sss.py

#############################################################################
#
# Version 0.1.118 - Author: Asaf Ravid <asaf.rvd@gmail.com>
#
#    Stock Screener and Scanner - based on yfinance
#    Copyright (C) 2021 Asaf Ravid
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
#    (at your option) any later version.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
#
#    You should have received a copy of the GNU General Public License
#    along with this program.  If not, see <https://www.gnu.org/licenses/>.
#
#############################################################################


# TODO: ASAFR: -1. (Highest Priority:) Update yfinance and commit the pull requests from internaly-used/improved yfinance
#               0. Auto-Update Nasdaq (All, NSR) Indices as done with TASE
#               1. Check and multi dim and investigate eqg_min and rqg_min: Check why Yahoo Finance always gives QRG values of 0? Unusable if that is always so
#               2. Implement:
#               2.1. https://en.wikipedia.org/wiki/Piotroski_F-score
#               2.1.1. ROA: https://www.investopedia.com/ask/answers/031215/what-formula-calculating-return-assets-roa.asp
#               2.2. https://en.wikipedia.org/wiki/Magic_formula_investing
#               2.3. https://www.oldschoolvalue.com/investing-strategy/backtest-graham-nnwc-ncav-screen/
#               3. Take latest yfinance base.py (and other - compare the whole folder) and updates - maybe not required - but just stay up to date
#               5. Investigate and add: https://www.investopedia.com/terms/o/operatingmargin.asp - operating margin
#               6. Add Free Cash flow [FCF] (EV/FreeCashFlow): Inverse of the Free Cash Flow Yield (https://www.stockopedia.com/ratios/ev-free-cash-flow-336/#:~:text=What%20is%20the%20definition%20of,the%20Free%20Cash%20Flow%20Yield.)
#               7. There is already an EV/CFO ratio.
#                    CFO - CapitalExpenditures = FCF
#                    EV/CFO * EV/FCF = EV^2 / (CFO * [CFO - CapitalExpenditures]) | EV/CFO + EV/FCF = EV*(1/CFO + 1/(CFO-CapitalExpenditures))
#                    Conclusion: EV/FCF is better as it provides moe information. But make this a lower priority for development
#                                Bonus: When there is no CFO, Use FCF, and Vice Versa - more information
#               9. Which are the most effective parameters? Correlate the sorting of sss_value to the results and each of the sorted-by-parameter list.
#              10. Important: https://www.oldschoolvalue.com/investing-strategy/walter-schloss-investing-strategy-producing-towering-returns/#:~:text=Walter%20Schloss%20ran%20with%20the,to%20perform%20complex%20valuations%20either.
#                  10.1.  3 years low, 5 years low
#                  10.2.  F-Score, M-Score, Z-Score
#                  10.3.  Multi-Dim scan over the distance from low, and over the Schloff Score - define a Walter-Schloss score
#                  10.4.  Remove the square root from DtoE ?
#                  10.5.  MktCapM is >= US$ 300 million (basis year 2000) adjusted yearly
#                  10.6.  Consider only stocks that are listed at least 10 years
#                  10.7.  Price 1 Day ago within 15% of the 52 week low
#                  10.8.  Take the top 1000 stocks with highest Number of Insiders owning shares#            11. Calculate share_price/52weekLow 0.1
#                  10.9.  Take the top 500 stocks with highest Current Dividend Yield %#            12. https://pyportfolioopt.readthedocs.io/en/latest/UserGuide.html -> Use
#                  10.10. Take the top 250 stocks with lowest Latest Filing P/E ratio#            13. Calculate the ROE - Return on equity
#                  10.11. Take the top 125 stocks with lowest Latest Filing P/B ratio#            14. Operating Cash Flow Growth - interesting: https://github.com/JerBouma/FundamentalAnalysis
#                  10.12. Take the top 75 stocks with lowest Latest Filing Long Term Debt#            15. Quick Ratio - https://github.com/JerBouma/FinanceDatabase - interesting
#
#               11. [Building DB: thread_id  0 Sleeping for        0.0 sec] Checking AVCT      ( 634/ 648/7231 [Diff:    0]):
#                American Virtual Cloud Technolo    :
# C:\Users\Administrator\Downloads\sss-master\venv\lib\site-packages\yfinance\base.py:519: UserWarning: DataFrame columns are not unique, some columns will be omitted.
#   return data.to_dict()
# C:\Users\Administrator\Downloads\sss-master\venv\lib\site-packages\yfinance\base.py:509: UserWarning: DataFrame columns are not unique, some columns will be omitted.
#   return data.to_dict()
# C:\Users\Administrator\Downloads\sss-master\venv\lib\site-packages\yfinance\base.py:502: UserWarning: DataFrame columns are not unique, some columns will be omitted.
#   return data.to_dict()


import time
import random                                                                                                          
import pandas   as pd
import yfinance as yf
import csv
import os
import sss_filenames
import sss_indices
import sss_post_processing
import math
import json

from threading import Thread
from dataclasses import dataclass
# from forex_python.converter import CurrencyRates
# from currency_converter import CurrencyConverter


VERBOSE_LOGS = 0

SKIP_5LETTER_Y_STOCK_LISTINGS                = False       # Skip ADRs - American Depositary receipts (5 Letter Stocks)
NUM_ROUND_DECIMALS                           = 6
NUM_EMPLOYEES_UNKNOWN                        = 10000000   # This will make the company very inefficient in terms of number of employees
PROFIT_MARGIN_UNKNOWN                        = 0.00001    # This will make the company almost not profitable terms of profit margins, thus less attractive
PRICE_TO_BOOK_UNKNOWN                        = 1000.0
PERCENT_HELD_INSTITUTIONS_LOW                = 0.01       # low, to make less relevant
PEG_UNKNOWN                                  = 10000      # Use a non-attractive value
QEG_MAX                                      = 10000
REG_MAX                                      = 10000
SHARES_OUTSTANDING_UNKNOWN                   = 100000000  # 100 Million Shares - just a value for calculation of a currently unused vaue
BAD_SSS                                      = 10.0 ** 50.0
PROFIT_MARGIN_WEIGHTS                        = [1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0, 512.0]  # from oldest to newest
CASH_FLOW_WEIGHTS                            = [1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0, 512.0]  # from oldest to newest
REVENUES_WEIGHTS                             = [1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0, 512.0]  # from oldest to newest
NO_WEIGHTS                                   = [1.0, 1.0, 1.0, 1.0,  1.0,  1.0,  1.0,   1.0,   1.0,   1.0]  # from oldest to newest
EARNINGS_WEIGHTS                             = [1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0, 512.0]  # from oldest to newest
BALANCE_SHEETS_WEIGHTS                       = [1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0, 512.0]  # from oldest to newest
EQG_UNKNOWN                                  = -0.9   # -90% TODO: ASAFR: 1. Scan (like pm and ever) values of eqg for big data research better recommendations
RQG_UNKNOWN                                  = -0.9   # -90% TODO: ASAFR: 1. Scan (like pm and ever) values of rqg for big data research better recommendations
EQG_POSITIVE_FACTOR                          = 10.0   # When positive, it will have a 5x factor on the 1 + function
RQG_POSITIVE_FACTOR                          = 10.0   # When positive, it will have a 5x factor on the 1 + function
EQG_WEIGHT_VS_YOY                            = 0.75   # the provided EQG is weighted more than the manually calculated one
RQG_WEIGHT_VS_YOY                            = 0.1   # the provided RQG (Actually Yahoo never provides it) is weighted less than the manually calculated one
EQG_DAMPER                                   = 0.25
RQG_DAMPER                                   = 0.25
TRAILING_EPS_PERCENTAGE_DAMP_FACTOR          = 0.01   # When the trailing_eps_percentage is very low (units are ratio here), this damper shall limit the affect to x100 not more)
PROFIT_MARGIN_DAMPER                         = 0.01   # When the profit_margin                   is very low (units are ratio here), this damper shall limit the affect to x100 not more)
RATIO_DAMPER                                 = 0.01   # When the total/current_other_other ratio is very low (units are ratio here), this damper shall limit the affect to x100 not more)
ROA_DAMPER                                   = 0.02   # When the ROA is very low (units are ratio here), this damper shall limit the affect to x50 not more)
REFERENCE_DB_MAX_VALUE_DIFF_FACTOR_THRESHOLD = 0.9   # if there is a parameter difference from reference db, in which the difference of values is higher than 0.75*abs(max_value) then something went wrong with the fetch of values from yfinance. Compensate smartly from reference database
QUARTERLY_YEARLY_MISSING_FACTOR              = 0.25  # if either yearly or quarterly values are missing - compensate by other with bad factor (less information means less attractive)

# TODO: ASAFR: All below boosters should be calibrated by:
#              1. The rarety (statistically comapred to all the stocks in scan) - proportionaly to it (the rarest the case - the more boost)
#              2. The ascent (slope) of the increase and the positive value -> the higher - the more boost
#              3. Add similar boosters for other annual and quarterly weighted-averaged parameters
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_INCREASE                = 2.25   # Provide a "bonus" for companies whose profit margins have increased continuously annually
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_INCREASE             = 1.75   # Provide a "bonus" for companies whose profit margins have increased continuously quarterly
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_POSITIVE                = 2.5    # Provide a "bonus" for companies whose profit margins have been continuously positive annually
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_POSITIVE             = 2.5    # Provide a "bonus" for companies whose profit margins have been continuously positive quarterly
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_INCREASE_IN_EARNINGS    = 2.75   # Provide a "bonus" for companies whose earnings       have been continuously increasing annually
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_INCREASE_IN_REVENUE     = 2.25   # Provide a "bonus" for companies whose revenue        has  been continuously increasing annually
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_INCREASE_IN_EARNINGS = 2.75   # Provide a "bonus" for companies whose earnings       have been continuously increasing quarterly
PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_INCREASE_IN_REVENUE  = 2.5    # Provide a "bonus" for companies whose revenue        has  been continuously increasing quarterly
PROFIT_MARGIN_DUPLICATION_FACTOR                                  = 8.0    # When copying profit margin (if either quarterized/annualized/profit_margin is missing) - devide by this factor
NEGATIVE_CFO_FACTOR                                               = 10000.0   #
NEGATIVE_PEG_RATIO_FACTOR                                         = 100000.0
NEGATIVE_DEBT_TO_EQUITY_FACTOR                                    = 100.0   # -0.5 -> 50, and -0.001 -> 0.1
NEGATIVE_EARNINGS_FACTOR                                          = 10000.0
DEBT_TO_EQUITY_MIN_BASE                                           = 0.001  # Clearing from 0 values for companies without debt

FORWARD_PRICE_TO_EARNINGS_WEIGHT  = 0.125 # Give less weight to forward (estimation)
TRAILING_PRICE_TO_EARNINGS_WEIGHT = 1-FORWARD_PRICE_TO_EARNINGS_WEIGHT

DIST_FROM_LOW_FACTOR_DAMPER                = 0.001
DIST_FROM_LOW_FACTOR_HIGHER_THAN_ONE_POWER = 6

EV_TO_EBITDA_MAX_UNKNOWN = 100000

#
# TODO: ASAFR: (https://www.gurufocus.com/letter.php)
#              1. Add Free Cash Flow
#              2. Lower Market Cap - Give more weight (in multi-dimensional scan)

@dataclass
class StockData:
    symbol:                                         str   = 'None'
    short_name:                                     str   = 'None'
    quote_type:                                     str   = 'None'
    sector:                                         str   = 'None'
    country:                                        str   = 'Unknown'
    sss_value:                                      float = BAD_SSS
    annualized_revenue:                             float = 0.0
    annualized_earnings:                            float = 0.0
    annualized_retained_earnings:                   float = 0.0
    quarterized_revenue:                            float = 0.0
    quarterized_earnings:                           float = 0.0
    quarterized_retained_earnings:                  float = 0.0
    effective_earnings:                             float = 0.0
    effective_retained_earnings:                    float = 0.0
    effective_revenue:                              float = 0.0
    annualized_total_revenue:                       float = 0.0
    annualized_net_income:                          float = 0.0
    quarterized_total_revenue:                      float = 0.0
    quarterized_net_income:                         float = 0.0
    effective_net_income:                           float = 0.0
    effective_total_revenue:                        float = 0.0
    enterprise_value_to_revenue:                    float = 0.0
    evr_effective:                                  float = 0.0
    trailing_price_to_earnings:                     float = 0.0
    forward_price_to_earnings:                      float = 0.0
    effective_price_to_earnings:                    float = 0.0
    trailing_12months_price_to_sales:               float = 0.0
    pe_effective:                                   float = 0.0
    enterprise_value_to_ebitda:                     float = 0.0
    effective_ev_to_ebitda:                         float = 0.0
    ebitda:                                         float = 0.0
    quarterized_ebitd:                              float = 0.0
    annualized_ebitd:                               float = 0.0
    ebitd:                                          float = 0.0
    profit_margin:                                  float = 0.0
    annualized_profit_margin:                       float = 0.0
    annualized_profit_margin_boost:                 float = 0.0
    quarterized_profit_margin:                      float = 0.0
    quarterized_profit_margin_boost:                float = 0.0
    effective_profit_margin:                        float = 0.0
    held_percent_institutions:                      float = 0.0
    forward_eps:                                    float = 0.0
    trailing_eps:                                   float = 0.0
    previous_close:                                 float = 0.0
    trailing_eps_percentage:                        float = 0.0 # trailing_eps / previousClose
    price_to_book:                                  float = 0.0
    shares_outstanding:                             float = 0.0
    net_income_to_common_shareholders:              float = 0.0
    nitcsh_to_shares_outstanding:                   float = 0.0
    employees:                                      int   = 0
    enterprise_value:                               int   = 0
    market_cap:                                     int   = 0
    nitcsh_to_num_employees:                        float = 0.0
    eqg:                                            float = 0.0  # Value is a ratio, such that when multiplied by 100, yields percentage (%) units
    rqg:                                            float = 0.0  # Value is a ratio, such that when multiplied by 100, yields percentage (%) units
    eqg_yoy:                                        float = 0.0  # calculated from the yearly earnings - if available
    rqg_yoy:                                        float = 0.0  # calculated from the yearly Revenues - if available
    niqg_yoy:                                       float = 0.0  # Net Income Quarterly Growth: calculated from the yearly net income - if available
    trqg_yoy:                                       float = 0.0  # Total Revenue Quarterly Growth: calculated from the yearly net income - if available
    eqg_effective:                                  float = 0.0  # average of eqg_yoy and eqg
    eqg_factor_effective:                           float = 0.0  # function with positive factor and damper
    rqg_effective:                                  float = 0.0  # average of rqg_yoy and rqg
    rqg_factor_effective:                           float = 0.0  # function with positive factor and damper
    price_to_earnings_to_growth_ratio:              float = 0.0
    effective_peg_ratio:                            float = 0.0
    annualized_cash_flow_from_operating_activities: float = 0.0
    quarterized_cash_flow_from_operating_activities:float = 0.0
    annualized_ev_to_cfo_ratio:                     float = 0.0  # https://investinganswers.com/dictionary/e/enterprise-value-cash-flow-operations-evcfo
    quarterized_ev_to_cfo_ratio:                    float = 0.0  # https://investinganswers.com/dictionary/e/enterprise-value-cash-flow-operations-evcfo
    ev_to_cfo_ratio_effective:                      float = 0.0
    annualized_debt_to_equity:                      float = 0.0
    quarterized_debt_to_equity:                     float = 0.0
    debt_to_equity_effective:                       float = 0.0
    debt_to_equity_effective_used:                  float = 0.0
    financial_currency:                             str   = 'None'
    summary_currency:                               str   = 'None'
    financial_currency_conversion_rate_mult_to_usd: float = 0.0
    summary_currency_conversion_rate_mult_to_usd:   float = 0.0
    last_dividend_0:                                float = 0.0
    last_dividend_1:                                float = 0.0
    last_dividend_2:                                float = 0.0
    last_dividend_3:                                float = 0.0
    fifty_two_week_change:                          float = 0.0
    fifty_two_week_low:                             float = 0.0
    fifty_two_week_high:                            float = 0.0
    two_hundred_day_average:                        float = 0.0
    previous_close_percentage_from_200d_ma:         float = 0.0
    previous_close_percentage_from_52w_low:         float = 0.0
    previous_close_percentage_from_52w_high:        float = 0.0
    dist_from_low_factor:                           float = 0.0
    eff_dist_from_low_factor:                       float = 0.0
    annualized_total_ratio:                         float = 0.0
    quarterized_total_ratio:                        float = 0.0
    annualized_other_current_ratio:                 float = 0.0
    quarterized_other_current_ratio:                float = 0.0
    annualized_other_ratio:                         float = 0.0
    quarterized_other_ratio:                        float = 0.0
    annualized_total_current_ratio:                 float = 0.0
    quarterized_total_current_ratio:                float = 0.0
    total_ratio_effective:                          float = 0.0
    other_current_ratio_effective:                  float = 0.0
    other_ratio_effective:                          float = 0.0
    total_current_ratio_effective:                  float = 0.0
    effective_current_ratio:                        float = 0.0
    annualized_total_assets:                        float = 0.0
    quarterized_total_assets:                       float = 0.0
    effective_total_assets:                         float = 0.0
    calculated_roa:                                 float = 0.0
    annualized_working_capital:                     float = 0.0
    quarterized_working_capital:                    float = 0.0
    effective_working_capital:                      float = 0.0
    annualized_total_liabilities:                   float = 0.0
    quarterized_total_liabilities:                  float = 0.0
    effective_total_liabilities:                    float = 0.0
    altman_z_score_factor:                          float = 0.0
    skip_reason:                                    str   = 'None'

@dataclass
class StockDataNormalized:
    symbol:                                         str   = 'None'
    short_name:                                     str   = 'None'
    quote_type:                                     str   = 'None'
    sector:                                         str   = 'None'
    country:                                        str   = 'Unknown'
    sss_value:                                      float = BAD_SSS
    sss_value_normalized:                           float = BAD_SSS
    annualized_revenue:                             float = 0.0
    annualized_earnings:                            float = 0.0
    annualized_retained_earnings:                   float = 0.0
    quarterized_revenue:                            float = 0.0
    quarterized_earnings:                           float = 0.0
    quarterized_retained_earnings:                  float = 0.0
    effective_earnings:                             float = 0.0
    effective_retained_earnings:                    float = 0.0
    effective_revenue:                              float = 0.0
    annualized_total_revenue:                       float = 0.0
    annualized_net_income:                          float = 0.0
    quarterized_total_revenue:                      float = 0.0
    quarterized_net_income:                         float = 0.0
    effective_net_income:                           float = 0.0
    effective_total_revenue:                        float = 0.0
    enterprise_value_to_revenue:                    float = 0.0
    evr_effective:                                  float = 0.0
    evr_effective_normalized:                       float = 0.0
    trailing_price_to_earnings:                     float = 0.0
    forward_price_to_earnings:                      float = 0.0
    effective_price_to_earnings:                    float = 0.0
    trailing_12months_price_to_sales:               float = 0.0
    trailing_12months_price_to_sales_normalized:    float = 0.0
    pe_effective:                                   float = 0.0
    pe_effective_normalized:                        float = 0.0
    enterprise_value_to_ebitda:                     float = 0.0
    effective_ev_to_ebitda:                         float = 0.0
    effective_ev_to_ebitda_normalized:              float = 0.0
    ebitda:                                         float = 0.0
    quarterized_ebitd:                              float = 0.0
    annualized_ebitd:                               float = 0.0
    ebitd:                                          float = 0.0
    profit_margin:                                  float = 0.0
    annualized_profit_margin:                       float = 0.0
    annualized_profit_margin_boost:                 float = 0.0
    quarterized_profit_margin:                      float = 0.0
    quarterized_profit_margin_boost:                float = 0.0
    effective_profit_margin:                        float = 0.0
    effective_profit_margin_normalized:             float = 0.0
    held_percent_institutions:                      float = 0.0
    forward_eps:                                    float = 0.0
    trailing_eps:                                   float = 0.0
    previous_close:                                 float = 0.0
    trailing_eps_percentage:                        float = 0.0 # trailing_eps / previousClose
    price_to_book:                                  float = 0.0
    price_to_book_normalized:                       float = 0.0
    shares_outstanding:                             float = 0.0
    net_income_to_common_shareholders:              float = 0.0
    nitcsh_to_shares_outstanding:                   float = 0.0
    employees:                                      int   = 0
    enterprise_value:                               int   = 0
    market_cap:                                     int   = 0
    nitcsh_to_num_employees:                        float = 0.0
    eqg:                                            float = 0.0  # Value is a ratio, such that when multiplied by 100, yields percentage (%) units
    rqg:                                            float = 0.0  # Value is a ratio, such that when multiplied by 100, yields percentage (%) units
    eqg_yoy:                                        float = 0.0  # calculated from the yearly earnings - if available
    rqg_yoy:                                        float = 0.0  # calculated from the yearly Revenues - if available
    niqg_yoy:                                       float = 0.0  # Net Income Quarterly Growth: calculated from the yearly net income - if available
    trqg_yoy:                                       float = 0.0  # Total Revenue Quarterly Growth: calculated from the yearly net income - if available
    eqg_effective:                                  float = 0.0  # average of eqg_yoy and eqg
    eqg_factor_effective:                           float = 0.0  # function with positive factor and damper
    eqg_factor_effective_normalized:                float = 0.0  # function with positive factor and damper
    rqg_effective:                                  float = 0.0  # average of rqg_yoy and rqg
    rqg_factor_effective:                           float = 0.0  # function with positive factor and damper
    rqg_factor_effective_normalized:                float = 0.0  # function with positive factor and damper
    price_to_earnings_to_growth_ratio:              float = 0.0
    effective_peg_ratio:                            float = 0.0
    effective_peg_ratio_normalized:                 float = 0.0
    annualized_cash_flow_from_operating_activities: float = 0.0
    quarterized_cash_flow_from_operating_activities:float = 0.0
    annualized_ev_to_cfo_ratio:                     float = 0.0  # https://investinganswers.com/dictionary/e/enterprise-value-cash-flow-operations-evcfo
    quarterized_ev_to_cfo_ratio:                    float = 0.0  # https://investinganswers.com/dictionary/e/enterprise-value-cash-flow-operations-evcfo
    ev_to_cfo_ratio_effective:                      float = 0.0
    ev_to_cfo_ratio_effective_normalized:           float = 0.0
    annualized_debt_to_equity:                      float = 0.0
    quarterized_debt_to_equity:                     float = 0.0
    debt_to_equity_effective:                       float = 0.0
    debt_to_equity_effective_used:                  float = 0.0
    debt_to_equity_effective_used_normalized:       float = 0.0
    financial_currency:                             str   = 'None'
    summary_currency:                               str   = 'None'
    financial_currency_conversion_rate_mult_to_usd: float = 0.0
    summary_currency_conversion_rate_mult_to_usd:   float = 0.0
    last_dividend_0:                                float = 0.0
    last_dividend_1:                                float = 0.0
    last_dividend_2:                                float = 0.0
    last_dividend_3:                                float = 0.0
    fifty_two_week_change:                          float = 0.0
    fifty_two_week_low:                             float = 0.0
    fifty_two_week_high:                            float = 0.0
    two_hundred_day_average:                        float = 0.0
    previous_close_percentage_from_200d_ma:         float = 0.0
    previous_close_percentage_from_52w_low:         float = 0.0
    previous_close_percentage_from_52w_high:        float = 0.0
    dist_from_low_factor:                           float = 0.0
    eff_dist_from_low_factor:                       float = 0.0
    eff_dist_from_low_factor_normalized:            float = 0.0
    annualized_total_ratio:                         float = 0.0
    quarterized_total_ratio:                        float = 0.0
    annualized_other_current_ratio:                 float = 0.0
    quarterized_other_current_ratio:                float = 0.0
    annualized_other_ratio:                         float = 0.0
    quarterized_other_ratio:                        float = 0.0
    annualized_total_current_ratio:                 float = 0.0
    quarterized_total_current_ratio:                float = 0.0
    total_ratio_effective:                          float = 0.0
    other_current_ratio_effective:                  float = 0.0
    other_ratio_effective:                          float = 0.0
    total_current_ratio_effective:                  float = 0.0
    effective_current_ratio:                        float = 0.0
    effective_current_ratio_normalized:             float = 0.0
    annualized_total_assets:                        float = 0.0
    quarterized_total_assets:                       float = 0.0
    effective_total_assets:                         float = 0.0
    calculated_roa:                                 float = 0.0
    calculated_roa_normalized:                      float = 0.0
    annualized_working_capital:                     float = 0.0
    quarterized_working_capital:                    float = 0.0
    effective_working_capital:                      float = 0.0
    annualized_total_liabilities:                   float = 0.0
    quarterized_total_liabilities:                  float = 0.0
    effective_total_liabilities:                    float = 0.0
    altman_z_score_factor:                          float = 0.0
    altman_z_score_factor_normalized:               float = 0.0
    skip_reason:                                    str   = 'None'


g_header_row = ["Symbol", "Name", "Sector", "Country", "sss_value", "annualized_revenue", "annualized_earnings", "annualized_retained_earnings", "quarterized_revenue", "quarterized_earnings", "quarterized_retained_earnings", "effective_earnings", "effective_retained_earnings", "effective_revenue", "annualized_total_revenue", "annualized_net_income", "quarterized_total_revenue", "quarterized_net_income", "effective_net_income", "effective_total_revenue", "enterprise_value_to_revenue", "evr_effective", "trailing_price_to_earnings", "forward_price_to_earnings", "effective_price_to_earnings", "trailing_12months_price_to_sales", "pe_effective", "enterprise_value_to_ebitda", "effective_ev_to_ebitda", "ebitda", "quarterized_ebitd", "annualized_ebitd", "ebitd", "profit_margin", "annualized_profit_margin", "annualized_profit_margin_boost", "quarterized_profit_margin", "quarterized_profit_margin_boost", "effective_profit_margin", "held_percent_institutions", "forward_eps", "trailing_eps", "previous_close", "trailing_eps_percentage","price_to_book", "shares_outstanding", "net_income_to_common_shareholders", "nitcsh_to_shares_outstanding", "employees", "enterprise_value", "market_cap", "nitcsh_to_num_employees", "eqg", "rqg", "eqg_yoy", "rqg_yoy", "niqg_yoy", "trqg_yoy", "eqg_effective", "eqg_factor_effective", "rqg_effective", "rqg_factor_effective", "price_to_earnings_to_growth_ratio", "effective_peg_ratio", "annualized_cash_flow_from_operating_activities", "quarterized_cash_flow_from_operating_activities", "annualized_ev_to_cfo_ratio", "quarterized_ev_to_cfo_ratio", "ev_to_cfo_ratio_effective", "annualized_debt_to_equity", "quarterized_debt_to_equity", "debt_to_equity_effective", "debt_to_equity_effective_used", "financial_currency", "summary_currency", "financial_currency_conversion_rate_mult_to_usd", "summary_currency_conversion_rate_mult_to_usd", "last_dividend_0", "last_dividend_1", "last_dividend_2", "last_dividend_3", "fifty_two_week_change", "fifty_two_week_low", "fifty_two_week_high", "two_hundred_day_average", "previous_close_percentage_from_200d_ma", "previous_close_percentage_from_52w_low", "previous_close_percentage_from_52w_high", "dist_from_low_factor", "eff_dist_from_low_factor", "annualized_total_ratio", "quarterized_total_ratio", "annualized_other_current_ratio", "quarterized_other_current_ratio", "annualized_other_ratio", "quarterized_other_ratio", "annualized_total_current_ratio", "quarterized_total_current_ratio", "total_ratio_effective", "other_current_ratio_effective", "other_ratio_effective", "total_current_ratio_effective", "effective_current_ratio", "annualized_total_assets", "quarterized_total_assets", "effective_total_assets", "calculated_roa", "annualized_working_capital", "quarterized_working_capital", "effective_working_capital", "annualized_total_liabilities", "quarterized_total_liabilities", "effective_total_liabilities", "altman_z_score_factor", "skip_reason" ]
g_symbol_index                                          = g_header_row.index("Symbol")
g_name_index                                            = g_header_row.index("Name")
g_sector_index                                          = g_header_row.index("Sector")
g_country_index                                         = g_header_row.index("Country")
g_sss_value_index                                       = g_header_row.index("sss_value")
g_annualized_revenue_index                              = g_header_row.index("annualized_revenue")
g_annualized_earnings_index                             = g_header_row.index("annualized_earnings")
g_annualized_retained_earnings_index                    = g_header_row.index("annualized_retained_earnings")
g_quarterized_revenue_index                             = g_header_row.index("quarterized_revenue")
g_quarterized_earnings_index                            = g_header_row.index("quarterized_earnings")
g_quarterized_retained_earnings_index                   = g_header_row.index("quarterized_retained_earnings")
g_effective_earnings_index                              = g_header_row.index("effective_earnings")
g_effective_retained_earnings_index                     = g_header_row.index("effective_retained_earnings")
g_effective_revenue_index                               = g_header_row.index("effective_revenue")
g_annualized_total_revenue_index                        = g_header_row.index("annualized_total_revenue")
g_annualized_net_income_index                           = g_header_row.index("annualized_net_income")
g_quarterized_total_revenue_index                       = g_header_row.index("quarterized_total_revenue")
g_quarterized_net_income_index                          = g_header_row.index("quarterized_net_income")
g_effective_net_income_index                            = g_header_row.index("effective_net_income")
g_effective_total_revenue_index                         = g_header_row.index("effective_total_revenue")
g_enterprise_value_to_revenue_index                     = g_header_row.index("enterprise_value_to_revenue")
g_evr_effective_index                                   = g_header_row.index("evr_effective")
g_trailing_price_to_earnings_index                      = g_header_row.index("trailing_price_to_earnings")
g_forward_price_to_earnings_index                       = g_header_row.index("forward_price_to_earnings")
g_effective_price_to_earnings_index                     = g_header_row.index("effective_price_to_earnings")
g_trailing_12months_price_to_sales_index                = g_header_row.index("trailing_12months_price_to_sales")
g_pe_effective_index                                    = g_header_row.index("pe_effective")
g_enterprise_value_to_ebitda_index                      = g_header_row.index("enterprise_value_to_ebitda")
g_effective_ev_to_ebitda_index                          = g_header_row.index("effective_ev_to_ebitda")
g_ebitda_index                                          = g_header_row.index("ebitda")
g_quarterized_ebitd_index                               = g_header_row.index("quarterized_ebitd")
g_annualized_ebitd_index                                = g_header_row.index("annualized_ebitd")
g_ebitd_index                                           = g_header_row.index("ebitd")
g_profit_margin_index                                   = g_header_row.index("profit_margin")
g_annualized_profit_margin_index                        = g_header_row.index("annualized_profit_margin")
g_annualized_profit_margin_boost_index                  = g_header_row.index("annualized_profit_margin_boost")
g_quarterized_profit_margin_index                       = g_header_row.index("quarterized_profit_margin")
g_quarterized_profit_margin_boost_index                 = g_header_row.index("quarterized_profit_margin_boost")
g_effective_profit_margin_index                         = g_header_row.index("effective_profit_margin")
g_held_percent_institutions_index                       = g_header_row.index("held_percent_institutions")
g_forward_eps_index                                     = g_header_row.index("forward_eps")
g_trailing_eps_index                                    = g_header_row.index("trailing_eps")
g_previous_close_index                                  = g_header_row.index("previous_close")
g_trailing_eps_percentage_index                         = g_header_row.index("trailing_eps_percentage")
g_price_to_book_index                                   = g_header_row.index("price_to_book")
g_shares_outstanding_index                              = g_header_row.index("shares_outstanding")
g_net_income_to_common_shareholders_index               = g_header_row.index("net_income_to_common_shareholders")
g_nitcsh_to_shares_outstanding_index                    = g_header_row.index("nitcsh_to_shares_outstanding")
g_employees_index                                       = g_header_row.index("employees")
g_enterprise_value_index                                = g_header_row.index("enterprise_value")
g_market_cap_index                                      = g_header_row.index("market_cap")
g_nitcsh_to_num_employees_index                         = g_header_row.index("nitcsh_to_num_employees")
g_eqg_index                                             = g_header_row.index("eqg")
g_rqg_index                                             = g_header_row.index("rqg")
g_eqg_yoy_index                                         = g_header_row.index("eqg_yoy")
g_rqg_yoy_index                                         = g_header_row.index("rqg_yoy")
g_niqg_yoy_index                                        = g_header_row.index("niqg_yoy")
g_trqg_yoy_index                                        = g_header_row.index("trqg_yoy")
g_eqg_effective_index                                   = g_header_row.index("eqg_effective")
g_eqg_factor_effective_index                            = g_header_row.index("eqg_factor_effective")
g_rqg_effective_index                                   = g_header_row.index("rqg_effective")
g_rqg_factor_effective_index                            = g_header_row.index("rqg_factor_effective")
g_price_to_earnings_to_growth_ratio_index               = g_header_row.index("price_to_earnings_to_growth_ratio")
g_effective_peg_ratio_index                             = g_header_row.index("effective_peg_ratio")
g_annualized_cash_flow_from_operating_activities_index  = g_header_row.index("annualized_cash_flow_from_operating_activities")
g_quarterized_cash_flow_from_operating_activities_index = g_header_row.index("quarterized_cash_flow_from_operating_activities")
g_annualized_ev_to_cfo_ratio_index                      = g_header_row.index("annualized_ev_to_cfo_ratio")
g_quarterized_ev_to_cfo_ratio_index                     = g_header_row.index("quarterized_ev_to_cfo_ratio")
g_ev_to_cfo_ratio_effective_index                       = g_header_row.index("ev_to_cfo_ratio_effective")
g_annualized_debt_to_equity_index                       = g_header_row.index("annualized_debt_to_equity")
g_quarterized_debt_to_equity_index                      = g_header_row.index("quarterized_debt_to_equity")
g_debt_to_equity_effective_index                        = g_header_row.index("debt_to_equity_effective")
g_debt_to_equity_effective_used_index                   = g_header_row.index("debt_to_equity_effective_used")
g_financial_currency_index                              = g_header_row.index("financial_currency")
g_summary_currency_index                                = g_header_row.index("summary_currency")
g_financial_currency_conversion_rate_mult_to_usd_index  = g_header_row.index("financial_currency_conversion_rate_mult_to_usd")
g_summary_currency_conversion_rate_mult_to_usd_index    = g_header_row.index("summary_currency_conversion_rate_mult_to_usd")
g_last_dividend_0_index                                 = g_header_row.index("last_dividend_0")
g_last_dividend_1_index                                 = g_header_row.index("last_dividend_1")
g_last_dividend_2_index                                 = g_header_row.index("last_dividend_2")
g_last_dividend_3_index                                 = g_header_row.index("last_dividend_3")
g_fifty_two_week_change_index                           = g_header_row.index("fifty_two_week_change")
g_fifty_two_week_low_index                              = g_header_row.index("fifty_two_week_low")
g_fifty_two_week_high_index                             = g_header_row.index("fifty_two_week_high")
g_two_hundred_day_average_index                         = g_header_row.index("two_hundred_day_average")
g_previous_close_percentage_from_200d_ma_index          = g_header_row.index("previous_close_percentage_from_200d_ma")
g_previous_close_percentage_from_52w_low_index          = g_header_row.index("previous_close_percentage_from_52w_low")
g_previous_close_percentage_from_52w_high_index         = g_header_row.index("previous_close_percentage_from_52w_high")
g_dist_from_low_factor_index                            = g_header_row.index("dist_from_low_factor")
g_eff_dist_from_low_factor_index                        = g_header_row.index("eff_dist_from_low_factor")
g_annualized_total_ratio_index                          = g_header_row.index("annualized_total_ratio")
g_quarterized_total_ratio_index                         = g_header_row.index("quarterized_total_ratio")
g_annualized_other_current_ratio_index                  = g_header_row.index("annualized_other_current_ratio")
g_quarterized_other_current_ratio_index                 = g_header_row.index("quarterized_other_current_ratio")
g_annualized_other_ratio_index                          = g_header_row.index("annualized_other_ratio")
g_quarterized_other_ratio_index                         = g_header_row.index("quarterized_other_ratio")
g_annualized_total_current_ratio_index                  = g_header_row.index("annualized_total_current_ratio")
g_quarterized_total_current_ratio_index                 = g_header_row.index("quarterized_total_current_ratio")
g_total_ratio_effective_index                           = g_header_row.index("total_ratio_effective")
g_other_current_ratio_effective_index                   = g_header_row.index("other_current_ratio_effective")
g_other_ratio_effective_index                           = g_header_row.index("other_ratio_effective")
g_total_current_ratio_effective_index                   = g_header_row.index("total_current_ratio_effective")
g_effective_current_ratio_index                         = g_header_row.index("effective_current_ratio")
g_annualized_total_assets_index                         = g_header_row.index("annualized_total_assets")
g_quarterized_total_assets_index                        = g_header_row.index("quarterized_total_assets")
g_effective_total_assets_index                          = g_header_row.index("effective_total_assets")
g_calculated_roa_index                                  = g_header_row.index("calculated_roa")
g_annualized_working_capital_index                      = g_header_row.index("annualized_working_capital")
g_quarterized_working_capital_index                     = g_header_row.index("quarterized_working_capital")
g_effective_working_capital_index                       = g_header_row.index("effective_working_capital")
g_annualized_total_liabilities_index                    = g_header_row.index("annualized_total_liabilities")
g_quarterized_total_liabilities_index                   = g_header_row.index("quarterized_total_liabilities")
g_effective_total_liabilities_index                     = g_header_row.index("effective_total_liabilities")
g_altman_z_score_factor_index                           = g_header_row.index("altman_z_score_factor")
g_skip_reason_index                                     = g_header_row.index("skip_reason")

g_header_row_normalized = ["Symbol", "Name", "Sector", "Country", "sss_value", "sss_value_normalized", "annualized_revenue", "annualized_earnings", "annualized_retained_earnings", "quarterized_revenue", "quarterized_earnings", "quarterized_retained_earnings", "effective_earnings", "effective_retained_earnings", "effective_revenue", "annualized_total_revenue", "annualized_net_income", "quarterized_total_revenue", "quarterized_net_income", "effective_net_income", "effective_total_revenue", "enterprise_value_to_revenue", "evr_effective", "evr_effective_normalized", "trailing_price_to_earnings", "forward_price_to_earnings", "effective_price_to_earnings", "trailing_12months_price_to_sales", "trailing_12months_price_to_sales_normalized", "pe_effective", "pe_effective_normalized", "enterprise_value_to_ebitda", "effective_ev_to_ebitda", "effective_ev_to_ebitda_normalized", "ebitda", "quarterized_ebitd", "annualized_ebitd", "ebitd", "profit_margin", "annualized_profit_margin", "annualized_profit_margin_boost", "quarterized_profit_margin", "quarterized_profit_margin_boost", "effective_profit_margin", "effective_profit_margin_normalized", "held_percent_institutions", "forward_eps", "trailing_eps", "previous_close", "trailing_eps_percentage", "price_to_book", "price_to_book_normalized", "shares_outstanding", "net_income_to_common_shareholders", "nitcsh_to_shares_outstanding", "employees", "enterprise_value", "market_cap", "nitcsh_to_num_employees", "eqg", "rqg", "eqg_yoy", "rqg_yoy", "niqg_yoy", "trqg_yoy", "eqg_effective", "eqg_factor_effective", "eqg_factor_effective_normalized", "rqg_effective", "rqg_factor_effective", "rqg_factor_effective_normalized", "price_to_earnings_to_growth_ratio", "effective_peg_ratio", "effective_peg_ratio_normalized", "annualized_cash_flow_from_operating_activities", "quarterized_cash_flow_from_operating_activities", "annualized_ev_to_cfo_ratio", "quarterized_ev_to_cfo_ratio", "ev_to_cfo_ratio_effective", "ev_to_cfo_ratio_effective_normalized", "annualized_debt_to_equity", "quarterized_debt_to_equity", "debt_to_equity_effective", "debt_to_equity_effective_used", "debt_to_equity_effective_used_normalized", "financial_currency", "summary_currency", "financial_currency_conversion_rate_mult_to_usd", "summary_currency_conversion_rate_mult_to_usd", "last_dividend_0", "last_dividend_1", "last_dividend_2", "last_dividend_3", "fifty_two_week_change", "fifty_two_week_low", "fifty_two_week_high", "two_hundred_day_average", "previous_close_percentage_from_200d_ma", "previous_close_percentage_from_52w_low", "previous_close_percentage_from_52w_high", "dist_from_low_factor", "eff_dist_from_low_factor", "eff_dist_from_low_factor_normalized", "annualized_total_ratio", "quarterized_total_ratio", "annualized_other_current_ratio", "quarterized_other_current_ratio", "annualized_other_ratio", "quarterized_other_ratio", "annualized_total_current_ratio", "quarterized_total_current_ratio", "total_ratio_effective", "other_current_ratio_effective", "other_ratio_effective", "total_current_ratio_effective", "effective_current_ratio", "effective_current_ratio_normalized", "annualized_total_assets", "quarterized_total_assets", "effective_total_assets", "calculated_roa", "calculated_roa_normalized", "annualized_working_capital", "quarterized_working_capital", "effective_working_capital", "annualized_total_liabilities", "quarterized_total_liabilities", "effective_total_liabilities", "altman_z_score_factor", "altman_z_score_factor_normalized", "skip_reason" ]
g_symbol_index_n                                          = g_header_row_normalized.index("Symbol")
g_name_index_n                                            = g_header_row_normalized.index("Name")
g_sector_index_n                                          = g_header_row_normalized.index("Sector")
g_country_index_n                                         = g_header_row_normalized.index("Country")
g_sss_value_index_n                                       = g_header_row_normalized.index("sss_value")
g_sss_value_normalized_index_n                            = g_header_row_normalized.index("sss_value_normalized")
g_annualized_revenue_index_n                              = g_header_row_normalized.index("annualized_revenue")
g_annualized_earnings_index_n                             = g_header_row_normalized.index("annualized_earnings")
g_annualized_retained_earnings_index_n                    = g_header_row_normalized.index("annualized_retained_earnings")
g_quarterized_revenue_index_n                             = g_header_row_normalized.index("quarterized_revenue")
g_quarterized_earnings_index_n                            = g_header_row_normalized.index("quarterized_earnings")
g_quarterized_retained_earnings_index_n                   = g_header_row_normalized.index("quarterized_retained_earnings")
g_effective_earnings_index_n                              = g_header_row_normalized.index("effective_earnings")
g_effective_retained_earnings_index_n                     = g_header_row_normalized.index("effective_retained_earnings")
g_effective_revenue_index_n                               = g_header_row_normalized.index("effective_revenue")
g_annualized_total_revenue_index_n                        = g_header_row_normalized.index("annualized_total_revenue")
g_annualized_net_income_index_n                           = g_header_row_normalized.index("annualized_net_income")
g_quarterized_total_revenue_index_n                       = g_header_row_normalized.index("quarterized_total_revenue")
g_quarterized_net_income_index_n                          = g_header_row_normalized.index("quarterized_net_income")
g_effective_net_income_index_n                            = g_header_row_normalized.index("effective_net_income")
g_effective_total_revenue_index_n                         = g_header_row_normalized.index("effective_total_revenue")
g_enterprise_value_to_revenue_index_n                     = g_header_row_normalized.index("enterprise_value_to_revenue")
g_evr_effective_index_n                                   = g_header_row_normalized.index("evr_effective")
g_evr_effective_normalized_index_n                        = g_header_row_normalized.index("evr_effective_normalized")
g_trailing_price_to_earnings_index_n                      = g_header_row_normalized.index("trailing_price_to_earnings")
g_forward_price_to_earnings_index_n                       = g_header_row_normalized.index("forward_price_to_earnings")
g_effective_price_to_earnings_index_n                     = g_header_row_normalized.index("effective_price_to_earnings")
g_trailing_12months_price_to_sales_index_n                = g_header_row_normalized.index("trailing_12months_price_to_sales")
g_trailing_12months_price_to_sales_normalized_index_n     = g_header_row_normalized.index("trailing_12months_price_to_sales_normalized")
g_pe_effective_index_n                                    = g_header_row_normalized.index("pe_effective")
g_pe_effective_normalized_index_n                         = g_header_row_normalized.index("pe_effective_normalized")
g_enterprise_value_to_ebitda_index_n                      = g_header_row_normalized.index("enterprise_value_to_ebitda")
g_effective_ev_to_ebitda_index_n                          = g_header_row_normalized.index("effective_ev_to_ebitda")
g_effective_ev_to_ebitda_normalized_index_n               = g_header_row_normalized.index("effective_ev_to_ebitda_normalized")
g_ebitda_index_n                                          = g_header_row_normalized.index("ebitda")
g_quarterized_ebitd_index_n                               = g_header_row_normalized.index("quarterized_ebitd")
g_annualized_ebitd_index_n                                = g_header_row_normalized.index("annualized_ebitd")
g_ebitd_index_n                                           = g_header_row_normalized.index("ebitd")
g_profit_margin_index_n                                   = g_header_row_normalized.index("profit_margin")
g_annualized_profit_margin_index_n                        = g_header_row_normalized.index("annualized_profit_margin")
g_annualized_profit_margin_boost_index_n                  = g_header_row_normalized.index("annualized_profit_margin_boost")
g_quarterized_profit_margin_index_n                       = g_header_row_normalized.index("quarterized_profit_margin")
g_quarterized_profit_margin_boost_index_n                 = g_header_row_normalized.index("quarterized_profit_margin_boost")
g_effective_profit_margin_index_n                         = g_header_row_normalized.index("effective_profit_margin")
g_effective_profit_margin_normalized_index_n              = g_header_row_normalized.index("effective_profit_margin_normalized")
g_held_percent_institutions_index_n                       = g_header_row_normalized.index("held_percent_institutions")
g_forward_eps_index_n                                     = g_header_row_normalized.index("forward_eps")
g_trailing_eps_index_n                                    = g_header_row_normalized.index("trailing_eps")
g_previous_close_index_n                                  = g_header_row_normalized.index("previous_close")
g_trailing_eps_percentage_index_n                         = g_header_row_normalized.index("trailing_eps_percentage")
g_price_to_book_index_n                                   = g_header_row_normalized.index("price_to_book")
g_price_to_book_normalized_index_n                        = g_header_row_normalized.index("price_to_book_normalized")
g_shares_outstanding_index_n                              = g_header_row_normalized.index("shares_outstanding")
g_net_income_to_common_shareholders_index_n               = g_header_row_normalized.index("net_income_to_common_shareholders")
g_nitcsh_to_shares_outstanding_index_n                    = g_header_row_normalized.index("nitcsh_to_shares_outstanding")
g_employees_index_n                                       = g_header_row_normalized.index("employees")
g_enterprise_value_index_n                                = g_header_row_normalized.index("enterprise_value")
g_market_cap_index_n                                      = g_header_row_normalized.index("market_cap")
g_nitcsh_to_num_employees_index_n                         = g_header_row_normalized.index("nitcsh_to_num_employees")
g_eqg_index_n                                             = g_header_row_normalized.index("eqg")
g_rqg_index_n                                             = g_header_row_normalized.index("rqg")
g_eqg_yoy_index_n                                         = g_header_row_normalized.index("eqg_yoy")
g_rqg_yoy_index_n                                         = g_header_row_normalized.index("rqg_yoy")
g_niqg_yoy_index_n                                        = g_header_row_normalized.index("niqg_yoy")
g_trqg_yoy_index_n                                        = g_header_row_normalized.index("trqg_yoy")
g_eqg_effective_index_n                                   = g_header_row_normalized.index("eqg_effective")
g_eqg_factor_effective_index_n                            = g_header_row_normalized.index("eqg_factor_effective")
g_eqg_factor_effective_normalized_index_n                 = g_header_row_normalized.index("eqg_factor_effective_normalized")
g_rqg_effective_index_n                                   = g_header_row_normalized.index("rqg_effective")
g_rqg_factor_effective_index_n                            = g_header_row_normalized.index("rqg_factor_effective")
g_rqg_factor_effective_normalized_index_n                 = g_header_row_normalized.index("rqg_factor_effective_normalized")
g_price_to_earnings_to_growth_ratio_index_n               = g_header_row_normalized.index("price_to_earnings_to_growth_ratio")
g_effective_peg_ratio_index_n                             = g_header_row_normalized.index("effective_peg_ratio")
g_effective_peg_ratio_normalized_index_n                  = g_header_row_normalized.index("effective_peg_ratio_normalized")
g_annualized_cash_flow_from_operating_activities_index_n  = g_header_row_normalized.index("annualized_cash_flow_from_operating_activities")
g_quarterized_cash_flow_from_operating_activities_index_n = g_header_row_normalized.index("quarterized_cash_flow_from_operating_activities")
g_annualized_ev_to_cfo_ratio_index_n                      = g_header_row_normalized.index("annualized_ev_to_cfo_ratio")
g_quarterized_ev_to_cfo_ratio_index_n                     = g_header_row_normalized.index("quarterized_ev_to_cfo_ratio")
g_ev_to_cfo_ratio_effective_index_n                       = g_header_row_normalized.index("ev_to_cfo_ratio_effective")
g_ev_to_cfo_ratio_effective_normalized_index_n            = g_header_row_normalized.index("ev_to_cfo_ratio_effective_normalized")
g_annualized_debt_to_equity_index_n                       = g_header_row_normalized.index("annualized_debt_to_equity")
g_quarterized_debt_to_equity_index_n                      = g_header_row_normalized.index("quarterized_debt_to_equity")
g_debt_to_equity_effective_index_n                        = g_header_row_normalized.index("debt_to_equity_effective")
g_debt_to_equity_effective_used_index_n                   = g_header_row_normalized.index("debt_to_equity_effective_used")
g_debt_to_equity_effective_used_normalized_index_n        = g_header_row_normalized.index("debt_to_equity_effective_used_normalized")
g_financial_currency_index_n                              = g_header_row_normalized.index("financial_currency")
g_summary_currency_index_n                                = g_header_row_normalized.index("summary_currency")
g_financial_currency_conversion_rate_mult_to_usd_index_n  = g_header_row_normalized.index("financial_currency_conversion_rate_mult_to_usd")
g_summary_currency_conversion_rate_mult_to_usd_index_n    = g_header_row_normalized.index("summary_currency_conversion_rate_mult_to_usd")
g_last_dividend_0_index_n                                 = g_header_row_normalized.index("last_dividend_0")
g_last_dividend_1_index_n                                 = g_header_row_normalized.index("last_dividend_1")
g_last_dividend_2_index_n                                 = g_header_row_normalized.index("last_dividend_2")
g_last_dividend_3_index_n                                 = g_header_row_normalized.index("last_dividend_3")
g_fifty_two_week_change_index_n                           = g_header_row_normalized.index("fifty_two_week_change")
g_fifty_two_week_low_index_n                              = g_header_row_normalized.index("fifty_two_week_low")
g_fifty_two_week_high_index_n                             = g_header_row_normalized.index("fifty_two_week_high")
g_two_hundred_day_average_index_n                         = g_header_row_normalized.index("two_hundred_day_average")
g_previous_close_percentage_from_200d_ma_index_n          = g_header_row_normalized.index("previous_close_percentage_from_200d_ma")
g_previous_close_percentage_from_52w_low_index_n          = g_header_row_normalized.index("previous_close_percentage_from_52w_low")
g_previous_close_percentage_from_52w_high_index_n         = g_header_row_normalized.index("previous_close_percentage_from_52w_high")
g_dist_from_low_factor_index_n                            = g_header_row_normalized.index("dist_from_low_factor")
g_eff_dist_from_low_factor_index_n                        = g_header_row_normalized.index("eff_dist_from_low_factor")
g_eff_dist_from_low_factor_normalized_index_n             = g_header_row_normalized.index("eff_dist_from_low_factor_normalized")
g_annualized_total_ratio_index_n                          = g_header_row_normalized.index("annualized_total_ratio")
g_quarterized_total_ratio_index_n                         = g_header_row_normalized.index("quarterized_total_ratio")
g_annualized_other_current_ratio_index_n                  = g_header_row_normalized.index("annualized_other_current_ratio")
g_quarterized_other_current_ratio_index_n                 = g_header_row_normalized.index("quarterized_other_current_ratio")
g_annualized_other_ratio_index_n                          = g_header_row_normalized.index("annualized_other_ratio")
g_quarterized_other_ratio_index_n                         = g_header_row_normalized.index("quarterized_other_ratio")
g_annualized_total_current_ratio_index_n                  = g_header_row_normalized.index("annualized_total_current_ratio")
g_quarterized_total_current_ratio_index_n                 = g_header_row_normalized.index("quarterized_total_current_ratio")
g_total_ratio_effective_index_n                           = g_header_row_normalized.index("total_ratio_effective")
g_other_current_ratio_effective_index_n                   = g_header_row_normalized.index("other_current_ratio_effective")
g_other_ratio_effective_index_n                           = g_header_row_normalized.index("other_ratio_effective")
g_total_current_ratio_effective_index_n                   = g_header_row_normalized.index("total_current_ratio_effective")
g_effective_current_ratio_index_n                         = g_header_row_normalized.index("effective_current_ratio")
g_effective_current_ratio_normalized_index_n              = g_header_row_normalized.index("effective_current_ratio_normalized")
g_annualized_total_assets_index_n                         = g_header_row_normalized.index("annualized_total_assets")
g_quarterized_total_assets_index_n                        = g_header_row_normalized.index("quarterized_total_assets")
g_effective_total_assets_index_n                          = g_header_row_normalized.index("effective_total_assets")
g_calculated_roa_index_n                                  = g_header_row_normalized.index("calculated_roa")
g_calculated_roa_normalized_index_n                       = g_header_row_normalized.index("calculated_roa_normalized")
g_annualized_working_capital_index_n                      = g_header_row_normalized.index("annualized_working_capital")
g_quarterized_working_capital_index_n                     = g_header_row_normalized.index("quarterized_working_capital")
g_effective_working_capital_index_n                       = g_header_row_normalized.index("effective_working_capital")
g_annualized_total_liabilities_index_n                    = g_header_row_normalized.index("annualized_total_liabilities")
g_quarterized_total_liabilities_index_n                   = g_header_row_normalized.index("quarterized_total_liabilities")
g_effective_total_liabilities_index_n                     = g_header_row_normalized.index("effective_total_liabilities")
g_altman_z_score_factor_index_n                           = g_header_row_normalized.index("altman_z_score_factor")
g_altman_z_score_factor_normalized_index_n                = g_header_row_normalized.index("altman_z_score_factor_normalized")
g_skip_reason_index_n                                     = g_header_row_normalized.index("skip_reason")


def check_quote_type(stock_data, research_mode):
    if stock_data.quote_type == 'MUTUALFUND' and not research_mode: # Definition of a mutual fund 'quoteType' field in base.py, those are not interesting
        print('Mutual Fund: Skip')
        return False  # Not interested in those and they lack all the below info[] properties so nothing to do with them anyways
    if stock_data.quote_type == 'ETF' and not research_mode: # Definition of a mutual fund 'quoteType' field in base.py, those are not interesting
        print('ETF: Skip')
        return False  # Not interested in those and they lack all the below info[] properties so nothing to do with them anyways
    return True


def check_sector(stock_data, sectors_list):
    # Fix stocks' Sectors to Correct Sector. yfinance sometimes has those mistaken
    if   stock_data.symbol in ['BRMG.TA',   'RLCO.TA',   'DELT.TA',  'TDRN.TA',   'ECP.TA'      ]: stock_data.sector = 'Consumer Cyclical'
    elif stock_data.symbol in ['EFNC.TA',   'GIBUI.TA',  'KMNK-M.TA'                            ]: stock_data.sector = 'Financial Services'
    elif stock_data.symbol in ['DEDR-L.TA', 'GLEX-L',    'RPAC.TA',  'CDEV.TA',   'GNRS.TA'     ]: stock_data.sector = 'Energy'
    elif stock_data.symbol in ['GLRS.TA',   'WILC.TA',   'MEDN.TA'                              ]: stock_data.sector = 'Consumer Defensive'
    elif stock_data.symbol in ['POLY.TA',   'WTS.TA',    'YBOX.TA',  'PLAZ-L.TA', 'TIGBUR.TA',
                               'ROTS.TA',   'AZRT.TA',   'SKBN.TA',  'DUNI.TA',   'DNYA.TA',
                               'HGG.TA',    'YAAC.TA',   'LZNR.TA',  'LSCO.TA',   'MGRT.TA',
                               'ALMA.TA',   'RTSN.TA',   'AVIV.TA',  'KRNV.TA',   'LAHAV.TA',
                               'NERZ.TA',   'SNEL.TA'                                           ]: stock_data.sector = 'Real Estate'
    elif stock_data.symbol in ['XTLB.TA',   'UNVO.TA',   'BONS.TA',  'CSURE.TA',  'GODM-M.TA',
                               'ILX.TA',    'LCTX.TA',   'ORMP.TA'                              ]: stock_data.sector = 'Healthcare'
    elif stock_data.symbol in ['BIRM.TA'                                                        ]: stock_data.sector = 'Industrials'
    elif stock_data.symbol in ['IGLD-M.TA'                                                      ]: stock_data.sector = 'Communication Services'
    elif stock_data.symbol in ['UNCT-L.TA', 'ROBO.TA',   'SONO.TA',  'SMAG-L.TA', 'STG.TA',
                               'BIGT-L.TA', 'BIMT-L.TA', 'BVC.TA',   'ECPA.TA',   'ELLO.TA',
                               'FLYS.TA',   'FORTY.TA',  'GFC-L.TA', 'IARG-L.TA', 'IBITEC-F.TA',
                               'MBMX-M.TA', 'MITC.TA',   'SMAG-L.TA','ORBI.TA',   'ARYT.TA',
                               'ORTC.TA',   'PERI.TA',   'PAYT.TA',  'TUZA.TA',   'ENLT.TA',
                               'ESLT.TA',   'ORA.TA',    'ENRG.TA',  'RADA.TA',   'DORL.TA',
                               'AUGN.TA',   'FRSX.TA',   'SLGN.TA',  'AQUA.TA',   'PNRG.TA',
                               'BMLK.TA',   'MSKE.TA',   'HMGS.TA',  'HICN.TA',   'ARDM.TA',
                               'ENOG.TA',   'BLND.TA',   'ARTS.TA',  'BNRG.TA',   'MIFT.TA',
                               'SNFL.TA',   'KVSR.TA',   'SNEL.TA',  'SVRT.TA',   'GIX.TA',
                               'NXFR.TA',   'FEAT-L.TA'                                         ]: stock_data.sector = 'Technology'

    if len(sectors_list) and stock_data.sector not in sectors_list:
        return False
    return True


def check_country(stock_data, countries_list):
    if len(countries_list) and stock_data.country not in countries_list:
        return False
    return True


def text_to_num(text):
    d = {
        'K': 1000,
        'M': 1000000,
        'B': 1000000000,
        'T': 1000000000000
    }
    if not isinstance(text, str):
        # Non-strings are bad are missing data in poster's submission
        return 0

    text = text.replace(' ','')

    if text[-1] in d:  # separate out the K, M, B or T
        num, magnitude = text[:-1], text[-1]
        return int(float(num) * d[magnitude])
    else:
        return float(text)


def weighted_average(values_list, weights):
    if VERBOSE_LOGS: print("[{} weighted_average]".format(__name__))
    return sum([values_list[i]*weights[i] for i in range(len(values_list))])/sum(weights)


def sss_core_equation_value_set(stock_data):
    if VERBOSE_LOGS: print("[{} sss_core_equation_value_set]".format(__name__))
    if stock_data.shares_outstanding and stock_data.net_income_to_common_shareholders != None: stock_data.nitcsh_to_shares_outstanding = float(stock_data.net_income_to_common_shareholders) / float(stock_data.shares_outstanding)
    if stock_data.employees          and stock_data.net_income_to_common_shareholders != None: stock_data.nitcsh_to_num_employees = float(stock_data.net_income_to_common_shareholders) / float(stock_data.employees)

    if stock_data.trailing_12months_price_to_sales != None and stock_data.trailing_12months_price_to_sales > 0 and stock_data.effective_profit_margin != None and stock_data.effective_profit_margin > 0 and stock_data.eqg_factor_effective and stock_data.eqg_factor_effective > 0 != None and stock_data.rqg_factor_effective and stock_data.rqg_factor_effective > 0 != None and stock_data.pe_effective != None and stock_data.pe_effective > 0 and stock_data.effective_ev_to_ebitda != None and stock_data.effective_ev_to_ebitda > 0 and stock_data.ev_to_cfo_ratio_effective != None and stock_data.ev_to_cfo_ratio_effective > 0 and stock_data.effective_peg_ratio != None and stock_data.effective_peg_ratio > 0 and stock_data.price_to_book != None and stock_data.price_to_book > 0 and stock_data.debt_to_equity_effective > 0 and stock_data.total_ratio_effective > 0 and stock_data.total_current_ratio_effective > 0 and stock_data.evr_effective != None and stock_data.evr_effective > 0.0 and stock_data.calculated_roa != None and stock_data.calculated_roa > 0 and stock_data.altman_z_score_factor != None and stock_data.altman_z_score_factor > 0:
        stock_data.sss_value = float(stock_data.eff_dist_from_low_factor * ((stock_data.evr_effective * stock_data.pe_effective * stock_data.effective_ev_to_ebitda * stock_data.trailing_12months_price_to_sales * stock_data.price_to_book) / (stock_data.effective_profit_margin * stock_data.effective_current_ratio * stock_data.calculated_roa)) * ((stock_data.effective_peg_ratio * stock_data.ev_to_cfo_ratio_effective * stock_data.debt_to_equity_effective_used) / (stock_data.eqg_factor_effective * stock_data.rqg_factor_effective * stock_data.altman_z_score_factor)))  # The lower  the better
    else:
        stock_data.sss_value = BAD_SSS


def get_used_parameters_names_in_core_equation():
    numerator_parameters_list   = ["eff_dist_from_low_factor", "evr_effective", "pe_effective", "effective_ev_to_ebitda", "trailing_12months_price_to_sales", "price_to_book",        "effective_peg_ratio", "ev_to_cfo_ratio_effective", "debt_to_equity_effective_used"]  # The lower  the better
    denominator_parameters_list = ["effective_profit_margin",  "effective_current_ratio",       "calculated_roa",         "eqg_factor_effective",             "rqg_factor_effective", "altman_z_score_factor"                                                            ]  # The higher the better
    return [numerator_parameters_list, denominator_parameters_list]


# Rounding to non-None values + set None values to 0 for simplicity:
def round_and_avoid_none_values(stock_data):
    if stock_data.sss_value                                       != None: stock_data.sss_value                                       = round(stock_data.sss_value,                                       NUM_ROUND_DECIMALS)
    if stock_data.annualized_revenue                              != None: stock_data.annualized_revenue                              = round(stock_data.annualized_revenue,                              NUM_ROUND_DECIMALS)
    if stock_data.annualized_earnings                             != None: stock_data.annualized_earnings                             = round(stock_data.annualized_earnings,                             NUM_ROUND_DECIMALS)
    if stock_data.annualized_retained_earnings                    != None: stock_data.annualized_retained_earnings                    = round(stock_data.annualized_retained_earnings,                    NUM_ROUND_DECIMALS)
    if stock_data.quarterized_revenue                             != None: stock_data.quarterized_revenue                             = round(stock_data.quarterized_revenue,                             NUM_ROUND_DECIMALS)
    if stock_data.quarterized_earnings                            != None: stock_data.quarterized_earnings                            = round(stock_data.quarterized_earnings,                            NUM_ROUND_DECIMALS)
    if stock_data.quarterized_retained_earnings                   != None: stock_data.quarterized_retained_earnings                   = round(stock_data.quarterized_retained_earnings,                   NUM_ROUND_DECIMALS)
    if stock_data.effective_earnings                              != None: stock_data.effective_earnings                              = round(stock_data.effective_earnings,                              NUM_ROUND_DECIMALS)
    if stock_data.effective_retained_earnings                     != None: stock_data.effective_retained_earnings                     = round(stock_data.effective_retained_earnings,                     NUM_ROUND_DECIMALS)
    if stock_data.effective_revenue                               != None: stock_data.effective_revenue                               = round(stock_data.effective_revenue,                               NUM_ROUND_DECIMALS)
    if stock_data.annualized_total_revenue                        != None: stock_data.annualized_total_revenue                        = round(stock_data.annualized_total_revenue,                        NUM_ROUND_DECIMALS)
    if stock_data.annualized_net_income                           != None: stock_data.annualized_net_income                           = round(stock_data.annualized_net_income,                           NUM_ROUND_DECIMALS)
    if stock_data.quarterized_total_revenue                       != None: stock_data.quarterized_total_revenue                       = round(stock_data.quarterized_total_revenue,                       NUM_ROUND_DECIMALS)
    if stock_data.quarterized_net_income                          != None: stock_data.quarterized_net_income                          = round(stock_data.quarterized_net_income,                          NUM_ROUND_DECIMALS)
    if stock_data.effective_net_income                            != None: stock_data.effective_net_income                            = round(stock_data.effective_net_income,                            NUM_ROUND_DECIMALS)
    if stock_data.effective_total_revenue                         != None: stock_data.effective_total_revenue                         = round(stock_data.effective_total_revenue,                         NUM_ROUND_DECIMALS)
    if stock_data.enterprise_value_to_revenue                     != None: stock_data.enterprise_value_to_revenue                     = round(stock_data.enterprise_value_to_revenue,                     NUM_ROUND_DECIMALS)
    if stock_data.evr_effective                                   != None: stock_data.evr_effective                                   = round(stock_data.evr_effective,                                   NUM_ROUND_DECIMALS)
    if stock_data.trailing_price_to_earnings                      != None: stock_data.trailing_price_to_earnings                      = round(stock_data.trailing_price_to_earnings,                      NUM_ROUND_DECIMALS)
    if stock_data.forward_price_to_earnings                       != None: stock_data.forward_price_to_earnings                       = round(stock_data.forward_price_to_earnings,                       NUM_ROUND_DECIMALS)
    if stock_data.effective_price_to_earnings                     != None: stock_data.effective_price_to_earnings                     = round(stock_data.effective_price_to_earnings,                     NUM_ROUND_DECIMALS)
    if stock_data.trailing_12months_price_to_sales                != None: stock_data.trailing_12months_price_to_sales                = round(stock_data.trailing_12months_price_to_sales,                NUM_ROUND_DECIMALS)
    if stock_data.pe_effective                                    != None: stock_data.pe_effective                                    = round(stock_data.pe_effective,                                    NUM_ROUND_DECIMALS)
    if stock_data.enterprise_value_to_ebitda                      != None: stock_data.enterprise_value_to_ebitda                      = round(stock_data.enterprise_value_to_ebitda,                      NUM_ROUND_DECIMALS)
    if stock_data.effective_ev_to_ebitda                          != None: stock_data.effective_ev_to_ebitda                          = round(stock_data.effective_ev_to_ebitda,                          NUM_ROUND_DECIMALS)
    if stock_data.ebitda                                          != None: stock_data.ebitda                                          = round(stock_data.ebitda,                                          NUM_ROUND_DECIMALS)
    if stock_data.quarterized_ebitd                               != None: stock_data.quarterized_ebitd                               = round(stock_data.quarterized_ebitd,                               NUM_ROUND_DECIMALS)
    if stock_data.annualized_ebitd                                != None: stock_data.annualized_ebitd                                = round(stock_data.annualized_ebitd,                                NUM_ROUND_DECIMALS)
    if stock_data.ebitd                                           != None: stock_data.ebitd                                           = round(stock_data.ebitd,                                           NUM_ROUND_DECIMALS)
    if stock_data.profit_margin                                   != None: stock_data.profit_margin                                   = round(stock_data.profit_margin,                                   NUM_ROUND_DECIMALS)
    if stock_data.annualized_profit_margin                        != None: stock_data.annualized_profit_margin                        = round(stock_data.annualized_profit_margin,                        NUM_ROUND_DECIMALS)
    if stock_data.annualized_profit_margin_boost                  != None: stock_data.annualized_profit_margin_boost                  = round(stock_data.annualized_profit_margin_boost,                  NUM_ROUND_DECIMALS)
    if stock_data.quarterized_profit_margin                       != None: stock_data.quarterized_profit_margin                       = round(stock_data.quarterized_profit_margin,                       NUM_ROUND_DECIMALS)
    if stock_data.quarterized_profit_margin_boost                 != None: stock_data.quarterized_profit_margin_boost                 = round(stock_data.quarterized_profit_margin_boost,                 NUM_ROUND_DECIMALS)
    if stock_data.effective_profit_margin                         != None: stock_data.effective_profit_margin                         = round(stock_data.effective_profit_margin,                         NUM_ROUND_DECIMALS)
    if stock_data.held_percent_institutions                       != None: stock_data.held_percent_institutions                       = round(stock_data.held_percent_institutions,                       NUM_ROUND_DECIMALS)
    if stock_data.forward_eps                                     != None: stock_data.forward_eps                                     = round(stock_data.forward_eps,                                     NUM_ROUND_DECIMALS)
    if stock_data.trailing_eps                                    != None: stock_data.trailing_eps                                    = round(stock_data.trailing_eps,                                    NUM_ROUND_DECIMALS)
    if stock_data.previous_close                                  != None: stock_data.previous_close                                  = round(stock_data.previous_close,                                  NUM_ROUND_DECIMALS)
    if stock_data.trailing_eps_percentage                         != None: stock_data.trailing_eps_percentage                         = round(stock_data.trailing_eps_percentage,                         NUM_ROUND_DECIMALS)
    if stock_data.price_to_book                                   != None: stock_data.price_to_book                                   = round(stock_data.price_to_book,                                   NUM_ROUND_DECIMALS)
    if stock_data.shares_outstanding                              != None: stock_data.shares_outstanding                              = round(stock_data.shares_outstanding,                              NUM_ROUND_DECIMALS)
    if stock_data.net_income_to_common_shareholders               != None: stock_data.net_income_to_common_shareholders               = round(stock_data.net_income_to_common_shareholders,               NUM_ROUND_DECIMALS)
    if stock_data.nitcsh_to_shares_outstanding                    != None: stock_data.nitcsh_to_shares_outstanding                    = round(stock_data.nitcsh_to_shares_outstanding,                    NUM_ROUND_DECIMALS)
    if stock_data.employees                                       != None: stock_data.employees                                       = int(  stock_data.employees                                                          )
    if stock_data.enterprise_value                                != None: stock_data.enterprise_value                                = int(  stock_data.enterprise_value                                                   )
    if stock_data.market_cap                                      != None: stock_data.market_cap                                      = int(  stock_data.market_cap                                                         )
    if stock_data.nitcsh_to_num_employees                         != None: stock_data.nitcsh_to_num_employees                         = round(stock_data.nitcsh_to_num_employees,                         NUM_ROUND_DECIMALS)
    if stock_data.eqg                                             != None: stock_data.eqg                                             = round(stock_data.eqg,                                             NUM_ROUND_DECIMALS)
    if stock_data.rqg                                             != None: stock_data.rqg                                             = round(stock_data.rqg,                                             NUM_ROUND_DECIMALS)
    if stock_data.eqg_yoy                                         != None: stock_data.eqg_yoy                                         = round(stock_data.eqg_yoy,                                         NUM_ROUND_DECIMALS)
    if stock_data.rqg_yoy                                         != None: stock_data.rqg_yoy                                         = round(stock_data.rqg_yoy,                                         NUM_ROUND_DECIMALS)
    if stock_data.niqg_yoy                                        != None: stock_data.niqg_yoy                                        = round(stock_data.niqg_yoy,                                        NUM_ROUND_DECIMALS)
    if stock_data.trqg_yoy                                        != None: stock_data.trqg_yoy                                        = round(stock_data.trqg_yoy,                                        NUM_ROUND_DECIMALS)
    if stock_data.eqg_effective                                   != None: stock_data.eqg_effective                                   = round(stock_data.eqg_effective,                                   NUM_ROUND_DECIMALS)
    if stock_data.eqg_factor_effective                            != None: stock_data.eqg_factor_effective                            = round(stock_data.eqg_factor_effective,                            NUM_ROUND_DECIMALS)
    if stock_data.rqg_effective                                   != None: stock_data.rqg_effective                                   = round(stock_data.rqg_effective,                                   NUM_ROUND_DECIMALS)
    if stock_data.rqg_factor_effective                            != None: stock_data.rqg_factor_effective                            = round(stock_data.rqg_factor_effective,                            NUM_ROUND_DECIMALS)
    if stock_data.price_to_earnings_to_growth_ratio               != None: stock_data.price_to_earnings_to_growth_ratio               = round(stock_data.price_to_earnings_to_growth_ratio,               NUM_ROUND_DECIMALS)
    if stock_data.effective_peg_ratio                             != None: stock_data.effective_peg_ratio                             = round(stock_data.effective_peg_ratio,                             NUM_ROUND_DECIMALS)
    if stock_data.annualized_cash_flow_from_operating_activities  != None: stock_data.annualized_cash_flow_from_operating_activities  = round(stock_data.annualized_cash_flow_from_operating_activities,  NUM_ROUND_DECIMALS)
    if stock_data.quarterized_cash_flow_from_operating_activities != None: stock_data.quarterized_cash_flow_from_operating_activities = round(stock_data.quarterized_cash_flow_from_operating_activities, NUM_ROUND_DECIMALS)
    if stock_data.annualized_ev_to_cfo_ratio                      != None: stock_data.annualized_ev_to_cfo_ratio                      = round(stock_data.annualized_ev_to_cfo_ratio,                      NUM_ROUND_DECIMALS)
    if stock_data.quarterized_ev_to_cfo_ratio                     != None: stock_data.quarterized_ev_to_cfo_ratio                     = round(stock_data.quarterized_ev_to_cfo_ratio,                     NUM_ROUND_DECIMALS)
    if stock_data.ev_to_cfo_ratio_effective                       != None: stock_data.ev_to_cfo_ratio_effective                       = round(stock_data.ev_to_cfo_ratio_effective,                       NUM_ROUND_DECIMALS)
    if stock_data.annualized_debt_to_equity                       != None: stock_data.annualized_debt_to_equity                       = round(stock_data.annualized_debt_to_equity,                       NUM_ROUND_DECIMALS)
    if stock_data.quarterized_debt_to_equity                      != None: stock_data.quarterized_debt_to_equity                      = round(stock_data.quarterized_debt_to_equity,                      NUM_ROUND_DECIMALS)
    if stock_data.debt_to_equity_effective                        != None: stock_data.debt_to_equity_effective                        = round(stock_data.debt_to_equity_effective,                        NUM_ROUND_DECIMALS)
    if stock_data.debt_to_equity_effective_used                   != None: stock_data.debt_to_equity_effective_used                   = round(stock_data.debt_to_equity_effective_used,                   NUM_ROUND_DECIMALS)
    if stock_data.financial_currency_conversion_rate_mult_to_usd  != None: stock_data.financial_currency_conversion_rate_mult_to_usd  = round(stock_data.financial_currency_conversion_rate_mult_to_usd,  NUM_ROUND_DECIMALS)
    if stock_data.summary_currency_conversion_rate_mult_to_usd    != None: stock_data.summary_currency_conversion_rate_mult_to_usd    = round(stock_data.summary_currency_conversion_rate_mult_to_usd,    NUM_ROUND_DECIMALS)
    if stock_data.last_dividend_0                                 != None: stock_data.last_dividend_0                                 = round(stock_data.last_dividend_0,                                 NUM_ROUND_DECIMALS)
    if stock_data.last_dividend_1                                 != None: stock_data.last_dividend_1                                 = round(stock_data.last_dividend_1,                                 NUM_ROUND_DECIMALS)
    if stock_data.last_dividend_2                                 != None: stock_data.last_dividend_2                                 = round(stock_data.last_dividend_2,                                 NUM_ROUND_DECIMALS)
    if stock_data.last_dividend_3                                 != None: stock_data.last_dividend_3                                 = round(stock_data.last_dividend_3,                                 NUM_ROUND_DECIMALS)
    if stock_data.fifty_two_week_change                           != None: stock_data.fifty_two_week_change                           = round(stock_data.fifty_two_week_change,                           NUM_ROUND_DECIMALS)
    if stock_data.fifty_two_week_low                              != None: stock_data.fifty_two_week_low                              = round(stock_data.fifty_two_week_low,                              NUM_ROUND_DECIMALS)
    if stock_data.fifty_two_week_high                             != None: stock_data.fifty_two_week_high                             = round(stock_data.fifty_two_week_high,                             NUM_ROUND_DECIMALS)
    if stock_data.two_hundred_day_average                         != None: stock_data.two_hundred_day_average                         = round(stock_data.two_hundred_day_average,                         NUM_ROUND_DECIMALS)
    if stock_data.previous_close_percentage_from_200d_ma          != None: stock_data.previous_close_percentage_from_200d_ma          = round(stock_data.previous_close_percentage_from_200d_ma,          NUM_ROUND_DECIMALS)
    if stock_data.previous_close_percentage_from_52w_low          != None: stock_data.previous_close_percentage_from_52w_low          = round(stock_data.previous_close_percentage_from_52w_low,          NUM_ROUND_DECIMALS)
    if stock_data.previous_close_percentage_from_52w_high         != None: stock_data.previous_close_percentage_from_52w_high         = round(stock_data.previous_close_percentage_from_52w_high,         NUM_ROUND_DECIMALS)
    if stock_data.dist_from_low_factor                            != None: stock_data.dist_from_low_factor                            = round(stock_data.dist_from_low_factor,                            NUM_ROUND_DECIMALS)
    if stock_data.eff_dist_from_low_factor                        != None: stock_data.eff_dist_from_low_factor                        = round(stock_data.eff_dist_from_low_factor,                        NUM_ROUND_DECIMALS)
    if stock_data.annualized_total_ratio                          != None: stock_data.annualized_total_ratio                          = round(stock_data.annualized_total_ratio,                          NUM_ROUND_DECIMALS)
    if stock_data.quarterized_total_ratio                         != None: stock_data.quarterized_total_ratio                         = round(stock_data.quarterized_total_ratio,                         NUM_ROUND_DECIMALS)
    if stock_data.annualized_other_current_ratio                  != None: stock_data.annualized_other_current_ratio                  = round(stock_data.annualized_other_current_ratio,                  NUM_ROUND_DECIMALS)
    if stock_data.quarterized_other_current_ratio                 != None: stock_data.quarterized_other_current_ratio                 = round(stock_data.quarterized_other_current_ratio,                 NUM_ROUND_DECIMALS)
    if stock_data.annualized_other_ratio                          != None: stock_data.annualized_other_ratio                          = round(stock_data.annualized_other_ratio,                          NUM_ROUND_DECIMALS)
    if stock_data.quarterized_other_ratio                         != None: stock_data.quarterized_other_ratio                         = round(stock_data.quarterized_other_ratio,                         NUM_ROUND_DECIMALS)
    if stock_data.annualized_total_current_ratio                  != None: stock_data.annualized_total_current_ratio                  = round(stock_data.annualized_total_current_ratio,                  NUM_ROUND_DECIMALS)
    if stock_data.quarterized_total_current_ratio                 != None: stock_data.quarterized_total_current_ratio                 = round(stock_data.quarterized_total_current_ratio,                 NUM_ROUND_DECIMALS)
    if stock_data.total_ratio_effective                           != None: stock_data.total_ratio_effective                           = round(stock_data.total_ratio_effective,                           NUM_ROUND_DECIMALS)
    if stock_data.other_current_ratio_effective                   != None: stock_data.other_current_ratio_effective                   = round(stock_data.other_current_ratio_effective,                   NUM_ROUND_DECIMALS)
    if stock_data.other_ratio_effective                           != None: stock_data.other_ratio_effective                           = round(stock_data.other_ratio_effective,                           NUM_ROUND_DECIMALS)
    if stock_data.total_current_ratio_effective                   != None: stock_data.total_current_ratio_effective                   = round(stock_data.total_current_ratio_effective,                   NUM_ROUND_DECIMALS)
    if stock_data.effective_current_ratio                         != None: stock_data.effective_current_ratio                         = round(stock_data.effective_current_ratio,                         NUM_ROUND_DECIMALS)
    if stock_data.annualized_total_assets                         != None: stock_data.annualized_total_assets                         = round(stock_data.annualized_total_assets,                         NUM_ROUND_DECIMALS)
    if stock_data.quarterized_total_assets                        != None: stock_data.quarterized_total_assets                        = round(stock_data.quarterized_total_assets,                        NUM_ROUND_DECIMALS)
    if stock_data.effective_total_assets                          != None: stock_data.effective_total_assets                          = round(stock_data.effective_total_assets,                          NUM_ROUND_DECIMALS)
    if stock_data.calculated_roa                                  != None: stock_data.calculated_roa                                  = round(stock_data.calculated_roa,                                  NUM_ROUND_DECIMALS)
    if stock_data.annualized_working_capital                      != None: stock_data.annualized_working_capital                      = round(stock_data.annualized_working_capital,                      NUM_ROUND_DECIMALS)
    if stock_data.quarterized_working_capital                     != None: stock_data.quarterized_working_capital                     = round(stock_data.quarterized_working_capital,                     NUM_ROUND_DECIMALS)
    if stock_data.effective_working_capital                       != None: stock_data.effective_working_capital                       = round(stock_data.effective_working_capital,                       NUM_ROUND_DECIMALS)
    if stock_data.annualized_total_liabilities                    != None: stock_data.annualized_total_liabilities                    = round(stock_data.annualized_total_liabilities,                    NUM_ROUND_DECIMALS)
    if stock_data.quarterized_total_liabilities                   != None: stock_data.quarterized_total_liabilities                   = round(stock_data.quarterized_total_liabilities,                   NUM_ROUND_DECIMALS)
    if stock_data.effective_total_liabilities                     != None: stock_data.effective_total_liabilities                     = round(stock_data.effective_total_liabilities,                     NUM_ROUND_DECIMALS)
    if stock_data.altman_z_score_factor                           != None: stock_data.altman_z_score_factor                           = round(stock_data.altman_z_score_factor,                           NUM_ROUND_DECIMALS)

    # TODO: ASAFR: Unify below with above to single line for each parameter
    if stock_data.sss_value                                       is None: stock_data.sss_value                                       = BAD_SSS
    if stock_data.annualized_revenue                              is None: stock_data.annualized_revenue                              = 0
    if stock_data.annualized_earnings                             is None: stock_data.annualized_earnings                             = 0
    if stock_data.annualized_retained_earnings                    is None: stock_data.annualized_retained_earnings                    = 0
    if stock_data.quarterized_revenue                             is None: stock_data.quarterized_revenue                             = 0
    if stock_data.quarterized_earnings                            is None: stock_data.quarterized_earnings                            = 0
    if stock_data.quarterized_retained_earnings                   is None: stock_data.quarterized_retained_earnings                   = 0
    if stock_data.effective_earnings                              is None: stock_data.effective_earnings                              = 0
    if stock_data.effective_retained_earnings                     is None: stock_data.effective_retained_earnings                     = 0
    if stock_data.effective_revenue                               is None: stock_data.effective_revenue                               = 0
    if stock_data.annualized_total_revenue                        is None: stock_data.annualized_total_revenue                        = 0
    if stock_data.annualized_net_income                           is None: stock_data.annualized_net_income                           = 0
    if stock_data.quarterized_total_revenue                       is None: stock_data.quarterized_total_revenue                       = 0
    if stock_data.quarterized_net_income                          is None: stock_data.quarterized_net_income                          = 0
    if stock_data.effective_net_income                            is None: stock_data.effective_net_income                            = 0
    if stock_data.effective_total_revenue                         is None: stock_data.effective_total_revenue                         = 0
    if stock_data.enterprise_value_to_revenue                     is None: stock_data.enterprise_value_to_revenue                     = 0
    if stock_data.evr_effective                                   is None: stock_data.evr_effective                                   = 0
    if stock_data.trailing_price_to_earnings                      is None: stock_data.trailing_price_to_earnings                      = 0
    if stock_data.forward_price_to_earnings                       is None: stock_data.forward_price_to_earnings                       = 0
    if stock_data.effective_price_to_earnings                     is None: stock_data.effective_price_to_earnings                     = 0
    if stock_data.trailing_12months_price_to_sales                is None: stock_data.trailing_12months_price_to_sales                = 0
    if stock_data.pe_effective                                    is None: stock_data.pe_effective                                    = 0
    if stock_data.enterprise_value_to_ebitda                      is None: stock_data.enterprise_value_to_ebitda                      = 0
    if stock_data.effective_ev_to_ebitda                          is None: stock_data.effective_ev_to_ebitda                          = 0
    if stock_data.ebitda                                          is None: stock_data.ebitda                                          = 0
    if stock_data.quarterized_ebitd                               is None: stock_data.quarterized_ebitd                               = 0
    if stock_data.annualized_ebitd                                is None: stock_data.annualized_ebitd                                = 0
    if stock_data.ebitd                                           is None: stock_data.ebitd                                           = 0
    if stock_data.profit_margin                                   is None: stock_data.profit_margin                                   = 0
    if stock_data.annualized_profit_margin                        is None: stock_data.annualized_profit_margin                        = 0
    if stock_data.annualized_profit_margin_boost                  is None: stock_data.annualized_profit_margin_boost                  = 0
    if stock_data.quarterized_profit_margin                       is None: stock_data.quarterized_profit_margin                       = 0
    if stock_data.quarterized_profit_margin_boost                 is None: stock_data.quarterized_profit_margin_boost                 = 0
    if stock_data.effective_profit_margin                         is None: stock_data.effective_profit_margin                         = 0
    if stock_data.held_percent_institutions                       is None: stock_data.held_percent_institutions                       = 0
    if stock_data.forward_eps                                     is None: stock_data.forward_eps                                     = 0
    if stock_data.trailing_eps                                    is None: stock_data.trailing_eps                                    = 0
    if stock_data.previous_close                                  is None: stock_data.previous_close                                  = 0
    if stock_data.trailing_eps_percentage                         is None: stock_data.trailing_eps_percentage                         = 0
    if stock_data.price_to_book                                   is None: stock_data.price_to_book                                   = 0
    if stock_data.shares_outstanding                              is None: stock_data.shares_outstanding                              = 0
    if stock_data.net_income_to_common_shareholders               is None: stock_data.net_income_to_common_shareholders               = 0
    if stock_data.nitcsh_to_shares_outstanding                    is None: stock_data.nitcsh_to_shares_outstanding                    = 0
    if stock_data.employees                                       is None: stock_data.employees                                       = 0
    if stock_data.enterprise_value                                is None: stock_data.enterprise_value                                = 0
    if stock_data.market_cap                                      is None: stock_data.market_cap                                      = 0
    if stock_data.nitcsh_to_num_employees                         is None: stock_data.nitcsh_to_num_employees                         = 0
    if stock_data.eqg                                             is None: stock_data.eqg                                             = 0
    if stock_data.rqg                                             is None: stock_data.rqg                                             = 0
    if stock_data.eqg_yoy                                         is None: stock_data.eqg_yoy                                         = 0
    if stock_data.rqg_yoy                                         is None: stock_data.rqg_yoy                                         = 0
    if stock_data.niqg_yoy                                        is None: stock_data.niqg_yoy                                        = 0
    if stock_data.trqg_yoy                                        is None: stock_data.trqg_yoy                                        = 0
    if stock_data.eqg_effective                                   is None: stock_data.eqg_effective                                   = 0
    if stock_data.eqg_factor_effective                            is None: stock_data.eqg_factor_effective                            = 0
    if stock_data.rqg_effective                                   is None: stock_data.rqg_effective                                   = 0
    if stock_data.rqg_factor_effective                            is None: stock_data.rqg_factor_effective                            = 0
    if stock_data.price_to_earnings_to_growth_ratio               is None: stock_data.price_to_earnings_to_growth_ratio               = 0
    if stock_data.effective_peg_ratio                             is None: stock_data.effective_peg_ratio                             = 0
    if stock_data.annualized_cash_flow_from_operating_activities  is None: stock_data.annualized_cash_flow_from_operating_activities  = 0
    if stock_data.quarterized_cash_flow_from_operating_activities is None: stock_data.quarterized_cash_flow_from_operating_activities = 0
    if stock_data.annualized_ev_to_cfo_ratio                      is None: stock_data.annualized_ev_to_cfo_ratio                      = 0
    if stock_data.quarterized_ev_to_cfo_ratio                     is None: stock_data.quarterized_ev_to_cfo_ratio                     = 0
    if stock_data.ev_to_cfo_ratio_effective                       is None: stock_data.ev_to_cfo_ratio_effective                       = 0
    if stock_data.annualized_debt_to_equity                       is None: stock_data.annualized_debt_to_equity                       = 0
    if stock_data.quarterized_debt_to_equity                      is None: stock_data.quarterized_debt_to_equity                      = 0
    if stock_data.debt_to_equity_effective                        is None: stock_data.debt_to_equity_effective                        = 0
    if stock_data.debt_to_equity_effective_used                   is None: stock_data.debt_to_equity_effective_used                   = 0
    if stock_data.financial_currency_conversion_rate_mult_to_usd  is None: stock_data.financial_currency_conversion_rate_mult_to_usd  = 0
    if stock_data.summary_currency_conversion_rate_mult_to_usd    is None: stock_data.summary_currency_conversion_rate_mult_to_usd    = 0
    if stock_data.last_dividend_0                                 is None: stock_data.last_dividend_0                                 = 0
    if stock_data.last_dividend_1                                 is None: stock_data.last_dividend_1                                 = 0
    if stock_data.last_dividend_2                                 is None: stock_data.last_dividend_2                                 = 0
    if stock_data.last_dividend_3                                 is None: stock_data.last_dividend_3                                 = 0
    if stock_data.fifty_two_week_change                           is None: stock_data.fifty_two_week_change                           = 0
    if stock_data.fifty_two_week_low                              is None: stock_data.fifty_two_week_low                              = 0
    if stock_data.fifty_two_week_high                             is None: stock_data.fifty_two_week_high                             = 0
    if stock_data.two_hundred_day_average                         is None: stock_data.two_hundred_day_average                         = 0
    if stock_data.previous_close_percentage_from_200d_ma          is None: stock_data.previous_close_percentage_from_200d_ma          = 0
    if stock_data.previous_close_percentage_from_52w_low          is None: stock_data.previous_close_percentage_from_52w_low          = 0
    if stock_data.previous_close_percentage_from_52w_high         is None: stock_data.previous_close_percentage_from_52w_high         = 0
    if stock_data.dist_from_low_factor                            is None: stock_data.dist_from_low_factor                            = 0
    if stock_data.eff_dist_from_low_factor                        is None: stock_data.eff_dist_from_low_factor                        = 0
    if stock_data.annualized_total_ratio                          is None: stock_data.annualized_total_ratio                          = 0
    if stock_data.quarterized_total_ratio                         is None: stock_data.quarterized_total_ratio                         = 0
    if stock_data.annualized_other_current_ratio                  is None: stock_data.annualized_other_current_ratio                  = 0
    if stock_data.quarterized_other_current_ratio                 is None: stock_data.quarterized_other_current_ratio                 = 0
    if stock_data.annualized_other_ratio                          is None: stock_data.annualized_other_ratio                          = 0
    if stock_data.quarterized_other_ratio                         is None: stock_data.quarterized_other_ratio                         = 0
    if stock_data.annualized_total_current_ratio                  is None: stock_data.annualized_total_current_ratio                  = 0
    if stock_data.quarterized_total_current_ratio                 is None: stock_data.quarterized_total_current_ratio                 = 0
    if stock_data.total_ratio_effective                           is None: stock_data.total_ratio_effective                           = 0
    if stock_data.other_current_ratio_effective                   is None: stock_data.other_current_ratio_effective                   = 0
    if stock_data.other_ratio_effective                           is None: stock_data.other_ratio_effective                           = 0
    if stock_data.total_current_ratio_effective                   is None: stock_data.total_current_ratio_effective                   = 0
    if stock_data.effective_current_ratio                         is None: stock_data.effective_current_ratio                         = 0
    if stock_data.annualized_total_assets                         is None: stock_data.annualized_total_assets                         = 0
    if stock_data.quarterized_total_assets                        is None: stock_data.quarterized_total_assets                        = 0
    if stock_data.effective_total_assets                          is None: stock_data.effective_total_assets                          = 0
    if stock_data.calculated_roa                                  is None: stock_data.calculated_roa                                  = 0
    if stock_data.annualized_working_capital                      is None: stock_data.annualized_working_capital                      = 0
    if stock_data.quarterized_working_capital                     is None: stock_data.quarterized_working_capital                     = 0
    if stock_data.effective_working_capital                       is None: stock_data.effective_working_capital                       = 0
    if stock_data.annualized_total_liabilities                    is None: stock_data.annualized_total_liabilities                    = 0
    if stock_data.quarterized_total_liabilities                   is None: stock_data.quarterized_total_liabilities                   = 0
    if stock_data.effective_total_liabilities                     is None: stock_data.effective_total_liabilities                     = 0
    if stock_data.altman_z_score_factor                           is None: stock_data.altman_z_score_factor                           = 0


def calculate_weighted_stock_data_on_dict(dict_input, dict_name, str_in_dict, weights, stock_data, reverse_required, force_only_sum=False):
    if VERBOSE_LOGS: print("[{} calculate_weighted_stock_data_on_dict]".format(__name__))
    weight_index  = 0
    weighted_list = []
    weights_sum   = 0
    try:
        if str_in_dict is None:
            for key in (reversed(list(dict_input)) if reverse_required else list(dict_input)):
                weighted_list.append((float(dict_input[key])) * weights[weight_index])
                weights_sum += weights[weight_index]
                weight_index += 1
        else:
            for key in (reversed(list(dict_input)) if reverse_required else list(dict_input)):  # The 1st element will be the oldest, receiving the lowest weight
                if str_in_dict in dict_input[key] and not math.isnan(dict_input[key][str_in_dict]):
                    weighted_list.append(dict_input[key][str_in_dict] * weights[weight_index])
                    weights_sum  += weights[weight_index]
                    weight_index += 1
        if weights_sum > 0:
            return_value = stock_data.financial_currency_conversion_rate_mult_to_usd * sum(weighted_list) / (1 if force_only_sum else weights_sum)  # Multiplying by the factor to get the valu in USD.
        else:
            return_value = None
    except Exception as e:
        print("Exception in {} {}}: {}".format(stock_data.symbol, dict_name, e))
        return_value = None
        pass

    return return_value


def calculate_weighted_ratio_from_dict(dict_input, dict_name, str_in_dict_numerator, str_in_dict_denominator, weights, stock_data, default_return_value, reverse_required):
    if VERBOSE_LOGS: print("[{} calculate_weighted_ratio_from_dict]".format(__name__))
    return_value         = default_return_value
    weighted_ratios_list = []
    try:
        for key in (reversed(list(dict_input)) if reverse_required else list(dict_input)):  # The 1st element will be the oldest, receiving the lowest weight
            if str_in_dict_denominator in dict_input[key] and not math.isnan(dict_input[key][str_in_dict_denominator]) and str_in_dict_numerator in dict_input[key] and not math.isnan(dict_input[key][str_in_dict_numerator]):
                weighted_ratios_list.append((dict_input[key][str_in_dict_numerator] / dict_input[key][str_in_dict_denominator]))
    except Exception as e:
        print("Exception in {} {}}: {}".format(stock_data.symbol, dict_name, e))
        pass
    if len(weighted_ratios_list): return_value = weighted_average(weighted_ratios_list, weights[:len(weighted_ratios_list)])

    return return_value


def calculate_weighted_diff_from_dict(dict_input, dict_name, str_in_dict_left_term, str_in_dict_right_term, weights, stock_data, default_return_value, reverse_required, force_only_sum=False):
    if VERBOSE_LOGS: print("[{} calculate_weighted_diff_from_dict]".format(__name__))
    return_value         = default_return_value
    weighted_diffs_list = []
    try:
        for key in (reversed(list(dict_input)) if reverse_required else list(dict_input)):  # The 1st element will be the oldest, receiving the lowest weight
            if str_in_dict_right_term in dict_input[key] and not math.isnan(dict_input[key][str_in_dict_right_term]) and str_in_dict_left_term in dict_input[key] and not math.isnan(dict_input[key][str_in_dict_left_term]):
                weighted_diffs_list.append((dict_input[key][str_in_dict_left_term] - dict_input[key][str_in_dict_right_term]))
    except Exception as e:
        print("Exception in {} {}}: {}".format(stock_data.symbol, dict_name, e))
        pass
    if len(weighted_diffs_list): return_value = sum(weighted_diffs_list) if force_only_sum else weighted_average(weighted_diffs_list, weights[:len(weighted_diffs_list)])

    return return_value


def calculate_weighted_sum_from_2_dicts(dict1_input, dict1_name, str_in_dict1, dict2_input, dict2_name, str_in_dict2, weights, stock_data, default_return_value, reverse_required1, reverse_required2, force_only_sum=False):
    if VERBOSE_LOGS: print("[{} calculate_weighted_sum_from_2_dicts]".format(__name__))
    return_value       = default_return_value
    weighted_sums_list = []
    try:
        for key in (reversed(list(dict1_input)) if reverse_required1 else list(dict1_input)):  # The 1st element will be the oldest, receiving the lowest weight
            if str_in_dict1 in dict1_input[key] and not math.isnan(dict1_input[key][str_in_dict1]) and str_in_dict2 in dict2_input[key] and not math.isnan(dict2_input[key][str_in_dict2]):
                weighted_sums_list.append((dict1_input[key][str_in_dict1] + dict2_input[key][str_in_dict2]))
    except Exception as e:
        print("Exception in {} {} {}}: {}".format(stock_data.symbol, dict1_name, dict2_name, e))
        pass
    if len(weighted_sums_list): return_value = sum(weighted_sums_list) if force_only_sum else weighted_average(weighted_sums_list, weights[:len(weighted_sums_list)])

    return return_value


def calculate_current_vs_previous_change_ratio(current_value, previous_value):
    if VERBOSE_LOGS: print("[{} calculate_current_vs_previous_change_ratio]".format(__name__))
    if   current_value >  0 and previous_value >  0: value_to_return = current_value /previous_value - 1  #  100/ 1000 - 1 = -0.9;  1000/ 100 - 1 = 9
    elif current_value == 0 and previous_value >  0: value_to_return = current_value /previous_value - 1  #  100/ 1000 - 1 = -0.9;  1000/ 100 - 1 = 9
    elif current_value >  0 and previous_value == 0: value_to_return =  1  #  100% Growth (from     0        to     positive)

    elif current_value <  0 and previous_value <  0: value_to_return = previous_value/current_value  - 1  # -100/-1000 - 1 = -0.9; -1000/-100 - 1 = 9
    elif current_value == 0 and previous_value <  0: value_to_return =  1  #  100% Growth (from     negative to            0)
    elif current_value <  0 and previous_value == 0: value_to_return = -1  # -100% Growth (from     0        to     negative)

    elif current_value >  0 and previous_value <  0: value_to_return =  1  #  100% Growth (from     negative to     positive)
   #elif current_value == 0 and previous_value <  0: value_to_return =  1  #  Already handled above
   #elif current_value >  0 and previous_value == 0: value_to_return =  1  #  Already handled above

    elif current_value <  0 and previous_value >  0: value_to_return = -1  # -100% Growth (from     positive to     negative)
   #elif current_value == 0 and previous_value >  0: value_to_return = -1  #  Already handled above
   #elif current_value <  0 and previous_value == 0: value_to_return = -1  #  Already handled above
    else: # current_value == 0 and previous_value == 0:
        value_to_return = 0.0  # No change

    return value_to_return


# Altman Z Score: The formula for Altman Z-Score is:
# Formula: 2 calculation methods: Simple is effective_<parameter1> / effective_<parameter2>. Complex (but better) is effective(parameter1/parameter2) and best (most complex) is effective((parameter1-parameter2)/(parameter3-parameter4))
#                 1.2*(working capital                       / total assets     ) +
#                 1.4*(retained earnings                     / total assets     ) +
#                 3.3*(earnings before interest and tax      / total assets     ) +
#                 0.6*(market value of equity [==market_cap] / total liabilities) +
#                 1.0*(sales                                 / total assets     )
# Instructions:
# How Should an Investor Interpret Altman Z-Score?
# - Investors can use Altman Z-score Plus to evaluate corporate credit risk. A score below 1.8 means it's likely the company is headed for bankruptcy, while companies with scores above 3 are not likely to go bankrupt. Investors may consider purchasing a stock if its Altman Z-Score value is closer to 3 and selling, or shorting, a stock if the value is closer to 1.8.
# Beneish M Score
def calculate_altman_z_score_factor(stock_data):
    if VERBOSE_LOGS: print("[{} calculate_altman_z_score_factor]".format(__name__))
    stock_data.altman_z_score_factor = 0

    # TODO: ASAFR: Each of the elements can (and should) most probably be calculated by quarterized and annualized (and then effective) ratio prior
    if stock_data.effective_total_assets != None and stock_data.effective_total_assets != 0:
        stock_data.altman_z_score_factor += 1.2 * ((stock_data.effective_working_capital   if stock_data.effective_working_capital   != None else 0)/ stock_data.effective_total_assets)
        stock_data.altman_z_score_factor += 1.4 * ((stock_data.effective_retained_earnings if stock_data.effective_retained_earnings != None else 0)/ stock_data.effective_total_assets)
        stock_data.altman_z_score_factor += 3.3 * ((stock_data.ebitda                      if stock_data.ebitda                      != None else 0)/ stock_data.effective_total_assets)
        stock_data.altman_z_score_factor += 1.0 * ((stock_data.effective_revenue           if stock_data.effective_revenue           != None else 0)/ stock_data.effective_total_assets)
    if stock_data.effective_total_liabilities != None and stock_data.effective_total_liabilities != 0:
        stock_data.altman_z_score_factor += 0.6 * ((stock_data.market_cap                  if stock_data.market_cap                  != None else 0)/ stock_data.effective_total_liabilities)

    # https://www.accountingtools.com/articles/the-altman-z-score-formula.html
    if    3 <= stock_data.altman_z_score_factor:          stock_data.altman_z_score_factor **= 0.5  # f(        x >= 3   ) = 1.71+...square root     growth
    elif 1.81 <= stock_data.altman_z_score_factor < 3:    stock_data.altman_z_score_factor  /= 2    # f(1.81 <= x <  3   ) = 1.5 -...         linear decay
    elif    0 <  stock_data.altman_z_score_factor < 1.81: stock_data.altman_z_score_factor  /= 4    # f(   0 <  x <  1.81) = 0.45-...stronger linear decay


def process_info(symbol, stock_data, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, db_filename):
    try:
        return_value = True
        info               = {}
        earnings_quarterly = {}
        earnings_yearly    = {}
        if build_csv_db:
            try:
                info                                   = symbol.get_info()
                cash_flows_yearly                      = symbol.get_cashflow(     as_dict=True, freq="yearly")
                cash_flows_quarterly                   = symbol.get_cashflow(     as_dict=True, freq="quarterly")
                balance_sheets_yearly                  = symbol.get_balance_sheet(as_dict=True, freq="yearly")
                balance_sheets_quarterly               = symbol.get_balance_sheet(as_dict=True, freq="quarterly")
                earnings_yearly                        = symbol.get_earnings(     as_dict=True, freq="yearly")
                earnings_quarterly                     = symbol.get_earnings(     as_dict=True, freq="quarterly")

                if   earnings_yearly    != None: stock_data.financial_currency = earnings_yearly[   'financialCurrency']
                elif earnings_quarterly != None: stock_data.financial_currency = earnings_quarterly['financialCurrency']

                stock_data.summary_currency = info['currency'] if 'currency' in info else stock_data.financial_currency  # Used for market_cap and enterprise_value conversions

                # Currency Conversion Rules, per Yahoo Finance Data Display Methods:
                #
                # +--------------------+-------------------+-------------------+
                # |       Mode         | earnings_ Exist   | earnings_ Missing |
                # +====================+===================+===================+
                # | All/Custom/Nsr     |                   |                   |
                # +--------------------| financialCurrency | info.currency     |
                # | TASE (<symbol>.TA) |                   |                   |
                # +--------------------+-------------------+-------------------+

                if stock_data.summary_currency == 'ILA':  # Sometimes (in TASE mode) in info['currency'] ILA may show instead of ILS so just substitute
                    stock_data.summary_currency = 'ILS'
                log_id = 0
                if VERBOSE_LOGS: print("[{} {}]".format(__name__, 1))
                if currency_conversion_tool:
                    if VERBOSE_LOGS: print("[{} {}]".format(__name__, 2))
                    stock_data.financial_currency_conversion_rate_mult_to_usd = round(1.0/currency_conversion_tool[stock_data.financial_currency], NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3
                    stock_data.summary_currency_conversion_rate_mult_to_usd   = round(1.0/currency_conversion_tool[stock_data.summary_currency],   NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3
                elif currency_conversion_tool_alternative:
                    try:
                        stock_data.financial_currency_conversion_rate_mult_to_usd = round(currency_conversion_tool_alternative.convert(1.0, stock_data.financial_currency, 'USD'), NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3
                        stock_data.summary_currency_conversion_rate_mult_to_usd   = round(currency_conversion_tool_alternative.convert(1.0, stock_data.summary_currency,   'USD'), NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3
                    except Exception as e:
                        stock_data.financial_currency_conversion_rate_mult_to_usd = round(1.0 / currency_conversion_tool_manual[stock_data.financial_currency], NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3
                        stock_data.summary_currency_conversion_rate_mult_to_usd   = round(1.0 / currency_conversion_tool_manual[stock_data.summary_currency],   NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3
                else:
                    stock_data.financial_currency_conversion_rate_mult_to_usd = round(1.0 / currency_conversion_tool_manual[stock_data.financial_currency], NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3
                    stock_data.summary_currency_conversion_rate_mult_to_usd   = round(1.0 / currency_conversion_tool_manual[stock_data.summary_currency],   NUM_ROUND_DECIMALS)  # conversion_rate is the value to multiply the foreign exchange (in which the stock's currency is) by to get the original value in USD. For instance if the currency is ILS, values should be divided by ~3.3

                financials_yearly                           = symbol.get_financials(as_dict=True, freq="yearly")
                financials_quarterly                        = symbol.get_financials(as_dict=True, freq="quarterly")
                # institutional_holders                = symbol.get_institutional_holders(as_dict=True)
                # sustainability                       = symbol.get_sustainability(as_dict=True)
                # major_holders                        = symbol.get_major_holders(as_dict=True)
                # mutualfund_holders                   = symbol.get_mutualfund_holders(as_dict=True)
            except Exception as e:
                if not research_mode: print("              Exception in {} symbol.get_info(): {}".format(stock_data.symbol, e))
                pass

            if 'shortName' in info: stock_data.short_name = info['shortName']
            else:                   stock_data.short_name = 'None'

            # Balance sheets are listed from newest to oldest, so for the weights, must reverse the dictionary:
            stock_data.annualized_total_ratio          = calculate_weighted_ratio_from_dict(balance_sheets_yearly,    'annualized_total_ratio',          'Total Assets',         'Total Liab',                BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)
            stock_data.annualized_other_current_ratio  = calculate_weighted_ratio_from_dict(balance_sheets_yearly,    'annualized_other_current_ratio',  'Other Current Assets', 'Other Current Liab',        BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)
            stock_data.annualized_other_ratio          = calculate_weighted_ratio_from_dict(balance_sheets_yearly,    'annualized_other_ratio',          'Other Assets',         'Other Liab',                BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)
            stock_data.annualized_total_current_ratio  = calculate_weighted_ratio_from_dict(balance_sheets_yearly,    'annualized_total_current_ratio',  'Total Current Assets', 'Total Current Liabilities', BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)

            stock_data.quarterized_total_ratio         = calculate_weighted_ratio_from_dict(balance_sheets_quarterly, 'quarterized_total_ratio',         'Total Assets',         'Total Liab',                BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)
            stock_data.quarterized_other_current_ratio = calculate_weighted_ratio_from_dict(balance_sheets_quarterly, 'quarterized_other_current_ratio', 'Other Current Assets', 'Other Current Liab',        BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)
            stock_data.quarterized_other_ratio         = calculate_weighted_ratio_from_dict(balance_sheets_quarterly, 'quarterized_other_ratio',         'Other Assets',         'Other Liab',                BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)
            stock_data.quarterized_total_current_ratio = calculate_weighted_ratio_from_dict(balance_sheets_quarterly, 'quarterized_total_current_ratio', 'Total Current Assets', 'Total Current Liabilities', BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)

            stock_data.annualized_working_capital      = calculate_weighted_diff_from_dict( balance_sheets_yearly,    'annualized_working_capital',      'Total Current Assets', 'Total Current Liabilities', BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)
            stock_data.quarterized_working_capital     = calculate_weighted_diff_from_dict( balance_sheets_quarterly, 'quarterized_working_capital',     'Total Current Assets', 'Total Current Liabilities', BALANCE_SHEETS_WEIGHTS, stock_data, 0, True)

            stock_data.annualized_total_liabilities    = calculate_weighted_stock_data_on_dict(balance_sheets_yearly,    'annualized_total_liabilities',  'Total Liab', BALANCE_SHEETS_WEIGHTS, stock_data, True)
            stock_data.quarterized_total_liabilities   = calculate_weighted_stock_data_on_dict(balance_sheets_quarterly, 'quarterized_total_liabilities', 'Total Liab', BALANCE_SHEETS_WEIGHTS, stock_data, True)

            if stock_data.annualized_total_ratio          == 0.0: stock_data.annualized_total_ratio          = stock_data.quarterized_total_ratio        *QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.quarterized_total_ratio         == 0.0: stock_data.quarterized_total_ratio         = stock_data.annualized_total_ratio         *QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.annualized_other_current_ratio  == 0.0: stock_data.annualized_other_current_ratio  = stock_data.quarterized_other_current_ratio*QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.quarterized_other_current_ratio == 0.0: stock_data.quarterized_other_current_ratio = stock_data.annualized_other_current_ratio *QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.annualized_other_ratio          == 0.0: stock_data.annualized_other_ratio          = stock_data.quarterized_other_ratio        *QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.quarterized_other_ratio         == 0.0: stock_data.quarterized_other_ratio         = stock_data.annualized_other_ratio         *QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.annualized_total_current_ratio  == 0.0: stock_data.annualized_total_current_ratio  = stock_data.quarterized_total_current_ratio*QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.quarterized_total_current_ratio == 0.0: stock_data.quarterized_total_current_ratio = stock_data.annualized_total_current_ratio *QUARTERLY_YEARLY_MISSING_FACTOR

            if stock_data.annualized_working_capital      == 0.0: stock_data.annualized_working_capital  = stock_data.quarterized_working_capital*QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.quarterized_working_capital     == 0.0: stock_data.quarterized_working_capital = stock_data.annualized_working_capital *QUARTERLY_YEARLY_MISSING_FACTOR

            if stock_data.annualized_total_liabilities    is None and stock_data.quarterized_total_liabilities != None: stock_data.annualized_total_liabilities  = stock_data.quarterized_total_liabilities*QUARTERLY_YEARLY_MISSING_FACTOR
            if stock_data.quarterized_total_liabilities   is None and stock_data.annualized_total_liabilities  != None: stock_data.quarterized_total_liabilities = stock_data.annualized_total_liabilities *QUARTERLY_YEARLY_MISSING_FACTOR

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 3))

            # The correct methos is the TTM only: avoid (quareterly+annual)/2 - Keep it Simple -> Apply to all calculations
            # TODO: ASAFR: In the next stage - add the other current and other ratio to a sum of the ratios Investigate prior
            stock_data.total_ratio_effective         = RATIO_DAMPER+(stock_data.quarterized_total_ratio        ) # Prefer TTM only
            stock_data.other_current_ratio_effective = RATIO_DAMPER+(stock_data.quarterized_other_current_ratio) # Prefer TTM only
            stock_data.other_ratio_effective         = RATIO_DAMPER+(stock_data.quarterized_other_ratio        ) # Prefer TTM only
            stock_data.total_current_ratio_effective = RATIO_DAMPER+(stock_data.quarterized_total_current_ratio) # Prefer TTM only
            stock_data.effective_current_ratio       = (stock_data.total_ratio_effective + stock_data.total_current_ratio_effective) / 2.0  # TODO: ASAFR: In the next stage - add the other and current other ratios - more information -> more completeness

            stock_data.effective_working_capital     = (stock_data.quarterized_working_capital) # Prefer TTM only

            if stock_data.quarterized_total_liabilities != None:
                stock_data.effective_total_liabilities = (stock_data.quarterized_total_liabilities) # Prefer TTM only

            # TODO: ASAFR: Add Other Current Liab / Other Stockholder Equity
            # Balance Sheets are listed from newest to olders, so for proper weight: Reverse required
            stock_data.annualized_debt_to_equity  = calculate_weighted_ratio_from_dict(balance_sheets_yearly,    'annualized_debt_to_equity',  'Total Liab', 'Total Stockholder Equity', BALANCE_SHEETS_WEIGHTS, stock_data, None, True)
            stock_data.quarterized_debt_to_equity = calculate_weighted_ratio_from_dict(balance_sheets_quarterly, 'quarterized_debt_to_equity', 'Total Liab', 'Total Stockholder Equity', BALANCE_SHEETS_WEIGHTS, stock_data, None, True)

            if stock_data.annualized_debt_to_equity is None and stock_data.quarterized_debt_to_equity is None:
                stock_data.annualized_debt_to_equity = stock_data.quarterized_debt_to_equity = 1000.0*debt_to_equity_limit
            else:
                if   stock_data.annualized_debt_to_equity is None and stock_data.quarterized_debt_to_equity != None:
                    stock_data.annualized_debt_to_equity = stock_data.quarterized_debt_to_equity*QUARTERLY_YEARLY_MISSING_FACTOR
                elif stock_data.annualized_debt_to_equity != None and stock_data.quarterized_debt_to_equity is None:
                    stock_data.quarterized_debt_to_equity = stock_data.annualized_debt_to_equity * QUARTERLY_YEARLY_MISSING_FACTOR

                stock_data.debt_to_equity_effective = (stock_data.quarterized_debt_to_equity)  # Prefer TTM only

                if stock_data.debt_to_equity_effective < 0.0:
                    stock_data.debt_to_equity_effective_used = (1.0-stock_data.debt_to_equity_effective * NEGATIVE_DEBT_TO_EQUITY_FACTOR)  # (https://www.investopedia.com/terms/d/debtequityratio.asp#:~:text=What%20does%20it%20mean%20for,has%20more%20liabilities%20than%20assets.) What does it mean for debt to equity to be negative? If a company has a negative D/E ratio, this means that the company has negative shareholder equity. In other words, it means that the company has more liabilities than assets
                else:
                    stock_data.debt_to_equity_effective_used = DEBT_TO_EQUITY_MIN_BASE + math.sqrt(stock_data.debt_to_equity_effective)

            # Cash Flows are listed from newest to oldest, so reverse required for weights:
            stock_data.annualized_cash_flow_from_operating_activities  = calculate_weighted_stock_data_on_dict(cash_flows_yearly,    'cash_flows_yearly',    'Total Cash From Operating Activities', CASH_FLOW_WEIGHTS, stock_data, True)
            stock_data.quarterized_cash_flow_from_operating_activities = calculate_weighted_stock_data_on_dict(cash_flows_quarterly, 'cash_flows_quarterly', 'Total Cash From Operating Activities', NO_WEIGHTS,        stock_data, True, True)

            # Balance Sheets are listed from newest to oldest, so reverse required for weights:
            stock_data.annualized_total_assets      = calculate_weighted_stock_data_on_dict(balance_sheets_yearly,    'balance_sheets_yearly',    'Total Assets', BALANCE_SHEETS_WEIGHTS, stock_data, True)
            stock_data.quarterized_total_assets     = calculate_weighted_stock_data_on_dict(balance_sheets_quarterly, 'balance_sheets_quarterly', 'Total Assets', BALANCE_SHEETS_WEIGHTS, stock_data, True)

            # Balance Sheets are listed from newest to oldest, so reverse required for weights:
            stock_data.annualized_retained_earnings  = calculate_weighted_stock_data_on_dict(balance_sheets_yearly,    'balance_sheets_yearly',    'Retained Earnings', BALANCE_SHEETS_WEIGHTS, stock_data, True)
            stock_data.quarterized_retained_earnings = calculate_weighted_stock_data_on_dict(balance_sheets_quarterly, 'balance_sheets_yearly',    'Retained Earnings', NO_WEIGHTS,             stock_data, True, True)

        if stock_data.short_name is     None:                       stock_data.short_name = 'None'
        if stock_data.short_name != None and not research_mode: print('              {:35}:'.format(stock_data.short_name))

        if build_csv_db and 'quoteType' in info: stock_data.quote_type = info['quoteType']
        if not check_quote_type(stock_data, research_mode):     return_value = False

        if build_csv_db and 'sector'  in info:    stock_data.sector = info['sector']
        if sectors_filter_out:
            if     check_sector(stock_data, sectors_list):    return_value = False
        else:
            if not check_sector(stock_data, sectors_list):    return_value = False

        if build_csv_db and 'country' in info:    stock_data.country = info['country']
        if len(countries_list):
            if countries_filter_out:
                if     check_country(stock_data, countries_list): return_value = False
            else:
                if not check_country(stock_data, countries_list): return_value = False

        if build_csv_db:
            if 'fullTimeEmployees' in info:      stock_data.employees = info['fullTimeEmployees']
            else:                                stock_data.employees = NUM_EMPLOYEES_UNKNOWN
            if stock_data.employees is None: stock_data.employees = NUM_EMPLOYEES_UNKNOWN

            # Oldest is the lower index
            if earnings_yearly != None and 'Revenue' in earnings_yearly and 'Earnings' in earnings_yearly:
                len_revenue_list  = len(earnings_yearly['Revenue'])
                len_earnings_list = len(earnings_yearly['Earnings'])
                if len_earnings_list == len_revenue_list:
                    weight_index              = 0
                    used_weights              = 0
                    earnings_to_revenues_list = []
                    weights_sum               = 0
                    boost_cont_inc_ratio    = True # Will be set to True if there is a continuous increase in the profit margin
                    boost_cont_inc_pos      = True # Will be set to True if there is a continuous positive in the profit margin
                    boost_cont_inc_earnings = True # Will be set to True if there is a continuous increase in the earnings
                    boost_cont_inc_revenue  = True # Will be set to True if there is a continuous increase in the revenue
                    try:
                        if VERBOSE_LOGS: print("[{} {}]".format(__name__, 4))

                        for key in earnings_yearly['Revenue']:
                            if float(earnings_yearly['Revenue'][key]) > 0:
                                earnings_to_revenues_list.append((float(earnings_yearly['Earnings'][key])/float(earnings_yearly['Revenue'][key]))*PROFIT_MARGIN_WEIGHTS[weight_index])
                                weights_sum  += PROFIT_MARGIN_WEIGHTS[weight_index]
                                used_weights += 1
                                current_ratio = float(earnings_yearly['Earnings'][key])/float(earnings_yearly['Revenue'][key])
                                if used_weights > 1:
                                    boost_cont_inc_ratio    = True if boost_cont_inc_ratio    and current_ratio                    > last_ratio                    else False
                                    boost_cont_inc_pos      = True if boost_cont_inc_pos      and current_ratio                    > 0          and last_ratio > 0 else False
                                    boost_cont_inc_earnings = True if boost_cont_inc_earnings and float(earnings_yearly['Earnings'][key]) > last_earnings                 else False
                                    boost_cont_inc_revenue  = True if boost_cont_inc_revenue  and float(earnings_yearly['Revenue' ][key]) > last_revenue                  else False
                                last_ratio    = current_ratio
                                last_earnings = float(earnings_yearly['Earnings'][key])
                                last_revenue  = float(earnings_yearly['Revenue' ][key])
                            weight_index += 1
                        if weights_sum > 0:
                            stock_data.annualized_profit_margin        = sum(earnings_to_revenues_list)/weights_sum
                            if stock_data.annualized_profit_margin > 0:
                                stock_data.annualized_profit_margin_boost  = 1.0 if not boost_cont_inc_ratio    or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_INCREASE
                                stock_data.annualized_profit_margin_boost *= 1.0 if not boost_cont_inc_pos      or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_POSITIVE
                                stock_data.annualized_profit_margin_boost *= 1.0 if not boost_cont_inc_earnings or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_INCREASE_IN_EARNINGS
                                stock_data.annualized_profit_margin_boost *= 1.0 if not boost_cont_inc_revenue  or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_ANNUAL_INCREASE_IN_REVENUE
                                stock_data.annualized_profit_margin       *= stock_data.annualized_profit_margin_boost
                    except Exception as e:
                        print("Exception in {} annualized_profit_margin: {}".format(stock_data.symbol, e))
                        stock_data.annualized_profit_margin = None
                        pass
            # TODO: ASAFR: This below can be duplicated for usage of total_revenue and net_income. Analyze and implement as/if required:
            if earnings_quarterly != None and 'Revenue' in earnings_quarterly and 'Earnings' in earnings_quarterly:
                len_revenue_list  = len(earnings_quarterly['Revenue'])
                len_earnings_list = len(earnings_quarterly['Earnings'])
                if len_earnings_list == len_revenue_list:
                    weight_index              = 0
                    used_weights              = 0
                    earnings_to_revenues_list = []
                    weights_sum               = 0
                    boost_cont_inc_ratio    = True # Will be set to True if there is a continuous increase in the profit   margin  TODO: ASAFR: Do the same boosting for cash flows ev_to_cfo
                    boost_cont_inc_pos      = True # Will be set to True if there is a continuous positive in the profit   margin
                    boost_cont_inc_earnings = True # Will be set to True if there is a continuous increase in the earnings
                    boost_cont_inc_revenue  = True # Will be set to True if there is a continuous increase in the revenue
                    try:
                        if VERBOSE_LOGS: print("[{} {}]".format(__name__, 5))

                        for key in earnings_quarterly['Revenue']:
                            if float(earnings_quarterly['Revenue'][key]) > 0:
                                earnings_to_revenues_list.append((float(earnings_quarterly['Earnings'][key])/float(earnings_quarterly['Revenue'][key]))*PROFIT_MARGIN_WEIGHTS[weight_index])
                                weights_sum  += PROFIT_MARGIN_WEIGHTS[weight_index]
                                used_weights += 1
                                current_ratio = float(earnings_quarterly['Earnings'][key])/float(earnings_quarterly['Revenue'][key])
                                if used_weights > 1:
                                    boost_cont_inc_ratio    = True if boost_cont_inc_ratio    and current_ratio                              > last_ratio                    else False
                                    boost_cont_inc_pos      = True if boost_cont_inc_pos      and current_ratio                              > 0          and last_ratio > 0 else False
                                    boost_cont_inc_earnings = True if boost_cont_inc_earnings and float(earnings_quarterly['Earnings'][key]) > last_earnings                 else False
                                    boost_cont_inc_revenue  = True if boost_cont_inc_revenue  and float(earnings_quarterly['Revenue' ][key]) > last_revenue                  else False
                                last_ratio    = current_ratio
                                last_earnings = float(earnings_quarterly['Earnings'][key])
                                last_revenue  = float(earnings_quarterly['Revenue' ][key])
                            weight_index += 1
                        if weights_sum > 0:
                            stock_data.quarterized_profit_margin            = sum(earnings_to_revenues_list)/weights_sum
                            if stock_data.quarterized_profit_margin > 0:
                                stock_data.quarterized_profit_margin_boost  = 1.0 if not boost_cont_inc_ratio    or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_INCREASE
                                stock_data.quarterized_profit_margin_boost *= 1.0 if not boost_cont_inc_pos      or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_POSITIVE
                                stock_data.quarterized_profit_margin_boost *= 1.0 if not boost_cont_inc_earnings or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_INCREASE_IN_EARNINGS
                                stock_data.quarterized_profit_margin_boost *= 1.0 if not boost_cont_inc_revenue  or used_weights <= 1 else PROFIT_MARGIN_BOOST_FOR_CONTINUOUS_QUARTERLY_INCREASE_IN_REVENUE
                                stock_data.quarterized_profit_margin       *= stock_data.quarterized_profit_margin_boost
                    except Exception as e:
                        print("Exception in {} quarterized_profit_margin: {}".format(stock_data.symbol, e))
                        stock_data.quarterized_profit_margin = None
                        pass

            # Earnings are ordered from oldest to newest so no reversing required for weights:
            if earnings_yearly != None and 'Revenue' in earnings_yearly: stock_data.annualized_revenue = calculate_weighted_stock_data_on_dict(earnings_yearly['Revenue'],    'earnings_yearly[Revenue]', None,            REVENUES_WEIGHTS, stock_data, False)
            else:                                                        stock_data.annualized_revenue = None

            # TODO: ASAFR: Add a calculation of net_income_to_total_revenue_list
            # Financials are ordered newest to oldest so reversing is required for weights:
            stock_data.annualized_total_revenue = calculate_weighted_stock_data_on_dict(financials_yearly,             'financials_yearly',        'Total Revenue', REVENUES_WEIGHTS, stock_data, True)

            if earnings_yearly != None and 'Earnings' in earnings_yearly:
                weight_index      = 0
                earnings_list     = []
                qeg_list          = []
                weights_sum       = 0
                qeg_weights_sum   = 0
                previous_earnings = None
                for key in earnings_yearly['Earnings']:
                    earnings_list.append((float(earnings_yearly['Earnings'][key])) * EARNINGS_WEIGHTS[weight_index])
                    weights_sum  += EARNINGS_WEIGHTS[weight_index]
                    if weight_index > 0:
                        current_earnings = earnings_yearly['Earnings'][key]
                        if float(previous_earnings) != 0.0 and float(current_earnings) != 0.0: # (this-prev)/(abs(this)+abs(prev))
                            value_to_append = calculate_current_vs_previous_change_ratio(current_earnings, previous_earnings)
                            qeg_list.append(value_to_append*EARNINGS_WEIGHTS[weight_index-1])
                        else:
                            qeg_list.append(0.0) # No change
                        qeg_weights_sum += EARNINGS_WEIGHTS[weight_index-1]
                    previous_earnings = earnings_yearly['Earnings'][key]
                    weight_index     += 1
                if VERBOSE_LOGS: print("[{} {}]".format(__name__, 6))

                stock_data.annualized_earnings = stock_data.financial_currency_conversion_rate_mult_to_usd*sum(earnings_list) / weights_sum  # Multiplying by the factor to get the valu in USD.
                if len(qeg_list):
                    stock_data.eqg_yoy         = sum(qeg_list) / qeg_weights_sum
                else:
                    stock_data.eqg_yoy         = None
            else:
                stock_data.eqg_yoy             = None
                stock_data.annualized_earnings = None

            if earnings_yearly != None and 'Revenue' in earnings_yearly:
                weight_index      = 0
                qrg_list          = []
                qrg_weights_sum   = 0
                previous_revenue = None
                for key in earnings_yearly['Revenue']:
                    if weight_index > 0:
                        current_revenue = earnings_yearly['Revenue'][key]
                        if float(previous_revenue) != 0.0 and float(current_revenue) != 0.0: # (this-prev)/(abs(this)+abs(prev))
                            value_to_append = calculate_current_vs_previous_change_ratio(current_revenue, previous_revenue)
                            qrg_list.append(value_to_append*REVENUES_WEIGHTS[weight_index-1])
                        else:
                            qrg_list.append(0.0) # No change
                        qrg_weights_sum += REVENUES_WEIGHTS[weight_index-1]
                    previous_revenue = earnings_yearly['Revenue'][key]
                    weight_index     += 1
                if len(qrg_list):
                    if VERBOSE_LOGS: print("[{} {}]".format(__name__, 7))

                    stock_data.rqg_yoy = sum(qrg_list) / qrg_weights_sum
                else:
                    stock_data.rqg_yoy = None
            else:
                stock_data.rqg_yoy     = None

            # Net Income Quarterly Growth Year-Over-Year Calculation:
            if financials_yearly != None and len(financials_yearly):
                qnig_weight_index = 0
                qtrg_weight_index = 0
                net_income_list = []
                qnig_list = []
                qtrg_list = [] # Quarterly Total Revenue Growth
                weights_sum = 0
                qnig_weights_sum = 0
                qtrg_weights_sum = 0
                previous_net_income = None
                previous_total_revenue = None
                if VERBOSE_LOGS: print("[{} {}]".format(__name__, 8))

                for key in reversed(list(financials_yearly)):  # 1st will be oldest
                    if 'Net Income' in financials_yearly[key]:
                        net_income_list.append((float(financials_yearly[key]['Net Income'])) * EARNINGS_WEIGHTS[qnig_weight_index])
                        weights_sum += EARNINGS_WEIGHTS[qnig_weight_index]
                        if qnig_weight_index > 0:
                            current_net_income = financials_yearly[key]['Net Income']
                            if float(previous_net_income) != 0.0 and float(current_net_income) != 0.0:  # (this-prev)/(abs(this)+abs(prev))
                                value_to_append = calculate_current_vs_previous_change_ratio(current_net_income, previous_net_income)
                                qnig_list.append(value_to_append * EARNINGS_WEIGHTS[qnig_weight_index - 1])
                            else:
                                qnig_list.append(0.0)  # No change
                            qnig_weights_sum += EARNINGS_WEIGHTS[qnig_weight_index - 1]
                        previous_net_income = financials_yearly[key]['Net Income']
                        qnig_weight_index += 1
                    if 'Total Revenue' in financials_yearly[key]:
                        if qtrg_weight_index > 0:
                            current_total_revenue = financials_yearly[key]['Total Revenue']
                            if float(previous_total_revenue) != 0.0 and float(current_total_revenue) != 0.0:  # (this-prev)/(abs(this)+abs(prev))
                                value_to_append = calculate_current_vs_previous_change_ratio(current_total_revenue, previous_total_revenue)
                                qtrg_list.append(value_to_append * REVENUES_WEIGHTS[qtrg_weight_index - 1])
                            else:
                                qtrg_list.append(0.0)  # No change
                            qtrg_weights_sum += REVENUES_WEIGHTS[qtrg_weight_index - 1]
                        previous_total_revenue = financials_yearly[key]['Total Revenue']
                        qtrg_weight_index += 1
                if len(net_income_list):
                    if VERBOSE_LOGS: print("[{} {}]".format(__name__, 9))

                    stock_data.annualized_net_income = stock_data.financial_currency_conversion_rate_mult_to_usd * sum(net_income_list) / weights_sum  # Multiplying by the factor to get the valu in USD.
                else:
                    stock_data.annualized_net_income = None
                if VERBOSE_LOGS: print("[{} {}]".format(__name__, 10))

                if len(qnig_list):
                    stock_data.niqg_yoy = sum(qnig_list) / qnig_weights_sum
                else:
                    stock_data.niqg_yoy = None
                if len(qtrg_list):
                    stock_data.trqg_yoy = sum(qtrg_list) / qtrg_weights_sum
                else:
                    stock_data.trqg_yoy = None
            else:
                stock_data.niqg_yoy              = None
                stock_data.trqg_yoy              = None
                stock_data.annualized_net_income = None

            # Earnings are ordered from oldest to newest so no reversing required for weights:
            if earnings_quarterly != None and 'Revenue' in earnings_quarterly: stock_data.quarterized_revenue = calculate_weighted_stock_data_on_dict(earnings_quarterly['Revenue'],   'earnings_quarterly[Revenue]',   None,            NO_WEIGHTS, stock_data, False, True)
            else:                                                              stock_data.quarterized_revenue = None

            # Financials are ordered newest to oldest so reversing is required for weights:
            stock_data.quarterized_total_revenue = calculate_weighted_stock_data_on_dict(financials_quarterly,            'financials_quarterly',          'Total Revenue', NO_WEIGHTS,       stock_data, True, True)

            # Earnings are ordered from oldest to newest so no reversing required for weights:
            if earnings_quarterly != None and 'Earnings' in earnings_quarterly: stock_data.quarterized_earnings = calculate_weighted_stock_data_on_dict(earnings_quarterly['Earnings'],  'earnings_quarterly[Earnings]',  None,            NO_WEIGHTS, stock_data, False, True)
            else:                                                               stock_data.quarterized_earnings = None

            # TODO: ASAFR: Add a calculation of net_income_to_total_revenue_list
            # Financials are ordered newest to oldest so reversing is required for weights:
            stock_data.quarterized_net_income    = calculate_weighted_stock_data_on_dict(financials_quarterly,            'financials_quarterly',          'Net Income',    NO_WEIGHTS, stock_data, True, True)

            # At this stage, use the Total Revenue and Net Income as backups for revenues and earnings. TODO: ASAFR: Later on run comparisons and take them into account as well!
            if   stock_data.annualized_net_income  is None and stock_data.quarterized_net_income is None: stock_data.effective_net_income = None
            elif stock_data.annualized_net_income  is None and stock_data.quarterized_net_income != None: stock_data.effective_net_income = stock_data.quarterized_net_income
            elif stock_data.quarterized_net_income is None and stock_data.annualized_net_income  != None: stock_data.effective_net_income = stock_data.annualized_net_income
            else                                                                                        : stock_data.effective_net_income = (stock_data.quarterized_net_income) # Prefer TTM only

            if   stock_data.annualized_total_revenue   is None and stock_data.quarterized_total_revenue  is None: stock_data.effective_total_revenue  = None
            elif stock_data.annualized_total_revenue   is None and stock_data.quarterized_total_revenue  != None: stock_data.effective_total_revenue  = stock_data.quarterized_total_revenue
            elif stock_data.quarterized_total_revenue  is None and stock_data.annualized_total_revenue   != None: stock_data.effective_total_revenue  = stock_data.annualized_total_revenue
            else                                                                                                : stock_data.effective_total_revenue  = (stock_data.quarterized_total_revenue ) # Prefer TTM only

            if   stock_data.annualized_earnings  is None: stock_data.annualized_earnings  = stock_data.annualized_net_income
            if   stock_data.quarterized_earnings is None: stock_data.quarterized_earnings = stock_data.quarterized_net_income

            if   stock_data.annualized_earnings  is None and stock_data.quarterized_earnings is None: stock_data.effective_earnings = stock_data.effective_net_income
            elif stock_data.annualized_earnings  is None and stock_data.quarterized_earnings != None: stock_data.effective_earnings = stock_data.quarterized_earnings
            elif stock_data.quarterized_earnings is None and stock_data.annualized_earnings  != None: stock_data.effective_earnings = stock_data.annualized_earnings
            else                                                                                    : stock_data.effective_earnings = (stock_data.quarterized_earnings) # Prefer TTM only

            if   stock_data.annualized_revenue   is None: stock_data.annualized_revenue   = stock_data.annualized_total_revenue
            if   stock_data.quarterized_revenue  is None: stock_data.quarterized_revenue  = stock_data.quarterized_total_revenue

            if   stock_data.annualized_revenue   is None and stock_data.quarterized_revenue  is None: stock_data.effective_revenue  = stock_data.effective_total_revenue
            elif stock_data.annualized_revenue   is None and stock_data.quarterized_revenue  != None: stock_data.effective_revenue  = stock_data.quarterized_revenue
            elif stock_data.quarterized_revenue  is None and stock_data.annualized_revenue   != None: stock_data.effective_revenue  = stock_data.annualized_revenue
            else                                                                                    : stock_data.effective_revenue  = (stock_data.quarterized_revenue ) # Prefer TTM only

            if 'country' in info:                stock_data.country = info['country']
            else:                                stock_data.country = 'Unknown'
            if stock_data.country is None: stock_data.country       = 'Unknown'

            if 'profitMargins' in info:          stock_data.profit_margin = info['profitMargins']
            else:                                stock_data.profit_margin = None

            if 'heldPercentInstitutions' in info:                                                         stock_data.held_percent_institutions = info['heldPercentInstitutions']
            else:                                                                                         stock_data.held_percent_institutions = PERCENT_HELD_INSTITUTIONS_LOW
            if stock_data.held_percent_institutions is None or stock_data.held_percent_institutions == 0: stock_data.held_percent_institutions = PERCENT_HELD_INSTITUTIONS_LOW

            if 'enterpriseToRevenue' in info:
                stock_data.enterprise_value_to_revenue = info['enterpriseToRevenue']
                if stock_data.enterprise_value_to_revenue != None: stock_data.enterprise_value_to_revenue *= stock_data.summary_currency_conversion_rate_mult_to_usd # https://www.investopedia.com/terms/e/ev-revenue-multiple.asp
            else:
                stock_data.enterprise_value_to_revenue = None # Mark as None, so as to try and calculate manually. TODO: ASAFR: Do the same to the Price and to the Earnings and the Price/Earnings (Also to sales if possible)
            if isinstance(stock_data.enterprise_value_to_revenue,str): stock_data.enterprise_value_to_revenue = None # Mark as None, so as to try and calculate manually.

            if 'marketCap' in info and info['marketCap'] != None:
                stock_data.market_cap = info['marketCap']*stock_data.summary_currency_conversion_rate_mult_to_usd
            else:
                stock_data.market_cap = None

            if 'enterpriseValue' in info and info['enterpriseValue'] != None: stock_data.enterprise_value = info['enterpriseValue']*stock_data.summary_currency_conversion_rate_mult_to_usd
            if market_cap_included:
                if stock_data.enterprise_value is None or stock_data.enterprise_value <= 0:
                    if   'marketCap' in info and info['marketCap'] != None:
                        stock_data.enterprise_value = int(info['marketCap']*stock_data.summary_currency_conversion_rate_mult_to_usd)
                elif stock_data.market_cap is None or stock_data.market_cap <= 0:
                    stock_data.market_cap = stock_data.enterprise_value

            # in order to calculate eibtd, take ebit from finantials and add deprecations from cash_flows to it:
            # Financials and Cash Flows are ordered newest to oldest so reversing is required for weights:
            stock_data.quarterized_ebitd = calculate_weighted_sum_from_2_dicts(financials_quarterly, 'financials_quarterly', 'Ebit', cash_flows_quarterly, 'cash_flows_quarterly', 'Depreciation', NO_WEIGHTS,       stock_data, 0, True, True, True)
            stock_data.annualized_ebitd  = calculate_weighted_sum_from_2_dicts(financials_yearly,    'financials_yearly',    'Ebit', cash_flows_yearly,    'cash_flows_yearly',    'Depreciation', EARNINGS_WEIGHTS, stock_data, 0, True, True)
            stock_data.ebitd             = (stock_data.quarterized_ebitd) # Prefer TTM only

            # TODO: ASAFR: 1. ebit (within financials) can be used instead of simply taking the earnings
            #              1.1. is ebit available for all/most TASE stocks? ebitd
            #              1.2. There is a similar field (same value different name) besides ebit - see if it is always the same? Low priority
            #              1.3. If enterpriseToEbitda is available (for most stocks it is? Check), then EBITDA = EV/enterpriseToEbitda
            #              1.3.1. But if EV is negative for some stocks... then Market Capital might be used...
            #              1.4. Suggestion: take average such that EBITDA = (EV/enterpriseToEbitda + CalculatedEbitFromFinancialsAndCashFlows)/2
            if 'enterpriseToEbitda' in info:
                stock_data.enterprise_value_to_ebitda = info['enterpriseToEbitda']
                if stock_data.enterprise_value_to_ebitda != None:
                    stock_data.enterprise_value_to_ebitda *= stock_data.summary_currency_conversion_rate_mult_to_usd  # The lower the better: https://www.investopedia.com/ask/answers/072715/what-considered-healthy-evebitda.asp

                    # Calculate ebitda from enterprise_value_to_ebitda:
                    if VERBOSE_LOGS: print("[{} {}]".format(__name__, 11))

                    if stock_data.enterprise_value_to_ebitda != 0: stock_data.ebitda = (stock_data.enterprise_value/stock_data.enterprise_value_to_ebitda + stock_data.ebitd)/2
            else:
                if stock_data.ebitd != 0:
                    if VERBOSE_LOGS: print("[{} {}]".format(__name__, 12))

                    stock_data.enterprise_value_to_ebitda = stock_data.enterprise_value/stock_data.ebitd

            if stock_data.enterprise_value_to_ebitda != None:
                if VERBOSE_LOGS: print("[{} {}]".format(__name__, 13))

                stock_data.effective_ev_to_ebitda = (stock_data.enterprise_value_to_ebitda + stock_data.enterprise_value/stock_data.ebitda if stock_data.ebitda != 0 else stock_data.enterprise_value_to_ebitda)/2
            else:
                stock_data.effective_ev_to_ebitda = EV_TO_EBITDA_MAX_UNKNOWN

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 14))

            if 'trailingPE' in info:
                stock_data.trailing_price_to_earnings  = info['trailingPE']  # https://www.investopedia.com/terms/t/trailingpe.asp
                if tase_mode and stock_data.trailing_price_to_earnings != None:
                    stock_data.trailing_price_to_earnings /= 100.0  # In TLV stocks, Yahoo multiplies trailingPE by a factor of 100, so compensate
                    stock_data.trailing_price_to_earnings *= stock_data.summary_currency_conversion_rate_mult_to_usd # Additionally, in TLV stocks this ratio is mistakenly calculated using PriceInNis/EarningsInUSD -> so Compensate
            elif stock_data.effective_earnings != None and stock_data.effective_earnings != 0:
                stock_data.trailing_price_to_earnings  = stock_data.market_cap / stock_data.effective_earnings # Calculate manually.
            if isinstance(stock_data.trailing_price_to_earnings,str):  stock_data.trailing_price_to_earnings  = None # Mark as None, so as to try and calculate manually.

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 15))

            if 'forwardPE' in info:
                stock_data.forward_price_to_earnings  = info['forwardPE']  # https://www.investopedia.com/terms/t/trailingpe.asp
                if tase_mode and stock_data.forward_price_to_earnings != None:
                    stock_data.forward_price_to_earnings /= 100.0 # In TLV stocks, Yahoo multiplies forwardPE by a factor of 100, so compensate
                    stock_data.forward_price_to_earnings *= stock_data.summary_currency_conversion_rate_mult_to_usd # Additionally, in TLV stocks this ratio is mistakenly calculated using PriceInNis/EarningsInUSD -> so Compensate
            else:  stock_data.forward_price_to_earnings  = None # Mark as None, so as to try and calculate manually. TODO: ASAFR: Calcualte using the forward_eps?

            if   stock_data.trailing_price_to_earnings is None and stock_data.forward_price_to_earnings  is None: stock_data.effective_price_to_earnings = None
            elif stock_data.forward_price_to_earnings  is None and stock_data.trailing_price_to_earnings != None: stock_data.forward_price_to_earnings   = stock_data.trailing_price_to_earnings
            elif stock_data.trailing_price_to_earnings is None and stock_data.forward_price_to_earnings  != None: stock_data.trailing_price_to_earnings  = stock_data.forward_price_to_earnings

            # Handle Negative Values of P/E: Negative P/E handling (TODO: ASAFR: Research this, maybe a smiling parabola is preferred, or some different less harsh function than 1/-x, like some damper n/[e+X])
            # Sources: https://www.investopedia.com/ask/answers/05/negativeeps.asp
            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 16))

            if stock_data.trailing_price_to_earnings != None:
                if   stock_data.trailing_price_to_earnings  < 0: stock_data.trailing_price_to_earnings = -NEGATIVE_EARNINGS_FACTOR/stock_data.trailing_price_to_earnings
                elif stock_data.trailing_price_to_earnings == 0: stock_data.trailing_price_to_earnings =  NEGATIVE_EARNINGS_FACTOR
            if stock_data.forward_price_to_earnings  != None:
                if   stock_data.forward_price_to_earnings   < 0: stock_data.forward_price_to_earnings  = -NEGATIVE_EARNINGS_FACTOR/stock_data.forward_price_to_earnings
                elif stock_data.forward_price_to_earnings  == 0: stock_data.forward_price_to_earnings  =  NEGATIVE_EARNINGS_FACTOR

            # Calculate the weighted average of the forward and trailing P/E:
            stock_data.effective_price_to_earnings = (stock_data.trailing_price_to_earnings*TRAILING_PRICE_TO_EARNINGS_WEIGHT+stock_data.forward_price_to_earnings*FORWARD_PRICE_TO_EARNINGS_WEIGHT)

            if 'forwardEps'                                 in info: stock_data.forward_eps                       = info['forwardEps']
            else:                                                    stock_data.forward_eps                       = None
            if isinstance(stock_data.forward_eps,str):               stock_data.forward_eps                       = None

            if 'trailingEps'                                in info: stock_data.trailing_eps                      = info['trailingEps']
            else:                                                    stock_data.trailing_eps                      = None
            if isinstance(stock_data.trailing_eps,str):              stock_data.trailing_eps                      = None

            if 'previousClose'                              in info: stock_data.previous_close                    = info['previousClose']
            else:                                                    stock_data.previous_close                    = None
            if isinstance(stock_data.previous_close,str):            stock_data.previous_close                    = None

            if build_csv_db and '52WeekChange'         in info: stock_data.fifty_two_week_change   = info['52WeekChange']
            if build_csv_db and 'fiftyTwoWeekLow'      in info: stock_data.fifty_two_week_low      = info['fiftyTwoWeekLow']
            if build_csv_db and 'fiftyTwoWeekHigh'     in info: stock_data.fifty_two_week_high     = info['fiftyTwoWeekHigh']
            if build_csv_db and 'twoHundredDayAverage' in info: stock_data.two_hundred_day_average = info['twoHundredDayAverage']

            if build_csv_db and stock_data.fifty_two_week_change                                                        is None: stock_data.fifty_two_week_change   = stock_data.previous_close
            if build_csv_db and stock_data.fifty_two_week_low                                                           is None: stock_data.fifty_two_week_low      = stock_data.previous_close
            if build_csv_db and stock_data.fifty_two_week_high                                                          is None: stock_data.fifty_two_week_high     = stock_data.previous_close
            if build_csv_db and stock_data.two_hundred_day_average                                                      is None: stock_data.two_hundred_day_average = stock_data.previous_close
            if build_csv_db and stock_data.previous_close != None and stock_data.previous_close < stock_data.fifty_two_week_low: stock_data.previous_close          = stock_data.fifty_two_week_low

            if build_csv_db:
                if VERBOSE_LOGS: print("[{} {}]".format(__name__, 17))

                if stock_data.two_hundred_day_average > 0.0: stock_data.previous_close_percentage_from_200d_ma  = 100.0 * ((stock_data.previous_close - stock_data.two_hundred_day_average) / stock_data.two_hundred_day_average)
                if stock_data.fifty_two_week_low      > 0.0: stock_data.previous_close_percentage_from_52w_low  = 100.0 * ((stock_data.previous_close - stock_data.fifty_two_week_low     ) / stock_data.fifty_two_week_low     )
                if stock_data.fifty_two_week_high     > 0.0: stock_data.previous_close_percentage_from_52w_high = 100.0 * ((stock_data.previous_close - stock_data.fifty_two_week_high    ) / stock_data.fifty_two_week_high    )
                if stock_data.fifty_two_week_low      > 0.0 and stock_data.fifty_two_week_high > 0.0 and stock_data.previous_close > 0.0:
                    if stock_data.fifty_two_week_high == stock_data.fifty_two_week_low:
                        stock_data.dist_from_low_factor = 1.0  # When there is no range, leave as neutral
                    else:
                        stock_data.dist_from_low_factor = (stock_data.previous_close - stock_data.fifty_two_week_low)/(0.5*(stock_data.fifty_two_week_high-stock_data.fifty_two_week_low))
                    stock_data.eff_dist_from_low_factor = (DIST_FROM_LOW_FACTOR_DAMPER + stock_data.dist_from_low_factor) if stock_data.dist_from_low_factor < 1.0 else (stock_data.dist_from_low_factor**DIST_FROM_LOW_FACTOR_HIGHER_THAN_ONE_POWER)

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 18))

            if stock_data.trailing_eps != None and stock_data.previous_close != None and stock_data.previous_close > 0:
                stock_data.trailing_eps_percentage = stock_data.trailing_eps / stock_data.previous_close

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 19))

            if 'priceToBook'                                in info:
                stock_data.price_to_book = info['priceToBook']
                if tase_mode and stock_data.price_to_book != None: # Yahoo Finance mistakenly multiplies value by 100 and doesn't convert price to USD, so compenate:
                    stock_data.price_to_book /= 100
                    stock_data.price_to_book *= stock_data.summary_currency_conversion_rate_mult_to_usd
            else:
                stock_data.price_to_book      = None # Mark as None, so as to try and calculate manually.
            if isinstance(stock_data.price_to_book,str): stock_data.price_to_book = None # Mark as None, so as to try and calculate manually.
            if stock_data.price_to_book is None:
                stock_data.price_to_book = PRICE_TO_BOOK_UNKNOWN * (10 if tase_mode else 1) # TODO: ASAFR: Until calculated manually, do not allow N/A in price2book to ruin the whole value, just set a very unatractive one, and let the rest of the parameters cope

            # Value is a ratio, such that when multiplied by 100, yields percentage (%) units:
            if 'earningsQuarterlyGrowth'                    in info: stock_data.eqg         = info['earningsQuarterlyGrowth']
            else:                                                    stock_data.eqg         = None

            # Value is a ratio, such that when multiplied by 100, yields percentage (%) units:
            if 'revenueQuarterlyGrowth'                     in info: stock_data.rqg         = info['revenueQuarterlyGrowth']
            else:                                                    stock_data.rqg         = None

            # TODO: ASAFR: Currently use the niqg_yoy and trqg_yoy as a simple backup. Later on - compare and add to calculations...
            if stock_data.eqg_yoy is None: stock_data.eqg_yoy = stock_data.niqg_yoy
            if stock_data.rqg_yoy is None: stock_data.rqg_yoy = stock_data.trqg_yoy

            # Now use above backup as required: TODO: ASAFR: One may use the yoy as direct backup... analyze this...
            if   stock_data.eqg is None and stock_data.eqg_yoy != None: stock_data.eqg     = stock_data.eqg_yoy
            elif stock_data.eqg != None and stock_data.eqg_yoy is None: stock_data.eqg_yoy = stock_data.eqg
            elif stock_data.eqg is None and stock_data.eqg_yoy is None: stock_data.eqg_yoy = stock_data.eqg = EQG_UNKNOWN

            if   stock_data.rqg is None and stock_data.rqg_yoy != None: stock_data.rqg     = stock_data.rqg_yoy
            elif stock_data.rqg != None and stock_data.rqg_yoy is None: stock_data.rqg_yoy = stock_data.rqg
            elif stock_data.rqg is None and stock_data.rqg_yoy is None: stock_data.rqg_yoy = stock_data.rqg = RQG_UNKNOWN

            stock_data.eqg_effective = EQG_WEIGHT_VS_YOY*stock_data.eqg + (1.0-EQG_WEIGHT_VS_YOY)*stock_data.eqg_yoy
            stock_data.rqg_effective = RQG_WEIGHT_VS_YOY*stock_data.rqg + (1.0-RQG_WEIGHT_VS_YOY)*stock_data.rqg_yoy

            if stock_data.eqg_effective > 0:
                stock_data.eqg_factor_effective = (EQG_DAMPER + EQG_POSITIVE_FACTOR * math.sqrt(stock_data.eqg_effective))
            else:
                stock_data.eqg_factor_effective = (EQG_DAMPER + stock_data.eqg_effective/EQG_POSITIVE_FACTOR)

            if stock_data.rqg_effective >= 0:
                stock_data.rqg_factor_effective = (RQG_DAMPER + RQG_POSITIVE_FACTOR * math.sqrt(stock_data.rqg_effective))
            else:
                stock_data.rqg_factor_effective = (RQG_DAMPER + stock_data.rqg_effective/RQG_POSITIVE_FACTOR)

            if 'sharesOutstanding'                          in info: stock_data.shares_outstanding                = info['sharesOutstanding']
            else:                                                    stock_data.shares_outstanding                = SHARES_OUTSTANDING_UNKNOWN
            if stock_data.shares_outstanding is None or stock_data.shares_outstanding == 0:
                stock_data.shares_outstanding = SHARES_OUTSTANDING_UNKNOWN

            if 'netIncomeToCommon' in info: stock_data.net_income_to_common_shareholders = info['netIncomeToCommon']
            else:                           stock_data.net_income_to_common_shareholders = None # TODO: ASAFR: It may be possible to calculate this manually

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 20))

            # if no effective_ev_to_ebitda, use earnings.
            if (stock_data.effective_ev_to_ebitda is None or stock_data.effective_ev_to_ebitda < 0) and stock_data.enterprise_value != None and stock_data.enterprise_value != 0 and stock_data.effective_earnings != None and stock_data.effective_earnings != 0:
                stock_data.effective_ev_to_ebitda = float(stock_data.enterprise_value) / stock_data.effective_earnings  #  effective_earnings is already in USD

            if stock_data.effective_ev_to_ebitda != None:
                if   stock_data.effective_ev_to_ebitda  < 0: stock_data.effective_ev_to_ebitda = -NEGATIVE_EARNINGS_FACTOR/stock_data.effective_ev_to_ebitda
                elif stock_data.effective_ev_to_ebitda == 0: stock_data.effective_ev_to_ebitda =  NEGATIVE_EARNINGS_FACTOR

            if stock_data.annualized_total_assets is None and stock_data.quarterized_total_assets is None:
                stock_data.effective_total_assets = None
            elif stock_data.annualized_total_assets is None and stock_data.quarterized_total_assets != None:
                stock_data.annualized_total_assets = stock_data.quarterized_total_assets*QUARTERLY_YEARLY_MISSING_FACTOR
            elif stock_data.annualized_total_assets != None and stock_data.quarterized_total_assets is None:
                stock_data.quarterized_total_assets = stock_data.annualized_total_assets*QUARTERLY_YEARLY_MISSING_FACTOR

            if stock_data.quarterized_total_assets != None:
                stock_data.effective_total_assets = (stock_data.quarterized_total_assets) # Prefer TTM only

            if stock_data.annualized_retained_earnings is None and stock_data.quarterized_retained_earnings is None:
                stock_data.effective_retained_earnings = None
            elif stock_data.annualized_retained_earnings is None and stock_data.quarterized_retained_earnings != None:
                stock_data.annualized_retained_earnings = stock_data.quarterized_retained_earnings*QUARTERLY_YEARLY_MISSING_FACTOR
            elif stock_data.annualized_retained_earnings != None and stock_data.quarterized_retained_earnings is None:
                stock_data.quarterized_retained_earnings = stock_data.annualized_retained_earnings*QUARTERLY_YEARLY_MISSING_FACTOR

            if stock_data.quarterized_retained_earnings != None:
                stock_data.effective_retained_earnings = (stock_data.quarterized_retained_earnings) # Prefer TTM only

            if stock_data.effective_total_assets != None and stock_data.effective_total_assets > 0 and stock_data.effective_net_income != None:
                stock_data.calculated_roa = ROA_DAMPER + stock_data.effective_net_income/stock_data.effective_total_assets

            if stock_data.annualized_cash_flow_from_operating_activities is None and stock_data.quarterized_cash_flow_from_operating_activities is None:
                if stock_data.effective_earnings != None:
                    stock_data.annualized_cash_flow_from_operating_activities = stock_data.quarterized_cash_flow_from_operating_activities = stock_data.effective_earnings
            elif stock_data.annualized_cash_flow_from_operating_activities is None and stock_data.quarterized_cash_flow_from_operating_activities != None:
                if stock_data.quarterized_cash_flow_from_operating_activities >= 0:
                    stock_data.annualized_cash_flow_from_operating_activities = stock_data.quarterized_cash_flow_from_operating_activities*QUARTERLY_YEARLY_MISSING_FACTOR
                else:
                    stock_data.annualized_cash_flow_from_operating_activities = stock_data.quarterized_cash_flow_from_operating_activities/QUARTERLY_YEARLY_MISSING_FACTOR
            elif stock_data.annualized_cash_flow_from_operating_activities != None and stock_data.quarterized_cash_flow_from_operating_activities is None:
                if stock_data.annualized_cash_flow_from_operating_activities >= 0:
                    stock_data.quarterized_cash_flow_from_operating_activities = stock_data.annualized_cash_flow_from_operating_activities*QUARTERLY_YEARLY_MISSING_FACTOR
                else:
                    stock_data.quarterized_cash_flow_from_operating_activities = stock_data.annualized_cash_flow_from_operating_activities/QUARTERLY_YEARLY_MISSING_FACTOR

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 100))

            if stock_data.annualized_cash_flow_from_operating_activities != None:
                if stock_data.annualized_cash_flow_from_operating_activities >= 0:
                    if stock_data.enterprise_value == 0 or stock_data.annualized_cash_flow_from_operating_activities == 0: # When 0, it means either EV is 0 (strange!) or a very very good cash flow (strange as well)
                        stock_data.annualized_ev_to_cfo_ratio = ev_to_cfo_ratio_limit * 1000  # Set a very high value to make stock unatractive
                    else:
                        stock_data.annualized_ev_to_cfo_ratio = float(stock_data.enterprise_value)/stock_data.annualized_cash_flow_from_operating_activities  # annualized_cash_flow_from_operating_activities has been converted to USD earlier
                else:  # stock_data.annualized_cash_flow_from_operating_activities < 0
                    stock_data.annualized_ev_to_cfo_ratio = -NEGATIVE_CFO_FACTOR/(float(stock_data.enterprise_value)/stock_data.annualized_cash_flow_from_operating_activities)
            else:
                stock_data.annualized_ev_to_cfo_ratio = ev_to_cfo_ratio_limit * 1000
            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 101))
            if stock_data.quarterized_cash_flow_from_operating_activities != None:
                if stock_data.quarterized_cash_flow_from_operating_activities >= 0:
                    if stock_data.enterprise_value == 0 or stock_data.quarterized_cash_flow_from_operating_activities == 0: # When 0, it means either EV is 0 (strange!) or a very very good cash flow (strange as well)
                        stock_data.quarterized_ev_to_cfo_ratio = ev_to_cfo_ratio_limit * 1000  # Set a very high value to make stock unatractive
                    else:
                        stock_data.quarterized_ev_to_cfo_ratio = float(stock_data.enterprise_value)/stock_data.quarterized_cash_flow_from_operating_activities  # quarterized_cash_flow_from_operating_activities has been converted to USD earlier
                else:  # stock_data.quarterized_cash_flow_from_operating_activities < 0
                    stock_data.quarterized_ev_to_cfo_ratio = -NEGATIVE_CFO_FACTOR*(float(stock_data.enterprise_value)/stock_data.quarterized_cash_flow_from_operating_activities)
            else:
                stock_data.quarterized_ev_to_cfo_ratio = ev_to_cfo_ratio_limit * 1000

            # Seems value is calculated relatively similarly in dual (TASE, NASDAQ) stocks so no compensation required
            stock_data.ev_to_cfo_ratio_effective = (stock_data.quarterized_ev_to_cfo_ratio) # Prefer TTM only
            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 200))
            if 'priceToSalesTrailing12Months' in info and info['priceToSalesTrailing12Months'] != None:
                stock_data.trailing_12months_price_to_sales = info['priceToSalesTrailing12Months'] # https://www.investopedia.com/articles/fundamental/03/032603.asp#:~:text=The%20price%2Dto%2Dsales%20ratio%20(Price%2FSales%20or,the%20more%20attractive%20the%20investment.
                if isinstance(stock_data.trailing_12months_price_to_sales, str):  stock_data.trailing_12months_price_to_sales = None
                if tase_mode and stock_data.trailing_12months_price_to_sales != None: stock_data.trailing_12months_price_to_sales *= (stock_data.summary_currency_conversion_rate_mult_to_usd/100) # Wrongly calculated by Yahoo for TASE
            else:
                if stock_data.effective_revenue != None and stock_data.effective_revenue > 0 and stock_data.market_cap != None and stock_data.market_cap > 0:
                    stock_data.trailing_12months_price_to_sales  = stock_data.market_cap / stock_data.effective_revenue  # effective_revenue and_market_cap are already in USD (converted earlier)
                elif stock_data.ev_to_cfo_ratio_effective != None:
                    stock_data.trailing_12months_price_to_sales  = stock_data.ev_to_cfo_ratio_effective
                else:
                    stock_data.trailing_12months_price_to_sales  = None # Mark as None, so as to try and calculate manually.
            if isinstance(stock_data.trailing_12months_price_to_sales,str):  stock_data.trailing_12months_price_to_sales  = None # Mark as None, so as to try and calculate manually.

            calculate_altman_z_score_factor(stock_data)

        # TODO: ASAFR: Here: id no previous_close, use market_high, low regular, or anything possible to avoid none!
        if not build_csv_db_only and (stock_data.previous_close is None or stock_data.previous_close < 1.0): # Avoid Penny Stocks
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} previous_close: {}'.format(stock_data.symbol, stock_data.previous_close)
            return_value = False

        if not build_csv_db_only and (stock_data.enterprise_value is None or (not research_mode_max_ev and stock_data.enterprise_value < enterprise_value_millions_usd_limit*1000000) or (research_mode_max_ev and stock_data.enterprise_value > enterprise_value_millions_usd_limit*1000000)):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} enterprise_value: {}'.format(stock_data.symbol, stock_data.enterprise_value)
            return_value = False

        # Checkout http://www.nasdaqtrader.com/trader.aspx?id=symboldirdefs for all symbol definitions (for instance - `$` in stock names, 5-letter stocks ending with `Y`)
        if not tase_mode and SKIP_5LETTER_Y_STOCK_LISTINGS and stock_data.symbol[-1] == 'Y' and len(stock_data.symbol) == 5 and '.' not in stock_data.symbol and '-' not in stock_data.symbol and '$' not in stock_data.symbol:
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} 5 letter stock listing'.format(stock_data.symbol)
            return_value = False

        if build_csv_db_only:
            if (stock_data.enterprise_value_to_revenue != None and stock_data.enterprise_value_to_revenue <= 0 or stock_data.enterprise_value_to_revenue is None) and stock_data.effective_revenue != None and stock_data.effective_revenue > 0:
                stock_data.evr_effective = stock_data.enterprise_value/stock_data.effective_revenue
            else:
                stock_data.evr_effective = stock_data.enterprise_value_to_revenue

        if not build_csv_db_only and (stock_data.evr_effective is None or stock_data.evr_effective <= 0 or stock_data.evr_effective > enterprise_value_to_revenue_limit):
            if return_value and (not research_mode or VERBOSE_LOGS):
                stock_data.skip_reason = 'Skipping {} enterprise_value_to_revenue: {}'.format(stock_data.symbol, stock_data.evr_effective)
            return_value = False

        if not build_csv_db_only and (stock_data.effective_ev_to_ebitda is None or stock_data.effective_ev_to_ebitda <= 0):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} effective_ev_to_ebitda: {}'.format(stock_data.symbol, stock_data.effective_ev_to_ebitda)
            return_value = False

        if not build_csv_db_only and (stock_data.effective_price_to_earnings is None or stock_data.effective_price_to_earnings <= 0):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} effective_price_to_earnings: {}'.format(stock_data.symbol, stock_data.effective_price_to_earnings)
            return_value = False

        if not build_csv_db_only and (stock_data.trailing_12months_price_to_sales is None or stock_data.trailing_12months_price_to_sales <= 0):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} trailing_12months_price_to_sales: {}'.format(stock_data.symbol, stock_data.trailing_12months_price_to_sales)
            return_value = False

        if not build_csv_db_only and (stock_data.price_to_book is None or stock_data.price_to_book <= 0):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} price_to_book: {}'.format(stock_data.symbol, stock_data.price_to_book)
            return_value = False
        if VERBOSE_LOGS: print("[{} {}]".format(__name__, 300))
        if build_csv_db_only and stock_data.effective_price_to_earnings != None:
            if stock_data.sector in favor_sectors:
                index = favor_sectors.index(stock_data.sector)
                stock_data.pe_effective = stock_data.effective_price_to_earnings / float(favor_sectors_by[index])  # ** 2
            else:
                stock_data.pe_effective = stock_data.effective_price_to_earnings

        if not build_csv_db_only and (stock_data.pe_effective is None or stock_data.pe_effective <= 0 or stock_data.pe_effective > price_to_earnings_limit):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} pe_effective: {}'.format(stock_data.symbol, stock_data.pe_effective)
            return_value = False

        if build_csv_db_only:
            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 400))
            if   stock_data.profit_margin            is None and stock_data.annualized_profit_margin is None and stock_data.quarterized_profit_margin is None:
                stock_data.profit_margin             = stock_data.annualized_profit_margin  = stock_data.quarterized_profit_margin = PROFIT_MARGIN_UNKNOWN
            elif stock_data.profit_margin            is None and stock_data.annualized_profit_margin is None                                                 :
                 stock_data.profit_margin            = stock_data.annualized_profit_margin  = stock_data.quarterized_profit_margin/PROFIT_MARGIN_DUPLICATION_FACTOR
            elif stock_data.annualized_profit_margin is None and stock_data.quarterized_profit_margin is None:
                 stock_data.annualized_profit_margin = stock_data.quarterized_profit_margin = stock_data.profit_margin/PROFIT_MARGIN_DUPLICATION_FACTOR
            elif stock_data.profit_margin            is None and stock_data.quarterized_profit_margin is None:
                 stock_data.profit_margin            = stock_data.quarterized_profit_margin = stock_data.annualized_profit_margin/PROFIT_MARGIN_DUPLICATION_FACTOR
            elif stock_data.profit_margin             is None: stock_data.profit_margin             = stock_data.annualized_profit_margin/PROFIT_MARGIN_DUPLICATION_FACTOR
            elif stock_data.annualized_profit_margin  is None: stock_data.annualized_profit_margin  = stock_data.profit_margin/PROFIT_MARGIN_DUPLICATION_FACTOR
            elif stock_data.quarterized_profit_margin is None: stock_data.quarterized_profit_margin = max(stock_data.profit_margin, stock_data.annualized_profit_margin)/PROFIT_MARGIN_DUPLICATION_FACTOR

            sorted_pms = sorted([stock_data.profit_margin, stock_data.annualized_profit_margin, stock_data.quarterized_profit_margin])
            weighted_average_pm = weighted_average(sorted_pms, PROFIT_MARGIN_WEIGHTS[:len(sorted_pms)]) # Higher weight to the higher profit margin when averaging out
            stock_data.effective_profit_margin = PROFIT_MARGIN_DAMPER + weighted_average_pm
            
        if not build_csv_db_only and stock_data.effective_profit_margin < profit_margin_limit:
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} effective_profit_margin: {}'.format(stock_data.symbol, stock_data.effective_profit_margin)
            return_value = False

        if not build_csv_db_only and (stock_data.ev_to_cfo_ratio_effective is None  or stock_data.ev_to_cfo_ratio_effective > ev_to_cfo_ratio_limit or stock_data.ev_to_cfo_ratio_effective <= 0):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} ev_to_cfo_ratio_effective: {}'.format(stock_data.symbol, stock_data.ev_to_cfo_ratio_effective)
            return_value = False

        if not build_csv_db_only and (stock_data.debt_to_equity_effective is None  or stock_data.debt_to_equity_effective > debt_to_equity_limit or stock_data.debt_to_equity_effective <= 0):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} debt_to_equity_effective: {}'.format(stock_data.symbol, stock_data.debt_to_equity_effective)
            return_value = False

        if not build_csv_db_only and (stock_data.eqg_factor_effective is None or stock_data.eqg_factor_effective < eqg_min):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} eqg_factor_effective: {}'.format(stock_data.symbol, stock_data.eqg_factor_effective)
            return_value = False

        if not build_csv_db_only and (stock_data.rqg_factor_effective is None or stock_data.rqg_factor_effective < rqg_min):
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} rqg_factor_effective: {}'.format(stock_data.symbol, stock_data.rqg_factor_effective)
            return_value = False

        if not build_csv_db_only and stock_data.price_to_earnings_to_growth_ratio is None:
            if return_value and (not research_mode or VERBOSE_LOGS): stock_data.skip_reason = 'Skipping {} price_to_earnings_to_growth_ratio: {}'.format(stock_data.symbol, stock_data.price_to_earnings_to_growth_ratio)
            return_value = False

        if build_csv_db_only:
            # The PEG Ratio is equal to (share_price / earnings_per_share) / (earnings_per_share_growth_ratio [% units])
            if 'pegRatio' in info:
                stock_data.price_to_earnings_to_growth_ratio = info['pegRatio']
            else:
                stock_data.price_to_earnings_to_growth_ratio = PEG_UNKNOWN

            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 500))
            if stock_data.price_to_earnings_to_growth_ratio is None or stock_data.price_to_earnings_to_growth_ratio == PEG_UNKNOWN:
                if stock_data.eqg_effective != 0 and stock_data.effective_price_to_earnings > 0:
                    stock_data.price_to_earnings_to_growth_ratio = stock_data.effective_price_to_earnings / stock_data.eqg_effective
                else:
                    stock_data.price_to_earnings_to_growth_ratio = stock_data.effective_price_to_earnings * PEG_UNKNOWN # At this stage effective_price_to_earnings is always positive so this code will not be reached
            if VERBOSE_LOGS: print("[{} {}]".format(__name__, 600))
            if   stock_data.price_to_earnings_to_growth_ratio > 0: stock_data.effective_peg_ratio =  stock_data.price_to_earnings_to_growth_ratio
            elif stock_data.price_to_earnings_to_growth_ratio < 0: stock_data.effective_peg_ratio = -NEGATIVE_PEG_RATIO_FACTOR/stock_data.price_to_earnings_to_growth_ratio
            else                                                 : stock_data.effective_peg_ratio =  1.0  # Something must be wrong, so take a neutral value of 1.0

        if build_csv_db:
            if return_value: sss_core_equation_value_set(stock_data)
            else:            stock_data.sss_value = BAD_SSS

            stock_data.last_dividend_0 = 0; stock_data.last_dividend_1 = 0
            stock_data.last_dividend_2 = 0; stock_data.last_dividend_3 = 0

            try:  # It is important to note that: 1. latest value is in index 0. 2. For the actual value in USD, need to translate the date of the dividend to the value of share at that time, because the dividends[] are pare share
                dividends = symbol.get_dividends()
                if len(dividends) > 0:
                    last_4_dividends = dividends[-1:]
                    stock_data.last_dividend_0 = last_4_dividends[-1] # Latest
                if len(dividends) > 1:
                    last_4_dividends = dividends[-2:]
                    stock_data.last_dividend_1 = last_4_dividends[-2] # One before latest
                if len(dividends) > 2:
                    last_4_dividends = dividends[-3:]
                    stock_data.last_dividend_2 = last_4_dividends[-3] # 2 before latest, etc
                if len(dividends) > 3:
                    last_4_dividends = dividends[-4:]
                    stock_data.last_dividend_3 = last_4_dividends[-4]

            except Exception as e:
                #if not research_mode: print("Exception in symbol.dividends: {}".format(e))
                pass

            round_and_avoid_none_values(stock_data)

        if return_value and not research_mode: print('                                          sector: {:10},     country: {:10},    sss_value: {:10},     annualized_revenue: {:10},     annualized_earnings: {:10},     annualized_retained_earnings: {:10},     quarterized_revenue: {:10},     quarterized_earnings: {:10},     quarterized_retained_earnings: {:10},     effective_earnings: {:10},     effective_retained_earnings: {:10},     effective_revenue: {:10},     annualized_total_revenue: {:10},     annualized_net_income: {:10},     quarterized_total_revenue: {:10},     quarterized_net_income: {:10},     effective_net_income: {:10},     effective_total_revenue: {:10},     enterprise_value_to_revenue: {:10},     evr_effective: {:10},     trailing_price_to_earnings: {:10},     forward_price_to_earnings: {:10},     effective_price_to_earnings: {:10},     trailing_12months_price_to_sales: {:10},     pe_effective: {:10},     effective_ev_to_ebitda: {:10},     profit_margin: {:10},     annualized_profit_margin: {:10},       annualized_profit_margin_boost: {:10},     quarterized_profit_margin: {:10},     quarterized_profit_margin_boost: {:10},     effective_profit_margin_boost: {:10}, held_percent_institutions: {:10},     forward_eps: {:10},     trailing_eps: {:10},     previous_close: {:10},     trailing_eps_percentage: {:10},     price_to_book: {:10},     shares_outstanding: {:10},     net_income_to_common_shareholders: {:10},     nitcsh_to_shares_outstanding: {:10},     employees: {:10},     enterprise_value: {:10},     market_cap: {:10},     nitcsh_to_num_employees: {:10},     eqg_factor_effective: {:10},     rqg_factor_effective: {:10},     price_to_earnings_to_growth_ratio: {:10},     effective_peg_ratio: {:10},     annualized_cash_flow_from_operating_activities: {:10},     quarterized_cash_flow_from_operating_activities: {:10},     annualized_ev_to_cfo_ratio: {:10},     quarterized_ev_to_cfo_ratio: {:10},     ev_to_cfo_ratio_effective: {:10},     annualized_debt_to_equity: {:10},     quarterized_debt_to_equity: {:10},     debt_to_equity_effective: {:10},     debt_to_equity_effective_used: {:10},     financial_currency: {:10},     summary_currency: {:10},     financial_currency_conversion_rate_mult_to_usd: {:10},     summary_currency_conversion_rate_mult_to_usd: {:10}'.format(
                                                                                                stock_data.sector, stock_data.country,stock_data.sss_value, stock_data.annualized_revenue, stock_data.annualized_earnings, stock_data.annualized_retained_earnings, stock_data.quarterized_revenue, stock_data.quarterized_earnings, stock_data.quarterized_retained_earnings, stock_data.effective_earnings, stock_data.effective_retained_earnings, stock_data.effective_revenue, stock_data.annualized_total_revenue, stock_data.annualized_net_income, stock_data.quarterized_total_revenue, stock_data.quarterized_net_income, stock_data.effective_net_income, stock_data.effective_total_revenue, stock_data.enterprise_value_to_revenue, stock_data.evr_effective, stock_data.trailing_price_to_earnings, stock_data.forward_price_to_earnings, stock_data.effective_price_to_earnings, stock_data.trailing_12months_price_to_sales, stock_data.pe_effective, stock_data.effective_ev_to_ebitda, stock_data.profit_margin, stock_data.annualized_profit_margin,   stock_data.annualized_profit_margin_boost, stock_data.quarterized_profit_margin, stock_data.quarterized_profit_margin_boost, stock_data.effective_profit_margin,   stock_data.held_percent_institutions, stock_data.forward_eps, stock_data.trailing_eps, stock_data.previous_close, stock_data.trailing_eps_percentage, stock_data.price_to_book, stock_data.shares_outstanding, stock_data.net_income_to_common_shareholders, stock_data.nitcsh_to_shares_outstanding, stock_data.employees, stock_data.enterprise_value, stock_data.market_cap, stock_data.nitcsh_to_num_employees, stock_data.eqg_factor_effective, stock_data.rqg_factor_effective, stock_data.price_to_earnings_to_growth_ratio, stock_data.effective_peg_ratio, stock_data.annualized_cash_flow_from_operating_activities, stock_data.quarterized_cash_flow_from_operating_activities, stock_data.annualized_ev_to_cfo_ratio, stock_data.quarterized_ev_to_cfo_ratio, stock_data.ev_to_cfo_ratio_effective, stock_data.annualized_debt_to_equity, stock_data.quarterized_debt_to_equity, stock_data.debt_to_equity_effective, stock_data.debt_to_equity_effective_used, stock_data.financial_currency, stock_data.summary_currency, stock_data.financial_currency_conversion_rate_mult_to_usd, stock_data.summary_currency_conversion_rate_mult_to_usd))
        if not return_value and (not research_mode or VERBOSE_LOGS): print('                            ' + stock_data.skip_reason)

        return return_value

    except Exception as e:  # More information is output when exception is used instead of Exception
        if not research_mode: print("              Exception in {} info: {}".format(stock_data.symbol, e))
        return False


def check_interval(thread_id, interval_threads, interval_secs_to_avoid_http_errors, research_mode):
    if thread_id > 0 and thread_id % interval_threads == 0 and not research_mode:
        print("\n===========================================================================")
        print(  "[thread_id {:2} is an interval {} point, going to sleep for {} seconds]".format(thread_id, interval_threads, interval_secs_to_avoid_http_errors))
        print(  "===========================================================================\n")
        time.sleep(interval_secs_to_avoid_http_errors)


# Assumption is that reference_db is sorted by symbol name, but just find it, no need to optimize as of now
def find_symbol_in_reference_db(symbol, reference_db):
    for index in range(len(reference_db)):
        if symbol == reference_db[index][g_symbol_index]:
            return index
    return -1


def get_db_row_from_stock_data(stock_data):
    return [stock_data.symbol,            stock_data.short_name,            stock_data.sector,            stock_data.country,            stock_data.sss_value,                                                        stock_data.annualized_revenue,            stock_data.annualized_earnings,            stock_data.annualized_retained_earnings,            stock_data.quarterized_revenue,            stock_data.quarterized_earnings,            stock_data.quarterized_retained_earnings,            stock_data.effective_earnings,            stock_data.effective_retained_earnings,            stock_data.effective_revenue,            stock_data.annualized_total_revenue,            stock_data.annualized_net_income,            stock_data.quarterized_total_revenue,            stock_data.quarterized_net_income,            stock_data.effective_net_income,            stock_data.effective_total_revenue,            stock_data.enterprise_value_to_revenue,            stock_data.evr_effective,                                                            stock_data.trailing_price_to_earnings,            stock_data.forward_price_to_earnings,            stock_data.effective_price_to_earnings,            stock_data.trailing_12months_price_to_sales,                                                                               stock_data.pe_effective,                                                           stock_data.enterprise_value_to_ebitda,            stock_data.effective_ev_to_ebitda,                                                                     stock_data.ebitda,            stock_data.quarterized_ebitd,            stock_data.annualized_ebitd,            stock_data.ebitd,            stock_data.profit_margin,            stock_data.annualized_profit_margin,            stock_data.annualized_profit_margin_boost,            stock_data.quarterized_profit_margin,            stock_data.quarterized_profit_margin_boost,            stock_data.effective_profit_margin,            stock_data.held_percent_institutions,            stock_data.forward_eps,            stock_data.trailing_eps,            stock_data.previous_close,            stock_data.trailing_eps_percentage,            stock_data.price_to_book,            stock_data.shares_outstanding,            stock_data.net_income_to_common_shareholders,            stock_data.nitcsh_to_shares_outstanding,            stock_data.employees,            stock_data.enterprise_value,            stock_data.market_cap,            stock_data.nitcsh_to_num_employees,            stock_data.eqg,            stock_data.rqg,            stock_data.eqg_yoy,            stock_data.rqg_yoy,            stock_data.niqg_yoy,            stock_data.trqg_yoy,            stock_data.eqg_effective,            stock_data.eqg_factor_effective,            stock_data.rqg_effective,            stock_data.rqg_factor_effective,            stock_data.price_to_earnings_to_growth_ratio,            stock_data.effective_peg_ratio,            stock_data.annualized_cash_flow_from_operating_activities,            stock_data.quarterized_cash_flow_from_operating_activities,            stock_data.annualized_ev_to_cfo_ratio,            stock_data.quarterized_ev_to_cfo_ratio,            stock_data.ev_to_cfo_ratio_effective,            stock_data.annualized_debt_to_equity,            stock_data.quarterized_debt_to_equity,            stock_data.debt_to_equity_effective,            stock_data.debt_to_equity_effective_used,            stock_data.financial_currency,            stock_data.summary_currency,            stock_data.financial_currency_conversion_rate_mult_to_usd,            stock_data.summary_currency_conversion_rate_mult_to_usd,            stock_data.last_dividend_0,            stock_data.last_dividend_1,            stock_data.last_dividend_2,            stock_data.last_dividend_3,            stock_data.fifty_two_week_change,            stock_data.fifty_two_week_low,            stock_data.fifty_two_week_high,            stock_data.two_hundred_day_average,            stock_data.previous_close_percentage_from_200d_ma,            stock_data.previous_close_percentage_from_52w_low,            stock_data.previous_close_percentage_from_52w_high,            stock_data.dist_from_low_factor,            stock_data.eff_dist_from_low_factor,            stock_data.annualized_total_ratio,            stock_data.quarterized_total_ratio,            stock_data.annualized_other_current_ratio,            stock_data.quarterized_other_current_ratio,            stock_data.annualized_other_ratio,            stock_data.quarterized_other_ratio,            stock_data.annualized_total_current_ratio,            stock_data.quarterized_total_current_ratio,            stock_data.total_ratio_effective,            stock_data.other_current_ratio_effective,            stock_data.other_ratio_effective,            stock_data.total_current_ratio_effective,            stock_data.effective_current_ratio,            stock_data.annualized_total_assets,            stock_data.quarterized_total_assets,            stock_data.effective_total_assets,            stock_data.calculated_roa,            stock_data.annualized_working_capital,            stock_data.quarterized_working_capital,            stock_data.effective_working_capital,            stock_data.annualized_total_liabilities,            stock_data.quarterized_total_liabilities,            stock_data.effective_total_liabilities,            stock_data.altman_z_score_factor,            stock_data.skip_reason]           


def get_db_row_from_stock_data_normalized(           stock_data_normalized):
    return [stock_data_normalized.symbol, stock_data_normalized.short_name, stock_data_normalized.sector, stock_data_normalized.country, stock_data_normalized.sss_value, stock_data_normalized.sss_value_normalized, stock_data_normalized.annualized_revenue, stock_data_normalized.annualized_earnings, stock_data_normalized.annualized_retained_earnings, stock_data_normalized.quarterized_revenue, stock_data_normalized.quarterized_earnings, stock_data_normalized.quarterized_retained_earnings, stock_data_normalized.effective_earnings, stock_data_normalized.effective_retained_earnings, stock_data_normalized.effective_revenue, stock_data_normalized.annualized_total_revenue, stock_data_normalized.annualized_net_income, stock_data_normalized.quarterized_total_revenue, stock_data_normalized.quarterized_net_income, stock_data_normalized.effective_net_income, stock_data_normalized.effective_total_revenue, stock_data_normalized.enterprise_value_to_revenue, stock_data_normalized.evr_effective, stock_data_normalized.evr_effective_normalized, stock_data_normalized.trailing_price_to_earnings, stock_data_normalized.forward_price_to_earnings, stock_data_normalized.effective_price_to_earnings, stock_data_normalized.trailing_12months_price_to_sales, stock_data_normalized.trailing_12months_price_to_sales_normalized, stock_data_normalized.pe_effective, stock_data_normalized.pe_effective_normalized, stock_data_normalized.enterprise_value_to_ebitda, stock_data_normalized.effective_ev_to_ebitda, stock_data_normalized.effective_ev_to_ebitda_normalized, stock_data_normalized.ebitda, stock_data_normalized.quarterized_ebitd, stock_data_normalized.annualized_ebitd, stock_data_normalized.ebitd, stock_data_normalized.profit_margin, stock_data_normalized.annualized_profit_margin, stock_data_normalized.annualized_profit_margin_boost, stock_data_normalized.quarterized_profit_margin, stock_data_normalized.quarterized_profit_margin_boost, stock_data_normalized.effective_profit_margin, stock_data_normalized.effective_profit_margin_normalized, stock_data_normalized.held_percent_institutions, stock_data_normalized.forward_eps, stock_data_normalized.trailing_eps, stock_data_normalized.previous_close, stock_data_normalized.trailing_eps_percentage, stock_data_normalized.price_to_book, stock_data_normalized.price_to_book_normalized, stock_data_normalized.shares_outstanding, stock_data_normalized.net_income_to_common_shareholders, stock_data_normalized.nitcsh_to_shares_outstanding, stock_data_normalized.employees, stock_data_normalized.enterprise_value, stock_data_normalized.market_cap, stock_data_normalized.nitcsh_to_num_employees, stock_data_normalized.eqg, stock_data_normalized.rqg, stock_data_normalized.eqg_yoy, stock_data_normalized.rqg_yoy, stock_data_normalized.niqg_yoy, stock_data_normalized.trqg_yoy, stock_data_normalized.eqg_effective, stock_data_normalized.eqg_factor_effective, stock_data_normalized.eqg_factor_effective_normalized, stock_data_normalized.rqg_effective, stock_data_normalized.rqg_factor_effective, stock_data_normalized.rqg_factor_effective_normalized, stock_data_normalized.price_to_earnings_to_growth_ratio, stock_data_normalized.effective_peg_ratio, stock_data_normalized.effective_peg_ratio_normalized, stock_data_normalized.annualized_cash_flow_from_operating_activities, stock_data_normalized.quarterized_cash_flow_from_operating_activities, stock_data_normalized.annualized_ev_to_cfo_ratio, stock_data_normalized.quarterized_ev_to_cfo_ratio, stock_data_normalized.ev_to_cfo_ratio_effective, stock_data_normalized.ev_to_cfo_ratio_effective_normalized, stock_data_normalized.annualized_debt_to_equity, stock_data_normalized.quarterized_debt_to_equity, stock_data_normalized.debt_to_equity_effective, stock_data_normalized.debt_to_equity_effective_used, stock_data_normalized.debt_to_equity_effective_used_normalized, stock_data_normalized.financial_currency, stock_data_normalized.summary_currency, stock_data_normalized.financial_currency_conversion_rate_mult_to_usd, stock_data_normalized.summary_currency_conversion_rate_mult_to_usd, stock_data_normalized.last_dividend_0, stock_data_normalized.last_dividend_1, stock_data_normalized.last_dividend_2, stock_data_normalized.last_dividend_3, stock_data_normalized.fifty_two_week_change, stock_data_normalized.fifty_two_week_low, stock_data_normalized.fifty_two_week_high, stock_data_normalized.two_hundred_day_average, stock_data_normalized.previous_close_percentage_from_200d_ma, stock_data_normalized.previous_close_percentage_from_52w_low, stock_data_normalized.previous_close_percentage_from_52w_high, stock_data_normalized.dist_from_low_factor, stock_data_normalized.eff_dist_from_low_factor, stock_data_normalized.eff_dist_from_low_factor_normalized, stock_data_normalized.annualized_total_ratio, stock_data_normalized.quarterized_total_ratio, stock_data_normalized.annualized_other_current_ratio, stock_data_normalized.quarterized_other_current_ratio, stock_data_normalized.annualized_other_ratio, stock_data_normalized.quarterized_other_ratio, stock_data_normalized.annualized_total_current_ratio, stock_data_normalized.quarterized_total_current_ratio, stock_data_normalized.total_ratio_effective, stock_data_normalized.other_current_ratio_effective, stock_data_normalized.other_ratio_effective, stock_data_normalized.total_current_ratio_effective, stock_data_normalized.effective_current_ratio, stock_data_normalized.effective_current_ratio_normalized, stock_data_normalized.annualized_total_assets, stock_data_normalized.quarterized_total_assets, stock_data_normalized.effective_total_assets, stock_data_normalized.calculated_roa, stock_data_normalized.calculated_roa_normalized, stock_data_normalized.annualized_working_capital, stock_data_normalized.quarterized_working_capital, stock_data_normalized.effective_working_capital, stock_data_normalized.annualized_total_liabilities, stock_data_normalized.quarterized_total_liabilities, stock_data_normalized.effective_total_liabilities, stock_data_normalized.altman_z_score_factor, stock_data_normalized.altman_z_score_factor_normalized, stock_data_normalized.skip_reason]


def get_stock_data_from_db_row(row, symbol=None):
    if symbol:
        stock_symbol = symbol
    else:
        stock_symbol = row[g_symbol_index]
    return StockData(symbol=stock_symbol, short_name=row[g_name_index],   sector=row[g_sector_index],   country=row[g_country_index],   sss_value=float(row[g_sss_value_index]   if row[g_sss_value_index]   != None else 0), annualized_revenue=float(row[g_annualized_revenue_index] if row[g_annualized_revenue_index] != None else 0), annualized_earnings=float(row[g_annualized_earnings_index] if row[g_annualized_earnings_index] != None else 0), annualized_retained_earnings=float(row[g_annualized_retained_earnings_index] if row[g_annualized_retained_earnings_index] != None else 0), quarterized_revenue=float(row[g_quarterized_revenue_index] if row[g_quarterized_revenue_index] != None else 0), quarterized_earnings=float(row[g_quarterized_earnings_index] if row[g_quarterized_earnings_index] != None else 0), quarterized_retained_earnings=float(row[g_quarterized_retained_earnings_index] if row[g_quarterized_retained_earnings_index] != None else 0), effective_earnings=float(row[g_effective_earnings_index] if row[g_effective_earnings_index] != None else 0), effective_retained_earnings=float(row[g_effective_retained_earnings_index] if row[g_effective_retained_earnings_index] != None else 0), effective_revenue=float(row[g_effective_revenue_index] if row[g_effective_revenue_index] != None else 0), annualized_total_revenue=float(row[g_annualized_total_revenue_index] if row[g_annualized_total_revenue_index] != None else 0), annualized_net_income=float(row[g_annualized_net_income_index] if row[g_annualized_net_income_index] != None else 0), quarterized_total_revenue=float(row[g_quarterized_total_revenue_index] if row[g_quarterized_total_revenue_index] != None else 0), quarterized_net_income=float(row[g_quarterized_net_income_index] if row[g_quarterized_net_income_index] != None else 0), effective_net_income=float(row[g_effective_net_income_index] if row[g_effective_net_income_index] != None else 0), effective_total_revenue=float(row[g_effective_total_revenue_index] if row[g_effective_total_revenue_index] != None else 0), enterprise_value_to_revenue=float(row[g_enterprise_value_to_revenue_index] if row[g_enterprise_value_to_revenue_index] != None else 0), evr_effective=float(row[g_evr_effective_index] if row[g_evr_effective_index] != None else 0), trailing_price_to_earnings=float(row[g_trailing_price_to_earnings_index] if row[g_trailing_price_to_earnings_index] != None else 0), forward_price_to_earnings=float(row[g_forward_price_to_earnings_index] if row[g_forward_price_to_earnings_index] != None else 0), effective_price_to_earnings=float(row[g_effective_price_to_earnings_index] if row[g_effective_price_to_earnings_index] != None else 0), trailing_12months_price_to_sales=float(row[g_trailing_12months_price_to_sales_index] if row[g_trailing_12months_price_to_sales_index] != None else 0), pe_effective=float(row[g_pe_effective_index] if row[g_pe_effective_index] != None else 0), enterprise_value_to_ebitda=float(row[g_enterprise_value_to_ebitda_index] if row[g_enterprise_value_to_ebitda_index] != None else 0), effective_ev_to_ebitda=float(row[g_effective_ev_to_ebitda_index] if row[g_effective_ev_to_ebitda_index] != None else 0), ebitda=float(row[g_ebitda_index] if row[g_ebitd_index] != None else 0), quarterized_ebitd=float(row[g_quarterized_ebitd_index] if row[g_quarterized_ebitd_index] != None else 0), annualized_ebitd=float(row[g_annualized_ebitd_index] if row[g_annualized_ebitd_index] != None else 0), ebitd=float(row[g_ebitd_index] if row[g_ebitd_index] != None else 0), profit_margin=float(row[g_profit_margin_index] if row[g_profit_margin_index] != None else 0), annualized_profit_margin=float(row[g_annualized_profit_margin_index] if row[g_annualized_profit_margin_index] != None else 0), annualized_profit_margin_boost=float(row[g_annualized_profit_margin_boost_index] if row[g_annualized_profit_margin_boost_index] != None else 0), quarterized_profit_margin=float(row[g_quarterized_profit_margin_index] if row[g_quarterized_profit_margin_index] != None else 0), quarterized_profit_margin_boost=float(row[g_quarterized_profit_margin_boost_index] if row[g_quarterized_profit_margin_boost_index] != None else 0), effective_profit_margin=float(row[g_effective_profit_margin_index] if row[g_effective_profit_margin_index] != None else 0), held_percent_institutions=float(row[g_held_percent_institutions_index] if row[g_held_percent_institutions_index] != None else 0), forward_eps=float(row[g_forward_eps_index] if row[g_forward_eps_index] != None else 0), trailing_eps=float(row[g_trailing_eps_index] if row[g_trailing_eps_index] != None else 0), previous_close=float(row[g_previous_close_index] if row[g_previous_close_index] != None else 0), trailing_eps_percentage=float(row[g_trailing_eps_percentage_index] if row[g_trailing_eps_percentage_index] != None else 0), price_to_book=float(row[g_price_to_book_index] if row[g_price_to_book_index] != None else 0), shares_outstanding=float(row[g_shares_outstanding_index] if row[g_shares_outstanding_index] != None else 0), net_income_to_common_shareholders=float(row[g_net_income_to_common_shareholders_index] if row[g_net_income_to_common_shareholders_index] != None else 0), nitcsh_to_shares_outstanding=float(row[g_nitcsh_to_shares_outstanding_index] if row[g_nitcsh_to_shares_outstanding_index] != None else 0), employees=int(float(row[g_employees_index] if row[g_employees_index] != None else 0)), enterprise_value=int(float(row[g_enterprise_value_index] if row[g_enterprise_value_index] != None else 0)), market_cap=int(float(row[g_market_cap_index] if row[g_market_cap_index] != None else 0)), nitcsh_to_num_employees=float(row[g_nitcsh_to_num_employees_index] if row[g_nitcsh_to_num_employees_index] != None else 0), eqg=float(row[g_eqg_index] if row[g_eqg_index] != None else 0), rqg=float(row[g_rqg_index] if row[g_rqg_index] != None else 0), eqg_yoy=float(row[g_eqg_yoy_index] if row[g_eqg_yoy_index] != None else 0), rqg_yoy=float(row[g_rqg_yoy_index] if row[g_rqg_yoy_index] != None else 0), niqg_yoy=float(row[g_niqg_yoy_index] if row[g_niqg_yoy_index] != None else 0), trqg_yoy=float(row[g_trqg_yoy_index] if row[g_trqg_yoy_index] != None else 0), eqg_effective=float(row[g_eqg_effective_index] if row[g_eqg_effective_index] != None else 0), eqg_factor_effective=float(row[g_eqg_factor_effective_index] if row[g_eqg_factor_effective_index] != None else 0), rqg_effective=float(row[g_rqg_effective_index] if row[g_rqg_effective_index] != None else 0), rqg_factor_effective=float(row[g_rqg_factor_effective_index] if row[g_rqg_factor_effective_index] != None else 0), price_to_earnings_to_growth_ratio=float(row[g_price_to_earnings_to_growth_ratio_index] if row[g_price_to_earnings_to_growth_ratio_index] != None else 0), effective_peg_ratio=float(row[g_effective_peg_ratio_index] if row[g_effective_peg_ratio_index] != None else 0), annualized_cash_flow_from_operating_activities=float(row[g_annualized_cash_flow_from_operating_activities_index] if row[g_annualized_cash_flow_from_operating_activities_index] != None else 0), quarterized_cash_flow_from_operating_activities=float(row[g_quarterized_cash_flow_from_operating_activities_index] if row[g_quarterized_cash_flow_from_operating_activities_index] != None else 0), annualized_ev_to_cfo_ratio=float(row[g_annualized_ev_to_cfo_ratio_index] if row[g_annualized_ev_to_cfo_ratio_index] != None else 0), quarterized_ev_to_cfo_ratio=float(row[g_quarterized_ev_to_cfo_ratio_index] if row[g_quarterized_ev_to_cfo_ratio_index] != None else 0), ev_to_cfo_ratio_effective=float(row[g_ev_to_cfo_ratio_effective_index] if row[g_ev_to_cfo_ratio_effective_index] != None else 0), annualized_debt_to_equity=float(row[g_annualized_debt_to_equity_index] if row[g_annualized_debt_to_equity_index] != None else 0), quarterized_debt_to_equity=float(row[g_quarterized_debt_to_equity_index] if row[g_quarterized_debt_to_equity_index] != None else 0), debt_to_equity_effective=float(row[g_debt_to_equity_effective_index] if row[g_debt_to_equity_effective_index] != None else 0), debt_to_equity_effective_used=float(row[g_debt_to_equity_effective_used_index] if row[g_debt_to_equity_effective_used_index] != None else 0), financial_currency=row[g_financial_currency_index], summary_currency=row[g_summary_currency_index], financial_currency_conversion_rate_mult_to_usd=float(row[g_financial_currency_conversion_rate_mult_to_usd_index] if row[g_financial_currency_conversion_rate_mult_to_usd_index] != None else 0), summary_currency_conversion_rate_mult_to_usd=float(row[g_summary_currency_conversion_rate_mult_to_usd_index] if row[g_summary_currency_conversion_rate_mult_to_usd_index] != None else 0), last_dividend_0=float(row[g_last_dividend_0_index] if row[g_last_dividend_0_index] != None else 0), last_dividend_1=float(row[g_last_dividend_1_index] if row[g_last_dividend_1_index] != None else 0), last_dividend_2=float(row[g_last_dividend_2_index] if row[g_last_dividend_2_index] != None else 0), last_dividend_3=float(row[g_last_dividend_3_index] if row[g_last_dividend_3_index] != None else 0), fifty_two_week_change=float(row[g_fifty_two_week_change_index] if row[g_fifty_two_week_change_index] != None else 0), fifty_two_week_low=float(row[g_fifty_two_week_low_index] if row[g_fifty_two_week_low_index] != None else 0), fifty_two_week_high=float(row[g_fifty_two_week_high_index] if row[g_fifty_two_week_high_index] != None else 0), two_hundred_day_average=float(row[g_two_hundred_day_average_index] if row[g_two_hundred_day_average_index] != None else 0), previous_close_percentage_from_200d_ma=float(row[g_previous_close_percentage_from_200d_ma_index] if row[g_previous_close_percentage_from_200d_ma_index] != None else 0), previous_close_percentage_from_52w_low=float(row[g_previous_close_percentage_from_52w_low_index] if row[g_previous_close_percentage_from_52w_low_index] != None else 0), previous_close_percentage_from_52w_high=float(row[g_previous_close_percentage_from_52w_high_index] if row[g_previous_close_percentage_from_52w_high_index] != None else 0), dist_from_low_factor=float(row[g_dist_from_low_factor_index] if row[g_dist_from_low_factor_index] != None else 0), eff_dist_from_low_factor=float(row[g_eff_dist_from_low_factor_index] if row[g_eff_dist_from_low_factor_index] != None else 0), annualized_total_ratio=float(row[g_annualized_total_ratio_index] if row[g_annualized_total_ratio_index] != None else 0), quarterized_total_ratio=float(row[g_quarterized_total_ratio_index] if row[g_quarterized_total_ratio_index] != None else 0), annualized_other_current_ratio=float(row[g_annualized_other_current_ratio_index] if row[g_annualized_other_current_ratio_index] != None else 0), quarterized_other_current_ratio=float(row[g_quarterized_other_current_ratio_index] if row[g_quarterized_other_current_ratio_index] != None else 0), annualized_other_ratio=float(row[g_annualized_other_ratio_index] if row[g_annualized_other_ratio_index] != None else 0), quarterized_other_ratio=float(row[g_quarterized_other_ratio_index] if row[g_quarterized_other_ratio_index] != None else 0), annualized_total_current_ratio=float(row[g_annualized_total_current_ratio_index] if row[g_annualized_total_current_ratio_index] != None else 0), quarterized_total_current_ratio=float(row[g_quarterized_total_current_ratio_index] if row[g_quarterized_total_current_ratio_index] != None else 0), total_ratio_effective=float(row[g_total_ratio_effective_index] if row[g_total_ratio_effective_index] != None else 0), other_current_ratio_effective=float(row[g_other_current_ratio_effective_index] if row[g_other_current_ratio_effective_index] != None else 0), other_ratio_effective=float(row[g_other_ratio_effective_index] if row[g_other_ratio_effective_index] != None else 0), total_current_ratio_effective=float(row[g_total_current_ratio_effective_index] if row[g_total_current_ratio_effective_index] != None else 0), effective_current_ratio=float(row[g_effective_current_ratio_index] if row[g_effective_current_ratio_index] != None else 0), annualized_total_assets=float(row[g_annualized_total_assets_index] if row[g_annualized_total_assets_index] != None else 0), quarterized_total_assets=float(row[g_quarterized_total_assets_index] if row[g_quarterized_total_assets_index] != None else 0), effective_total_assets=float(row[g_effective_total_assets_index] if row[g_effective_total_assets_index] != None else 0), calculated_roa=float(row[g_calculated_roa_index] if row[g_calculated_roa_index] != None else 0), annualized_working_capital=float(row[g_annualized_working_capital_index] if row[g_annualized_working_capital_index] != None else 0), quarterized_working_capital=float(row[g_quarterized_working_capital_index] if row[g_quarterized_working_capital_index] != None else 0), effective_working_capital=float(row[g_effective_working_capital_index] if row[g_effective_working_capital_index] != None else 0), annualized_total_liabilities=float(row[g_annualized_total_liabilities_index] if row[g_annualized_total_liabilities_index] != None else 0), quarterized_total_liabilities=float(row[g_quarterized_total_liabilities_index] if row[g_quarterized_total_liabilities_index] != None else 0), effective_total_liabilities=float(row[g_effective_total_liabilities_index] if row[g_effective_total_liabilities_index] != None else 0), altman_z_score_factor=float(row[g_altman_z_score_factor_index] if row[g_altman_z_score_factor_index] != None else 0), skip_reason=row[g_skip_reason_index])


def get_stock_data_normalized_from_db_row(row, symbol=None):
    if symbol:
        stock_symbol = symbol
    else:
        stock_symbol = row[g_symbol_index_n]
    return StockDataNormalized(symbol=stock_symbol, short_name=row[g_name_index_n], sector=row[g_sector_index_n], country=row[g_country_index_n], sss_value=float(row[g_sss_value_index_n] if row[g_sss_value_index_n] != None else 0), sss_value_normalized=float(row[g_sss_value_normalized_index_n] if row[g_sss_value_normalized_index_n] != None else 0), annualized_revenue=float(row[g_annualized_revenue_index_n] if row[g_annualized_revenue_index_n] != None else 0), annualized_earnings=float(row[g_annualized_earnings_index_n] if row[g_annualized_earnings_index_n] != None else 0), annualized_retained_earnings=float(row[g_annualized_retained_earnings_index_n] if row[g_annualized_retained_earnings_index_n] != None else 0), quarterized_revenue=float(row[g_quarterized_revenue_index_n] if row[g_quarterized_revenue_index_n] != None else 0), quarterized_earnings=float(row[g_quarterized_earnings_index_n] if row[g_quarterized_earnings_index_n] != None else 0), quarterized_retained_earnings=float(row[g_quarterized_retained_earnings_index_n] if row[g_quarterized_retained_earnings_index_n] != None else 0), effective_earnings=float(row[g_effective_earnings_index_n] if row[g_effective_earnings_index_n] != None else 0), effective_retained_earnings=float(row[g_effective_retained_earnings_index_n] if row[g_effective_retained_earnings_index_n] != None else 0), effective_revenue=float(row[g_effective_revenue_index_n] if row[g_effective_revenue_index_n] != None else 0), annualized_total_revenue=float(row[g_annualized_total_revenue_index_n] if row[g_annualized_total_revenue_index_n] != None else 0), annualized_net_income=float(row[g_annualized_net_income_index_n] if row[g_annualized_net_income_index_n] != None else 0), quarterized_total_revenue=float(row[g_quarterized_total_revenue_index_n] if row[g_quarterized_total_revenue_index_n] != None else 0), quarterized_net_income=float(row[g_quarterized_net_income_index_n] if row[g_quarterized_net_income_index_n] != None else 0), effective_net_income=float(row[g_effective_net_income_index_n] if row[g_effective_net_income_index_n] != None else 0), effective_total_revenue=float(row[g_effective_total_revenue_index_n] if row[g_effective_total_revenue_index_n] != None else 0), enterprise_value_to_revenue=float(row[g_enterprise_value_to_revenue_index_n] if row[g_enterprise_value_to_revenue_index_n] != None else 0), evr_effective=float(row[g_evr_effective_index_n] if row[g_evr_effective_index_n] != None else 0), evr_effective_normalized=float(row[g_evr_effective_normalized_index_n] if row[g_evr_effective_normalized_index_n] != None else 0), trailing_price_to_earnings=float(row[g_trailing_price_to_earnings_index_n] if row[g_trailing_price_to_earnings_index_n] != None else 0), forward_price_to_earnings=float(row[g_forward_price_to_earnings_index_n] if row[g_forward_price_to_earnings_index_n] != None else 0), effective_price_to_earnings=float(row[g_effective_price_to_earnings_index_n] if row[g_effective_price_to_earnings_index_n] != None else 0), trailing_12months_price_to_sales=float(row[g_trailing_12months_price_to_sales_index_n] if row[g_trailing_12months_price_to_sales_index_n] != None else 0), trailing_12months_price_to_sales_normalized=float(row[g_trailing_12months_price_to_sales_normalized_index_n] if row[g_trailing_12months_price_to_sales_normalized_index_n] != None else 0), pe_effective=float(row[g_pe_effective_index_n] if row[g_pe_effective_index_n] != None else 0), pe_effective_normalized=float(row[g_pe_effective_normalized_index_n] if row[g_pe_effective_normalized_index_n] != None else 0), enterprise_value_to_ebitda=float(row[g_enterprise_value_to_ebitda_index_n] if row[g_enterprise_value_to_ebitda_index_n] != None else 0), effective_ev_to_ebitda=float(row[g_effective_ev_to_ebitda_index_n] if row[g_effective_ev_to_ebitda_index_n] != None else 0), effective_ev_to_ebitda_normalized=float(row[g_effective_ev_to_ebitda_normalized_index_n] if row[g_effective_ev_to_ebitda_normalized_index_n] != None else 0), ebitda=float(row[g_ebitda_index_n] if row[g_ebitd_index_n] != None else 0), quarterized_ebitd=float(row[g_quarterized_ebitd_index_n] if row[g_quarterized_ebitd_index_n] != None else 0), annualized_ebitd=float(row[g_annualized_ebitd_index_n] if row[g_annualized_ebitd_index_n] != None else 0), ebitd=float(row[g_ebitd_index_n] if row[g_ebitd_index_n] != None else 0), profit_margin=float(row[g_profit_margin_index_n] if row[g_profit_margin_index_n] != None else 0), annualized_profit_margin=float(row[g_annualized_profit_margin_index_n] if row[g_annualized_profit_margin_index_n] != None else 0), annualized_profit_margin_boost=float(row[g_annualized_profit_margin_boost_index_n] if row[g_annualized_profit_margin_boost_index_n] != None else 0), quarterized_profit_margin=float(row[g_quarterized_profit_margin_index_n] if row[g_quarterized_profit_margin_index_n] != None else 0), quarterized_profit_margin_boost=float(row[g_quarterized_profit_margin_boost_index_n] if row[g_quarterized_profit_margin_boost_index_n] != None else 0), effective_profit_margin=float(row[g_effective_profit_margin_index_n] if row[g_effective_profit_margin_index_n] != None else 0), effective_profit_margin_normalized=float(row[g_effective_profit_margin_normalized_index_n] if row[g_effective_profit_margin_normalized_index_n] != None else 0), held_percent_institutions=float(row[g_held_percent_institutions_index_n] if row[g_held_percent_institutions_index_n] != None else 0), forward_eps=float(row[g_forward_eps_index_n] if row[g_forward_eps_index_n] != None else 0), trailing_eps=float(row[g_trailing_eps_index_n] if row[g_trailing_eps_index_n] != None else 0), previous_close=float(row[g_previous_close_index_n] if row[g_previous_close_index_n] != None else 0), trailing_eps_percentage=float(row[g_trailing_eps_percentage_index_n] if row[g_trailing_eps_percentage_index_n] != None else 0), price_to_book=float(row[g_price_to_book_index_n] if row[g_price_to_book_index_n] != None else 0), price_to_book_normalized=float(row[g_price_to_book_normalized_index_n] if row[g_price_to_book_normalized_index_n] != None else 0), shares_outstanding=float(row[g_shares_outstanding_index_n] if row[g_shares_outstanding_index_n] != None else 0), net_income_to_common_shareholders=float(row[g_net_income_to_common_shareholders_index_n] if row[g_net_income_to_common_shareholders_index_n] != None else 0), nitcsh_to_shares_outstanding=float(row[g_nitcsh_to_shares_outstanding_index_n] if row[g_nitcsh_to_shares_outstanding_index_n] != None else 0), employees=int(float(row[g_employees_index_n] if row[g_employees_index_n] != None else 0)), enterprise_value=int(float(row[g_enterprise_value_index_n] if row[g_enterprise_value_index_n] != None else 0)), market_cap=int(float(row[g_market_cap_index_n] if row[g_market_cap_index_n] != None else 0)), nitcsh_to_num_employees=float(row[g_nitcsh_to_num_employees_index_n] if row[g_nitcsh_to_num_employees_index_n] != None else 0), eqg=float(row[g_eqg_index_n] if row[g_eqg_index_n] != None else 0), rqg=float(row[g_rqg_index_n] if row[g_rqg_index_n] != None else 0), eqg_yoy=float(row[g_eqg_yoy_index_n] if row[g_eqg_yoy_index_n] != None else 0), rqg_yoy=float(row[g_rqg_yoy_index_n] if row[g_rqg_yoy_index_n] != None else 0), niqg_yoy=float(row[g_niqg_yoy_index_n] if row[g_niqg_yoy_index_n] != None else 0), trqg_yoy=float(row[g_trqg_yoy_index_n] if row[g_trqg_yoy_index_n] != None else 0), eqg_effective=float(row[g_eqg_effective_index_n] if row[g_eqg_effective_index_n] != None else 0), eqg_factor_effective=float(row[g_eqg_factor_effective_index_n] if row[g_eqg_factor_effective_index_n] != None else 0), eqg_factor_effective_normalized=float(row[g_eqg_factor_effective_normalized_index_n] if row[g_eqg_factor_effective_normalized_index_n] != None else 0), rqg_effective=float(row[g_rqg_effective_index_n] if row[g_rqg_effective_index_n] != None else 0), rqg_factor_effective=float(row[g_rqg_factor_effective_index_n] if row[g_rqg_factor_effective_index_n] != None else 0), rqg_factor_effective_normalized=float(row[g_rqg_factor_effective_normalized_index_n] if row[g_rqg_factor_effective_normalized_index_n] != None else 0), price_to_earnings_to_growth_ratio=float(row[g_price_to_earnings_to_growth_ratio_index_n] if row[g_price_to_earnings_to_growth_ratio_index_n] != None else 0), effective_peg_ratio=float(row[g_effective_peg_ratio_index_n] if row[g_effective_peg_ratio_index_n] != None else 0), effective_peg_ratio_normalized=float(row[g_effective_peg_ratio_normalized_index_n] if row[g_effective_peg_ratio_normalized_index_n] != None else 0), annualized_cash_flow_from_operating_activities=float(row[g_annualized_cash_flow_from_operating_activities_index_n] if row[g_annualized_cash_flow_from_operating_activities_index_n] != None else 0), quarterized_cash_flow_from_operating_activities=float(row[g_quarterized_cash_flow_from_operating_activities_index_n] if row[g_quarterized_cash_flow_from_operating_activities_index_n] != None else 0), annualized_ev_to_cfo_ratio=float(row[g_annualized_ev_to_cfo_ratio_index_n] if row[g_annualized_ev_to_cfo_ratio_index_n] != None else 0), quarterized_ev_to_cfo_ratio=float(row[g_quarterized_ev_to_cfo_ratio_index_n] if row[g_quarterized_ev_to_cfo_ratio_index_n] != None else 0), ev_to_cfo_ratio_effective=float(row[g_ev_to_cfo_ratio_effective_index_n] if row[g_ev_to_cfo_ratio_effective_index_n] != None else 0), ev_to_cfo_ratio_effective_normalized=float(row[g_ev_to_cfo_ratio_effective_normalized_index_n] if row[g_ev_to_cfo_ratio_effective_normalized_index_n] != None else 0), annualized_debt_to_equity=float(row[g_annualized_debt_to_equity_index_n] if row[g_annualized_debt_to_equity_index_n] != None else 0), quarterized_debt_to_equity=float(row[g_quarterized_debt_to_equity_index_n] if row[g_quarterized_debt_to_equity_index_n] != None else 0), debt_to_equity_effective=float(row[g_debt_to_equity_effective_index_n] if row[g_debt_to_equity_effective_index_n] != None else 0), debt_to_equity_effective_used=float(row[g_debt_to_equity_effective_used_index_n] if row[g_debt_to_equity_effective_used_index_n] != None else 0), debt_to_equity_effective_used_normalized=float(row[g_debt_to_equity_effective_used_normalized_index_n] if row[g_debt_to_equity_effective_used_normalized_index_n] != None else 0), financial_currency=row[g_financial_currency_index_n], summary_currency=row[g_summary_currency_index_n], financial_currency_conversion_rate_mult_to_usd=float(row[g_financial_currency_conversion_rate_mult_to_usd_index_n] if row[g_financial_currency_conversion_rate_mult_to_usd_index_n] != None else 0), summary_currency_conversion_rate_mult_to_usd=float(row[g_summary_currency_conversion_rate_mult_to_usd_index_n] if row[g_summary_currency_conversion_rate_mult_to_usd_index_n] != None else 0), last_dividend_0=float(row[g_last_dividend_0_index_n] if row[g_last_dividend_0_index_n] != None else 0), last_dividend_1=float(row[g_last_dividend_1_index_n] if row[g_last_dividend_1_index_n] != None else 0), last_dividend_2=float(row[g_last_dividend_2_index_n] if row[g_last_dividend_2_index_n] != None else 0), last_dividend_3=float(row[g_last_dividend_3_index_n] if row[g_last_dividend_3_index_n] != None else 0), fifty_two_week_change=float(row[g_fifty_two_week_change_index_n] if row[g_fifty_two_week_change_index_n] != None else 0), fifty_two_week_low=float(row[g_fifty_two_week_low_index_n] if row[g_fifty_two_week_low_index_n] != None else 0), fifty_two_week_high=float(row[g_fifty_two_week_high_index_n] if row[g_fifty_two_week_high_index_n] != None else 0), two_hundred_day_average=float(row[g_two_hundred_day_average_index_n] if row[g_two_hundred_day_average_index_n] != None else 0), previous_close_percentage_from_200d_ma=float(row[g_previous_close_percentage_from_200d_ma_index_n] if row[g_previous_close_percentage_from_200d_ma_index_n] != None else 0), previous_close_percentage_from_52w_low=float(row[g_previous_close_percentage_from_52w_low_index_n] if row[g_previous_close_percentage_from_52w_low_index_n] != None else 0), previous_close_percentage_from_52w_high=float(row[g_previous_close_percentage_from_52w_high_index_n] if row[g_previous_close_percentage_from_52w_high_index_n] != None else 0), dist_from_low_factor=float(row[g_dist_from_low_factor_index_n] if row[g_dist_from_low_factor_index_n] != None else 0), eff_dist_from_low_factor=float(row[g_eff_dist_from_low_factor_index_n] if row[g_eff_dist_from_low_factor_index_n] != None else 0), eff_dist_from_low_factor_normalized=float(row[g_eff_dist_from_low_factor_normalized_index_n] if row[g_eff_dist_from_low_factor_normalized_index_n] != None else 0), annualized_total_ratio=float(row[g_annualized_total_ratio_index_n] if row[g_annualized_total_ratio_index_n] != None else 0), quarterized_total_ratio=float(row[g_quarterized_total_ratio_index_n] if row[g_quarterized_total_ratio_index_n] != None else 0), annualized_other_current_ratio=float(row[g_annualized_other_current_ratio_index_n] if row[g_annualized_other_current_ratio_index_n] != None else 0), quarterized_other_current_ratio=float(row[g_quarterized_other_current_ratio_index_n] if row[g_quarterized_other_current_ratio_index_n] != None else 0), annualized_other_ratio=float(row[g_annualized_other_ratio_index_n] if row[g_annualized_other_ratio_index_n] != None else 0), quarterized_other_ratio=float(row[g_quarterized_other_ratio_index_n] if row[g_quarterized_other_ratio_index_n] != None else 0), annualized_total_current_ratio=float(row[g_annualized_total_current_ratio_index_n] if row[g_annualized_total_current_ratio_index_n] != None else 0), quarterized_total_current_ratio=float(row[g_quarterized_total_current_ratio_index_n] if row[g_quarterized_total_current_ratio_index_n] != None else 0), total_ratio_effective=float(row[g_total_ratio_effective_index_n] if row[g_total_ratio_effective_index_n] != None else 0), other_current_ratio_effective=float(row[g_other_current_ratio_effective_index_n] if row[g_other_current_ratio_effective_index_n] != None else 0), other_ratio_effective=float(row[g_other_ratio_effective_index_n] if row[g_other_ratio_effective_index_n] != None else 0), total_current_ratio_effective=float(row[g_total_current_ratio_effective_index_n] if row[g_total_current_ratio_effective_index_n] != None else 0), effective_current_ratio=float(row[g_effective_current_ratio_index_n] if row[g_effective_current_ratio_index_n] != None else 0), effective_current_ratio_normalized=float(row[g_effective_current_ratio_normalized_index_n] if row[g_effective_current_ratio_normalized_index_n] != None else 0), annualized_total_assets=float(row[g_annualized_total_assets_index_n] if row[g_annualized_total_assets_index_n] != None else 0), quarterized_total_assets=float(row[g_quarterized_total_assets_index_n] if row[g_quarterized_total_assets_index_n] != None else 0), effective_total_assets=float(row[g_effective_total_assets_index_n] if row[g_effective_total_assets_index_n] != None else 0), calculated_roa=float(row[g_calculated_roa_index_n] if row[g_calculated_roa_index_n] != None else 0), calculated_roa_normalized=float(row[g_calculated_roa_normalized_index_n] if row[g_calculated_roa_normalized_index_n] != None else 0), annualized_working_capital=float(row[g_annualized_working_capital_index_n] if row[g_annualized_working_capital_index_n] != None else 0), quarterized_working_capital=float(row[g_quarterized_working_capital_index_n] if row[g_quarterized_working_capital_index_n] != None else 0), effective_working_capital=float(row[g_effective_working_capital_index_n] if row[g_effective_working_capital_index_n] != None else 0), annualized_total_liabilities=float(row[g_annualized_total_liabilities_index_n] if row[g_annualized_total_liabilities_index_n] != None else 0), quarterized_total_liabilities=float(row[g_quarterized_total_liabilities_index_n] if row[g_quarterized_total_liabilities_index_n] != None else 0), effective_total_liabilities=float(row[g_effective_total_liabilities_index_n] if row[g_effective_total_liabilities_index_n] != None else 0), altman_z_score_factor=float(row[g_altman_z_score_factor_index_n] if row[g_altman_z_score_factor_index_n] != None else 0), altman_z_score_factor_normalized=float(row[g_altman_z_score_factor_normalized_index_n] if row[g_altman_z_score_factor_normalized_index_n] != None else 0), skip_reason=row[g_skip_reason_index_n])


def process_symbols(symbols, csv_db_data, rows, rows_no_div, rows_only_div, thread_id, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, diff_rows, db_filename):
    iteration = 0
    if build_csv_db:
        for symb in symbols:
            iteration += 1
            sleep_seconds = round(random.uniform(float(relaxed_access)/2.0, float(relaxed_access)*2), NUM_ROUND_DECIMALS)
            time.sleep(sleep_seconds)
            if not research_mode: print('[Building DB: thread_id {:2} Sleeping for {:10} sec] Checking {:9} ({:4}/{:4}/{:4} [Diff: {:4}]):'.format(thread_id, sleep_seconds, symb, len(rows), iteration, len(symbols), len(diff_rows)))
            if tase_mode:
                symbol = yf.Ticker(symb)
            else:
                symbol = yf.Ticker(symb.replace('.','-'))  # TODO: ASFAR: Sometimes the '.' Is needed, especially for non-US companies. See for instance 5205.kl. In this case the parameter is also case-sensitive! -> https://github.com/pydata/pandas-datareader/issues/810#issuecomment-789684354
            stock_data = StockData(symbol=symb)
            process_info_result = process_info(symbol=symbol, stock_data=stock_data, build_csv_db_only=build_csv_db_only, tase_mode=tase_mode, sectors_list=sectors_list, sectors_filter_out=sectors_filter_out, countries_list=countries_list, countries_filter_out=countries_filter_out, build_csv_db=build_csv_db, profit_margin_limit=profit_margin_limit, ev_to_cfo_ratio_limit=ev_to_cfo_ratio_limit, debt_to_equity_limit=debt_to_equity_limit, enterprise_value_millions_usd_limit=enterprise_value_millions_usd_limit, research_mode_max_ev=research_mode_max_ev, eqg_min=eqg_min, rqg_min=rqg_min, price_to_earnings_limit=price_to_earnings_limit, enterprise_value_to_revenue_limit=enterprise_value_to_revenue_limit, favor_sectors=favor_sectors, favor_sectors_by=favor_sectors_by, market_cap_included=market_cap_included, research_mode=research_mode, currency_conversion_tool=currency_conversion_tool, currency_conversion_tool_alternative=currency_conversion_tool_alternative, currency_conversion_tool_manual=currency_conversion_tool_manual, reference_db=reference_db, reference_db_title_row=reference_db_title_row, db_filename=None)
            if tase_mode and 'TLV:' not in stock_data.symbol: stock_data.symbol = 'TLV:' + stock_data.symbol.replace('.TA', '').replace('.', '-')

            row_to_append = get_db_row_from_stock_data(stock_data)
            # Find symbol in reference_db:
            if len(reference_db):
                symbol_index_in_reference_db = find_symbol_in_reference_db(stock_data.symbol, reference_db)
                if symbol_index_in_reference_db >= 0:
                    found_differences = False
                    for index in range(len(g_header_row)):
                        if (VERBOSE_LOGS): print('      comparing column {}'.format(g_header_row[index]))
                        if type(row_to_append[index]) == int or type(row_to_append[index]) == float:
                            try:
                                if g_header_row[index] in reference_db_title_row:
                                    column_index_in_reference_db = reference_db_title_row.index(g_header_row[index])
                                    min_val = min(float(row_to_append[index]), float(reference_db[symbol_index_in_reference_db][column_index_in_reference_db]))
                                    max_val = max(float(row_to_append[index]), float(reference_db[symbol_index_in_reference_db][column_index_in_reference_db]))
                                    diff    = abs(max_val-min_val)
                                    # TODO: ASAFR: 52-week change is not really working and not really needed - fix or eliminate
                                    indices_list_to_ignore_changes_in = [g_sss_value_index, g_financial_currency_index, g_summary_currency_index] # [g_fifty_two_week_change_index, g_sss_value_index, g_two_hundred_day_average_index, g_previous_close_percentage_from_200d_ma_index]
                                    if diff > abs(max_val)*REFERENCE_DB_MAX_VALUE_DIFF_FACTOR_THRESHOLD and index not in indices_list_to_ignore_changes_in:
                                        if 0.0 < float(reference_db[symbol_index_in_reference_db][g_sss_value_index]) < float(row_to_append[g_sss_value_index]):
                                            found_differences = True
                                            compensated_value = round(REFERENCE_DB_MAX_VALUE_DIFF_FACTOR_THRESHOLD*float(reference_db[symbol_index_in_reference_db][column_index_in_reference_db]) + (1-REFERENCE_DB_MAX_VALUE_DIFF_FACTOR_THRESHOLD)*float(row_to_append[index]), NUM_ROUND_DECIMALS)
                                            if type(row_to_append[index]) == int:
                                                compensated_value = int(round(compensated_value))
                                            print('                            {:5} - Suspicious Difference detected (taking lower sss_value [{} < {}] row as correct row): reference_db[{:25}]={:6}, db[{:25}]={:6} -> compensated_value = {:6}'.format(row_to_append[g_symbol_index], (reference_db[symbol_index_in_reference_db][g_sss_value_index]), (row_to_append[g_sss_value_index]), g_header_row[index], reference_db[symbol_index_in_reference_db][column_index_in_reference_db], g_header_row[index], row_to_append[index], compensated_value))
                                            row_to_append[index] = compensated_value  # Overwrite specific index value with compensated value from reference db
                            except Exception as e:
                                print("Exception {} in comparison of {}: row_to_append is {} while reference_db is {}".format(e, g_header_row[index], row_to_append[index], reference_db[symbol_index_in_reference_db][column_index_in_reference_db]))
                                pass

                    if found_differences:
                        get_stock_data_from_db_row(row_to_append)
                        if stock_data.sector             in ['None', '', 'Unknown']: stock_data.sector             = reference_db[symbol_index_in_reference_db][g_sector_index]
                        if stock_data.country            in ['None', '', 'Unknown']: stock_data.country            = reference_db[symbol_index_in_reference_db][g_country_index]
                        if stock_data.short_name         in ['None', '', 'Unknown']: stock_data.short_name         = reference_db[symbol_index_in_reference_db][g_name_index]
                        if stock_data.financial_currency in ['None', '', 'Unknown']: stock_data.financial_currency = reference_db[symbol_index_in_reference_db][g_financial_currency_index]
                        if stock_data.summary_currency   in ['None', '', 'Unknown']: stock_data.summary_currency   = reference_db[symbol_index_in_reference_db][g_summary_currency_index]

                        # stock_data = get_stock_data_from_db_row(reference_db[symbol_index_in_reference_db])
                        # Re-process with more correct information:
                        sss_core_equation_value_set(stock_data)
                        diff_rows.append(reference_db[symbol_index_in_reference_db])

            dividends_sum = stock_data.last_dividend_0+stock_data.last_dividend_1+stock_data.last_dividend_2+stock_data.last_dividend_3

            if process_info_result:
                rows.append(                           row_to_append)
                if dividends_sum: rows_only_div.append(row_to_append)
                else:             rows_no_div.append(  row_to_append)
            csv_db_data.append(                    row_to_append)
    else: # DB already present
        for row_index, row in enumerate(csv_db_data):
            iteration += 1

            symbol = row[g_symbol_index_n if "normalized" in db_filename else g_symbol_index]
            # Below: 4 represents 1st index in row after "Country" which is index 3 (counting from 0 of course)
            for fix_row_index in range((g_country_index_n if "normalized" in db_filename else g_country_index)+1,len(row)):  # for empty strings - convert value to 0
                if row[fix_row_index] == '':
                    if fix_row_index == (g_name_index_n if "normalized" in db_filename else g_name_index):  # Name == '' --> 'None'
                        row[fix_row_index] = 'None'
                    else:
                        row[fix_row_index] = 0
            stock_data = get_stock_data_normalized_from_db_row(row, symbol) if "normalized" in db_filename else get_stock_data_from_db_row(row, symbol)
            if not process_info(symbol=symbol, stock_data=stock_data, build_csv_db_only=build_csv_db_only, tase_mode=tase_mode, sectors_list=sectors_list, sectors_filter_out=sectors_filter_out, countries_list=countries_list, countries_filter_out=countries_filter_out, build_csv_db=build_csv_db, profit_margin_limit=profit_margin_limit, enterprise_value_millions_usd_limit=enterprise_value_millions_usd_limit, research_mode_max_ev=research_mode_max_ev, ev_to_cfo_ratio_limit=ev_to_cfo_ratio_limit, debt_to_equity_limit=debt_to_equity_limit, eqg_min=eqg_min, rqg_min=rqg_min, price_to_earnings_limit=price_to_earnings_limit, enterprise_value_to_revenue_limit=enterprise_value_to_revenue_limit, favor_sectors=favor_sectors, favor_sectors_by=favor_sectors_by, market_cap_included=market_cap_included, research_mode=research_mode, currency_conversion_tool=currency_conversion_tool, currency_conversion_tool_alternative=currency_conversion_tool_alternative, currency_conversion_tool_manual=currency_conversion_tool_manual, reference_db=reference_db, reference_db_title_row=reference_db_title_row, db_filename=db_filename):
                if research_mode: continue

            if tase_mode and 'TLV:' not in stock_data.symbol: stock_data.symbol = 'TLV:' + stock_data.symbol.replace('.TA', '').replace('.','-')

            dividends_sum = stock_data.last_dividend_0 + stock_data.last_dividend_1 + stock_data.last_dividend_2 + stock_data.last_dividend_3

            row_to_append = get_db_row_from_stock_data_normalized(stock_data) if "normalized" in db_filename else get_db_row_from_stock_data(stock_data)
            rows.append(                           row_to_append)
            if dividends_sum: rows_only_div.append(row_to_append)
            else:             rows_no_div.append(  row_to_append)


# reference_run : Used for identifying anomalies in which some symbol information is completely different from last run. It can be different but only in new quartely reports
#                 It is sometimes observed that stocks information is wrongly fetched. Is such cases, the last run's reference point shall be used, with a forgetting factor
def sss_run(reference_run, sectors_list, sectors_filter_out, countries_list, countries_filter_out,build_csv_db_only, build_csv_db, csv_db_path, db_filename, read_united_states_input_symbols, tase_mode, num_threads, market_cap_included, research_mode, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, generate_result_folders=1, appearance_counter_dict_sss={}, appearance_counter_min=25, appearance_counter_max=35, custom_portfolio=[]):
    currency_conversion_tool = currency_conversion_tool_alternative = None

    # https://en.wikipedia.org/wiki/ISO_4217
    currency_filename = 'Indices/currencies.json'
    with open(currency_filename, 'r') as file:
        currency_rates_raw_dict = json.loads(file.read())
    currency_conversion_tool_manual = {k: round(float(v), NUM_ROUND_DECIMALS) for k, v in currency_rates_raw_dict.items()}
    # print(currency_conversion_tool_manual)

    # try:
    #     currency_conversion_tool = CurrencyRates().get_rates('USD') if build_csv_db else None
    # except Exception as e:
    #     try:
    #         currency_conversion_tool_alternative = CurrencyConverter() if build_csv_db else None
    #     except Exception as e:
    #         print('Exchange Rates down, some countries shall be filtered out unless exchange rate provided manually')

    reference_db           = []
    reference_db_title_row = []
    if not research_mode and reference_run != None and len(reference_run):  # in non-research mode, compare to reference run
        reference_csv_db_filename = reference_run+'/db.csv'
        with open(reference_csv_db_filename, mode='r', newline='') as engine:
            reader = csv.reader(engine, delimiter=',')
            row_index = 0
            for row in reader:
                if row_index <= 1: # first row is just a title of evr and pm, then a title of columns
                    if row_index == 1: reference_db_title_row = row
                    row_index += 1
                    continue
                else:
                    reference_db.append(row)
                    row_index += 1

    # Working Mode:
    relaxed_access                     = (num_threads-1)/10.0                                       if build_csv_db else 0  # In seconds
    interval_threads                   = 4 +     1*tase_mode -    read_united_states_input_symbols  if build_csv_db else 0
    interval_secs_to_avoid_http_errors = num_threads*(num_threads - 1*tase_mode + num_threads*read_united_states_input_symbols) if build_csv_db else 0  # Every interval_threads, a INTERVALS_TO_AVOID_HTTP_ERRORS sec sleep will take place

    # Working Parameters:
    eqg_min = EQG_UNKNOWN     # The earnings can decrease but there is still a requirement that price_to_earnings_to_growth_ratio > 0. TODO: ASAFR: Add to multi-dimension
    rqg_min = RQG_UNKNOWN     # The revenue  can decrease there is still a requirement that price_to_earnings_to_growth_ratio > 0. TODO: ASAFR: Add to multi-dimension

    symbols                 = []
    symbols_tase            = []
    symbols_snp500          = []
    symbols_snp500_download = []
    symbols_nasdaq100       = []
    symbols_nasdaq_100_csv  = []
    symbols_russel1000      = []
    symbols_russel1000_csv  = []
    stocks_list_tase        = []

    if not tase_mode and not research_mode and build_csv_db:
        payload            = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies') # There are 2 tables on the Wikipedia page, get the first table
        first_table        = payload[0]
        second_table       = payload[1]
        df                 = first_table
        symbols_snp500     = df['Symbol'].values.tolist()
        symbols_nasdaq100  = ['ATVI', 'ADBE', 'AMD', 'ALXN', 'ALGN', 'GOOG', 'GOOGL', 'AMZN', 'AMGN', 'ADI', 'ANSS', 'AAPL', 'AMAT', 'ASML', 'ADSK', 'ADP', 'BIDU', 'BIIB', 'BMRN', 'BKNG', 'AVGO', 'CDNS', 'CDW', 'CERN', 'CHTR', 'CHKP', 'CTAS', 'CSCO', 'CTXS', 'CTSH', 'CMCSA', 'CPRT', 'COST', 'CSX', 'DXCM', 'DOCU', 'DLTR', 'EBAY', 'EA', 'EXC', 'EXPE', 'FB', 'FAST', 'FISV', 'FOX', 'FOXA', 'GILD', 'IDXX', 'ILMN', 'INCY', 'INTC', 'INTU', 'ISRG', 'JD', 'KLAC', 'LRCX', 'LBTYA', 'LBTYK', 'LULU', 'MAR', 'MXIM', 'MELI', 'MCHP', 'MU', 'MSFT', 'MRNA', 'MDLZ', 'MNST', 'NTES', 'NFLX', 'NVDA', 'NXPI', 'ORLY', 'PCAR', 'PAYX', 'PYPL', 'PEP', 'PDD', 'QCOM', 'REGN', 'ROST', 'SGEN', 'SIRI', 'SWKS', 'SPLK', 'SBUX', 'SNPS', 'TMUS', 'TTWO', 'TSLA', 'TXN', 'KHC', 'TCOM', 'ULTA', 'VRSN', 'VRSK', 'VRTX', 'WBA', 'WDC', 'WDAY', 'XEL', 'XLNX', 'ZM']
        symbols_russel1000 = ['TWOU', 'MMM', 'ABT', 'ABBV', 'ABMD', 'ACHC', 'ACN', 'ATVI', 'AYI', 'ADNT', 'ADBE', 'ADT', 'AAP', 'AMD', 'ACM', 'AES', 'AMG', 'AFL', 'AGCO', 'A', 'AGIO', 'AGNC', 'AL', 'APD', 'AKAM', 'ALK', 'ALB', 'AA', 'ARE', 'ALXN', 'ALGN', 'ALKS', 'Y', 'ALLE', 'AGN', 'ADS', 'LNT', 'ALSN', 'ALL', 'ALLY', 'ALNY', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCX', 'DOX', 'UHAL', 'AEE', 'AAL', 'ACC', 'AEP', 'AXP', 'AFG', 'AMH', 'AIG', 'ANAT', 'AMT', 'AWK', 'AMP', 'ABC', 'AME', 'AMGN', 'APH', 'ADI', 'NLY', 'ANSS', 'AR', 'ANTM', 'AON', 'APA', 'AIV', 'APY', 'APLE', 'AAPL', 'AMAT', 'ATR', 'APTV', 'WTR', 'ARMK', 'ACGL', 'ADM', 'ARNC', 'ARD', 'ANET', 'AWI', 'ARW', 'ASH', 'AZPN', 'ASB', 'AIZ', 'AGO', 'T', 'ATH', 'TEAM', 'ATO', 'ADSK', 'ADP', 'AN', 'AZO', 'AVB', 'AGR', 'AVY', 'AVT', 'EQH', 'AXTA', 'AXS', 'BKR', 'BLL', 'BAC', 'BOH', 'BK', 'OZK', 'BKU', 'BAX', 'BDX', 'WRB', 'BRK.B', 'BERY', 'BBY', 'BYND', 'BGCP', 'BIIB', 'BMRN', 'BIO', 'TECH', 'BKI', 'BLK', 'HRB', 'BLUE', 'BA', 'BOKF', 'BKNG', 'BAH', 'BWA', 'BSX', 'BDN', 'BFAM', 'BHF', 'BMY', 'BRX', 'AVGO', 'BR', 'BPYU', 'BRO', 'BFA', 'BFB', 'BRKR', 'BC', 'BG', 'BURL', 'BWXT', 'CHRW', 'CABO', 'CBT', 'COG', 'CACI', 'CDNS', 'CZR', 'CPT', 'CPB', 'CMD', 'COF', 'CAH', 'CSL', 'KMX', 'CCL', 'CRI', 'CASY', 'CTLT', 'CAT', 'CBOE', 'CBRE', 'CBS', 'CDK', 'CDW', 'CE', 'CELG', 'CNC', 'CDEV', 'CNP', 'CTL', 'CDAY', 'BXP', 'CF', 'CRL', 'CHTR', 'CHE', 'LNG', 'CHK', 'CVX', 'CIM', 'CMG', 'CHH', 'CB', 'CHD', 'CI', 'XEC', 'CINF', 'CNK', 'CTAS', 'CSCO', 'CIT', 'C', 'CFG', 'CTXS', 'CLH', 'CLX', 'CME', 'CMS', 'CNA', 'CNX', 'KO', 'CGNX', 'CTSH', 'COHR', 'CFX', 'CL', 'CLNY', 'CXP', 'COLM', 'CMCSA', 'CMA', 'CBSH', 'COMM', 'CAG', 'CXO', 'CNDT', 'COP', 'ED', 'STZ', 'CERN', 'CPA', 'CPRT', 'CLGX', 'COR', 'GLW', 'OFC', 'CSGP', 'COST', 'COTY', 'CR', 'CACC', 'CCI', 'CCK', 'CSX', 'CUBE', 'CFR', 'CMI', 'CW', 'CVS', 'CY', 'CONE', 'DHI', 'DHR', 'DRI', 'DVA', 'SITC', 'DE', 'DELL', 'DAL', 'XRAY', 'DVN', 'DXCM', 'FANG', 'DKS', 'DLR', 'DFS', 'DISCA', 'DISCK', 'DISH', 'DIS', 'DHC', 'DOCU', 'DLB', 'DG', 'DLTR', 'D', 'DPZ', 'CLR', 'COO', 'DEI', 'DOV', 'DD', 'DPS', 'DTE', 'DUK', 'DRE', 'DNB', 'DNKN', 'DXC', 'ETFC', 'EXP', 'EWBC', 'EMN', 'ETN', 'EV', 'EBAY', 'SATS', 'ECL', 'EIX', 'EW', 'EA', 'EMR', 'ESRT', 'EHC', 'EGN', 'ENR', 'ETR', 'EVHC', 'EOG', 'EPAM', 'EPR', 'EQT', 'EFX', 'EQIX', 'EQC', 'ELS', 'EQR', 'ERIE', 'ESS', 'EL', 'EEFT', 'EVBG', 'EVR', 'RE', 'EVRG', 'ES', 'UFS', 'DCI', 'EXPE', 'EXPD', 'STAY', 'EXR', 'XOG', 'XOM', 'FFIV', 'FB', 'FDS', 'FICO', 'FAST', 'FRT', 'FDX', 'FIS', 'FITB', 'FEYE', 'FAF', 'FCNCA', 'FDC', 'FHB', 'FHN', 'FRC', 'FSLR', 'FE', 'FISV', 'FLT', 'FLIR', 'FND', 'FLO', 'FLS', 'FLR', 'FMC', 'FNB', 'FNF', 'FL', 'F', 'FTNT', 'FTV', 'FBHS', 'FOXA', 'FOX', 'BEN', 'FCX', 'AJG', 'GLPI', 'GPS', 'EXAS', 'EXEL', 'EXC', 'GTES', 'GLIBA', 'GD', 'GE', 'GIS', 'GM', 'GWR', 'G', 'GNTX', 'GPC', 'GILD', 'GPN', 'GL', 'GDDY', 'GS', 'GT', 'GRA', 'GGG', 'EAF', 'GHC', 'GWW', 'LOPE', 'GPK', 'GRUB', 'GWRE', 'HAIN', 'HAL', 'HBI', 'THG', 'HOG', 'HIG', 'HAS', 'HE', 'HCA', 'HDS', 'HTA', 'PEAK', 'HEI.A', 'HEI', 'HP', 'JKHY', 'HLF', 'HSY', 'HES', 'GDI', 'GRMN', 'IT', 'HGV', 'HLT', 'HFC', 'HOLX', 'HD', 'HON', 'HRL', 'HST', 'HHC', 'HPQ', 'HUBB', 'HPP', 'HUM', 'HBAN', 'HII', 'HUN', 'H', 'IAC', 'ICUI', 'IEX', 'IDXX', 'INFO', 'ITW', 'ILMN', 'INCY', 'IR', 'INGR', 'PODD', 'IART', 'INTC', 'IBKR', 'ICE', 'IGT', 'IP', 'IPG', 'IBM', 'IFF', 'INTU', 'ISRG', 'IVZ', 'INVH', 'IONS', 'IPGP', 'IQV', 'HPE', 'HXL', 'HIW', 'HRC', 'JAZZ', 'JBHT', 'JBGS', 'JEF', 'JBLU', 'JNJ', 'JCI', 'JLL', 'JPM', 'JNPR', 'KSU', 'KAR', 'K', 'KEY', 'KEYS', 'KRC', 'KMB', 'KIM', 'KMI', 'KEX', 'KLAC', 'KNX', 'KSS', 'KOS', 'KR', 'LB', 'LHX', 'LH', 'LRCX', 'LAMR', 'LW', 'LSTR', 'LVS', 'LAZ', 'LEA', 'LM', 'LEG', 'LDOS', 'LEN', 'LEN.B', 'LII', 'LBRDA', 'LBRDK', 'FWONA', 'IRM', 'ITT', 'JBL', 'JEC', 'LLY', 'LECO', 'LNC', 'LGF.A', 'LGF.B', 'LFUS', 'LYV', 'LKQ', 'LMT', 'L', 'LOGM', 'LOW', 'LPLA', 'LULU', 'LYFT', 'LYB', 'MTB', 'MAC', 'MIC', 'M', 'MSG', 'MANH', 'MAN', 'MRO', 'MPC', 'MKL', 'MKTX', 'MAR', 'MMC', 'MLM', 'MRVL', 'MAS', 'MASI', 'MA', 'MTCH', 'MAT', 'MXIM', 'MKC', 'MCD', 'MCK', 'MDU', 'MPW', 'MD', 'MDT', 'MRK', 'FWONK', 'LPT', 'LSXMA', 'LSXMK', 'LSI', 'CPRI', 'MIK', 'MCHP', 'MU', 'MSFT', 'MAA', 'MIDD', 'MKSI', 'MHK', 'MOH', 'TAP', 'MDLZ', 'MPWR', 'MNST', 'MCO', 'MS', 'MORN', 'MOS', 'MSI', 'MSM', 'MSCI', 'MUR', 'MYL', 'NBR', 'NDAQ', 'NFG', 'NATI', 'NOV', 'NNN', 'NAVI', 'NCR', 'NKTR', 'NTAP', 'NFLX', 'NBIX', 'NRZ', 'NYCB', 'NWL', 'NEU', 'NEM', 'NWSA', 'NWS', 'MCY', 'MET', 'MTD', 'MFA', 'MGM', 'JWN', 'NSC', 'NTRS', 'NOC', 'NLOK', 'NCLH', 'NRG', 'NUS', 'NUAN', 'NUE', 'NTNX', 'NVT', 'NVDA', 'NVR', 'NXPI', 'ORLY', 'OXY', 'OGE', 'OKTA', 'ODFL', 'ORI', 'OLN', 'OHI', 'OMC', 'ON', 'OMF', 'OKE', 'ORCL', 'OSK', 'OUT', 'OC', 'OI', 'PCAR', 'PKG', 'PACW', 'PANW', 'PGRE', 'PK', 'PH', 'PE', 'PTEN', 'PAYX', 'PAYC', 'PYPL', 'NEE', 'NLSN', 'NKE', 'NI', 'NBL', 'NDSN', 'PEP', 'PKI', 'PRGO', 'PFE', 'PCG', 'PM', 'PSX', 'PPC', 'PNFP', 'PF', 'PNW', 'PXD', 'ESI', 'PNC', 'PII', 'POOL', 'BPOP', 'POST', 'PPG', 'PPL', 'PRAH', 'PINC', 'TROW', 'PFG', 'PG', 'PGR', 'PLD', 'PFPT', 'PB', 'PRU', 'PTC', 'PSA', 'PEG', 'PHM', 'PSTG', 'PVH', 'QGEN', 'QRVO', 'QCOM', 'PWR', 'PBF', 'PEGA', 'PAG', 'PNR', 'PEN', 'PBCT', 'RLGY', 'RP', 'O', 'RBC', 'REG', 'REGN', 'RF', 'RGA', 'RS', 'RNR', 'RSG', 'RMD', 'RPAI', 'RNG', 'RHI', 'ROK', 'ROL', 'ROP', 'ROST', 'RCL', 'RGLD', 'RES', 'RPM', 'RSPP', 'R', 'SPGI', 'SABR', 'SAGE', 'CRM', 'SC', 'SRPT', 'SBAC', 'HSIC', 'SLB', 'SNDR', 'SCHW', 'SMG', 'SEB', 'SEE', 'DGX', 'QRTEA', 'RL', 'RRC', 'RJF', 'RYN', 'RTN', 'NOW', 'SVC', 'SHW', 'SBNY', 'SLGN', 'SPG', 'SIRI', 'SIX', 'SKX', 'SWKS', 'SLG', 'SLM', 'SM', 'AOS', 'SJM', 'SNA', 'SON', 'SO', 'SCCO', 'LUV', 'SPB', 'SPR', 'SRC', 'SPLK', 'S', 'SFM', 'SQ', 'SSNC', 'SWK', 'SBUX', 'STWD', 'STT', 'STLD', 'SRCL', 'STE', 'STL', 'STOR', 'SYK', 'SUI', 'STI', 'SIVB', 'SWCH', 'SGEN', 'SEIC', 'SRE', 'ST', 'SCI', 'SERV', 'TPR', 'TRGP', 'TGT', 'TCO', 'TCF', 'AMTD', 'TDY', 'TFX', 'TDS', 'TPX', 'TDC', 'TER', 'TEX', 'TSRO', 'TSLA', 'TCBI', 'TXN', 'TXT', 'TFSL', 'CC', 'KHC', 'WEN', 'TMO', 'THO', 'TIF', 'TKR', 'TJX', 'TOL', 'TTC', 'TSCO', 'TDG', 'RIG', 'TRU', 'TRV', 'THS', 'TPCO', 'TRMB', 'TRN', 'TRIP', 'SYF', 'SNPS', 'SNV', 'SYY', 'DATA', 'TTWO', 'TMUS', 'TFC', 'UBER', 'UGI', 'ULTA', 'ULTI', 'UMPQ', 'UAA', 'UA', 'UNP', 'UAL', 'UPS', 'URI', 'USM', 'X', 'UTX', 'UTHR', 'UNH', 'UNIT', 'UNVR', 'OLED', 'UHS', 'UNM', 'URBN', 'USB', 'USFD', 'VFC', 'MTN', 'VLO', 'VMI', 'VVV', 'VAR', 'VVC', 'VEEV', 'VTR', 'VER', 'VRSN', 'VRSK', 'VZ', 'VSM', 'VRTX', 'VIAC', 'TWLO', 'TWTR', 'TWO', 'TYL', 'TSN', 'USG', 'UI', 'UDR', 'VMC', 'WPC', 'WBC', 'WAB', 'WBA', 'WMT', 'WM', 'WAT', 'WSO', 'W', 'WFTLF', 'WBS', 'WEC', 'WRI', 'WBT', 'WCG', 'WFC', 'WELL', 'WCC', 'WST', 'WAL', 'WDC', 'WU', 'WLK', 'WRK', 'WEX', 'WY', 'WHR', 'WTM', 'WLL', 'JW.A', 'WMB', 'WSM', 'WLTW', 'WTFC', 'WDAY', 'WP', 'WPX', 'WYND', 'WH', 'VIAB', 'VICI', 'VIRT', 'V', 'VC', 'VST', 'VMW', 'VNO', 'VOYA', 'ZAYO', 'ZBRA', 'ZEN', 'ZG', 'Z', 'ZBH', 'ZION', 'ZTS', 'ZNGA', 'WYNN', 'XEL', 'XRX', 'XLNX', 'XPO', 'XYL', 'YUMC', 'YUM']

        # nasdaq100: https://www.nasdaq.com/market-activity/quotes/nasdaq-ndx-index
        symbols_nasdaq_100_csv = [] # nasdaq100.csv
        nasdq100_filenames_list = ['Indices/nasdaq100.csv']
        for filename in nasdq100_filenames_list:
            with open(filename, mode='r', newline='') as engine:
                reader = csv.reader(engine, delimiter=',')
                row_index = 0
                for row in reader:
                    if row_index == 0:
                        row_index += 1
                        continue
                    else:
                        symbols_nasdaq_100_csv.append(row[0])
                        row_index += 1

        # s&p500:
        symbols_snp500_download_csv = [] # snp500.csv
        symbols_snp500_download_filenames_list = ['Indices/snp500.csv']
        for filename in symbols_snp500_download_filenames_list:
            with open(filename, mode='r', newline='') as engine:
                reader = csv.reader(engine, delimiter=',')
                row_index = 0
                for row in reader:
                    if row_index == 0:
                        row_index += 1
                        continue
                    else:
                        symbols_snp500_download_csv.append(row[0])
                        row_index += 1

        symbols_russel1000_wiki = [] # https://en.wikipedia.org/wiki/Russell_1000_Index
        russel1000_filenames_wiki_list = ['Indices/Russel_1000_index_wiki.csv']
        for filename in russel1000_filenames_wiki_list:
            with open(filename, mode='r', newline='', encoding='cp1252') as engine:
                reader = csv.reader(engine, delimiter=',')
                for row in reader:
                    symbols_russel1000_wiki.append(row[1])


        symbols_russel1000_csv = []  # TODO: ASAFR: Make a general CSV reading function (with title row and withour, and which component in row to take, etc...
        russel1000_filenames_list = ['Indices/russell1000.csv']
        for filename in russel1000_filenames_list:
            with open(filename, mode='r', newline='') as engine:
                reader = csv.reader(engine, delimiter=',')
                row_index = 0
                for row in reader:
                    if row_index == 0:
                        row_index += 1
                        continue
                    else:
                        symbols_russel1000_csv.append(row[0])
                        row_index += 1

        symbols_tase     = []  # symbols_tase       = ['ALD.TA', 'ABIL.TA', 'ACCL.TA', 'ADGR.TA', 'ADKA.TA', 'ARDM.TA', 'AFHL.TA', 'AFPR.TA', 'AFID.TA', 'AFRE.TA', 'AICS.TA', 'ARPT.TA', 'ALBA.TA', 'ALMD.TA', 'ALLT.TA', 'AMDA.L.TA', 'ALMA.TA', 'ALGS.TA', 'ALHE.TA', 'ALRPR.TA', 'ASPF.TA', 'AMAN.TA', 'AMRK.TA', 'AMOT.TA', 'ANLT.TA', 'ANGL.TA', 'APIO.M.TA', 'APLP.TA', 'ARD.TA', 'ARAD.TA', 'ARAN.TA', 'ARNA.TA', 'ARKO.TA', 'ARYT.TA', 'ASHO.TA', 'ASHG.TA', 'ASPR.TA', 'ASGR.TA', 'ATRY.TA', 'AUDC.TA', 'AUGN.TA', 'AURA.TA', 'SHVA.TA', 'AVER.TA', 'AVGL.TA', 'AVIA.TA', 'AVIV.TA', 'AVLN.TA', 'AVRT.TA', 'AYAL.TA', 'AZRM.TA', 'AZRG.TA', 'BCOM.TA', 'BYAR.TA', 'BBYL.TA', 'BRAN.TA', 'BVC.TA', 'BYSD.TA', 'ORL.TA', 'BSEN.TA', 'BEZQ.TA', 'BGI-M.TA', 'BIG.TA', 'BIOV.TA', 'BOLT.TA', 'BLRX.TA', 'PHGE.TA', 'BIRM.TA', 'BLSR.TA', 'BOTI.TA', 'BONS.TA', 'BCNV.TA', 'BWAY.TA', 'BRAM.TA', 'BRND.TA', 'BNRG.TA', 'BRIL.TA', 'BRMG.TA', 'CISY.TA', 'CAMT.TA', 'CANF.TA', 'CSURE.TA', 'CNMD.TA', 'CNZN.TA', 'CPTP.TA', 'CRSO.TA', 'CRMT.TA', 'CAST.TA', 'CEL.TA', 'CHAM.TA', 'CHR.TA', 'CMCT.TA', 'CMCTP.TA', 'CTPL5.TA', 'CTPL1.TA', 'CLBV.TA', 'CBI.TA', 'CLIS.TA', 'CFX.TA', 'CDEV.TA', 'CGEN.TA', 'CMDR.TA', 'DNA.TA', 'DANH.TA', 'DANE.TA', 'DCMA.TA', 'DLRL.TA', 'DLEA.TA', 'DEDR.L.TA', 'DLEKG.TA', 'DELT.TA', 'DIMRI.TA', 'DIFI.TA', 'DSCT.TA', 'DISI.TA', 'DRAL.TA', 'DORL.TA', 'DRSL.TA', 'DUNI.TA', 'EMCO.TA', 'EDRL.TA', 'ELAL.TA', 'EMITF.TA', 'EMTC.TA', 'ESLT.TA', 'ELCO.TA', 'ELDAV.TA', 'ELTR.TA', 'ECP.TA', 'ELCRE.TA', 'ELWS.TA', 'ELLO.TA', 'ELMR.TA', 'ELRN.TA', 'ELSPC.TA', 'EMDV.TA', 'ENDY.TA', 'ENOG.TA', 'ENRG.TA', 'ENLT.TA', 'ENLV.TA', 'EQTL.TA', 'EFNC.TA', 'EVGN.TA', 'EXPO.TA', 'FNTS.TA', 'FTAL.TA', 'FIBI.TA', 'FIBIH.TA', 'FGAS.TA', 'FBRT.TA', 'FRSX.TA', 'FORTY.TA', 'FOX.TA', 'FRSM.TA', 'FRDN.TA', 'GOSS.TA', 'GFC-L.TA', 'GPGB.TA', 'GADS.TA', 'GSFI.TA', 'GAON.TA', 'GAGR.TA', 'GZT.TA', 'GNRS.TA', 'GIBUI.TA', 'GILT.TA', 'GNGR.TA', 'GIVO.L.TA', 'GIX.TA', 'GLTC.TA', 'GLEX.L.TA', 'GKL.TA', 'GLRS.TA', 'GODM-M.TA', 'GLPL.TA', 'GOLD.TA', 'GOHO.TA', 'GOLF.TA', 'HDST.TA', 'HAP.TA', 'HGG.TA', 'HAIN.TA', 'HMAM.TA', 'MSBI.TA', 'HAMAT.TA', 'HAML.TA', 'HNMR.TA', 'HARL.TA', 'HLAN.TA', 'HRON.TA', 'HOD.TA', 'HLMS.TA', 'IBI.TA', 'IBITEC.F.TA', 'ICB.TA', 'ICCM.TA', 'ICL.TA', 'IDIN.TA', 'IES.TA', 'IFF.TA', 'ILDR.TA', 'ILX.TA', 'IMCO.TA', 'INBR.TA', 'INFR.TA', 'INRM.TA', 'INTL.TA', 'ININ.TA', 'INCR.TA', 'INTR.TA', 'IGLD-M.TA', 'ISCD.TA', 'ISCN.TA', 'ILCO.TA', 'ISOP.L.TA', 'ISHI.TA', 'ISRA.L.TA', 'ISRS.TA', 'ISRO.TA', 'ISTA.TA', 'ITMR.TA', 'JBNK.TA', 'KDST.TA', 'KAFR.TA', 'KMDA.TA', 'KRNV-L.TA', 'KARE.TA', 'KRDI.TA', 'KEN.TA', 'KRUR.TA', 'KTOV.TA', 'KLIL.TA', 'KMNK-M.TA', 'KNFM.TA', 'LHIS.TA', 'LAHAV.TA', 'ILDC.TA', 'LPHL.L.TA', 'LAPD.TA', 'LDER.TA', 'LSCO.TA', 'LUMI.TA', 'LEOF.TA', 'LEVI.TA', 'LVPR.TA', 'LBTL.TA', 'LCTX.TA', 'LPSN.TA', 'LODZ.TA', 'LUDN.TA', 'LUZN.TA', 'LZNR.TA', 'MGIC.TA', 'MLTM.TA', 'MMAN.TA', 'MSLA.TA', 'MTMY.TA', 'MTRX.TA', 'MAXO.TA', 'MTRN.TA', 'MEAT.TA', 'MDGS.TA', 'MDPR.TA', 'MDTR.TA', 'MDVI.TA', 'MGOR.TA', 'MEDN.TA', 'MTDS.TA', 'MLSR.TA', 'MNIN.TA', 'MNRT.TA', 'MMHD.TA', 'CMER.TA', 'MRHL.TA', 'MSKE.TA', 'MGRT.TA', 'MCRNT.TA', 'MGDL.TA', 'MIFT.TA', 'MNGN.TA', 'MNRV.TA', 'MLD.TA', 'MSHR.TA', 'MVNE.TA', 'MISH.TA', 'MZTF.TA', 'MBMX-M.TA', 'MDIN.L.TA', 'MRIN.TA', 'MYSZ.TA', 'MYDS.TA', 'NFTA.TA', 'NVPT.L.TA', 'NAWI.TA', 'NTGR.TA', 'NTO.TA', 'NTML.TA', 'NERZ-M.TA', 'NXTG.TA', 'NXTM.TA', 'NXGN-M.TA', 'NICE.TA', 'NISA.TA', 'NSTR.TA', 'NVMI.TA', 'NVLG.TA', 'ORTC.TA', 'ONE.TA', 'OPAL.TA', 'OPCE.TA', 'OPK.TA', 'OBAS.TA', 'ORAD.TA', 'ORMP.TA', 'ORBI.TA', 'ORIN.TA', 'ORA.TA', 'ORON.TA', 'OVRS.TA', 'PCBT.TA', 'PLTF.TA', 'PLRM.TA', 'PNAX.TA', 'PTNR.TA', 'PAYT.TA', 'PZOL.TA', 'PEN.TA', 'PFLT.TA', 'PERI.TA', 'PRGO.TA', 'PTCH.TA', 'PTX.TA', 'PMCN.TA', 'PHOE.TA', 'PLSN.TA', 'PLCR.TA', 'PPIL-M.TA', 'PLAZ-L.TA', 'PSTI.TA', 'POLI.TA', 'PIU.TA', 'POLY.TA', 'PWFL.TA', 'PRSK.TA', 'PRTC.TA', 'PTBL.TA', 'PLX.TA', 'QLTU.TA', 'QNCO.TA', 'RLCO.TA', 'RMN.TA', 'RMLI.TA', 'RANI.TA', 'RPAC.TA', 'RATI.L.TA', 'RTPT.L.TA', 'RAVD.TA', 'RVL.TA', 'RIT1.TA', 'AZRT.TA', 'REKA.TA', 'RIMO.TA', 'ROBO.TA', 'RTEN.L.TA', 'ROTS.TA', 'RSEL.TA', 'SRAC.TA', 'SFET.TA', 'SANO1.TA', 'SPNS.TA', 'SRFT.TA', 'STCM.TA', 'SAVR.TA', 'SHNP.TA', 'SCOP.TA', 'SEMG.TA', 'SLARL.TA', 'SHGR.TA', 'SALG.TA', 'SHAN.TA', 'SPEN.TA', 'SEFA.TA', 'SMNIN.TA', 'SKBN.TA', 'SHOM.TA', 'SAE.TA', 'SKLN.TA', 'SLGN.TA', 'SMTO.TA', 'SCC.TA', 'SPRG.TA', 'SPNTC.TA', 'STG.TA', 'STRS.TA', 'SMT.TA', 'SNFL.TA', 'SNCM.TA', 'SPGE.TA', 'SNEL.TA', 'TDGN-L.TA', 'TDRN.TA', 'TALD.TA', 'TMRP.TA', 'TASE.TA', 'TATT.TA', 'TAYA.TA', 'TNPV.TA', 'TEDE.TA', 'TFRLF.TA', 'TLRD.TA', 'TLSY.TA', 'TUZA.TA', 'TEVA.TA', 'TIGBUR.TA', 'TKUN.TA', 'TTAM.TA', 'TGTR.TA', 'TOPS.TA', 'TSEM.TA', 'TREN.TA', 'UNCR.TA', 'UNCT.L.TA', 'UNIT.TA', 'UNVO.TA', 'UTRN.TA', 'VCTR.TA', 'VILR.TA', 'VISN.TA', 'VTLC-M.TA', 'VTNA.TA', 'VNTZ-M.TA', 'WSMK.TA', 'WTS.TA', 'WILC.TA', 'WLFD.TA', 'XENA.TA', 'XTLB.TA', 'YAAC.TA', 'YBOX.TA', 'YHNF.TA', 'ZNKL.TA', 'ZMH.TA', 'ZUR.TA']
        stocks_list_tase = []  # https://info.tase.co.il/eng/MarketData/Stocks/MarketData/Pages/MarketData.aspx

    if tase_mode and not research_mode and build_csv_db:
        try:
            sss_indices.update_tase_indices()
        except Exception as e:
            print("Error updating Indices/Data_TASE.csv: {}".format(e))
        tase_filenames_list = ['Indices/Data_TASE.csv']

        for filename in tase_filenames_list:
            with open(filename, mode='r', newline='') as engine:
                reader = csv.reader(engine, delimiter=',')
                row_index = 0
                for row in reader:
                    if row_index <= 3:
                        row_index += 1
                        continue
                    else:
                        symbols_tase.append(row[1].replace('.','-')+'.TA')
                        row_index += 1

    # All nasdaq and others: ftp://ftp.nasdaqtrader.com/symboldirectory/ -> TODO: ASAFR: Download automatically as in here: https://stackoverflow.com/questions/11768214/python-download-a-file-from-an-ftp-server
    # Legend: http://www.nasdaqtrader.com/trader.aspx?id=symboldirdefs
    # ftp.nasdaqtrader.com/SymbolDirectory/nasdaqlisted.txt
    # ftp.nasdaqtrader.com/SymbolDirectory/otherlisted.txt
    # ftp.nasdaqtrader.com/SymbolDirectory/nasdaqtraded.txt
    if not research_mode and build_csv_db:
        symbols_united_states               = []
        etf_and_nextshares_list             = []
        if read_united_states_input_symbols:
            nasdaq_filenames_list = ['Indices/nasdaqlisted.csv', 'Indices/otherlisted.csv', 'Indices/nasdaqtraded.csv']  # Checkout http://www.nasdaqtrader.com/trader.aspx?id=symboldirdefs for all symbol definitions (for instance - `$` in stock names, 5-letter stocks ending with `Y`)
            ticker_clumn_list     = [0,                          0,                         1                         ]  # nasdaqtraded.csv - 1st column is Y/N (traded or not) - so take row[1] instead!!!
            for index, filename in enumerate(nasdaq_filenames_list):
                with open(filename, mode='r', newline='') as engine:
                    reader = csv.reader(engine, delimiter='|')
                    next_shares_column = None
                    etf_column         = None
                    row_index = 0
                    for row in reader:
                        if row_index == 0:
                            row_index += 1
                            # Find ETF and next shares Column:
                            for column_index, column in enumerate(row):
                                if   column == 'ETF':        etf_column         = column_index
                                elif column == 'NextShares': next_shares_column = column_index
                            continue
                        else:
                            row_index += 1
                            if next_shares_column and row[next_shares_column] == 'Y':
                                etf_and_nextshares_list.append(row[ticker_clumn_list[index]])
                                continue
                            if etf_column         and row[etf_column]         == 'Y':
                                etf_and_nextshares_list.append(row[ticker_clumn_list[index]])
                                continue
                            if '$' in row[ticker_clumn_list[index]]: # AAIC$B -> <stock_symbol>$<letter> --> keep just the stock_Symbol
                                stock_symbol = row[ticker_clumn_list[index]].split('$')[0]
                            else:
                                stock_symbol = row[ticker_clumn_list[index]]
                            if len(stock_symbol) == 5: # https://www.investopedia.com/ask/answers/06/nasdaqfifthletter.asp
                                if stock_symbol[4] in ['Q', 'W', 'C']: # Q: Bankruptcy, W: Warrant, C: Nextshares (Example: https://funds.eatonvance.com/includes/loadDocument.php?fn=20939.pdf&dt=fundPDFs)
                                    continue
                                if stock_symbol[4] in ['Y'] and SKIP_5LETTER_Y_STOCK_LISTINGS: # Configurable - harder to buy (from Israel, at least), but not impossible of coure
                                    continue
                            elif len(stock_symbol) == 6: # https://www.investopedia.com/ask/answers/06/nasdaqfifthletter.asp
                                if stock_symbol[5] in ['Q', 'W', 'C']: # Q: Bankruptcy, W: Warrant, C: Nextshares (Example: https://funds.eatonvance.com/includes/loadDocument.php?fn=20939.pdf&dt=fundPDFs)
                                    continue
                                if stock_symbol[5] in ['Y'] and SKIP_5LETTER_Y_STOCK_LISTINGS: # Configurable - harder to buy (from Israel, at least), but not impossible of coure
                                    continue
                            symbols_united_states.append(stock_symbol)

        symbols = symbols_snp500 + symbols_snp500_download + symbols_nasdaq100 + symbols_nasdaq_100_csv + symbols_russel1000 + symbols_russel1000_csv + symbols_united_states

    if not research_mode and tase_mode and build_csv_db:
        symbols = symbols_tase + stocks_list_tase

    if not research_mode and build_csv_db: symbols = sorted(list(set(symbols)))

    # Temporary to test and debug: DEBUG MODE
    # =======================================
    if len(custom_portfolio):
        symbols = custom_portfolio

    if not research_mode: print('\n{} Symbols for SSS to Scan (Using {} threads): {}\n'.format(len(symbols), num_threads, symbols))

    csv_db_data   = [];	rows   = []; rows_no_div   = []; rows_only_div   = []; rows_diff   = []
    csv_db_data0  = []; rows0  = []; rows0_no_div  = []; rows0_only_div  = []; rows0_diff  = []; csv_db_data1  = []; rows1  = []; rows1_no_div  = []; rows1_only_div  = []; rows1_diff  = []
    csv_db_data2  = []; rows2  = []; rows2_no_div  = []; rows2_only_div  = []; rows2_diff  = []; csv_db_data3  = []; rows3  = []; rows3_no_div  = []; rows3_only_div  = []; rows3_diff  = []
    csv_db_data4  = []; rows4  = []; rows4_no_div  = []; rows4_only_div  = []; rows4_diff  = []; csv_db_data5  = []; rows5  = []; rows5_no_div  = []; rows5_only_div  = []; rows5_diff  = []
    csv_db_data6  = []; rows6  = []; rows6_no_div  = []; rows6_only_div  = []; rows6_diff  = []; csv_db_data7  = []; rows7  = []; rows7_no_div  = []; rows7_only_div  = []; rows7_diff  = []
    csv_db_data8  = []; rows8  = []; rows8_no_div  = []; rows8_only_div  = []; rows8_diff  = []; csv_db_data9  = []; rows9  = []; rows9_no_div  = []; rows9_only_div  = []; rows9_diff  = []
    csv_db_data10 = []; rows10 = []; rows10_no_div = []; rows10_only_div = []; rows10_diff = []; csv_db_data11 = []; rows11 = []; rows11_no_div = []; rows11_only_div = []; rows11_diff = []
    csv_db_data12 = []; rows12 = []; rows12_no_div = []; rows12_only_div = []; rows12_diff = []; csv_db_data13 = []; rows13 = []; rows13_no_div = []; rows13_only_div = []; rows13_diff = []
    csv_db_data14 = []; rows14 = []; rows14_no_div = []; rows14_only_div = []; rows14_diff = []; csv_db_data15 = []; rows15 = []; rows15_no_div = []; rows15_only_div = []; rows15_diff = []
    csv_db_data16 = []; rows16 = []; rows16_no_div = []; rows16_only_div = []; rows16_diff = []; csv_db_data17 = []; rows17 = []; rows17_no_div = []; rows17_only_div = []; rows17_diff = []
    csv_db_data18 = []; rows18 = []; rows18_no_div = []; rows18_only_div = []; rows18_diff = []; csv_db_data19 = []; rows19 = []; rows19_no_div = []; rows19_only_div = []; rows19_diff = []

    if build_csv_db == 0: # if DB is already present, read from it and prepare input to threads
        symbols = []
        csv_db_filename = csv_db_path+'/'+db_filename
        num_title_rows = 1 if "normalized" in db_filename else 2
        with open(csv_db_filename, mode='r', newline='') as engine:
            reader = csv.reader(engine, delimiter=',')
            row_index = 0
            for row in reader:
                if row_index < num_title_rows:  # first row (only in non-normalized sss_engine.csv) is just a title of evr and pm, then a title of columns
                    row_index += 1
                    continue
                else:
                    symbols.append(row[0])
                    if   (row_index-2) % num_threads ==  0: csv_db_data0.append(row)
                    elif (row_index-2) % num_threads ==  1: csv_db_data1.append(row)
                    elif (row_index-2) % num_threads ==  2: csv_db_data2.append(row)
                    elif (row_index-2) % num_threads ==  3: csv_db_data3.append(row)
                    elif (row_index-2) % num_threads ==  4: csv_db_data4.append(row)
                    elif (row_index-2) % num_threads ==  5: csv_db_data5.append(row)
                    elif (row_index-2) % num_threads ==  6: csv_db_data6.append(row)
                    elif (row_index-2) % num_threads ==  7: csv_db_data7.append(row)
                    elif (row_index-2) % num_threads ==  8: csv_db_data8.append(row)
                    elif (row_index-2) % num_threads ==  9: csv_db_data9.append(row)
                    elif (row_index-2) % num_threads == 10: csv_db_data10.append(row)
                    elif (row_index-2) % num_threads == 11: csv_db_data11.append(row)
                    elif (row_index-2) % num_threads == 12: csv_db_data12.append(row)
                    elif (row_index-2) % num_threads == 13: csv_db_data13.append(row)
                    elif (row_index-2) % num_threads == 14: csv_db_data14.append(row)
                    elif (row_index-2) % num_threads == 15: csv_db_data15.append(row)
                    elif (row_index-2) % num_threads == 16: csv_db_data16.append(row)
                    elif (row_index-2) % num_threads == 17: csv_db_data17.append(row)
                    elif (row_index-2) % num_threads == 18: csv_db_data18.append(row)
                    elif (row_index-2) % num_threads == 19: csv_db_data19.append(row)
                    row_index += 1

    if num_threads >=  1: symbols0  = symbols[ 0:][::num_threads] #  0,    num_threads,    2*num_threads,    3*num_threads, ...
    if num_threads >=  2: symbols1  = symbols[ 1:][::num_threads] #  1,  1+num_threads,  2+2*num_threads,  2+3*num_threads, ...
    if num_threads >=  3: symbols2  = symbols[ 2:][::num_threads] #  2,  2+num_threads,  3+2*num_threads,  3+3*num_threads, ...
    if num_threads >=  4: symbols3  = symbols[ 3:][::num_threads] #  3,  3+num_threads,  4+2*num_threads,  4+3*num_threads, ...
    if num_threads >=  5: symbols4  = symbols[ 4:][::num_threads] #  4,  4+num_threads,  5+2*num_threads,  5+3*num_threads, ...
    if num_threads >=  6: symbols5  = symbols[ 5:][::num_threads] #  5,  5+num_threads,  6+2*num_threads,  6+3*num_threads, ...
    if num_threads >=  7: symbols6  = symbols[ 6:][::num_threads] #  6,  6+num_threads,  7+2*num_threads,  7+3*num_threads, ...
    if num_threads >=  8: symbols7  = symbols[ 7:][::num_threads] #  7,  7+num_threads,  8+2*num_threads,  8+3*num_threads, ...
    if num_threads >=  9: symbols8  = symbols[ 8:][::num_threads] #  8,  8+num_threads,  9+2*num_threads,  9+3*num_threads, ...
    if num_threads >= 10: symbols9  = symbols[ 9:][::num_threads] #  9,  9+num_threads, 10+2*num_threads, 10+3*num_threads, ...
    if num_threads >= 11: symbols10 = symbols[10:][::num_threads] # 10, 10+num_threads, 11+2*num_threads, 11+3*num_threads, ...
    if num_threads >= 12: symbols11 = symbols[11:][::num_threads] # 11, 11+num_threads, 12+2*num_threads, 12+3*num_threads, ...
    if num_threads >= 13: symbols12 = symbols[12:][::num_threads] # 12, 12+num_threads, 13+2*num_threads, 13+3*num_threads, ...
    if num_threads >= 14: symbols13 = symbols[13:][::num_threads] # 13, 13+num_threads, 14+2*num_threads, 14+3*num_threads, ...
    if num_threads >= 15: symbols14 = symbols[14:][::num_threads] # 14, 14+num_threads, 15+2*num_threads, 15+3*num_threads, ...
    if num_threads >= 16: symbols15 = symbols[15:][::num_threads] # 15, 15+num_threads, 16+2*num_threads, 16+3*num_threads, ...
    if num_threads >= 17: symbols16 = symbols[16:][::num_threads] # 16, 16+num_threads, 17+2*num_threads, 17+3*num_threads, ...
    if num_threads >= 18: symbols17 = symbols[17:][::num_threads] # 17, 17+num_threads, 18+2*num_threads, 18+3*num_threads, ...
    if num_threads >= 19: symbols18 = symbols[18:][::num_threads] # 18, 18+num_threads, 19+2*num_threads, 19+3*num_threads, ...
    if num_threads >= 20: symbols19 = symbols[19:][::num_threads] # 19, 19+num_threads, 20+2*num_threads, 20+3*num_threads, ...

    if num_threads ==  1:
        process_symbols(symbols0, csv_db_data0, rows0, rows0_no_div, rows0_only_div, 0, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows0_diff, db_filename)
    elif num_threads >= 1:
        check_interval(0, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread0  = Thread(target=process_symbols, args=(symbols0,  csv_db_data0,  rows0,  rows0_no_div,  rows0_only_div,   0, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows0_diff, db_filename))
        thread0.start()
    if num_threads >=  2:
        check_interval(1, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread1  = Thread(target=process_symbols, args=(symbols1,  csv_db_data1,  rows1,  rows1_no_div,  rows1_only_div,   1, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows1_diff, db_filename))
        thread1.start()
    if num_threads >=  3:
        check_interval(2, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread2  = Thread(target=process_symbols, args=(symbols2,  csv_db_data2,  rows2,  rows2_no_div,  rows2_only_div,   2, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows2_diff, db_filename))
        thread2.start()
    if num_threads >=  4:
        check_interval(3, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread3  = Thread(target=process_symbols, args=(symbols3,  csv_db_data3,  rows3,  rows3_no_div,  rows3_only_div,   3, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows3_diff, db_filename))
        thread3.start()
    if num_threads >=  5:
        check_interval(4, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread4  = Thread(target=process_symbols, args=(symbols4,  csv_db_data4,  rows4,  rows4_no_div,  rows4_only_div,   4, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows4_diff, db_filename))
        thread4.start()
    if num_threads >=  6:
        check_interval(5, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread5  = Thread(target=process_symbols, args=(symbols5,  csv_db_data5,  rows5,  rows5_no_div,  rows5_only_div,   5, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows5_diff, db_filename))
        thread5.start()
    if num_threads >=  7:
        check_interval(6, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread6  = Thread(target=process_symbols, args=(symbols6,  csv_db_data6,  rows6,  rows6_no_div,  rows6_only_div,   6, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows6_diff, db_filename))
        thread6.start()
    if num_threads >=  8:
        check_interval(7, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread7  = Thread(target=process_symbols, args=(symbols7,  csv_db_data7,  rows7,  rows7_no_div,  rows7_only_div,   7, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows7_diff, db_filename))
        thread7.start()
    if num_threads >=  9:
        check_interval(8, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread8  = Thread(target=process_symbols, args=(symbols8,  csv_db_data8,  rows8,  rows8_no_div,  rows8_only_div,   8, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows8_diff, db_filename))
        thread8.start()
    if num_threads >= 10:
        check_interval(9, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread9  = Thread(target=process_symbols, args=(symbols9,  csv_db_data9,  rows9,  rows9_no_div,  rows9_only_div,   9, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows9_diff, db_filename))
        thread9.start()
    if num_threads >= 11:
        check_interval(10, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread10 = Thread(target=process_symbols, args=(symbols10, csv_db_data10, rows10, rows10_no_div, rows10_only_div, 10, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows10_diff, db_filename))
        thread10.start()
    if num_threads >= 12:
        check_interval(11, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread11 = Thread(target=process_symbols, args=(symbols11, csv_db_data11, rows11, rows11_no_div, rows11_only_div, 11, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows11_diff, db_filename))
        thread11.start()
    if num_threads >= 13:
        check_interval(12, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread12 = Thread(target=process_symbols, args=(symbols12, csv_db_data12, rows12, rows12_no_div, rows12_only_div, 12, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows12_diff, db_filename))
        thread12.start()
    if num_threads >= 14:
        check_interval(13, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread13 = Thread(target=process_symbols, args=(symbols13, csv_db_data13, rows13, rows13_no_div, rows13_only_div, 13, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows13_diff, db_filename))
        thread13.start()
    if num_threads >= 15:
        check_interval(14, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread14 = Thread(target=process_symbols, args=(symbols14, csv_db_data14, rows14, rows14_no_div, rows14_only_div, 14, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows14_diff, db_filename))
        thread14.start()
    if num_threads >= 16:
        check_interval(15, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread15 = Thread(target=process_symbols, args=(symbols15, csv_db_data15, rows15, rows15_no_div, rows15_only_div, 15, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows15_diff, db_filename))
        thread15.start()
    if num_threads >= 17:
        check_interval(16, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread16 = Thread(target=process_symbols, args=(symbols16, csv_db_data16, rows16, rows16_no_div, rows16_only_div, 16, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows16_diff, db_filename))
        thread16.start()
    if num_threads >= 18:
        check_interval(17, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread17 = Thread(target=process_symbols, args=(symbols17, csv_db_data17, rows17, rows17_no_div, rows17_only_div, 17, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows17_diff, db_filename))
        thread17.start()
    if num_threads >= 19:
        check_interval(18, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread18 = Thread(target=process_symbols, args=(symbols18, csv_db_data18, rows18, rows18_no_div, rows18_only_div, 18, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows18_diff, db_filename))
        thread18.start()
    if num_threads >= 20:
        check_interval(19, interval_threads, interval_secs_to_avoid_http_errors, research_mode)
        thread19 = Thread(target=process_symbols, args=(symbols19, csv_db_data19, rows19, rows19_no_div, rows19_only_div, 19, build_csv_db_only, tase_mode, sectors_list, sectors_filter_out, countries_list, countries_filter_out, build_csv_db, relaxed_access, profit_margin_limit, ev_to_cfo_ratio_limit, debt_to_equity_limit, enterprise_value_millions_usd_limit, research_mode_max_ev, eqg_min, rqg_min, price_to_earnings_limit, enterprise_value_to_revenue_limit, favor_sectors, favor_sectors_by, market_cap_included, research_mode, currency_conversion_tool, currency_conversion_tool_alternative, currency_conversion_tool_manual, reference_db, reference_db_title_row, rows19_diff, db_filename))
        thread19.start()

    if num_threads == 1:
        pass
    elif num_threads >=  1: thread0.join()
    if num_threads >=  2: thread1.join()
    if num_threads >=  3: thread2.join()
    if num_threads >=  4: thread3.join()
    if num_threads >=  5: thread4.join()
    if num_threads >=  6: thread5.join()
    if num_threads >=  7: thread6.join()
    if num_threads >=  8: thread7.join()
    if num_threads >=  9: thread8.join()
    if num_threads >= 10: thread9.join()
    if num_threads >= 11: thread10.join()
    if num_threads >= 12: thread11.join()
    if num_threads >= 13: thread12.join()
    if num_threads >= 14: thread13.join()
    if num_threads >= 15: thread14.join()
    if num_threads >= 16: thread15.join()
    if num_threads >= 17: thread16.join()
    if num_threads >= 18: thread17.join()
    if num_threads >= 19: thread18.join()
    if num_threads >= 20: thread19.join()

    csv_db_data.extend(  csv_db_data0   + csv_db_data1   + csv_db_data2   + csv_db_data3   + csv_db_data4   + csv_db_data5   + csv_db_data6   + csv_db_data7   + csv_db_data8   + csv_db_data9   + csv_db_data10   + csv_db_data11   + csv_db_data12   + csv_db_data13   + csv_db_data14   + csv_db_data15   + csv_db_data16   + csv_db_data17   + csv_db_data18   + csv_db_data19  )
    rows.extend(         rows0          + rows1          + rows2          + rows3          + rows4          + rows5          + rows6          + rows7          + rows8          + rows9          + rows10          + rows11          + rows12          + rows13          + rows14          + rows15          + rows16          + rows17          + rows18          + rows19         )
    rows_no_div.extend(  rows0_no_div   + rows1_no_div   + rows2_no_div   + rows3_no_div   + rows4_no_div   + rows5_no_div   + rows6_no_div   + rows7_no_div   + rows8_no_div   + rows9_no_div   + rows10_no_div   + rows11_no_div   + rows12_no_div   + rows13_no_div   + rows14_no_div   + rows15_no_div   + rows16_no_div   + rows17_no_div   + rows18_no_div   + rows19_no_div  )
    rows_only_div.extend(rows0_only_div + rows1_only_div + rows2_only_div + rows3_only_div + rows4_only_div + rows5_only_div + rows6_only_div + rows7_only_div + rows8_only_div + rows9_only_div + rows10_only_div + rows11_only_div + rows12_only_div + rows13_only_div + rows14_only_div + rows15_only_div + rows16_only_div + rows17_only_div + rows18_only_div + rows19_only_div)
    rows_diff.extend(    rows0_diff     + rows1_diff     + rows2_diff     + rows3_diff     + rows4_diff     + rows5_diff     + rows6_diff     + rows7_diff     + rows8_diff     + rows9_diff     + rows10_diff     + rows11_diff     + rows12_diff     + rows13_diff     + rows14_diff     + rows15_diff     + rows16_diff     + rows17_diff     + rows18_diff     + rows19_diff    )

    # remove (from rows, not from db or diff) rows whose sss[s[s]]_value is a bad one - irrelevant:
    compact_rows          = []
    compact_rows_no_div   = []
    compact_rows_only_div = []
    for row in rows:
        if row[g_sss_value_index_n if "normalized" in db_filename else g_sss_value_index] != BAD_SSS: compact_rows.append(row)
    for row_no_div in rows_no_div:
        if row_no_div[g_sss_value_index_n if "normalized" in db_filename else g_sss_value_index] != BAD_SSS: compact_rows_no_div.append(row_no_div)
    for row_only_div in rows_only_div:
        if row_only_div[g_sss_value_index_n if "normalized" in db_filename else g_sss_value_index] != BAD_SSS: compact_rows_only_div.append(row_only_div)

    # Now, Sort the compact_rows using the sss_value formula: [1:] skips the 1st title row
    sorted_list_db               = sorted(csv_db_data,           key=lambda row:          row[         g_symbol_index_n               if "normalized" in db_filename else g_symbol_index   ],  reverse=False)  # Sort by symbol
    sorted_list_sss              = sorted(compact_rows,          key=lambda row:          row[         g_sss_value_normalized_index_n if "normalized" in db_filename else g_sss_value_index],  reverse=False)  # Sort by sss_value     -> The lower  - the more attractive
    sorted_list_sss_no_div       = sorted(compact_rows_no_div,   key=lambda row_no_div:   row_no_div[  g_sss_value_normalized_index_n if "normalized" in db_filename else g_sss_value_index],  reverse=False)  # Sort by sss_value     -> The lower  - the more attractive
    sorted_list_sss_only_div     = sorted(compact_rows_only_div, key=lambda row_only_div: row_only_div[g_sss_value_normalized_index_n if "normalized" in db_filename else g_sss_value_index],  reverse=False)  # Sort by sss_value     -> The lower  - the more attractive
    sorted_list_diff             = sorted(rows_diff,             key=lambda row_diff:     row_diff[    g_symbol_index_n               if "normalized" in db_filename else g_symbol_index   ],  reverse=False)  # Sort by symbol

    if research_mode: # Update the appearance counter
        list_len_sss = len(sorted_list_sss)
        if appearance_counter_min <= list_len_sss   <= appearance_counter_max:
            for index, row in enumerate(sorted_list_sss):
                # Debug mode:
                # if 'ISRA-L' in row[g_symbol_index]:
                #     print('ISRA-L - index is {}'.format(index))
                if "normalized" in db_filename:
                    appearance_counter_dict_sss[(row[g_symbol_index_n], row[g_name_index_n], row[g_sector_index_n], row[g_sss_value_normalized_index_n], row[g_previous_close_index_n])] = appearance_counter_dict_sss[(row[g_symbol_index_n], row[g_name_index_n], row[g_sector_index_n], row[g_sss_value_normalized_index_n], row[g_previous_close_index_n])] + math.sqrt(float(list_len_sss - index)) / float(list_len_sss)
                else:
                    appearance_counter_dict_sss[(row[g_symbol_index],   row[g_name_index],   row[g_sector_index],   row[g_sss_value_index],              row[g_previous_close_index])]   = appearance_counter_dict_sss[(row[g_symbol_index],   row[g_name_index],   row[g_sector_index],   row[g_sss_value_index],              row[g_previous_close_index])]   + math.sqrt(float(list_len_sss - index)) / float(list_len_sss)

    sorted_lists_list = [
        sorted_list_db,
        sorted_list_sss,
        sorted_list_sss_no_div,
        sorted_list_sss_only_div,
        sorted_list_diff
    ]

    for sorted_list in sorted_lists_list:
        sorted_list.insert(0, (g_header_row_normalized if "normalized" in db_filename else g_header_row))

    tase_str              = ""
    sectors_str           = ""
    countries_str         = ""
    all_str               = ""
    csv_db_str            = ""
    custom_portfolio_str  = ""
    num_results_str       = "_nRes{}".format(len(compact_rows))
    build_csv_db_only_str = ""
    if tase_mode:         tase_str       = "_Tase"

    if len(sectors_list):
        if sectors_filter_out:
            sectors_list       += 'FO_'
        sectors_str            += '_'+'_'.join(sectors_list)
    else:
        for index, sector in enumerate(favor_sectors):
            sectors_str += '_{}{}'.format(sector.replace(' ',''),round(favor_sectors_by[index],NUM_ROUND_DECIMALS))

    if len(countries_list):
        if countries_filter_out:
            countries_list       += 'FO_'
        countries_str            += '_'+'_'.join(countries_list).replace(' ','')

    mode_str = 'Nsr' # Default is Nasdaq100+S&P500+Russel1000
    if read_united_states_input_symbols: mode_str              = 'All'
    elif tase_mode:                      mode_str              = 'Tase'
    if   len(custom_portfolio):          mode_str              = 'Custom'
    if read_united_states_input_symbols: all_str               = '_A'
    if build_csv_db == 0:                csv_db_str            = '_DBR'
    if build_csv_db_only:                build_csv_db_only_str = '_Bdb'
    if len(custom_portfolio):            custom_portfolio_str  = '_Custom'
    date_and_time = time.strftime("Results/{}/%Y%m%d-%H%M%S{}{}{}{}{}{}{}{}".format(mode_str, tase_str, sectors_str.replace(' ','').replace('a','').replace('e','').replace('i','').replace('o','').replace('u',''), countries_str, all_str, csv_db_str, build_csv_db_only_str, num_results_str, custom_portfolio_str))

    filenames_list = sss_filenames.create_filenames_list(date_and_time)

    evr_pm_col_title_row = ['Maximal price_to_earnings_limit: {}, Maximal enterprise_value_to_revenue_limit: {}, Minimal profit_margin_limit: {}'.format(price_to_earnings_limit, enterprise_value_to_revenue_limit, profit_margin_limit)]

    if generate_result_folders:
        for index in range(len(filenames_list)):
            os.makedirs(os.path.dirname(filenames_list[index]), exist_ok=True)
            with open(filenames_list[index], mode='w', newline='') as engine:
                writer = csv.writer(engine)
                sorted_lists_list[index].insert(0, evr_pm_col_title_row)
                writer.writerows(sorted_lists_list[index])

    # Normalized sss_engine:
    if generate_result_folders:
        sss_post_processing.process_engine_csv(date_and_time)

    return len(compact_rows)