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‎01_Basics_of_Python.ipynb

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‎02_Python_for_DataSciences.ipynb

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‎03 Introduction .ipynb

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‎04 Import financial data.ipynb

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‎05 Send orders.ipynb

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‎06_money_management.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "735390d1",
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"metadata": {},
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"source": [
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"# Chapter 06: Money management\n",
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"\n",
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"📈Join our community: https://discord.gg/wXjNPAc5BH\n",
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"\n",
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"📚Read our book: https://www.amazon.com/gp/product/B09HG18CYL \n",
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"\n",
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"🖥️Quantreo's YouTube channel: https://www.youtube.com/channel/UCp7jckfiEglNf_Gj62VR0pw"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c9512371",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import the libraries\n",
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"import MetaTrader5 as mt5\n",
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"from datetime import datetime\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "24a9d118",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Create the bound between MT5 and Python\n",
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"mt5.initialize()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d07b406f",
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"metadata": {},
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"source": [
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"### Place an order with take profit & stop loss"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "da38caed",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Done'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Place a BUY order with take profit\n",
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"# Initialization value\n",
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"lot = 0.01\n",
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"symbol = \"EURUSD\"\n",
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"\n",
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"# Extract symbol point\n",
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"point = mt5.symbol_info(symbol).point\n",
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"\n",
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"# Choose the deviation\n",
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"deviation = 10\n",
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"\n",
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"# Find the filling mode of symbol\n",
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"filling_type = mt5.symbol_info(symbol).filling_mode\n",
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"\n",
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"# Create dictionnary request\n",
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"request = {\n",
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" \"action\": mt5.TRADE_ACTION_DEAL,\n",
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" \"symbol\": symbol,\n",
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" \"volume\": lot,\n",
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" \"type\": mt5.ORDER_TYPE_BUY,\n",
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" \"price\": mt5.symbol_info_tick(symbol).ask,\n",
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" \"deviation\": deviation,\n",
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" \"tp\": mt5.symbol_info_tick(symbol).ask + 100*point,\n",
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" \"sl\": mt5.symbol_info_tick(symbol).ask - 100*point, \n",
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" \"type_filling\": filling_type,\n",
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" \"type_time\": mt5.ORDER_TIME_GTC,\n",
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"}\n",
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"\n",
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"mt5.order_check(request).comment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "cdb0bbb3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Done'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Place a SELL order with take profit\n",
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"# Initialization value\n",
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"lot = 0.01\n",
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"symbol = \"EURUSD\"\n",
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"\n",
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"# Extract symbol point\n",
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"point = mt5.symbol_info(symbol).point\n",
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"\n",
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"# Choose the deviation\n",
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"deviation = 10\n",
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"\n",
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"# Find the filling mode of symbol\n",
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"filling_type = mt5.symbol_info(symbol).filling_mode\n",
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"\n",
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"# Create dictionnary request\n",
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"request = {\n",
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" \"action\": mt5.TRADE_ACTION_DEAL,\n",
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" \"symbol\": symbol,\n",
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" \"volume\": lot,\n",
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" \"type\": mt5.ORDER_TYPE_SELL,\n",
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" \"price\": mt5.symbol_info_tick(symbol).bid,\n",
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" \"deviation\": deviation,\n",
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" \"tp\": mt5.symbol_info_tick(symbol).ask - 100 * point,\n",
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" \"sl\": mt5.symbol_info_tick(symbol).ask + 100 * point, \n",
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" \"type_filling\": filling_type,\n",
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" \"type_time\": mt5.ORDER_TIME_GTC,\n",
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"}\n",
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"\n",
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"mt5.order_check(request).comment"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2beacd7",
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"metadata": {},
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"source": [
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"### Find stop loss and take profit level for a specific risk percentage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "43d6988c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def risk_reward_threshold(symbol, buy=True, risk=0.01, reward=0.02):\n",
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" \n",
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" # Extract the leverage\n",
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" leverage = mt5.account_info().leverage\n",
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"\n",
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" # Compute the price\n",
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" price = mt5.symbol_info(symbol).ask\n",
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"\n",
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" # Extract the number of decimals\n",
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" nb_decimal = str(price)[::-1].find(\".\")\n",
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"\n",
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"\n",
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" # Compute the variations in percentage\n",
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" var_down = risk/leverage\n",
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" var_up = reward/leverage\n",
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"\n",
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"\n",
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" # Find the TP and SL threshold in absolute price\n",
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" if buy:\n",
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" price = mt5.symbol_info(symbol).ask\n",
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"\n",
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" # Compute the variations in absolute price\n",
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" price_var_down = var_down*price\n",
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" price_var_up = var_up * price\n",
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"\n",
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" tp = np.round(price + price_var_up, nb_decimal)\n",
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" sl = np.round(price - price_var_down, nb_decimal)\n",
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"\n",
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" else:\n",
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"\n",
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" price = mt5.symbol_info(symbol).bid\n",
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"\n",
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" # Compute the variations in absolute price\n",
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" price_var_down = var_down*price\n",
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" price_var_up = var_up * price\n",
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"\n",
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" tp = np.round(price - price_var_up, nb_decimal)\n",
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" sl = np.round(price + price_var_down, nb_decimal)\n",
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"\n",
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"\n",
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" \n",
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" print(f\"PRICE: {price} \\t Take Profit: {tp} \\t Stop Loss: {sl}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "bc012463",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PRICE: 1.1015 \t Take Profit: 1.1022 \t Stop Loss: 1.1011\n"
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]
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}
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],
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"source": [
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"risk_reward_threshold(\"EURUSD\",buy=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "fc2136a8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"-1.002798720064676"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"(1.09682 - 1.09693) / 1.09693 *100 * 100"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d98cfa33",
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"metadata": {},
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"source": [
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"### Find the volume depending of your capital"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "a1a2de6c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INVESTED CAPITAL: 120\n",
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"LEVERAGE: 100\n",
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"INVESTED CAPITAL LEVERAGED: 12000\n",
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"TRADE SIZE: 10.0\n",
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"PRICE: 4312.75\n",
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"LOT SIZE: 0.27824473943539507\n",
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"MIN LOT: 0.1\n",
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"MAX LOT: 5.0\n",
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"NUMBER DECIMAL: 1\n",
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"LOT SIZE ROUNDED: 0.3\n",
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"LOT DOWN ROUNDED: 0.2\n",
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"GOOD SIZE LOT: 0.2\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"0.2"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def position_size(capital, symbol):\n",
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" mt5.initialize()\n",
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" print(f\"INVESTED CAPITAL: {capital}\")\n",
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" \n",
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" leverage = mt5.account_info().leverage\n",
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" print(f\"LEVERAGE: {leverage}\")\n",
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" \n",
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" invested_capital = capital * leverage\n",
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" print(f\"INVESTED CAPITAL LEVERAGED: {invested_capital}\")\n",
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" \n",
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" trade_size = mt5.symbol_info(symbol).trade_contract_size\n",
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" print(f\"TRADE SIZE: {trade_size}\")\n",
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" \n",
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" price = (mt5.symbol_info(symbol).ask + mt5.symbol_info(symbol).bid)/2\n",
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" print(f\"PRICE: {price}\")\n",
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"\n",
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" lot_size = invested_capital / trade_size / price\n",
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" print(f\"LOT SIZE: {lot_size}\")\n",
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" \n",
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" min_lot = mt5.symbol_info(symbol).volume_min\n",
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" print(f\"MIN LOT: {min_lot}\")\n",
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" \n",
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" max_lot = mt5.symbol_info(symbol).volume_max\n",
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" print(f\"MAX LOT: {max_lot}\")\n",
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"\n",
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"\n",
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" if min_lot<lot_size:\n",
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" number_decimal = str(min_lot)[::-1].find(\".\")\n",
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" print(f\"NUMBER DECIMAL: {number_decimal}\")\n",
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"\n",
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" if number_decimal>0:\n",
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" lot_size_rounded = np.round(lot_size, number_decimal)\n",
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" print(f\"LOT SIZE ROUNDED: {lot_size_rounded}\")\n",
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"\n",
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" if lot_size < lot_size_rounded:\n",
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" lot_size_rounded = np.round(lot_size_rounded - min_lot, number_decimal)\n",
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" print(f\"LOT DOWN ROUNDED: {lot_size_rounded}\")\n",
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"\n",
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" else:\n",
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" number_size_lot = len(str(min_lot))\n",
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"\n",
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" lot_size_rounded = int(np.round(lot_size, -number_size_lot))\n",
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"\n",
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" if lot_size < lot_size_rounded:\n",
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" lot_size_rounded = int(np.round(lot_size_rounded - number_size_lot, - number_size_lot))\n",
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" \n",
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" if lot_size_rounded>max_lot:\n",
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" lot_size_rounded = max_lot\n",
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" \n",
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" print(f\"GOOD SIZE LOT: {lot_size_rounded}\")\n",
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" return lot_size_rounded\n",
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" else: \n",
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" print(\"Invested capital is too small to be able to place an order\")\n",
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" \n",
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" \n",
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" \n",
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"position_size(120, \"SP500m\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "055bf5a5",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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