Merge pull request #4 from devfinwiz/deepsource-autofix-908482d2

Convert string with anomalous backslash into a raw string

Author: devfinwiz

Dataset/Dataset_Gathering_Scripts/csv_generator.py

@@ -6,7 +6,7 @@
 
 def get_symbols():
     flag=1
-    comp=csv.reader(open("Prerequisites\Tickers.csv"))     
+    comp=csv.reader(open(r"Prerequisites\Tickers.csv"))     
     tickers=[]
 
     for c in comp:
@@ -27,7 +27,7 @@ def dataset_generator(symbol):
 
     #Creation of individual CSVs for all listed tickers in Tickers.csv
 
-    history_filename="Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol)  
+    history_filename=r"Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol)  
     f=open(history_filename,'w',newline="")
 
     #---------------------------------------------------------------------------------

FundamentalScans/valuation_f.py

@@ -120,7 +120,7 @@ def valuation_determiner(ticker):
 
 def get_symbols():
     flag=1
-    comp=csv.reader(open("Prerequisites\Tickers.csv"))     
+    comp=csv.reader(open(r"Prerequisites\Tickers.csv"))     
     tickers=[]
 
     for c in comp:

Prerequisites/Symbols_Generator.py

@@ -36,10 +36,9 @@ def store_in_tickers_csv():
                 correct_ticker = re.sub(r'[^a-zA-Z\d\s]', u'', str(hold[i]), flags=re.UNICODE)
                 hold[i]=str(correct_ticker).split(",")
 
-        with open('Prerequisites\Tickers.csv','a',newline="") as f:
+        with open(r'Prerequisites\Tickers.csv','a',newline="") as f:
             writer = csv.writer(f)
             writer.writerows(hold)
 
 
-store_in_tickers_csv()
-   
+store_in_tickers_csv()

Prerequisites/garbage_eliminator.py

@@ -18,12 +18,12 @@ def unique_everseen(iterable, key=None):
                 seen_add(k)
                 yield element        
 
-with open("Prerequisites\Tickers.csv", "r") as file:
+with open(r"Prerequisites\Tickers.csv", "r") as file:
     lines = []
     for line in file:
         lines.append(line.strip("\n").split(","))
 
-with open("Prerequisites\Tickers.csv", "w") as file:
+with open(r"Prerequisites\Tickers.csv", "w") as file:
     for line in unique_everseen(lines, key=frozenset):
         file.write(",".join(line)+"\n")
 

Prerequisites/nse_indices_pe_history.py

@@ -7,7 +7,7 @@
 #Generates csv files for all NSE indices with their PE,P/B
 
 def dataset_generator(indice):  
-    history_filename="Dataset\Resultant Dataset\\nse_indices_pe_dataset\{}.csv".format(indice)  
+    history_filename=r"Dataset\Resultant Dataset\\nse_indices_pe_dataset\{}.csv".format(indice)  
     f=open(history_filename,'w',newline="")
 
     indice_pe = get_index_pe_history(symbol="{}".format(indice),

TechnicalScans/sma_scanner.py

@@ -7,7 +7,7 @@
 
 def get_symbols():
     flag=1
-    comp=csv.reader(open("Prerequisites\Tickers.csv"))     
+    comp=csv.reader(open(r"Prerequisites\Tickers.csv"))     
     tickers=[]
 
     for c in comp:
@@ -29,7 +29,7 @@ def ma_above_scanner(period):
 
     for symbol in tickers:
         try:
-            df=pd.read_csv("Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
+            df=pd.read_csv(r"Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
             sma=talib.SMA(df['Close'],period)
             sma,close=list(sma)[-1],list(df['Close'])[-1]
             if(close>sma):
@@ -48,7 +48,7 @@ def ma_below_scanner(period):
 
     for symbol in tickers:
         try:
-            df=pd.read_csv("Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
+            df=pd.read_csv(r"Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
             sma=talib.SMA(df['Close'],period)
             sma,close=list(sma)[-1],list(df['Close'])[-1]
             if(close<sma):
@@ -67,7 +67,7 @@ def price_just_crossed_over_ma(period):
 
     for symbol in tickers:
         try:
-            df=pd.read_csv("Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
+            df=pd.read_csv(r"Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
             sma=talib.SMA(df['Close'],period)
             sma,close=list(sma)[-1],list(df['Close'])[-1]
             if(close>sma and list(df['Close'][-200])<sma):
@@ -86,7 +86,7 @@ def price_just_crossed_below_ma(period):
 
     for symbol in tickers:
         try:
-            df=pd.read_csv("Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
+            df=pd.read_csv(r"Dataset\Resultant Dataset\\ticker_csvs\{}.csv".format(symbol))
             sma=talib.SMA(df['Close'],period)
             sma,close=list(sma)[-1],list(df['Close'])[-1]
             if(close<sma and list(df['Close'][-200])>sma):
@@ -96,8 +96,4 @@ def price_just_crossed_below_ma(period):
 
     return stocks_crossed_sma
 
-print(price_just_crossed_over_ma(200))
-
-
-
-            
+print(price_just_crossed_over_ma(200))

Valuations/multiple_parameters_valuation.py

@@ -5,7 +5,7 @@
 #Returns valuation of the requested ticker as per its book value
 
 def valuation_book_value(ticker):
-    with open("Dataset\Resultant Dataset\Financials.csv",'r') as f:
+    with open(r"Dataset\Resultant Dataset\Financials.csv",'r') as f:
         reader=csv.DictReader(f)
         for row in reader:
             if(row['Ticker']==ticker):
@@ -32,7 +32,7 @@ def valuation_book_value(ticker):
 #Returns valuation of the requested ticker as per its annual sales
 
 def valuation_sales(ticker):
-    with open("Dataset\Resultant Dataset\Financials.csv",'r') as f:
+    with open(r"Dataset\Resultant Dataset\Financials.csv",'r') as f:
         reader=csv.DictReader(f)
         for row in reader:
             if(row['Ticker']==ticker):
@@ -59,7 +59,7 @@ def valuation_sales(ticker):
 #Returns valuation of the requested ticker as per Graham
 
 def valuation_graham(ticker):
-    with open("Dataset\Resultant Dataset\Financials.csv",'r') as f:
+    with open(r"Dataset\Resultant Dataset\Financials.csv",'r') as f:
         reader=csv.DictReader(f)
         for row in reader:
             if(row['Ticker']==ticker):
@@ -86,7 +86,7 @@ def valuation_graham(ticker):
 #Returns valuation of the requested ticker as per its earnings
 
 def valuation_earnings(ticker):
-    with open("Dataset\Resultant Dataset\Financials.csv",'r') as f:
+    with open(r"Dataset\Resultant Dataset\Financials.csv",'r') as f:
         reader=csv.DictReader(f)
         for row in reader:
             if(row['Ticker']==ticker):

Valuations/nse_indices_valuation_check.py

@@ -47,7 +47,7 @@ def indices_flag_allocator():
     exceptions=['NIFTY FMCG','NIFTY IT']
 
     for indice in NSE_INDICES:
-        df=pd.read_csv("Dataset\Resultant Dataset\\nse_indices_pe_dataset\\{}.csv".format(indice))
+        df=pd.read_csv(r"Dataset\Resultant Dataset\\nse_indices_pe_dataset\\{}.csv".format(indice))
         indices_median_pe[indice]=round(df['P/E'].median(),2)
         indices_latest_pe[indice]=df['P/E'].iloc[-1]