minor fixs

get_data.py

@@ -3,33 +3,25 @@
 from pathlib import Path
 import ssl
 
-ssl._create_default_https_context = ssl._create_unverified_context
-
-#Load data from WEB OR CSV
+#Load data from WEB
 def DataFromWeb(names:list) -> pd.DataFrame:
+    ssl._create_default_https_context = ssl._create_unverified_context
 
     data = pd.DataFrame()
 
     for _ in range(len(names)):
+
+        data_price = pd.read_csv(r'https://www.cryptodatadownload.com/cdd/Binance_'+names[_]+r'USDT_d.csv',header=1) #Load data from cryptodatadownload.com
 
-        #Load data from cryptodatadownload.com
-        data_price = pd.read_csv(r'https://www.cryptodatadownload.com/cdd/Binance_'+names[_]+r'USDT_d.csv',header=1) 
-
-        #Keep close price anda date 
-        data_price.drop(data_price.columns.difference(['date','close']), 1, inplace=True)
-
-        #Rename colums
+        data_price.drop(data_price.columns.difference(['date','close']), 1, inplace=True) #Keep 'close price' and 'date' fields
         data_price.columns = ['date', names[_]]
-
-        #Set date as index
         data_price.set_index('date', inplace=True)
-
-        #Merge data, drop NAN rows and save csv
         data = data.join(data_price, how='outer')
         data.dropna(inplace= True)
 
     return data
 
+#Load data from CSV
 def DataFromCSV(Path: pathlib.Path) -> pd.DataFrame:
 
     data = pd.read_csv(Path, index_col=0) 

main.py

@@ -2,34 +2,38 @@
 import matplotlib.pyplot as plt
 from matplotlib.ticker import FuncFormatter
 
-#Parametros
-Monedas=['BTC','ETH','LTC']
-number_of_portfolios = 10000
-RiskFreeRate=0
-
-#Assets Risk/Return
-RiskReturn_asset = optimization.RiskByAsset(Monedas,optimization.SimpleReturns)
-
-#Assets Risk/Return graph
-fig, ax = plt.subplots()
-ax.yaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))
-ax.xaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))
-RiskReturn_asset.plot(x ='Risk', y='Return', kind = 'scatter',ax=ax,title='Return/Risk by asset',alpha=0.5)
-for k, v in RiskReturn_asset.iterrows():
-    ax.annotate(k, v)
-plt.grid()
-
-#Portfolio Risk/Retun 
-MaxSharpeRatioRisk,MaxSharpeRatioReturn,portfolio_risk,portfolio_returns,portfolio_sharpe_ratio= optimization.EfficientFrontier(Monedas,'','',number_of_portfolios,RiskFreeRate,optimization.SimpleReturns)
-
-#Portfolio Risk/Retun graph
-plt.figure(figsize=(12, 8))
-plt.scatter(portfolio_risk, portfolio_returns, c=portfolio_sharpe_ratio) 
-plt.colorbar(label='Sharpe Ratio')
-plt.xlabel('volatility')
-plt.ylabel('returns')
-plt.scatter(MaxSharpeRatioRisk, MaxSharpeRatioReturn,c='red', s=50)
-
-#Show graph
-plt.show()
+def main():
+    #Parametros
+    Monedas=['BTC','ETH','LTC']
+    number_of_portfolios = 10000
+    RiskFreeRate=0
+
+    #Assets Risk/Return
+    RiskReturn_asset = optimization.RiskByAsset(Monedas,optimization.SimpleReturns)
+
+    #Assets Risk/Return graph
+    fig, ax = plt.subplots()
+    ax.yaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))
+    ax.xaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))
+    RiskReturn_asset.plot(x ='Risk', y='Return', kind = 'scatter',ax=ax,title='Return/Risk by asset',alpha=0.5)
+    for k, v in RiskReturn_asset.iterrows():
+        ax.annotate(k, v)
+    plt.grid()
+
+    #Portfolio Risk/Retun 
+    MaxSharpeRatioRisk,MaxSharpeRatioReturn,portfolio_risk,portfolio_returns,portfolio_sharpe_ratio= optimization.EfficientFrontier(Monedas,'','',number_of_portfolios,RiskFreeRate,optimization.SimpleReturns)
+
+    #Portfolio Risk/Retun graph
+    plt.figure(figsize=(12, 8))
+    plt.scatter(portfolio_risk, portfolio_returns, c=portfolio_sharpe_ratio) 
+    plt.colorbar(label='Sharpe Ratio')
+    plt.xlabel('volatility')
+    plt.ylabel('returns')
+    plt.scatter(MaxSharpeRatioRisk, MaxSharpeRatioReturn,c='red', s=50)
+
+    #Show graph
+    plt.show()
+
+if __name__ == '__main__':
+    main()