Jul-31-2022, 05:11 PM
import numpy as np import pandas as pd import yfinance as yf import matplotlib.pyplot as plt plt.style.use ('fivethirtyeight') start_date = '2010-01-01' # start and end date end_date = '2020-01-01' ticker = 'SPY' # Set the ticker cash=10_000 # cash df=yf.download(ticker,start_date, end_date) nShare=int(cash/df['Close'][1]) n=np.shape(df)[0] totalRet=(1+df['Adj Close'].pct_change()).prod()-1 totalRet # Out[836]: 2.468748704035219 totalValue=cash*(1+totalRet) totalValue # 34687.50232472107 # benchmark def SMA(data, period=10): return data['Close'].rolling(window=period).mean() df['SMA10']=SMA(df) df['SMA30']=SMA(df,30) # Set strategie buy = [] sell = [] long=0 # flag = 0 dd=[] flag2=[] aa=[] bb=[] for i in range(0, len(df)): #for i in range(31, len(df)): a=df['SMA10'][i] b=df['SMA30'][i] aa.append(a) bb.append(b) dd.append(df.index[i]) if(a!=np.NaN and b!=np.NaN): if a > b and long == 0: buy.append(df['Close'][i]) sell.append(np.nan) long=1 # flag = 1 elif a < b and long == 1: sell.append(df['Close'][i]) buy.append(np.nan) long=0 # flag = 0 # sell thus postion is zero else: sell.append(np.nan) buy.append(np.nan) p=1 # place holder flag2.append(long) # final=pd.DataFrame([buy,sell,flag2,aa,bb]).T final.index=dd final.columns=['buyPrice','sellPrice','long','SMA10','SMA30'] #final.to_csv("strategy.csv") ret2=df['Adj Close'].pct_change()*final['long'] totalRet2=(1+ret2).prod()-1 totalValue2=cash*(1+totalRet2) totalValue2 # Out[834]: 23747.6188958821 totalRet2 # Out[832]: 1.37476188958821