import pandas as pd import matplotlib.pyplot as plt import yfinance as yf

# Load stock data stock_ticker = '^GSPC' df = yf.download(stock_ticker) # List arrays for close price and dates close_price = df["Adj Close"] dates = df.index.tolist() # Returns daily_returns = close_price.pct_change(fill_method='pad') monthly_returns = close_price.resample('M').ffill().pct_change(fill_method='pad') monthly_returns

# Plot stock price over time plt.figure(figsize=(8, 5), dpi = 100) plt.plot(close_price) #Can use [-timescale:] to slice plot plt.xlabel("Year") plt.ylabel("Price (USD)") plt.title(stock_ticker + " Stock Price") plt.grid() plt.show() # Plot daily and monthly returns plt.figure(figsize=(8, 5), dpi = 100) plt.plot(daily_returns, label = "Daily Returns") plt.plot(monthly_returns, label = "Monthly Returns") plt.xlabel("Year") plt.ylabel("Returns (USD)") plt.title(stock_ticker + " Daily and Monthly Returns") plt.grid() plt.legend() plt.show()

timescape = 36 #how long we want to simulate in months # Buy and sell conditions buy_x = 100 value1 = buy_x / close_price[0] def monthly_buy(_timescape): global value1, buy_x, timescape #print(_timescape) if _timescape < 0 : print('You have this many shares: ' + str(value1)) print('Portfolio value:' + str(value1*close_price[30 * (timescape - _timescape)])) return elif _timescape == timescape - 1: print('Starting shares: ' + str(value1)) print('Portfolio value:' + str(value1*close_price[30 * (timescape - _timescape)])) this_month = close_price[30 * (timescape - _timescape)] value1 += buy_x / this_month monthly_buy(_timescape - 1) monthly_buy(35) #1. Buy $x on the first date. Buy $x every month thereafter. # Calculate the portfolio value (which would be my expected returns) on a specified date.

taken_out = 0 timescape = 36 _value2 = buy_x / close_price[0] # shares _limit = (timescape - 30) * 30 _baseP = close_price[0] _lastb = 0 def strategic_sell(): global taken_out, _limit global _baseP, _lastb, _value2 for day in range(0, _limit): if close_price[day] <= close_price[day-1] * .98: # buy value of buy_x print("bought +" + str(buy_x/close_price[day])) _value2 += buy_x / close_price[day] _lastb = day _baseP = close_price[day] ''' if close_price[day] >= (close_price[day-1] * 0.1): #print("selling, old shares: " + str(_value2)) # sell half value of buy_x _val = (close_price[day] * _value2) - (50) #substract 50$ from share value in $ _value2 = _val / close_price[day] #convert $ value back to shares #print("selling, new shares: " + str(_value2)) taken_out += 50 _lastb = day _baseP = close_price[day] ''' if day - _lastb >= 30: #print("bought +" + str(buy_x/close_price[day])) # buy value of buy_x _value2 += buy_x / close_price[day] _lastb = day _baseP = close_price[day] print("final portfolio price: " + str(_value2 * close_price[timescape * 30])) print("final account balance: " + str(taken_out)) print("last traded day " + str(_lastb)) print("last traded price: " + str(_baseP)); strategic_sell() #2. Buy $x on the first date. Buy $x again if the price drops by 10%. # Sell 0.5*$x if the price increases by 10%. # Buy $x if 30 days have surpassed and no buy/sell order has been made. #DO WE WANNA CONSIDER SELLS???

close_price[0] / close_price[_limit-1]

plt.plot(range(0, 180), close_price[0:180])

_value2