import pandas as pd import numpy as np import matplotlib.pyplot as plt import requests from math import floor from termcolor import colored as cl plt.style.use('fivethirtyeight') plt.rcParams['figure.figsize'] = (20,10) # EXTRACTING DATA import yfinance as yf start1 = '2020-01-01' end1='2021-06-10' # looping over tickers and creating a dataframe with close prices stocks = yf.download('msft',start1,end1,interval='1d') tsla =pd.DataFrame() tsla['open'] = stocks['Open'] tsla['high'] =stocks['High'] tsla['low'] = stocks['Low'] tsla['close'] = stocks['Close'] tsla['volume'] = stocks['Volume'] tsla['adjclose'] = stocks['Adj Close'] # SUPERTREND CALCULATION def get_supertrend(high, low, close, lookback, multiplier): # ATR tr1 = pd.DataFrame(high - low) tr2 = pd.DataFrame(abs(high - close.shift(1))) tr3 = pd.DataFrame(abs(low - close.shift(1))) frames = [tr1, tr2, tr3] tr = pd.concat(frames, axis = 1, join = 'inner').max(axis = 1) atr = tr.ewm(lookback).mean() # H/L AVG AND BASIC UPPER & LOWER BAND hl_avg = (high + low) / 2 upper_band = (hl_avg + multiplier * atr).dropna() lower_band = (hl_avg - multiplier * atr).dropna() # FINAL UPPER BAND final_bands = pd.DataFrame(columns = ['upper', 'lower']) final_bands.iloc[:,0] = [x for x in upper_band - upper_band] final_bands.iloc[:,1] = final_bands.iloc[:,0] for i in range(len(final_bands)): if i == 0: final_bands.iloc[i,0] = 0 else: if (upper_band[i] < final_bands.iloc[i-1,0]) | (close[i-1] > final_bands.iloc[i-1,0]): final_bands.iloc[i,0] = upper_band[i] else: final_bands.iloc[i,0] = final_bands.iloc[i-1,0] # FINAL LOWER BAND for i in range(len(final_bands)): if i == 0: final_bands.iloc[i, 1] = 0 else: if (lower_band[i] > final_bands.iloc[i-1,1]) | (close[i-1] < final_bands.iloc[i-1,1]): final_bands.iloc[i,1] = lower_band[i] else: final_bands.iloc[i,1] = final_bands.iloc[i-1,1] # SUPERTREND supertrend = pd.DataFrame(columns = [f'supertrend_{lookback}']) supertrend.iloc[:,0] = [x for x in final_bands['upper'] - final_bands['upper']] for i in range(len(supertrend)): if i == 0: supertrend.iloc[i, 0] = 0 elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] < final_bands.iloc[i, 0]: supertrend.iloc[i, 0] = final_bands.iloc[i, 0] elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] > final_bands.iloc[i, 0]: supertrend.iloc[i, 0] = final_bands.iloc[i, 1] elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] > final_bands.iloc[i, 1]: supertrend.iloc[i, 0] = final_bands.iloc[i, 1] elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] < final_bands.iloc[i, 1]: supertrend.iloc[i, 0] = final_bands.iloc[i, 0] supertrend = supertrend.set_index(upper_band.index) supertrend = supertrend.dropna()[1:] # ST UPTREND/DOWNTREND upt = [] dt = [] close = close.iloc[len(close) - len(supertrend):] for i in range(len(supertrend)): if close[i] > supertrend.iloc[i, 0]: upt.append(supertrend.iloc[i, 0]) dt.append(np.nan) elif close[i] < supertrend.iloc[i, 0]: upt.append(np.nan) dt.append(supertrend.iloc[i, 0]) else: upt.append(np.nan) dt.append(np.nan) st, upt, dt = pd.Series(supertrend.iloc[:, 0]), pd.Series(upt), pd.Series(dt) upt.index, dt.index = supertrend.index, supertrend.index return st, upt, dt tsla['st'], tsla['s_upt'], tsla['st_dt'] = get_supertrend(tsla['high'], tsla['low'], tsla['close'], 10, 3) tsla = tsla[1:] print(tsla.head()) # SUPERTREND PLOT plt.plot(tsla['close'], linewidth = 2, label = 'CLOSING PRICE') plt.plot(tsla['st'], color = 'green', linewidth = 2, label = 'ST UPTREND 10,3') plt.plot(tsla['st_dt'], color = 'r', linewidth = 2, label = 'ST DOWNTREND 10,3') plt.legend(loc = 'upper left') plt.show() # SUPERTREND STRATEGY def implement_st_strategy(prices, st): buy_price = [] sell_price = [] st_signal = [] signal = 0 for i in range(len(st)): if st[i-1] > prices[i-1] and st[i] < prices[i]: if signal != 1: buy_price.append(prices[i]) sell_price.append(np.nan) signal = 1 st_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) st_signal.append(0) elif st[i-1] < prices[i-1] and st[i] > prices[i]: if signal != -1: buy_price.append(np.nan) sell_price.append(prices[i]) signal = -1 st_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) st_signal.append(0) else: buy_price.append(np.nan) sell_price.append(np.nan) st_signal.append(0) return buy_price, sell_price, st_signal buy_price, sell_price, st_signal = implement_st_strategy(tsla['close'], tsla['st']) # SUPERTREND SIGNALS # SPY ETF COMPARISON def get_benchmark(start_date, investment_value): spy = get_historical_data('SPY', start_date)['close'] benchmark = pd.DataFrame(np.diff(spy)).rename(columns = {0:'benchmark_returns'}) investment_value = investment_value number_of_stocks = floor(investment_value/spy[-1]) benchmark_investment_ret = [] for i in range(len(benchmark['benchmark_returns'])): returns = number_of_stocks*benchmark['benchmark_returns'][i] benchmark_investment_ret.append(returns) benchmark_investment_ret_df = pd.DataFrame(benchmark_investment_ret).rename(columns = {0:'investment_returns'}) return benchmark_investment_ret_df benchmark = get_benchmark('2020-01-01', 100000) investment_value = 100000 total_benchmark_investment_ret = round(sum(benchmark['investment_returns']), 2) benchmark_profit_percentage = floor((total_benchmark_investment_ret/investment_value)*100) print(cl('Benchmark profit by investing $100k : {}'.format(total_benchmark_investment_ret), attrs = ['bold'])) print(cl('Benchmark Profit percentage : {}%'.format(benchmark_profit_percentage), attrs = ['bold'])) print(cl('ST Strategy profit is {}% higher than the Benchmark Profit'.format(profit_percentage - benchmark_profit_percentage), attrs = ['bold']))