import pandas as pd import numpy as np import datetime as dt import requests import math import matplotlib.pyplot as plt from termcolor import colored as cl plt.style.use('fivethirtyeight') plt.rcParams['figure.figsize'] = (20, 10) def get_historic_data(symbol): ticker = symbol iex_api_key = 'Tpk_9e3166e4053b4d99addc43ba300c2d42' api_url = f'https://sandbox.iexapis.com/stable/stock/{ticker}/chart/max?token={iex_api_key}' df = requests.get(api_url).json() date = [] open = [] high = [] low = [] close = [] for i in range(len(df)): date.append(df[i]['date']) open.append(df[i]['open']) high.append(df[i]['high']) low.append(df[i]['low']) close.append(df[i]['close']) date_df = pd.DataFrame(date).rename(columns = {0:'date'}) open_df = pd.DataFrame(open).rename(columns = {0:'open'}) high_df = pd.DataFrame(high).rename(columns = {0:'high'}) low_df = pd.DataFrame(low).rename(columns = {0:'low'}) close_df = pd.DataFrame(close).rename(columns = {0:'close'}) frames = [date_df, open_df, high_df, low_df, close_df] df = pd.concat(frames, axis = 1, join = 'inner') return df stock = input ( "Please enter stock symbol: " ) #stock = 'stock' data = get_historic_data(stock) data .to_csv(r'Desktop\stocks.csv') stocks= pd.read_csv(r'Desktop\stocks.csv').set_index('date') #startyear=int(input("Enter a Start Year #### : ")) #startmonth=int(input("Enter a Start Month # : ")) #startday=int(input("Enter a Start Day # : ")) startyear=2019 startmonth=1 startday=1 start=dt.datetime(startyear,startmonth,startday) now=dt.datetime.now() date = dt.datetime(startyear,startmonth,startday) stocks.index = pd.to_datetime(stocks.index) stocks = stocks[stocks.index >= str(date)] plt.plot(stocks.index, stocks['close']) plt.xlabel('Date') plt.ylabel('Closing Prices') plt.title('stocks Stock Prices'+str(date)+'-2019') #plt.show() import yfinance as yf start1 = '2018-01-01' end1='2021-03-01' # looping over tickers and creating a dataframe with close prices stocks = yf.download('msft',start1,end1,interval='1d') stocks['close'] = stocks['Close'] def sma(data, window): sma = data.rolling(window = window).mean() return sma stocks['sma_X'] = sma(stocks['close'], 3) print(stocks.tail()) #stocks['close'].plot(label = 'CLOSE', alpha = 0.6) #stocks['sma_X'].plot(label = 'SMA X', linewidth = 2) #plt.xlabel('Date') #plt.ylabel('Closing Prices') #plt.legend(loc = 'upper left') #plt.show() def bb(data, sma, window): std = data.rolling(window = window).std() upper_bb = sma + std * 2 mid_bb = sma lower_bb = sma - std * 2 return upper_bb, mid_bb, lower_bb stocks['upper_bb'], stocks['lower_bb'], stocks['mid_bb'] = bb(stocks['close'], stocks['sma_X'], 3) stocks['bb_w'] = (stocks['upper_bb'] - stocks['lower_bb'])/stocks['close'] print(stocks.tail()) def ema(data, window): ema= data.rolling(window = window).mean() return ema #$def ema(data, window): # $ ema = round(data.ewm(span= window, adjust=False).mean(),2) # return ema stocks['ema_1'] = ema(stocks['close'], 1) stocks['ema_3'] = ema(stocks['close'], 3) stocks['ema_5'] = ema(stocks['close'], 5) stocks['ema_8'] = ema(stocks['close'], 8) #stocks['ema_13'] = ema(stocks['close'], 13) #stocks['ema_14'] = ema(stocks['close'], 14) stocks['ema_21'] = ema(stocks['close'], 21) stocks['ema_34'] = ema(stocks['close'], 34) stocks['ema_55'] = ema(stocks['close'], 55) #stocks['ema_144'] = ema(stocks['close'], 144) #stocks['ema_233'] = ema(stocks['close'], 233) ######Calculating ATR def ATR(DF,n): "function to calculate True Range and Average True Range" df = DF.copy() df['H-L']=abs(df['High']-df['Low']) df['H-PC']=abs(df['High']-df['Adj Close'].shift(1)) df['L-PC']=abs(df['Low']-df['Adj Close'].shift(1)) df['TR']=df[['H-L','H-PC','L-PC']].max(axis=1,skipna=False) df['ATR'] = df['TR'].rolling(n).mean() #df['ATR'] = df['TR'].ewm(span=n,adjust=False,min_periods=n).mean() df2 = df.drop(['H-L','H-PC','L-PC'],axis=1) return df2 stocks1 = ATR(stocks, 14) data = stocks.copy() h_l = data["High"] - data["Low"] h_cp = abs(data["High"] - data["Close"].shift(1)) l_cp = abs(data["Low"] - data["Close"].shift(1)) true_range2 = (h_l, h_cp, l_cp) true_range2 true_range3 = pd.DataFrame(true_range2) true_range4 = true_range3.T true_range4.columns = ["h_l", "h_cp", "l_cp"] data["maximum_value"] = true_range4.max(axis = 1) length = 21 data['ATR'] = data["maximum_value"].rolling(length).mean() data['atr_ts1'] = '' data['atr_ts2'] = '' df = data df['crossover'] = '' mul =3.5 long_short = input("Are you long or short?") if long_short == 'long': df['atr_ts1'] = df['Close'] - (mul * df['ATR']) df['atr_ts2'] = df['atr_ts1'].cummax() df['crossover'] = df['atr_ts2'] > df['Close'] elif long_short == 'short': df['atr_ts1'] = df['Close'] + (mul * df['ATR']) df['atr_ts2'] = df['atr_ts1'].cummin() df['crossover'] = df['atr_ts2'] < df['Close'] fig, ax = plt.subplots(figsize=(20,10), sharex = False, sharey = False) df.loc[df.index, 'Adj Close'].plot(ax=ax) df.loc[df.index, 'atr_ts2'].plot(ax=ax) ax.set_title('Trailing Stop Loss Against Close Price') def implement_sma_strategy(data1, ema_1, ema_3,ema_5,ema_8,ema_34, ema_55, ema_21 ): buy_price = [] sell_price = [] sma_signal = [] tickers_ret =[] mul= 3.5 signal = 0 data1 = df.copy() for i in range(len(df)): if signal !=1: if(ema_1[i] < ema_3[i]) or (ema_21[i] < ema_55[i]): buy_price.append(data1['Close'][i]) sell_price.append(np.nan) signal = 1 sma_signal.append(signal) elif (ema_3[i] < ema_8[i]) or (ema_1[i] > ema_55[i]) or (ema_3[i] > ema_5[i]): buy_price.append(np.nan) sell_price.append(data1['Close'][i]) signal = -1 sma_signal.append(-1) elif signal == 1: if data1['Close'][i] < data1['atr_ts1'][i]: sell_price.append(data1['atr_ts2'][i]) buy_price.append(np.nan) signal = -1 sma_signal.append(signal) # else: # buy_price.append(np.nan) # sell_price.append(np.nan) # sma_signal.append(0) return buy_price, sell_price, sma_signal # identifying signals and calculating daily return (stop loss factored in) ema_1 = stocks['ema_1'] ema_8 = stocks['ema_8'] ema_21= stocks['ema_21'] ema_5 = stocks['ema_5'] ema_34 = stocks['ema_34'] ema_55 = stocks['ema_55'] ema_3 = stocks['ema_3'] buy_price, sell_price, signal = implement_sma_strategy(df, ema_1, ema_3,ema_5,ema_8,ema_34, ema_55, ema_21) position = [] for i in range(len(signal)): if signal[i] > 1: position.append(0) else: position.append(1) for i in range(len(stocks['close'])): if signal[i] == 1: position[i] = 1 elif signal[i] == -1: position[i] = 0 else: position[i] = position[i-1] data1 =stocks.index ema_1 = pd.DataFrame(ema_1).rename(columns = {0:'ema_1'}) ema_8 = pd.DataFrame(ema_8 ).rename(columns = {0:'ema_8'}) ema_21= pd.DataFrame(ema_21).rename(columns = {0:'ema_21'}) ema_5 = pd.DataFrame(ema_5).rename(columns = {0:'ema_5'}) ema_34 = pd.DataFrame(ema_34).rename(columns = {0:'ema_34'}) ema_55 = pd.DataFrame(ema_55).rename(columns = {0:'ema_55'}) ema_3 = pd.DataFrame(ema_3).rename(columns = {0:'ema_3'}) signal = pd.DataFrame(signal).rename(columns = {0:'ema_signal'}).set_index(data1) position = pd.DataFrame(position).rename(columns = {0:'ema_position'}).set_index(data1) frames = [ema_1, ema_8,ema_21,ema_5,ema_34 , ema_55,ema_3, signal, position] strategy = pd.concat(frames, join = 'inner', axis = 1) #strategy = strategy.reset_index().drop('date', axis = 1) ##绾垮浘 stocks_ret= pd.DataFrame(df['Close'].pct_change()[1:]) stocks_ret.columns = ['returns'] ema_strategy_ret = [] for i in range(len(stocks_ret)): try: returns = stocks_ret['returns'][i]*strategy['ema_position'][i] ema_strategy_ret.append(returns) except: pass ema_strategy_ret_df = pd.DataFrame(ema_strategy_ret).rename(columns = {0:'ema_returns'}).set_index(stocks_ret.index) fig, ax = plt.subplots() plt.plot(((1+ema_strategy_ret_df['ema_returns']).cumprod())*10000000, color = 'blue') ##with total amount money stocks_ret1= pd.DataFrame(np.diff(stocks['close'])).rename(columns = {0:'returns'}) ema_strategy_ret1= [] for i in range(len(stocks_ret1)): try: returns =stocks_ret1['returns'][i]*strategy['ema_position'][i] ema_strategy_ret1.append(returns) except: pass ema_strategy_ret_df1= pd.DataFrame(ema_strategy_ret1).rename(columns = {0:'ema_returns'}) investment_value = 10000000 number_of_stocks = math.floor(investment_value/stocks['close'][1]) ema_investment_ret = [] for i in range(len(ema_strategy_ret_df1['ema_returns'])): returns = number_of_stocks*ema_strategy_ret_df1['ema_returns'][i] ema_investment_ret.append(returns) ema_investment_ret_df = pd.DataFrame(ema_investment_ret).rename(columns = {0:'investment_returns'}).set_index(stocks_ret.index) total_investment_ret = round(sum(ema_investment_ret_df['investment_returns']), 2) GainedinP=round((total_investment_ret/investment_value)*100,2) #print(str(stocks)) print(cl('\n\n------------------------------------------\n\nStart from '+str(date)+'锛?\nProfit (cumulative returns) gained '+str(GainedinP)+'% by using SFIM-2-03 strategy \nBy investing $'+str(investment_value)+' in '+str(stock)+', the profit will be : ${}'.format(total_investment_ret), attrs = ['bold'])) df['Close'].plot(label = 'CLOSE PRICES', color = 'skyblue') df['ema_1'].plot(label = 'KeyLine', linestyle = '--', linewidth = 1) df['ema_3'].plot(label = 'Fast', linestyle = '--', linewidth = 1.2) df['ema_5'] .plot(label = 'Fast2', linestyle = '--', linewidth = 1.2) df['ema_8'].plot(label = 'Mid0', linestyle = '--', linewidth = 1.2) #stocks['ema_13'].plot(label = 'ema_13', linestyle = '--', linewidth = 1.2) #stocks['ema_14'].plot(label = 'ema_14', linestyle = '--', linewidth = 1.2) df['ema_21'].plot(label = 'LifeLine', linestyle = '--', linewidth = 1.2) df['ema_34'].plot(label = 'Neck', linestyle = '--', linewidth = 1.2) df['ema_55'].plot(label = 'BloodLine', linestyle = '--', linewidth = 1.2) df['upper_bb'].plot(label = 'UPPER SD', linestyle = '--', linewidth = 1, color = 'black') df['mid_bb'].plot(label = 'MIDDLE SD', linestyle = '--', linewidth = 1.2, color = 'grey') df['lower_bb'].plot(label = 'LOWER SD', linestyle = '--', linewidth = 1, color = 'black') #stocks['ema_144'].plot(label = 'ema_144', linestyle = '--', linewidth = 1.2) #stocks['ema_233'].plot(label = 'ema_233', linestyle = '--', linewidth = 1.2) plt.scatter(df.index, buy_price, marker = '^', s = 200, color = 'darkblue', label = 'BUY SIGNAL') plt.scatter(df.index, sell_price, marker = 'v', s = 200, color = 'crimson', label = 'SELL SIGNAL') plt.legend(loc = 'upper left') plt.title('Seabridge Fintech SFIM+ Buy Low Sell High \nStock ticker: Cumulative Returns Since 2019-01-01: '+str(GainedinP)+'%') plt.show() stockabc=str(stock) ###without money invested #print('\n\n------------------------------------------\n\nSeabridge Fintech SFIM+ Strategy Compared With Benchmark stock') def get_benchmark(stock_prices, start_date, investment_value): stock = get_historic_data(stockabc) stock = stock.set_index('date') stock.index = pd.to_datetime(stock.index) stock = stock[stock.index >= start_date] benchmark= pd.DataFrame(stock['close'].pct_change()[1:]) benchmark.columns = ['benchmark_returns'] investment_value = investment_value number_of_stocks = math.floor(investment_value/stock_prices[-1]) benchmark_investment_ret = [] for i in range(len(benchmark['benchmark_returns'])): #returns = number_of_stocks*benchmark['benchmark_returns'][i] returns =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(stocks['close'], '2019-1-1', 10000000) benchmark = benchmark.set_index(stocks_ret.index) fig, ax = plt.subplots() plt.plot(((1+ema_strategy_ret_df['ema_returns']).cumprod())*10000000, color = 'blue') plt.plot(((1+benchmark['investment_returns']).cumprod())*10000000, color = 'red') plt.title("Ticker: "+str(stock)+ "'s Return vs Seabridge Fintech Scalping Swing Return") plt.ylabel("Profit") plt.xlabel("Year") ax.legend(["Seabridge Fintech Scalping Swing Return", str(stock)+"'s Return"],loc ="upper left") #lt.yticks(np.arange(10000, 70000, step=5000)) plt.xticks(rotation = 45) ##with 10000000 invested def get_benchmark1(stock_prices, start_date, investment_value): stock = get_historic_data(stockabc) stock = stock.set_index('date') stock.index = pd.to_datetime(stock.index) stock = stock[stock.index >= start_date] benchmark=pd.DataFrame(np.diff(stock['close'])).rename(columns = {0:'returns'}) benchmark.columns = ['benchmark_returns'] investment_value = investment_value number_of_stocks = math.floor(investment_value/stock_prices[-1]) benchmark_investment_ret1= [] for i in range(len(benchmark['benchmark_returns'])): returns1= number_of_stocks*benchmark['benchmark_returns'][i] benchmark_investment_ret1.append(returns1) benchmark_investment_ret_df1= pd.DataFrame(benchmark_investment_ret1).rename(columns = {0:'investment_returns'}) return benchmark_investment_ret_df1 investment_value = 10000000 total_benchmark_investment_ret = round(sum(benchmark['investment_returns']), 2) benchmark_profit_percentage = math.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('Seabridge Fintech Scalping Swing Strategy profit is {}% higher than the Benchmark Profit'.format(GainedinP- benchmark_profit_percentage), attrs = ['bold'])) benchmark_total = get_benchmark1(stocks['close'], '2019-1-1', 10000000) benchmark_total = benchmark_total.set_index(stocks_ret.index) fig, ax = plt.subplots() plt.plot(ema_investment_ret_df['investment_returns'],linewidth = 2, color = 'red') plt.plot(benchmark_total['investment_returns'], linestyle = '--', linewidth = 1, color = 'blue') plt.title(str(stock)+"'s Return vs Seabridge Fintech Scalping Swing Return") plt.ylabel("Profit") plt.xlabel("Year") ax.legend(["Seabridge Fintech Scalping Swing Return",str(stock)+"'s Return"],loc ="upper left") #lt.yticks(np.arange(10000, 70000, step=5000)) plt.xticks(rotation = 45)