import pandas as pd import requests import matplotlib.pyplot as plt import numpy as np from math import floor from termcolor import colored as cl import pandas as pd import finnhub from sklearn.cluster import KMeans import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import matplotlib.dates as mpl_dates from datetime import datetime as dt import datetime from datetime import datetime as dt import datetime # today = dt.today() # a=str(today.year) # b=str(today.month) # c=str(today.day) # c1= pd.read_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') # c1 = c1.rename(columns = {'Unnamed: 0':'sy # # today = dt.today() # a = str(today.year) # b = str(today.month) # c = str(today.day) # d = pd.read_csv( r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') # d = d.rename(columns = {'Unnamed: 0':'symbol'}) # strategy2 = d.set_index('symbol') # #strategy1=pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') # today = dt.today() # a = str(today.year) # b = str(today.month) # c = str(today.day) # e = pd.read_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy3.csv_'+a+'_'+b+'_'+c+'.csv') # e = e.rename(columns = {'Unnamed: 0':'symbol'}) # strategy3 = e.set_index('symbol') # ticker1 = strategy1.index.tolist() # ticker2 = strategy2.index.tolist() # ticker3 = strategy3.index.tolist() # ticker_sum = ticker1+ticker2+ticker3 # ticker_sum = np.unique(ticker_sum).tolist() # #ticker_sum = f['0'].tolist() # f =pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\support_resistop_last_peak_data.csv') # f = f.rename(columns = {'Unnamed: 0':'symbol'}) # # stock_info_data2 = f.set_index('symbol')mbol'}) 除了等 # strategy1 = c1.set_index('symbol') #stock_info_data2.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\support_resistop_last_peak_data.csv') #stock_info_data1 def strategy_performance(ticker, start, end): # EXTRACTING STOCK DATA # Setup client # Stock candles # start =[start] # end =[end] import requests api_key = '86dd63f6b8ae774b061232685b78eb52' bs = requests.get(f'https://financialmodelingprep.com/api/v3/historical-price-full/{ticker}?from={start}&to={end}&apikey={api_key}').json()['historical'] stock = pd.DataFrame(bs) stock =stock[['date','open','high','low','close','volume']] stock = stock.rename(columns = {'date':'Date', 'open':'Open', 'high':'High','low':'Low', 'close':'Close', 'volume':'volumn'}) stock = stock.set_index('Date') stock= stock.sort_index() data1 = stock.copy() # from datetime import datetime # start =list(start) # end = list(end) # finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # start_time = int(datetime(start[0], start[1], start[2], 0, 0).replace(tzinfo = timezone.utc).timestamp()) # end_time = int(datetime(end[0], end[1], end[2], 0, 0).replace(tzinfo = timezone.utc).timestamp()) # res = finnhub_client.stock_candles(ticker, 'D', start_time, end_time) # stock = pd.DataFrame(res) # stock = stock.rename(columns = {'t':'Date', 'o':'Open', 'h':'High', 'l':'Low', 'c':'Close', 's':'status', 'v':'volumn'}) # stock['Date'] = pd.to_datetime(stock['Date'], unit = 's') # stock = stock.set_index('Date') # data1 = stock.copy() # strategy CALCULATION def get_tsi(Close, long, short, signal): diff = Close - Close.shift(1) abs_diff = abs(diff) diff_smoothed = diff.ewm(span = long, adjust = False).mean() diff_double_smoothed = diff_smoothed.ewm(span = short, adjust = False).mean() abs_diff_smoothed = abs_diff.ewm(span = long, adjust = False).mean() abs_diff_double_smoothed = abs_diff_smoothed.ewm(span = short, adjust = False).mean() tsi = (diff_double_smoothed / abs_diff_double_smoothed) * 100 signal = tsi.ewm(span = signal, adjust = False).mean() # tsi = tsi[tsi.index >= '2020-01-01'].dropna() # signal = signal[signal.index >= '2020-01-01'].dropna() return tsi, signal data1['tsi'], data1['signal_line'] = get_tsi(data1['Close'], 25, 13, 12) # TRUE STRENGTH INDEX STRATEGY def implement_tsi_strategy(prices, tsi, signal_line): buy_price = [] sell_price = [] tsi_signal = [] signal = 0 for i in range(len(prices)): if tsi[i-1] < signal_line[i-1] and tsi[i] > signal_line[i]: if signal != 1: buy_price.append(prices[i]) sell_price.append(np.nan) signal = 1 tsi_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) tsi_signal.append(0) elif tsi[i-1] > signal_line[i-1] and tsi[i] < signal_line[i]: if signal != -1: buy_price.append(np.nan) sell_price.append(prices[i]) signal = -1 tsi_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) tsi_signal.append(0) else: buy_price.append(np.nan) sell_price.append(np.nan) tsi_signal.append(0) return buy_price, sell_price, tsi_signal buy_price, sell_price, tsi_signal = implement_tsi_strategy(data1['Close'], data1['tsi'], data1['signal_line']) # STOCK POSITION position = [] for i in range(len(tsi_signal)): if tsi_signal[i] > 1: position.append(0) else: position.append(1) for i in range(len(data1['Close'])): if tsi_signal[i] == 1: position[i] = 1 elif tsi_signal[i] == -1: position[i] = 0 else: position[i] = position[i-1] Close_price = data1['Close'] tsi = data1['tsi'] signal_line = data1['signal_line'] tsi_signal = pd.DataFrame(tsi_signal).rename(columns = {0:'tsi_signal'}).set_index(data1.index) position = pd.DataFrame(position).rename(columns = {0:'tsi_position'}).set_index(data1.index) frames_data1 = [Close_price, tsi, signal_line, tsi_signal, position] strategy1 = pd.concat(frames_data1, join = 'inner', axis = 1) strategy1['ret'] =pd.DataFrame(strategy1['Close'].pct_change()) strategy1['tsi_returns'] = strategy1['ret'] *strategy1['tsi_position'] ####################second strategy###################### # SUPERTREND CALCULATION data2 =stock.copy() 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 data2['st'], data2['s_upt'], data2['st_dt'] = get_supertrend(data2['High'], data2['Low'], data2['Close'], 9, 2) #data2 = data2[1:] # 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(data2['Close'], data2['st']) # SUPERTREND SIGNALS position = [] for i in range(len(st_signal)): if st_signal[i] > 1: position.append(0) else: position.append(1) for i in range(len(data2['Close'])): if st_signal[i] == 1: position[i] = 1 elif st_signal[i] == -1: position[i] = 0 else: position[i] = position[i-1] Close_price = data2['Close'] st = data2['st'] st_signal = pd.DataFrame(st_signal).rename(columns = {0:'st_signal'}).set_index(data2.index) position = pd.DataFrame(position).rename(columns = {0:'st_position'}).set_index(data2.index) frames_daat2 = [Close_price, st, st_signal, position] strategy2 = pd.concat(frames_daat2 , join = 'inner', axis = 1) # BACKTESTING strategy2['ret'] =pd.DataFrame(strategy2['Close'].pct_change()) strategy2['st_returns'] = strategy2['ret']*strategy2['st_position'] # fig, ax = plt.subplots() # plt.plot(((1+strategy2['st_returns'][1:] ).cumprod()*10000), color = 'green') # plt.plot(((1+strategy2['ret'][1:]).cumprod()*10000), color = 'red') #ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y))) ##############strategy3######################## data3 = stock.copy() def get_di(data, lookback): ma = data.rolling(lookback).mean() di = ((data - ma) / ma) * 100 return di data3['di_14'] = get_di(data3['Close'], 14) data3 = data3.dropna() # DISPARITY INDEX PLOT def implement_di_strategy(prices, di): buy_price = [] sell_price = [] di_signal = [] signal = 0 for i in range(len(prices)): if di[i-4] < 0 and di[i-3] < 0 and di[i-2] < 0 and di[i-1] < 0 and di[i] > 0: if signal != 1: buy_price.append(prices[i]) sell_price.append(np.nan) signal = 1 di_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) di_signal.append(0) elif di[i-4] > 0 and di[i-3] > 0 and di[i-2] > 0 and di[i-1] > 0 and di[i] < 0: if signal != -1: buy_price.append(np.nan) sell_price.append(prices[i]) signal = -1 di_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) di_signal.append(0) else: buy_price.append(np.nan) sell_price.append(np.nan) di_signal.append(0) return buy_price, sell_price, di_signal buy_price, sell_price, di_signal = implement_di_strategy(data3['Close'], data3['di_14']) # STOCK POSITION position = [] for i in range(len(di_signal)): if di_signal[i] > 1: position.append(0) else: position.append(1) for i in range(len(data3['Close'])): if di_signal[i] == 1: position[i] = 1 elif di_signal[i] == -1: position[i] = 0 else: position[i] = position[i-1] Close_price = data3['Close'] di = data3['di_14'] di_signal = pd.DataFrame(di_signal).rename(columns = {0:'di_signal'}).set_index(data3.index) position = pd.DataFrame(position).rename(columns = {0:'di_position'}).set_index(data3.index) frames = [Close_price, di, di_signal, position] strategy3 = pd.concat(frames, join = 'inner', axis = 1) # BACKTESTING strategy3['ret'] =pd.DataFrame(strategy3['Close'].pct_change()) strategy3['di_returns'] = strategy3['ret']*strategy3['di_position'] def CAGR(DF): "function to calculate the Cumulative Annual Growth Rate of a trading strategy" df = DF.copy() df["cum_return"] = (1 + df["mon_ret"]).cumprod() n = len(df)/360 CAGR = (df["cum_return"].tolist()[-1])**(1/n) - 1 return CAGR def volatility(DF): "function to calculate annualized volatility of a trading strategy" df = DF.copy() vol = df["mon_ret"].std() * np.sqrt(360) return vol def sharpe(DF,rf): "function to calculate sharpe ratio ; rf is the risk free rate" df = DF.copy() sr = (CAGR(df) - rf)/volatility(df) return sr def max_dd(DF): "function to calculate max drawdown" df = DF.copy() df["cum_return"] = (1 + df["mon_ret"]).cumprod() df["cum_roll_max"] = df["cum_return"].cummax() df["drawdown"] = df["cum_roll_max"] - df["cum_return"] df["drawdown_pct"] = df["drawdown"]/df["cum_roll_max"] max_dd = df["drawdown_pct"].max() return max_dd def sortino(DF,rf): "function to calculate sortino ratio ; rf is the risk free rate" df = DF.copy() df["neg_ret"] = np.where(df["mon_ret"]<0,df["mon_ret"],0) neg_vol = df["neg_ret"].std() * np.sqrt(252) sr = (CAGR(df) - rf)/neg_vol return sr # def calmar(DF): # "function to calculate calmar ratio" # df = DF.copy() # clmr = CAGR(df)/max_dd(df) # return clmr strategy1_ret = pd.DataFrame(strategy1.loc[:,'tsi_returns']) strategy1_ret.columns = ['mon_ret'] strategy1_ret.dropna(inplace = True) strategy2_ret = pd.DataFrame(strategy2.loc[:,'st_returns']) strategy2_ret.columns = ['mon_ret'] strategy2_ret.dropna(inplace = True) strategy3_ret = pd.DataFrame(strategy3.loc[:,'di_returns']) strategy3_ret.columns = ['mon_ret'] strategy3_ret.dropna(inplace = True) strategy_b = pd.DataFrame(strategy1.loc[:,'ret']) strategy_b.columns = ['mon_ret'] strategy_b.dropna(inplace = True) #strategy1_ret =strategy1_ret.rename({'tsi_returns':"mon_ret"}) #traetgy1: 'Super Momentum'###########tsi_return green ############strategy3: Super Oscillator di_return green #strateg2:'Super Trend" purple st_return cagr1=CAGR(strategy1_ret) cagr2=CAGR(strategy2_ret) cagr3= CAGR(strategy3_ret) cagr_benchmark = CAGR(strategy_b) data =[{'cagr_green':cagr1, 'cagr_purple':cagr2, 'cagr_blue':cagr3, 'cagr_benchmark': cagr_benchmark}] da = pd.DataFrame(data) da.index= [ticker] vol1 =volatility(strategy1_ret) vol2 =volatility(strategy2_ret) vol3 = volatility(strategy3_ret) vol_benchmark = volatility(strategy_b) data2= [{'vol_green': vol1, 'vol_purple':vol2, 'vol_blue':vol3,'vlo_benchmark': vol_benchmark }] da3= pd.DataFrame(data2) da3.index= [ticker] sharp1 = sharpe(strategy1_ret,0.025) sharp2 = sharpe(strategy2_ret,0.025) sharp3 =sharpe(strategy3_ret,0.025) sharp_benchmark = sharpe(strategy_b,0.025) data3 = [{'sharp_green': sharp1, 'sharp_purple': sharp2, 'sharp_blue': sharp3, 'sharp_benchmark' : sharp_benchmark}] da4 = pd.DataFrame(data3) da4.index= [ticker] max_dd1 = max_dd(strategy1_ret) max_dd2 = max_dd(strategy2_ret) max_dd3 = max_dd(strategy3_ret) max_dd_benchmark = max_dd(strategy_b) data4 = [{'max_dd_green': max_dd1, 'max_dd_purple': max_dd2, 'max_dd_blue': max_dd3, 'max_dd_benchmark': max_dd_benchmark}] da5 = pd.DataFrame(data4) da5.index= [ticker] sortino1 = sortino(strategy1_ret,0.025) sortino2= sortino(strategy2_ret,0.025) sortino3= sortino(strategy3_ret,0.025) sortino_benchmark = sortino(strategy_b,0.025) data5 = [{'sortino_green':sortino1, 'sortino_purple': sortino2, 'sortino_blue' : sortino3,'sortino_benchmark ': sortino_benchmark}] da6 = pd.DataFrame(data5) da6.index= [ticker] # calmar1= calmar(strategy1_ret) # calmar2= calmar(strategy2_ret) # calmar3= calmar(strategy3_ret) # calmar_benchmark = calmar(strategy_b) # data7 = [{'calmar_green':calmar1, ' calmar_purple': calmar2, ' calmar_blue': calmar3,' calmar_benchmark': calmar_benchmark}] # da7 =pd.DataFrame(data6) # da7.index= [ticker] k= da.merge(da3, left_index=True, right_index=True) k1 = k.merge(da4, left_index=True, right_index=True) k2 = k1.merge(da5, left_index=True, right_index=True) kpi = k2.merge(da6, left_index=True, right_index=True) kpi.columns =kpi.columns.to_series().apply(lambda x: x.strip()) kpi[['sharp_green','sharp_purple','sharp_blue','sharp_benchmark','sortino_green','sortino_purple','sortino_blue','sortino_benchmark']] = kpi[['sharp_green','sharp_purple','sharp_blue','sharp_benchmark','sortino_green','sortino_purple','sortino_blue','sortino_benchmark']].applymap("{0:,.2f}".format) kpi[['cagr_green','cagr_purple','cagr_blue','cagr_benchmark','vol_green','vol_purple','vol_blue','vlo_benchmark','max_dd_green','max_dd_purple','max_dd_blue','max_dd_benchmark']] =kpi[['cagr_green','cagr_purple','cagr_blue','cagr_benchmark','vol_green','vol_purple','vol_blue','vlo_benchmark','max_dd_green','max_dd_purple','max_dd_blue','max_dd_benchmark']].applymap("{0:,.2%}".format) return kpi # df[['var1','var2']] = df[['var1','var2']].applymap("{0:.2f}".format) # df['var3'] = df['var3'].applymap(lambda x: "{0:.2f}%".format(x*100)) # df[['var1','var2']].apply(lambda x: map(lambda x:'{:.2f}%'.format(x),x),axis=1) from datetime import datetime as dt import datetime #start=(2010,1,21) start='2010-01-21' today = dt.today() end = today-datetime.timedelta(1) #end = (yesterday.year, yesterday.month, yesterday.day) kpi = pd.DataFrame() for ticker in ticker_sum: data = strategy_performance( ticker , start,end) kpi= pd.concat([kpi, data ],axis =0) kpi=kpi.sort_index() # kpi= kpi.astype(float) # kpi[['sharp_green','sharp_purple','sharp_blue','sharp_benchmark','sortino_green','sortino_purple','sortino_blue','sortino_benchmark']] = kpi[['sharp_green','sharp_purple','sharp_blue','sharp_benchmark','sortino_green','sortino_purple','sortino_blue','sortino_benchmark']].applymap("{0:,.2f}".format) # kpi= kpi.astype(float) # kpi[['cagr_green','cagr_purple','cagr_blue','cagr_benchmark','vol_green','vol_purple','vol_blue','vlo_benchmark','max_dd_green','max_dd_purple','max_dd_blue','max_dd_benchmark']] =kpi[['cagr_green','cagr_purple','cagr_blue','cagr_benchmark','vol_green','vol_purple','vol_blue','vlo_benchmark','max_dd_green','max_dd_purple','max_dd_blue','max_dd_benchmark']].applymap("{0:,.2%}".format) #stock_info_data2= stock_info_data1.merge(strategy_sum1,right_index = True, left_index = True) stock_info_data3 = stock_info_data2.merge(kpi,right_index = True, left_index = True) stock_info_data3 .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\all_data.csv')