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 * import urllib3, socket from urllib3.connection import HTTPConnection HTTPConnection.default_socket_options = ( HTTPConnection.default_socket_options + [ (socket.SOL_SOCKET, socket.SO_SNDBUF, 10000000), #1MB in byte (socket.SOL_SOCKET, socket.SO_RCVBUF, 10000000) ]) #pip install --default-timeout=100 future import requests #return requests.get(url).json() def get_signal(ticker, start, end): print(ticker) # EXTRACTING STOCK DATA # Setup client # Stock candles # finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # # Stock candles # start =list(start) # end = list(end) # from datetime import datetime # 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') import requests api_key = '86dd63f6b8ae774b061232685b78eb52' from datetime import datetime 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':'volume'}) stock = stock.set_index('Date') stock= stock.sort_index() 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'] # fig, ax = plt.subplots() # plt.plot(((1+strategy1['tsi_returns'][1:] ).cumprod()), color = 'green') # plt.plot(((1+strategy1['ret'][1:]).cumprod()), color = 'red') return strategy1 #s= get_signal('OGN', start = (2020,1,1), end = end) def strategy_signal1(symbols): strategy1 = pd.DataFrame() for tick in symbols: #print(tick) # tick = 'TBIO' data = get_signal(tick, start , end ) f =pd.DataFrame(columns = ['g_bp','g_sp','g_p_bp','g_p_sp','g_tdate' ,'strategy1_g_signal']) if data['tsi_signal'][-1] == 1 and data['tsi_position'][-1] ==1: d =pd.DataFrame( data.iloc[-1,:]).T # buy_price.append(d['Close'].values) # d.index = d.index.strftime('%d/%m/%Y') d.index = pd.to_datetime(d.index, format='%Y-%m-%d') d = d.reset_index() f= f.append({'g_bp':d['Close'][0], 'g_sp':0,'g_p_bp':0,'g_p_sp':0,'g_tdate': d['index'][0],'strategy1_g_signal':1},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # date = d.index.values.tolist() elif data['tsi_signal'][-1] == -1 and data['tsi_position'][-1] ==0: d =pd.DataFrame( data.iloc[-1,:]).T # buy_price.append(d['Close'].values) # d.index = d.index.strftime('%d/%m/%Y') d.index = pd.to_datetime(d.index, format='%Y-%m-%d') d = d.reset_index() f= f.append({'g_bp':0, 'g_sp':d['Close'][0],'g_p_bp':0,'g_p_sp':0,'g_tdate': d['index'][0],'strategy1_g_signal':-1},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') elif data['tsi_signal'][-1] == 0 and data['tsi_position'][-1] ==1: data1 = data[(data['tsi_signal'] == 1) & (data['tsi_position'] ==1)] d =pd.DataFrame( data1.iloc[-1,:]).T # buy_p.append(d['Close'].values) # d.index = d.index.strftime('%d/%m/%Y') d.index = pd.to_datetime(d.index, format='%Y-%m-%d') d = d.reset_index() f= f.append({'g_bp':0, 'g_sp':0,'g_p_bp':d['Close'][0],'g_p_sp':0,'g_tdate': d['index'][0], 'strategy1_g_signal':0},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') elif data['tsi_signal'][-1] == 0 and data['tsi_position'][-1] ==0: data1= data[(data['tsi_signal'] == -1) & (data['tsi_position'] ==0)] d =pd.DataFrame( data1.iloc[-1,:]).T # d.index = d.index.strftime('%d/%m/%Y') d.index = pd.to_datetime(d.index, format='%Y-%m-%d') d = d.reset_index() f= f.append({'g_bp':0, 'g_sp':0,'g_p_bp':0,'g_p_sp':d['Close'][0],'g_tdate': d['index'][0],'strategy1_g_signal':0},ignore_index = True) f.index = [tick] Close = data.loc[data.index[-1],'Close'] f['g_cur_p'] = [Close] # dat = data.index[-1] today = dt.today() f['g_tdate'] = [today] f['g_date'] =[today] strategy1 = pd.concat([f,strategy1], axis=0) strategy1['g_name'] = 'Green Momentum' strategy1=strategy1.sort_index() return strategy1 #s = pd.DataFrame(symbols) #s.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_3_13.csv') #f.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_09_20_2022.csv' ) #stock_lists = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_09_20_2022.csv') #f1.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_1_26_2022.csv' ) #stock_list0 = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_1_26_2022.csv') stock_lists = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_05_27_2023.csv' ) #stock_lists = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_3_13.csv') symbols = list(stock_lists['0']) #symbols = ['AAL','AON','ARW','ASGN'] symbols.remove('GEHCV') symbols.remove('ASML') symbols.remove('RXDX') symbols.remove('ALTM') symbols.remove('YUM') symbols.remove('L&TFH.NS') symbols.remove('LB') # symbols.remove('DRE') from datetime import datetime as dt import datetime today = dt.today() end = today-datetime.timedelta(1) end =today #start = '2021-03-01' start = today-datetime.timedelta(200) #start = '2021-03-01' n=4#umber of groups groups = np.array_split(symbols, n) group1 = groups[0].tolist() group2 = groups[1].tolist() group3 = groups[2].tolist() group41 =groups[3].tolist() # group42 =groups[4].tolist() # group43 =groups[5].tolist() #group41.remove('RIVN') from datetime import datetime as dt import datetime import time data1 = strategy_signal1(group1) time.sleep(60) #单独run ########################################################################################## data2 = strategy_signal1(group2) time.sleep(60) #我来学习了 #单独run ############################################################################### data3 = strategy_signal1(group3) time.sleep(60) #单独run ############################################################################### data41 = strategy_signal1(group41) time.sleep(60) #单独run ############################################################################### # data42 = strategy_signal1(group42) # data43 = strategy_signal1(group43) #单独run ############################################################################### strategy1_signal = pd.concat([data1, data2, data3, data41],axis =0) strategy1_signal = strategy1_signal[ strategy1_signal['strategy1_g_signal'] ==1] strategy1_signal=strategy1_signal.sort_index() import matplotlib.image as mpimg import matplotlib.pyplot as plt # Read Images img1= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_buy.jpg') img2= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_downtrend.jpg') img3= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_no_signal.jpg') img4= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_sell.jpg') img5= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_sideways.jpg') img6= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_uptrend.jpg') # Output Images def get_pa_signal(symblos1): for ticker in symblos1: print(ticker) fig = plt.figure(figsize=(10,10)) plt.imshow(img1) plt.axis("off") # plt.grid(b=None) plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0) path ="C:\\users\jizha\Desktop\seabridge fintech\greenline_pa_icon\\" plt.savefig(path +'{}.jpg'.format(ticker+"_pa_greenline_signal"), dpi = 72,transparent = True, bbox_inches = 'tight', pad_inches = 0) plt.show() #dr= 'https://3arbzfbsh-cname-us.ngrok.io' g_pa_signal_url = pd.DataFrame() for ticker in symblos1: print(ticker) image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/greenline_pa_icon/"+ticker+"_pa_greenline_signal.jpg" tick = ticker image['g_PA_image_buy_sell']=[ dr] image.index =[tick] g_pa_signal_url = pd.concat([g_pa_signal_url ,image], axis =0) g_pa_signal_url=g_pa_signal_url.sort_index() return g_pa_signal_url symblos1 = list(strategy1_signal.index) g_pa_signal_url = get_pa_signal(symblos1) strategy1 =strategy1_signal.merge(g_pa_signal_url, left_index=True, right_index=True) strategy1 ['g_b_r_p'] = strategy1['g_bp']+strategy1['g_bp']*0.005 strategy1['g_b_r_m'] = strategy1['g_bp'] - strategy1['g_bp']*0.005 strategy1['g_b_zone'] = 'Yes' strategy1[['g_bp','g_sp','g_p_bp','g_p_sp','g_cur_p','g_b_r_p','g_b_r_m']] =strategy1[['g_bp','g_sp','g_p_bp','g_p_sp','g_cur_p','g_b_r_p','g_b_r_m']].astype(float) strategy1[['g_bp','g_sp','g_p_bp','g_p_sp','g_cur_p','g_b_r_p','g_b_r_m']]= strategy1[['g_bp','g_sp','g_p_bp','g_p_sp','g_cur_p','g_b_r_p','g_b_r_m']].applymap("{0:,.2f}".format) #strategy1[['g_bp','g_sp','g_p_bp','g_p_sp','g_cur_p']]= strategy1[['g_bp','g_sp','g_p_bp','g_p_sp','g_cur_p']].applymap("{0:,.2f}".format) #strategy1.columns =strategy1.columns.to_series().apply(lambda x: x.strip()) #strategy1['g_tdate']= pd.to_datetime(strategy1['g_tdate']).dt.date #strategy1.dtypespip install scikit-optimize from datetime import datetime as dt import datetime today = dt.today() a=str(today.year) b=str(today.month) c=str(today.day) print(c) strategy1['g_tdate']= pd.to_datetime(strategy1['g_tdate']).dt.date strategy1['g_date']= pd.to_datetime(strategy1['g_date']).dt.date strategy1 .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') #strategy1 .to_csv('C:\\Users\\jizha\\Desktop\\seabridge_datapool1\\final_strategy_data_temporaly\\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') #strategy1= pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') #strategy1 .to_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\startegy1.csv_'+a+'_'+b+'_'+c+'.csv',encoding='utf-8') #C:\Users\jizha\Desktop\Strategy_auto_2_21_2022 #C:\Users\jizha\Desktop\Strategy_auto_2_21_2022 #strategy1_1 = strategy1.copy() #strategy1_1[['g_tdate','g_date']] = pd.Timestamp(strategy1_1[['g_tdate','g_date']]) #strategy1_1[['g_tdate','g_date']]= strategy1_1[['g_tdate','g_date']].astype(str) # file = r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results.csv' # with open(file, 'w') as the_file: # the_file.write(strategy1_1+'\n') # # the_file.write(enddate+'\n') # C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results # #print(startdate) # print(enddate) strategy1 = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') #strategy1 .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') strategy1 = strategy1.drop_duplicates()