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 import requests def get_signal2(ticker, start, end): print(ticker) # from datetime import datetime # start =list(start) # end = list(end) # finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # # Stock candles # 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() data2 =stock.copy() # 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 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']) # GENERATING STOCK POSITION 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'] return strategy2 def strategy_signal2(symbols): strategy2 = pd.DataFrame() for tick in symbols: data = get_signal2(tick, start, end ) f =pd.DataFrame(columns = ['p_bp','p_sp','p_p_bp','p_p_sp','p_tdate' ,'strtategy2_trend_signal']) if data['st_signal'][-1] == 1 and data['st_position'][-1] ==1: d =pd.DataFrame( data.iloc[-1,:]).T # buy_price.append(d['Close'].values) d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # df.index = pd.to_datetime(df.index).strftime('%d-%m-%Y') # d.index = d.index.strftime('%d/%m/%Y') d = d.reset_index() f= f.append({'p_bp':d['Close'][0], 'p_sp':0,'p_p_bp':0,'p_p_sp':0,'p_tdate': d['index'][0],'strtategy2_trend_signal':1},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # date = d.index.values.tolist() elif data['st_signal'][-1] == -1 and data['st_position'][-1] ==0: d =pd.DataFrame( data.iloc[-1,:]).T # buy_price.append(d['Close'].values) d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # d.index = d.index.strftime('%d/%m/%Y') d = d.reset_index() f= f.append({'p_bp':0, 'p_sp':d['Close'][0],'p_p_bp':0,'p_p_sp':0,'p_tdate': d['index'][0],'strtategy2_trend_signal':-1},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') elif data['st_signal'][-1] == 0 and data['st_position'][-1] ==1: # data1 = data[(data['st_signal'] == 1) & (data['st_position'] ==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({'p_bp':0, 'p_sp':0,'p_p_bp':d['Close'][0],'p_p_sp':0,'p_tdate': d['index'][0], 'strtategy2_trend_signal':0},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') elif data['st_signal'][-1] == 0 and data['st_position'][-1] ==0: # data1= data[(data['st_signal'] == -1) & (data['st_position'] ==0)] d =pd.DataFrame( data.iloc[-1,:]).T d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # d.index = d.index.strftime('%d/%m/%Y') d = d.reset_index() f= f.append({'p_bp':0, 'p_sp':0,'p_p_bp':0,'p_p_sp':d['Close'][0],'p_tdate': d['index'][0],'strtategy2_trend_signal':0},ignore_index = True) f.index = [tick] Close = data.loc[data.index[-1],'Close'] f['p_cur_p'] = [Close] #dat = data.index[-1] # dat1 =dat.date() today = dt.today() f['p_tdate'] = [today] f['p_date'] =[today] strategy2 = pd.concat([f,strategy2], axis=0) strategy2['p_name'] = 'Purple Trend' return strategy2 # stock_lists = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_3_13.csv') # symbols = list(stock_lists['0']) stock_lists = 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_3_13.csv') symbols = list(stock_lists['0']) #start = '2021-03-01' from datetime import datetime as dt import datetime symbols.remove('BCE') #symbols.remove('GEHCV') #symbols.remove('ASML') symbols.remove('RXDX') #symbols.remove('GEHCV') symbols.remove('YUM') symbols.remove('ZEUS') symbols.remove('BBY') symbols.remove('AAWW') # symbols.remove('MANT') #symbols.remove('CTXS') #symbols.remove('DRE') #ticker = 'AWR' # h = ['META'] # g = symbols+h #symbols = g # yesterday = today-datetime.timedelta(1) # start = (2020,3,1) # end = (yesterday.year, yesterday.month, yesterday.day) today = dt.today() end = today-datetime.timedelta(1) #start = '2021-03-01' start = today-datetime.timedelta(200) n= 3# number of groups groups = np.array_split(symbols, n) group1 = groups[0].tolist() group2 = groups[1].tolist() group3 = groups[2].tolist() # group4 = groups[3].tolist() # group5 = groups[4].tolist() # group6 = groups[5].tolist() # group7 = groups[6].tolist() from datetime import datetime as dt import datetime import time data4 = strategy_signal2(group1) time.sleep(60) data5 = strategy_signal2(group2) time.sleep(60) data6= strategy_signal2(group3) time.sleep(60) #单独run ########################################################################################## #time.sleep(60) #group3.remove('TTC') # data61= strategy_signal2(group4) # data62= strategy_signal2(group5) #data6= strategy_signal2(group3) strategy2_signal = pd.concat([data4, data5, data6], axis=0) strategy2_signal = strategy2_signal[strategy2_signal['strtategy2_trend_signal'] == 1] #strategy2_signal=strategy2_signal.sort_index() #strategy2_signal.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\startegy2.csv') # data4 = strategy_signal2(group4) # data5 = strategy_signal2(group5) # data6= strategy_signal2(group6) # data7 = strategy_signal2(group7) # strategy2 = pd.concat([data1, data2, data3, data4, data5, data6, data7], axis = 0) 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_signal2(strategy2_symbol): for ticker in strategy2_symbol: img1 = mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_buy.jpg') 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\purpleline_pa_icon\\" plt.savefig(path + '{}.jpg'.format(ticker+"_pa_purpleline_signal"), dpi=72, transparent=True, bbox_inches='tight', pad_inches=0) plt.show() #dr= 'https://3arbzfbsh-cname-us.ngrok.io' p_pa_signal_url = pd.DataFrame() for ticker in strategy2_symbol: image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/purpleline_pa_icon/" + \ ticker+"_pa_purpleline_signal.jpg" tick = ticker image['p_PA_image_buy_sell'] = [dr] image.index = [tick] p_pa_signal_url = pd.concat([p_pa_signal_url, image], axis=0) p_pa_signal_url = p_pa_signal_url.sort_index() return p_pa_signal_url strategy2_symbol = list(strategy2_signal.index) p_pa_signal_url = get_pa_signal2(strategy2_symbol) strategy2_signal = strategy2_signal.sort_index() strategy2 = strategy2_signal.merge(p_pa_signal_url, left_index=True, right_index=True) strategy2['p_b_r_p'] = strategy2['p_bp']+strategy2['p_bp']*0.005 strategy2['p_b_r_m'] = strategy2['p_bp'] - strategy2['p_bp']*0.005 strategy2['g_b_zon'] = 'Yes' strategy2.columns = strategy2.columns.to_series().apply(lambda x: x.strip()) strategy2[['p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']] = strategy2[[ 'p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']].astype(float) strategy2[['p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']] = strategy2[[ 'p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']].applymap("{:,.2f}".format) # stocks[['lastDiv','price']] = stocks[['lastDiv','price']].applymap("{0:,.2f}".format) # stocks['div_yiel'] =stocks['div_yiel'].apply(lambda x: "{0:.2f}%".format(x*100)) 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 import requests def get_signal2(ticker, start, end): print(ticker) # from datetime import datetime # start =list(start) # end = list(end) # finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # # Stock candles # 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() data2 =stock.copy() # 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 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']) # GENERATING STOCK POSITION 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'] return strategy2 def strategy_signal2(symbols): strategy2 = pd.DataFrame() for tick in symbols: data = get_signal2(tick, start, end ) f =pd.DataFrame(columns = ['p_bp','p_sp','p_p_bp','p_p_sp','p_tdate' ,'strtategy2_trend_signal']) if data['st_signal'][-1] == 1 and data['st_position'][-1] ==1: d =pd.DataFrame( data.iloc[-1,:]).T # buy_price.append(d['Close'].values) d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # df.index = pd.to_datetime(df.index).strftime('%d-%m-%Y') # d.index = d.index.strftime('%d/%m/%Y') d = d.reset_index() f= f.append({'p_bp':d['Close'][0], 'p_sp':0,'p_p_bp':0,'p_p_sp':0,'p_tdate': d['index'][0],'strtategy2_trend_signal':1},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # date = d.index.values.tolist() elif data['st_signal'][-1] == -1 and data['st_position'][-1] ==0: d =pd.DataFrame( data.iloc[-1,:]).T # buy_price.append(d['Close'].values) d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # d.index = d.index.strftime('%d/%m/%Y') d = d.reset_index() f= f.append({'p_bp':0, 'p_sp':d['Close'][0],'p_p_bp':0,'p_p_sp':0,'p_tdate': d['index'][0],'strtategy2_trend_signal':-1},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') elif data['st_signal'][-1] == 0 and data['st_position'][-1] ==1: # data1 = data[(data['st_signal'] == 1) & (data['st_position'] ==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({'p_bp':0, 'p_sp':0,'p_p_bp':d['Close'][0],'p_p_sp':0,'p_tdate': d['index'][0], 'strtategy2_trend_signal':0},ignore_index = True) # d.index = pd.to_datetime(d.index, format='%Y-%m-%d') elif data['st_signal'][-1] == 0 and data['st_position'][-1] ==0: # data1= data[(data['st_signal'] == -1) & (data['st_position'] ==0)] d =pd.DataFrame( data.iloc[-1,:]).T d.index = pd.to_datetime(d.index, format='%Y-%m-%d') # d.index = d.index.strftime('%d/%m/%Y') d = d.reset_index() f= f.append({'p_bp':0, 'p_sp':0,'p_p_bp':0,'p_p_sp':d['Close'][0],'p_tdate': d['index'][0],'strtategy2_trend_signal':0},ignore_index = True) f.index = [tick] Close = data.loc[data.index[-1],'Close'] f['p_cur_p'] = [Close] #dat = data.index[-1] # dat1 =dat.date() today = dt.today() f['p_tdate'] = [today] f['p_date'] =[today] strategy2 = pd.concat([f,strategy2], axis=0) strategy2['p_name'] = 'Purple Trend' return strategy2 # stock_lists = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stock_lists_pool_3_13.csv') # symbols = list(stock_lists['0']) stock_lists = 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_3_13.csv') symbols = list(stock_lists['0']) #start = '2021-03-01' from datetime import datetime as dt import datetime # symbols.remove('BCE') # #symbols.remove('GEHCV') # symbols.remove('RXDX') # symbols.remove('ALTM') # symbols.remove('YUM') # symbols.remove('ZEUS') # symbols.remove('SAFM') # symbols.remove('MANT') #symbols.remove('CTXS') #symbols.remove('DRE') #ticker = 'AWR' # h = ['META'] # g = symbols+h #symbols = g # yesterday = today-datetime.timedelta(1) # start = (2020,3,1) # end = (yesterday.year, yesterday.month, yesterday.day) today = dt.today() end = today-datetime.timedelta(1) #start = '2021-03-01' start = today-datetime.timedelta(200) n= 3# number of groups groups = np.array_split(symbols, n) group1 = groups[0].tolist() group2 = groups[1].tolist() group3 = groups[2].tolist() # group4 = groups[3].tolist() # group5 = groups[4].tolist() # group6 = groups[5].tolist() # group7 = groups[6].tolist() from datetime import datetime as dt import datetime import time data4 = strategy_signal2(group1) time.sleep(60) data5 = strategy_signal2(group2) time.sleep(60) data6= strategy_signal2(group3) time.sleep(60) #单独run ########################################################################################## #time.sleep(60) #group3.remove('TTC') # data61= strategy_signal2(group4) # data62= strategy_signal2(group5) #data6= strategy_signal2(group3) strategy2_signal = pd.concat([data4, data5, data6], axis=0) strategy2_signal = strategy2_signal[strategy2_signal['strtategy2_trend_signal'] == 1] #strategy2_signal=strategy2_signal.sort_index() #strategy2_signal.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\startegy2.csv') # data4 = strategy_signal2(group4) # data5 = strategy_signal2(group5) # data6= strategy_signal2(group6) # data7 = strategy_signal2(group7) # strategy2 = pd.concat([data1, data2, data3, data4, data5, data6, data7], axis = 0) 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_signal2(strategy2_symbol): for ticker in strategy2_symbol: img1 = mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_buy.jpg') 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\purpleline_pa_icon\\" plt.savefig(path + '{}.jpg'.format(ticker+"_pa_purpleline_signal"), dpi=72, transparent=True, bbox_inches='tight', pad_inches=0) plt.show() #dr= 'https://3arbzfbsh-cname-us.ngrok.io' p_pa_signal_url = pd.DataFrame() for ticker in strategy2_symbol: image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/purpleline_pa_icon/" + \ ticker+"_pa_purpleline_signal.jpg" tick = ticker image['p_PA_image_buy_sell'] = [dr] image.index = [tick] p_pa_signal_url = pd.concat([p_pa_signal_url, image], axis=0) p_pa_signal_url = p_pa_signal_url.sort_index() return p_pa_signal_url strategy2_symbol = list(strategy2_signal.index) p_pa_signal_url = get_pa_signal2(strategy2_symbol) strategy2_signal = strategy2_signal.sort_index() strategy2 = strategy2_signal.merge(p_pa_signal_url, left_index=True, right_index=True) strategy2['p_b_r_p'] = strategy2['p_bp']+strategy2['p_bp']*0.005 strategy2['p_b_r_m'] = strategy2['p_bp'] - strategy2['p_bp']*0.005 strategy2['g_b_zon'] = 'Yes' strategy2.columns = strategy2.columns.to_series().apply(lambda x: x.strip()) strategy2[['p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']] = strategy2[[ 'p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']].astype(float) strategy2[['p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']] = strategy2[[ 'p_bp', 'p_sp', 'p_p_bp', 'p_p_sp', 'p_cur_p', 'p_b_r_p', 'p_b_r_m']].applymap("{:,.2f}".format) # stocks[['lastDiv','price']] = stocks[['lastDiv','price']].applymap("{0:,.2f}".format) # stocks['div_yiel'] =stocks['div_yiel'].apply(lambda x: "{0:.2f}%".format(x*100)) 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 strategy2['p_tdate']= pd.to_datetime(strategy2['p_tdate']).dt.date strategy2['p_date']= pd.to_datetime(strategy2['p_date']).dt.date #strategy2 .to_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') strategy2.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') #strategy2 .to_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') strategy2 = strategy2.drop_duplicates() 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 strategy2['p_tdate']= pd.to_datetime(strategy2['p_tdate']).dt.date strategy2['p_date']= pd.to_datetime(strategy2['p_date']).dt.date strategy2 =pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') #strategy2 .to_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') #strategy2 .to_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') strategy2 = strategy2.drop_duplicates()