import pandas as pd import requests import matplotlib.pyplot as plt import numpy as np import requests import matplotlib.pyplot as plt from math import floor import finnhub from termcolor import colored as cl # plt.style.use('fivethirtyeight') # plt.rcParams['figure.figsize'] = (20, 10) # 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\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') # c1 = c1.rename(columns = {'Unnamed: 0':'symbol'}) # strategy1 = c1.set_index('symbol') # today = dt.today() # a = str(today.year) # b = str(today.month) # c = str(today.day) # d = pd.read_csv( r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\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\seabridge_datapool1\final_strategy_data_temporaly\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() # f=pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\all_data.csv') # f = f.rename(columns = {'Unnamed: 0':'symbol'}) # stock_info_data3 = f.set_index('symbol') #stock_info_data3 .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\all_data.csv') def get_trend (ticker, start, end): #finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # Stock candles 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':'volume'}) stock = stock.set_index('Date') stock= stock.sort_index() # data1 = stock.copy() # from datetime import datetime # 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') tsla = stock.copy() def get_ci(high, low, close, lookback): tr1 = pd.DataFrame(high - low).rename(columns = {0:'tr1'}) tr2 = pd.DataFrame(abs(high - close.shift(1))).rename(columns = {0:'tr2'}) tr3 = pd.DataFrame(abs(low - close.shift(1))).rename(columns = {0:'tr3'}) frames = [tr1, tr2, tr3] tr = pd.concat(frames, axis = 1, join = 'inner').dropna().max(axis = 1) atr = tr.rolling(1).mean() highh = high.rolling(lookback).max() lowl = low.rolling(lookback).min() ci = 100 * np.log10((atr.rolling(lookback).sum()) / (high - lowl)) / np.log10(lookback) return ci tsla['ci_14'] = get_ci(tsla['high'], tsla['low'], tsla['close'], 14) tsla = tsla.dropna() def get_adx(high, low, close, lookback): plus_dm = high.diff() minus_dm = low.diff() plus_dm[plus_dm < 0] = 0 minus_dm[minus_dm > 0] = 0 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.rolling(lookback).mean() plus_di = 100 * (plus_dm.ewm(alpha = 1/lookback).mean() / atr) minus_di = abs(100 * (minus_dm.ewm(alpha = 1/lookback).mean() / atr)) dx = (abs(plus_di - minus_di) / abs(plus_di + minus_di)) * 100 adx = ((dx.shift(1) * (lookback - 1)) + dx) / lookback adx_smooth = adx.ewm(alpha = 1/lookback).mean() return plus_di, minus_di, adx_smooth tsla['plus_di'] = pd.DataFrame(get_adx(tsla['high'], tsla['low'], tsla['close'], 14)[0]).rename(columns = {0:'plus_di'}) tsla['minus_di'] = pd.DataFrame(get_adx(tsla['high'], tsla['low'], tsla['close'], 14)[1]).rename(columns = {0:'minus_di'}) tsla['adx'] = pd.DataFrame(get_adx(tsla['high'], tsla['low'], tsla['close'], 14)[2]).rename(columns = {0:'adx'}) tsla = tsla.dropna() tsla['ci_14_considating'] = np.where(tsla['ci_14'] >61.8,0,1) #tsla['ADX_considating'] = np.where(tsla['adx']<20,0,1) tsla['trend'] = np.where(tsla['plus_di'] >tsla['minus_di'],1,0) tsla['market_trend'] = '' for i in tsla.index: if tsla['ci_14_considating'][i] ==1 and tsla['trend'][i] ==1: tsla['market_trend'][i] = 1 elif tsla['ci_14_considating'][i] ==1 and tsla['trend'][i] ==0: tsla['market_trend'][i] = -1 elif tsla['ci_14_considating'][i] ==0: tsla['market_trend'][i] = 0 signal = tsla.iloc[-1,:] signal =pd.DataFrame(signal).T return signal def get_markett(symbols): from datetime import datetime as dt import datetime today = dt.today() end= today-datetime.timedelta(1) # end = (yesterday.year, yesterday.month, yesterday.day) start = '2021-02-15' market_trend = pd.DataFrame() for ticker in symbols: data = get_trend (ticker, start, end) da = pd.DataFrame(data['market_trend']) da.index = [ticker] market_trend = pd.concat([market_trend, da], axis =0) market_trend = market_trend.sort_index() return market_trend stock_priceaction = get_markett(ticker_sum) # Output Images def get_pa_url(symbols): for ticker in symbols: 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 if stock_priceaction['market_trend'][ticker] ==1: img6= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_uptrend.jpg') fig = plt.figure(figsize=(10,10)) plt.imshow(img6) 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\icon\\" plt.savefig(path +'{}.jpg'.format(ticker+"_pa_trend_signal"), dpi = 72,transparent = True, bbox_inches = 'tight', pad_inches = 0) plt.show() elif stock_priceaction['market_trend'][ticker] ==-1: img2= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_downtrend.jpg') fig = plt.figure(figsize=(10,10)) plt.imshow(img2) 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\icon\\" plt.savefig(path +'{}.jpg'.format(ticker+"_pa_trend_signal"), dpi = 72,transparent = True, bbox_inches = 'tight', pad_inches = 0) plt.show() elif stock_priceaction['market_trend'][ticker] ==0: img5= mpimg.imread(r'C:/Users/jizha/App-googlesheet/icon/pa_sideways.jpg') fig = plt.figure(figsize=(10,10)) plt.imshow(img5) 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\icon\\" plt.savefig(path +'{}.jpg'.format(ticker+"_pa_trend_signal"), dpi = 72,transparent = True, bbox_inches = 'tight', pad_inches = 0) plt.show() priceaction_url = pd.DataFrame() for ticker in symbols: image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/icon/"+ticker+"_pa_trend_signal.jpg" image['priceaction_url']=[ dr] image.index =[ticker] priceaction_url = pd.concat([priceaction_url ,image], axis =0) priceaction_url = priceaction_url .sort_index() return priceaction_url trend_ticker= list(stock_priceaction.index) priceaction_url = get_pa_url(trend_ticker) stock_info_data4 = stock_info_data3 .merge(priceaction_url,right_index = True, left_index = True) stock_info_data4 ['strb_na'] = '' stock_info_data4 ['strp_na'] = '' stock_info_data4 ['strg_na'] = '' # for ticker in ticker_sum: # if ticker in strategy1_signal.index: # stock_info_data4 ['strg_na'][ticker] ='1' # else: # stock_info_data4 ['strg_na'][ticker] = '0' # if ticker in strategy2_signal.index: # stock_info_data4 ['strp_na'][ticker] ='1' # else: # stock_info_data4 ['strp_na'] [ticker]= '0' # if ticker in strategy3_signal.index: # stock_info_data4 ['strb_na'][ticker] ='1' # else: # stock_info_data4 ['strb_na'] [ticker]= '0' for ticker in ticker_sum: if ticker in strategy1.index: stock_info_data4 ['strg_na'][ticker] ='1' else: stock_info_data4 ['strg_na'][ticker] = '0' if ticker in strategy2.index: stock_info_data4 ['strp_na'][ticker] ='1' else: stock_info_data4 ['strp_na'] [ticker]= '0' if ticker in strategy3.index: stock_info_data4 ['strb_na'][ticker] ='1' else: stock_info_data4 ['strb_na'] [ticker]= '0' ######################## import requests def get_companyinfo(symbols): api_key1 = '86dd63f6b8ae774b061232685b78eb52' stocks1 = pd.DataFrame(columns = [ 'description','symbol', 'weburl', 'logourl', 'name','mktCap','volAvg', 'industry','lastDiv','price']) for symb in symbols: comp = requests.get(f'https://financialmodelingprep.com/api/v3/profile/{symb}?apikey={api_key1}').json()[0] stocks1 = stocks1.append({'description': comp['description'], 'symbol': comp['symbol'],\ 'weburl': comp['website'], 'logourl': comp['image'], 'name': comp['companyName'], 'mktCap':comp['mktCap'],'volAvg':comp['volAvg'],'industry':comp['industry'],'lastDiv':comp['lastDiv'],'price':comp['price']}, ignore_index = True) return stocks1 a =get_companyinfo(ticker_sum) a['div_yiel']= a['lastDiv']/a['price'] a[['lastDiv','price']] = a[['lastDiv','price']].applymap("{0:,.2f}".format) a['div_yiel'] =a['div_yiel'].apply(lambda x: "{0:.2f}%".format(x*100)) a =a.sort_index() b = a[['symbol','volAvg','price','div_yiel']] b =b.set_index('symbol') b =b.sort_index() stock_info_data5 =stock_info_data4.merge(b,right_index = True, left_index = True) #stock_info_data5 ['AI_Signal'] ='None' api_key = '86dd63f6b8ae774b061232685b78eb52' da = pd.DataFrame(columns = ['symbol','52wk_high','52wk_low']) for ticker in ticker_sum: #ticker = 'AAPL' bs5 = requests.get(f'https://financialmodelingprep.com/api/v3/quote/{ticker}?apikey={api_key}').json() da =da.append({'symbol':bs5[0]['symbol'],'52wk_high':bs5[0]['yearHigh'], '52wk_low':bs5[0]['yearLow']}, ignore_index = True) da = da.set_index('symbol') da[['52wk_high','52wk_low']] =da [['52wk_high','52wk_low']] .applymap("{0:,.2f}".format) stock_info_data5 = stock_info_data5.merge(da, right_index = True, left_index = True) stock_info_data5['Ticker']=stock_info_data5.index from datetime import datetime as dt today = dt.today() a=str(today.year) b=str(today.month) import datetime c=str(today.day) stock_info_data5 .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\all_data_final_'+a+'_'+b+'_'+c+'.csv') stock_info_data2 .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\etf_'+a+'_'+b+'_'+c+'.csv') #stock_info_data5.columns =stock_info_data5.columns.to_series().apply(lambda x: x.strip()) ###############export to airtable########### #stock_info_data5 =stock_info_data5.dtypes #stock_info_data5 = stock_info_data5.astype(str) #stock_info_data5['price'] = stock_info_data5['price'].astype(float) #stock_info_data5['prev_close'] = stock_info_data5['prev_close'].astype(float) stock_info_data5 [['description', 'logourl', 'name', 'mktCap', 'lastDiv', 'Date_access', 'earning_date', 'pe_ratio(TTM)', 'eps(TTM)', 'im_res_sup', 'image_return', 'center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'atr','last_peak','last_peakday','cagr_green', 'cagr_purple', 'cagr_blue', 'cagr_benchmark', 'vol_green', 'vol_purple', 'vol_blue', 'vlo_benchmark', 'sharp_green', 'sharp_purple', 'sharp_blue', 'sharp_benchmark', 'max_dd_green', 'max_dd_purple', 'max_dd_blue', 'max_dd_benchmark', 'sortino_green', 'sortino_purple', 'sortino_blue', 'sortino_benchmark', 'priceaction_url', 'strb_na', 'strp_na', 'strg_na', 'volAvg', 'price', 'div_yiel', '52wk_high', '52wk_low', 'Ticker']] =stock_info_data5 [['description', 'logourl', 'name', 'mktCap', 'lastDiv', 'Date_access', 'earning_date', 'pe_ratio(TTM)', 'eps(TTM)', 'im_res_sup', 'image_return', 'center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'atr', 'last_peak','last_peakday','cagr_green', 'cagr_purple', 'cagr_blue', 'cagr_benchmark', 'vol_green', 'vol_purple', 'vol_blue', 'vlo_benchmark', 'sharp_green', 'sharp_purple', 'sharp_blue', 'sharp_benchmark', 'max_dd_green', 'max_dd_purple', 'max_dd_blue', 'max_dd_benchmark', 'sortino_green', 'sortino_purple', 'sortino_blue', 'sortino_benchmark', 'priceaction_url', 'strb_na', 'strp_na', 'strg_na', 'volAvg', 'price', 'div_yiel', '52wk_high', '52wk_low', 'Ticker']].astype(str) #stock_info_data5[['recent_support', # 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss']] = stock_info_data5[['recent_support', # 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss']].astype(float) stock_info_data5['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data5['recent_support']] stock_info_data5['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data5['recent_resistance']] stock_info_data5['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data5['recent_stoploss']] stock_info_data5['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data5['recent_trailingstoploss']] stock_info_data5['last_peak'] =[float(str(i).replace(",", "")) for i in stock_info_data5['last_peak']]