import pandas as pd import finnhub import requests import numpy as np import time from datetime import datetime from sklearn.cluster import KMeans import matplotlib.pyplot as plt import pandas as pd import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import finnhub import matplotlib.dates as mpl_dates import numpy as np from datetime import * import seaborn as sns from datetime import datetime import requests pd.set_option('display.float_format', '{:.2E}'.format) from IPython.display import display #symbols = ticker_sum # return stocks def get_ticker(symbols): import pandas as pd import finnhub import requests import numpy as np import time from datetime import datetime from sklearn.cluster import KMeans import matplotlib.pyplot as plt import pandas as pd import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import finnhub import matplotlib.dates as mpl_dates import numpy as np import seaborn as sns from datetime import datetime import requests def get_profile(symbols): def get_companyinfo(symbols): import requests from datetime import datetime api_key1 = '86dd63f6b8ae774b061232685b78eb52' stocks = pd.DataFrame(columns = [ 'description','symbol', 'logourl', 'name','mktCap','lastDiv']) for symb in symbols: comp = requests.get(f'https://financialmodelingprep.com/api/v3/profile/{symb}?apikey={api_key1}').json()[0] stocks = stocks.append({'description': comp['description'], 'symbol': comp['symbol'], 'logourl': comp['image'], 'name': comp['companyName'], 'mktCap':comp['mktCap'],'lastDiv':comp['lastDiv']}, ignore_index = True) stocks['Date_access'] = datetime.now().strftime("%d/%m/%Y %H:%M:%S") # stocks['div_yiel'] = stocks['lastDiv']/stocks['price'] # stocks[['lastDiv','price']] = stocks[['lastDiv','price']].applymap("{0:,.2f}".format) # stocks['div_yiel'] =stocks['div_yiel'].apply(lambda x: "{0:.2f}%".format(x*100)) # df['var3'] = df['var3'].applymap(lambda x: "{0:.2f}%".format(x*100)) # stocks = stocks.style.format({'lastDiv': "{:.2f}".format,'price': "{:.2f}".format,'div_yiel': "{:.2%}".format}) stocks['mktCap'] = ( stocks['mktCap'].astype(float)/1000000).round(2).astype(str) + 'MM' stocks =stocks.set_index('symbol') stocks =stocks.sort_index() # stocks = stocks.style.format({'mktCap': "{:.2f}",'volAvg': "{:.2f}",'var3': "{:.2%}"}) return stocks stocks = get_companyinfo(symbols) # df[['var1','var2']] = df[['var1','var2']].applymap("{0:.2f}".format) # df['var3'] = df['var3'].applymap(lambda x: "{0:.2f}%".format(x*100)) ######################earningdate######################### def earning_date1(symbols): api_key = '86dd63f6b8ae774b061232685b78eb52' bs = requests.get(f'https://financialmodelingprep.com/api/v3/earning_calendar?apikey={api_key}').json() df =pd.DataFrame() for i in range(len(bs)): d =pd.DataFrame() d1=bs[i]['symbol'] d2 = bs[i]['date'] d['earning_date'] =[d2] d.index = [d1] df = pd.concat([d,df],axis =0) earning_date = pd.DataFrame() earning_date = pd.DataFrame() for tick in symbols: dd= pd.DataFrame() if tick not in df.index: d1 ="Not Available" else: d1=df['earning_date'][tick] dd['symbol'] = [tick] dd['earning_date'] = [d1] dd = dd.set_index('symbol') earning_date = pd.concat([earning_date ,dd],axis =0) earning_date= earning_date.sort_index() return earning_date earning_date = earning_date1(symbols) ##################peratio################## def eps_pe(symbols): api_key1 = '86dd63f6b8ae774b061232685b78eb52' da1 = pd.DataFrame(columns = [ 'pe_ratio(TTM)','symbol', 'eps(TTM)']) for symb in symbols: comp = requests.get(f'https://financialmodelingprep.com/api/v3/quote/{symb}?apikey={api_key1}').json()[0] da1 = da1.append({'pe_ratio(TTM)': comp['pe'], 'symbol': comp['symbol'], 'eps(TTM)': comp['eps']}, ignore_index = True) da1 = da1.set_index('symbol') da1[['pe_ratio(TTM)','eps(TTM)']] = da1[['pe_ratio(TTM)','eps(TTM)']] .applymap("{0:,.2f}".format) da1= da1.sort_index() return da1 # stocks['Date_access'] = datetime.now().strftime("%d/%m/%Y %H:%M:%S") da2 = eps_pe(symbols) stock_info=stocks.merge(earning_date, left_index=True, right_index=True) stock_info_data = stock_info.merge(da2, left_index=True, right_index=True) stock_info_data = stock_info_data.sort_index() return stock_info_data stock_info_data = get_profile(symbols) #stock_info_data1 =stock_info_data.merge(strategy_sum3, right_index = True, left_index = True) stock_info_data =stock_info_data .sort_index() def get_image(symbols): image_strategy_return = pd.DataFrame() for ticker in symbols: print(ticker) image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/profit_graph_app/" + ticker + "_Return.jpg" tick = ticker image['image_return'] =[ dr] image.index =[tick] image_strategy_return = pd.concat([image_strategy_return ,image], axis =0) image_rs = pd.DataFrame() for ticker in symbols: image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/image_app/" + ticker+"_resistance_support.jpg" tick = ticker image['im_res_sup']=[ dr] image.index =[tick] image_rs = pd.concat([image_rs,image], axis =0) return image_strategy_return,image_rs image_strategy_return,image_rs = get_image(symbols) image_rs = image_rs.sort_index() image_strategy_return = image_strategy_return.sort_index() image_data = image_rs.merge(image_strategy_return, left_index=True, right_index=True) stock_info_data1 = stock_info_data.merge(image_data, right_index =True, left_index =True) from sklearn.cluster import KMeans import matplotlib.pyplot as plt import pandas as pd import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import finnhub import matplotlib.dates as mpl_dates import numpy as np import seaborn as sns from datetime import datetime def get_data(ticker): print(ticker) from datetime import datetime as dt import datetime import dateutil today = datetime.datetime.today() delta = dateutil.relativedelta.relativedelta(months=6) end = ( today-datetime.timedelta(1)).strftime('%Y-%m-%d') start = (today - delta).strftime('%Y-%m-%d') # ticker= 'AAPL' 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() stock1 = stock.iloc[:-4,:] def get_optimum_clusters(df, saturation_point = 0.05): wcss = [] k_models = [] labels = [] days = df.shape[0] size = range(1,9) for i in size: # kmeans = KMeans(n_clusters = i, init='k-means++', max_iter=300, n_init=10, random_state=0) kmeans = KMeans(n_clusters = i, init='random', max_iter=300, n_init=10) kmeans.fit(df) wcss.append(kmeans.inertia_) # Sum of squared distances of samples to their Closest cluster center k_models.append(kmeans) labels.append(kmeans.labels_) # Compare differences in inertias until it's no more than saturation_point optimum_k = len(wcss)-1 for i in range(0, len(wcss)-1): diff = abs(wcss[i+1] - wcss[i]) if diff < saturation_point: optimum_k = i break # optimum_clusters = k_models[optimum_k] labels = labels[optimum_k] return labels Lows = pd.DataFrame(data = stock1, index = stock1.index, columns = ['Low']) Highs = pd.DataFrame(data = stock1, index = stock1.index, columns = ['High']) low_labels = get_optimum_clusters(Lows) high_labels = get_optimum_clusters(Highs) Lows['labels'] = pd.Series(low_labels, index = Lows.index) Highs['labels'] = pd.Series(high_labels, index = Highs.index) res = [max(Highs.loc[Highs.labels == i, 'High']) for i in np.unique(high_labels)] sup = [min(Lows.loc[Lows.labels == i, 'Low']) for i in np.unique(low_labels)] # pivotpoints last_day = stock.iloc[-1].to_frame().T not_pivots = last_day.columns last_day['center_gravity'] = (last_day['High'] + last_day['Low'] + last_day['Close'])/3 last_day['vol_up1'] = 2*last_day['center_gravity'] - last_day['Low'] last_day['vol_down1'] = 2*last_day['center_gravity'] - last_day['High'] last_day['vol_up2'] = last_day['center_gravity'] + (last_day['High'] - last_day['Low']) last_day['vol_down2'] = last_day['center_gravity'] - (last_day['High'] - last_day['Low']) last_day['vol_up3'] = last_day['center_gravity'] + 2*(last_day['High'] - last_day['Low']) last_day['vol_down3'] = last_day['center_gravity'] - 2*(last_day['High'] - last_day['Low']) Highs['Date'] = pd.to_datetime(Highs.index) Highs['Date'] = Highs['Date'].apply(mpl_dates.date2num) min_date_high = min(Highs.Date) max_date_high = max(Highs.Date) Lows['Date'] = pd.to_datetime(Lows.index) Lows['Date'] = Lows['Date'].apply(mpl_dates.date2num) min_date_low = min(Lows.Date) max_date_low = max(Lows.Date) Highs['res'] = 0 for i in range(len(Highs)): if Highs['High'][i] in res: Highs['res'][i]= 1 Lows['sup'] = 0 for i in range(len(Lows)): if Lows['Low'][i] in sup: Lows['sup'][i]= 1 resistance= pd.DataFrame() for i in range(len(Highs)): if Highs['res'][i] ==1: current_res = Highs.iloc[i,:] resistance = resistance.append(current_res) support = pd.DataFrame() for i in range(len(Lows)): if Lows['sup'][i] ==1: current_sup = Lows.iloc[i,:] support = support.append(current_sup) rec_res = resistance['High'][-1] rec_sup = support['Low'][-1] last_day['recent_support'] = rec_sup last_day['recent_resistance'] = rec_res tick = ticker last_day.index = [tick] return last_day def strategy_signal4(symbols): strategy_data= pd.DataFrame() for tick in symbols: data = get_data( tick) strategy_data = pd.concat([strategy_data,data], axis=0) return strategy_data support_resistance_data= strategy_signal4(symbols) support_resistance_data = support_resistance_data[['center_gravity','vol_up1','vol_up2','vol_up3','vol_down1', "vol_down2" , "vol_down3","recent_support","recent_resistance"]] #support_resistance_data .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\support_resistance_data.csv') #support_resistance_data = pd.concat([data1, data2, data3, data4, data5, data6, data7], axis = 0) # strategy_sum1 = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool\support_resistop_last_peak_data.csv') # strategy_sum1 =strategy_sum1.rename({'Unnamed: 0':'symbol'}, axis =1) # strategy_sum1= strategy_sum1.rename({'pivot':'center_gravity'}, axis =1) #####################stop loss#################################### def get_data2(ticker): # ticker ='KO' # finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # from datetime import datetime # start = np.array(start).tolist() # end =np.array(end).tolist() # # start = start # # end = end # 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_d = finnhub_client.stock_candles(ticker, 'D', start_time, end_time) # stock = pd.DataFrame(res_d) # 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') # stock_1= stock[['Close', 'High', 'Low','Open','volumn']] from datetime import datetime as dt import datetime import dateutil today = datetime.datetime.today() delta = dateutil.relativedelta.relativedelta(months=6) end = ( today-datetime.timedelta(1)).strftime('%Y-%m-%d') start = (today - delta).strftime('%Y-%m-%d') # ticker= 'AAPL' 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() def get_stop(stock): # pivotpoints last_day = stock.iloc[-1].to_frame().T not_pivots = last_day.columns last_day['center_gravity'] = (last_day['High'] + last_day['Low'] + last_day['Close'])/3 last_day['vol_up1'] = 2*last_day['center_gravity'] - last_day['Low'] last_day['vol_down1'] = 2*last_day['center_gravity'] - last_day['High'] last_day['vol_up2'] = last_day['center_gravity'] + (last_day['High'] - last_day['Low']) last_day['vol_down2'] = last_day['center_gravity'] - (last_day['High'] - last_day['Low']) last_day['vol_up3'] = last_day['center_gravity'] + 2*(last_day['High'] - last_day['Low']) last_day['vol_down3'] = last_day['center_gravity'] - 2*(last_day['High'] - last_day['Low']) tick = ticker last_day.index = [tick] last_day['stop_loss'] = (last_day['vol_down2'] +last_day['vol_down3'] )/2 last_day['stop_loss_pct'] = (last_day['Close']- last_day['stop_loss'])/last_day['Close']*100 if last_day['stop_loss_pct'][-1] >3.5: last_day['stop_loss'][-1]= (last_day['vol_down1'][-1] +last_day['Close'][-1] )/2 return last_day last_day = get_stop(stock) ema_s = 13 ema_l = 21 stock["EMA_S"] = stock.Close.ewm(span = ema_s, min_periods = ema_s).mean() stock["EMA_L"] = stock.Close.ewm(span = ema_l, min_periods = ema_l).mean() # display(stock.head(30)) def get_ATR(stock, n): hl = stock.High - stock.Low hc = abs(stock.High - stock.Close.shift(1)) lc = abs(stock.Low - stock.Close.shift(1)) stock['TR'] = np.maximum(np.maximum(hl, hc), lc) stock['ATR'] = stock.TR.rolling(n).mean() return stock stock = get_ATR(stock, 20) stock = stock.dropna() mul =2 stock['ATR_stop'] = (stock.Close - mul * stock.ATR).shift(1) # ATR_stop for current day rec_trailingstoploss = stock.loc[stock.index[-1],'ATR_stop'] rec_stoploss = last_day.loc[last_day.index[-1],'stop_loss'] atr = stock.loc[stock.index[-1],'ATR'] strategy_data2=pd.DataFrame() tick = ticker strategy_data2 ['recent_stoploss'] =[rec_stoploss] strategy_data2['recent_trailingstoploss'] = [rec_trailingstoploss] strategy_data2['atr'] = [atr] strategy_data2.index = [tick] return strategy_data2 # from datetime import datetime as dt # import datetime # today = dt.today() # yesterday = today-datetime.timedelta(1) # start = (2021,1,1) # end = (yesterday.year, yesterday.month, yesterday.day) def strategy_signal5(symbols): strategy_data3= pd.DataFrame() for ticker in symbols: data = get_data2( ticker) strategy_data3= pd.concat([strategy_data3,data], axis=0) return strategy_data3 stop_loss_data = strategy_signal5(symbols) #stop_loss = strategy_signal5(symbols) # n= 4# number of groups # groups = np.array_split(symbols, n) # import time # stop_loss_data = pd.DataFrame() # for symbol in groups: # f= strategy_signal5(symbol) # stop_loss_data = pd.concat([f, stop_loss_data], axis =0) # time.sleep(60) #stop_loss_data .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\stop_loss_data.csv') #####################last peak price################### def get_data3(ticker): # from datetime import datetime as dt # import datetime # today = dt.today() # yesterday = today-datetime.timedelta(1) # start = (2021,1,1) # end = (yesterday.year, yesterday.month, yesterday.day) # from datetime import datetime # finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # start = np.array(start).tolist() # end =np.array(end).tolist() # start = start # end = end # # 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_d = finnhub_client.stock_candles(ticker, 'D', start_time, end_time) # stock = pd.DataFrame(res_d) # 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') # stock= stock[['Close', 'High', 'Low','Open','volumn']] from datetime import datetime as dt import datetime import dateutil today = datetime.datetime.today() delta = dateutil.relativedelta.relativedelta(months=6) end = ( today-datetime.timedelta(1)).strftime('%Y-%m-%d') start = (today - delta).strftime('%Y-%m-%d') 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':'volumn'}) #stock = stock.set_index('Date') # stock= stock.sort_index() stock['Date'] = pd.to_datetime(stock['Date']) stock = stock.set_index('Date') stock= stock.sort_index() # stock= stock[['Close', 'High', 'Low','Open','volumn']] df =stock.copy() df.drop(df[df["volumn"]<1000].index, inplace=True) dfday=df.groupby(pd.Grouper(freq="D"))["High"].max() now = datetime.now() glDate=0 lastGLV=0 currentDate="" curentGLV=0 for index, value in dfday.items(): if value > curentGLV: curentGLV=value currentDate=index counter=0 if value < curentGLV: counter=counter+1 if counter==5 and ((index.day != now.day) or (index.month != now.month)): if curentGLV != lastGLV: print(curentGLV) glDate=currentDate lastGLV=curentGLV counter=0 if lastGLV==0: last_peak = ticker+" has not formed a green line yet" last_peak_date = 0 else: last_peak = lastGLV last_peak_date = glDate strategy_data6 =pd.DataFrame() strategy_data6['last_peak'] = [last_peak] strategy_data6['last_peakday'] = [last_peak_date] tick = ticker strategy_data6.index= [tick] return strategy_data6 def strategy_signal6(symbols): strategy_data7= pd.DataFrame() for tick in symbols: print(tick) data = get_data3(tick) strategy_data7 = pd.concat([strategy_data7,data], axis=0) return strategy_data7 last_peak_data = strategy_signal6(symbols) # last_peak_data.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\last_peak_data.csv') # last_peak_data.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool\last_peak_data.csv') ########################################## #strategy= strategy.sort_index() support_resistance_data = support_resistance_data.sort_index() stop_loss_data = stop_loss_data.sort_index() last_peak_data = last_peak_data.sort_index() strategy_sum = support_resistance_data .merge(stop_loss_data , left_index=True, right_index=True) strategy_sum[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr']]=strategy_sum[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr']].astype(float) strategy_sum[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr']]= strategy_sum[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr']].applymap("{0:,.2f}".format) strategy_sum1 =strategy_sum.merge(last_peak_data,left_index=True, right_index=True) strategy_sum1 = strategy_sum1 .sort_index() # strategy_sum2 =strategy_sum1.merge(strategy_data4,left_index=True, right_index=True) # strategy_sum2['market_trend'] =1 # strategy_sum1.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\support_resistop_last_peak_data.csv') stock_info_data2= stock_info_data1.merge(strategy_sum1,right_index = True, left_index = True) stock_info_data3 =stock_info_data2.copy() #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') 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) def get_trend (ticker, start, end): print(ticker) #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(symbols) # 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) ######################## # 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(symbols) # 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 symbols: # #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_data51 = stock_info_data5.merge(da, right_index = True, left_index = True) # stock_info_data51['Ticker']=stock_info_data51.index return stock_info_data4 #####################################################stockdatat############################ symbols.remove('O') symbols.remove('ON') symbols.remove('MRK') group2.remove('EXR') group3.remove('KR') #group3.remove('META') n =7# 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() # group8= groups[7].tolist() # group9= groups[8].tolist() # group10= groups[9].tolist() from datetime import datetime as dt import datetime import time stock_info_data51 = get_ticker(group1) time.sleep(60) stock_info_data52 = get_ticker(group2) time.sleep(60) stock_info_data53 = get_ticker(group3) time.sleep(60) stock_info_data54 = get_ticker(group4) time.sleep(60) stock_info_data55 = get_ticker(group5) time.sleep(60) stock_info_data56 = get_ticker(group6) time.sleep(60) # stock_info_data57= get_ticker(group7) # time.sleep(60) # stock_info_data58= get_ticker(group8) # time.sleep(60) # stock_info_data59= get_ticker(group9) # time.sleep(60) # stock_info_data60= get_ticker(group10) # time.sleep(60) #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_data51 [['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', 'priceaction_url']] =stock_info_data51 [['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', 'priceaction_url']].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_data51['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data51['recent_support']] stock_info_data51['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data51['recent_resistance']] stock_info_data51['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data51['recent_stoploss']] stock_info_data51['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data51['recent_trailingstoploss']] import os from pprint import pprint from airtable import Airtable import airtable import requests import airtable import json base_key = 'appeRvxdi9NzuVglI' table_name ='all_ticker' api_key0 ='keyoQLQK3ktA58PBx' airtable = Airtable(base_key, table_name, api_key0) #records = airtable.get_all() records = airtable.get_all(sort="symbol") a=[] for i in range(len(records)): a.append(records[i]['id']) airtable.batch_delete(a) ###############insert_dataframe################ df = stock_info_data51.copy() df =df.reset_index() df=df.rename({"index":"symbol"}, axis = 1) ab = upload_pandas_dataframe(df, table_name, api_key, base_key) stock_info_data52 [['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', 'priceaction_url']] =stock_info_data52 [['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', 'priceaction_url']].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_data52 ['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data52 ['recent_support']] stock_info_data52 ['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data52 ['recent_resistance']] stock_info_data52 ['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data52 ['recent_stoploss']] stock_info_data52 ['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data52['recent_trailingstoploss']] import os from pprint import pprint from airtable import Airtable import airtable import requests import airtable import json base_key = 'appeRvxdi9NzuVglI' table_name ='all_ticker1' api_key0 ='keyoQLQK3ktA58PBx' airtable = Airtable(base_key, table_name, api_key0) #records = airtable.get_all() records = airtable.get_all(sort="symbol") a=[] for i in range(len(records)): a.append(records[i]['id']) airtable.batch_delete(a) ###############insert_dataframe################ df = stock_info_data52.copy() df =df.reset_index() df=df.rename({"index":"symbol"}, axis = 1) ab = upload_pandas_dataframe(df, table_name, api_key, base_key) stock_info_data53 [['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', 'priceaction_url']] =stock_info_data53 [['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', 'priceaction_url']].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_data53 ['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data53 ['recent_support']] stock_info_data53 ['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data53 ['recent_resistance']] stock_info_data53 ['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data53 ['recent_stoploss']] stock_info_data53 ['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data53['recent_trailingstoploss']] import os from pprint import pprint from airtable import Airtable import airtable import requests import airtable import json base_key = 'appeRvxdi9NzuVglI' table_name ='all_ticker2' api_key ='keyoQLQK3ktA58PBx' airtable = Airtable(base_key, table_name, api_key) #records = airtable.get_all() records = airtable.get_all(sort="symbol") a=[] for i in range(len(records)): a.append(records[i]['id']) airtable.batch_delete(a) ###############insert_dataframe################ df = stock_info_data53.copy() df =df.reset_index() df=df.rename({"index":"symbol"}, axis = 1) ab = upload_pandas_dataframe(df, table_name, api_key, base_key) stock_info_data54 [['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', 'priceaction_url']] =stock_info_data54 [['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', 'priceaction_url']].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_data54['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data54['recent_support']] stock_info_data54['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data54['recent_resistance']] stock_info_data54['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data54['recent_stoploss']] stock_info_data54['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data54['recent_trailingstoploss']] import os from pprint import pprint from airtable import Airtable import airtable import requests import airtable import json base_key = 'appeRvxdi9NzuVglI' table_name ='all_ticker3' api_key ='keyoQLQK3ktA58PBx' airtable = Airtable(base_key, table_name, api_key) #records = airtable.get_all() records = airtable.get_all(sort="symbol") a=[] for i in range(len(records)): a.append(records[i]['id']) airtable.batch_delete(a) ###############insert_dataframe################ df = stock_info_data54.copy() df =df.reset_index() df=df.rename({"index":"symbol"}, axis = 1) ab = upload_pandas_dataframe(df, table_name, api_key, base_key) stock_info_data55 [['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', 'priceaction_url']] =stock_info_data55 [['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', 'priceaction_url']].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_data55['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data55['recent_support']] stock_info_data55['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data55['recent_resistance']] stock_info_data55['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data55['recent_stoploss']] stock_info_data55['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data55['recent_trailingstoploss']] import os from pprint import pprint from airtable import Airtable import airtable import requests import airtable import json base_key = 'appeRvxdi9NzuVglI' table_name ='all_ticker4' api_key ='keyoQLQK3ktA58PBx' airtable = Airtable(base_key, table_name, api_key) #records = airtable.get_all() records = airtable.get_all(sort="symbol") a=[] for i in range(len(records)): a.append(records[i]['id']) airtable.batch_delete(a) ###############insert_dataframe################ df = stock_info_data55.copy() df =df.reset_index() df=df.rename({"index":"symbol"}, axis = 1) ab = upload_pandas_dataframe(df, table_name, api_key, base_key) stock_info_data56 [['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', 'priceaction_url']] =stock_info_data56 [['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', 'priceaction_url']].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_data56['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data56['recent_support']] stock_info_data56['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data56['recent_resistance']] stock_info_data56['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data56['recent_stoploss']] stock_info_data56['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data56['recent_trailingstoploss']] import os from pprint import pprint from airtable import Airtable import airtable import requests import airtable import json base_key = 'appeRvxdi9NzuVglI' table_name ='all_ticker5' api_key ='keyoQLQK3ktA58PBx' airtable = Airtable(base_key, table_name, api_key) #records = airtable.get_all() records = airtable.get_all(sort="symbol") a=[] for i in range(len(records)): a.append(records[i]['id']) airtable.batch_delete(a) ###############insert_dataframe################ df = stock_info_data56.copy() df =df.reset_index() df=df.rename({"index":"symbol"}, axis = 1) ab = upload_pandas_dataframe(df, table_name, api_key, base_key) # stock_info_data57 [['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', 'priceaction_url']] =stock_info_data57 [['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', 'priceaction_url']].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_data57['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data57['recent_support']] # stock_info_data57['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data57['recent_resistance']] # stock_info_data57['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data57['recent_stoploss']] # stock_info_data57['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data57['recent_trailingstoploss']] # import os # from pprint import pprint # from airtable import Airtable # import airtable # import requests # import airtable # import json # base_key = 'appeRvxdi9NzuVglI' # table_name ='all_ticker6' # api_key ='keyoQLQK3ktA58PBx' # airtable = Airtable(base_key, table_name, api_key) # #records = airtable.get_all() # records = airtable.get_all(sort="symbol") # a=[] # for i in range(len(records)): # a.append(records[i]['id']) # airtable.batch_delete(a) # ###############insert_dataframe################ # df = stock_info_data57.copy() # df =df.reset_index() # df=df.rename({"index":"symbol"}, axis = 1) # ab = upload_pandas_dataframe(df, table_name, api_key, base_key) # stock_info_data58 [['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', 'priceaction_url']] =stock_info_data58 [['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', 'priceaction_url']].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_data58['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data58['recent_support']] # stock_info_data58['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data58['recent_resistance']] # stock_info_data58['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data58['recent_stoploss']] # stock_info_data58['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data58['recent_trailingstoploss']] # import os # from pprint import pprint # from airtable import Airtable # import airtable # import requests # import airtable # import json # base_key = 'appeRvxdi9NzuVglI' # table_name ='all_ticker7' # api_key ='keyoQLQK3ktA58PBx' # airtable = Airtable(base_key, table_name, api_key) # #records = airtable.get_all() # records = airtable.get_all(sort="symbol") # a=[] # for i in range(len(records)): # a.append(records[i]['id']) # airtable.batch_delete(a) # ###############insert_dataframe################ # df = stock_info_data58.copy() # df =df.reset_index() # df=df.rename({"index":"symbol"}, axis = 1) # ab = upload_pandas_dataframe(df, table_name, api_key, base_key) # stock_info_data59 [['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', 'priceaction_url']] =stock_info_data59 [['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', 'priceaction_url']].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_data59['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data59['recent_support']] # stock_info_data59['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data59['recent_resistance']] # stock_info_data59['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data59['recent_stoploss']] # stock_info_data59['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data59['recent_trailingstoploss']] # import os # from pprint import pprint # from airtable import Airtable # import airtable # import requests # import airtable # import json # base_key = 'appeRvxdi9NzuVglI' # table_name ='all_ticker8' # api_key ='keyoQLQK3ktA58PBx' # airtable = Airtable(base_key, table_name, api_key) # #records = airtable.get_all() # records = airtable.get_all(sort="symbol") # a=[] # for i in range(len(records)): # a.append(records[i]['id']) # airtable.batch_delete(a) # ###############insert_dataframe################ # df = stock_info_data59.copy() # df =df.reset_index() # df=df.rename({"index":"symbol"}, axis = 1) # ab = upload_pandas_dataframe(df, table_name, api_key, base_key) # stock_info_data60 [['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', 'priceaction_url']] =stock_info_data60 [['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', 'priceaction_url']].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_data60['recent_support'] =[float(str(i).replace(",", "")) for i in stock_info_data60['recent_support']] # stock_info_data60['recent_resistance'] =[float(str(i).replace(",", "")) for i in stock_info_data60['recent_resistance']] # stock_info_data60['recent_stoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data60['recent_stoploss']] # stock_info_data60['recent_trailingstoploss'] =[float(str(i).replace(",", "")) for i in stock_info_data60['recent_trailingstoploss']] # import os # from pprint import pprint # from airtable import Airtable # import airtable # import requests # import airtable # import json # base_key = 'appeRvxdi9NzuVglI' # table_name ='all_ticker9' # api_key ='keyoQLQK3ktA58PBx' # airtable = Airtable(base_key, table_name, api_key) # #records = airtable.get_all() # records = airtable.get_all(sort="symbol") # a=[] # for i in range(len(records)): # a.append(records[i]['id']) # airtable.batch_delete(a) # ###############insert_dataframe################ # df = stock_info_data60.copy() # df =df.reset_index() # df=df.rename({"index":"symbol"}, axis = 1) # ab = upload_pandas_dataframe(df, table_name, api_key, base_key)