ticker1 = list(strategy1.index)

ticker2 = list(strategy2.index)

ticker3 = list(strategy3.index)
ticker_sum =ticker_sum2

ticker_sum = ticker1+ticker2+ticker3
ticker_sum = np.unique(ticker_sum).tolist()
#ticker_sum = np.unique(ticker_sum).tolist()
f = pd.DataFrame(ticker_sum)
#f =pd.DataFrame(symbols)
today = dt.today()
a = str(today.year)
b = str(today.month)
c = str(today.day)
f.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv')
#ticker_sum = ticker_sum2

stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] !='2022-05-05']
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] !='2022-05-07']
#remove yesterdaystock####################
#remove yesterdaystock####################
today = dt.today()
a = str(today.year)
b = str(today.month)
c = str(today.day-1)
f = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv')
#d = f['0'].tolist()
# ticker1.remove("XLF")
# ticker2.remove("DIS")
# ticker3.remove('XLI')
# ticker1.remove("GME")
d = f[0].tolist()

for tic in d:
    if tic in ticker1:
        ticker1.remove(tic)
for tic in d:
    if tic in ticker2:
        ticker2.remove(tic)

for tic in d:
    if tic in ticker3:
        ticker3.remove(tic)

ticker_sum2 = ticker1+ticker2+ticker3
ticker_sum2 = np.unique(ticker_sum2).tolist()
ticker_sum =ticker_sum2 
#ticker_sum = np.unique(ticker_sum).tolist()
f = pd.DataFrame(ticker_sum)
#f =pd.DataFrame(symbols)
strategy11 = strategy1.loc[ticker1, :]

strategy21 = strategy2.loc[ticker2,:]
strategy31 = strategy3.loc[ticker3,:]

strategy1 =strategy11 .copy()
strategy2 =strategy21 .copy()
strategy3 =strategy31 .copy()        

a = str(today.year)
b = str(today.month)
c = str(today.day)
f.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv')
#ticker_sum = ticker_sum2

##########if stilll >150###########
def get_companyinfo(symbols):
       import requests
       from datetime import datetime
       api_key1 = '86dd63f6b8ae774b061232685b78eb52'
       stocks = pd.DataFrame(columns=['relative_vol', 'symbol'])
       for symb in symbols:
          #  print(symb)
          #  symb = 'BRO'
            comp = requests.get(
                f'https://financialmodelingprep.com/api/v3/historical-price-full/{symb}?apikey={api_key1}').json()['historical']
            comp = pd.DataFrame(comp)
            comp = comp.iloc[:10, :]
            comp['avg_vol'] = comp.loc[:, 'volume'].rolling(window=6).mean()
            comp_rv = comp.loc[0, 'volume']/comp.loc[5, 'avg_vol']
            stocks = stocks.append(
                {'relative_vol': comp_rv, 'symbol': symb}, ignore_index=True)

       # stocks = stocks.style.format({'mktCap': "{:.2f}",'volAvg': "{:.2f}",'var3': "{:.2%}"})
       return stocks


#b = list(stock_lists['symbol'])
a = get_companyinfo(ticker_sum)

today = dt.today()
a = str(today.year)
b = str(today.month)
c = str(today.day)
f.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv')
ticker_sum = a
#####################################################################
d = f[0].tolist()
ticker5 = ticker1
ticker6 =ticker2 
ticker7 = ticker3
ticker1 = []
ticker2 = []
ticker3 = []
for tic in ticker5:
    if tic in ticker_sum:
        ticker1.append(tic)
for tic in ticker6:
    if tic in ticker_sum:
        ticker2.append(tic)

for tic in ticker7:
    if tic in ticker_sum:
        ticker3.append(tic)

strategy11 = strategy1.loc[ticker1, :]

strategy21 = strategy2.loc[ticker2,:]
strategy31 = strategy3.loc[ticker3,:]

strategy1 =strategy11 .copy()
strategy2 =strategy21 .copy()
strategy3 =strategy31 .copy()        
ticker_sum2=ticker1+ticker2+ticker3
ticker_sum2= np.unique(ticker_sum2).tolist()
ticker_sum = ticker_sum2

#ticker_sum = tickers


for tic in ticker2:
    if tic in ticker1:
        ticker1.remove(tic)
for tic in ticker2:
    if tic in ticker3:
        ticker3.remove(tic)
        
for tic in ticker1:
    if tic in ticker3:
        ticker3.remove(tic)
        
        
#############################
a = str(today.year)
b = str(today.month)
c = str(today.day+1)
d =str(today.day+2)
e = str(today.day+3)
f =str(today.day)
e1 =str(a+'-'+b+'-'+c)
e2 =str(a+'-'+b+'-'+d)
e3 =str(a+'-'+b+'-'+e)
e4 =str(a+'-'+b+'-'+f)
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] != '2022-04-21']
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] != '2022-04-22']
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] != '2022-04-23']
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] != e1]
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] != e2]
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] != e3]
stock_info_data5 = stock_info_data5[stock_info_data5['earning_date'] != e4]

tickers =stock_info_data5.index.tolist()



f1 =pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_2021_12_9.csv')  
ticker_sum1 =list(f1['0'])


def get_companyinfo(symbols):
        import requests
        from datetime import datetime
        api_key1 = '86dd63f6b8ae774b061232685b78eb52'    
        stocks = pd.DataFrame(columns = [ 'description','symbol', 'logourl', 'name','mktCap','lastDiv','volAvg','price'])
        for symb  in symbols:
           # symb = 'FB'
            print(symb)
            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'],'volAvg':comp['volAvg'], 'price':comp['price']}, 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(ticker_sum)
    
stocks =stocks.sort_values(['div_yiel','volAvg','mktCap'], ascending =False)
import re
stocks['price'] = stocks['price'].apply(pd.to_numeric,errors='coerce')

#stocks['price'] = stocks['price'].astype(float)
stocks = stocks[stocks['price'] >60]
stocks = stocks.iloc[:70,:]
ticker_sum= list(stocks.index)
#ticker_sum = ticker_sum2d = f[0].tolist()






ticker5 = ticker1
ticker6 =ticker2 
ticker7 = ticker3
ticker1 = []
ticker2 = []
ticker3 = []
for tic in ticker5:
    if tic in ticker_sum:
        ticker1.append(tic)
for tic in ticker6:
    if tic in ticker_sum:
        ticker2.append(tic)

for tic in ticker7:
    if tic in ticker_sum:
        ticker3.append(tic)

strategy11 = strategy1.loc[ticker1, :]

strategy21 = strategy2.loc[ticker2,:]
strategy31 = strategy3.loc[ticker3,:]

strategy1 =strategy11 .copy()
strategy2 =strategy21 .copy()
strategy3 =strategy31 .copy()        
ticker_sum2=ticker1+ticker2+ticker3
ticker_sum2= np.unique(ticker_sum2).tolist()
ticker_sum = ticker_sum2





def get_companyinfo(symbols):
        import requests
        from datetime import datetime
        api_key1 = '86dd63f6b8ae774b061232685b78eb52'    
        stocks = pd.DataFrame(columns = [ 'description','symbol', 'logourl', 'name','mktCap','lastDiv','volAvg','price'])
        for symb  in symbols:
           # symb = 'FB'
            print(symb)
            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'],'volAvg':comp['volAvg'], 'price':comp['price']}, 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(ticker_sum)
    
stocks =stocks.sort_values(['div_yiel','volAvg','mktCap'], ascending =False)
import re
stocks['price'] = stocks['price'].apply(pd.to_numeric,errors='coerce')

#stocks['price'] = stocks['price'].astype(float)
stocks = stocks[stocks['price'] >60]
stocks = stocks.iloc[:70,:]
ticker_sum= list(stocks.index)
#ticker_sum = ticker_sum2d = f[0].tolist()