# IMPORTING PACKAGES
import pandas as pd
import requests
import matplotlib
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 seaborn as sns
from datetime import datetime
#rcParams['figure.figsize'] = 25,16

def strategy_performance(ticker, start, end):
    # EXTRACTING STOCK DATA
    
    
    
    from datetime import datetime
    start = np.array(start).tolist()
    end = np.array(end).tolist()
    finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg")
    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')
    
    
    
    data1 = stock.copy()
##############################trend#############   
    # strategy CALCULATION 
    def get_tsi(Close, long, short, signal):
        diff = Close - Close.shift(1)
        abs_diff = abs(diff)
        
        diff_smoothed = diff.ewm(span = long, adjust = False).mean()
        diff_double_smoothed = diff_smoothed.ewm(span = short, adjust = False).mean()
        abs_diff_smoothed = abs_diff.ewm(span = long, adjust = False).mean()
        abs_diff_double_smoothed = abs_diff_smoothed.ewm(span = short, adjust = False).mean()
        
        tsi = (diff_double_smoothed / abs_diff_double_smoothed) * 100
        signal = tsi.ewm(span = signal, adjust = False).mean()
       # tsi = tsi[tsi.index >= '2020-01-01'].dropna()
       # signal = signal[signal.index >= '2020-01-01'].dropna()
        
        return tsi, signal
    data1['tsi'], data1['signal_line'] = get_tsi(data1['Close'], 25, 13, 12)
    
    # TRUE STRENGTH INDEX STRATEGY
    def implement_tsi_strategy(prices, tsi, signal_line):
        buy_price = []
        sell_price = []
        tsi_signal = []
        signal = 0
        
        for i in range(len(prices)):
            if tsi[i-1] < signal_line[i-1] and tsi[i] > signal_line[i]:
                if signal != 1:
                    buy_price.append(prices[i])
                    sell_price.append(np.nan)
                    signal = 1
                    tsi_signal.append(signal)
                else:
                    buy_price.append(np.nan)
                    sell_price.append(np.nan)
                    tsi_signal.append(0)
            elif tsi[i-1] > signal_line[i-1] and tsi[i] < signal_line[i]:
                if signal != -1:
                    buy_price.append(np.nan)
                    sell_price.append(prices[i])
                    signal = -1
                    tsi_signal.append(signal)
                else:
                    buy_price.append(np.nan)
                    sell_price.append(np.nan)
                    tsi_signal.append(0)
            else:
                buy_price.append(np.nan)
                sell_price.append(np.nan)
                tsi_signal.append(0)
                
        return buy_price, sell_price, tsi_signal
    
    buy_price, sell_price, tsi_signal = implement_tsi_strategy(data1['Close'], data1['tsi'], data1['signal_line'])
    
    # STOCK POSITION
    
    position = []
    for i in range(len(tsi_signal)):
        if tsi_signal[i] > 1:
            position.append(0)
        else:
            position.append(1)
            
    for i in range(len(data1['Close'])):
        if tsi_signal[i] == 1:
            position[i] = 1
        elif tsi_signal[i] == -1:
            position[i] = 0
        else:
            position[i] = position[i-1]
            
    Close_price = data1['Close']  
    tsi = data1['tsi']
    signal_line = data1['signal_line']
    tsi_signal = pd.DataFrame(tsi_signal).rename(columns = {0:'tsi_signal'}).set_index(data1.index)
    position = pd.DataFrame(position).rename(columns = {0:'tsi_position'}).set_index(data1.index)
    
    frames_data1 = [Close_price, tsi, signal_line, tsi_signal, position]
    strategy1 = pd.concat(frames_data1, join = 'inner', axis = 1)
    
    strategy1['ret'] =pd.DataFrame(strategy1['Close'].pct_change())
    
    strategy1['tsi_returns'] = strategy1['ret'] *strategy1['tsi_position']
    
   
####################second strategy######################
# SUPERTREND CALCULATION
    data2 =stock.copy()
    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'])
    
    # SUPERTREND SIGNALS
   
    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']
    
    
   # fig, ax = plt.subplots()
   # plt.plot(((1+strategy2['st_returns'][1:]  ).cumprod()*10000), color = 'green')
  #  plt.plot(((1+strategy2['ret'][1:]).cumprod()*10000), color = 'red')
    #ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y))) 
##############strategy3########################
    data3 = stock.copy()
    def get_di(data, lookback):
        ma = data.rolling(lookback).mean()
        di = ((data - ma) / ma) * 100
        return di
    
    data3['di_14'] = get_di(data3['Close'], 14)
    data3 = data3.dropna()
    # DISPARITY INDEX PLOT
    
    def implement_di_strategy(prices, di):
        buy_price = []
        sell_price = []
        di_signal = []
        signal = 0
        
        for i in range(len(prices)):
            if di[i-4] < 0 and di[i-3] < 0 and di[i-2] < 0 and di[i-1] < 0 and di[i] > 0:
                if signal != 1:
                    buy_price.append(prices[i])
                    sell_price.append(np.nan)
                    signal = 1
                    di_signal.append(signal)
                else:
                    buy_price.append(np.nan)
                    sell_price.append(np.nan)
                    di_signal.append(0)
            elif di[i-4] > 0 and di[i-3] > 0 and di[i-2] > 0 and di[i-1] > 0 and di[i] < 0:
                if signal != -1:
                    buy_price.append(np.nan)
                    sell_price.append(prices[i])
                    signal = -1
                    di_signal.append(signal)
                else:
                    buy_price.append(np.nan)
                    sell_price.append(np.nan)
                    di_signal.append(0)
            else:
                buy_price.append(np.nan)
                sell_price.append(np.nan)
                di_signal.append(0)
                
        return buy_price, sell_price, di_signal
    
    buy_price, sell_price, di_signal = implement_di_strategy(data3['Close'], data3['di_14'])
    
        
    # STOCK POSITION
    
    position = []
    for i in range(len(di_signal)):
        if di_signal[i] > 1:
            position.append(0)
        else:
            position.append(1)
            
    for i in range(len(data3['Close'])):
        if di_signal[i] == 1:
            position[i] = 1
        elif di_signal[i] == -1:
            position[i] = 0
        else:
            position[i] = position[i-1]
            
    Close_price = data3['Close']
    di = data3['di_14']
    di_signal = pd.DataFrame(di_signal).rename(columns = {0:'di_signal'}).set_index(data3.index)
    position = pd.DataFrame(position).rename(columns = {0:'di_position'}).set_index(data3.index)
    
    frames = [Close_price, di, di_signal, position]
    strategy3 = pd.concat(frames, join = 'inner', axis = 1)
      # BACKTESTING
    strategy3['ret'] =pd.DataFrame(strategy3['Close'].pct_change())
    strategy3['di_returns'] = strategy3['ret']*strategy3['di_position']
    
    strategy1 =strategy1.loc['2010-01-23':,:]
    strategy2 =strategy2.loc['2010-01-23':,:]
    strategy3 = strategy3.loc['2010-01-23':,:]

  #  import seaborn as sns
   #sns.set_style("whitegrid")
    plt.rcParams['figure.figsize'] = [11, 7]
    plt.rc('font', size =12)
    #sns.set_theme()
 #  plt.rcParams['figure.facecolor'] = '
    # plt.rcParams['figure.figsize'] = [12, 7]
    # plt.rc('font', size = 14)
    
#   ax.set_axisbelow(True)
 #  ax.yaxis.grid(color='gray', linestyle='dashed')
 #  sns.set_style("whitegrid")
   # plt.rcParams['figure.figsize'] = [12, 7]
  #  plt.rc('font', size =14)
    
  #  sns.set_theme()
    fig, ax = plt.subplots(dpi = 300 )
    ax.set_facecolor('white') 
    fig.patch.set_facecolor('white')
    ax.ticklabel_format(style='plain')
    plt.plot(((1+strategy3['di_returns'][1:]  ).cumprod()*10000), color = 'blue', linewidth = 0.9, label = 'Super Oscillator')
    plt.plot(((1+strategy2 ['st_returns'][1:]  ).cumprod()*10000), color = 'purple', linewidth = 0.9, label= 'Super Trend')
    plt.plot(((1+strategy1['tsi_returns'][1:]  ).cumprod()*10000), color = 'green', linewidth =0.9 , label = 'Super Momentum')
    plt.plot(((1+strategy3['ret'][1:]).cumprod()*10000), color = 'red', linewidth = 0.9, label = ticker)
    
    plt.grid(True, linewidth=0.3, color='grey',linestyle='dashed')
    ax.spines["right"].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines["left"].set_visible(False)
    plt.legend(loc='upper left')
    #plt.title(ticker+ '   Supports and Resistances')
    plt.title("AI return VS "+ ticker + " return", size = 14, y =0.95)
    plt.xticks(rotation=30)
    path ="C:\\users\jizha\Desktop\seabridge fintech\profit_graph_app\\"
    plt.savefig(path +'{}.jpg'.format(ticker+"_Return"),transparent=True, dpi=300, pad_inches = 0)
   # ax.set_title('Title', pad=15)
    plt.show()
    
 
############strategy3: Super Oscillator di_return blue
#strateg2:'Super Trend')"   purple  st_return
#traetgy1: 'Super Momentum'###########tsi_return green
stock_lists = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\stocks_list_7-29\stock_lists_7_29.csv' )  
symbols = list(stock_lists['0'])

symbols.remove('LZ')   
symbols.remove('MRK')   
n = 4# 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()
from datetime import datetime as dt
import datetime
today = dt.today()
yesterday = today-datetime.timedelta(1)
end = (yesterday.year, yesterday.month, yesterday.day)


for ticker in group1:
  
    data1 =  strategy_performance( ticker ,  start = (2010,1,21),  end = end)
   
 
for ticker in group2:
    data2 =  strategy_performance(ticker = ticker ,start = (2010,1,21),  end = end)
    
for ticker in group3:
    data3 =  strategy_performance(ticker = ticker , start = (2010,1,21),  end = end)
       
    
for ticker in group4:
     data4 =  strategy_performance(ticker = ticker , start = (2010,1,21),  end = end)

# import time    
# for ticker in group5:
#     data5 =  strategy_performance(ticker = ticker , start = (2010,1,21),  end = end)
#     time.sleep(60)
    
list = ['ALLY','AJG','AMAT','BKR','BSX','BX','XLV','SIRI','TTD','VER','GOOG','HOG','KMB','NOV','NXPI','O','ON','XEL'] ###not good
list_backtest = ['ASO','DASH','CARR','DISCK','OTIS','OGN','PPD','U','ABNB']##not good

stocks1 =['QQQ','SPY']
for ticker in stocks1:
     data7 =  strategy_performance(ticker = ticker , start = (2010,1,21),  end = end)

d =symbols.copy()
for i in range(len(list_backtest)):
    if list_backtest[i] in symbols:
       d.remove( list_backtest[i])

tickers =pd.DataFrame(d)
tickers.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool\backtest_symbols.csv')





stock_lists= pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool\backtest_symbols.csv')
symbols = list(stock_lists['0'])  


dr= 'https://3arbzfbsh-cname-us.ngrok.io'
image_strategy_return = pd.DataFrame()
for ticker in symbols:
    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_strategy_return .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\image_strategy_return.csv')

################resistance and resiistance #############

dr= 'https://3arbzfbsh-cname-us.ngrok.io'
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['image_resistance_support'] =[ dr]
    image.index =[tick]
    image_rs = pd.concat([image_rs,image], axis =0)
image_rs.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\image_rs.csv') 
         
#pip install oauth2client
#stock_info.to_csv(r'C:\Users\jizha\Desktop\seabridge fintech\stock_info' )
#pip install gspread
#pip install PyOpenSSL
#pip install pygsheets
import gspread
from oauth2client.service_account import ServiceAccountCredentials
import json
import pygsheets
import pandas as pd
#authorization
import pandas as pd
import pygsheets
import pandas as pdSeabridger
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)

#authorization
df = image_data .copy()
df = df.reset_index()
gc = pygsheets.authorize(service_file=r'C:/Users/jizha/App-googlesheet/crenditals.json')
# Create empty dataframe
# Create a column
#df =pd.read_csv(r'C:\Users\jizha\Desktop\seabridge fintech\strategy_sum2' )
#df = ema_strategy_ret_df.merge(benchmark, how = 'inner')
df=df.rename({"index":"symbol"}, axis = 1)
#open the google spreadsheet (where 'PY to Gsheet Test' is the name of my sheet)
sh =  gc.open_by_key('136vIMVig4r4j0b-gOwg25IlL-evFF_hqLNY3FxIUHWU')
#select the first sheet 
wks = sh[7]
#update the first sheet with df, starting at cell B2. 
wks.set_dataframe(df,(1,1))


 
 #####icon################
icon = ['market_trend_down','market_trend_sideway','market_trend_up','pa_buy','pa_downtrend','pa_sell','pa_sideways', 'pa_uptrend']

dr= 'https://3arbzfbsh-cname-us.ngrok.io'
image_icon = pd.DataFrame()
for item in icon:
    image = pd.DataFrame()
    dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/icon/" + item+".jpg"
    tick = item
    image['price_action_icon'] =[ dr]
    image.index =[tick]
    image_icon = pd.concat([image_icon,image], axis =0)
       
 

    
 
    
import gspread
from oauth2client.service_account import ServiceAccountCredentials
import json
import pygsheets
import pandas as pd
#authorization
import pandas as pd

import pygsheets
import pandas as pdSeabridger

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)

df = image_icon.reset_index()
#authorization

gc = pygsheets.authorize(service_file=r'C:/Users/jizha/App-googlesheet/crenditals.json')
# Create empty dataframe
# Create a column
#df =pd.read_csv(r'C:\Users\jizha\Desktop\seabridge fintech\strategy_sum2' )
#df = ema_strategy_ret_df.merge(benchmark, how = 'inner')
df=df.rename({"index":"symbol"}, axis = 1)
#open the google spreadsheet (where 'PY to Gsheet Test' is the name of my sheet)
sh =  gc.open_by_key('136vIMVig4r4j0b-gOwg25IlL-evFF_hqLNY3FxIUHWU')
#select the first sheet 
wks = sh[5]
#update the first sheet with df, starting at cell B2. 
wks.set_dataframe(df,'F2')