import pandas as pd
import pandas_datareader as pdr
import numpy as np
import yfinance as yf
import requests
from bs4 import BeautifulSoup
from typing import List
from tqdm import tqdm
import os
import datetime
import json
from pandas.tseries.offsets import BDay
from sqlalchemy import create_engine
# from dotenv import load_dotenv
# load_dotenv()

data_start_date = '2018-07-01'

def get_daily(mode='daily', periods_30m=None):
    '''
    Method to get daily data and create daily features. Optionally append intra data if specified.
    `mode`: 'daily' or 'intra'.
    `periods_30m`: How many 30m periods to bring in. Only specify if mode == 'intra'.
    '''
    
    vix = yf.Ticker('^VIX')
    vvix = yf.Ticker('^VVIX')
    spx = yf.Ticker('^GSPC')

    # Grab data from db
    engine = create_engine(
        f"mysql+mysqldb://{os.getenv('DATABASE_USERNAME')}:" \
        f"{os.getenv('DATABASE_PASSWORD')}@{os.getenv('DATABASE_HOST')}/" \
        f"{os.getenv('DATABASE')}?ssl_ca=ca-certificates.crt&ssl_mode=VERIFY_IDENTITY"
    )

    query = f'''SELECT
        spx.Datetime AS Datetime,
        spx.Open AS Open,
        spx.High AS High,
        spx.Low AS Low,
        spx.Close AS Close,
        vix.Open AS Open_VIX,
        vix.High AS High_VIX,
        vix.Low AS Low_VIX,
        vix.Close AS Close_VIX,
        vvix.Open AS Open_VVIX,
        vvix.High AS High_VVIX,
        vvix.Low AS Low_VVIX,
        vvix.Close AS Close_VVIX
    FROM 
        SPX_full_1day AS spx
    LEFT JOIN 
        VIX_full_1day AS vix ON spx.Datetime = vix.Datetime AND vix.Datetime > '{data_start_date}'
    LEFT JOIN 
        VVIX_full_1day AS vvix ON spx.Datetime = vvix.Datetime AND vvix.Datetime > '{data_start_date}'
    WHERE 
        spx.Datetime > '{data_start_date}'

    '''
    data = pd.read_sql_query(sql=query, con=engine.connect())
    data['Datetime'] = pd.to_datetime(data['Datetime'])
    data = data.set_index('Datetime',drop=True)

    # Get incremental date
    last_date = data.index.date[-1]
    last_date = last_date + BDay(1) 

    prices_vix = vix.history(start=last_date, interval='1d')
    prices_vvix = vvix.history(start=last_date, interval='1d')
    prices_spx = spx.history(start=last_date, interval='1d')

    if len(prices_spx) > 0:

        prices_spx['index'] = [str(x).split()[0] for x in prices_spx.index]
        prices_spx['index'] = pd.to_datetime(prices_spx['index']).dt.date
        prices_spx.index = prices_spx['index']
        prices_spx = prices_spx.drop(columns='index')
        prices_spx.index = pd.DatetimeIndex(prices_spx.index)

        prices_vix['index'] = [str(x).split()[0] for x in prices_vix.index]
        prices_vix['index'] = pd.to_datetime(prices_vix['index']).dt.date
        prices_vix.index = prices_vix['index']
        prices_vix = prices_vix.drop(columns='index')
        prices_vix.index = pd.DatetimeIndex(prices_vix.index)

        prices_vvix['index'] = [str(x).split()[0] for x in prices_vvix.index]
        prices_vvix['index'] = pd.to_datetime(prices_vvix['index']).dt.date
        prices_vvix.index = prices_vvix['index']
        prices_vvix = prices_vvix.drop(columns='index')
        prices_vvix.index = pd.DatetimeIndex(prices_vvix.index)

        data1 = prices_spx.merge(prices_vix[['Open','High','Low','Close']], left_index=True, right_index=True, suffixes=['','_VIX'])
        data1 = data1.merge(prices_vvix[['Open','High','Low','Close']], left_index=True, right_index=True, suffixes=['','_VVIX'])
        data = pd.concat([data, data1])

    else:
        data = data.copy()

    if mode == 'intra':
        from getIntraData import get_intra
        df_intra = get_intra(periods_30m)
        data = data.merge(df_intra, left_index=True, right_index=True)
    else:
        data = data.copy()

    # Features
    data['PrevClose'] = data['Close'].shift(1)
    data['Perf5Day'] = data['Close'] > data['Close'].shift(5)
    data['Perf5Day_n1'] = data['Perf5Day'].shift(1)
    data['Perf5Day_n1'] = data['Perf5Day_n1'].astype(bool)
    data['GreenDay'] = (data['Close'] > data['PrevClose']) * 1
    data['RedDay'] = (data['Close'] <= data['PrevClose']) * 1

    data['VIX5Day'] = data['Close_VIX'] > data['Close_VIX'].shift(5)
    data['VIX5Day_n1'] = data['VIX5Day'].astype(bool)

    data['VVIX5Day'] = data['Close_VVIX'] > data['Close_VVIX'].shift(5)
    data['VVIX5Day_n1'] = data['VVIX5Day'].astype(bool)

    data['VIXOpen'] = data['Open_VIX'] > data['Close_VIX'].shift(1)
    data['VVIXOpen'] = data['Open_VVIX'] > data['Close_VVIX'].shift(1)
    data['VIXOpen'] = data['VIXOpen'].astype(bool)
    data['VVIXOpen'] = data['VVIXOpen'].astype(bool)

    data['Range'] = data[['Open','High']].max(axis=1) - data[['Low','Open']].min(axis=1) # Current day range in points
    data['RangePct'] = data['Range'] / data['Close']
    data['VIXLevel'] = pd.qcut(data['Close_VIX'], 4)
    data['OHLC4_VIX'] = data[['Open_VIX','High_VIX','Low_VIX','Close_VIX']].mean(axis=1)
    data['OHLC4'] = data[['Open','High','Low','Close']].mean(axis=1)
    data['OHLC4_Trend'] = data['OHLC4'] > data['OHLC4'].shift(1)
    data['OHLC4_Trend'] = data['OHLC4_Trend'].astype(bool)
    data['OHLC4_Trend_n1'] = data['OHLC4_Trend'].shift(1)
    data['OHLC4_Trend_n1'] = data['OHLC4_Trend_n1'].astype(float)
    data['OHLC4_Trend_n2'] = data['OHLC4_Trend'].shift(1)
    data['OHLC4_Trend_n2'] = data['OHLC4_Trend_n2'].astype(float)
    data['RangePct_n1'] = data['RangePct'].shift(1)
    data['RangePct_n2'] = data['RangePct'].shift(2)
    data['OHLC4_VIX_n1'] = data['OHLC4_VIX'].shift(1)
    data['OHLC4_VIX_n2'] = data['OHLC4_VIX'].shift(2)
    data['CurrentGap'] = (data['Open'] - data['PrevClose']) / data['PrevClose']
    data['CurrentGapHist'] = data['CurrentGap'].copy()
    data['CurrentGap'] = data['CurrentGap'].shift(-1)
    data['DayOfWeek'] = pd.to_datetime(data.index)
    data['DayOfWeek'] = data['DayOfWeek'].dt.day

    # Target -- the next day's low
    data['Target'] = (data['OHLC4'] / data['PrevClose']) - 1
    data['Target'] = data['Target'].shift(-1)
    # data['Target'] = data['RangePct'].shift(-1)

    # Target for clf -- whether tomorrow will close above or below today's close
    data['Target_clf'] = data['Close'] > data['PrevClose']
    data['Target_clf'] = data['Target_clf'].shift(-1)
    data['DayOfWeek'] = pd.to_datetime(data.index)
    data['Quarter'] = data['DayOfWeek'].dt.quarter
    data['DayOfWeek'] = data['DayOfWeek'].dt.weekday

    # Calculate up
    data['up'] = 100 * (data['High'].shift(1) - data['Open'].shift(1)) / data['Close'].shift(1)

    # Calculate upSD
    data['upSD'] = data['up'].rolling(30).std(ddof=0)

    # Calculate aveUp
    data['aveUp'] = data['up'].rolling(30).mean()
    data['H1'] = data['Open'] + (data['aveUp'] / 100) * data['Open']
    data['H2'] = data['Open'] + ((data['aveUp'] + data['upSD']) / 100) * data['Open']
    data['down'] = 100 * (data['Open'].shift(1) - data['Low'].shift(1)) / data['Close'].shift(1)
    data['downSD'] = data['down'].rolling(30).std(ddof=0)
    data['aveDown'] = data['down'].rolling(30).mean()
    data['L1'] = data['Open'] - (data['aveDown'] / 100) * data['Open']
    data['L2'] = data['Open'] - ((data['aveDown'] + data['downSD']) / 100) * data['Open']

    data = data.assign(
        L1Touch = lambda x: x['Low'] < x['L1'],
        L2Touch = lambda x: x['Low'] < x['L2'],
        H1Touch = lambda x: x['High'] > x['H1'],
        H2Touch = lambda x: x['High'] > x['H2'],
        L1Break = lambda x: x['Close'] < x['L1'],
        L1TouchRed = lambda x: (x['Low'] < x['L2']) & (x['Close'] < x['PrevClose']),
        L2TouchL1Break = lambda x: (x['Low'] < x['L2']) & (x['Close'] < x['L1']),
        L2Break = lambda x: x['Close'] < x['L2'],
        H1Break = lambda x: x['Close'] > x['H1'],
        H1TouchGreen = lambda x: (x['High'] > x['H1']) & (x['Close'] > x['PrevClose']),
        H2TouchH1Break = lambda x: (x['High'] > x['H2']) & (x['Close'] > x['H1']),
        H2Break = lambda x: x['Close'] > x['H2'],
        OpenL1 = lambda x: np.where(x['Open'] < x['L1'], 1, 0),
        OpenL2 = lambda x: np.where(x['Open'] < x['L2'], 1, 0),
        OpenH1 = lambda x: np.where(x['Open'] > x['H1'], 1, 0),
        OpenH2 = lambda x: np.where(x['Open'] > x['H2'], 1, 0)
    )

    data['OpenL1'] = data['OpenL1'].shift(-1)
    data['OpenL2'] = data['OpenL2'].shift(-1)
    data['OpenH1'] = data['OpenH1'].shift(-1)
    data['OpenH2'] = data['OpenH2'].shift(-1)


    level_cols = [
        'L1Touch',
        'L2Touch',
        'H1Touch',
        'H2Touch',
        'L1Break',
        'L2Break',
        'H1Break',
        'H2Break'
    ]

    for col in level_cols:
        data[col+'Pct'] = data[col].rolling(100).mean()
        # data[col+'Pct'] = data[col+'Pct'].shift(-1)

    data['H1BreakTouchPct'] = data['H1Break'].rolling(100).sum() / data['H1Touch'].rolling(100).sum()
    data['H2BreakTouchPct'] = data['H2Break'].rolling(100).sum() / data['H2Touch'].rolling(100).sum()
    data['L1BreakTouchPct'] = data['L1Break'].rolling(100).sum() / data['L1Touch'].rolling(100).sum()
    data['L2BreakTouchPct'] = data['L2Break'].rolling(100).sum() / data['L2Touch'].rolling(100).sum()
    data['L1TouchRedPct'] = data['L1TouchRed'].rolling(100).sum() / data['L1Touch'].rolling(100).sum()
    data['H1TouchGreenPct'] = data['H1TouchGreen'].rolling(100).sum() / data['H1Touch'].rolling(100).sum()

    data['H1BreakH2TouchPct'] = data['H2TouchH1Break'].rolling(100).sum() / data['H2Touch'].rolling(100).sum()
    data['L1BreakL2TouchPct'] = data['L2TouchL1Break'].rolling(100).sum() / data['L2Touch'].rolling(100).sum()

    if mode=='intra':
        # Intraday features
        data['CurrentOpen30'] = data['Open30'].shift(-1)
        data['CurrentHigh30'] = data['High30'].shift(-1)
        data['CurrentLow30'] = data['Low30'].shift(-1)
        data['CurrentClose30'] = data['Close30'].shift(-1)
        data['CurrentOHLC430'] = data[['CurrentOpen30','CurrentHigh30','CurrentLow30','CurrentClose30']].max(axis=1)
        data['OHLC4_Current_Trend'] = data['CurrentOHLC430'] > data['OHLC4']
        data['OHLC4_Current_Trend'] = data['OHLC4_Current_Trend'].astype(bool)
        data['HistClose30toPrevClose'] = (data['Close30'] / data['PrevClose']) - 1

        data['CurrentCloseVIX30'] = data['Close_VIX30'].shift(-1)
        data['CurrentOpenVIX30'] = data['Open_VIX30'].shift(-1)

        data['CurrentVIXTrend'] = data['CurrentCloseVIX30'] > data['Close_VIX']

        # Open to High
        data['CurrentHigh30toClose'] = (data['CurrentHigh30'] / data['Close']) - 1
        data['CurrentLow30toClose'] = (data['CurrentLow30'] / data['Close']) - 1
        data['CurrentClose30toClose'] = (data['CurrentClose30'] / data['Close']) - 1
        data['CurrentRange30'] = (data['CurrentHigh30'] - data['CurrentLow30']) / data['Close']
        data['GapFill30'] = [low <= prev_close if gap > 0 else high >= prev_close for high, low, prev_close, gap in zip(data['CurrentHigh30'], data['CurrentLow30'], data['Close'], data['CurrentGap'])]
        data['CloseL1'] = np.where(data['Close30'] < data['L1'], 1, 0)
        data['CloseL2'] = np.where(data['Close30'] < data['L2'], 1, 0)
        data['CloseH1'] = np.where(data['Close30'] > data['H1'], 1, 0)
        data['CloseH2'] = np.where(data['Close30'] > data['H2'], 1, 0)
        data['CloseL1'] = data['CloseL1'].shift(-1)
        data['CloseL2'] = data['CloseL2'].shift(-1)
        data['CloseH1'] = data['CloseH1'].shift(-1)
        data['CloseH2'] = data['CloseH2'].shift(-1)

        def get_quintiles(df, col_name, q):
            return df.groupby(pd.qcut(df[col_name], q))['GreenDay'].mean()

        probas = []
        # Given the current price level
        for i, pct in enumerate(data['CurrentClose30toClose']):
            try:
                # Split
                df_q = get_quintiles(data.iloc[:i], 'HistClose30toPrevClose', 10)
                for q in df_q.index:
                    if q.left <= pct <= q.right:
                        p = df_q[q]
            except:
                p = None

            probas.append(p)

        data['GreenProbas'] = probas

    engine = create_engine(
        f"mysql+mysqldb://{os.getenv('DATABASE_USERNAME')}:" \
        f"{os.getenv('DATABASE_PASSWORD')}@{os.getenv('DATABASE_HOST')}/" \
        f"{os.getenv('DATABASE')}?ssl_ca=ca-certificates.crt&ssl_mode=VERIFY_IDENTITY"
    )

    df_releases = pd.read_sql_query('select * from releases', con=engine)
    df_releases = df_releases.set_index('Datetime')
    data = data.merge(df_releases, how = 'left', left_index=True, right_index=True)

    for n in tqdm(df_releases.columns, desc='Merging econ data'):
        # Get the name of the release
        # n = releases[rid]['name']
        # Merge the corresponding DF of the release
        # data = data.merge(releases[rid]['df'], how = 'left', left_index=True, right_index=True)
        # Create a column that shifts the value in the merged column up by 1
        data[f'{n}_shift'] = data[n].shift(-1)
        # Fill the rest with zeroes
        data[n] = data[n].fillna(0)
        data[f'{n}_shift'] = data[f'{n}_shift'].fillna(0)
        
    data['BigNewsDay'] = data[[x for x in data.columns if '_shift' in x]].max(axis=1)

    def cumul_sum(col):
        nums = []
        s = 0
        for x in col:
            if x == 1:
                s += 1
            elif x == 0:
                s = 0
            nums.append(s)
        return nums

    consec_green = cumul_sum(data['GreenDay'].values)
    consec_red = cumul_sum(data['RedDay'].values)

    data['DaysGreen'] = consec_green
    data['DaysRed'] = consec_red

    final_row = data.index[-2]

    if mode=='daily':
        from dailyCols import model_cols

    elif mode=='intra':
        from intraCols import model_cols

    df_final = data.loc[:final_row, model_cols + ['Target', 'Target_clf']]
    df_final = df_final.dropna(subset=['Target','Target_clf'])
    # df_final = df_final.dropna(subset=['Target','Target_clf','Perf5Day_n1'])
    return data, df_final, final_row