import pandas as pd
import numpy as np
import os
import gc
import copy
from pathlib import Path
from datetime import datetime, timedelta, time, date

#this package is to download equity price data from yahoo finance
#the source code of this package can be found here: https://github.com/ranaroussi/yfinance/blob/main
import yfinance as yf

pd.options.display.max_rows = 100
pd.options.display.max_columns = 100

import warnings
warnings.filterwarnings("ignore")

import pytorch_lightning as pl
random_seed=1234
pl.seed_everything(random_seed)

#S&P 500 (^GSPC),  Dow Jones Industrial Average (^DJI), NASDAQ Composite (^IXIC)
#Russell 2000 (^RUT), Crude Oil Nov 21 (CL=F), Gold Dec 21 (GC=F)
#Treasury Yield 10 Years (^TNX)
#CBOE Volatility Index (^VIX) Chicago Options - Chicago Options Delayed Price. Currency in USD

#benchmark_tickers = ['^GSPC', '^DJI', '^IXIC', '^RUT',  'CL=F', 'GC=F', '^TNX']

benchmark_tickers = ['^GSPC', '^VIX']
tickers = benchmark_tickers + ['GSK', 'BST', 'PFE']

#https://github.com/ranaroussi/yfinance/blob/main/yfinance/base.py
#     def history(self, period="1mo", interval="1d",
#                 start=None, end=None, prepost=False, actions=True,
#                 auto_adjust=True, back_adjust=False,
#                 proxy=None, rounding=False, tz=None, timeout=None, **kwargs):

dfs = {}

for ticker in tickers:
    cur_data = yf.Ticker(ticker)
    hist = cur_data.history(period="max", start='2000-01-01')
    print(datetime.now(), ticker, hist.shape, hist.index.min(), hist.index.max())
    dfs[ticker] = hist

ticker = 'GSK'
dfs[ticker].tail(5)

from core.finta import TA

df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]
df = df.round(2)

TA.MABW

df_ta = TA.MABW(df, fast_period = 10, slow_period = 50, multiplier = 1.0)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

df_ta = TA.EMA(df, period = 20, column="close")
df_ta.name='EMA'
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

df['SIGNAL'] = ((df['EMA']>=df['MAB_UPPER']) & (df['EMA'].shift(1)<df['MAB_UPPER'].shift(1))).astype(int)
df['B'] = df['SIGNAL']*(df["High"] + df["Low"])/2
df['SIGNAL'].value_counts()

df['MAB_WIDTH'].rolling(20).min()

display(df.head(5))
display(df.tail(5))

df[['MAB_WIDTH', 'MAB_LLV']].hist(bins=50)

from core.visuals import *

start = -1000
end = df.shape[0]
df_sub = df.iloc[start:end]
# df_sub = df[(df.index<='2019-04-01') & (df.index>='2019-01-24')]
names = {'main_title': f'{ticker}'}

lines0 = basic_lines(df_sub[['MAB_UPPER', 'EMA', 'MAB_LOWER']], 
                     colors = [], 
                     **dict(panel=0, width=1.5, secondary_y=False))

lines1 = basic_lines(df_sub[['MAB_WIDTH', 'MAB_LLV']], 
                     colors = ['cadetblue'], 
                     **dict(panel=1, width=1))

lines2 = basic_lines(df_sub[[ 'B']],
                     colors = ['navy'], 
                     **dict(panel=0, type='scatter', marker=r'${B}$' , markersize=100, secondary_y=False))


lines_ = dict(**lines0, **lines1)
lines_.update(lines2)

#shadows_ = basic_shadows(bands=[-0.01, 0.01], nsamples=df.iloc[start:end].shape[0], **dict(panel=1, color="lightskyblue",alpha=0.1,interpolate=True))
shadows_ = []
fig_config_ = dict(figratio=(18,10), volume=True, volume_panel=2,panel_ratios=(4,2, 2), tight_layout=True, returnfig=True,)

ax_cfg_ = {0:dict(basic=[5, 2, ['MAB_UPPER', 'EMA', 'MAB_LOWER']], 
                 title=dict(label = 'MA', fontsize=9, style='italic',  loc='left'), 
                ),
           2:dict(basic=[2, 0, ['MAB_WIDTH', 'MAB_LLV']]
                ),
          }


names = {'main_title': f'{ticker}'}

aa_, bb_ = make_panels(main_data = df_sub[['Open', 'High', 'Low', 'Close', 'Volume']], 
                       added_plots = lines_,
                       fill_betweens = shadows_, 
                       fig_config = fig_config_, 
                       axes_config = ax_cfg_,  
                       names = names)