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)

##### Download data#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 = 'PFE'
dfs[ticker].tail(5)

"""
    Function: __C_Crow
    // _C_Crow Function
    // TASC OCT 2017
    // A Candlestick Strategy
    // With Soldiers and Crows
    // D'Ambrosio and Star

    _C_Crow = Close[1] > Close[2] and
            Close[1] > Open[1] and 
            Open < Close[1] and
            Close < Open[1] and 
            Open > Open[1] and
            Close[1] > Close[2] and 
            Close[2] > Close[3] ;
"""
"""
One Black Crow:
minPrice:= 10;    {minimum price}
minVol:= 100000; {minimum volume}
trend:= Sum( C > Ref(C, -1), 3) = 3; 
Ref(C > O, -1) AND O < Ref(C, -1) AND
C < Ref(O, -1) AND O > Ref(O, -1) AND
Ref(trend, -1) AND C > minPrice AND
Mov(V, 50, S) > minVol



"""
def _scan_crow(c, o):
    crow_ = True
    crow_ = crow_ & (c.shift(1)>c.shift(2)) 
    crow_ = crow_ & (c.shift(1)>o.shift(1)) 
    crow_ = crow_ & (o<c.shift(1)) 
    crow_ = crow_ & (c<o.shift(1)) 
    crow_ = crow_ & (o>o.shift(1)) 
    crow_ = crow_ & (c.shift(2)>c.shift(3))
    return crow_



"""

    Function: _C_Soldier
    // _C_Soldier Function
    // TASC OCT 2017
    // A Candlestick Strategy
    // With Soldiers and Crows
    // D'Ambrosio and Star

    _C_Soldier = Close[1] < Close[2] and
                Close[1] < Open[1] and 
                Open > Close[1] and
                Close > Open[1] and 
                Open < Open[1] and
                Close[1] < Close[2] and 
                Close[2] < Close[3] ;

"""
"""
One White Soldier:
minPrice:= 1;    {minimum price}
minVol:= 100000; {minimum volume}
trend:= Sum( C < Ref(C, -1), 3) = 3; 
Ref(C < O, -1) AND O > Ref(C, -1) AND
C > Ref(O, -1) AND O < Ref(O, -1) AND
Ref(trend, -1) AND C > minPrice AND
Mov(V, 50, S) > minVol

"""


def _scan_soldier(c, o):
    soldier_ = True
    soldier_ = soldier_ & (c.shift(1)<c.shift(2)) 
    soldier_ = soldier_ & (c.shift(1)<o.shift(1)) 
    soldier_ = soldier_ & (o>c.shift(1)) 
    soldier_ = soldier_ & (c>o.shift(1)) 
    soldier_ = soldier_ & (o<o.shift(1)) 
    soldier_ = soldier_ & (c.shift(2)<c.shift(3))
    return soldier_

from core.finta import TA

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

df_ref = dfs['^GSPC'][[ 'Close']]
df_ref = df_ref.round(2)
df_ref.columns = ['ref_close']

print(df.shape, df_ref.shape)
df = df.merge(df_ref, left_index=True, right_index=True, how='inner')
print(df.shape)
df.isna().sum()

df_ta = TA.BBANDS(df, period = 20, std_multiplier=2.2, column="close")
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

df_ta = TA.XSII(df, slow_period = 102, fast_period = 7)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

df_ta = TA.RSMK(df, rsmk_period = 60, ema_period = 5, column = "close", ref_column = "ref_close")
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

df_ta = TA.SQZMI(df, period = 20,  column = "close")
df['SQZMI'] = df_ta

del df_ta
gc.collect()

df['SOLDIER'] = _scan_soldier(df['Close'], df['Open'])
df['CROW'] = _scan_crow(df['Close'], df['Open'])

df_ta = TA.SMA(df, period = 50, column = "volume") 
df['MAV'] = df_ta.values

df[['SOLDIER', 'CROW', 'SQZMI']].value_counts()

min_avg_volume = 100000 
df['B'] = (df['SOLDIER'] & (df['MAV']>min_avg_volume)).astype(int)*((df['High'] + df['Low'])/2)
df['S'] = (df['CROW'] & (df['MAV']>min_avg_volume)).astype(int)*((df['High'] + df['Low'])/2)
df['B2'] = (df['SOLDIER'] & (df['MAV']>min_avg_volume) & df['SQZMI']).astype(int)*((df['High'] + df['Low'])/2)

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

df.columns

from core.visuals import *

start = -1500
end = df.shape[0]
# df_sub = df.iloc[start:end]
df_sub = df[(df.index<='2016-06-02') & (df.index>='2015-09-02')]
names = {'main_title': f'{ticker}'}

lines0 = basic_lines(df_sub[['XSII1', 'XSII2', 'XSII3', 'XSII4']], 
                     colors = [], 
                     **dict(panel=0, width=1.5, secondary_y=False))

lines1 = basic_lines(df_sub[['RSMK']], 
                     colors = ['cadetblue'], 
                     **dict(panel=1, width=1, secondary_y=False))

lines3 = basic_lines(df_sub[[ 'S']],
                     colors = ['black'], 
                     **dict(panel=0, type='scatter', marker=r'${S}$' , markersize=100, secondary_y=False))

lines2 = basic_lines(df_sub[[ 'B', 'B2']],
                     colors = ['navy', 'red'], 
                     **dict(panel=0, type='scatter', marker=r'${B}$' , markersize=100, secondary_y=False))


lines_ = dict(**lines0, **lines1)
lines_.update(lines2)
lines_.update(lines3)

shadows_ = basic_shadows(bands=[-5, 5], nsamples=df_sub.shape[0], **dict(panel=1, color="lightskyblue",alpha=0.1,interpolate=True))

fig_config_ = dict(figratio=(18,10), volume=False, volume_panel=2,panel_ratios=(5,2), tight_layout=True, returnfig=True,)

ax_cfg_ = {0:dict(basic=[6, 2, ['XSII1', 'XSII2', 'XSII3', 'XSII4']], 
                 title=dict(label = 'XSII', fontsize=9, style='italic',  loc='left'), 
                ),
           2:dict(basic=[1, 0, ['RSMK']]
                ),
          }


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)