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

#!pip install yfinance
#!pip install mplfinance

#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(f"{datetime.now()}\t {ticker}\t {hist.shape}\t {hist.index.min()}\t {hist.index.max()}")
    dfs[ticker] = hist

ticker = '^GSPC'
dfs[ticker].tail(5)

import sys
sys.path.append(r"/kaggle/input/technical-indicators-core")
#from core.finta import TA
from finta import TA

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

#help(TA.DMI)

df_ta = TA.DMI(df, period = 10)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

df.tail(5)

#help(TA.SMA)

df['SMA'] = TA.SMA(df, period=18, column='close')

df.tail(5)

#help(TA.CCI)

df['CCI'] = TA.CCI(df, period=13)

#DMIOsc = oDMIPlus - oDMIMinus;
df['DMI_OSC']  = df['DI_PLUS'] - df['DI_MINUS']
#df['PRE_CLOSE'] = df['Close'].shift(1)
# df['PRE_Low'] = df['Low'].shift(1)
# df['PRE_Low2'] = df['Low'].shift(2)
# df['PRE_High'] = df['High'].shift(1)
# df['PRE_High2'] = df['High'].shift(2)

# df['PRE_CCI'] = df['CCI'].shift(1)
# df['PRE_CCI2'] = df['CCI'].shift(2)

df.tail(5)

df['DMI_BUY'] = 0
buy_cond = (df['Close']>df['SMA']) 
buy_cond = buy_cond & (df['DI_PLUS'] > df['DI_MINUS'])
buy_cond = buy_cond & (df['Low'] > (df['Low'].shift(1)))
buy_cond = buy_cond & ((df['Low'].shift(1)) < (df['Low'].shift(2)))
df.loc[buy_cond, 'DMI_BUY'] = 1

df['DMI_SELL'] = 0
sell_cond = (df['Close']<df['SMA']) 
sell_cond = sell_cond & (df['DI_PLUS'] < df['DI_MINUS'])
sell_cond = sell_cond & (df['High'] < (df['High'].shift(1)))
sell_cond = sell_cond & ((df['High'].shift(1)) > (df['High'].shift(2)))
df.loc[sell_cond, 'DMI_SELL'] = 1

df.tail(5)

df['CCI_BUY'] = 0
buy_cond = (df['CCI']>=-100) 
buy_cond = buy_cond & (df['CCI'].shift(1) < -100)
buy_cond = buy_cond & (df['CCI'].shift(2) < -100)
df.loc[buy_cond, 'CCI_BUY'] = 1

df['CCI_SELL'] = 0
sell_cond = (df['CCI']<=100) 
sell_cond = sell_cond & (df['CCI'].shift(1) > 100)
sell_cond = sell_cond & (df['CCI'].shift(2) > 100)
df.loc[sell_cond, 'CCI_SELL'] = 1

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

df['DMI_BUY_Close'] = df['Close']*df['DMI_BUY']
df['DMI_SELL_Close'] = df['Close']*df['DMI_SELL']
df['CCI_BUY_Close'] = df['Close']*df['CCI_BUY']
df['CCI_SELL_Close'] = df['Close']*df['CCI_SELL']

display(df.tail(5))

#from core.visuals import *
from visuals import *

start = -100
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}'}

names = {'main_title': f'{ticker} - DMI oscillator continuation signals'}

lines0,  ax_cfg0 = plot_overlay_lines(data = df_sub, overlay_columns = ['SMA'])

lines1, ax_cfg1 = plot_macd(data = df_sub, macd= 'DI_PLUS', macd_signal = 'DI_MINUS', panel =1)

lines2, shadows2, ax_cfg2 = plot_add_lines(data = df_sub, line_columns=['CCI'], 
                                           panel =2, bands = [-100, 100])
b_s_ = plot_buy_sell(data=df_sub, buy_column='DMI_BUY_Close', sell_column='DMI_SELL_Close')
lines_ = dict(**lines0, **lines1)
lines_.update(lines2)
lines_.update(b_s_)

shadows_ =    shadows2


fig_config_ = dict(figratio=(18,9), volume=False, volume_panel=2,panel_ratios=(4,2,2), tight_layout=True, returnfig=True,)

ax_cfg_ = ax_cfg0
ax_cfg_.update(ax_cfg1)
ax_cfg_.update(ax_cfg2)


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)    

names = {'main_title': f'{ticker} - CCI reversal signals'}

lines0,  ax_cfg0 = plot_overlay_lines(data = df_sub, overlay_columns = ['SMA'])

lines1, ax_cfg1 = plot_macd(data = df_sub, macd= 'DI_PLUS', macd_signal = 'DI_MINUS', panel =1)

lines2, shadows2, ax_cfg2 = plot_add_lines(data = df_sub, line_columns=['CCI'], 
                                           panel =2, bands = [-100, 100])
b_s_ = plot_buy_sell(data=df_sub, buy_column='CCI_BUY_Close', sell_column='CCI_SELL_Close')
lines_ = dict(**lines0, **lines1)
lines_.update(lines2)
lines_.update(b_s_)

shadows_ =    shadows2


fig_config_ = dict(figratio=(18,9), volume=False, volume_panel=2,panel_ratios=(4,2,2), tight_layout=True, returnfig=True,)

ax_cfg_ = ax_cfg0
ax_cfg_.update(ax_cfg1)
ax_cfg_.update(ax_cfg2)


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)    

df_list = []
for ticker, df in dfs.items():
    df = df[['Open', 'High', 'Low', 'Close', 'Volume']].round(2)

    df_ta = TA.DMI(df, period = 10)
    df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

    del df_ta
    gc.collect()

    df['SMA'] = TA.SMA(df, period=18, column='close')
    df['CCI'] = TA.CCI(df, period=13)
    df['DMI_OSC']  = df['DI_PLUS'] - df['DI_MINUS']

    df['DMI_BUY'] = 0
    buy_cond = (df['Close']>df['SMA']) 
    buy_cond = buy_cond & (df['DI_PLUS'] > df['DI_MINUS'])
    buy_cond = buy_cond & (df['Low'] > (df['Low'].shift(1)))
    buy_cond = buy_cond & ((df['Low'].shift(1)) < (df['Low'].shift(2)))
    df.loc[buy_cond, 'DMI_BUY'] = 1

    df['DMI_SELL'] = 0
    sell_cond = (df['Close']<df['SMA']) 
    sell_cond = sell_cond & (df['DI_PLUS'] < df['DI_MINUS'])
    sell_cond = sell_cond & (df['High'] < (df['High'].shift(1)))
    sell_cond = sell_cond & ((df['High'].shift(1)) > (df['High'].shift(2)))
    df.loc[sell_cond, 'DMI_SELL'] = 1

    df['CCI_BUY'] = 0
    buy_cond = (df['CCI']>=-100) 
    buy_cond = buy_cond & (df['CCI'].shift(1) < -100)
    buy_cond = buy_cond & (df['CCI'].shift(2) < -100)
    df.loc[buy_cond, 'CCI_BUY'] = 1

    df['CCI_SELL'] = 0
    sell_cond = (df['CCI']<=100) 
    sell_cond = sell_cond & (df['CCI'].shift(1) > 100)
    sell_cond = sell_cond & (df['CCI'].shift(2) > 100)
    df.loc[sell_cond, 'CCI_SELL'] = 1
    
    df['ticker'] = ticker
    df_list.append(df)

df_all = pd.concat(df_list, axis=0)
print(df_all.shape)
df_all.tail(5)

dd = df_all.index
df_all.index = dd.date
df_all.index.name='Date'

df_all.to_csv('c_011_tasc202212.csv', index=True)