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

from core.finta import TA

def cal_vpn(ohlc: pd.DataFrame, 
            period: int = 30,
            ema_period: int = 3,
            mav_period: int = 30,
            adjust: bool = True 
            ) -> pd.Series:
    """
    source: https://traders.com/Documentation/FEEDbk_docs/2021/04/TradersTips.html

    Volume Positive Negative:

    pds:= Input( "Periods", 5, 100, 30);
    apr:= Typical();
    at:= ATR(pds) * 0.1;
    vpd:= If( apr >= Ref(apr, -1) + at, V, 0);
    vnd:= If( apr <= Ref(apr, -1) - at, V, 0);
    VP:= Sum(vpd, pds);
    VN:= Sum(vnd, pds);
    VPN:= (((VP - VN)/Mov(V, pds, S))/pds) * 100;
    VPN;
    Mov(VPN, 3, E)
    
    #------------------------------------------
    MF	Momentum( Avg3( High, Low, Close ), 1)
    MC	Mul2( 0.1, ATR( High, Low, Close, 30 ) )
    VP	Sum( IfThenElse( A>B( MF,  MC ), Volume, 0 ), 30 )
    VN	Sum( IfThenElse( A<B( MF, Neg(MC) ), Volume, 0), 30 )
    VPN	ExpAvg( Mul2( Divide( Divide( Sub( VP, VN ), Avg( Volume, 30 ) ), 30), 100 ), 3 )
    MAVPN	Avg( VPN, 30 )
   
"""      

    ohlc = ohlc.copy()
    ohlc.columns = [c.lower() for c in ohlc.columns]

    v = ohlc['volume']
    mav_ = v.rolling(period).mean()

    tp_ = TA.TP(ohlc) #typical price: (high + low + close)/3
    atr_ =  TA.ATR(ohlc, period = period)#Average True Range is moving average of True Range
    mf_ = tp_.diff(1) #momentum of typical price
    mc_ = atr_*0.1
    vol_up = (mf_ > mc_).astype(int)*v
    vol_down = (mf_ < (-1*mc_)).astype(int)*v
    vp_ = vol_up.rolling(period).sum()
    vn_ = vol_down.rolling(period).sum()
    
    mav_[mav_<=0] = 1
    vpn_ = ((vp_ - vn_)/mav_/period*100).ewm(span=ema_period, adjust=adjust).mean()
    ma_vpn_ = vpn_.rolling(mav_period).mean()   

    return pd.DataFrame(data = {'VPN': vpn_.values, 
                                'MA_VPN': ma_vpn_.values, 
                               },
                        index=ohlc.index, )    

df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]

df = df.round(2)

cal_vpn

df_ta = cal_vpn(df,  period=30, ema_period=5, mav_period=10)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

from core.finta import TA

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()

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

df[['VPN']].hist(bins=50)

#https://github.com/matplotlib/mplfinance
#this package help visualize financial data
import mplfinance as mpf
import matplotlib.colors as mcolors

# all_colors = list(mcolors.CSS4_COLORS.keys())#"CSS Colors"
# all_colors = list(mcolors.TABLEAU_COLORS.keys()) # "Tableau Palette",
# all_colors = list(mcolors.BASE_COLORS.keys()) #"Base Colors",
all_colors = ['dodgerblue', 'firebrick','limegreen','skyblue','lightgreen',  'navy','yellow','plum',  'yellowgreen']
#https://github.com/matplotlib/mplfinance/issues/181#issuecomment-667252575
#list of colors: https://matplotlib.org/stable/gallery/color/named_colors.html
#https://github.com/matplotlib/mplfinance/blob/master/examples/styles.ipynb

def make_3panels2(main_data, add_data, mid_panel=None, chart_type='candle', names=None, figratio=(14,9)):

    style = mpf.make_mpf_style(base_mpf_style='yahoo',  #charles
                               base_mpl_style = 'seaborn-whitegrid',
#                                marketcolors=mpf.make_marketcolors(up="r", down="#0000CC",inherit=True),
                               gridcolor="whitesmoke", 
                               gridstyle="--", #or None, or - for solid
                               gridaxis="both", 
                               edgecolor = 'whitesmoke',
                               facecolor = 'white', #background color within the graph edge
                               figcolor = 'white', #background color outside of the graph edge
                               y_on_right = False,
                               rc =  {'legend.fontsize': 'small',#or number
                                      #'figure.figsize': (14, 9),
                                     'axes.labelsize': 'small',
                                     'axes.titlesize':'small',
                                     'xtick.labelsize':'small',#'x-small', 'small','medium','large'
                                     'ytick.labelsize':'small'
                                     }, 
                              )   

    if (chart_type is None) or (chart_type not in ['ohlc', 'line', 'candle', 'hollow_and_filled']):
        chart_type = 'candle'
    len_dict = {'candle':2, 'ohlc':3, 'line':1, 'hollow_and_filled':2}    
        
    kwargs = dict(type=chart_type, figratio=figratio, volume=False, volume_panel=1, 
                  panel_ratios=(4,2), tight_layout=True, style=style, returnfig=True)
    
    if names is None:
        names = {'main_title': '', 'sub_tile': ''}
    
    added_plots = { 
#         'S':  mpf.make_addplot(add_data['S'], panel=0, color='blue', type='scatter', marker=r'${S}$' , markersize=100, secondary_y=False),   
#         'B':  mpf.make_addplot(add_data['B'], panel=0, color='blue', type='scatter', marker=r'${B}$' , markersize=100, secondary_y=False), 
        
        'EMA': mpf.make_addplot(add_data['EMA'], panel=0, color='dodgerblue', secondary_y=False), 
    }


      

    fb_bbands= []
    
    
    if mid_panel is not None:
        i = 0
        for name_, data_ in mid_panel.iteritems():
            added_plots[name_] = mpf.make_addplot(data_, panel=1, color=all_colors[i], secondary_y=False)
            i = i + 1
        fb_bbands2_ = dict(y1=-10*np.ones(mid_panel.shape[0]),
                      y2=10*np.ones(mid_panel.shape[0]),color="lightskyblue",alpha=0.1,interpolate=True)
        fb_bbands2_['panel'] = 1
        fb_bbands.append(fb_bbands2_)


    fig, axes = mpf.plot(main_data,  **kwargs,
                         addplot=list(added_plots.values()), 
                         fill_between=fb_bbands)
    # add a new suptitle
    fig.suptitle(names['main_title'], y=1.05, fontsize=12, x=0.1285)

    axes[0].legend([None]*4)
    handles = axes[0].get_legend().legendHandles
    axes[0].legend(handles=handles[2:],labels=['EMA'])
    axes[0].set_title(names['sub_tile'], fontsize=10, style='italic',  loc='left')
    axes[2].set_title('VPN/MA VPN', fontsize=10, style='italic',  loc='left')
    

#     axes[0].set_ylabel(names['y_tiles'][0])
#     axes[2].set_ylabel(names['y_tiles'][1])
    return fig, axes
   

start = -300
end = df.shape[0]

names = {'main_title': f'{ticker}', 
         'sub_tile': 'volume positive negative (VPN) '}


aa_, bb_ = make_3panels2(df.iloc[start:end][['Open', 'High', 'Low', 'Close', 'Volume']], 
            df.iloc[start:end][['EMA']],
            df.iloc[start:end][['VPN', 'MA_VPN']],
             chart_type='hollow_and_filled',names = names)