#!/usr/bin/env python3

import sys
import logging
import argparse

import matplotlib
# matplotlib.use("Qt5Agg")
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
from pandas import DataFrame
import talib.abstract as ta

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade import exchange, analyze
from freqtrade.misc import common_args_parser
from freqtrade.strategy.strategy import Strategy
import freqtrade.misc as misc
import freqtrade.optimize as optimize
import freqtrade.analyze as analyze


logger = logging.getLogger(__name__)


def plot_parse_args(args):
    parser = misc.common_args_parser('Graph dataframe')
    misc.backtesting_options(parser)
    misc.scripts_options(parser)
    return parser.parse_args(args)


def plot_analyzed_dataframe(args) -> None:
    """
    Calls analyze() and plots the returned dataframe
    :param pair: pair as str
    :return: None
    """
    pair = args.pair
    pairs = [pair]
    timerange = misc.parse_timerange(args.timerange)

    # Init strategy
    strategy = Strategy()
    strategy.init({'strategy': args.strategy})
    tick_interval = strategy.ticker_interval

    tickers = {}
    if args.live:
        logger.info('Downloading pair.')
        # Init Bittrex to use public API
        exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
        tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
    else:
        tickers = optimize.load_data(args.datadir, pairs=pairs,
                                     ticker_interval=tick_interval,
                                     refresh_pairs=False,
                                     timerange=timerange)
    dataframes = optimize.tickerdata_to_dataframe(tickers)
    dataframe = dataframes[pair]
    dataframe = analyze.populate_buy_trend(dataframe)
    dataframe = analyze.populate_sell_trend(dataframe)
    dates = misc.datesarray_to_datetimearray(dataframe['date'])

    # Two subplots sharing x axis
    fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
    fig.suptitle(pair + " " +     str(tick_interval), fontsize=14, fontweight='bold')

    ax1.plot(dates, dataframe['close'], label='close')
    # ax1.plot(dates, dataframe['sell'], 'ro', label='sell')
    ax1.plot(dates, dataframe['sma'], '--', label='SMA')
    ax1.plot(dates, dataframe['tema'], ':', label='TEMA')
    ax1.plot(dates, dataframe['blower'], '-.', label='BB low')
    ax1.plot(dates, dataframe['close'] * dataframe['buy'], 'bo', label='buy')
    ax1.plot(dates, dataframe['close'] * dataframe['sell'], 'ro', label='sell')

    ax1.legend()

    ax2.plot(dates, dataframe['adx'], label='ADX')
    ax2.plot(dates, dataframe['mfi'], label='MFI')
    # ax2.plot(dates, [25] * len(dataframe.index.values))
    ax2.legend()

    ax3.plot(dates, dataframe['fastk'], label='k')
    ax3.plot(dates, dataframe['fastd'], label='d')
    ax3.plot(dates, [20] * len(dataframe.index.values))
    ax3.legend()
    xfmt = mdates.DateFormatter('%d-%m-%y %H:%M')  # Dont let matplotlib autoformat date
    ax3.xaxis.set_major_formatter(xfmt)

    # Fine-tune figure; make subplots close to each other and hide x ticks for
    # all but bottom plot.
    fig.subplots_adjust(hspace=0)
    fig.autofmt_xdate()  # Rotate the dates
    plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
    plt.show()

if __name__ == '__main__':
    args = plot_parse_args(sys.argv[1:])
    plot_analyzed_dataframe(args)