# pragma pylint: disable=missing-docstring,W0212


import logging
from typing import Tuple, Dict

import arrow
from pandas import DataFrame, Series
from tabulate import tabulate

from freqtrade import exchange
from freqtrade.analyze import populate_buy_trend, populate_sell_trend
from freqtrade.exchange import Bittrex
from freqtrade.main import min_roi_reached
from freqtrade.misc import load_config
from freqtrade.optimize import load_data, preprocess
from freqtrade.persistence import Trade

logger = logging.getLogger(__name__)


def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
    """
    Get the maximum timeframe for the given backtest data
    :param data: dictionary with preprocessed backtesting data
    :return: tuple containing min_date, max_date
    """
    all_dates = Series([])
    for pair, pair_data in data.items():
        all_dates = all_dates.append(pair_data['date'])
    all_dates.sort_values(inplace=True)
    return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1])


def generate_text_table(
        data: Dict[str, Dict], results: DataFrame, stake_currency, ticker_interval) -> str:
    """
    Generates and returns a text table for the given backtest data and the results dataframe
    :return: pretty printed table with tabulate as str
    """
    floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
    tabular_data = []
    headers = ['pair', 'buy count', 'avg profit %',
               'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
    for pair in data:
        result = results[results.currency == pair]
        tabular_data.append([
            pair,
            len(result.index),
            result.profit_percent.mean() * 100.0,
            result.profit_BTC.sum(),
            result.duration.mean() * ticker_interval,
            result.profit.sum(),
            result.loss.sum()
        ])

    # Append Total
    tabular_data.append([
        'TOTAL',
        len(results.index),
        results.profit_percent.mean() * 100.0,
        results.profit_BTC.sum(),
        results.duration.mean() * ticker_interval,
        results.profit.sum(),
        results.loss.sum()
    ])
    return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)


def backtest(stake_amount: float, processed: Dict[str, DataFrame],
             max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False,
             stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame:
    """
    Implements backtesting functionality
    :param stake_amount: btc amount to use for each trade
    :param processed: a processed dictionary with format {pair, data}
    :param max_open_trades: maximum number of concurrent trades (default: 0, disabled)
    :param realistic: do we try to simulate realistic trades? (default: True)
    :return: DataFrame
    """
    trades = []
    trade_count_lock: dict = {}
    exchange._API = Bittrex({'key': '', 'secret': ''})
    for pair, pair_data in processed.items():
        pair_data['buy'], pair_data['sell'] = 0, 0
        ticker = populate_sell_trend(populate_buy_trend(pair_data))
        # for each buy point
        lock_pair_until = None
        buy_subset = ticker[ticker.buy == 1][['buy', 'open', 'close', 'date', 'sell']]
        for row in buy_subset.itertuples(index=True):
            if realistic:
                if lock_pair_until is not None and row.Index <= lock_pair_until:
                    continue
            if max_open_trades > 0:
                # Check if max_open_trades has already been reached for the given date
                if not trade_count_lock.get(row.date, 0) < max_open_trades:
                    continue

            if max_open_trades > 0:
                # Increase lock
                trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1

            trade = Trade(
                open_rate=row.close,
                open_date=row.date,
                stake_amount=stake_amount,
                amount=stake_amount / row.open,
                fee=exchange.get_fee()
            )

            # calculate win/lose forwards from buy point
            sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
            for row2 in sell_subset.itertuples(index=True):
                if max_open_trades > 0:
                    # Increase trade_count_lock for every iteration
                    trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1

                current_profit_percent = trade.calc_profit_percent(rate=row2.close)
                if (sell_profit_only and current_profit_percent < 0):
                    continue
                if min_roi_reached(trade, row2.close, row2.date) or \
                    (row2.sell == 1 and use_sell_signal) or \
                        current_profit_percent <= stoploss:
                        current_profit_btc = trade.calc_profit(rate=row2.close)
                        lock_pair_until = row2.Index

                        trades.append(
                            (
                                pair,
                                current_profit_percent,
                                current_profit_btc,
                                row2.Index - row.Index,
                                current_profit_btc > 0,
                                current_profit_btc < 0
                            )
                        )
                        break
    labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss']
    return DataFrame.from_records(trades, columns=labels)


def start(args):
    # Initialize logger
    logging.basicConfig(
        level=args.loglevel,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    )

    exchange._API = Bittrex({'key': '', 'secret': ''})

    logger.info('Using config: %s ...', args.config)
    config = load_config(args.config)

    logger.info('Using ticker_interval: %s ...', args.ticker_interval)

    data = {}
    pairs = config['exchange']['pair_whitelist']
    if args.live:
        logger.info('Downloading data for all pairs in whitelist ...')
        for pair in pairs:
            data[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
    else:
        logger.info('Using local backtesting data (using whitelist in given config) ...')
        data = load_data(pairs=pairs, ticker_interval=args.ticker_interval,
                         refresh_pairs=args.refresh_pairs)

        logger.info('Using stake_currency: %s ...', config['stake_currency'])
        logger.info('Using stake_amount: %s ...', config['stake_amount'])

    max_open_trades = 0
    if args.realistic_simulation:
        logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
        max_open_trades = config['max_open_trades']

    # Monkey patch config
    from freqtrade import main
    main._CONF = config

    preprocessed = preprocess(data)
    # Print timeframe
    min_date, max_date = get_timeframe(preprocessed)
    logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())

    # Execute backtest and print results
    results = backtest(
        stake_amount=config['stake_amount'],
        processed=preprocessed,
        max_open_trades=max_open_trades,
        realistic=args.realistic_simulation,
        sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
        stoploss=config.get('stoploss'),
        use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
    )
    logger.info(
        '\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
        generate_text_table(data, results, config['stake_currency'], args.ticker_interval)
    )