# pragma pylint: disable=missing-docstring,W0212

import logging
from typing import Dict, Tuple

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

import freqtrade.misc as misc
import freqtrade.optimize as optimize
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.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 = misc.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) ...')
        logger.info('Using stake_currency: %s ...', config['stake_currency'])
        logger.info('Using stake_amount: %s ...', config['stake_amount'])

        data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval,
                                  refresh_pairs=args.refresh_pairs)

    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 = optimize.tickerdata_to_dataframe(data, timerange=args.timerange)
    # 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)
    )