# pragma pylint: disable=missing-docstring import json import logging import os from typing import Tuple, Dict import arrow import pytest from arrow import Arrow from pandas import DataFrame from tabulate import tabulate from freqtrade import exchange from freqtrade.analyze import parse_ticker_dataframe, populate_indicators, \ 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 format_results(results: DataFrame): return 'Made {} buys. Average profit {:.2f}%. ' \ 'Total profit was {:.3f}. Average duration {:.1f} mins.'.format( len(results.index), results.profit.mean() * 100.0, results.profit.sum(), results.duration.mean() * 5, ) def print_pair_results(pair: str, results: DataFrame): print('For currency {}:'.format(pair)) print(format_results(results[results.currency == pair])) def preprocess(backdata) -> Dict[str, DataFrame]: processed = {} for pair, pair_data in backdata.items(): processed[pair] = populate_indicators(parse_ticker_dataframe(pair_data)) return processed def get_timeframe(data: Dict[str, Dict]) -> Tuple[Arrow, Arrow]: """ Get the maximum timeframe for the given backtest data :param data: dictionary with backtesting data :return: tuple containing min_date, max_date """ min_date, max_date = None, None for values in data.values(): values = sorted(values, key=lambda d: arrow.get(d['T'])) if not min_date or values[0]['T'] < min_date: min_date = values[0]['T'] if not max_date or values[-1]['T'] > max_date: max_date = values[-1]['T'] return arrow.get(min_date), arrow.get(max_date) def generate_text_table(data: Dict[str, Dict], results: DataFrame) -> str: """ Generates and returns a text table for the given backtest data and the results dataframe :return: pretty printed table with tabulate as str """ tabular_data = [] headers = ['pair', 'buy count', 'avg profit', 'total profit', 'avg duration'] for pair in data: result = results[results.currency == pair] tabular_data.append([ pair, len(result.index), '{:.2f}%'.format(result.profit.mean() * 100.0), '{:.8f}'.format(result.profit.sum()), '{:.2f}'.format(result.duration.mean() * 5), ]) # Append Total tabular_data.append([ 'TOTAL', len(results.index), '{:.2f}%'.format(results.profit.mean() * 100.0), '{:.8f}'.format(results.profit.sum()), '{:.2f}'.format(results.duration.mean() * 5), ]) return tabulate(tabular_data, headers=headers) def backtest(backtest_conf, processed, mocker): trades = [] exchange._API = Bittrex({'key': '', 'secret': ''}) mocker.patch.dict('freqtrade.main._CONF', backtest_conf) for pair, pair_data in processed.items(): pair_data['buy'] = 0 pair_data['sell'] = 0 ticker = populate_sell_trend(populate_buy_trend(pair_data)) # for each buy point for row in ticker[ticker.buy == 1].itertuples(index=True): trade = Trade( open_rate=row.close, open_date=row.date, amount=1, fee=exchange.get_fee() * 2 ) # calculate win/lose forwards from buy point for row2 in ticker[row.Index:].itertuples(index=True): if min_roi_reached(trade, row2.close, row2.date) or row2.sell == 1: current_profit = trade.calc_profit(row2.close) trades.append((pair, current_profit, row2.Index - row.Index)) break labels = ['currency', 'profit', 'duration'] return DataFrame.from_records(trades, columns=labels) @pytest.mark.skipif(not os.environ.get('BACKTEST'), reason="BACKTEST not set") def test_backtest(backtest_conf, backdata, mocker): print('') config = None conf_path = os.environ.get('BACKTEST_CONFIG') if conf_path: print('Using config: {} ...'.format(conf_path)) with open(conf_path, 'r') as conf_file: config = json.load(conf_file) ticker_interval = int(os.environ.get('BACKTEST_TICKER_INTERVAL') or 5) print('Using ticker_interval: {} ...'.format(ticker_interval)) livedata = {} if os.environ.get('BACKTEST_LIVE'): print('Downloading data for all pairs in whitelist ...') exchange._API = Bittrex({'key': '', 'secret': ''}) for pair in config['exchange']['pair_whitelist']: livedata[pair] = exchange.get_ticker_history(pair, ticker_interval) config = config or backtest_conf data = livedata or backdata min_date, max_date = get_timeframe(data) print('Measuring data from {} up to {} ...'.format( min_date.isoformat(), max_date.isoformat() )) results = backtest(config, preprocess(data), mocker) print('====================== BACKTESTING REPORT ======================================\n') print(generate_text_table(data, results))