diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 52b1d6aca..f907a53b2 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -48,8 +48,8 @@ def generate_text_table( tabular_data.append([ pair, len(result.index), - '{:.2f}%'.format(result.profit.mean() * 100.0), - '{:.08f} {}'.format(result.profit.sum(), stake_currency), + '{:.2f}%'.format(result.profit_percent.mean() * 100.0), + '{:.08f} {}'.format(result.profit_BTC.sum(), stake_currency), '{:.2f}'.format(result.duration.mean() * ticker_interval), ]) @@ -57,8 +57,8 @@ def generate_text_table( tabular_data.append([ 'TOTAL', len(results.index), - '{:.2f}%'.format(results.profit.mean() * 100.0), - '{:.08f} {}'.format(results.profit.sum(), stake_currency), + '{:.2f}%'.format(results.profit_percent.mean() * 100.0), + '{:.08f} {}'.format(results.profit_BTC.sum(), stake_currency), '{:.2f}'.format(results.duration.mean() * ticker_interval), ]) return tabulate(tabular_data, headers=headers) @@ -98,8 +98,9 @@ def backtest(config: Dict, processed: Dict[str, DataFrame], trade = Trade( open_rate=row.close, open_date=row.date, - amount=config['stake_amount'], - fee=exchange.get_fee() * 2 + stake_amount=config['stake_amount'], + amount= config['stake_amount'] / row.open, + fee=exchange.get_fee() ) # calculate win/lose forwards from buy point @@ -109,12 +110,13 @@ def backtest(config: Dict, processed: Dict[str, DataFrame], trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1 if min_roi_reached(trade, row2.close, row2.date) or row2.sell == 1: - current_profit = trade.calc_profit_percent(row2.close) + current_profit_percent = trade.calc_profit_percent(rate=row2.close) + current_profit_BTC = trade.calc_profit(rate=row2.close) lock_pair_until = row2.Index - trades.append((pair, current_profit, row2.Index - row.Index)) + trades.append((pair, current_profit_percent, current_profit_BTC, row2.Index - row.Index)) break - labels = ['currency', 'profit', 'duration'] + labels = ['currency', 'profit_percent', 'profit_BTC', 'duration'] return DataFrame.from_records(trades, columns=labels)