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Fix wrong implementation of total_profit in Backtesting

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This commit is contained in:
Gerald Lonlas 2017-12-17 23:00:16 -08:00
parent 7541bf8c65
commit 08d6dd6a70
2 changed files with 15 additions and 13 deletions
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20
freqtrade/optimize/backtesting.py
View File

@ -48,8 +48,8 @@ def generate_text_table(
tabular_data.append([ tabular_data.append([
pair, pair,
len(result.index), len(result.index),
'{:.2f}%'.format(result.profit.mean() * 100.0), '{:.2f}%'.format(result.profit_percent.mean() * 100.0),
'{:.08f} {}'.format(result.profit.sum(), stake_currency), '{:.08f} {}'.format(result.profit_BTC.sum(), stake_currency),
'{:.2f}'.format(result.duration.mean() * ticker_interval), '{:.2f}'.format(result.duration.mean() * ticker_interval),
]) ])
@ -57,8 +57,8 @@ def generate_text_table(
tabular_data.append([ tabular_data.append([
'TOTAL', 'TOTAL',
len(results.index), len(results.index),
'{:.2f}%'.format(results.profit.mean() * 100.0), '{:.2f}%'.format(results.profit_percent.mean() * 100.0),
'{:.08f} {}'.format(results.profit.sum(), stake_currency), '{:.08f} {}'.format(results.profit_BTC.sum(), stake_currency),
'{:.2f}'.format(results.duration.mean() * ticker_interval), '{:.2f}'.format(results.duration.mean() * ticker_interval),
]) ])
return tabulate(tabular_data, headers=headers) return tabulate(tabular_data, headers=headers)
@ -98,8 +98,9 @@ def backtest(config: Dict, processed: Dict[str, DataFrame],
trade = Trade( trade = Trade(
open_rate=row.close, open_rate=row.close,
open_date=row.date, open_date=row.date,
amount=config['stake_amount'], stake_amount=config['stake_amount'],
fee=exchange.get_fee() * 2 amount= config['stake_amount'] / row.open,
fee=exchange.get_fee()
) )
# calculate win/lose forwards from buy point # 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 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: 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 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 break
labels = ['currency', 'profit', 'duration'] labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
return DataFrame.from_records(trades, columns=labels) return DataFrame.from_records(trades, columns=labels)

8
freqtrade/optimize/hyperopt.py
View File

@ -131,7 +131,7 @@ def optimizer(params):
result = format_results(results) result = format_results(results)
total_profit = results.profit.sum() * 1000 total_profit = results.profit_percent.sum() * 1000
trade_count = len(results.index) trade_count = len(results.index)
trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2) trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
@ -144,7 +144,7 @@ def optimizer(params):
'total_profit': total_profit, 'total_profit': total_profit,
'trade_loss': trade_loss, 'trade_loss': trade_loss,
'profit_loss': profit_loss, 'profit_loss': profit_loss,
'avg_profit': results.profit.mean() * 100.0, 'avg_profit': results.profit_percent.mean() * 100.0,
'avg_duration': results.duration.mean() * 5, 'avg_duration': results.duration.mean() * 5,
'current_tries': _CURRENT_TRIES, 'current_tries': _CURRENT_TRIES,
'total_tries': TOTAL_TRIES, 'total_tries': TOTAL_TRIES,
@ -166,8 +166,8 @@ def format_results(results: DataFrame):
return ('Made {:6d} buys. Average profit {: 5.2f}%. ' return ('Made {:6d} buys. Average profit {: 5.2f}%. '
'Total profit was {: 7.3f}. Average duration {:5.1f} mins.').format( 'Total profit was {: 7.3f}. Average duration {:5.1f} mins.').format(
len(results.index), len(results.index),
results.profit.mean() * 100.0, results.profit_percent.mean() * 100.0,
results.profit.sum(), results.profit_BTC.sum(),
results.duration.mean() * 5, results.duration.mean() * 5,
) )