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Don't use profit_percent for backtesting results anymore

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Matthias
2021-01-23 13:02:48 +01:00
parent 48977493bb
commit 8ee264bc59
17 changed files with 78 additions and 101 deletions
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34
freqtrade/optimize/optimize_reports.py
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@@ -58,14 +58,14 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
"""
Generate one result dict, with "first_column" as key.
"""
profit_sum = result['profit_percent'].sum()
profit_sum = result['profit_ratio'].sum()
profit_total = profit_sum / max_open_trades
return {
'key': first_column,
'trades': len(result),
'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100.0, 2),
'profit_total_abs': result['profit_abs'].sum(),
@@ -124,8 +124,8 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
for reason, count in results['sell_reason'].value_counts().iteritems():
result = results.loc[results['sell_reason'] == reason]
profit_mean = result['profit_percent'].mean()
profit_sum = result['profit_percent'].sum()
profit_mean = result['profit_ratio'].mean()
profit_sum = result['profit_ratio'].sum()
profit_total = profit_sum / max_open_trades
tabular_data.append(
@@ -150,7 +150,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
@@ -199,15 +199,15 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_ratio'].sum()
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_percent'] > 0]
losing_trades = results.loc[results['profit_percent'] < 0]
winning_trades = results.loc[results['profit_ratio'] > 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
return {
'backtest_best_day': best,
@@ -273,8 +273,8 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_percent'].sum(),
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_ratio'].sum(),
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
@@ -314,7 +314,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_percent')
results, value_col='profit_ratio')
strat_stats.update({
'max_drawdown': max_drawdown,
'drawdown_start': drawdown_start,
@@ -392,7 +392,7 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
Generate summary table per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
:return: pretty printed table with tabulate as string
"""
floatfmt = _get_line_floatfmt()
@@ -409,8 +409,8 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_percent'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_percent'])
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
@@ -424,9 +424,9 @@ def text_table_add_metrics(strat_results: Dict) -> str:
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
('Worst Pair', f"{strat_results['worst_pair']['key']} "
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
('Best trade', f"{best_trade['pair']} {round(best_trade['profit_percent'] * 100, 2)}%"),
('Best trade', f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"),
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_percent'] * 100, 2)}%"),
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),