def test_hyperopt_format_results(hyperopt):
bt_result = {
'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"is_open": [False, False, False, True],
"stake_amount": [0.01, 0.01, 0.01, 0.01],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
}),
'config': hyperopt.config,
'locks': [],
'final_balance': 0.02,
'rejected_signals': 2,
'backtest_start_time': 1619718665,
'backtest_end_time': 1619718665,
}
results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
Arrow(2017, 11, 14, 19, 32, 00),
Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
results_explanation = HyperoptTools.format_results_explanation_string(results_metrics, 'BTC')
total_profit = results_metrics['profit_total_abs']
results = {
'loss': 0.0,
'params_dict': None,
'params_details': None,
'results_metrics': results_metrics,
'results_explanation': results_explanation,
'total_profit': total_profit,
'current_epoch': 1,
'is_initial_point': True,
}
result = HyperoptTools._format_explanation_string(results, 1)
assert ' 0.71%' in result
assert 'Total profit 0.00003100 BTC' in result
assert '0:50:00 min' in result
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict,
processed: Dict[str, DataFrame]) -> Dict[str, Any]:
params_details = self._get_params_details(params_dict)
strat_stats = generate_strategy_stats(
processed,
self.backtesting.strategy.get_strategy_name(),
backtesting_results,
min_date,
max_date,
market_change=0)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
not_optimized = self.backtesting.strategy.get_no_optimize_params()
not_optimized = deep_merge_dicts(not_optimized,
self._get_no_optimize_details())
trade_count = strat_stats['total_trades']
total_profit = strat_stats['profit_total']
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
# in order to cast this hyperspace point away from optimization
# path. We do not want to optimize 'hodl' strategies.
loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results['results'],
trade_count=trade_count,
min_date=min_date,
max_date=max_date,
config=self.config,
processed=processed,
backtest_stats=strat_stats)
return {
'loss': loss,
'params_dict': params_dict,
'params_details': params_details,
'params_not_optimized': not_optimized,
'results_metrics': strat_stats,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
