Fix documentation
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@ -63,7 +63,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
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* 0.25: Avoiding trade loss
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* 0.25: Avoiding trade loss
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* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
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* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
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"""
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"""
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total_profit = results['profit_percent'].sum()
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total_profit = results['profit_ratio'].sum()
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trade_duration = results['trade_duration'].mean()
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trade_duration = results['trade_duration'].mean()
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trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
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trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
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@ -77,10 +77,10 @@ Currently, the arguments are:
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* `results`: DataFrame containing the result
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* `results`: DataFrame containing the result
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The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
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The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
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`pair, profit_percent, profit_abs, open_date, open_rate, open_fee, close_date, close_rate, close_fee, amount, trade_duration, open_at_end, sell_reason`
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`pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, sell_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs`
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* `trade_count`: Amount of trades (identical to `len(results)`)
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* `trade_count`: Amount of trades (identical to `len(results)`)
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* `min_date`: Start date of the hyperopting TimeFrame
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* `min_date`: Start date of the timerange used
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* `min_date`: End date of the hyperopting TimeFrame
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* `min_date`: End date of the timerange used
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This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
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This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
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@ -9,11 +9,10 @@ from freqtrade.configuration import TimeRange
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from freqtrade.constants import LAST_BT_RESULT_FN
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from freqtrade.constants import LAST_BT_RESULT_FN
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from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
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from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
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analyze_trade_parallelism, calculate_market_change,
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analyze_trade_parallelism, calculate_market_change,
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calculate_max_drawdown,
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calculate_max_drawdown, combine_dataframes_with_mean,
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combine_dataframes_with_mean, create_cum_profit,
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create_cum_profit, extract_trades_of_period,
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extract_trades_of_period, get_latest_backtest_filename,
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get_latest_backtest_filename, get_latest_hyperopt_file,
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get_latest_hyperopt_file, load_backtest_data, load_trades,
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load_backtest_data, load_trades, load_trades_from_db)
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load_trades_from_db)
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from freqtrade.data.history import load_data, load_pair_history
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from freqtrade.data.history import load_data, load_pair_history
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from tests.conftest import create_mock_trades
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from tests.conftest import create_mock_trades
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from tests.conftest_trades import MOCK_TRADE_COUNT
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from tests.conftest_trades import MOCK_TRADE_COUNT
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