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Fix documentation

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Matthias 2021-01-25 19:47:44 +01:00
parent c659150d9f
commit 13ad6dd461
2 changed files with 8 additions and 9 deletions
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8
docs/advanced-hyperopt.md
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@ -63,7 +63,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
* 0.25: Avoiding trade loss * 0.25: Avoiding trade loss
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
""" """
total_profit = results['profit_percent'].sum() total_profit = results['profit_ratio'].sum()
trade_duration = results['trade_duration'].mean() trade_duration = results['trade_duration'].mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8) trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
@ -77,10 +77,10 @@ Currently, the arguments are:
* `results`: DataFrame containing the result * `results`: DataFrame containing the result
The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`): The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
`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` `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`
* `trade_count`: Amount of trades (identical to `len(results)`) * `trade_count`: Amount of trades (identical to `len(results)`)
* `min_date`: Start date of the hyperopting TimeFrame * `min_date`: Start date of the timerange used
* `min_date`: End date of the hyperopting TimeFrame * `min_date`: End date of the timerange used
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. 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.

9
tests/data/test_btanalysis.py
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@ -9,11 +9,10 @@ from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD, from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
analyze_trade_parallelism, calculate_market_change, analyze_trade_parallelism, calculate_market_change,
calculate_max_drawdown, calculate_max_drawdown, combine_dataframes_with_mean,
combine_dataframes_with_mean, create_cum_profit, create_cum_profit, extract_trades_of_period,
extract_trades_of_period, get_latest_backtest_filename, get_latest_backtest_filename, get_latest_hyperopt_file,
get_latest_hyperopt_file, load_backtest_data, load_trades, load_backtest_data, load_trades, load_trades_from_db)
load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history from freqtrade.data.history import load_data, load_pair_history
from tests.conftest import create_mock_trades from tests.conftest import create_mock_trades
from tests.conftest_trades import MOCK_TRADE_COUNT from tests.conftest_trades import MOCK_TRADE_COUNT