Merge pull request #6165 from freqtrade/drawdown_fixes
Improved drawdown calculation
This commit is contained in:
Matthias
GitHub
commit
addba6597a
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@ -312,7 +312,7 @@ A backtesting result will look like that:
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| Min balance | 0.00945123 BTC |
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| Max balance | 0.01846651 BTC |
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| Drawdown | 50.63% |
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| Drawdown (Account) | 13.33% |
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| Drawdown | 0.0015 BTC |
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| Drawdown high | 0.0013 BTC |
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| Drawdown low | -0.0002 BTC |
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@ -399,7 +399,7 @@ It contains some useful key metrics about performance of your strategy on backte
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| Min balance | 0.00945123 BTC |
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| Max balance | 0.01846651 BTC |
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| Drawdown | 50.63% |
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| Drawdown (Account) | 13.33% |
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| Drawdown | 0.0015 BTC |
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| Drawdown high | 0.0013 BTC |
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| Drawdown low | -0.0002 BTC |
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@ -426,7 +426,8 @@ It contains some useful key metrics about performance of your strategy on backte
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- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
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- `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
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- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
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- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
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- `Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$.
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- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
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- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
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- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
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- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
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@ -19,11 +19,6 @@ logger = logging.getLogger(__name__)
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BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
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"trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
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# Mid-term format, created by BacktestResult Named Tuple
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BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
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'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
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'fee_close', 'amount', 'profit_abs', 'profit_ratio']
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# Newest format
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BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
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'open_rate', 'close_rate',
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@ -392,15 +387,17 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
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def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_ratio'
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) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
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value_col: str = 'profit_abs', starting_balance: float = 0
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) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
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"""
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Calculate max drawdown and the corresponding close dates
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:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
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high and low time and high and low value.
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
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:param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
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with absolute max drawdown, high and low time and high and low value,
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and the relative account drawdown
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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@ -416,7 +413,17 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
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high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
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['high_value'].idxmax(), 'cumulative']
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low_val = max_drawdown_df.loc[idxmin, 'cumulative']
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return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
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max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
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return (
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abs(min(max_drawdown_df['drawdown'])),
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high_date,
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low_date,
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high_val,
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low_val,
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max_drawdown_rel
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)
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def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
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@ -47,10 +47,9 @@ class CalmarHyperOptLoss(IHyperOptLoss):
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# calculate max drawdown
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try:
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_, _, _, high_val, low_val = calculate_max_drawdown(
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_, _, _, _, _, max_drawdown = calculate_max_drawdown(
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results, value_col="profit_abs"
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)
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max_drawdown = (high_val - low_val) / high_val
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except ValueError:
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max_drawdown = 0
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@ -308,8 +308,7 @@ class HyperoptTools():
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if not has_drawdown:
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# Ensure compatibility with older versions of hyperopt results
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trials['results_metrics.max_drawdown_abs'] = None
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trials['results_metrics.max_drawdown'] = None
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trials['results_metrics.max_drawdown_account'] = None
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# New mode, using backtest result for metrics
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trials['results_metrics.winsdrawslosses'] = trials.apply(
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@ -320,12 +319,15 @@ class HyperoptTools():
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'results_metrics.winsdrawslosses',
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'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
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'results_metrics.profit_total', 'results_metrics.holding_avg',
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'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
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'results_metrics.max_drawdown',
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'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
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'loss', 'is_initial_point', 'is_best']]
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trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
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'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
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'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
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trials.columns = [
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'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
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'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
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'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
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]
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return trials
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@ -341,9 +343,9 @@ class HyperoptTools():
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tabulate.PRESERVE_WHITESPACE = True
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trials = json_normalize(results, max_level=1)
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has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
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has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
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trials = HyperoptTools.prepare_trials_columns(trials, has_drawdown)
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trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '* '
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@ -368,19 +370,20 @@ class HyperoptTools():
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stake_currency = config['stake_currency']
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if has_drawdown:
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trials['Max Drawdown'] = trials.apply(
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lambda x: '{} {}'.format(
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round_coin_value(x['max_drawdown_abs'], stake_currency),
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f"({x['Max Drawdown']:,.2%})".rjust(10, ' ')
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).rjust(25 + len(stake_currency))
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if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
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axis=1
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)
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else:
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trials = trials.drop(columns=['Max Drawdown'])
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trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
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lambda x: "{} {}".format(
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round_coin_value(x['max_drawdown_abs'], stake_currency),
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(f"({x['max_drawdown_account']:,.2%})"
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if has_account_drawdown
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else f"({x['max_drawdown']:,.2%})"
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).rjust(10, ' ')
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).rjust(25 + len(stake_currency))
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if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
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else '--'.rjust(25 + len(stake_currency)),
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axis=1
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)
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trials = trials.drop(columns=['max_drawdown_abs'])
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trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
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trials['Profit'] = trials.apply(
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lambda x: '{} {}'.format(
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@ -1,4 +1,5 @@
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import logging
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from copy import deepcopy
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Union
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@ -194,29 +195,21 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
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return tabular_data
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def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
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def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
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"""
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Generate summary per strategy
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:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
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:param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
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:return: List of Dicts containing the metrics per Strategy
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"""
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tabular_data = []
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for strategy, results in all_results.items():
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tabular_data.append(_generate_result_line(
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results['results'], results['config']['dry_run_wallet'], strategy)
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)
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try:
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max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
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value_col='profit_ratio')
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max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
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value_col='profit_abs')
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except ValueError:
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max_drawdown_per = 0
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max_drawdown_abs = 0
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tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
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tabular_data[-1]['max_drawdown_abs'] = \
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round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
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for strategy, result in bt_stats.items():
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tabular_data.append(deepcopy(result['results_per_pair'][-1]))
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# Update "key" to strategy (results_per_pair has it as "Total").
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tabular_data[-1]['key'] = strategy
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tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
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tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
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result['max_drawdown_abs'], result['stake_currency'], False)
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return tabular_data
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@ -462,12 +455,14 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
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}
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try:
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max_drawdown, _, _, _, _ = calculate_max_drawdown(
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max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
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results, value_col='profit_ratio')
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drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
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results, value_col='profit_abs')
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(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
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max_drawdown) = calculate_max_drawdown(
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results, value_col='profit_abs', starting_balance=starting_balance)
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strat_stats.update({
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'max_drawdown': max_drawdown,
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'max_drawdown': max_drawdown_legacy, # Deprecated - do not use
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'max_drawdown_account': max_drawdown,
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'max_drawdown_abs': drawdown_abs,
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'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
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'drawdown_start_ts': drawdown_start.timestamp() * 1000,
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@ -487,6 +482,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
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except ValueError:
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strat_stats.update({
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'max_drawdown': 0.0,
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'max_drawdown_account': 0.0,
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'max_drawdown_abs': 0.0,
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'max_drawdown_low': 0.0,
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'max_drawdown_high': 0.0,
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@ -521,7 +517,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
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min_date, max_date, market_change=market_change)
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result['strategy'][strategy] = strat_stats
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strategy_results = generate_strategy_comparison(all_results=all_results)
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strategy_results = generate_strategy_comparison(bt_stats=result['strategy'])
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result['strategy_comparison'] = strategy_results
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@ -646,7 +642,7 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
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headers.append('Drawdown')
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# Align drawdown string on the center two space separator.
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drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
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drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
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dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
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dd_pad_per = max([len(dd) for dd in drawdown])
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drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
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@ -716,7 +712,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
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('Max balance', round_coin_value(strat_results['csum_max'],
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strat_results['stake_currency'])),
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('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
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# Compatibility to show old hyperopt results
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('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
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if 'max_drawdown_account' in strat_results else (
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'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
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('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
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strat_results['stake_currency'])),
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('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
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@ -161,7 +161,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
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Add scatter points indicating max drawdown
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"""
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try:
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max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
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_, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(trades)
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drawdown = go.Scatter(
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x=[highdate, lowdate],
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@ -55,7 +55,8 @@ class MaxDrawdown(IProtection):
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# Drawdown is always positive
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try:
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drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
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# TODO: This should use absolute profit calculation, considering account balance.
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drawdown, _, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
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except ValueError:
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return False, None, None
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@ -2020,7 +2020,7 @@ def saved_hyperopt_results():
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'params_dict': {
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'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501
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'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501
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'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501
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'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501
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'total_profit': -0.00125625,
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'current_epoch': 1,
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@ -2036,7 +2036,7 @@ def saved_hyperopt_results():
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'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501
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'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501
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'stoploss': {'stoploss': -0.338070047333259}},
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'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
|
||||
'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501
|
||||
'total_profit': 6.185e-05,
|
||||
'current_epoch': 2,
|
||||
|
|
@ -2046,7 +2046,7 @@ def saved_hyperopt_results():
|
|||
'loss': 14.241196856510731,
|
||||
'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'max_drawdown': 0.25, 'max_drawdown_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
|
||||
'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501
|
||||
'total_profit': -0.13639474,
|
||||
'current_epoch': 3,
|
||||
|
|
@ -2063,7 +2063,7 @@ def saved_hyperopt_results():
|
|||
'loss': 0.22195522184191518,
|
||||
'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'max_drawdown': 0.34, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
|
||||
'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501
|
||||
'total_profit': -0.002480140000000001,
|
||||
'current_epoch': 5,
|
||||
|
|
@ -2073,7 +2073,7 @@ def saved_hyperopt_results():
|
|||
'loss': 0.545315889154162,
|
||||
'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'max_drawdown': 0.45, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
|
||||
'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501
|
||||
'total_profit': -0.0041773,
|
||||
'current_epoch': 6,
|
||||
|
|
@ -2085,7 +2085,7 @@ def saved_hyperopt_results():
|
|||
'params_details': {
|
||||
'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501
|
||||
'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'max_drawdown': 0.50, 'max_drawdown_abs': -200.955321, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
|
||||
'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501
|
||||
'total_profit': -0.06339929,
|
||||
'current_epoch': 7,
|
||||
|
|
@ -2095,7 +2095,7 @@ def saved_hyperopt_results():
|
|||
'loss': 20.0, # noqa: E501
|
||||
'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.52, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
|
||||
'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501
|
||||
'total_profit': 0.0,
|
||||
'current_epoch': 8,
|
||||
|
|
@ -2105,7 +2105,7 @@ def saved_hyperopt_results():
|
|||
'loss': 2.4731817780991223,
|
||||
'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'max_drawdown': 0.41, 'max_drawdown_abs': -150.955321, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
|
||||
'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501
|
||||
'total_profit': -0.044050070000000004, # noqa: E501
|
||||
'current_epoch': 9,
|
||||
|
|
@ -2115,7 +2115,7 @@ def saved_hyperopt_results():
|
|||
'loss': -0.2604606005845212, # noqa: E501
|
||||
'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'max_drawdown': 0.13, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
|
||||
'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501
|
||||
'total_profit': 0.00021629,
|
||||
'current_epoch': 10,
|
||||
|
|
@ -2126,7 +2126,7 @@ def saved_hyperopt_results():
|
|||
'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501
|
||||
# New Hyperopt mode!
|
||||
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'max_drawdown': 0.52, 'max_drawdown_abs': -224.955321, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
|
||||
'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501
|
||||
'total_profit': -0.07436117,
|
||||
'current_epoch': 11,
|
||||
|
|
@ -2136,7 +2136,7 @@ def saved_hyperopt_results():
|
|||
'loss': 100000,
|
||||
'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
|
||||
'total_profit': 0,
|
||||
'current_epoch': 12,
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from pandas import DataFrame, DateOffset, Timestamp, to_datetime
|
|||
|
||||
from freqtrade.configuration import TimeRange
|
||||
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_OLD,
|
||||
analyze_trade_parallelism, calculate_csum,
|
||||
calculate_market_change, calculate_max_drawdown,
|
||||
calculate_underwater, combine_dataframes_with_mean,
|
||||
|
|
@ -72,7 +72,7 @@ def test_load_backtest_data_new_format(testdatadir):
|
|||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
assert isinstance(bt_data, DataFrame)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS_MID)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS + ['close_timestamp', 'open_timestamp'])
|
||||
assert len(bt_data) == 179
|
||||
|
||||
# Test loading from string (must yield same result)
|
||||
|
|
@ -96,7 +96,7 @@ def test_load_backtest_data_multi(testdatadir):
|
|||
for strategy in ('StrategyTestV2', 'TestStrategy'):
|
||||
bt_data = load_backtest_data(filename, strategy=strategy)
|
||||
assert isinstance(bt_data, DataFrame)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS_MID)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS + ['close_timestamp', 'open_timestamp'])
|
||||
assert len(bt_data) == 179
|
||||
|
||||
# Test loading from string (must yield same result)
|
||||
|
|
@ -280,23 +280,24 @@ def test_create_cum_profit1(testdatadir):
|
|||
|
||||
|
||||
def test_calculate_max_drawdown(testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(bt_data)
|
||||
_, hdate, lowdate, hval, lval, drawdown = calculate_max_drawdown(
|
||||
bt_data, value_col="profit_abs")
|
||||
assert isinstance(drawdown, float)
|
||||
assert pytest.approx(drawdown) == 0.21142322
|
||||
assert pytest.approx(drawdown) == 0.12071099
|
||||
assert isinstance(hdate, Timestamp)
|
||||
assert isinstance(lowdate, Timestamp)
|
||||
assert isinstance(hval, float)
|
||||
assert isinstance(lval, float)
|
||||
assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
|
||||
assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
|
||||
assert hdate == Timestamp('2018-01-25 01:30:00', tz='UTC')
|
||||
assert lowdate == Timestamp('2018-01-25 03:50:00', tz='UTC')
|
||||
|
||||
underwater = calculate_underwater(bt_data)
|
||||
assert isinstance(underwater, DataFrame)
|
||||
|
||||
with pytest.raises(ValueError, match='Trade dataframe empty.'):
|
||||
drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
|
||||
calculate_max_drawdown(DataFrame())
|
||||
|
||||
with pytest.raises(ValueError, match='Trade dataframe empty.'):
|
||||
calculate_underwater(DataFrame())
|
||||
|
|
@ -331,12 +332,13 @@ def test_calculate_max_drawdown2():
|
|||
# sort by profit and reset index
|
||||
df = df.sort_values('profit').reset_index(drop=True)
|
||||
df1 = df.copy()
|
||||
drawdown, hdate, ldate, hval, lval = calculate_max_drawdown(
|
||||
drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
|
||||
df, date_col='open_date', value_col='profit')
|
||||
# Ensure df has not been altered.
|
||||
assert df.equals(df1)
|
||||
|
||||
assert isinstance(drawdown, float)
|
||||
assert isinstance(drawdown_rel, float)
|
||||
# High must be before low
|
||||
assert hdate < ldate
|
||||
# High value must be higher than low value
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import datetime
|
||||
import re
|
||||
from datetime import timedelta
|
||||
from pathlib import Path
|
||||
|
|
@ -103,7 +102,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
|
|||
assert strat_stats['backtest_end'] == max_date.strftime(DATETIME_PRINT_FORMAT)
|
||||
assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
|
||||
# Above sample had no loosing trade
|
||||
assert strat_stats['max_drawdown'] == 0.0
|
||||
assert strat_stats['max_drawdown_account'] == 0.0
|
||||
|
||||
# Retry with losing trade
|
||||
results = {'DefStrat': {
|
||||
|
|
@ -143,7 +142,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
|
|||
assert 'strategy_comparison' in stats
|
||||
strat_stats = stats['strategy']['DefStrat']
|
||||
|
||||
assert strat_stats['max_drawdown'] == 0.013803
|
||||
assert pytest.approx(strat_stats['max_drawdown_account']) == 1.399999e-08
|
||||
assert strat_stats['drawdown_start'] == '2017-11-14 22:10:00'
|
||||
assert strat_stats['drawdown_end'] == '2017-11-14 22:43:00'
|
||||
assert strat_stats['drawdown_end_ts'] == 1510699380000
|
||||
|
|
@ -165,7 +164,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
|
|||
filename1 = Path(tmpdir / last_fn)
|
||||
assert filename1.is_file()
|
||||
content = filename1.read_text()
|
||||
assert 'max_drawdown' in content
|
||||
assert 'max_drawdown_account' in content
|
||||
assert 'strategy' in content
|
||||
assert 'pairlist' in content
|
||||
|
||||
|
|
@ -227,9 +226,9 @@ def test_generate_daily_stats(testdatadir):
|
|||
assert isinstance(res, dict)
|
||||
assert round(res['backtest_best_day'], 4) == 0.1796
|
||||
assert round(res['backtest_worst_day'], 4) == -0.1468
|
||||
assert res['winning_days'] == 14
|
||||
assert res['draw_days'] == 4
|
||||
assert res['losing_days'] == 3
|
||||
assert res['winning_days'] == 19
|
||||
assert res['draw_days'] == 0
|
||||
assert res['losing_days'] == 2
|
||||
|
||||
# Select empty dataframe!
|
||||
res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
|
||||
|
|
@ -324,51 +323,25 @@ def test_generate_sell_reason_stats():
|
|||
assert stop_result['profit_mean_pct'] == round(stop_result['profit_mean'] * 100, 2)
|
||||
|
||||
|
||||
def test_text_table_strategy(default_conf):
|
||||
default_conf['max_open_trades'] = 2
|
||||
default_conf['dry_run_wallet'] = 3
|
||||
results = {}
|
||||
date = datetime.datetime(year=2020, month=1, day=1, hour=12, minute=30)
|
||||
delta = datetime.timedelta(days=1)
|
||||
results['TestStrategy1'] = {'results': pd.DataFrame(
|
||||
{
|
||||
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
|
||||
'close_date': [date, date + delta, date + delta * 2],
|
||||
'profit_ratio': [0.1, 0.2, 0.3],
|
||||
'profit_abs': [0.2, 0.4, 0.5],
|
||||
'trade_duration': [10, 30, 10],
|
||||
'wins': [2, 0, 0],
|
||||
'draws': [0, 0, 0],
|
||||
'losses': [0, 0, 1],
|
||||
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
|
||||
}
|
||||
), 'config': default_conf}
|
||||
results['TestStrategy2'] = {'results': pd.DataFrame(
|
||||
{
|
||||
'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
|
||||
'close_date': [date, date + delta, date + delta * 2],
|
||||
'profit_ratio': [0.4, 0.2, 0.3],
|
||||
'profit_abs': [0.4, 0.4, 0.5],
|
||||
'trade_duration': [15, 30, 15],
|
||||
'wins': [4, 1, 0],
|
||||
'draws': [0, 0, 0],
|
||||
'losses': [0, 0, 1],
|
||||
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
|
||||
}
|
||||
), 'config': default_conf}
|
||||
def test_text_table_strategy(testdatadir):
|
||||
filename = testdatadir / "backtest-result_multistrat.json"
|
||||
bt_res_data = load_backtest_stats(filename)
|
||||
|
||||
bt_res_data_comparison = bt_res_data.pop('strategy_comparison')
|
||||
|
||||
result_str = (
|
||||
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
|
||||
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
|
||||
' Tot Profit % | Avg Duration | Win Draw Loss Win% | Drawdown |\n'
|
||||
'|---------------+--------+----------------+----------------+------------------+'
|
||||
'|----------------+--------+----------------+----------------+------------------+'
|
||||
'----------------+----------------+-------------------------+-----------------------|\n'
|
||||
'| TestStrategy1 | 3 | 20.00 | 60.00 | 1.10000000 |'
|
||||
' 36.67 | 0:17:00 | 3 0 0 100 | 0.00000000 BTC 0.00% |\n'
|
||||
'| TestStrategy2 | 3 | 30.00 | 90.00 | 1.30000000 |'
|
||||
' 43.33 | 0:20:00 | 3 0 0 100 | 0.00000000 BTC 0.00% |'
|
||||
'| StrategyTestV2 | 179 | 0.08 | 14.39 | 0.02608550 |'
|
||||
' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |\n'
|
||||
'| TestStrategy | 179 | 0.08 | 14.39 | 0.02608550 |'
|
||||
' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |'
|
||||
)
|
||||
|
||||
strategy_results = generate_strategy_comparison(all_results=results)
|
||||
strategy_results = generate_strategy_comparison(bt_stats=bt_res_data['strategy'])
|
||||
assert strategy_results == bt_res_data_comparison
|
||||
assert text_table_strategy(strategy_results, 'BTC') == result_str
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -343,7 +343,7 @@ def test_generate_profit_graph(testdatadir):
|
|||
|
||||
profit = find_trace_in_fig_data(figure.data, "Profit")
|
||||
assert isinstance(profit, go.Scatter)
|
||||
drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
|
||||
drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 35.69%")
|
||||
assert isinstance(drawdown, go.Scatter)
|
||||
parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
|
||||
assert isinstance(parallel, go.Scatter)
|
||||
|
|
|
|||
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Loading…
Reference in New Issue