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Merge pull request #6165 from freqtrade/drawdown_fixes

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Improved drawdown calculation
This commit is contained in:
Matthias
2022-01-06 09:56:05 +01:00
committed by GitHub
parent 5451972456 2a2392fd73
commit addba6597a
13 changed files with 117 additions and 132 deletions
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29
freqtrade/data/btanalysis.py
View File

@@ -19,11 +19,6 @@ logger = logging.getLogger(__name__)
BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
"trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
# Mid-term format, created by BacktestResult Named Tuple
BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
'fee_close', 'amount', 'profit_abs', 'profit_ratio']
# Newest format
BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
'open_rate', 'close_rate',
@@ -392,15 +387,17 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
value_col: str = 'profit_abs', starting_balance: float = 0
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
high and low time and high and low value.
:param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
:param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
with absolute max drawdown, high and low time and high and low value,
and the relative account drawdown
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
@@ -416,7 +413,17 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
['high_value'].idxmax(), 'cumulative']
low_val = max_drawdown_df.loc[idxmin, 'cumulative']
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
return (
abs(min(max_drawdown_df['drawdown'])),
high_date,
low_date,
high_val,
low_val,
max_drawdown_rel
)
def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:

3
freqtrade/optimize/hyperopt_loss_calmar.py
View File

@@ -47,10 +47,9 @@ class CalmarHyperOptLoss(IHyperOptLoss):
# calculate max drawdown
try:
_, _, _, high_val, low_val = calculate_max_drawdown(
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
results, value_col="profit_abs"
)
max_drawdown = (high_val - low_val) / high_val
except ValueError:
max_drawdown = 0

43
freqtrade/optimize/hyperopt_tools.py
View File

@@ -308,8 +308,7 @@ class HyperoptTools():
if not has_drawdown:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.max_drawdown_abs'] = None
trials['results_metrics.max_drawdown'] = None
trials['results_metrics.max_drawdown_account'] = None
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
@@ -320,12 +319,15 @@ class HyperoptTools():
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
'results_metrics.max_drawdown',
'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
trials.columns = [
'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
]
return trials
@@ -341,9 +343,9 @@ class HyperoptTools():
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
trials = HyperoptTools.prepare_trials_columns(trials, has_drawdown)
trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
@@ -368,19 +370,20 @@ class HyperoptTools():
stake_currency = config['stake_currency']
if has_drawdown:
trials['Max Drawdown'] = trials.apply(
lambda x: '{} {}'.format(
round_coin_value(x['max_drawdown_abs'], stake_currency),
f"({x['Max Drawdown']:,.2%})".rjust(10, ' ')
).rjust(25 + len(stake_currency))
if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
axis=1
)
else:
trials = trials.drop(columns=['Max Drawdown'])
trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
lambda x: "{} {}".format(
round_coin_value(x['max_drawdown_abs'], stake_currency),
(f"({x['max_drawdown_account']:,.2%})"
if has_account_drawdown
else f"({x['max_drawdown']:,.2%})"
).rjust(10, ' ')
).rjust(25 + len(stake_currency))
if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
else '--'.rjust(25 + len(stake_currency)),
axis=1
)
trials = trials.drop(columns=['max_drawdown_abs'])
trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
trials['Profit'] = trials.apply(
lambda x: '{} {}'.format(

47
freqtrade/optimize/optimize_reports.py
View File

@@ -1,4 +1,5 @@
import logging
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
@@ -194,29 +195,21 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
return tabular_data
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
:param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(_generate_result_line(
results['results'], results['config']['dry_run_wallet'], strategy)
)
try:
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_ratio')
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_abs')
except ValueError:
max_drawdown_per = 0
max_drawdown_abs = 0
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
tabular_data[-1]['max_drawdown_abs'] = \
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
for strategy, result in bt_stats.items():
tabular_data.append(deepcopy(result['results_per_pair'][-1]))
# Update "key" to strategy (results_per_pair has it as "Total").
tabular_data[-1]['key'] = strategy
tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
result['max_drawdown_abs'], result['stake_currency'], False)
return tabular_data
@@ -462,12 +455,14 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
}
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
max_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=starting_balance)
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown': max_drawdown_legacy, # Deprecated - do not use
'max_drawdown_account': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
@@ -487,6 +482,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_account': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
@@ -521,7 +517,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
min_date, max_date, market_change=market_change)
result['strategy'][strategy] = strat_stats
strategy_results = generate_strategy_comparison(all_results=all_results)
strategy_results = generate_strategy_comparison(bt_stats=result['strategy'])
result['strategy_comparison'] = strategy_results
@@ -646,7 +642,7 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
headers.append('Drawdown')
# Align drawdown string on the center two space separator.
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
dd_pad_per = max([len(dd) for dd in drawdown])
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
@@ -716,7 +712,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Max balance', round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
# Compatibility to show old hyperopt results
('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
if 'max_drawdown_account' in strat_results else (
'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],

2
freqtrade/plot/plotting.py
View File

@@ -161,7 +161,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
Add scatter points indicating max drawdown
"""
try:
max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
_, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(trades)
drawdown = go.Scatter(
x=[highdate, lowdate],

3
freqtrade/plugins/protections/max_drawdown_protection.py
View File

@@ -55,7 +55,8 @@ class MaxDrawdown(IProtection):
# Drawdown is always positive
try:
drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
# TODO: This should use absolute profit calculation, considering account balance.
drawdown, _, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
except ValueError:
return False, None, None