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freqtrade
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generate_backtest_stats must take config options from the strategy

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config

as a strategy can override certain options.
pull/3803/head
Matthias 6 years ago
parent 378f03a5b1
commit ff3e2641ae
3 changed files with 85 additions and 72 deletions
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23
freqtrade/optimize/backtesting.py
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@ -380,12 +380,6 @@ class Backtesting:
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
position_stacking = self.config.get('position_stacking', False)
data, timerange = self.load_bt_data()
@ -395,6 +389,15 @@ class Backtesting:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
@ -407,7 +410,7 @@ class Backtesting:
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
results = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date,
@ -415,8 +418,12 @@ class Backtesting:
max_open_trades=max_open_trades,
position_stacking=position_stacking,
)
all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
}
stats = generate_backtest_stats(self.config, data, all_results,
stats = generate_backtest_stats(data, all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)

33
freqtrade/optimize/optimize_reports.py
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@ -1,7 +1,7 @@
import logging
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List
from typing import Any, Dict, List, Union
from arrow import Arrow
from pandas import DataFrame
@ -143,19 +143,18 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
return tabular_data
def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
all_results: Dict) -> List[Dict]:
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> 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, max_open_trades, strategy))
tabular_data.append(_generate_result_line(
results['results'], results['config']['max_open_trades'], strategy)
)
return tabular_data
@ -219,25 +218,29 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
}
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame],
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
:param all_results: backtest result - dictionary in the form:
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items():
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = config['max_open_trades']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
@ -310,9 +313,7 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
'drawdown_end_ts': 0,
})
strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
max_open_trades=max_open_trades,
all_results=all_results)
strategy_results = generate_strategy_metrics(all_results=all_results)
result['strategy_comparison'] = strategy_results

101
tests/optimize/test_optimize_reports.py
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@ -60,32 +60,35 @@ def test_generate_backtest_stats(default_conf, testdatadir):
default_conf.update({'strategy': 'DefaultStrategy'})
StrategyResolver.load_strategy(default_conf)
results = {'DefStrat': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [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],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
results = {'DefStrat': {
'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [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],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
}),
'config': default_conf}
}
timerange = TimeRange.parse_timerange('1510688220-1510700340')
min_date = Arrow.fromtimestamp(1510688220)
max_date = Arrow.fromtimestamp(1510700340)
btdata = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
stats = generate_backtest_stats(default_conf, btdata, results, min_date, max_date)
stats = generate_backtest_stats(btdata, results, min_date, max_date)
assert isinstance(stats, dict)
assert 'strategy' in stats
assert 'DefStrat' in stats['strategy']
@ -93,29 +96,32 @@ def test_generate_backtest_stats(default_conf, testdatadir):
strat_stats = stats['strategy']['DefStrat']
assert strat_stats['backtest_start'] == min_date.datetime
assert strat_stats['backtest_end'] == max_date.datetime
assert strat_stats['total_trades'] == len(results['DefStrat'])
assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
# Above sample had no loosing trade
assert strat_stats['max_drawdown'] == 0.0
results = {'DefStrat': pd.DataFrame(
{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [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, 0.0032903, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
results = {'DefStrat': {
'results': pd.DataFrame(
{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [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, 0.0032903, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
}),
'config': default_conf}
}
assert strat_stats['max_drawdown'] == 0.0
assert strat_stats['drawdown_start'] == Arrow.fromtimestamp(0).datetime
@ -283,9 +289,10 @@ def test_generate_sell_reason_stats(default_conf):
assert stop_result['profit_mean_pct'] == round(stop_result['profit_mean'] * 100, 2)
def test_text_table_strategy(default_conf, mocker):
def test_text_table_strategy(default_conf):
default_conf['max_open_trades'] = 2
results = {}
results['TestStrategy1'] = pd.DataFrame(
results['TestStrategy1'] = {'results': pd.DataFrame(
{
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'profit_percent': [0.1, 0.2, 0.3],
@ -296,8 +303,8 @@ def test_text_table_strategy(default_conf, mocker):
'losses': [0, 0, 1],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}
)
results['TestStrategy2'] = pd.DataFrame(
), 'config': default_conf}
results['TestStrategy2'] = {'results': pd.DataFrame(
{
'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
'profit_percent': [0.4, 0.2, 0.3],
@ -308,7 +315,7 @@ def test_text_table_strategy(default_conf, mocker):
'losses': [0, 0, 1],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}
)
), 'config': default_conf}
result_str = (
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot'
@ -321,9 +328,7 @@ def test_text_table_strategy(default_conf, mocker):
' 45.00 | 0:20:00 | 3 | 0 | 0 |'
)
strategy_results = generate_strategy_metrics(stake_currency='BTC',
max_open_trades=2,
all_results=results)
strategy_results = generate_strategy_metrics(all_results=results)
assert text_table_strategy(strategy_results, 'BTC') == result_str

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