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Merge branch 'develop' into pr/TreborNamor/5607

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Matthias
2021-10-24 08:55:10 +02:00
parent ca973c05d1 b4bedc22d7
commit dffb4c5d53
75 changed files with 2416 additions and 1389 deletions
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3
freqtrade/optimize/hyperopt.py
View File

@@ -45,7 +45,7 @@ progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__)
INITIAL_POINTS = 5
INITIAL_POINTS = 30
# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt model queue, to optimize memory consumption
@@ -258,6 +258,7 @@ class Hyperopt:
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+ self.roi_space + self.stoploss_space + self.trailing_space)

27
freqtrade/optimize/hyperopt_auto.py
View File

@@ -3,6 +3,7 @@ HyperOptAuto class.
This module implements a convenience auto-hyperopt class, which can be used together with strategies
that implement IHyperStrategy interface.
"""
import logging
from contextlib import suppress
from typing import Callable, Dict, List
@@ -15,12 +16,19 @@ with suppress(ImportError):
from freqtrade.optimize.hyperopt_interface import EstimatorType, IHyperOpt
def _format_exception_message(space: str) -> str:
raise OperationalException(
f"The '{space}' space is included into the hyperoptimization "
f"but no parameter for this space was not found in your Strategy. "
f"Please make sure to have parameters for this space enabled for optimization "
f"or remove the '{space}' space from hyperoptimization.")
logger = logging.getLogger(__name__)
def _format_exception_message(space: str, ignore_missing_space: bool) -> None:
msg = (f"The '{space}' space is included into the hyperoptimization "
f"but no parameter for this space was not found in your Strategy. "
)
if ignore_missing_space:
logger.warning(msg + "This space will be ignored.")
else:
raise OperationalException(
msg + f"Please make sure to have parameters for this space enabled for optimization "
f"or remove the '{space}' space from hyperoptimization.")
class HyperOptAuto(IHyperOpt):
@@ -48,13 +56,16 @@ class HyperOptAuto(IHyperOpt):
if attr.optimize:
yield attr.get_space(attr_name)
def _get_indicator_space(self, category):
def _get_indicator_space(self, category) -> List:
# TODO: is this necessary, or can we call "generate_space" directly?
indicator_space = list(self._generate_indicator_space(category))
if len(indicator_space) > 0:
return indicator_space
else:
_format_exception_message(category)
_format_exception_message(
category,
self.config.get("hyperopt_ignore_missing_space", False))
return []
def buy_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy')

41
freqtrade/optimize/hyperopt_loss_max_drawdown.py Normal file
View File

@@ -0,0 +1,41 @@
"""
MaxDrawDownHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
from freqtrade.data.btanalysis import calculate_max_drawdown
from freqtrade.optimize.hyperopt import IHyperOptLoss
class MaxDrawDownHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation optimizes for max draw down and profit
Less max drawdown more profit -> Lower return value
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function.
Uses profit ratio weighted max_drawdown when drawdown is available.
Otherwise directly optimizes profit ratio.
"""
total_profit = results['profit_abs'].sum()
try:
max_drawdown = calculate_max_drawdown(results, value_col='profit_abs')
except ValueError:
# No losing trade, therefore no drawdown.
return -total_profit
return -total_profit / max_drawdown[0]

87
freqtrade/optimize/optimize_reports.py
View File

@@ -4,7 +4,7 @@ from pathlib import Path
from typing import Any, Dict, List, Union
from numpy import int64
from pandas import DataFrame
from pandas import DataFrame, to_datetime
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
@@ -189,7 +189,6 @@ def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
tabular_data = []
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
@@ -214,6 +213,41 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def _get_resample_from_period(period: str) -> str:
if period == 'day':
return '1d'
if period == 'week':
return '1w'
if period == 'month':
return '1M'
raise ValueError(f"Period {period} is not supported.")
def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dict[str, Any]]:
results = DataFrame.from_records(trade_list)
if len(results) == 0:
return []
results['close_date'] = to_datetime(results['close_date'], utc=True)
resample_period = _get_resample_from_period(period)
resampled = results.resample(resample_period, on='close_date')
stats = []
for name, day in resampled:
profit_abs = day['profit_abs'].sum().round(10)
wins = sum(day['profit_abs'] > 0)
draws = sum(day['profit_abs'] == 0)
loses = sum(day['profit_abs'] < 0)
stats.append(
{
'date': name.strftime('%d/%m/%Y'),
'profit_abs': profit_abs,
'wins': wins,
'draws': draws,
'loses': loses
}
)
return stats
def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate overall trade statistics """
if len(results) == 0:
@@ -329,7 +363,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
results['open_timestamp'] = results['open_date'].view(int64) // 1e6
results['close_timestamp'] = results['close_date'].view(int64) // 1e6
backtest_days = (max_date - min_date).days
backtest_days = (max_date - min_date).days or 1
strat_stats = {
'trades': results.to_dict(orient='records'),
'locks': [lock.to_json() for lock in content['locks']],
@@ -338,6 +372,8 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
# 'days_breakdown_stats': days_breakdown_stats,
'total_trades': len(results),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
@@ -354,7 +390,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'trades_per_day': round(len(results) / backtest_days, 2),
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
@@ -506,6 +542,28 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
stake_currency: str, period: str) -> str:
"""
Generate small table with Backtest results by days
:param days_breakdown_stats: Days breakdown metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
period.capitalize(),
f'Tot Profit {stake_currency}',
'Wins',
'Draws',
'Losses',
]
output = [[
d['date'], round_coin_value(d['profit_abs'], stake_currency, False),
d['wins'], d['draws'], d['loses'],
] for d in days_breakdown_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
def text_table_strategy(strategy_results, stake_currency: str) -> str:
"""
Generate summary table per strategy
@@ -557,7 +615,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',
f"{round(strat_results['profit_total'] / strat_results['backtest_days'] * 100, 2)}%"),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
@@ -614,7 +675,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
return message
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str,
backtest_breakdown=[]):
"""
Print results for one strategy
"""
@@ -636,6 +698,15 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
for period in backtest_breakdown:
days_breakdown_stats = generate_periodic_breakdown_stats(
trade_list=results['trades'], period=period)
table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats,
stake_currency=stake_currency, period=period)
if isinstance(table, str) and len(table) > 0:
print(f' {period.upper()} BREAKDOWN '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
@@ -650,7 +721,9 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
show_backtest_result(strategy, results, stake_currency)
show_backtest_result(
strategy, results, stake_currency,
config.get('backtest_breakdown', []))
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table