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Add sharpe ratio as loss function

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This commit is contained in:
Matthias
2019-07-15 22:52:33 +02:00
parent e5170582de
commit 55e8092cbf
5 changed files with 73 additions and 13 deletions
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2
freqtrade/configuration/arguments.py
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@@ -235,7 +235,7 @@ AVAILABLE_CLI_OPTIONS = {
help='Define the loss-function to use for hyperopt.'
'Possibilities are `legacy`, and `custom` (providing a custom loss-function).'
'Default: `%(default)s`.',
choices=['legacy', 'custom'],
choices=['legacy', 'sharpe', 'custom'],
default='legacy',
),
# List exchanges

4
freqtrade/optimize/hyperopt.py
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@@ -23,7 +23,7 @@ from freqtrade.configuration import Arguments
from freqtrade.data.history import load_data, get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
from freqtrade.optimize.hyperopt_loss import hyperopt_loss_legacy
from freqtrade.optimize.hyperopt_loss import hyperopt_loss_legacy, hyperopt_loss_sharpe
logger = logging.getLogger(__name__)
@@ -74,6 +74,8 @@ class Hyperopt(Backtesting):
# Assign loss function
if self.config.get('loss_function', 'legacy') == 'legacy':
self.calculate_loss = hyperopt_loss_legacy
elif self.config.get('loss_function', 'sharpe') == 'sharpe':
self.calculate_loss = hyperopt_loss_sharpe
elif (self.config['loss_function'] == 'custom' and
hasattr(self.custom_hyperopt, 'hyperopt_loss_custom')):
self.calculate_loss = self.custom_hyperopt.hyperopt_loss_custom # type: ignore

27
freqtrade/optimize/hyperopt_loss.py
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@@ -1,4 +1,7 @@
from datetime import datetime
from math import exp
import numpy as np
from pandas import DataFrame
# Define some constants:
@@ -35,3 +38,27 @@ def hyperopt_loss_legacy(results: DataFrame, trade_count: int,
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result
def hyperopt_loss_sharpe(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results
Using sharpe ratio calculation
"""
total_profit = results.profit_percent
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_yearly_return = total_profit.sum() / days_period
if (np.std(total_profit) != 0.):
sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
else:
sharp_ratio = 1.
# print(expected_yearly_return, np.std(total_profit), sharp_ratio)
# Negate sharp-ratio so lower is better (??)
return -sharp_ratio

39
freqtrade/tests/optimize/test_hyperopt.py
View File

@@ -15,6 +15,7 @@ from freqtrade.optimize import setup_configuration, start_hyperopt
from freqtrade.optimize.default_hyperopt import DefaultHyperOpts
from freqtrade.optimize.hyperopt import (HYPEROPT_LOCKFILE, TICKERDATA_PICKLE,
Hyperopt)
from freqtrade.optimize.hyperopt_loss import hyperopt_loss_legacy, hyperopt_loss_sharpe
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import SellType
@@ -273,32 +274,48 @@ def test_start_filelock(mocker, default_conf, caplog) -> None:
)
def test_loss_calculation_prefer_correct_trade_count(hyperopt, hyperopt_results) -> None:
correct = hyperopt.calculate_loss(hyperopt_results, hyperopt.target_trades)
over = hyperopt.calculate_loss(hyperopt_results, hyperopt.target_trades + 100)
under = hyperopt.calculate_loss(hyperopt_results, hyperopt.target_trades - 100)
def test_loss_calculation_prefer_correct_trade_count(hyperopt_results) -> None:
correct = hyperopt_loss_legacy(hyperopt_results, 600)
over = hyperopt_loss_legacy(hyperopt_results, 600 + 100)
under = hyperopt_loss_legacy(hyperopt_results, 600 - 100)
assert over > correct
assert under > correct
def test_loss_calculation_prefer_shorter_trades(hyperopt, hyperopt_results) -> None:
def test_loss_calculation_prefer_shorter_trades(hyperopt_results) -> None:
resultsb = hyperopt_results.copy()
resultsb['trade_duration'][1] = 20
longer = hyperopt.calculate_loss(hyperopt_results, 100)
shorter = hyperopt.calculate_loss(resultsb, 100)
longer = hyperopt_loss_legacy(hyperopt_results, 100)
shorter = hyperopt_loss_legacy(resultsb, 100)
assert shorter < longer
def test_loss_calculation_has_limited_profit(hyperopt, hyperopt_results) -> None:
def test_loss_calculation_has_limited_profit(hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
results_under = hyperopt_results.copy()
results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
correct = hyperopt.calculate_loss(hyperopt_results, hyperopt.target_trades)
over = hyperopt.calculate_loss(results_over, hyperopt.target_trades)
under = hyperopt.calculate_loss(results_under, hyperopt.target_trades)
correct = hyperopt_loss_legacy(hyperopt_results, 600)
over = hyperopt_loss_legacy(results_over, 600)
under = hyperopt_loss_legacy(results_under, 600)
assert over < correct
assert under > correct
def test_sharpe_loss_prefers_higher_profits(hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
results_under = hyperopt_results.copy()
results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
correct = hyperopt_loss_sharpe(hyperopt_results, len(
hyperopt_results), datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hyperopt_loss_sharpe(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hyperopt_loss_sharpe(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct