freqtrade_origin/freqtrade/optimize/hyperopt_loss_sharpe_daily.py
Yazeed Al Oyoun 9639ffb140
added daily sharpe ratio hyperopt loss method, ty @djacky (#2826)
* more consistent backtesting tables and labels

* added rounding to Tot Profit % on Sell Reasosn table to be consistent with other percentiles on table.

* added daily sharpe ratio hyperopt loss method, ty @djacky

* removed commented code

* removed unused profit_abs

* added proper slippage to each trade

* replaced use of old value total_profit

* Align quotes in same area

* added daily sharpe ratio test and modified hyperopt_loss_sharpe_daily

* fixed some more line alignments

* updated docs to include SharpeHyperOptLossDaily

* Update dockerfile to 3.8.1

* Run tests against 3.8

* added daily sharpe ratio hyperopt loss method, ty @djacky

* removed commented code

* removed unused profit_abs

* added proper slippage to each trade

* replaced use of old value total_profit

* added daily sharpe ratio test and modified hyperopt_loss_sharpe_daily

* updated docs to include SharpeHyperOptLossDaily

* docs fixes

* missed one fix

* fixed standard deviation line

* fixed to bracket notation

* fixed to bracket notation

* fixed syntax error

* better readability, kept np.sqrt(365) which results in  annualized sharpe ratio

* fixed method arguments indentation

* updated commented out debug print line

* renamed after slippage profit_percent so it wont affect _calculate_results_metrics()

* Reworked to fill leading and trailing days

* No need for np; make flake happy

* Fix risk free rate

Co-authored-by: Matthias <xmatthias@outlook.com>
Co-authored-by: hroff-1902 <47309513+hroff-1902@users.noreply.github.com>
2020-02-06 06:49:08 +01:00

62 lines
2.0 KiB
Python

"""
SharpeHyperOptLossDaily
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
import math
from datetime import datetime
from pandas import DataFrame, date_range
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SharpeHyperOptLossDaily(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sharpe Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sharpe Ratio calculation.
"""
resample_freq = '1D'
slippage_per_trade_ratio = 0.0005
days_in_year = 365
annual_risk_free_rate = 0.0
risk_free_rate = annual_risk_free_rate / days_in_year
# apply slippage per trade to profit_percent
results.loc[:, 'profit_percent_after_slippage'] = \
results['profit_percent'] - slippage_per_trade_ratio
# create the index within the min_date and end max_date
t_index = date_range(start=min_date, end=max_date, freq=resample_freq)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
expected_returns_mean = total_profit.mean()
up_stdev = total_profit.std()
if (up_stdev != 0.):
sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
# print(t_index, sum_daily, total_profit)
# print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
return -sharp_ratio