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>
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Yazeed Al Oyoun
GitHub
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@ -337,8 +337,8 @@ optional arguments:
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generate completely different results, since the
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target for optimization is different. Built-in
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Hyperopt-loss-functions are: DefaultHyperOptLoss,
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OnlyProfitHyperOptLoss, SharpeHyperOptLoss (default:
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`DefaultHyperOptLoss`).
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OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
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SharpeHyperOptLossDaily (default: `DefaultHyperOptLoss`).
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Common arguments:
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-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
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@ -75,8 +75,8 @@ Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts
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There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
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- Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
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- Inside `populate_buy_trend()` - applying the parameters.
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* Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
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* Inside `populate_buy_trend()` - applying the parameters.
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There you have two different types of indicators: 1. `guards` and 2. `triggers`.
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@ -192,6 +192,7 @@ Currently, the following loss functions are builtin:
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* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
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* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
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* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
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* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily trade returns)
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Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
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@ -372,6 +373,7 @@ In order to use this best ROI table found by Hyperopt in backtesting and for liv
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118: 0
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}
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```
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As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
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#### Default ROI Search Space
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@ -379,7 +381,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
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If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
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| # step | 1m | | 5m | | 1h | | 1d | |
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|---|---|---|---|---|---|---|---|---|
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| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
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| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 |
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| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
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| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
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@ -416,6 +418,7 @@ In order to use this best stoploss value found by Hyperopt in backtesting and fo
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# This attribute will be overridden if the config file contains "stoploss"
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stoploss = -0.27996
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```
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As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file.
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#### Default Stoploss Search Space
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@ -452,6 +455,7 @@ In order to use these best trailing stop parameters found by Hyperopt in backtes
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trailing_stop_positive_offset = 0.06038
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trailing_only_offset_is_reached = True
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```
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As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
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#### Default Trailing Stop Search Space
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@ -256,7 +256,7 @@ AVAILABLE_CLI_OPTIONS = {
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help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
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'Different functions can generate completely different results, '
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'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
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'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss.'
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'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily.'
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'(default: `%(default)s`).',
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metavar='NAME',
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default=constants.DEFAULT_HYPEROPT_LOSS,
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@ -0,0 +1,61 @@
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"""
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SharpeHyperOptLossDaily
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This module defines the alternative HyperOptLoss class which can be used for
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Hyperoptimization.
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"""
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import math
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from datetime import datetime
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from pandas import DataFrame, date_range
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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class SharpeHyperOptLossDaily(IHyperOptLoss):
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"""
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Defines the loss function for hyperopt.
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This implementation uses the Sharpe Ratio calculation.
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"""
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@staticmethod
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def hyperopt_loss_function(results: DataFrame, trade_count: int,
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min_date: datetime, max_date: datetime,
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*args, **kwargs) -> float:
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"""
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Objective function, returns smaller number for more optimal results.
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Uses Sharpe Ratio calculation.
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"""
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resample_freq = '1D'
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slippage_per_trade_ratio = 0.0005
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days_in_year = 365
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annual_risk_free_rate = 0.0
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risk_free_rate = annual_risk_free_rate / days_in_year
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# apply slippage per trade to profit_percent
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results.loc[:, 'profit_percent_after_slippage'] = \
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results['profit_percent'] - slippage_per_trade_ratio
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# create the index within the min_date and end max_date
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t_index = date_range(start=min_date, end=max_date, freq=resample_freq)
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sum_daily = (
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results.resample(resample_freq, on='close_time').agg(
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{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
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)
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total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
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expected_returns_mean = total_profit.mean()
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up_stdev = total_profit.std()
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if (up_stdev != 0.):
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sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
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else:
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# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
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sharp_ratio = -20.
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# print(t_index, sum_daily, total_profit)
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# print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
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return -sharp_ratio
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@ -42,7 +42,13 @@ def hyperopt_results():
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'profit_percent': [-0.1, 0.2, 0.3],
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'profit_abs': [-0.2, 0.4, 0.6],
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'trade_duration': [10, 30, 10],
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'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI]
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'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI],
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'close_time':
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[
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datetime(2019, 1, 1, 9, 26, 3, 478039),
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datetime(2019, 2, 1, 9, 26, 3, 478039),
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datetime(2019, 3, 1, 9, 26, 3, 478039)
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]
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}
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)
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@ -336,6 +342,24 @@ def test_sharpe_loss_prefers_higher_profits(default_conf, hyperopt_results) -> N
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assert under > correct
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def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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default_conf.update({'hyperopt_loss': 'SharpeHyperOptLossDaily'})
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over < correct
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assert under > correct
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def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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