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
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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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@ -57,7 +57,7 @@ Rarely you may also need to override:
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!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
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You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything (i.e. without creation of a "complete" Hyperopt class with dimensions, parameters, triggers and guards, as described in this document) from the default hyperopt template by relying on your strategy to do most of the calculations.
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``` python
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```python
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# Have a working strategy at hand.
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freqtrade new-hyperopt --hyperopt EmptyHyperopt
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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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@ -141,7 +141,7 @@ one we call `trigger` and use it to decide which buy trigger we want to use.
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So let's write the buy strategy using these values:
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``` python
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```python
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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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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@ -206,7 +207,7 @@ We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
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freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spaces all
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```
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Use `<hyperoptname>` as the name of the custom hyperopt used.
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Use `<hyperoptname>` as the name of the custom hyperopt used.
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The `-e` option will set how many evaluations hyperopt will do. We recommend
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running at least several thousand evaluations.
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@ -323,7 +324,7 @@ method, what those values match to.
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So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
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``` python
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```python
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(dataframe['rsi'] < 29.0)
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```
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@ -372,18 +373,19 @@ 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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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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| 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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| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
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| # step | 1m | | 5m | | 1h | | 1d | |
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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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| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
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These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
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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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61
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
Normal file
61
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
Normal file
@ -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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