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1235 lines
34 KiB
HTML
1235 lines
34 KiB
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<link rel="canonical" href="https://www.freqtrade.io/2021.11/advanced-hyperopt/">
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<meta name="generator" content="mkdocs-1.2.3, mkdocs-material-7.3.6">
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<title>Advanced Hyperopt - Freqtrade</title>
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<link rel="stylesheet" href="../assets/stylesheets/main.a57b2b03.min.css">
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</head>
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Skip to content
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Freqtrade
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<div class="md-header__topic" data-md-component="header-topic">
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<span class="md-ellipsis">
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Advanced Hyperopt
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Initializing search
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</div>
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<a href="https://github.com/freqtrade/freqtrade/" title="Go to repository" class="md-source" data-md-component="source">
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<div class="md-source__repository">
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GitHub
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</a>
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<div class="md-container" data-md-component="container">
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<img src="../images/logo.png" alt="logo">
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</a>
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Freqtrade
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</label>
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<div class="md-nav__source">
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<a href="https://github.com/freqtrade/freqtrade/" title="Go to repository" class="md-source" data-md-component="source">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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</div>
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<div class="md-source__repository">
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GitHub
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</div>
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</a>
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Home
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<li class="md-nav__item">
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<a href="../docker_quickstart/" class="md-nav__link">
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Quickstart with Docker
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</a>
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<label class="md-nav__link" for="__nav_3">
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Installation
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<nav class="md-nav" aria-label="Installation" data-md-level="1">
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<label class="md-nav__title" for="__nav_3">
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<span class="md-nav__icon md-icon"></span>
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Installation
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
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<a href="../installation/" class="md-nav__link">
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Linux/MacOS/Raspberry
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../windows_installation/" class="md-nav__link">
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Windows
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</ul>
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</nav>
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<li class="md-nav__item">
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<a href="../bot-basics/" class="md-nav__link">
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Freqtrade Basics
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../configuration/" class="md-nav__link">
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Configuration
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../strategy-customization/" class="md-nav__link">
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Strategy Customization
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../strategy-callbacks/" class="md-nav__link">
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Strategy Callbacks
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../stoploss/" class="md-nav__link">
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Stoploss
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../plugins/" class="md-nav__link">
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Plugins
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../bot-usage/" class="md-nav__link">
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Start the bot
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</a>
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</li>
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<li class="md-nav__item md-nav__item--nested">
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_11" type="checkbox" id="__nav_11" >
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<label class="md-nav__link" for="__nav_11">
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Control the bot
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<span class="md-nav__icon md-icon"></span>
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</label>
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<nav class="md-nav" aria-label="Control the bot" data-md-level="1">
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<label class="md-nav__title" for="__nav_11">
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<span class="md-nav__icon md-icon"></span>
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Control the bot
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
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<a href="../telegram-usage/" class="md-nav__link">
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Telegram
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../rest-api/" class="md-nav__link">
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REST API & FreqUI
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../webhook-config/" class="md-nav__link">
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Web Hook
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</a>
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</li>
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</ul>
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</nav>
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</li>
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<li class="md-nav__item">
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<a href="../data-download/" class="md-nav__link">
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Data Downloading
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../backtesting/" class="md-nav__link">
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Backtesting
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../hyperopt/" class="md-nav__link">
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Hyperopt
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../utils/" class="md-nav__link">
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Utility Sub-commands
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</a>
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</li>
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<h1 id="advanced-hyperopt">Advanced Hyperopt<a class="headerlink" href="#advanced-hyperopt" title="Permanent link">¶</a></h1>
|
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<p>This page explains some advanced Hyperopt topics that may require higher
|
|
coding skills and Python knowledge than creation of an ordinal hyperoptimization
|
|
class.</p>
|
|
<h2 id="creating-and-using-a-custom-loss-function">Creating and using a custom loss function<a class="headerlink" href="#creating-and-using-a-custom-loss-function" title="Permanent link">¶</a></h2>
|
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<p>To use a custom loss function class, make sure that the function <code>hyperopt_loss_function</code> is defined in your custom hyperopt loss class.
|
|
For the sample below, you then need to add the command line parameter <code>--hyperopt-loss SuperDuperHyperOptLoss</code> to your hyperopt call so this function is being used.</p>
|
|
<p>A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in <a href="https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py">userdata/hyperopts</a>.</p>
|
|
<p>``` python
|
|
from datetime import datetime
|
|
from typing import Dict</p>
|
|
<p>from pandas import DataFrame</p>
|
|
<p>from freqtrade.optimize.hyperopt import IHyperOptLoss</p>
|
|
<p>TARGET_TRADES = 600
|
|
EXPECTED_MAX_PROFIT = 3.0
|
|
MAX_ACCEPTED_TRADE_DURATION = 300</p>
|
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<p>class SuperDuperHyperOptLoss(IHyperOptLoss):
|
|
"""
|
|
Defines the default loss function for hyperopt
|
|
"""</p>
|
|
<div class="codehilite"><pre><span></span><code>@staticmethod
|
|
def hyperopt_loss_function(results: DataFrame, trade_count: int,
|
|
min_date: datetime, max_date: datetime,
|
|
config: Dict, processed: Dict[str, DataFrame],
|
|
backtest_stats: Dict[str, Any],
|
|
*args, **kwargs) -> float:
|
|
"""
|
|
Objective function, returns smaller number for better results
|
|
This is the legacy algorithm (used until now in freqtrade).
|
|
Weights are distributed as follows:
|
|
* 0.4 to trade duration
|
|
* 0.25: Avoiding trade loss
|
|
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
|
|
"""
|
|
total_profit = results['profit_ratio'].sum()
|
|
trade_duration = results['trade_duration'].mean()
|
|
|
|
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
|
|
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
|
|
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
|
|
result = trade_loss + profit_loss + duration_loss
|
|
return result
|
|
</code></pre></div>
|
|
|
|
<p>```</p>
|
|
<p>Currently, the arguments are:</p>
|
|
<ul>
|
|
<li><code>results</code>: DataFrame containing the resulting trades.
|
|
The following columns are available in results (corresponds to the output-file of backtesting when used with <code>--export trades</code>):<br />
|
|
<code>pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, sell_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs</code></li>
|
|
<li><code>trade_count</code>: Amount of trades (identical to <code>len(results)</code>)</li>
|
|
<li><code>min_date</code>: Start date of the timerange used</li>
|
|
<li><code>min_date</code>: End date of the timerange used</li>
|
|
<li><code>config</code>: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).</li>
|
|
<li><code>processed</code>: Dict of Dataframes with the pair as keys containing the data used for backtesting.</li>
|
|
<li><code>backtest_stats</code>: Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in <code>generate_strategy_stats()</code> in <code>optimize_reports.py</code>.</li>
|
|
</ul>
|
|
<p>This function needs to return a floating point number (<code>float</code>). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.</p>
|
|
<div class="admonition note">
|
|
<p class="admonition-title">Note</p>
|
|
<p>This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.</p>
|
|
</div>
|
|
<div class="admonition note">
|
|
<p class="admonition-title"><code>*args</code> and <code>**kwargs</code></p>
|
|
<p>Please keep the arguments <code>*args</code> and <code>**kwargs</code> in the interface to allow us to extend this interface in the future.</p>
|
|
</div>
|
|
<h2 id="overriding-pre-defined-spaces">Overriding pre-defined spaces<a class="headerlink" href="#overriding-pre-defined-spaces" title="Permanent link">¶</a></h2>
|
|
<p>To override a pre-defined space (<code>roi_space</code>, <code>generate_roi_table</code>, <code>stoploss_space</code>, <code>trailing_space</code>), define a nested class called Hyperopt and define the required spaces as follows:</p>
|
|
<p>```python
|
|
class MyAwesomeStrategy(IStrategy):
|
|
class HyperOpt:
|
|
# Define a custom stoploss space.
|
|
def stoploss_space():
|
|
return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]</p>
|
|
<div class="codehilite"><pre><span></span><code> # Define custom ROI space
|
|
def roi_space() -> List[Dimension]:
|
|
return [
|
|
Integer(10, 120, name='roi_t1'),
|
|
Integer(10, 60, name='roi_t2'),
|
|
Integer(10, 40, name='roi_t3'),
|
|
SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
|
|
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
|
|
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
|
|
]
|
|
</code></pre></div>
|
|
|
|
<p>```</p>
|
|
<div class="admonition note">
|
|
<p class="admonition-title">Note</p>
|
|
<p>All overrides are optional and can be mixed/matched as necessary.</p>
|
|
</div>
|
|
<h3 id="overriding-base-estimator">Overriding Base estimator<a class="headerlink" href="#overriding-base-estimator" title="Permanent link">¶</a></h3>
|
|
<p>You can define your own estimator for Hyperopt by implementing <code>generate_estimator()</code> in the Hyperopt subclass.</p>
|
|
<p>```python
|
|
class MyAwesomeStrategy(IStrategy):
|
|
class HyperOpt:
|
|
def generate_estimator():
|
|
return "RF"</p>
|
|
<p>```</p>
|
|
<p>Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the <a href="https://scikit-optimize.github.io/">scikit-optimize documentation</a>), or "an instance of a class that inherits from <code>RegressorMixin</code> (from sklearn) and where the <code>predict</code> method has an optional <code>return_std</code> argument, which returns <code>std(Y | x)</code> along with <code>E[Y | x]</code>".</p>
|
|
<p>Some research will be necessary to find additional Regressors.</p>
|
|
<p>Example for <code>ExtraTreesRegressor</code> ("ET") with additional parameters:</p>
|
|
<p>```python
|
|
class MyAwesomeStrategy(IStrategy):
|
|
class HyperOpt:
|
|
def generate_estimator():
|
|
from skopt.learning import ExtraTreesRegressor
|
|
# Corresponds to "ET" - but allows additional parameters.
|
|
return ExtraTreesRegressor(n_estimators=100)</p>
|
|
<p>```</p>
|
|
<div class="admonition note">
|
|
<p class="admonition-title">Note</p>
|
|
<p>While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
|
|
If you're unsure about this, best use one of the Defaults (<code>"ET"</code> has proven to be the most versatile) without further parameters.</p>
|
|
</div>
|
|
<h2 id="space-options">Space options<a class="headerlink" href="#space-options" title="Permanent link">¶</a></h2>
|
|
<p>For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:</p>
|
|
<ul>
|
|
<li><code>Categorical</code> - Pick from a list of categories (e.g. <code>Categorical(['a', 'b', 'c'], name="cat")</code>)</li>
|
|
<li><code>Integer</code> - Pick from a range of whole numbers (e.g. <code>Integer(1, 10, name='rsi')</code>)</li>
|
|
<li><code>SKDecimal</code> - Pick from a range of decimal numbers with limited precision (e.g. <code>SKDecimal(0.1, 0.5, decimals=3, name='adx')</code>). <em>Available only with freqtrade</em>.</li>
|
|
<li><code>Real</code> - Pick from a range of decimal numbers with full precision (e.g. <code>Real(0.1, 0.5, name='adx')</code></li>
|
|
</ul>
|
|
<p>You can import all of these from <code>freqtrade.optimize.space</code>, although <code>Categorical</code>, <code>Integer</code> and <code>Real</code> are only aliases for their corresponding scikit-optimize Spaces. <code>SKDecimal</code> is provided by freqtrade for faster optimizations.</p>
|
|
<p><code>python
|
|
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa</code></p>
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<div class="admonition hint">
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<p class="admonition-title">SKDecimal vs. Real</p>
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<p>We recommend to use <code>SKDecimal</code> instead of the <code>Real</code> space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.</p>
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<p>Assuming the definition of a rather small space (<code>SKDecimal(0.10, 0.15, decimals=2, name='xxx')</code>) - SKDecimal will have 5 possibilities (<code>[0.10, 0.11, 0.12, 0.13, 0.14, 0.15]</code>).</p>
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<p>A corresponding real space <code>Real(0.10, 0.15 name='xxx')</code> on the other hand has an almost unlimited number of possibilities (<code>[0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]</code>).</p>
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