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<h1 id="advanced-hyperopt">Advanced Hyperopt<a class="headerlink" href="#advanced-hyperopt" title="Permanent link">&para;</a></h1>
<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="derived-hyperopt-classes">Derived hyperopt classes<a class="headerlink" href="#derived-hyperopt-classes" title="Permanent link">&para;</a></h2>
<p>Custom hyperopt classes can be derived in the same way <a href="../strategy-customization/#derived-strategies">it can be done for strategies</a>.</p>
<p>Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:</p>
<p>```python
class MyAwesomeHyperOpt(IHyperOpt):
...
# Uses default stoploss dimension</p>
<p>class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
@staticmethod
def stoploss_space() -&gt; List[Dimension]:
# Override boundaries for stoploss
return [
Real(-0.33, -0.01, name='stoploss'),
]
```</p>
<p>and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:</p>
<p><code>$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
or
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...</code></p>
<h2 id="sharing-methods-with-your-strategy">Sharing methods with your strategy<a class="headerlink" href="#sharing-methods-with-your-strategy" title="Permanent link">&para;</a></h2>
<p>Hyperopt classes provide access to the Strategy via the <code>strategy</code> class attribute.
This can be a great way to reduce code duplication if used correctly, but will also complicate usage for inexperienced users.</p>
<p>``` python
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import freqtrade.vendor.qtpylib.indicators as qtpylib</p>
<p>class MyAwesomeStrategy(IStrategy):</p>
<div class="codehilite"><pre><span></span><code>buy_params = {
&#39;rsi-value&#39;: 30,
&#39;adx-value&#39;: 35,
}
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -&gt; DataFrame:
return self.buy_strategy_generator(self.buy_params, dataframe, metadata)
@staticmethod
def buy_strategy_generator(params, dataframe: DataFrame, metadata: dict) -&gt; DataFrame:
dataframe.loc[
(
qtpylib.crossed_above(dataframe[&#39;rsi&#39;], params[&#39;rsi-value&#39;]) &amp;
dataframe[&#39;adx&#39;] &gt; params[&#39;adx-value&#39;]) &amp;
dataframe[&#39;volume&#39;] &gt; 0
)
, &#39;buy&#39;] = 1
return dataframe
</code></pre></div>
<p>class MyAwesomeHyperOpt(IHyperOpt):
...
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -&gt; Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -&gt; DataFrame:
# Call strategy's buy strategy generator
return self.StrategyClass.buy_strategy_generator(params, dataframe, metadata)</p>
<div class="codehilite"><pre><span></span><code> return populate_buy_trend
</code></pre></div>
<p>```</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">&para;</a></h2>
<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>
<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],
*args, **kwargs) -&gt; float:
&quot;&quot;&quot;
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
&quot;&quot;&quot;
total_profit = results[&#39;profit_ratio&#39;].sum()
trade_duration = results[&#39;trade_duration&#39;].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 result<br />
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>
</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 iteration - 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">Note</p>
<p>Please keep the arguments <code>*args</code> and <code>**kwargs</code> in the interface to allow us to extend this interface later.</p>
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