280 lines
9.9 KiB
Markdown
280 lines
9.9 KiB
Markdown
# Hyperopt
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This page explains how to tune your strategy by finding the optimal
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parameters with Hyperopt.
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## Table of Contents
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- [Prepare your Hyperopt](#prepare-hyperopt)
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- [1. Configure your Guards and Triggers](#1-configure-your-guards-and-triggers)
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- [2. Update the hyperopt config file](#2-update-the-hyperopt-config-file)
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- [Advanced Hyperopt notions](#advanced-notions)
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- [Understand the Guards and Triggers](#understand-the-guards-and-triggers)
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- [Execute Hyperopt](#execute-hyperopt)
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- [Understand the hyperopts result](#understand-the-backtesting-result)
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## Prepare Hyperopt
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Before we start digging in Hyperopt, we recommend you to take a look at
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your strategy file located into [user_data/strategies/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
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### 1. Configure your Guards and Triggers
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There are two places you need to change in your strategy file to add a
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new buy strategy for testing:
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- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294).
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- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`.
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There you have two different type of indicators: 1. `guards` and 2.
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`triggers`.
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1. Guards are conditions like "never buy if ADX < 10", or never buy if
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current price is over EMA10.
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2. Triggers are ones that actually trigger buy in specific moment, like
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"buy when EMA5 crosses over EMA10" or buy when close price touches lower
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bollinger band.
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HyperOpt will, for each eval round, pick just ONE trigger, and possibly
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multiple guards. So that the constructed strategy will be something like
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"*buy exactly when close price touches lower bollinger band, BUT only if
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ADX > 10*".
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If you have updated the buy strategy, means change the content of
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`populate_buy_trend()` method you have to update the `guards` and
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`triggers` hyperopts must used.
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As for an example if your `populate_buy_trend()` method is:
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```python
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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dataframe.loc[
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(dataframe['rsi'] < 35) &
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(dataframe['adx'] > 65),
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'buy'] = 1
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return dataframe
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```
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Your hyperopt file must contain `guards` to find the right value for
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`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That
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means you will need to enable/disable triggers.
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In our case the `SPACE` and `populate_buy_trend` in your strategy file
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will look like:
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```python
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space = {
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema5_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'stochf_cross'},
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{'type': 'ht_sine'},
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]),
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}
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...
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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# TRIGGERS
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triggers = {
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'lower_bb': dataframe['tema'] <= dataframe['blower'],
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'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
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'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
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'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
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'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
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'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
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'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
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'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
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}
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...
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```
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### 2. Update the hyperopt config file
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Hyperopt is using a dedicated config file. Currently hyperopt
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cannot use your config file. It is also made on purpose to allow you
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testing your strategy with different configurations.
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The Hyperopt configuration is located in
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[user_data/hyperopt_conf.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopt_conf.py).
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## Advanced notions
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### Understand the Guards and Triggers
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When you need to add the new guards and triggers to be hyperopt
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parameters, you do this by adding them into the [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297).
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If it's a trigger, you add one line to the 'trigger' choice group and that's it.
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If it's a guard, you will add a line like this:
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```
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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```
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This says, "*one of the guards is RSI, it can have two values, enabled or
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disabled. If it is enabled, try different values for it between 20 and 40*".
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So, the part of the strategy builder using the above setting looks like
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this:
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```
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if params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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```
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It checks if Hyperopt wants the RSI guard to be enabled for this
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round `params['rsi']['enabled']` and if it is, then it will add a
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condition that says RSI must be smaller than the value hyperopt picked
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for this evaluation, which is given in the `params['rsi']['value']`.
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That's it. Now you can add new parts of strategies to Hyperopt and it
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will try all the combinations with all different values in the search
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for best working algo.
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### Add a new Indicators
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If you want to test an indicator that isn't used by the bot currently,
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you need to add it to the `populate_indicators()` method in `hyperopt.py`.
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## Execute Hyperopt
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Once you have updated your hyperopt configuration you can run it.
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Because hyperopt tries a lot of combination to find the best parameters
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it will take time you will have the result (more than 30 mins).
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We strongly recommend to use `screen` to prevent any connection loss.
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```bash
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python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
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```
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The `-e` flag will set how many evaluations hyperopt will do. We recommend
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running at least several thousand evaluations.
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### Execute hyperopt with different ticker-data source
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If you would like to hyperopt parameters using an alternate ticker data that
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you have on-disk, use the `--datadir PATH` option. Default hyperopt will
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use data from directory `user_data/data`.
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### Running hyperopt with smaller testset
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Use the `--timeperiod` argument to change how much of the testset
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you want to use. The last N ticks/timeframes will be used.
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Example:
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```bash
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python3 ./freqtrade/main.py hyperopt --timeperiod -200
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```
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### Running hyperopt with smaller search space
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Use the `--spaces` argument to limit the search space used by hyperopt.
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Letting Hyperopt optimize everything is a huuuuge search space. Often it
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might make more sense to start by just searching for initial buy algorithm.
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Or maybe you just want to optimize your stoploss or roi table for that awesome
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new buy strategy you have.
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Legal values are:
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- `all`: optimize everything
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- `buy`: just search for a new buy strategy
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- `roi`: just optimize the minimal profit table for your strategy
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- `stoploss`: search for the best stoploss value
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- space-separated list of any of the above values for example `--spaces roi stoploss`
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## Understand the hyperopts result
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Once Hyperopt is completed you can use the result to adding new buy
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signal. Given following result from hyperopt:
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```
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Best parameters:
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{
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"adx": {
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"enabled": true,
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"value": 15.0
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},
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"fastd": {
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"enabled": true,
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"value": 40.0
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},
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"green_candle": {
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"enabled": true
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},
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"mfi": {
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"enabled": false
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},
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"over_sar": {
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"enabled": false
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},
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"rsi": {
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"enabled": true,
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"value": 37.0
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},
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"trigger": {
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"type": "lower_bb"
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},
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"uptrend_long_ema": {
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"enabled": true
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},
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"uptrend_short_ema": {
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"enabled": false
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},
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"uptrend_sma": {
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"enabled": false
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}
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}
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Best Result:
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2197 trades. Avg profit 1.84%. Total profit 0.79367541 BTC. Avg duration 241.0 mins.
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```
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You should understand this result like:
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- You should **consider** the guard "adx" (`"adx"` is `"enabled": true`)
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and the best value is `15.0` (`"value": 15.0,`)
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- You should **consider** the guard "fastd" (`"fastd"` is `"enabled":
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true`) and the best value is `40.0` (`"value": 40.0,`)
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- You should **consider** to enable the guard "green_candle"
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(`"green_candle"` is `"enabled": true`) but this guards as no
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customizable value.
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- You should **ignore** the guard "mfi" (`"mfi"` is `"enabled": false`)
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- and so on...
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You have to look inside your strategy file into `buy_strategy_generator()`
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method, what those values match to.
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So for example you had `adx:` with the `value: 15.0` so we would look
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at `adx`-block, that translates to the following code block:
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```
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(dataframe['adx'] > 15.0)
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```
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Translating your whole hyperopt result to as the new buy-signal
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would be the following:
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```
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['adx'] > 15.0) & # adx-value
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(dataframe['fastd'] < 40.0) & # fastd-value
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(dataframe['close'] > dataframe['open']) & # green_candle
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(dataframe['rsi'] < 37.0) & # rsi-value
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(dataframe['ema50'] > dataframe['ema100']) # uptrend_long_ema
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),
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'buy'] = 1
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return dataframe
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```
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## Next step
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Now you have a perfect bot and want to control it from Telegram. Your
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next step is to learn the [Telegram usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md).
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