stable/docs/hyperopt.md

Hyperopt

This page explains how to tune your strategy by finding the optimal parameters, a process called hyperparameter optimization. The bot uses several algorithms included in the scikit-optimize package to accomplish this. The search will burn all your CPU cores, make your laptop sound like a fighter jet and still take a long time.

!!! Bug Hyperopt will crash when used with only 1 CPU Core as found out in Issue #1133

Prepare Hyperopting

Before we start digging into Hyperopt, we recommend you to take a look at an example hyperopt file located into user_data/hyperopts/

Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.

1. Install a Custom Hyperopt File

Put your hyperopt file into the folderuser_data/hyperopts.

Let assume you want a hyperopt file awesome_hyperopt.py:
Copy the file user_data/hyperopts/sample_hyperopt.py into user_data/hyperopts/awesome_hyperopt.py

2. Configure your Guards and Triggers

There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:

• Inside indicator_space() - the parameters hyperopt shall be optimizing.
• Inside populate_buy_trend() - applying the parameters.

There you have two different types of indicators: 1. guards and 2. triggers.

1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".

Hyperoptimization will, for each eval round, pick one trigger and possibly multiple guards. The constructed strategy will be something like "buy exactly when close price touches lower bollinger band, BUT only if ADX > 10".

If you have updated the buy strategy, ie. changed the contents of populate_buy_trend() method you have to update the guards and triggers hyperopts must use.

Sell optimization

Similar to the buy-signal above, sell-signals can also be optimized. Place the corresponding settings into the following methods

• Inside sell_indicator_space() - the parameters hyperopt shall be optimizing.
• Inside populate_sell_trend() - applying the parameters.

The configuration and rules are the same than for buy signals. To avoid naming collisions in the search-space, please prefix all sell-spaces with sell-.

Solving a Mystery

Let's say you are curious: should you use MACD crossings or lower Bollinger Bands to trigger your buys. And you also wonder should you use RSI or ADX to help with those buy decisions. If you decide to use RSI or ADX, which values should I use for them? So let's use hyperparameter optimization to solve this mystery.

We will start by defining a search space:

    def indicator_space() -> List[Dimension]:
        """
        Define your Hyperopt space for searching strategy parameters
        """
        return [
            Integer(20, 40, name='adx-value'),
            Integer(20, 40, name='rsi-value'),
            Categorical([True, False], name='adx-enabled'),
            Categorical([True, False], name='rsi-enabled'),
            Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
        ]

Above definition says: I have five parameters I want you to randomly combine to find the best combination. Two of them are integer values (adx-value and rsi-value) and I want you test in the range of values 20 to 40. Then we have three category variables. First two are either True or False. We use these to either enable or disable the ADX and RSI guards. The last one we call trigger and use it to decide which buy trigger we want to use.

So let's write the buy strategy using these values:

        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
            conditions = []
            # GUARDS AND TRENDS
            if 'adx-enabled' in params and params['adx-enabled']:
                conditions.append(dataframe['adx'] > params['adx-value'])
            if 'rsi-enabled' in params and params['rsi-enabled']:
                conditions.append(dataframe['rsi'] < params['rsi-value'])

            # TRIGGERS
            if 'trigger' in params:
                if params['trigger'] == 'bb_lower':
                    conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
                if params['trigger'] == 'macd_cross_signal':
                    conditions.append(qtpylib.crossed_above(
                        dataframe['macd'], dataframe['macdsignal']
                    ))

            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'buy'] = 1

            return dataframe

        return populate_buy_trend

Hyperopting will now call this populate_buy_trend as many times you ask it (epochs) with different value combinations. It will then use the given historical data and make buys based on the buy signals generated with the above function and based on the results it will end with telling you which paramter combination produced the best profits.

The search for best parameters starts with a few random combinations and then uses a regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination that minimizes the value of the objective function calculate_loss in hyperopt.py.

The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators. When you want to test an indicator that isn't used by the bot currently, remember to add it to the populate_indicators() method in hyperopt.py.

Execute Hyperopt

Once you have updated your hyperopt configuration you can run it. Because hyperopt tries a lot of combinations to find the best parameters it will take time you will have the result (more than 30 mins).

We strongly recommend to use screen or tmux to prevent any connection loss.

python3 ./freqtrade/main.py --hyperopt <hyperoptname> -c config.json hyperopt -e 5000

Use <hyperoptname> as the name of the custom hyperopt used.

The -e flag will set how many evaluations hyperopt will do. We recommend running at least several thousand evaluations.

Execute Hyperopt with Different Ticker-Data Source

If you would like to hyperopt parameters using an alternate ticker data that you have on-disk, use the --datadir PATH option. Default hyperopt will use data from directory user_data/data.

Running Hyperopt with Smaller Testset

Use the --timerange argument to change how much of the testset you want to use. The last N ticks/timeframes will be used. Example:

python3 ./freqtrade/main.py hyperopt --timerange -200

Running Hyperopt with Smaller Search Space

Use the --spaces argument to limit the search space used by hyperopt. Letting Hyperopt optimize everything is a huuuuge search space. Often it might make more sense to start by just searching for initial buy algorithm. Or maybe you just want to optimize your stoploss or roi table for that awesome new buy strategy you have.

Legal values are:

• all: optimize everything
• buy: just search for a new buy strategy
• sell: just search for a new sell strategy
• roi: just optimize the minimal profit table for your strategy
• stoploss: search for the best stoploss value
• space-separated list of any of the above values for example --spaces roi stoploss

Understand the Hyperopt Result

Once Hyperopt is completed you can use the result to create a new strategy. Given the following result from hyperopt:

Best result:
   135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values:
{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower'}

You should understand this result like:

• The buy trigger that worked best was bb_lower.
• You should not use ADX because adx-enabled: False)
• You should consider using the RSI indicator (rsi-enabled: True and the best value is 29.0 (rsi-value: 29.0)

You have to look inside your strategy file into buy_strategy_generator() method, what those values match to.

So for example you had rsi-value: 29.0 so we would look at rsi-block, that translates to the following code block:

(dataframe['rsi'] < 29.0)

Translating your whole hyperopt result as the new buy-signal would then look like:

def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
    dataframe.loc[
        (
            (dataframe['rsi'] < 29.0) &  # rsi-value
            dataframe['close'] < dataframe['bb_lowerband']  # trigger
        ),
        'buy'] = 1
    return dataframe

Understand Hyperopt ROI results

If you are optimizing ROI, you're result will look as follows and include a ROI table.

Best result:
   135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values:
{'adx-value': 44, 'rsi-value': 29, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'bb_lower', 'roi_t1': 40, 'roi_t2': 57, 'roi_t3': 21, 'roi_p1': 0.03634636907306948, 'roi_p2': 0.055237357937802885, 'roi_p3': 0.015163796015548354, 'stoploss': -0.37996664668703606}
ROI table:
{0: 0.10674752302642071, 21: 0.09158372701087236, 78: 0.03634636907306948, 118: 0}

This would translate to the following ROI table:

 minimal_roi = {
        "118": 0,
        "78": 0.0363463,
        "21": 0.0915,
        "0": 0.106
    }

Validate backtest result

Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected. To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag --disable-max-market-positions. This setting is the default for hyperopt for speed reasons. You can overwrite this in the configuration by setting "position_stacking"=false or by changing the relevant line in your hyperopt file here.

!!! Note: Dry/live runs will NOT use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.

Next Step

Now you have a perfect bot and want to control it from Telegram. Your next step is to learn the Telegram usage.