stable/docs/hyperopt.md

Hyperopt

This page explains how to tune your strategy by finding the optimal parameters with Hyperopt.

Table of Contents

• Prepare your Hyperopt
  • 1. Configure your Guards and Triggers
  • 2. Update the hyperopt config file
• Advanced Hyperopt notions
  • Understand the Guards and Triggers
• Execute Hyperopt
  • Hyperopt with MongoDB
• Understand the hyperopts result

Prepare Hyperopt

Before we start digging in Hyperopt, we recommend you to take a look at your strategy file located into user_data/strategies/

1. Configure your Guards and Triggers

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

• Inside populate_buy_trend().
• Inside indicator_space().

There you have two different type 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.

HyperOpt will, for each eval round, pick just ONE trigger, and possibly multiple guards. So that 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, means change the content of populate_buy_trend() method you have to update the guards and triggers hyperopts must used.

As for an example if your populate_buy_trend() method is:

def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
    dataframe.loc[
        (dataframe['rsi'] < 35) &
        (dataframe['adx'] > 65),
        'buy'] = 1

    return dataframe

Your hyperopt file must contain guards to find the right value for (dataframe['adx'] > 65) & and (dataframe['plus_di'] > 0.5). That means you will need to enable/disable triggers.

In our case the indicator_space and populate_buy_trend in your strategy file will look like:

    def indicator_space(self) -> Dict[str, Any]:
        """
        Define your Hyperopt space for searching strategy parameters
        """
        return {
            'rsi': hp.choice('rsi', [
                {'enabled': False},
                {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
            ]),
            'adx': hp.choice('adx', [
                {'enabled': False},
                {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
            ]),
            'trigger': hp.choice('trigger', [
                {'type': 'lower_bb'},
                {'type': 'faststoch10'},
                {'type': 'ao_cross_zero'},
                {'type': 'ema5_cross_ema10'},
                {'type': 'macd_cross_signal'},
                {'type': 'sar_reversal'},
                {'type': 'stochf_cross'},
                {'type': 'ht_sine'},
            ]),
        }

... 

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

        # TRIGGERS
        triggers = {
            'lower_bb': dataframe['tema'] <= dataframe['blower'],
            'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
            'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
            'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
            'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
            'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
        }
        conditions.append(triggers.get(params['trigger']['type']))

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

        return dataframe

2. Update the hyperopt config file

Hyperopt is using a dedicated config file. Currently hyperopt cannot use your config file. It is also made on purpose to allow you testing your strategy with different configurations.

The Hyperopt configuration is located in user_data/hyperopt_conf.py.

Advanced notions

Understand the Guards and Triggers

When you need to add the new guards and triggers to be hyperopt parameters, you do this by adding them into the indicator_space().

If it's a trigger, you add one line to the 'trigger' choice group and that's it.

If it's a guard, you will add a line like this:

'rsi': hp.choice('rsi', [
    {'enabled': False},
    {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),

This says, "one of the guards is RSI, it can have two values, enabled or disabled. If it is enabled, try different values for it between 20 and 40".

So, the part of the strategy builder using the above setting looks like this:

if params['rsi']['enabled']:
    conditions.append(dataframe['rsi'] < params['rsi']['value'])

It checks if Hyperopt wants the RSI guard to be enabled for this round params['rsi']['enabled'] and if it is, then it will add a condition that says RSI must be smaller than the value hyperopt picked for this evaluation, which is given in the params['rsi']['value'].

That's it. Now you can add new parts of strategies to Hyperopt and it will try all the combinations with all different values in the search for best working algo.

Add a new Indicators

If you want to test an indicator that isn't used by the bot currently, you need 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 combination to find the best parameters it will take time you will have the result (more than 30 mins).

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

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

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 --timeperiod 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 --timeperiod -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
• 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

Hyperopt with MongoDB

Hyperopt with MongoDB, is like Hyperopt under steroids. As you saw by executing the previous command is the execution takes a long time. To accelerate it you can use hyperopt with MongoDB.

To run hyperopt with MongoDb you will need 3 terminals.

Terminal 1: Start MongoDB

cd <freqtrade> 
source .env/bin/activate
python3 scripts/start-mongodb.py

Terminal 2: Start Hyperopt worker

cd <freqtrade> 
source .env/bin/activate
python3 scripts/start-hyperopt-worker.py

Terminal 3: Start Hyperopt with MongoDB

cd <freqtrade> 
source .env/bin/activate
python3 ./freqtrade/main.py -c config.json hyperopt --use-mongodb

Re-run an Hyperopt To re-run Hyperopt you have to delete the existing MongoDB table.

cd <freqtrade> 
rm -rf .hyperopt/mongodb/

Understand the hyperopts result

Once Hyperopt is completed you can use the result to adding new buy signal. Given following result from hyperopt:

Best parameters:
{
    "adx": {
        "enabled": true,
        "value": 15.0
    },
    "fastd": {
        "enabled": true,
        "value": 40.0
    },
    "green_candle": {
        "enabled": true
    },
    "mfi": {
        "enabled": false
    },
    "over_sar": {
        "enabled": false
    },
    "rsi": {
        "enabled": true,
        "value": 37.0
    },
    "trigger": {
        "type": "lower_bb"
    },
    "uptrend_long_ema": {
        "enabled": true
    },
    "uptrend_short_ema": {
        "enabled": false
    },
    "uptrend_sma": {
        "enabled": false
    }
}

Best Result:
  2197 trades. Avg profit  1.84%. Total profit  0.79367541 BTC. Avg duration 241.0 mins.

You should understand this result like:

• You should consider the guard "adx" ("adx" is "enabled": true) and the best value is 15.0 ("value": 15.0,)
• You should consider the guard "fastd" ("fastd" is "enabled": true) and the best value is 40.0 ("value": 40.0,)
• You should consider to enable the guard "green_candle" ("green_candle" is "enabled": true) but this guards as no customizable value.
• You should ignore the guard "mfi" ("mfi" is "enabled": false)
• and so on...

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

So for example you had adx: with the value: 15.0 so we would look at adx-block, that translates to the following code block:

(dataframe['adx'] > 15.0)

Translating your whole hyperopt result to as the new buy-signal would be the following:

def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
    dataframe.loc[
        (
            (dataframe['adx'] > 15.0) & # adx-value
            (dataframe['fastd'] < 40.0) & # fastd-value
            (dataframe['close'] > dataframe['open']) & # green_candle
            (dataframe['rsi'] < 37.0) & # rsi-value
            (dataframe['ema50'] > dataframe['ema100']) # uptrend_long_ema
        ),
        'buy'] = 1
    return dataframe

Next step

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