freqtrade_origin/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.

## Table of Contents
- [Prepare your Hyperopt](#prepare-hyperopt)
- [Configure your Guards and Triggers](#configure-your-guards-and-triggers)
- [Solving a Mystery](#solving-a-mystery)
- [Adding New Indicators](#adding-new-indicators)
- [Execute Hyperopt](#execute-hyperopt)
- [Understand the hyperopts result](#understand-the-backtesting-result)

## Prepare Hyperopting
We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
 
### Configure your Guards and Triggers
There are two places you need to change to add a new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L278-L294).
- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L218-L229) 
and the associated methods `indicator_space`, `roi_space`, `stoploss_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".

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.

## 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 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 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.
```bash
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:

```bash
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`

## Understand the Hyperopts 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
```

## Next Step
Now you have a perfect bot and want to control it from Telegram. Your
next step is to learn the [Telegram usage](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md).