# 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. *Note:* Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133) ## 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 hyperopt result](#understand-the-hyperopt-result) ## Prepare Hyperopting ## Prepare Hyperopt Before we start digging in Hyperopt, we recommend you to take a look at an example hyperopt file located into [user_data/hyperopts/](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py) ### 1. Install a Custom Hyperopt File This is very simple. Put your hyperopt file into the folder `user_data/hyperopts`. Let assume you want a hyperopt file `awesome_hyperopt.py`: 1. 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 [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251). - Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223). 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 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. ```bash python3 ./freqtrade/main.py -s --hyperopt -c config.json hyperopt -e 5000 ``` Use `` and `` as the names of the custom strategy (only required for generating sells) and 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: ```bash 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 - `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: ```python 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: ``` python 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](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283). 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](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md).