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FreqAI is configured through the typical Freqtrade config file and the standard Freqtrade strategy. Examples of FreqAI config and strategy files can be found in config_examples/config_freqai.example.json and freqtrade/templates/, respectively.

Setting up the configuration file

Although there are plenty of additional parameters to choose from, as highlighted in the parameter table, a FreqAI config must at minimum include the following parameters (the parameter values are only examples):

    "freqai": {
        "enabled": true,
        "purge_old_models": true,
        "train_period_days": 30,
        "backtest_period_days": 7,
        "identifier" : "unique-id",
        "feature_parameters" : {
            "include_timeframes": ["5m","15m","4h"],
            "include_corr_pairlist": [
            "label_period_candles": 24,
            "include_shifted_candles": 2,
            "indicator_periods_candles": [10, 20]
        "data_split_parameters" : {
            "test_size": 0.25
        "model_training_parameters" : {
            "n_estimators": 100

A full example config is available in config_examples/config_freqai.example.json.

Building a FreqAI strategy

The FreqAI strategy requires including the following lines of code in the standard Freqtrade strategy:

    # user should define the maximum startup candle count (the largest number of candles
    # passed to any single indicator)
    startup_candle_count: int = 20

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # the model will return all labels created by user in `populate_any_indicators`
        # (& appended targets), an indication of whether or not the prediction should be accepted,
        # the target mean/std values for each of the labels created by user in
        # `populate_any_indicators()` for each training period.

        dataframe = self.freqai.start(dataframe, metadata, self)

        return dataframe

    def populate_any_indicators(
        self, pair, df, tf, informative=None, set_generalized_indicators=False
        Function designed to automatically generate, name and merge features
        from user indicated timeframes in the configuration file. User controls the indicators
        passed to the training/prediction by prepending indicators with `'%-' + coin `
        (see convention below). I.e. user should not prepend any supporting metrics
        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
        :param pair: pair to be used as informative
        :param df: strategy dataframe which will receive merges from informatives
        :param tf: timeframe of the dataframe which will modify the feature names
        :param informative: the dataframe associated with the informative pair
        :param coin: the name of the coin which will modify the feature names.

        coin = pair.split('/')[0]

        if informative is None:
            informative = self.dp.get_pair_dataframe(pair, tf)

        # first loop is automatically duplicating indicators for time periods
        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
            t = int(t)
            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)

        indicators = [col for col in informative if col.startswith("%")]
        # This loop duplicates and shifts all indicators to add a sense of recency to data
        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
            if n == 0:
            informative_shift = informative[indicators].shift(n)
            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
            informative = pd.concat((informative, informative_shift), axis=1)

        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
        skip_columns = [
            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
        df = df.drop(columns=skip_columns)

        # Add generalized indicators here (because in live, it will call this
        # function to populate indicators during training). Notice how we ensure not to
        # add them multiple times
        if set_generalized_indicators:

            # user adds targets here by prepending them with &- (see convention below)
            # If user wishes to use multiple targets, a multioutput prediction model
            # needs to be used such as templates/
            df["&-s_close"] = (
                / df["close"]
                - 1

        return df

Notice how the populate_any_indicators() is where features and labels/targets are added. A full example strategy is available in templates/

Notice also the location of the labels under if set_generalized_indicators: at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as include_timeframes.


The self.freqai.start() function cannot be called outside the populate_indicators().


Features must be defined in populate_any_indicators(). Defining FreqAI features in populate_indicators() will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside populate_any_indicators() should be used (as exemplified in freqtrade/templates/

    def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):


        # Add generalized indicators here (because in live, it will call only this function to populate
        # indicators for retraining). Notice how we ensure not to add them multiple times by associating
        # these generalized indicators to the basepair/timeframe
        if set_generalized_indicators:
            df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
            df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25

            # user adds targets here by prepending them with &- (see convention below)
            # If user wishes to use multiple targets, a multioutput prediction model
            # needs to be used such as templates/
            df["&-s_close"] = (
                / df["close"]
                - 1

Please see the example script located in freqtrade/templates/ for a full example of populate_any_indicators().

Important dataframe key patterns

Below are the values you can expect to include/use inside a typical strategy dataframe (df[]):

DataFrame Key Description
df['&*'] Any dataframe column prepended with & in populate_any_indicators() is treated as a training target (label) inside FreqAI (typically following the naming convention &-s*). For example, to predict the close price 40 candles into the future, you would set df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) with "label_period_candles": 40 in the config. FreqAI makes the predictions and gives them back under the same key (df['&-s_close']) to be used in populate_entry/exit_trend().
Datatype: Depends on the output of the model.
df['&*_std/mean'] Standard deviation and mean values of the defined labels during training (or live tracking with fit_live_predictions_candles). Commonly used to understand the rarity of a prediction (use the z-score as shown in templates/ and explained here to evaluate how often a particular prediction was observed during training or historically with fit_live_predictions_candles).
Datatype: Float.
df['do_predict'] Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. do_predict==1 means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details here) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from do_predict, resulting in do_predict==0. If use_SVM_to_remove_outliers() is active, the Support Vector Machine (SVM, see details here) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from do_predict. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be do_predict==0. If both the DI and the SVM considers the input data point to be an outlier, the result will be do_predict==-1. As with the SVM, if use_DBSCAN_to_remove_outliers is active, DBSCAN (see details here) may also detect outliers and subtract 1 from do_predict. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be do_predict==-2. A particular case is when do_predict == 2, which means that the model has expired due to exceeding expired_hours.
Datatype: Integer between -2 and 2.
df['DI_values'] Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI here.
Datatype: Float.
df['%*'] Any dataframe column prepended with % in populate_any_indicators() is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in templates/ by setting df['%-rsi']. See more details on how this is done here.
Note: Since the number of features prepended with % can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., include_shifted_candles and include_timeframes as described in the parameter table), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with %%.
Datatype: Depends on the output of the model.

Setting the startup_candle_count

The startup_candle_count in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details here). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the dataprovider, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, startup_candle_count is 20 since this is the maximum value in indicators_periods_candles.


There are instances where the Ta-Lib functions actually require more data than just the passed period or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the startup_candle_count by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected startup_candle_count by 2. Look out for this log message to confirm that the data is clean:

2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.

Creating a dynamic target threshold

Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info here). For example, the &*_std/mean return values describe the statistical distribution of the target/label during the most recent training. Comparing a given prediction to these values allows you to know the rarity of the prediction. In templates/, the target_roi and sell_roi are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.

dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25

To consider the population of historical predictions for creating the dynamic target instead of information from the training as discussed above, you would set fit_live_predictions_candles in the config to the number of historical prediction candles you wish to use to generate target statistics.

    "freqai": {
        "fit_live_predictions_candles": 300,

If this value is set, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if you stop and restart a model with the same identifier.

Using different prediction models

FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag --freqaimodel. These libraries include CatBoost, LightGBM, and XGBoost regression, classification, and multi-target models, and can be found in freqai/prediction_models/.

Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details below).

All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:

There are also numerous online articles describing and comparing the algorithms. Some relatively light-weight examples would be CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm? and XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?. Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.

Apart from the models already available in FreqAI, it is also possible to customize and create your own prediction models using the IFreqaiModel class. You are encouraged to inherit fit(), train(), and predict() to customize various aspects of the training procedures. You can place custom FreqAI models in user_data/freqaimodels - and freqtrade will pick them up from there based on the provided --freqaimodel name - which has to correspond to the class name of your custom model. Make sure to use unique names to avoid overriding built-in models.

Setting model targets


If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the CatboostRegressorvia the flag --freqaimodel CatboostRegressor. An example of how you could set a regression target for predicting the price 100 candles into the future would be

df['&s-close_price'] = df['close'].shift(-100)

If you want to predict multiple targets, you need to define multiple labels using the same syntax as shown above.


If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the CatboostClassifier via the flag --freqaimodel CatboostClassifier. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set

df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')

If you want to predict multiple targets you must specify all labels in the same label column. You could, for example, add the label same to define where the price was unchanged by setting

df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])