stable/docs/freqai.md

Freqai

!!! Note Freqai is still experimental, and should be used at the user's own discretion.

Freqai is a module designed to automate a variety of tasks associated with training a regressor to predict signals based on input features. Among the the features includes:

• Easy large feature set construction based on simple user input
• Sweep model training and backtesting to simulate consistent model retraining through time
• Smart outlier removal of data points from prediction sets using a Dissimilarity Index.
• Data dimensionality reduction with Principal Component Analysis
• Automatic file management for storage of models to be reused during live
• Smart and safe data standardization
• Cleaning of NaNs from the data set before training and prediction.

TODO:

• live is not automated, still some architectural work to be done

Background and vocabulary

Features are the quantities with which a model is trained. X_i represents the vector of all features for a single candle. In Freqai, the user builds the features from anything they can construct in the strategy.

Labels are the target values with which the weights inside a model are trained toward. Each set of features is associated with a single label, which is also defined within the strategy by the user. These labels look forward into the future, and are not available to the model during dryrun/live/backtesting.

Training refers to the process of feeding individual feature sets into the model with associated labels with the goal of matching input feature sets to associated labels.

Train data is a subset of the historic data which is fed to the model during training to adjust weights. This data directly influences weight connections in the model.

Test data is a subset of the historic data which is used to evaluate the intermediate performance of the model during training. This data does not directly influence nodal weights within the model.

Configuring the bot

Example config file

The user interface is isolated to the typical config file. A typical Freqai config setup includes:

    "freqai": {
                "timeframes" : ["5m","15m","4h"],
                "full_timerange" : "20211220-20220220",
                "train_period" : "month",
                "backtest_period" : "week",
                "identifier" :  "unique-id",
                "base_features": [
                        "rsi",
                        "mfi",
                        "roc",
                ],
                "corr_pairlist": [
                        "ETH/USD",
                        "LINK/USD",
                        "BNB/USD"
                ],
                "train_params" : {
                        "period": 24,
                        "shift": 2,
                        "drop_features": false,
                        "DI_threshold": 1,
                        "weight_factor":  0,
                },
                "SPLIT_PARAMS" : {
                    "test_size": 0.25,
                    "random_state": 42
                },
                "CLASSIFIER_PARAMS" : {
                    "n_estimators": 100,
                    "random_state": 42,
                    "learning_rate": 0.02,
                    "task_type": "CPU",
                },
        },

Building the feature set

Most of these parameters are controlling the feature data set. The base_features indicates the basic indicators the user wishes to include in the feature set. The timeframes are the timeframes of each base_feature that the user wishes to include in the feature set. In the present case, the user is asking for the 5m, 15m, and 4h timeframes of the rsi, mfi, roc, etc. to be included in the feature set.

In addition, the user can ask for each of these features to be included from informative pairs using the corr_pairlist. This means that the present feature set will include all the base_features on all the timeframes for each of ETH/USD, LINK/USD, and BNB/USD.

shift is another user controlled parameter which indicates the number of previous candles to include in the present feature set. In other words, shift: 2, tells Freqai to include the the past 2 candles for each of the features included in the dataset.

In total, the number of features the present user has created is:_

no. timeframes * no. base_features * no. corr_pairlist * no. shift_ 3 * 3 * 3 * 2 = 54._

Deciding the sliding training window and backtesting duration

full_timerange lets the user set the full backtesting range to train and backtest through. Meanwhile train_period is the sliding training window and backtest_period is the sliding backtesting window. In the present example, the user is asking Freqai to train and backtest the range of 20211220-20220220 (month). The user wishes to backtest each week with a newly trained model. This means that Freqai will train 8 separate models (because the full range comprises 8 weeks), and then backtest the subsequent week associated with each of the 8 training data set timerange months. Users can think of this as a "sliding window" which emulates Freqai retraining itself once per week in live using the previous month of data.

Running Freqai

Training and backtesting

The freqai training/backtesting module can be executed with the following command:

freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel

where the user needs to have a FreqaiExampleStrategy that fits to the requirements outlined below. The ExamplePredictionModel is a user built class which lets users design their own training procedures and data analysis.

Building a freqai strategy

The Freqai strategy requires the user to include the following lines of code in populate_ any _indicators()

        from freqtrade.freqai.strategy_bridge import CustomModel

        def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
                # the configuration file parameters are stored here
                self.freqai_info = self.config['freqai']

                # the model is instantiated here
                self.model = CustomModel(self.config)

                print('Populating indicators...')

                # the following loops are necessary for building the features 
                # indicated by the user in the configuration file.
                for tf in self.freqai_info['timeframes']:
                        dataframe = self.populate_any_indicators(metadata['pair'],
                                                                dataframe.copy(), tf)
                        for i in self.freqai_info['corr_pairlist']:
                        dataframe = self.populate_any_indicators(i,
                                        dataframe.copy(), tf, coin=i.split("/")[0]+'-')

                # the model will return 4 values, its prediction, an indication of whether or not the prediction 
                # should be accepted, the target mean/std values from the labels used during each training period.
                (dataframe['prediction'], dataframe['do_predict'], 
                        dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata)

                return dataframe

The user should also include populate_any_indicators() from templates/FreqaiExampleStrategy.py which builds the feature set with a proper naming convention for the IFreqaiModel to use later.

Building an IFreqaiModel

Freqai has a base example model in templates/ExamplePredictionModel.py, but users can customize and create their own prediction models using the IFreqaiModel class. Users are encouraged to inherit train(), predict(), and make_labels() to let them customize various aspects of their training procedures.

Running the model live

After the user has designed a desirable featureset, Freqai can be run in dry/live using the typical trade command:

freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --training_timerange '20211220-20220120'

Where the user has now specified exactly which of the models from the sliding window that they wish to run live using --training_timerange (typically this would be the most recent model trained). As of right now, freqai will not automatically retain itself, so the user needs to manually retrain and then reload the config file with a new --training_timerange in order to update the model.

Data anylsis techniques

Controlling the model learning process

The user can define model settings for the data split data_split_parameters and learning parameters model_training_parameters. Users are encouraged to visit the Catboost documentation for more information on how to select these values. n_estimators increases the computational effort and the fit to the training data. If a user has a GPU installed in their system, they may benefit from changing task_type to GPU. The weight_factor allows the user to weight more recent data more strongly than past data via an exponential function:

 W_i = \exp(\frac{-i}{\alpha*n}) 

where W_i is the weight of data point i in a total set of n data points._

drop_features tells Freqai to train the model on the user defined features, followed by a feature importance evaluation where it drops the top and bottom performing features (there is evidence to suggest the top features may not be helpful in equity/crypto trading since the ultimate objective is to predict low frequency patterns, source: numerai)._

Finally, period defines the offset used for the labels. In the present example, the user is asking for labels that are 24 candles in the future.

Removing outliers with the Dissimilarity Index

The Dissimilarity Index (DI) aims to quantiy the uncertainty associated with each prediction by the model. To do so, Freqai measures the distance between each training data point and all other training data points:

 d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} 

where d_{ab} is the distance between the standardized points a and b. $p$ is the number of features i.e. the length of the vector X. The characteristic distance, \overline{d} for a set of training data points is simply the mean of the average distances:

 \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) 

\overline{d} quantifies the spread of the training data, which is compared to the distance between the new prediction feature vectors, X_k and all the training data:

 d_k = \argmin_i d_{k,i} 

which enables the estimation of a Dissimilarity Index:

 DI_k = d_k/\overline{d} 

Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The dissimilarity index allows predictions which are outliers and not existent in the model feature space, to be thrown out due to low levels of certainty. The user can tweak the DI with DI_threshold to increase or decrease the extrapolation of the trained model.

Reducing data dimensionality with Principal Component Analysis

TO BE WRITTEN

Additional information

Feature standardization

The feature set created by the user is automatically standardized to the training data only. This includes all test data and unseen prediction data (dry/live/backtest).

File structure

user_data_dir/models/ contains all the data associated with the trainings and backtestings. This file structure is heavily controlled and read by the DataHandler() and should thus not be modified.