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Installation
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Linux/MacOS/Raspberry
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Windows
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Freqtrade Basics
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Configuration
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Strategy Customization
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Strategy Callbacks
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Stoploss
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Plugins
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Start the bot
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Control the bot
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Control the bot
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Telegram
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freqUI
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REST API
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Web Hook
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Data Downloading
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Backtesting
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Hyperopt
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FreqAI
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FreqAI
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Introduction
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Configuration
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Parameter table
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< h1 id = "development" > Development< a class = "headerlink" href = "#development" title = "Permanent link" > ¶ < / a > < / h1 >
< h2 id = "project-architecture" > Project architecture< a class = "headerlink" href = "#project-architecture" title = "Permanent link" > ¶ < / a > < / h2 >
< p > The architecture and functions of FreqAI are generalized to encourages development of unique features, functions, models, etc.< / p >
< p > The class structure and a detailed algorithmic overview is depicted in the following diagram:< / p >
< p > < img alt = "image" src = "../assets/freqai_algorithm-diagram.jpg" / > < / p >
< p > As shown, there are three distinct objects comprising FreqAI:< / p >
< ul >
< li > < strong > IFreqaiModel< / strong > - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models.< / li >
< li > < strong > FreqaiDataKitchen< / strong > - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.< / li >
< li > < strong > FreqaiDataDrawer< / strong > - A singular persistent object containing all the historical predictions, models, and save/load methods.< / li >
< / ul >
< p > There are a variety of built-in < a href = "../freqai-configuration/#using-different-prediction-models" > prediction models< / a > which inherit directly from < code > IFreqaiModel< / code > . Each of these models have full access to all methods in < code > IFreqaiModel< / code > and can therefore override any of those functions at will. However, advanced users will likely stick to overriding < code > fit()< / code > , < code > train()< / code > , < code > predict()< / code > , and < code > data_cleaning_train/predict()< / code > .< / p >
< h2 id = "data-handling" > Data handling< a class = "headerlink" href = "#data-handling" title = "Permanent link" > ¶ < / a > < / h2 >
< p > FreqAI aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,< code > user_data_dir/models/< / code > , which contains all the data associated with the trainings and backtests. The < code > FreqaiDataKitchen()< / code > relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.< / p >
< h3 id = "file-structure" > File structure< a class = "headerlink" href = "#file-structure" title = "Permanent link" > ¶ < / a > < / h3 >
< p > The file structure is automatically generated based on the model < code > identifier< / code > set in the < a href = "../freqai-configuration/#setting-up-the-configuration-file" > config< / a > . The following structure shows where the data is stored for post processing:< / p >
< table >
< thead >
< tr >
< th > Structure< / th >
< th > Description< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > < code > config_*.json< / code > < / td >
< td > A copy of the model specific configuration file.< / td >
< / tr >
< tr >
< td > < code > historic_predictions.pkl< / code > < / td >
< td > A file containing all historic predictions generated during the lifetime of the < code > identifier< / code > model during live deployment. < code > historic_predictions.pkl< / code > is used to reload the model after a crash or a config change. A backup file is always held in case of corruption on the main file. FreqAI < strong > automatically< / strong > detects corruption and replaces the corrupted file with the backup.< / td >
< / tr >
< tr >
< td > < code > pair_dictionary.json< / code > < / td >
< td > A file containing the training queue as well as the on disk location of the most recently trained model.< / td >
< / tr >
< tr >
< td > < code > sub-train-*_TIMESTAMP< / code > < / td >
< td > A folder containing all the files associated with a single model, such as: < br > < / td >
< / tr >
< tr >
< td > < / td >
< td > < code > *_metadata.json< / code > - Metadata for the model, such as normalization max/min, expected training feature list, etc. < br > < / td >
< / tr >
< tr >
< td > < / td >
< td > < code > *_model.*< / code > - The model file saved to disk for reloading from a crash. Can be < code > joblib< / code > (typical boosting libs), < code > zip< / code > (stable_baselines), < code > hd5< / code > (keras type), etc. < br > < / td >
< / tr >
< tr >
< td > < / td >
< td > < code > *_pca_object.pkl< / code > - The < a href = "../freqai-feature-engineering/#data-dimensionality-reduction-with-principal-component-analysis" > Principal component analysis (PCA)< / a > transform (if < code > principal_component_analysis: True< / code > is set in the config) which will be used to transform unseen prediction features. < br > < / td >
< / tr >
< tr >
< td > < / td >
< td > < code > *_svm_model.pkl< / code > - The < a href = "../freqai-feature-engineering/#identifying-outliers-using-a-support-vector-machine-svm" > Support Vector Machine (SVM)< / a > model (if < code > use_SVM_to_remove_outliers: True< / code > is set in the config) which is used to detect outliers in unseen prediction features. < br > < / td >
< / tr >
< tr >
< td > < / td >
< td > < code > *_trained_df.pkl< / code > - The dataframe containing all the training features used to train the < code > identifier< / code > model. This is used for computing the < a href = "../freqai-feature-engineering/#identifying-outliers-with-the-dissimilarity-index-di" > Dissimilarity Index (DI)< / a > and can also be used for post-processing. < br > < / td >
< / tr >
< tr >
< td > < / td >
< td > < code > *_trained_dates.df.pkl< / code > - The dates associated with the < code > trained_df.pkl< / code > , which is useful for post-processing.< / td >
< / tr >
< / tbody >
< / table >
< p > The example file structure would look like this:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > ├── models
│ └── unique-id
│ ├── config_freqai.example.json
│ ├── historic_predictions.backup.pkl
│ ├── historic_predictions.pkl
│ ├── pair_dictionary.json
│ ├── sub-train-1INCH_1662821319
│ │ ├── cb_1inch_1662821319_metadata.json
│ │ ├── cb_1inch_1662821319_model.joblib
│ │ ├── cb_1inch_1662821319_pca_object.pkl
│ │ ├── cb_1inch_1662821319_svm_model.joblib
│ │ ├── cb_1inch_1662821319_trained_dates_df.pkl
│ │ └── cb_1inch_1662821319_trained_df.pkl
│ ├── sub-train-1INCH_1662821371
│ │ ├── cb_1inch_1662821371_metadata.json
│ │ ├── cb_1inch_1662821371_model.joblib
│ │ ├── cb_1inch_1662821371_pca_object.pkl
│ │ ├── cb_1inch_1662821371_svm_model.joblib
│ │ ├── cb_1inch_1662821371_trained_dates_df.pkl
│ │ └── cb_1inch_1662821371_trained_df.pkl
│ ├── sub-train-ADA_1662821344
│ │ ├── cb_ada_1662821344_metadata.json
│ │ ├── cb_ada_1662821344_model.joblib
│ │ ├── cb_ada_1662821344_pca_object.pkl
│ │ ├── cb_ada_1662821344_svm_model.joblib
│ │ ├── cb_ada_1662821344_trained_dates_df.pkl
│ │ └── cb_ada_1662821344_trained_df.pkl
│ └── sub-train-ADA_1662821399
│ ├── cb_ada_1662821399_metadata.json
│ ├── cb_ada_1662821399_model.joblib
│ ├── cb_ada_1662821399_pca_object.pkl
│ ├── cb_ada_1662821399_svm_model.joblib
│ ├── cb_ada_1662821399_trained_dates_df.pkl
│ └── cb_ada_1662821399_trained_df.pkl
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