intelligent-trading-bot/docs/configuration.md

Configuration and parameterization

Parameterization

All parameters for scripts, server and some high-level functions are provided as JSON object. It is represented as a JSON file with comments or as a Python dictionary object at run time. Not all parameters and parameter sections are used by all scripts and functions.

Global parameters

Here the most important parameters:

• General parameters:
  • symbol In particular, it is used as a subfolder name for all generated files (within data_folder)
  • description Any textual desciprition of this configuraiton. It can be then used by various plugable components, for example, for text or visual output.
  • freq Frequency of data in pandas format, for example, 1h for hourly data or 1min for minutely data.
  • train Boolean attribute specifying if the analysis has to run in train (if true) or predict (if false) mode. If it is true, then all trainable features will train their models based on historic data. If false, then the existing models will be used for prediction.
• Persistence:
  • data_folder Location of all the data files for this analysis
• Data providers:
  • venue Name of the data provider and the corresponding connector. Currently these values are supported: binance, yahoo, mt5.
  • api_key and api_secret Credentials for the selected venue (data provider). They will be passed to the connector.
  • client_args Dictionary with arbitrary arguments passed to the data connector
• Output:
  • telegram_bot_token and telegram_chat_id Used to send notifications to Telegram bot by the corresponding output adapters

Analaysis table parameters

All data during analysis are represented as a dataframe which has these parameters. This dataframe has to have certain shape which is specified via the following parameters which are used mainly by the server:

• label_horizon The minimum number of future rows required to compute a label. It is our prediction horizon. It is taken into account in training by ignoring rows which do not have enough future data.
• features_horizon The minimum number of past rows for a feature to be valid. For example, if we want to computing moving average for 10 days then the feature requires 10 previous rows. Essentially, the very first features_horizon rows will be considered invalid and ignored in analysis. This value should be taken from the feature defintions.
• train_length Default limit for the train data set size. It is a maximum value for all ML-features but individual features can set their own values. 0 means all available data.
• predict_length This minimum number of rows will be kept up-to-date and valid in online mode. Since their values must be valid, these rows must have at least features_horizon before.
• append_overlap_records In online mode, the server will request more records than strictly required (missing). This additional number is specified in this parameter. The received new records will be (again) evaluated and overwrite previuos values. It is desirable in case of connection errors or in case last rows have small deviations and differ from what is provided later.

Parameter sections

Model registry is a list model_registry with entries consisting of name and file attributes.

Feature definitions are specified in four sections:

• feature_sets
• label_sets labels needed in train mode
• train_feature_sets trainable features
• signal_sets features evaluated after ML-features Features use these global parameters:
• train_features a list of all column names which will be by default selected to pass data to train algorithms (in both train and predict modes) Algorithms (trainable features) can set their own lists of features they want to use for learning and prediction.
• labels A list of all labels (if not overwritten by individual trainable algorithms)
• algorithms Obsolete. Use either train_feature_sets or normal feature_sets to define trainable features

Outputs are defined in the output_sets which is a list of dictionaries passed to the output adapters after analysis. For example, here a trading or notification adapter can be specified.

Other sections can describe utilities, for example, rolling_predict or simulate_model.