# Configuration and parameterization

## Parameterization

All parameters for scripts, the server, and high-level functions are provided as a JSON object.
This configuration is represented either as a JSON file (with comment support) or as a Python dictionary (еt runtime).
Note that not all parameters and sections are required by every script or function.

## Global parameters

The most important parameters are listed below:

- General parameters:
  - `symbol`: Used primarily as the subfolder name for all generated files within the `data_folder`.
  - `description`: A textual description of this configuration. It can be consumed by various pluggable components, such as text or visual output adapters.
  - `freq`: Data frequency in `pandas` format (e.g., `1h` for hourly data or `1min` for minutely data).
  - `train`: Boolean flag specifying whether the analysis runs in train (`true`) or predict (`false`) mode.
    If `true`, all trainable features will fit their models using historical data.
    If `false`, existing models will be loaded for prediction.
- Persistence:
  - `data_folder`: Path to the directory containing all data files for this analysis.
  - `model_folder`: Path to the directory where ML models produced during training are stored.
- Data providers:
  - `venue`: Name of the data provider and its corresponding connector. Currently supported values: `binance`, `yahoo`, `mt5`.
  - `api_key` and `api_secret`: Credentials for the selected venue. These are passed directly to the connector.
  - `client_args`: Dictionary of arbitrary arguments passed to the data connector.
  - `time_column`: Column name used for timestamps (defaults to `"timestamp"`).
- Output:
  - `telegram_bot_token` and `telegram_chat_id`: Credentials used by the Telegram output adapter to send notifications.

## Analysis table parameters

During analysis, all data is represented as a DataFrame configured via the following parameters.
These parameters define the required shape of the DataFrame and are used primarily by the server:

- `label_horizon`: The minimum number of future rows required to compute a label (i.e., the prediction horizon).
  During training, rows lacking sufficient future data are excluded.
- `features_horizon`: The minimum number of past rows required for a feature to be valid. For example, computing a 10-day moving average requires 10 previous rows.
  Consequently, the first `features_horizon` rows are considered invalid and excluded from analysis.
  This value should be derived from the feature definitions.
- `train_length`: Default upper limit for the training dataset size.
  This serves as a global maximum for all ML features, though individual features may override it.
  A value of `0` indicates that all available data should be used.
- `predict_length`: The minimum number of rows kept up-to-date and valid in online mode.
  To ensure validity, these rows must be preceded by at least `features_horizon` historical rows.
- `append_overlap_records`: In online mode, the server requests additional records beyond those strictly missing.
  This parameter specifies the size of that overlap buffer.
  Overlapping records are re-evaluated and overwrite previous values, which helps mitigate connection errors or minor discrepancies in recently received data.

## Parameter sections

The model registry is defined as a list named `model_registry`, where each entry contains `name` and `file` attributes.

Feature definitions are organized into four sections:
- `feature_sets`
- `label_sets`: Labels required during training.
- `train_feature_sets`: Trainable features.
- `signal_sets`: Features evaluated after ML features.

Features rely on the following global parameters:
- `train_features`: A list of column names selected by default as input for training algorithms (in both train and predict modes).
  Individual trainable features may override this list with their own specific inputs.
- `labels`: A list of all labels (unless overridden by individual trainable algorithms).
- `algorithms`: **Obsolete.** Use either `train_feature_sets` or standard `feature_sets` to define trainable features.

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

Additional sections may describe utilities such as `rolling_predict` or `simulate_model`.