intelligent-trading-bot/configs/config-sample-v0.6.0.jsonc

{
  "api_key": "<binance-key>",
  "api_secret": "<binance-secret>",

  "telegram_bot_token": "<token>",
  "telegram_chat_id": "<chat-id-to-publish-messages>",

  "data_folder": "C:/DATA_ITB",

  // === DOWNLOAD AND MERGE ===

  "symbol": "BTCUSDT",

  "data_sources": [
    {"folder": "BTCUSDT", "file": "klines", "column_prefix": ""}
  ],

  // === GENERATE FEATURES ===

  "feature_sets": [
      {"column_prefix": "", "generator": "talib", "feature_prefix": "", "config":  {"columns": ["close"], "functions": ["SMA"], "windows": [1, 5, 10, 15, 60]}},
      {"column_prefix": "", "generator": "talib", "feature_prefix": "", "config":  {"columns": ["close"], "functions": ["LINEARREG_SLOPE"], "windows": [5, 10, 15, 60]}},
      {"column_prefix": "", "generator": "talib", "feature_prefix": "", "config":  {"columns": ["close"], "functions": ["STDDEV"], "windows": [5, 10, 15, 60]}}
  ],

  // === LABELS ===

  "label_sets": [
      {"column_prefix": "", "generator": "highlow2", "feature_prefix": "", "config":  {"columns": ["close", "high", "low"], "function": "high", "thresholds": [2.0], "tolerance": 0.2, "horizon": 60, "names": ["high_20"]}},
      {"column_prefix": "", "generator": "highlow2", "feature_prefix": "", "config":  {"columns": ["close", "high", "low"], "function": "low", "thresholds": [2.0], "tolerance": 0.2, "horizon": 60, "names": ["low_20"]}}
  ],

  // === TRAIN ===

  "label_horizon": 60,
  "train_length": 525600,

  "train_features": [
    "close_SMA_1", "close_SMA_5", "close_SMA_10", "close_SMA_15", "close_SMA_60",
    "close_LINEARREG_SLOPE_5", "close_LINEARREG_SLOPE_10", "close_LINEARREG_SLOPE_15", "close_LINEARREG_SLOPE_60",
    "close_STDDEV_5", "close_STDDEV_10", "close_STDDEV_15", "close_STDDEV_60"
  ],

  "labels": [
      "high_20",
      "low_20"
  ],

  "algorithms": [
    {
      "name": "lc",  // Unique name will be used as a column suffix
      "algo": "lc",  // Altorithm type is used to choose the train/predict function
      "params": {"penalty": "l2", "C": 1.0, "class_weight": null, "solver": "sag", "max_iter": 100},
      "train": {"is_scale": true, "length": 1000000, "shifts": []},
      "predict": {"length": 1440}
    }
  ],

  "features_horizon": 100, // Minimum data length for computing features (in online mode). Take it from feature generator parameters

  // === AGGREGATION AND POST-PROCESSING ===

  "score_aggregation_sets": [
      {
          "column": "trade_score", // Output column name: positive values - buy, negative values - sell

          "buy_labels": ["high_20_lc"],
          "sell_labels": ["low_20_lc"],

          "parameters": {
            "point_threshold": null, // Produce boolean column (optional)
            "window": 3, // Aggregate in time
            "combine": "difference", // "no_combine" (or empty), "relative", "difference"
            "coefficient": 1.0,
            "constant": 0.0
          }
      }
  ],

  // === SIGNAL RULES FOR TRADING ===

  "signal_model": {
      "rule_name": "",

      "signal_columns": ["buy_signal_column", "sell_signal_column"],

      "parameters": {
        "buy_signal_threshold": 0.05,
        "sell_signal_threshold": -0.05
      },
      "notification": {
        "buy_notify_threshold": 0.02,
        "sell_notify_threshold": -0.02,
        "trade_icon_step": 0.05,
        "notify_frequency_minutes": 1,
        "notify_signal": true,
        "notify_transaction": true,
        "notify_diagram": false
      }
  },

  "train_signal_model": {
      "data_start": 0,
      "data_end": null,

      "buy_sell_equal": false,
      "direction": "long",
      "topn_to_store": 10,

      "grid": {
          "buy_signal_threshold": [0.02, 0.03, 0.04, 0.05, 0.1, 0.15],
          "sell_signal_threshold": [-0.02, -0.03, -0.04, -0.05, -0.1, -0.15]
      }
    },

  "rolling_predict": {
    // int, null or string with date which will be resolved using time_column and removed from source data
    "data_start": "2020-02-01 00:00:00",
    "data_end": null,

    // One of these 3 parameters can be null and will be computed from the other two
    "prediction_start": null,  // First row for starting predictions, for example, "2022-02-01 00:00:00"
    "prediction_size": 10080, // How many predictions, for example, 1 week 7*1440
    "prediction_steps": 4, // How many train-prediction steps

    "use_multiprocessing": false,
    "max_workers": 8
  }
}