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Quick Start

Backtester Quick Start

In this example, we use Backtester in the context of financial markets, hence our user-defined logic is based on a notion of trading strategy. This can be extended to any other scenario which user wants to emulate. Once we have our best model from Trainer, we can plug it in our BaseStrategy derived class and run Backtester

  • Define config 

    featurizer_config_path: featurizer-config.yaml
inference_config:
  model_uri: <your-best-model-uri>
  predictor_class_name: 'XGBoostPredictor'
  num_replicas: <number-of-predictor-replicas>
simulation_class_name: 'backtester.strategy.ml_strategy.MLStrategy'
simulation_params:
  buy_delta: 0
  sell_delta: 0
user_defined_params:
  portfolio_config: <portfolio_config>
  tradable_instruments_params:
    - exchange: 'BINANCE'
      instrument_type: 'spot'
      symbol: 'BTC-USDT'
    See MLStrategy for example implementation

  • Run Backtester

    svoe backtester run --config-path <config-path> --ray-address <addr> --num-workers <num-workers>

    config = BacktesterConfig.load_config(config_path)
backtester = Backtester.from_config(config)
backtester.run_remotely(ray_address=ray_address, num_workers=num_workers)

This will run a distributed event-driven backtest using features and models defined earlier

  • Get statistics with 

    stats = backtester.get_stats()