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Extending

Three common extension points. Each touches one layer.

Add a strategy​

  1. Subclass Strategy in src/strategies/:

    class MyStrategy(Strategy):
        TIMEFRAME = "5Min"
        PARAM_RANGES = {  # min/max/step/default/type per tunable param
            "lookback": {"type": "int", "min": 5, "max": 50, "step": 5, "default": 20},
            "risk_per_trade": {"type": "float", "min": 0.01, "max": 0.05, "step": 0.01, "default": 0.02},
            "stop_loss": {"type": "float", "min": 0.01, "max": 0.05, "step": 0.01, "default": 0.02},
            "take_profit": {"type": "float", "min": 0.02, "max": 0.10, "step": 0.02, "default": 0.04},
        }

        def calculate_required_lookback(self): return self.config["lookback"] + 1
        def initialize(self): ...
        def process_data(self, df): ...                 # add indicator columns
        def generate_signals(self, df): ...             # -> {timestamp: signal}

  2. Register it in main.STRATEGIES. It now works in backtest, live, and optimize — sizing, fills, execution, and metrics come for free because they only depend on the base interface.

Use the pure indicators; don't reach for a compiled TA library.

Add a scanner​

  1. Subclass ScannerStrategy in src/scanners/ — implement process_data and generate_signals_df (emit SCANNER_BUY/SCANNER_SELL/SCANNER_HOLD plus a signal_strength).
  2. Register it in SymbolScanner.SCANNERS. Keep it TA-Lib-free.

Add a broker​

  1. Implement Broker (and optionally MarketDataProvider) for the venue in a new src/brokers/<vendor>/ package, mapping the SDK to the domain types.
  2. Construct it in main.build_data_and_broker().

Nothing in engine/, execution/, strategies/, scanners/, or the optimizer changes — they only ever knew the interface. Prove it the same way the suite does: run against your adapter, or against FakeBroker first.

Add an optimization objective​

Any key in the metrics dict (sharpe_ratio, total_return, calmar_ratio, ...) is a valid --objective. To add a new one, compute it in analytics.performance.compute_backtest_metrics and it's immediately selectable.