Extending

Three common extension points. Each touches one layer.

Add a strategy

1. Subclass Strategy in tradeflow/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 calculate_scores(self, df): ...             # -> {timestamp: signed score}

   You implement calculate_scores (one signed conviction per bar) and nothing else for decisions: the base class derives BUY/SELL/HOLD from the score, and the alpha layer scales the same score. Set LONG_ONLY = False to allow shorts, and override signal_thresholds() for asymmetric entry/exit bands.

2. Register it in STRATEGIES in tradeflow/services/registry.py. It now works in backtest, live, optimize, the MCP server, and the research agent — sizing, fills, execution, and metrics come for free because they only depend on the base interface. (create_with_defaults() is inherited from Strategy; no need to write it.)

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

Add a scanner

1. Subclass ScannerStrategy in tradeflow/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 tradeflow/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.