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Document and abstract portfolio trading costs

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Yuxuan Yan
2026-06-10 15:41:38 +08:00
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CLAUDE.md
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@@ -69,6 +69,8 @@ Data is stored **long/tidy**, not wide, as a Hive-partitioned dataset keyed by `
`portfolio simulate` must execute `position_shares`, not continuous `target_shares`. It fills at the next available open and clips desired deltas through repeatable constraints (`suspension`, `price_limit`, `volume_cap`). `portfolio eval` uses `target_weight` for a continuous research view, so zero-gross carry dates remain flat there. Keep IC/IR out of portfolio metrics too.
Trading cost uses the simplified open-execution proportional cash-cost model in `docs/portfolio_trading_cost_model.md`: `abs(traded_shares * open) * (cost_bps + slippage_bps) / 10000`. Slippage is cash cost only; do not also adjust execution prices for slippage.
## Alphas: factory + plugin pattern
Each alpha is a class subclassing `BaseAlpha` (`pipeline/alpha/base.py`), living in its own module. It implements `signal(close) -> wide DataFrame` (the raw score); the base class's `to_weights` cross-sectionally z-scores that into position weights (override for custom normalization). Subclasses declare their own typed `__init__` params (e.g. `lookback`, `vol_window`, or anything custom).