dennisthiessen
605f95098c
Part 1 — long-only. The momentum edge is long top-momentum; the gate was
qualifying shorts on high-momentum names (fighting the trend), which showed as
the -0.13R Short(qual.) drag. While the gate is active, shorts no longer qualify
(backend qualification, backtest _momentum_qualifies, and the frontend mirror).
Part 2 — production wiring. Live setups now carry a real momentum rank, so the
dashboard, the Track Record's qualified stats, and outcome evaluation all gate on
the same value instead of deferring to floors:
- new momentum_service.compute_momentum_percentiles: 12-1 momentum per ticker,
ranked across the universe into a {symbol: percentile} map.
- the daily R:R scan ranks the universe up front and stores each setup's
percentile (new trade_setups.momentum_percentile column, migration 010).
- enhance_trade_setup mutates the same row, so the percentile is preserved;
_trade_setup_to_dict + TradeSetupResponse expose it to the API.
Until a fresh scan runs, pre-existing setups have a null percentile and the gate
falls back to floors for them (longs) / excludes them (shorts) — they fill in on
the next scan. 341 backend tests pass; frontend build clean.
Needs the alembic upgrade (migration 010) on deploy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
"""Cross-sectional 12-1 momentum ranking for the universe.
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The activation gate selects the top ``min_momentum_percentile`` of the universe
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by 12-1 month momentum (return from ~12 months ago to ~1 month ago — the one
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price signal the backtest showed sorts forward returns). The daily scan ranks
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every ticker and stores each setup's percentile (see ``rr_scanner_service``), so
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the live list, the Track Record's qualified stats, and outcome evaluation all gate
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on the same value.
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"""
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from __future__ import annotations
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import json
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import logging
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.ticker import Ticker
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from app.services.price_service import query_ohlcv
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logger = logging.getLogger(__name__)
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# 12-1 momentum: ~12 months of daily history (252 bars) with the last ~1 month
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# (21 bars) skipped. Matches the backtest's _signal_values / _window_setups.
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_MOM_LOOKBACK = 252
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_MOM_SKIP = 21
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def compute_12_1_momentum(closes: list[float]) -> float | None:
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"""Return over the window ending ~1 month ago, starting ~12 months ago.
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None when there isn't a full year of history."""
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if len(closes) >= _MOM_LOOKBACK + 1 and closes[-(_MOM_LOOKBACK + 1)] > 0:
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return closes[-(_MOM_SKIP + 1)] / closes[-(_MOM_LOOKBACK + 1)] - 1.0
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return None
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async def compute_momentum_percentiles(db: AsyncSession) -> dict[str, float]:
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"""Compute each ticker's 12-1 momentum and rank the universe into a
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``{symbol: percentile}`` map (0–100, 100 = strongest momentum). Tickers
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without a full year of history are absent (can't be ranked)."""
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result = await db.execute(select(Ticker).order_by(Ticker.symbol))
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tickers = list(result.scalars().all())
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momentum: dict[str, float] = {}
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for ticker in tickers:
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try:
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records = await query_ohlcv(db, ticker.symbol)
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except Exception:
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logger.exception("Momentum fetch failed for %s", ticker.symbol)
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continue
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m = compute_12_1_momentum([float(r.close) for r in records])
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if m is not None:
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momentum[ticker.symbol] = m
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ranked = sorted(momentum, key=lambda s: momentum[s])
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n = len(ranked)
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percentiles = {
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sym: round((rank / (n - 1) * 100.0) if n > 1 else 100.0, 2)
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for rank, sym in enumerate(ranked)
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}
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logger.info(json.dumps({"event": "momentum_ranked", "tickers": n}))
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return percentiles
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