diff --git a/README.md b/README.md index 0295196..9bf3204 100644 --- a/README.md +++ b/README.md @@ -13,8 +13,8 @@ Scheduled pipelines turn raw prices into a ranked, gated list of tradeable setup Once a day (default 07:00). Steps run **in dependency order**, each consuming the previous step's fresh output: 1. **OHLCV** — fetch the latest daily bars for every tracked ticker (Alpaca); new tickers backfill ~5 years. -2. **Sentiment** — fetch sentiment for the names that matter and are stale (> 5 days): top-pick feeders (momentum leaders with a tradeable long setup), the watchlist, and open paper trades, plus a top-N-by-composite discovery net. Runs *before* the scan so the scan sees fresh sentiment. -3. **R:R Scan** — recompute S/R zones, the 5-dimension scores and long/short setups (ATR stops, S/R targets) for every ticker, and attach each ticker's 12‑1 momentum percentile. +2. **Sentiment** — fetch sentiment for the names that matter and are stale (> 5 days): top-pick feeders (residual-momentum leaders with a tradeable long setup), the watchlist, and open paper trades, plus a top-N-by-composite discovery net. Runs *before* the scan so the scan sees fresh sentiment. +3. **R:R Scan** — recompute S/R zones, the 5-dimension scores and long/short setups (ATR stops, S/R targets) for every ticker, and attach each ticker's residual 12‑1 momentum activation percentile. 4. **Outcome Eval** — resolve setups that hit target/stop or expired (default 30 trading days) and auto-close paper trades per the exit policy (default: hold 30 trading days with the initial stop — the backtest-validated exit). 5. **Market Regime** — recompute the regime index (breadth/trend). 6. **Regime Monitor** — observational early-warning snapshot (VIX, credit spreads via FRED); feeds nothing else. @@ -33,8 +33,8 @@ Fundamentals (weekly, early Monday) · Alerts (hourly, Telegram) · Backtest (we 1. **Composite score** — technical, S/R-quality, sentiment, fundamental and momentum sub-scores (0–100) combine into a weighted composite (weights configurable; missing dimensions re-normalize). 2. **Setups** — the scanner builds long/short setups with ATR stops and S/R targets, then adds a confidence score, conflict flags and a target reach-probability. -3. **Activation gate** — a setup *qualifies* only if it clears the R:R floor **and** ranks in the top momentum percentile of the universe (the validated edge is long-only momentum; the confidence floor was ablated to zero effect and defaults off). -4. **Top pick** — the highest-momentum qualified setup; highlighted on the Dashboard and labelled on the ticker page. +3. **Activation gate** — a setup *qualifies* only if it clears the R:R floor **and** ranks in the top residual-momentum percentile of the universe (the validated edge is long-only; the confidence floor was ablated to zero effect and defaults off). +4. **Top pick** — the highest residual-momentum qualified setup; highlighted on the Dashboard and labelled on the ticker page. ## Strategy Status — What's Validated and What Isn't @@ -42,7 +42,7 @@ Fundamentals (weekly, early Monday) · Alerts (hourly, Telegram) · Backtest (we | Component | Verdict | Evidence | |---|---|---| -| **12-1 cross-sectional momentum** (the activation gate, long-only) | **The only demonstrated edge — in-sample** | Qualified setups beat the all-setups baseline after costs; rank-IC ≈ 0.05. Residual 12-1 momentum is now evaluated as a research signal, but is not production ranking yet | +| **Residual 12-1 cross-sectional momentum** (the activation gate, long-only) | **Production ranking — in-sample edge** | Promoted July 2026 after the portfolio variant beat raw 80 on CAGR, Sharpe and drawdown. Raw 12-1 remains a fallback only when benchmark data is unavailable | | S/R setup engine (ATR stops, S/R targets, reach-probability) | **Filter/execution context, not the exit** | R:R/room-to-run still earns its keep as a filter, but S/R targets underperform the time exit. The probability model is display-only | | Composite score + 5 dimensions | **Display/ranking only** | Sub-scores are hand-built heuristics; none has a measured IC. Note: the "momentum" *dimension* is 5/20-day ROC — NOT the validated 12-1 factor (that lives in `momentum_service`) | | LLM sentiment | Display + a bounded composite adjustment (± weight × 100 pts around neutral 50) | Deliberately kept out of the setup engine; no point-in-time history to validate against yet | @@ -50,7 +50,7 @@ Fundamentals (weekly, early Monday) · Alerts (hourly, Telegram) · Backtest (we | Short setups | **Excluded while the momentum gate is active** | Backtest showed shorts fight the trend and drag expectancy | | Expected-value gate (removed June 2026) | Degenerate — do not resurrect | Structurally favored distant lottery targets; selected *worse*-than-random setups | -Caveats on the momentum result: in-sample, roughly one market regime, costs/slippage approximated at 0.1% per side, and the factor is beta-heavy (6-month volatility often posts the top IC — that's beta, not alpha). The **out-of-sample proof is the forward paper-trade record**: Signals → Track Record compares live qualified expectancy against the backtest. +Caveats on the momentum result: in-sample, roughly one market regime, costs/slippage approximated at 0.1% per side, and residual momentum still needs SPY benchmark history to compute. The **out-of-sample proof is the forward paper-trade record**: Signals → Track Record compares live qualified expectancy against the backtest. ### The iron rule for strategy changes @@ -64,14 +64,14 @@ Corollaries: never let an unvalidated score gate setups; the outcome evaluator m ### Highest-value next experiments (in order) -1. **Residual momentum portfolio variants** — compare raw vs beta-adjusted 12-1 momentum in the strategy-variant simulator before changing production ranking. -2. **Capacity checks for promising variants** — compare max-position variants for raw 90 and residual 80 so a good-looking row is not just a book-slot artifact. +1. **Raw 90 challenger** — keep comparing raw 12-1 momentum at cutoff 90 against production residual 80; promote only if it beats residual production on Sharpe and drawdown without a meaningful CAGR hit. +2. **Capacity check** — keep only the residual 80 / max 15 portfolio row as a guardrail; max 20 and raw max 15 added no information in the July 2026 run. 3. **Signal context snapshots** — accumulate point-in-time composite/sentiment/fundamental context for every new setup so the discretionary overlay can be tested forward-only. 4. **More breadth, not more history** — widening the ranked universe (e.g. `nasdaq_all`) strengthens each week's cross-section and the IC t-stat, even if only the top slice is traded. (Deeper history was considered and declined.) ## Key Use Cases -- **Find today's best long setup.** On the **Dashboard**, the *Top Setups* table lists qualified setups ranked by momentum with the #1 flagged "Top pick". Each row opens the ticker page for the chart, scores, S/R targets and entry/stop. +- **Find today's best long setup.** On the **Dashboard**, the *Top Setups* table lists qualified setups ranked by residual momentum with the #1 flagged "Top pick". Each row opens the ticker page for the chart, scores, S/R targets and entry/stop. - **Track a trade you took.** Mark a setup as a **paper trade**: it's marked-to-market against the latest close, auto-closed by the active exit policy (default: 30 trading days with the initial stop), and its sentiment stays fresh while open. *Signals → Track Record* shows the realized edge. ## Stack @@ -412,7 +412,7 @@ Context for whoever — human or AI — continues this work. The owner pushes st | Concern | File | |---|---| | Composite + 5 dimension scores, weights | `app/services/scoring_service.py` | -| 12-1 momentum ranking (the validated factor) | `app/services/momentum_service.py` | +| Residual 12-1 momentum ranking (the validated activation factor) | `app/services/momentum_service.py` | | Setup construction (ATR stop, S/R targets) | `app/services/rr_scanner_service.py` | | Confidence, targets, reach-probability, action | `app/services/recommendation_service.py` | | Activation gate predicate (mirrored in TS) | `app/services/qualification.py` | diff --git a/app/config.py b/app/config.py index efae61a..7d9ca21 100644 --- a/app/config.py +++ b/app/config.py @@ -51,7 +51,7 @@ class Settings(BaseSettings): # Sentiment search-budget controls (Gemini grounding free tier = 5000/month). # Scope (see _get_sentiment_priority_tickers): everything that matters is always # refreshed in full — open paper trades + the curated watchlist + top-pick - # feeders (momentum leaders with a tradeable long setup) — plus a top-N composite + # feeders (residual-momentum leaders with a tradeable long setup) — plus a top-N composite # discovery net. No per-run cap: the set is naturally bounded (watchlist <= 20, # composite <= top_composite), so a full refresh stays well inside the free tier. # Skip anything refreshed within fresh_hours (5 days: sentiment shifts slowly and diff --git a/app/models/benchmark_price.py b/app/models/benchmark_price.py index 9956e5a..0b266ce 100644 --- a/app/models/benchmark_price.py +++ b/app/models/benchmark_price.py @@ -10,8 +10,8 @@ class BenchmarkPrice(Base): """Daily close for a benchmark index (e.g. SPY), used to compute trade alpha. A standalone price series, deliberately NOT a tracked ``Ticker`` — so the - benchmark never enters the scanner, the momentum-percentile ranking, or the - rankings table. One row per (symbol, date). + benchmark never becomes a trade candidate or rankings-table row. Its closes + are used for residual momentum and trade alpha. One row per (symbol, date). """ __tablename__ = "benchmark_prices" diff --git a/app/models/trade_setup.py b/app/models/trade_setup.py index 69f0e95..a7cebb9 100644 --- a/app/models/trade_setup.py +++ b/app/models/trade_setup.py @@ -26,8 +26,9 @@ class TradeSetup(Base): ) confidence_score: Mapped[float | None] = mapped_column(Float, nullable=True) - # Ticker's 12-1 momentum percentile across the universe at detection time - # (0–100, 100 = strongest). Drives the activation gate's core selection. + # Ticker's activation momentum percentile across the universe at detection + # time. Since July 2026 this is residual 12-1 momentum when benchmark data is + # available, with raw 12-1 as a fallback. momentum_percentile: Mapped[float | None] = mapped_column(Float, nullable=True) targets_json: Mapped[str | None] = mapped_column(Text, nullable=True) conflict_flags_json: Mapped[str | None] = mapped_column(Text, nullable=True) diff --git a/app/scheduler.py b/app/scheduler.py index 82d7ae5..23fd41b 100644 --- a/app/scheduler.py +++ b/app/scheduler.py @@ -222,11 +222,11 @@ async def _get_ohlcv_priority_tickers(db: AsyncSession) -> list[str]: async def _get_top_pick_feeder_ids(db: AsyncSession) -> set[int]: """Ticker ids whose latest LONG setup makes them a top-pick feeder. - A dashboard 'top pick' is the highest-momentum *qualified* setup. Sentiment - can never move a ticker's momentum percentile (the gate's core axis) — only - its confidence and EV ranking. So the only tickers that are, or could become - with positive sentiment, a top pick are momentum leaders that already have a - tradeable long setup clearing the R:R floor. That set is exactly: + A dashboard 'top pick' is the highest residual-momentum *qualified* setup. + Sentiment can never move a ticker's activation percentile (the gate's core + axis) — only its confidence and EV ranking. So the only tickers that are, or + could become with positive sentiment, a top pick are residual-momentum leaders + that already have a tradeable long setup clearing the R:R floor. That set is exactly: latest long setup with momentum_percentile >= gate AND rr_ratio >= floor. @@ -311,7 +311,7 @@ async def _get_sentiment_priority_tickers(db: AsyncSession) -> list[str]: is always fully covered. The two tiers only affect ORDER, so a mid-run provider rate limit still lands the names we care about first: - Priority: top-pick feeders (momentum leaders with a tradeable long setup, see + Priority: top-pick feeders (residual-momentum leaders with a tradeable long setup, see ``_get_top_pick_feeder_ids``) + the curated watchlist + open paper trades — the set we never want shown without sentiment. Filler: top-N by composite — a cheap discovery net for names not yet covered. diff --git a/app/services/admin_service.py b/app/services/admin_service.py index 700249b..83344b7 100644 --- a/app/services/admin_service.py +++ b/app/services/admin_service.py @@ -39,8 +39,8 @@ SUPPORTED_TICKER_UNIVERSES = {"sp500", "nasdaq100", "nasdaq_all"} # Track Record's qualified stats. The outcome evaluator deliberately ignores # these — every setup is evaluated so the gate itself can be validated. # -# The core selection is cross-sectional 12-1 momentum (top percentile of the -# universe, long-only). R:R and confidence are floors; high-conviction / +# The core selection is residual cross-sectional 12-1 momentum (top percentile +# of the universe, long-only). R:R and confidence are floors; high-conviction / # clean-read are optional tighteners (off by default). _ACTIVATION_FLOAT_KEYS: dict[str, str] = { "min_momentum_percentile": "activation_min_momentum_percentile", diff --git a/app/services/backtest_service.py b/app/services/backtest_service.py index 07c8d5f..40ea099 100644 --- a/app/services/backtest_service.py +++ b/app/services/backtest_service.py @@ -93,6 +93,9 @@ ATR_MULTIPLIER = 1.5 # signal, not the outcome of a target/stop structure built on top of one. MIN_CROSS_SECTION = 20 # min tickers present in a week to score that week MIN_RELIABLE_PERIODS = 12 # min non-overlapping windows before a signal's IC is trusted +PRODUCTION_PERCENTILE_KEY = "activation_momentum_percentile" +RAW_PERCENTILE_KEY = "momentum_percentile" +RESIDUAL_PERCENTILE_KEY = "residual_momentum_percentile" def _wrap_levels(level_dicts: list[dict]) -> list[Any]: @@ -845,12 +848,26 @@ def _assign_momentum_percentiles(candidates: list[dict]) -> None: def _assign_residual_momentum_percentiles(candidates: list[dict]) -> None: - """Research-only residual-momentum percentile used by strategy variants.""" + """Residual-momentum percentile promoted to production activation ranking.""" _assign_signal_percentiles( - candidates, "residual_momentum", "residual_momentum_percentile" + candidates, "residual_momentum", RESIDUAL_PERCENTILE_KEY ) +def _assign_activation_momentum_percentiles(candidates: list[dict]) -> None: + """Production activation rank: residual 12-1 when available, raw fallback. + + The raw fallback mirrors the live scanner's behavior when benchmark history + is unavailable. In normal backtests, SPY is loaded and this is residual. + """ + for c in candidates: + c[PRODUCTION_PERCENTILE_KEY] = ( + c.get(RESIDUAL_PERCENTILE_KEY) + if c.get(RESIDUAL_PERCENTILE_KEY) is not None + else c.get(RAW_PERCENTILE_KEY) + ) + + def _momentum_qualifies(cand: dict, threshold: float) -> bool: """Whether a candidate clears the floors (meets_core) and the momentum gate. Threshold 0 disables the momentum gate (floors only). The gate is long-only: @@ -861,7 +878,7 @@ def _momentum_qualifies(cand: dict, threshold: float) -> bool: return True if cand["direction"] == "short": return False - mp = cand.get("momentum_percentile") + mp = cand.get(PRODUCTION_PERCENTILE_KEY) return mp is not None and mp >= threshold @@ -890,7 +907,7 @@ def _gate_ablation(candidates: list[dict], activation: dict, threshold: float) - return True if c["direction"] == "short": return False - mp = c.get("momentum_percentile") + mp = c.get(PRODUCTION_PERCENTILE_KEY) return mp is not None and mp >= threshold def rr_ok(c: dict) -> bool: @@ -965,7 +982,7 @@ def _simulate_portfolio( hold_days: int, *, qualified_fn: Callable[[dict], bool] | None = None, - ranking_key: str = "momentum_percentile", + ranking_key: str = PRODUCTION_PERCENTILE_KEY, max_positions: int = SIM_MAX_POSITIONS, risk_per_trade: float = SIM_RISK_PER_TRADE, ) -> dict | None: @@ -1189,9 +1206,18 @@ def _simulate_portfolio( STRATEGY_VARIANTS: tuple[dict, ...] = ( { - "variant": "production_raw_80_fixed10", - "label": "Production raw 80 / max 10", - "percentile_key": "momentum_percentile", + "variant": "production_residual_80_fixed10", + "label": "Production residual 80 / max 10", + "percentile_key": PRODUCTION_PERCENTILE_KEY, + "cutoff": 80.0, + "max_positions": 10, + "risk_per_trade": 0.01, + "risk_scale": None, + }, + { + "variant": "legacy_raw_80_fixed10", + "label": "Legacy raw 80 / max 10", + "percentile_key": RAW_PERCENTILE_KEY, "cutoff": 80.0, "max_positions": 10, "risk_per_trade": 0.01, @@ -1200,52 +1226,16 @@ STRATEGY_VARIANTS: tuple[dict, ...] = ( { "variant": "raw_90_fixed10", "label": "Raw 90 / max 10", - "percentile_key": "momentum_percentile", + "percentile_key": RAW_PERCENTILE_KEY, "cutoff": 90.0, "max_positions": 10, "risk_per_trade": 0.01, "risk_scale": None, }, - { - "variant": "raw_90_fixed15", - "label": "Raw 90 / max 15", - "percentile_key": "momentum_percentile", - "cutoff": 90.0, - "max_positions": 15, - "risk_per_trade": 0.01, - "risk_scale": None, - }, - { - "variant": "residual_80_fixed10", - "label": "Residual 80 / max 10", - "percentile_key": "residual_momentum_percentile", - "cutoff": 80.0, - "max_positions": 10, - "risk_per_trade": 0.01, - "risk_scale": None, - }, { "variant": "residual_80_fixed15", - "label": "Residual 80 / max 15", - "percentile_key": "residual_momentum_percentile", - "cutoff": 80.0, - "max_positions": 15, - "risk_per_trade": 0.01, - "risk_scale": None, - }, - { - "variant": "residual_80_fixed20", - "label": "Residual 80 / max 20", - "percentile_key": "residual_momentum_percentile", - "cutoff": 80.0, - "max_positions": 20, - "risk_per_trade": 0.01, - "risk_scale": None, - }, - { - "variant": "raw_80_fixed15", - "label": "Raw 80 / max 15", - "percentile_key": "momentum_percentile", + "label": "Residual 80 / max 15 capacity check", + "percentile_key": PRODUCTION_PERCENTILE_KEY, "cutoff": 80.0, "max_positions": 15, "risk_per_trade": 0.01, @@ -1295,7 +1285,7 @@ def _strategy_variant_sims( rows.append({ "variant": cfg["variant"], "label": cfg["label"], - "ranking": "residual" if "residual" in percentile_key else "raw", + "ranking": "raw" if percentile_key == RAW_PERCENTILE_KEY else "residual", "cutoff": cutoff, "max_positions": int(cfg["max_positions"]), "risk_per_trade_pct": round(float(cfg["risk_per_trade"]) * 100, 2), @@ -1312,14 +1302,12 @@ def _pct_loss(base: float | None, candidate: float | None) -> float | None: def _build_research_recommendation(report: dict) -> dict: - """Advisory rules for research variants. These are deliberately conservative: - production only changes later if a portfolio variant beats the baseline under - transparent drawdown/Sharpe/CAGR constraints.""" + """Advisory rules for the remaining research variants after residual promotion.""" variants = { v.get("variant"): v for v in (report.get("strategy_variants") or {}).get("variants", []) } - base = variants.get("production_raw_80_fixed10") + base = variants.get("production_residual_80_fixed10") items: list[dict] = [] if base is None: return { @@ -1331,25 +1319,25 @@ def _build_research_recommendation(report: dict) -> dict: base_dd = base.get("max_drawdown_pct") base_cagr = base.get("cagr_pct") - residuals = [ - v for key, v in variants.items() - if key.startswith("residual_80_") and v.get("risk_scale") is None - ] - residual = max(residuals, key=lambda v: v.get("sharpe") or -999, default=None) + capacity = variants.get("residual_80_fixed15") if ( - residual and base_sharpe is not None and residual.get("sharpe") is not None - and base_dd is not None and residual.get("max_drawdown_pct") is not None + capacity and base_sharpe is not None and base_cagr is not None + and capacity.get("sharpe") is not None and capacity.get("cagr_pct") is not None + and capacity.get("max_drawdown_pct") is not None and base_dd is not None ): - sharpe_delta = residual["sharpe"] - base_sharpe - dd_delta = residual["max_drawdown_pct"] - base_dd - candidate = sharpe_delta >= 0.10 and dd_delta <= 2.0 + candidate = ( + capacity["sharpe"] > base_sharpe + and capacity["cagr_pct"] > base_cagr + and capacity["max_drawdown_pct"] <= base_dd + 1.0 + ) items.append({ - "topic": "residual_momentum", + "topic": "capacity_15", "candidate": candidate, "text": ( - f"Residual momentum {'is a promotion candidate' if candidate else 'stays research-only'}: " - f"{residual['label']} Sharpe {residual['sharpe']:.2f} vs {base_sharpe:.2f}, " - f"drawdown {residual['max_drawdown_pct']:.1f}% vs {base_dd:.1f}%." + f"Max-15 capacity {'is worth promoting' if candidate else 'is not needed yet'}: " + f"Sharpe {capacity['sharpe']:.2f} vs {base_sharpe:.2f}, " + f"CAGR {capacity['cagr_pct']:+.1f}% vs {base_cagr:+.1f}%, " + f"skipped {capacity.get('skipped_book_full', 0)} vs {base.get('skipped_book_full', 0)}." ), }) @@ -1385,8 +1373,8 @@ def _build_research_recommendation(report: dict) -> dict: return { "items": items, "note": ( - "Advisory only. Production changes require a variant to pass the rule " - "and then be adopted explicitly in a later strategy-version change." + "Residual 12-1 momentum is now the production activation rank. " + "Remaining rows are research comparisons only." ), } @@ -1473,7 +1461,8 @@ def _build_recommendation(report: dict) -> dict: "text": f"Gate: keep the {label} (worth {delta:+.2f}R/trade under the hold exit).", }) - # Momentum cutoff: best per-trade net among the active-gate sweep rows. + # Activation cutoff: best per-trade net among the promoted residual-momentum + # sweep rows. sweep_rows = [ r for r in report.get("sweep") or [] if r.get("net_avg_r") is not None and (r.get("min_momentum_percentile") or 0) > 0 @@ -1483,7 +1472,7 @@ def _build_recommendation(report: dict) -> dict: items.append({ "topic": "cutoff", "text": ( - f"Momentum cutoff: {best_cut['min_momentum_percentile']:.0f} has the best " + f"Residual-momentum cutoff: {best_cut['min_momentum_percentile']:.0f} has the best " f"per-trade net ({best_cut['net_avg_r']:+.2f}R over {best_cut['total']} setups)." ), }) @@ -1653,9 +1642,11 @@ async def run_backtest( progress_cb(total, total, "") # Cross-sectional momentum: rank every week's universe, then "qualified" means - # floors + top ``min_momentum_percentile`` by 12-1 momentum. + # floors + top ``min_momentum_percentile`` by promoted residual 12-1 momentum + # (raw 12-1 fallback only when benchmark data is unavailable). _assign_momentum_percentiles(candidates) _assign_residual_momentum_percentiles(candidates) + _assign_activation_momentum_percentiles(candidates) current_min_pct = float(activation.get("min_momentum_percentile", 80.0)) for c in candidates: c["qualified"] = _momentum_qualifies(c, current_min_pct) @@ -1779,10 +1770,9 @@ async def run_backtest( "strategy_variants": { "variants": strategy_variant_rows, "note": ( - "Research-only hold-to-horizon portfolio variants. These compare " - "raw vs residual momentum ranking, cutoff 80 vs 90, and max 10/15/20 " - "position capacity. They do not change live " - "qualification or paper-trade behavior." + "Research-only hold-to-horizon portfolio variants. Production now " + "uses residual 12-1 momentum at cutoff 80; the remaining rows compare " + "the legacy raw rank, raw cutoff 90, and one max-15 capacity check." ), }, "signal_eval": _signal_evaluation(collected), diff --git a/app/services/benchmark_service.py b/app/services/benchmark_service.py index 805e371..b9bb546 100644 --- a/app/services/benchmark_service.py +++ b/app/services/benchmark_service.py @@ -3,8 +3,8 @@ Fetches the S&P 500 proxy (SPY) daily closes via Alpaca and persists them, so paper-trade alpha — a trade's return minus the benchmark's return over the same holding period — can be computed. The benchmark is a standalone series, NOT a -tracked ``Ticker``, so it never contaminates the scanner, momentum-percentile -ranking, or rankings. +tracked ``Ticker``; its closes feed residual momentum and alpha, but it never +becomes a trade candidate or rankings-table row. """ from __future__ import annotations diff --git a/app/services/momentum_service.py b/app/services/momentum_service.py index 569ff73..37f89ca 100644 --- a/app/services/momentum_service.py +++ b/app/services/momentum_service.py @@ -1,17 +1,18 @@ -"""Cross-sectional 12-1 momentum ranking for the universe. +"""Cross-sectional residual 12-1 momentum ranking for the universe. The activation gate selects the top ``min_momentum_percentile`` of the universe -by 12-1 month momentum (return from ~12 months ago to ~1 month ago — the one -price signal the backtest showed sorts forward returns). The daily scan ranks -every ticker and stores each setup's percentile (see ``rr_scanner_service``), so -the live list, the Track Record's qualified stats, and outcome evaluation all gate -on the same value. +by residual 12-1 month momentum: the stock's 12-1 return after subtracting its +estimated benchmark beta contribution over the same formation window. The daily +scan ranks every ticker and stores each setup's percentile (see +``rr_scanner_service``), so the live list, the Track Record's qualified stats, +and outcome evaluation all gate on the same value. """ from __future__ import annotations import json import logging +from datetime import date from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession @@ -35,29 +36,101 @@ def compute_12_1_momentum(closes: list[float]) -> float | None: return None +def compute_residual_12_1_momentum( + dates: list[date], + closes: list[float], + benchmark_closes: dict[date, float], +) -> float | None: + """12-1 momentum after removing linear benchmark exposure. + + Estimate beta from daily stock/benchmark returns over the standard 12-1 + formation window, then sum stock return minus beta * benchmark return. No + intercept is subtracted: fitting an intercept over the same window would make + residuals sum to roughly zero and destroy the ranking signal. + """ + i = len(closes) - 1 + if not benchmark_closes or len(dates) != len(closes) or i - _MOM_LOOKBACK < 0: + return None + + stock_rets: list[float] = [] + market_rets: list[float] = [] + for k in range(i - _MOM_LOOKBACK + 1, i - _MOM_SKIP + 1): + prev_close = closes[k - 1] + bench_prev = benchmark_closes.get(dates[k - 1]) + bench_cur = benchmark_closes.get(dates[k]) + if prev_close <= 0 or bench_prev is None or bench_cur is None or bench_prev <= 0: + continue + stock_rets.append(closes[k] / prev_close - 1.0) + market_rets.append(bench_cur / bench_prev - 1.0) + + if len(stock_rets) < 100: + return None + mean_market = sum(market_rets) / len(market_rets) + mean_stock = sum(stock_rets) / len(stock_rets) + var_market = sum((x - mean_market) ** 2 for x in market_rets) + if var_market <= 0: + return None + cov = sum( + (stock_rets[k] - mean_stock) * (market_rets[k] - mean_market) + for k in range(len(stock_rets)) + ) + beta = cov / var_market + return sum(stock_rets[k] - beta * market_rets[k] for k in range(len(stock_rets))) + + +async def _load_activation_benchmark(db: AsyncSession) -> dict[date, float]: + """Load SPY closes for residual momentum; refresh once if the table is empty.""" + try: + from app.services.benchmark_service import load_benchmark_closes, refresh_benchmark_prices + + closes = await load_benchmark_closes(db) + if closes: + return closes + await refresh_benchmark_prices(db) + return await load_benchmark_closes(db) + except Exception: + logger.exception("Residual momentum benchmark load failed; falling back to raw momentum") + return {} + + async def compute_momentum_percentiles(db: AsyncSession) -> dict[str, float]: - """Compute each ticker's 12-1 momentum and rank the universe into a - ``{symbol: percentile}`` map (0–100, 100 = strongest momentum). Tickers - without a full year of history are absent (can't be ranked).""" + """Compute each ticker's activation momentum rank. + + Production uses residual 12-1 momentum when benchmark data is available. If + SPY data is absent, fall back to raw 12-1 momentum rather than disabling the + scanner. Tickers without enough stock/benchmark history are absent. + """ result = await db.execute(select(Ticker).order_by(Ticker.symbol)) tickers = list(result.scalars().all()) - momentum: dict[str, float] = {} + benchmark_closes = await _load_activation_benchmark(db) + using_residual = len(benchmark_closes) >= _MOM_LOOKBACK + + values: dict[str, float] = {} for ticker in tickers: try: records = await query_ohlcv(db, ticker.symbol) except Exception: logger.exception("Momentum fetch failed for %s", ticker.symbol) continue - m = compute_12_1_momentum([float(r.close) for r in records]) - if m is not None: - momentum[ticker.symbol] = m + closes = [float(r.close) for r in records] + value = ( + compute_residual_12_1_momentum([r.date for r in records], closes, benchmark_closes) + if using_residual + else compute_12_1_momentum(closes) + ) + if value is not None: + values[ticker.symbol] = value - ranked = sorted(momentum, key=lambda s: momentum[s]) + ranked = sorted(values, key=lambda s: values[s]) n = len(ranked) percentiles = { sym: round((rank / (n - 1) * 100.0) if n > 1 else 100.0, 2) for rank, sym in enumerate(ranked) } - logger.info(json.dumps({"event": "momentum_ranked", "tickers": n})) + logger.info(json.dumps({ + "event": "momentum_ranked", + "signal": "residual_12_1" if using_residual else "raw_12_1_fallback", + "tickers": n, + })) return percentiles diff --git a/app/services/qualification.py b/app/services/qualification.py index 46412ac..b9fbff6 100644 --- a/app/services/qualification.py +++ b/app/services/qualification.py @@ -2,12 +2,12 @@ A single predicate, driven by the admin activation config, used by the performance stats (server) and mirrored on the frontend. The core selection is -cross-sectional momentum: a setup's ticker must rank in the top -``min_momentum_percentile`` of the universe by 12-1 month momentum — the one -signal the backtest showed actually sorts forward returns. R:R and confidence -remain as floors, and conviction/conflict survive as optional tighteners (off by -default). The momentum percentile is computed across the universe and attached to -each setup upstream; when it's absent the gate falls back to the floors. +residual cross-sectional momentum: a setup's ticker must rank in the top +``min_momentum_percentile`` of the universe by beta-adjusted 12-1 month momentum. +R:R and confidence remain as floors, and conviction/conflict survive as optional +tighteners (off by default). The activation percentile is computed across the +universe and attached to each setup upstream; when it's absent the gate falls +back to the floors. """ from __future__ import annotations @@ -65,12 +65,12 @@ def setup_qualifies(setup: Any, config: dict) -> bool: return False if (setup.confidence_score or 0.0) < config["min_confidence"]: return False - # Cross-sectional momentum: the core selection. A setup's ticker must rank in - # the top ``min_momentum_percentile`` of the universe by 12-1 momentum. The - # validated edge is long-only, so while the gate is active shorts (which fight - # the trend) never qualify. The percentile floor is only enforced when a - # percentile is attached (live setups / backtest); callers that don't attach - # it defer to the floors above. + # Residual cross-sectional momentum: the core selection. A setup's ticker + # must rank in the top ``min_momentum_percentile`` of the universe by + # beta-adjusted 12-1 momentum. The validated edge is long-only, so while the + # gate is active shorts (which fight the trend) never qualify. The percentile + # floor is only enforced when a percentile is attached (live setups / + # backtest); callers that don't attach it defer to the floors above. min_pct = float(config.get("min_momentum_percentile", 0.0)) if min_pct > 0: if (getattr(setup, "direction", "long") or "long") == "short": @@ -81,7 +81,7 @@ def setup_qualifies(setup: Any, config: dict) -> bool: # A NEUTRAL recommendation means the engine found no clear directional setup — # not an actionable signal, so by default it doesn't qualify (and can't be a # top pick). ``exclude_neutral`` defaults on; turn it off to also count - # no-clear-direction momentum leaders. + # no-clear-direction residual momentum leaders. if config.get("exclude_neutral"): if (setup.recommended_action or "NEUTRAL") == "NEUTRAL": return False diff --git a/app/services/rr_scanner_service.py b/app/services/rr_scanner_service.py index c8a0e5f..b58f682 100644 --- a/app/services/rr_scanner_service.py +++ b/app/services/rr_scanner_service.py @@ -31,7 +31,7 @@ from app.services.recommendation_service import enhance_trade_setup logger = logging.getLogger(__name__) -STRATEGY_VERSION = "momentum_12_1_rr_time_v1" +STRATEGY_VERSION = "residual_momentum_12_1_rr_time_v2" async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker: @@ -219,9 +219,9 @@ async def scan_ticker( ) -> list[TradeSetup]: """Scan a single ticker for trade setups meeting the R:R threshold. - ``momentum_percentile`` is the ticker's 12-1 momentum rank across the universe - (computed by the caller), stored on each setup so the activation gate can - select the top slice.""" + ``momentum_percentile`` is the ticker's residual 12-1 momentum activation + rank across the universe (computed by the caller), stored on each setup so + the activation gate can select the top slice.""" ticker = await _get_ticker(db, symbol) records = await query_ohlcv(db, symbol) @@ -393,8 +393,9 @@ async def scan_all_tickers( tickers = list(result.scalars().all()) total = len(tickers) - # Rank the universe by 12-1 momentum up front so each new setup carries its - # ticker's percentile (used by the activation gate). Best-effort. + # Rank the universe by residual 12-1 momentum up front so each new setup + # carries its activation percentile. Best-effort; the ranker falls back to + # raw 12-1 momentum only if benchmark data is unavailable. try: from app.services import momentum_service diff --git a/app/services/watchlist_service.py b/app/services/watchlist_service.py index 557fb5d..e3e8266 100644 --- a/app/services/watchlist_service.py +++ b/app/services/watchlist_service.py @@ -173,8 +173,8 @@ async def _enrich_entry( "dimensions": dims, "rr_ratio": setup.rr_ratio if setup else None, "rr_direction": setup.direction if setup else None, - # 12-1 cross-sectional momentum percentile (the top-pick selector); ticker- - # level, so any of the ticker's setups carries the same value. + # Residual 12-1 activation percentile (the top-pick selector); ticker-level, + # so any of the ticker's setups carries the same value. "momentum_percentile": setup.momentum_percentile if setup else None, "sr_levels": sr_levels, "last_close": last_close, diff --git a/frontend/src/components/admin/ActivationSettings.tsx b/frontend/src/components/admin/ActivationSettings.tsx index 86468f2..bd00d6e 100644 --- a/frontend/src/components/admin/ActivationSettings.tsx +++ b/frontend/src/components/admin/ActivationSettings.tsx @@ -41,16 +41,16 @@ export function ActivationSettings() {

What counts as a signal worth acting on. Drives the Dashboard's "Qualified" metric, the Signals "Qualified only" view, and the Track Record's qualified stats. The core selection is - cross-sectional momentum — the ticker must rank in the - top slice of the universe by 12-1 month momentum, the one signal the backtest showed predicts - forward returns. R:R and confidence stay as floors. Tune the cutoff against the Track Record's + residual cross-sectional momentum — the ticker must rank in the + top slice of the universe by beta-adjusted 12-1 month momentum, the production signal promoted + from the backtest. R:R and confidence stay as floors. Tune the cutoff against the Track Record's momentum sweep to see what actually wins.

diff --git a/frontend/src/components/signals/BacktestPanel.tsx b/frontend/src/components/signals/BacktestPanel.tsx index 4594448..58d1034 100644 --- a/frontend/src/components/signals/BacktestPanel.tsx +++ b/frontend/src/components/signals/BacktestPanel.tsx @@ -308,11 +308,11 @@ export function BacktestPanel() { {report.sweep && report.sweep.length > 0 && report.sweep[0].min_momentum_percentile != null && (

- Momentum-percentile sweep + Residual-momentum percentile sweep

- How many setups qualify — and how they perform — at each momentum-rank cutoff (floors - held fixed). 80 = only the top 20% of the universe by 12-1 momentum each week; 0 = + How many setups qualify — and how they perform — at each production-rank cutoff (floors + held fixed). 80 = only the top 20% of the universe by residual 12-1 momentum each week; 0 = floors only. Lower = more trades, watch that expectancy holds. Your current setting is highlighted; set it in Admin → Settings → Activation.

@@ -320,7 +320,7 @@ export function BacktestPanel() { - + @@ -541,10 +541,7 @@ export function BacktestPanel() { Strategy variants

- {report.strategy_variants.note ?? 'Research-only portfolio variants.'}{' '} - - Residual momentum stays research-only until a variant beats production under the promotion rules. - + {report.strategy_variants.note ?? 'Research-only portfolio variants.'}

Min momentum %ileMin residual %ile Qualified Wins Losses
diff --git a/frontend/src/components/ticker/StandingMatrix.tsx b/frontend/src/components/ticker/StandingMatrix.tsx index edb655c..9a10119 100644 --- a/frontend/src/components/ticker/StandingMatrix.tsx +++ b/frontend/src/components/ticker/StandingMatrix.tsx @@ -24,7 +24,7 @@ export interface FieldPoint { interface StandingMatrixProps { symbol: string; composite: number | null; // X for the highlighted dot (authoritative, from the scores endpoint) - momentum: number | null; // Y for the highlighted dot (the ticker's 12-1 momentum percentile) + momentum: number | null; // Y for the highlighted dot (residual 12-1 momentum percentile) field: FieldPoint[]; // every tracked ticker, for the background cloud gateMomentum: number; // Y divider = the activation gate's momentum percentile status: 'top-pick' | 'qualified' | 'none'; @@ -186,7 +186,7 @@ export default function StandingMatrix({

{v.note}

- + {confidence != null && }
@@ -206,7 +206,7 @@ export default function StandingMatrix({

Each dot is a tracked ticker; this one is highlighted. The dashed line is the - activation gate ({Math.round(gate)}th-pct momentum) — above it qualifies for a top pick. Click any peer to open it. + activation gate ({Math.round(gate)}th-pct residual momentum) — above it qualifies for a top pick. Click any peer to open it.

); diff --git a/frontend/src/lib/qualification.ts b/frontend/src/lib/qualification.ts index 9ced85d..5c04c5c 100644 --- a/frontend/src/lib/qualification.ts +++ b/frontend/src/lib/qualification.ts @@ -33,9 +33,9 @@ export function qualifiesSetup(setup: TradeSetup, config: ActivationConfig): boo return false; } if ((setup.confidence_score ?? 0) < config.min_confidence) return false; - // Cross-sectional momentum is the core selection (long-only). While the gate is - // active, shorts never qualify; the percentile floor is enforced only when a - // percentile is attached, otherwise defer to the floors. + // Residual cross-sectional momentum is the core selection (long-only). While + // the gate is active, shorts never qualify; the percentile floor is enforced + // only when a percentile is attached, otherwise defer to the floors. if (config.min_momentum_percentile > 0) { if (setup.direction === 'short') return false; if (setup.momentum_percentile != null && setup.momentum_percentile < config.min_momentum_percentile) { @@ -53,7 +53,7 @@ export function qualifiesSetup(setup: TradeSetup, config: ActivationConfig): boo /** * Symbol of the current single 'top pick' — the #1 row the dashboard highlights: - * the highest 12-1 momentum percentile among qualified setups (or among all + * the highest residual 12-1 momentum percentile among qualified setups (or among all * setups when none qualify). Returns null when there are no setups. Keep in step * with the Top Setups ranking in DashboardPage. */ @@ -74,7 +74,7 @@ export function topPickSymbol( /** Short human summary of the active gate, e.g. for tooltips/labels. */ export function activationSummary(config: ActivationConfig): string { const parts = []; - if (config.min_momentum_percentile > 0) parts.push(`top ${(100 - config.min_momentum_percentile).toFixed(0)}% momentum`); + if (config.min_momentum_percentile > 0) parts.push(`top ${(100 - config.min_momentum_percentile).toFixed(0)}% residual momentum`); parts.push(`R:R ≥ ${config.min_rr.toFixed(1)}`, `conf ≥ ${config.min_confidence.toFixed(0)}%`); if (config.exclude_neutral) parts.push('directional'); if (config.require_high_conviction) parts.push('high-conviction'); diff --git a/frontend/src/pages/DashboardPage.tsx b/frontend/src/pages/DashboardPage.tsx index 59c8c21..d3351c2 100644 --- a/frontend/src/pages/DashboardPage.tsx +++ b/frontend/src/pages/DashboardPage.tsx @@ -77,7 +77,7 @@ export default function DashboardPage() { ); // Show qualified setups first; fall back to the full list when none qualify. - // Rank by 12-1 momentum percentile so the strongest names sit at the top. + // Rank by residual 12-1 momentum percentile so the strongest names sit at the top. const showingQualified = qualifiedSetups.length > 0; const topSetups: TradeSetup[] = useMemo(() => { const pool = showingQualified ? qualifiedSetups : trades.data ?? []; @@ -214,7 +214,7 @@ export default function DashboardPage() { - + diff --git a/frontend/src/pages/TickerDetailPage.tsx b/frontend/src/pages/TickerDetailPage.tsx index 2f91697..bd855ef 100644 --- a/frontend/src/pages/TickerDetailPage.tsx +++ b/frontend/src/pages/TickerDetailPage.tsx @@ -216,7 +216,7 @@ export default function TickerDetailPage() { [setupsForSymbol], ); - // Standing matrix: this ticker's momentum percentile + long confidence (from its + // Standing matrix: this ticker's residual momentum percentile + long confidence (from its // setup), the field (every ticker's composite × momentum) for the cloud, and // whether it qualifies / is the top pick. const myMomentum = longSetup?.momentum_percentile ?? shortSetup?.momentum_percentile ?? null; @@ -296,7 +296,7 @@ export default function TickerDetailPage() { )} {hasOpenTrade && ( diff --git a/tests/unit/test_backtest_service.py b/tests/unit/test_backtest_service.py index 5e5929c..199b373 100644 --- a/tests/unit/test_backtest_service.py +++ b/tests/unit/test_backtest_service.py @@ -118,14 +118,30 @@ def test_assigns_raw_and_residual_percentiles_independently(): assert by_resid[0.10] == 0.0 +def test_activation_percentile_prefers_residual_with_raw_fallback(): + cands = [ + {"momentum_percentile": 80.0, "residual_momentum_percentile": 95.0}, + {"momentum_percentile": 70.0, "residual_momentum_percentile": None}, + ] + + bt._assign_activation_momentum_percentiles(cands) + + assert cands[0][bt.PRODUCTION_PERCENTILE_KEY] == 95.0 + assert cands[1][bt.PRODUCTION_PERCENTILE_KEY] == 70.0 + + def test_strategy_variants_keep_only_current_research_candidates(): variants = {cfg["variant"]: cfg for cfg in bt.STRATEGY_VARIANTS} + assert "production_raw_80_fixed10" not in variants assert "raw_80_regime_scaled" not in variants assert "residual_80_regime_scaled" not in variants assert "residual_90_fixed10" not in variants - assert variants["raw_90_fixed15"]["max_positions"] == 15 - assert variants["residual_80_fixed20"]["max_positions"] == 20 + assert "raw_90_fixed15" not in variants + assert "residual_80_fixed20" not in variants + assert variants["production_residual_80_fixed10"]["percentile_key"] == bt.PRODUCTION_PERCENTILE_KEY + assert variants["legacy_raw_80_fixed10"]["percentile_key"] == bt.RAW_PERCENTILE_KEY + assert variants["residual_80_fixed15"]["max_positions"] == 15 assert all(cfg["risk_scale"] is None for cfg in bt.STRATEGY_VARIANTS) @@ -136,6 +152,7 @@ def test_strategy_variant_sims_emit_fixed_variants_without_mutating_qualified(mo "direction": "long", "momentum_percentile": 90.0, "residual_momentum_percentile": 91.0, + "activation_momentum_percentile": 91.0, }] calls = [] @@ -168,34 +185,31 @@ def test_strategy_variant_sims_emit_fixed_variants_without_mutating_qualified(mo assert [r["variant"] for r in rows] == [cfg["variant"] for cfg in bt.STRATEGY_VARIANTS] assert all(call["exit_policy"] == "hold" for call in calls) - assert any(call["ranking_key"] == "residual_momentum_percentile" for call in calls) - assert any(call["max_positions"] == 20 for call in calls) + assert any(call["ranking_key"] == bt.PRODUCTION_PERCENTILE_KEY for call in calls) + assert any(call["ranking_key"] == bt.RAW_PERCENTILE_KEY for call in calls) + assert any(call["max_positions"] == 15 for call in calls) assert cands[0]["qualified"] is False def test_build_research_recommendation_applies_promotion_rules(): report = { "strategy_variants": {"variants": [ - {"variant": "production_raw_80_fixed10", "label": "Base", "sharpe": 1.20, - "max_drawdown_pct": 20.0, "cagr_pct": 30.0}, - {"variant": "residual_80_fixed10", "label": "Residual", "sharpe": 1.35, - "max_drawdown_pct": 21.0, "cagr_pct": 31.0, "risk_scale": None}, - {"variant": "residual_80_fixed20", "label": "Residual 20", "sharpe": 1.40, - "max_drawdown_pct": 20.5, "cagr_pct": 32.0, "risk_scale": None}, + {"variant": "production_residual_80_fixed10", "label": "Base", "sharpe": 1.40, + "max_drawdown_pct": 20.0, "cagr_pct": 32.0, "skipped_book_full": 7}, + {"variant": "residual_80_fixed15", "label": "Capacity", "sharpe": 1.39, + "max_drawdown_pct": 20.0, "cagr_pct": 32.0, "skipped_book_full": 0}, {"variant": "raw_90_fixed10", "label": "Cutoff 90", "sharpe": 1.25, "max_drawdown_pct": 19.0, "cagr_pct": 28.0}, - {"variant": "raw_90_fixed15", "label": "Cutoff 90 / 15", "sharpe": 1.30, - "max_drawdown_pct": 18.0, "cagr_pct": 29.0}, ]}, } rec = bt._build_research_recommendation(report) by_topic = {item["topic"]: item for item in rec["items"]} - assert by_topic["residual_momentum"]["candidate"] is True - assert "Residual 20" in by_topic["residual_momentum"]["text"] - assert by_topic["cutoff_90"]["candidate"] is True - assert "Cutoff 90 / 15" in by_topic["cutoff_90"]["text"] + assert by_topic["capacity_15"]["candidate"] is False + assert "not needed yet" in by_topic["capacity_15"]["text"] + assert by_topic["cutoff_90"]["candidate"] is False + assert "Cutoff 90" in by_topic["cutoff_90"]["text"] class TestStopFillR: @@ -305,6 +319,7 @@ def _acand( "confidence": conf, "action": action, "momentum_percentile": mp, + "activation_momentum_percentile": mp, "direction": direction, "meets_core": meets, "risk_level": "Low", @@ -380,6 +395,7 @@ def _sim_cand( "stop": stop, "target": target, "momentum_percentile": mp, + "activation_momentum_percentile": mp, } diff --git a/tests/unit/test_momentum_service.py b/tests/unit/test_momentum_service.py index b69556e..bb9c19c 100644 --- a/tests/unit/test_momentum_service.py +++ b/tests/unit/test_momentum_service.py @@ -1,4 +1,4 @@ -"""Unit tests for the cross-sectional 12-1 momentum ranking.""" +"""Unit tests for the cross-sectional activation momentum ranking.""" from __future__ import annotations @@ -35,6 +35,21 @@ async def _seed(session, symbol: str, rate: float, n: int = 280) -> None: await session.commit() +async def _seed_closes(session, symbol: str, closes: list[float]) -> None: + t = Ticker(symbol=symbol) + session.add(t) + await session.flush() + base = date(2024, 1, 1) + for i, close in enumerate(closes): + session.add(OHLCVRecord( + ticker_id=t.id, + date=base + timedelta(days=i), + open=close, high=close, low=close, close=close, + volume=1_000_000, + )) + await session.commit() + + def test_compute_momentum_insufficient_history(): assert ms.compute_12_1_momentum([100.0] * 100) is None @@ -47,7 +62,11 @@ def test_compute_momentum_value(): assert m > 0 -async def test_ranks_universe_into_percentiles(session): +async def test_ranks_universe_into_raw_percentiles_when_benchmark_missing(session, monkeypatch): + async def no_benchmark(_db): + return {} + + monkeypatch.setattr(ms, "_load_activation_benchmark", no_benchmark) await _seed(session, "HIGH", rate=1.010) # strong uptrend → top momentum await _seed(session, "MID", rate=1.002) await _seed(session, "LOW", rate=0.999) # declining → bottom momentum @@ -58,7 +77,31 @@ async def test_ranks_universe_into_percentiles(session): assert pct["LOW"] == 0.0 -async def test_short_history_ticker_is_unranked(session): +async def test_ranks_universe_into_residual_percentiles_when_benchmark_available(session, monkeypatch): + base = date(2024, 1, 1) + n = 280 + benchmark = {base + timedelta(days=i): 100.0 * (1.001 ** i) for i in range(n)} + + async def with_benchmark(_db): + return benchmark + + monkeypatch.setattr(ms, "_load_activation_benchmark", with_benchmark) + market = [benchmark[base + timedelta(days=i)] for i in range(n)] + await _seed_closes(session, "DRIFT", [market[i] * (1.0008 ** i) for i in range(n)]) + await _seed_closes(session, "BETA", market) + await _seed_closes(session, "LAG", [market[i] * (0.9992 ** i) for i in range(n)]) + + pct = await ms.compute_momentum_percentiles(session) + assert pct["DRIFT"] == 100.0 + assert pct["BETA"] == 50.0 + assert pct["LAG"] == 0.0 + + +async def test_short_history_ticker_is_unranked(session, monkeypatch): + async def no_benchmark(_db): + return {} + + monkeypatch.setattr(ms, "_load_activation_benchmark", no_benchmark) await _seed(session, "LONG", rate=1.005) await _seed(session, "SHORTHX", rate=1.005, n=100) # < 1y → no momentum @@ -67,5 +110,9 @@ async def test_short_history_ticker_is_unranked(session): assert "SHORTHX" not in pct -async def test_empty_universe_returns_empty(session): +async def test_empty_universe_returns_empty(session, monkeypatch): + async def no_benchmark(_db): + return {} + + monkeypatch.setattr(ms, "_load_activation_benchmark", no_benchmark) assert await ms.compute_momentum_percentiles(session) == {} diff --git a/tests/unit/test_sentiment_priority.py b/tests/unit/test_sentiment_priority.py index 0dbcf5b..0e20f17 100644 --- a/tests/unit/test_sentiment_priority.py +++ b/tests/unit/test_sentiment_priority.py @@ -1,9 +1,9 @@ """Tests for sentiment-collection scoping (``_get_sentiment_priority_tickers``). -A dashboard 'top pick' is the highest-momentum *qualified* long setup. Sentiment -can never move a ticker's momentum percentile (the gate's core axis) — only its +A dashboard 'top pick' is the highest residual-momentum *qualified* long setup. Sentiment +can never move a ticker's activation percentile (the gate's core axis) — only its confidence and EV ranking. So the tickers that are, or could become with positive -sentiment, a top pick are exactly the momentum leaders that already carry a +sentiment, a top pick are exactly the residual-momentum leaders that already carry a tradeable long setup over the R:R floor. These tests pin that priority tier (always refreshed, cap-exempt) and the capped filler tier behind it. """
Entry R:R Target ProbMomentumResidual Mom. Action