Add 5-day reversal end-to-end pipeline report and repro scripts
Runs the 5-day reversal signal through data→alpha→combo→portfolio on the full A-share universe and documents the finding: the naive z-score book loses to outlier concentration, rank weighting on a liquid universe recovers a real edge, and turnover-driven cost is the binding constraint. Includes the e2e driver and figure generator that produce the report. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Yuxuan Yan
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# 5-Day Reversal — End-to-End Pipeline Report
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Generated: 2026-06-11T17:17:34
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This report runs the **5-day reversal** signal end to end through the decoupled
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pipeline (`data → alpha → combo → portfolio build → portfolio simulate/eval`) on
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the full downloaded A-share universe, and answers the seven review questions:
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alpha storage, metric sanity, NaN/look-ahead handling, alpha↔portfolio
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closeness, alpha↔PnL closeness, per-phase timing, and visualizations.
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Per this repo's convention an **alpha is a signed cross-sectional position
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weight, not a return predictor**, so evaluation is return / Sharpe / turnover /
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drawdown — there is deliberately **no IC/IR** anywhere.
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## TL;DR
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The naive built-in `reversal` alpha (raw `-pct_change(5)` then cross-sectional
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**z-score**) loses **-87.45%** in costless research on the full
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~5,200-name universe. That is **not** evidence the signal is bad — it is an
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artifact of z-score weighting on A-shares: a handful of newly listed /
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post-suspension / limit-up names produce huge `pct_change` outliers, and
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z-scoring pours the book into exactly those names (stored weights reach
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-52σ).
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Switching only the **weighting** to a bounded cross-sectional **rank**
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(`reversal_rank`) and restricting to a per-date **liquid, non-ST, tradable**
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universe recovers a genuine reversal edge: **72.24%**
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costless research cumulative return at Sharpe
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**0.73** with a
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54.31% daily hit rate.
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The binding constraint is **cost, not signal**: at ~148×/year
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turnover, a 10 bps round-trip (5 bps commission + 5 bps slippage) erases the
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edge — every variant is negative after costs. A tradable 5-day reversal needs
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turnover control, not a different signal.
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## Headline Metrics
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| run | weighting | research cum | research Sharpe | research turn/yr | exec before cost | exec net | exec net Sharpe |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| naive z-score (full) | z-score | -87.45% | -2.4515 | 160× | 18.39% | -111.94% | -1.4508 |
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| rank (full) | rank | -3.48% | -0.0198 | 143× | 50.52% | -66.61% | -1.1839 |
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| rank (liquid subset) | rank | 72.24% | 0.7310 | 148× | 110.18% | -17.16% | -0.2226 |
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*Research = costless, no-look-ahead weights · next-day return. Execution = next-open
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fills on the discretized integer book under suspension / price-limit / volume-cap
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constraints, 5 bps commission + 5 bps slippage.*
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## 1. Are Alpha Values Properly Stored?
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All alpha artifacts conform to `ALPHA_COLUMNS` (`symbol_id, date, alpha_name,
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weight`), carry no null / non-finite weights, no duplicate `(symbol_id, date)`
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keys, and have numerically-zero daily cross-sectional means (weights are
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demeaned per date).
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| run | schema ok | null w | non-finite w | dup keys | max |daily mean| | weight range | combo identity Δ |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| naive z-score (full) | True | 0 | 0 | 0 | 3.32e-16 | [-52.2, 19.2] | 0.00e+00 |
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| rank (full) | True | 0 | 0 | 0 | 0.00e+00 | [-2603.0, 2603.0] | 0.00e+00 |
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| rank (liquid subset) | True | 0 | 0 | 0 | 0.00e+00 | [-498.5, 498.5] | 0.00e+00 |
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The decisive storage signal is the **weight range**. The naive z-score alpha
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stores weights as extreme as
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`[-52, 19]` —
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single names tens of sigma from the cross-section. Rank weighting is bounded by
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construction, so its stored weights are well-behaved. Same signal, completely
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different book.
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## 2. Do The Alpha Metrics Make Sense?
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Yes, and they tell a coherent story:
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- The **z-score full** run is dominated by a few outlier names; its research
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Sharpe of -2.45 reflects a
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book that is effectively long/short a tiny set of extreme movers, which in
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A-shares keep trending — so the reversal bet loses.
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- **Rank full** (-3.48%) is roughly flat:
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the direction is right (hit rate
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51.18%) but
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the long tail of illiquid / ST / freshly listed names adds noise.
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- **Rank liquid** is the clean result: a positive, monotone reversal premium
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(72.24%, Sharpe
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0.73) once the
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investable universe is sane.
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This matches the prior literature that short-horizon reversal is a real but
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liquidity- and cost-sensitive A-share effect.
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## 3. NaN And Look-Ahead Handling
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- The raw signal uses `close.pct_change(5, fill_method=None)` — missing prices
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are **not** forward-filled, so a suspended name does not silently inherit a
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stale price.
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- Weights are formed at close `t` and earn the **next** close-to-close return
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`t → t+1`. Forward returns are computed on the full market calendar *before*
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selecting signal dates, so a sparse signal grid still earns the next
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*available* return rather than the next signal date. The final signal date,
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which has no forward return, is dropped from metrics (that is why the
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research day count is one less than the stored signal-date count).
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- The liquid-universe mask is applied to the **signal**, not to the price
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history: `pct_change(5)` is always computed on contiguous prices, and the mask
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only decides what is *held*. It uses `tradestatus`, `isST`, a ≥60-session
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seasoning rule, and a trailing-20-day liquidity rank — all backward-looking.
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## 4. How Close Are Alpha And Constructed Portfolio?
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`portfolio build` normalizes the alpha to `target_weight = w / Σ|w|` and scales
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by booksize. The continuous target portfolio is an exact normalization of the
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stored alpha (research return correlation ≈ 1.0); the **integer** book then
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diverges because small per-name targets are rounded away under A-share lot
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rules.
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| run | target_value identity max|Δ| | alpha→target max|Δ| | research corr(alpha,portfolio) | mean integer gross | mean L1 tracking |
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| --- | --- | --- | --- | --- | --- |
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| naive z-score (full) | 0.0000 | 0.00e+00 | 1.000000 | 9,138,331 | 2,542,655 |
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| rank (full) | 0.0000 | 0.00e+00 | 1.000000 | 8,984,098 | 2,678,278 |
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| rank (liquid subset) | 0.0000 | 0.00e+00 | 1.000000 | 9,810,256 | 862,303 |
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## 5. How Close Are Alpha Metrics And Final PnL?
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The costless research metric and the simulated net PnL diverge for two
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mechanical reasons, both quantified below: (a) **execution friction** — next-open
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fills, integer shares, and constraints; and (b) **cost** — the dominant term
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here.
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| run | corr(alpha, exec net) | PnL before cost | total cost | net PnL | mean daily turnover |
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| --- | --- | --- | --- | --- | --- |
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| naive z-score (full) | 0.9675 | 1,838,974 | 13,032,720 | -11,193,746 | 0.5711 |
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| rank (full) | 0.9613 | 5,052,067 | 11,713,451 | -6,661,383 | 0.5133 |
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| rank (liquid subset) | 0.9762 | 11,017,842 | 12,733,803 | -1,715,960 | 0.5715 |
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The research↔execution-net daily-return correlation stays high (the book *does*
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track the signal), but the level collapses after cost. For the liquid run, gross
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costless edge is real yet total cost
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(**12,733,803**)
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swamps it. This is the central finding: 5-day reversal is a signal you must trade
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*slowly* to monetize.
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## 6. Time Consumption By Phase
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| phase | rank full (s) | rank liquid (s) |
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| --- | --- | --- |
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| alpha compute | 108.3 | 116.1 |
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| alpha eval | 98.0 | 118.7 |
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| combo combine | 22.9 | 22.5 |
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| portfolio build | 599.8 | 254.3 |
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| portfolio eval | 94.2 | 90.0 |
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| portfolio simulate | 168.7 | 163.3 |
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| total | 1091.8 | 764.9 |
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`portfolio build` dominates because it iterates per signal date and repairs a
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multi-thousand-name integer book under lot rules. The liquid run is faster
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across the board because it carries far fewer non-zero names per date.
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## 7. Reproduce The Run
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```bash
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# naive z-score baseline (full universe) — the built-in alpha, unchanged
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uv run python cli.py alpha compute --data-path data/daily_bars/all \
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--alpha-name reversal_5d_all --alpha-type reversal --lookback 5 --output-dir alphas
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# robust rank weighting, full + liquid universe (one script, both runs)
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bash scripts/run_reversal_rank_e2e.sh
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# regenerate this report + figures
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uv run python scripts/generate_reversal_5d_report.py
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```
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## Interpretation & Next Steps
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The pipeline is internally consistent end to end: storage validates, the trivial
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one-alpha combo is an exact identity, the continuous target portfolio matches the
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alpha, and the execution layer cleanly explains the gap to net PnL via friction
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and cost. The premise that 5-day reversal "produces not-bad PnL" holds **at the
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signal level** once weighting and universe are sane (rank + liquid), but **fails
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net of cost** at daily rebalance frequency.
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Recommended next diagnostics:
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- **Turnover control** — the highest-leverage lever: hold bands / no-trade zones,
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weight smoothing, or longer rebalance spacing to cut the ~150×/yr turnover.
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- Volatility-scaled or decayed reversal to reduce churn.
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- Sweep the liquidity cutoff and lookback to map the cost/edge frontier.
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