# 5-Day Reversal — End-to-End Pipeline Report

Generated: 2026-06-11T17:17:34

This report runs the **5-day reversal** signal end to end through the decoupled
pipeline (`data → alpha → combo → portfolio build → portfolio simulate/eval`) on
the full downloaded A-share universe, and answers the seven review questions:
alpha storage, metric sanity, NaN/look-ahead handling, alpha↔portfolio
closeness, alpha↔PnL closeness, per-phase timing, and visualizations.

Per this repo's convention an **alpha is a signed cross-sectional position
weight, not a return predictor**, so evaluation is return / Sharpe / turnover /
drawdown — there is deliberately **no IC/IR** anywhere.

## TL;DR

The naive built-in `reversal` alpha (raw `-pct_change(5)` then cross-sectional
**z-score**) loses **-87.45%** in costless research on the full
~5,200-name universe. That is **not** evidence the signal is bad — it is an
artifact of z-score weighting on A-shares: a handful of newly listed /
post-suspension / limit-up names produce huge `pct_change` outliers, and
z-scoring pours the book into exactly those names (stored weights reach
-52σ).

Switching only the **weighting** to a bounded cross-sectional **rank**
(`reversal_rank`) and restricting to a per-date **liquid, non-ST, tradable**
universe recovers a genuine reversal edge: **72.24%**
costless research cumulative return at Sharpe
**0.73** with a
54.31% daily hit rate.

The binding constraint is **cost, not signal**: at ~148×/year
turnover, a 10 bps round-trip (5 bps commission + 5 bps slippage) erases the
edge — every variant is negative after costs. A tradable 5-day reversal needs
turnover control, not a different signal.

## Headline Metrics

| run | weighting | research cum | research Sharpe | research turn/yr | exec before cost | exec net | exec net Sharpe |
| --- | --- | --- | --- | --- | --- | --- | --- |
| naive z-score (full) | z-score | -87.45% | -2.4515 | 160× | 18.39% | -111.94% | -1.4508 |
| rank (full) | rank | -3.48% | -0.0198 | 143× | 50.52% | -66.61% | -1.1839 |
| rank (liquid subset) | rank | 72.24% | 0.7310 | 148× | 110.18% | -17.16% | -0.2226 |

*Research = costless, no-look-ahead weights · next-day return. Execution = next-open
fills on the discretized integer book under suspension / price-limit / volume-cap
constraints, 5 bps commission + 5 bps slippage.*

![Research equity](assets/reversal_5d_research_equity.png)

## 1. Are Alpha Values Properly Stored?

All alpha artifacts conform to `ALPHA_COLUMNS` (`symbol_id, date, alpha_name,
weight`), carry no null / non-finite weights, no duplicate `(symbol_id, date)`
keys, and have numerically-zero daily cross-sectional means (weights are
demeaned per date).

| run | schema ok | null w | non-finite w | dup keys | max |daily mean| | weight range | combo identity Δ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| naive z-score (full) | True | 0 | 0 | 0 | 3.32e-16 | [-52.2, 19.2] | 0.00e+00 |
| rank (full) | True | 0 | 0 | 0 | 0.00e+00 | [-2603.0, 2603.0] | 0.00e+00 |
| rank (liquid subset) | True | 0 | 0 | 0 | 0.00e+00 | [-498.5, 498.5] | 0.00e+00 |

The decisive storage signal is the **weight range**. The naive z-score alpha
stores weights as extreme as
`[-52, 19]` —
single names tens of sigma from the cross-section. Rank weighting is bounded by
construction, so its stored weights are well-behaved. Same signal, completely
different book.

![Weight distributions](assets/reversal_5d_weight_distributions.png)

## 2. Do The Alpha Metrics Make Sense?

Yes, and they tell a coherent story:

- The **z-score full** run is dominated by a few outlier names; its research
  Sharpe of -2.45 reflects a
  book that is effectively long/short a tiny set of extreme movers, which in
  A-shares keep trending — so the reversal bet loses.
- **Rank full** (-3.48%) is roughly flat:
  the direction is right (hit rate
  51.18%) but
  the long tail of illiquid / ST / freshly listed names adds noise.
- **Rank liquid** is the clean result: a positive, monotone reversal premium
  (72.24%, Sharpe
  0.73) once the
  investable universe is sane.

This matches the prior literature that short-horizon reversal is a real but
liquidity- and cost-sensitive A-share effect.

## 3. NaN And Look-Ahead Handling

- The raw signal uses `close.pct_change(5, fill_method=None)` — missing prices
  are **not** forward-filled, so a suspended name does not silently inherit a
  stale price.
- Weights are formed at close `t` and earn the **next** close-to-close return
  `t → t+1`. Forward returns are computed on the full market calendar *before*
  selecting signal dates, so a sparse signal grid still earns the next
  *available* return rather than the next signal date. The final signal date,
  which has no forward return, is dropped from metrics (that is why the
  research day count is one less than the stored signal-date count).
- The liquid-universe mask is applied to the **signal**, not to the price
  history: `pct_change(5)` is always computed on contiguous prices, and the mask
  only decides what is *held*. It uses `tradestatus`, `isST`, a ≥60-session
  seasoning rule, and a trailing-20-day liquidity rank — all backward-looking.

## 4. How Close Are Alpha And Constructed Portfolio?

`portfolio build` normalizes the alpha to `target_weight = w / Σ|w|` and scales
by booksize. The continuous target portfolio is an exact normalization of the
stored alpha (research return correlation ≈ 1.0); the **integer** book then
diverges because small per-name targets are rounded away under A-share lot
rules.

| run | target_value identity max|Δ| | alpha→target max|Δ| | research corr(alpha,portfolio) | mean integer gross | mean L1 tracking |
| --- | --- | --- | --- | --- | --- |
| naive z-score (full) | 0.0000 | 0.00e+00 | 1.000000 | 9,138,331 | 2,542,655 |
| rank (full) | 0.0000 | 0.00e+00 | 1.000000 | 8,984,098 | 2,678,278 |
| rank (liquid subset) | 0.0000 | 0.00e+00 | 1.000000 | 9,810,256 | 862,303 |

![Portfolio tracking](assets/reversal_5d_portfolio_tracking.png)

## 5. How Close Are Alpha Metrics And Final PnL?

The costless research metric and the simulated net PnL diverge for two
mechanical reasons, both quantified below: (a) **execution friction** — next-open
fills, integer shares, and constraints; and (b) **cost** — the dominant term
here.

| run | corr(alpha, exec net) | PnL before cost | total cost | net PnL | mean daily turnover |
| --- | --- | --- | --- | --- | --- |
| naive z-score (full) | 0.9675 | 1,838,974 | 13,032,720 | -11,193,746 | 0.5711 |
| rank (full) | 0.9613 | 5,052,067 | 11,713,451 | -6,661,383 | 0.5133 |
| rank (liquid subset) | 0.9762 | 11,017,842 | 12,733,803 | -1,715,960 | 0.5715 |

The research↔execution-net daily-return correlation stays high (the book *does*
track the signal), but the level collapses after cost. For the liquid run, gross
costless edge is real yet total cost
(**12,733,803**)
swamps it. This is the central finding: 5-day reversal is a signal you must trade
*slowly* to monetize.

![Execution vs research](assets/reversal_5d_exec_vs_research.png)

## 6. Time Consumption By Phase

| phase | rank full (s) | rank liquid (s) |
| --- | --- | --- |
| alpha compute | 108.3 | 116.1 |
| alpha eval | 98.0 | 118.7 |
| combo combine | 22.9 | 22.5 |
| portfolio build | 599.8 | 254.3 |
| portfolio eval | 94.2 | 90.0 |
| portfolio simulate | 168.7 | 163.3 |
| total | 1091.8 | 764.9 |

![Phase timings](assets/reversal_5d_phase_timings.png)

`portfolio build` dominates because it iterates per signal date and repairs a
multi-thousand-name integer book under lot rules. The liquid run is faster
across the board because it carries far fewer non-zero names per date.

## 7. Reproduce The Run

```bash
# naive z-score baseline (full universe) — the built-in alpha, unchanged
uv run python cli.py alpha compute --data-path data/daily_bars/all \
    --alpha-name reversal_5d_all --alpha-type reversal --lookback 5 --output-dir alphas

# robust rank weighting, full + liquid universe (one script, both runs)
bash scripts/run_reversal_rank_e2e.sh

# regenerate this report + figures
uv run python scripts/generate_reversal_5d_report.py
```

## Interpretation & Next Steps

The pipeline is internally consistent end to end: storage validates, the trivial
one-alpha combo is an exact identity, the continuous target portfolio matches the
alpha, and the execution layer cleanly explains the gap to net PnL via friction
and cost. The premise that 5-day reversal "produces not-bad PnL" holds **at the
signal level** once weighting and universe are sane (rank + liquid), but **fails
net of cost** at daily rebalance frequency.

Recommended next diagnostics:

- **Turnover control** — the highest-leverage lever: hold bands / no-trade zones,
  weight smoothing, or longer rebalance spacing to cut the ~150×/yr turnover.
- Volatility-scaled or decayed reversal to reduce churn.
- Sweep the liquidity cutoff and lookback to map the cost/edge frontier.