Restore reversal tutorial wording

The simulator charges (cost_bps + slippage_bps) on each fill, so a full
round trip is charged twice. Correct the cost-model doc, the reversal_5d
report, and the report generator to state the rate is one-way per-trade
(~20 bps round trip for 5+5), rather than mislabeling it round-trip.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

CLAUDE.md

@@ -26,6 +26,10 @@ uv run python cli.py portfolio eval --positions-path portfolio/eq_10m.pq --data-
 
 Add a runtime dep with `uv add <pkg>`, a dev/test dep with `uv add --dev <pkg>` (both update `pyproject.toml` + `uv.lock`).
 
+Backtrader is optional (`uv sync --extra backtrader`) and is not used by the
+current pipeline. Keep `portfolio simulate` as the canonical backtest/execution
+path unless an explicit future adapter is requested.
+
 Note: `tests/test_downloader.py` hits the network (live baostock/akshare); `tests/test_alpha.py` and `tests/test_portfolio.py` are pure and fast.
 
 ## Architecture: one decoupled pipeline
@@ -65,6 +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).

README.md

@@ -48,6 +48,13 @@ The env is managed with [uv](https://docs.astral.sh/uv/). `uv sync` builds `.ven
 uv sync
 ```
 
+Backtrader is an optional dependency and is **not used by the current pipeline**.
+Install it only for future experiments or adapter work:
+
+```bash
+uv sync --extra backtrader
+```
+
 ## Quick start
 
 ```bash
@@ -203,7 +210,9 @@ uv run python cli.py portfolio build \
 
 Executes the constructed `position_shares` book at the next available open,
 clipping trades through repeatable constraints. It writes `fills/<name>.pq` and
-`pnl/<name>.pq`.
+`pnl/<name>.pq`. Trading costs use the simplified open-execution proportional
+cash-cost model documented in
+[`docs/portfolio_trading_cost_model.md`](docs/portfolio_trading_cost_model.md).
 
 | Option | Default | Description |
 | --- | --- | --- |
@@ -401,6 +410,9 @@ constructed positions, fills/costs, P&L, and target-weight research metrics.
 - [x] **Reference execution simulation** — next-open fills over constructed
   `position_shares`, with suspension, price-limit, volume-cap, transaction-cost,
   and slippage controls.
+- [ ] **Optional Backtrader adapter** — Backtrader is available as the
+  `backtrader` extra for possible future event-driven/broker-style experiments,
+  but it is not part of the current canonical portfolio workflow.
 - [ ] **Forward / paper trading** — run the same construction logic on live
   daily data, track simulated fills and a running P&L without real capital.
 - [ ] **Intraday / microstructure data** — bid/ask prices & sizes, mid-price,

docs/portfolio_trading_cost_model.md

@@ -0,0 +1,140 @@
+# Portfolio Trading Cost Model
+
+This document describes the trading cost model used by `portfolio simulate`.
+The current implementation is a simplified open-execution proportional cost
+model. It is intentionally small, explicit, and easy to audit.
+
+## Open-Execution Timeline
+
+The simulator runs once per trading day:
+
+1. A constructed portfolio row provides the target book for an execution date.
+   In the current file layout, a target dated `t` is executed at the next
+   available market date `d = next(t)`.
+2. Trades are executed at `open[d]`.
+3. Realized positions are held during the trading day.
+4. Daily PnL is marked from `open[d]` to `close[d]` on the newly realized book,
+   plus any overnight gap from the previous realized holdings.
+5. Trading cost is charged only on actually realized `traded_shares`, after all
+   constraints have clipped the desired trade.
+
+This means a fully blocked order has `traded_shares = 0` and therefore zero
+trading cost.
+
+## Current Formula
+
+For each symbol:
+
+```text
+trade_value_i = abs(traded_shares_i * execution_price_i)
+trade_cost_i  = trade_value_i * (cost_bps + slippage_bps) / 10000
+```
+
+where:
+
+```text
+execution_price_i = open_price_i
+```
+
+`cost_bps` is the proportional explicit trading-cost rate in basis points.
+`slippage_bps` is modeled as an additional cash cost in basis points. The two
+rates are added linearly. The CLI options `--cost-bps` and `--slippage-bps`
+both default to `0.0`.
+
+Both rates are **one-way, per-trade**: the combined `(cost_bps + slippage_bps)`
+is charged on the traded notional of *each* fill, buy and sell alike. A full
+round trip (enter then exit a position) is therefore charged twice — e.g.
+`5 + 5` bps becomes ~20 bps over a complete round trip, not 10. Quote any
+round-trip figure by doubling, or convert a round-trip budget to a per-trade
+rate by halving before passing it in.
+
+Example:
+
+```text
+traded_shares = 1000
+execution_price = 20 yuan
+cost_bps = 10
+slippage_bps = 5
+
+abs(1000 * 20) * 15 / 10000 = 30 yuan
+```
+
+## Slippage Convention
+
+Slippage is not applied by changing the execution price. It is charged only as
+a cash cost through `trade_cost`.
+
+Do not double-count slippage by doing both:
+
+```text
+execution_price = open * (1 +/- slippage_bps / 10000)
+trade_cost += trade_value * slippage_bps / 10000
+```
+
+The simulator should execute at the open price and subtract the slippage cash
+cost from PnL.
+
+## Relationship To The Simulator
+
+`ReferenceSimulator.fill()` clips desired trades through constraints first, then
+passes the actual `traded_shares` to the cost model. The per-name result is
+stored in the fills parquet as `trade_cost`.
+
+`ReferenceSimulator.run()` sums per-name `trade_cost` into the daily PnL row's
+`cost` column and subtracts that total from daily PnL:
+
+```text
+pnl = overnight + intraday - cost_total
+```
+
+## What This Model Does Not Cover
+
+The current model intentionally does not model:
+
+- Minimum commissions.
+- Buy/sell asymmetric fees.
+- Sell-side stamp duty.
+- Exchange handling fees.
+- Regulatory fees.
+- Transfer fees.
+- Date-aware fee schedule changes.
+- Nonlinear price impact.
+- Auction liquidity / queue effects.
+- Partial fills caused by open auction depth.
+
+These omissions are deliberate. The current model is the default reference
+model, not a detailed brokerage fee simulator.
+
+## Future Extension
+
+The simulator is structured around a cost model abstraction:
+
+```python
+class CostModel:
+    def compute(
+        self,
+        traded_shares,
+        execution_price,
+        side,
+        date,
+        metadata,
+    ):
+        ...
+```
+
+The current implementation is `SimpleProportionalCostModel`.
+
+A future `AShareDetailedCostModel` can add:
+
+- Commission, optionally subject to minimum commission.
+- Sell-side stamp duty.
+- Transfer fee.
+- Exchange handling fee.
+- Regulatory fee.
+- Date-aware fee rates.
+- Separate buy-side and sell-side rates.
+- Optional nonlinear slippage / market-impact model.
+
+Any future model must preserve the same high-level simulator contract: costs
+are computed from realized trades after constraints, and slippage must not be
+counted both through execution-price adjustment and cash cost.

docs/reversal_5d_all_universe_pipeline_report.md

@@ -0,0 +1,509 @@
+# Tutorial: Testing a 5-Day Reversal Alpha
+
+This document is a teaching walkthrough for someone who is new to this research
+framework and only lightly familiar with quant research. We will use one
+concrete experiment, a 5-day reversal alpha on the full downloaded Chinese
+A-share universe, to learn how the framework defines an alpha, stores it, tests
+it, turns it into a portfolio, and explains the gap between a research result
+and simulated trading PnL.
+
+This generated version was refreshed at 2026-06-12T22:52:56.
+The important point is not the timestamp; it is the research method.
+
+## The Research Question
+
+A quant research project starts with a hypothesis:
+
+> If a stock fell a lot over the last few trading days, it may rebound soon; if
+> it rose a lot, it may cool off soon.
+
+This is called **short-horizon reversal**. It is a simple idea: recent losers
+are candidates to buy, and recent winners are candidates to sell or underweight.
+In this repo, the tested version looks back 5 trading days.
+
+The central research question is:
+
+> Does this 5-day reversal rule create useful portfolio returns after the
+> framework applies realistic storage, portfolio construction, execution
+> constraints, and trading costs?
+
+The answer from this run is nuanced:
+
+- The naive built-in version is positive under the tradable
+  next-open-to-next-open research convention (**41.40%**),
+  but its stored weights still show that raw z-score weighting is too sensitive
+  to A-share outliers.
+- A rank-weighted version on a liquid, non-ST, tradable universe has a positive
+  costless research result: **209.58%**
+  at Sharpe **1.44**.
+- The daily-traded implementation is still not tradable after costs because
+  turnover is too high.
+
+That is a normal research outcome. Good research is not just asking "did the
+backtest go up?" It is asking **which layer explains the result**: signal,
+weighting, universe, construction, execution, or cost.
+
+## How This Framework Defines An Alpha
+
+In many quant textbooks, an alpha is described as a **prediction** of future
+returns. This framework uses a stricter and more practical convention:
+
+> An alpha is a signed cross-sectional position weight.
+
+That sentence is the key to the whole repo.
+
+- **Signed** means positive values are long exposure and negative values are
+  short exposure.
+- **Cross-sectional** means the alpha compares stocks to other stocks on the
+  same date.
+- **Position weight** means the output is already an instruction about what the
+  portfolio wants to own. It is not merely a score to correlate with future
+  returns.
+
+The stored alpha file always has this schema:
+
+| column | meaning |
+| --- | --- |
+| `symbol_id` | Stock identifier such as `sh600000` or `sz000001`. |
+| `date` | The signal date. The alpha is formed using information known by this date's close. |
+| `alpha_name` | A label for this particular run, such as `reversal_5d_all`. |
+| `weight` | Signed desired exposure. Positive means long; negative means short. |
+
+Because the framework treats alphas as position weights, it evaluates them with
+portfolio metrics: return, Sharpe, turnover, drawdown, and hit rate. It does
+**not** use IC/IR, because IC/IR would treat the alpha as a return predictor.
+
+## The Pipeline In One Picture
+
+Every phase reads parquet files and writes parquet files. That makes the system
+easy to inspect and rerun one layer at a time.
+
+```text
+daily bars
+   -> alpha weights
+   -> combined weights
+   -> portfolio targets and integer positions
+   -> simulated fills and PnL
+   -> evaluation metrics
+```
+
+For this experiment, the important phases are:
+
+| phase | command family | what it teaches you |
+| --- | --- | --- |
+| Data | `cli.py data download` | What market data is available. |
+| Alpha compute | `cli.py alpha compute` | How a raw research idea becomes stored weights. |
+| Alpha eval | `cli.py alpha eval` | How close-formed weights perform over the tradable next-open-to-next-open interval. |
+| Combo | `cli.py combo combine` | How one or more alphas become one combined book. |
+| Portfolio build | `cli.py portfolio build` | How weights become target values and integer shares. |
+| Portfolio simulate | `cli.py portfolio simulate` | How the integer book trades at next open with constraints and costs. |
+| Portfolio eval | `cli.py portfolio eval` | How the continuous target portfolio behaves over the same costless open-to-open research interval. |
+
+In a real research workflow, you should learn to pause after every phase and
+inspect the parquet output. Most mistakes are easier to find at the interface
+between two phases than at the final PnL line.
+
+## Step 1: Define The Raw Reversal Signal
+
+The built-in 5-day reversal alpha is implemented as:
+
+```python
+signal = -close.pct_change(5, fill_method=None)
+```
+
+For stock `i` on date `t`, this is approximately:
+
+```text
+signal[i, t] = -(close[i, t] / close[i, t-5] - 1)
+```
+
+So:
+
+- If a stock rose by 10% over the last 5 trading days, the raw signal is `-10%`.
+  It becomes a candidate short or underweight.
+- If a stock fell by 10% over the last 5 trading days, the raw signal is `+10%`.
+  It becomes a candidate long or overweight.
+
+Notice the timing. The signal uses prices through date `t`. It must not use the
+return from `t` to `t+1`, because that is the future. The costless alpha
+evaluator tests the weight formed on date `t` over the tradable interval from
+`open[t+1]` to `open[t+2]`; the later execution simulator is the separate layer
+that trades the constructed integer book at the next open.
+
+The code lives in `pipeline/alpha/library/reversal.py`:
+
+```python
+class ReversalAlpha(BaseAlpha):
+    name = "reversal"
+
+    def __init__(self, lookback: int = 5):
+        self.lookback = lookback
+
+    def signal(self, close: pd.DataFrame) -> pd.DataFrame:
+        return -close.pct_change(self.lookback, fill_method=None)
+```
+
+The alpha class only defines the raw signal. The base class then turns that
+signal into weights.
+
+## Step 2: Turn A Signal Into Cross-Sectional Weights
+
+By default, `BaseAlpha.to_weights()` does a cross-sectional z-score each date:
+
+```text
+weight[i, t] = (signal[i, t] - mean_signal[t]) / std_signal[t]
+```
+
+This means the framework asks:
+
+> On this date, which stocks have stronger reversal scores than the rest of the
+> market, and by how much?
+
+That is useful, but it has a weakness. If a few stocks have extreme trailing
+returns because they are newly listed, suspended, illiquid, or limit-constrained,
+z-scoring can put a very large amount of relative exposure into exactly those
+names.
+
+That is visible in the naive full-universe run. Stored weights reached about
+`-52` standard deviations. The
+result is positive under the open-to-open convention, but it is much weaker and
+less robust than the rank-weighted versions:
+
+| run | weighting | research cumulative return | research Sharpe | research turnover/year |
+| --- | --- | --- | --- | --- |
+| naive z-score, full universe | z-score | 41.40% | 0.4514 | 160x |
+
+The lesson is not "reversal is solved." The lesson is:
+
+> The same raw signal can become a fragile portfolio if the weighting method
+> reacts badly to outliers.
+
+## Step 3: Make The Weighting More Robust
+
+The repo also has a rank-weighted version, `reversal_rank`. It uses the same raw
+5-day reversal signal, but converts the cross-section to ranks instead of
+z-scores:
+
+```python
+ranks = signal.rank(axis=1)
+weights = ranks.subtract(ranks.mean(axis=1), axis=0)
+```
+
+Rank weighting keeps the ordering of stocks but removes the importance of the
+exact outlier magnitude. A stock can be "the worst recent loser" or "the best
+recent winner," but it cannot become dozens of standard deviations important
+just because its raw percentage move is unusual.
+
+The full-universe rank version was much less pathological, but still not a
+clean signal:
+
+| run | weighting | research cumulative return | research Sharpe | research turnover/year |
+| --- | --- | --- | --- | --- |
+| rank, full universe | rank | 73.44% | 0.8860 | 143x |
+
+That tells us the weighting fix helped, but the universe still contains many
+names that are poor candidates for a daily reversal strategy.
+
+## Step 4: Define The Investable Universe
+
+An alpha should be tested on stocks that could plausibly be traded. The liquid
+run applies a per-date mask before weights are created. A stock must be:
+
+- seasoned, with at least 60 observed closes;
+- currently tradable, using `tradestatus == 1`;
+- not ST, using `isST == 0`;
+- inside the top 1000 names by trailing 20-day average traded amount.
+
+This mask is applied to the signal, not to the price history used to compute the
+5-day return. That distinction matters. We still compute `pct_change(5)` on the
+full contiguous price history, then decide which names are eligible to hold on
+each signal date.
+
+The liquid rank result is the cleanest research result:
+
+| run | weighting | universe | research cumulative return | research Sharpe | hit rate |
+| --- | --- | --- | --- | --- | --- |
+| rank, liquid subset | rank | top 1000 liquid, tradable, non-ST | 209.58% | 1.4422 | 55.68% |
+
+This is the first point where a researcher can say:
+
+> There appears to be a real 5-day reversal effect in a cleaner A-share
+> universe, before trading costs.
+
+That last phrase, **before trading costs**, is essential.
+
+![Research equity](assets/reversal_5d_research_equity.png)
+
+When reading this chart, focus on the shape and relative behavior:
+
+- The naive z-score line shows why outlier-sensitive weighting is fragile.
+- The rank full-universe line shows that robust weighting helps, but the full
+  universe still contains noisy and hard-to-trade names.
+- The liquid rank line shows the signal-level edge before execution costs.
+
+## Step 5: Check That The Alpha File Is Sane
+
+Before trusting any metric, inspect the stored alpha artifact. The run checked:
+
+- The columns match `ALPHA_COLUMNS`.
+- There are no null weights.
+- There are no non-finite weights.
+- There are no duplicate `(symbol_id, date)` rows.
+- The daily cross-sectional mean is approximately zero.
+- A one-alpha combo is an exact identity transform.
+
+| 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 rank ranges look numerically large because rank weights scale with the
+number of names. That is fine: later evaluation divides by gross exposure, and
+portfolio construction normalizes by `sum(abs(weight))`. The important
+difference is that rank weights are bounded by cross-sectional rank, not by the
+raw size of an abnormal stock move.
+
+![Weight distributions](assets/reversal_5d_weight_distributions.png)
+
+This is a good habit: when a backtest looks strange, plot the weights before
+debugging the PnL. A broken or concentrated weight distribution often explains
+the result.
+
+## Step 6: Understand The Alpha Evaluation Formula
+
+The costless alpha evaluator now asks:
+
+> If we compute alpha weights after close on date `t`, trade them at `open[t+1]`,
+> and hold them until `open[t+2]`, what return would we earn before costs?
+
+This is still a **research-layer approximation**, not the trading simulator. At
+this stage the framework has only an alpha weight file. It has not yet rounded
+shares, checked limits, clipped trades, or paid costs. The purpose is to answer
+a clean signal question: "Do these close-formed weights line up with returns
+over the interval we could actually own after next-open execution?"
+
+The daily research return is:
+
+```text
+R[t] = sum_i(weight[i, t] * (open[i, t+2] / open[i, t+1] - 1)) / sum_i(abs(weight[i, t]))
+```
+
+This has three important consequences:
+
+- The alpha is normalized by its gross exposure, so the scale of raw weights
+  does not by itself create a higher return.
+- The new signal does not receive credit for the overnight gap from `close[t]`
+  to `open[t+1]`, because it cannot be traded until `open[t+1]`.
+- The final two signal dates are dropped from performance metrics because they
+  do not have a complete next-open-to-next-open holding interval.
+
+Turnover is still measured from the weights:
+
+```text
+turnover[t] = sum_i(abs(weight[i, t] - weight[i, t-1])) / sum_i(abs(weight[i, t-1]))
+```
+
+The annualized turnover numbers are a warning. Even a positive signal can be
+hard to monetize if it asks the portfolio to trade too much every day.
+
+## Step 7: Build A Portfolio From The Alpha
+
+The alpha file is still an abstract research book. `portfolio build` turns it
+into target exposures and integer shares.
+
+The main normalization is:
+
+```text
+target_weight[i, t] = weight[i, t] / sum_i(abs(weight[i, t]))
+target_value[i, t] = booksize * target_weight[i, t]
+target_shares[i, t] = target_value[i, t] / construction_price[i, t]
+```
+
+Then the framework creates an integer A-share book using lot rules and repair
+logic. This is where a research portfolio starts to become a tradable portfolio.
+
+The continuous target portfolio matched the stored alpha almost exactly:
+
+| 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 |
+
+The integer book is not exact because small target positions can be rounded
+away. The liquid subset has lower tracking error because it spreads the book
+over fewer and more tradable names.
+
+![Portfolio tracking](assets/reversal_5d_portfolio_tracking.png)
+
+When you research a new alpha, ask two separate questions:
+
+- Does the continuous target portfolio match the alpha? It should.
+- Does the integer tradable portfolio still resemble the target? It may not,
+  especially for small books or very broad universes.
+
+## Step 8: Simulate Execution And Costs
+
+Research returns are not the same as tradable PnL. The simulator executes the
+integer `position_shares` at the next available open and applies constraints:
+
+- suspension;
+- price limit;
+- volume cap;
+- proportional trading cost.
+
+The cost model is:
+
+```text
+cost = abs(traded_shares * open) * (cost_bps + slippage_bps) / 10000
+```
+
+For this run, cost is 5 bps commission plus 5 bps slippage. Slippage is treated
+as cash cost, not as an additional execution price adjustment.
+
+The execution results explain the final research conclusion:
+
+| run | corr(alpha, exec net) | PnL before cost | total cost | net PnL | mean daily turnover |
+| --- | --- | --- | --- | --- | --- |
+| naive z-score (full) | 0.8956 | 1,838,974 | 13,032,720 | -11,193,746 | 0.5711 |
+| rank (full) | 0.9126 | 5,052,067 | 11,713,451 | -6,661,383 | 0.5133 |
+| rank (liquid subset) | 0.8884 | 11,017,842 | 12,733,803 | -1,715,960 | 0.5715 |
+
+For the liquid rank run, simulated PnL before cost is about
+11,017,842, but total cost is about
+12,733,803. That is why the final net PnL is
+weak or negative.
+
+This is not a contradiction. It is exactly what a research pipeline should show:
+
+> The signal can exist in the costless layer, but the daily implementation can
+> still trade too much to keep the edge.
+
+![Execution vs research](assets/reversal_5d_exec_vs_research.png)
+
+## Step 9: Read The Headline Metrics Like A Researcher
+
+The complete summary is:
+
+| run | weighting | research cum | research Sharpe | research turn/yr | exec before cost | exec net | exec net Sharpe |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| naive z-score (full) | z-score | 41.40% | 0.4514 | 160× | 18.39% | -111.94% | -1.4508 |
+| rank (full) | rank | 73.44% | 0.8860 | 143× | 50.52% | -66.61% | -1.1839 |
+| rank (liquid subset) | rank | 209.58% | 1.4422 | 148× | 110.18% | -17.16% | -0.2226 |
+
+*Research = costless, no-look-ahead weights over the next-open-to-next-open
+holding interval. Execution = next-open fills on the discretized integer book
+under suspension / price-limit / volume-cap constraints, 5 bps commission + 5
+bps slippage.*
+
+Here is the interpretation:
+
+- **Naive z-score full universe**: positive under open-to-open research, but a
+  less reliable test of the reversal idea because the weighting scheme lets
+  outliers dominate parts of the book.
+- **Rank full universe**: a better test of the same idea, but still noisy
+  because the universe includes too many problematic names.
+- **Rank liquid subset**: the best signal-level test; it finds the cleanest
+  costless reversal effect.
+- **Execution net**: daily rebalancing remains heavily constrained by cost.
+
+A beginner might look only at the final net PnL and say "the alpha failed." A
+researcher should be more precise:
+
+> The raw 5-day reversal idea has signal value in a liquid universe, but the
+> current daily trading rule has too much turnover for the assumed cost model.
+
+## Step 10: Reproduce The Experiment
+
+These commands reproduce the important artifacts, assuming the full daily-bar
+dataset already exists at `data/daily_bars/all`.
+
+```bash
+# Naive z-score baseline: built-in reversal alpha, full universe.
+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
+
+# Rank-weighted full and liquid runs.
+bash scripts/run_reversal_rank_e2e.sh
+
+# Regenerate figures, diagnostics, and this tutorial report.
+uv run python scripts/generate_reversal_5d_report.py
+```
+
+If you are learning the framework, do not run the whole pipeline blindly. Run
+one phase, inspect the output parquet, then continue.
+
+## How To Research Your Own Alpha
+
+Use this checklist for a new idea.
+
+1. State the hypothesis in plain language.
+   Example: "Stocks with poor 5-day returns may rebound over the next day."
+
+2. Write the raw signal.
+   Implement `signal(close) -> wide DataFrame` in an alpha class. Higher values
+   should mean stronger long preference.
+
+3. Choose the weighting method.
+   The default z-score is useful, but it can be fragile. Consider rank weights,
+   caps, neutralization, or liquidity-aware filters if outliers dominate.
+
+4. Define the investable universe before trusting results.
+   Make sure the strategy is not depending on suspended, ST, newly listed, or
+   illiquid names.
+
+5. Evaluate the alpha as a portfolio, not as a prediction.
+   Check cumulative return, Sharpe, drawdown, hit rate, and turnover over the
+   next-open-to-next-open holding interval. Do not add IC/IR unless the
+   framework's alpha convention changes.
+
+6. Build the portfolio and inspect tracking.
+   Confirm that target weights match the alpha, then check whether integer
+   shares still track the target book.
+
+7. Simulate execution with costs.
+   The final research question is not only "is there a signal?" It is "is there
+   enough signal left after realistic trading?"
+
+8. Diagnose the failure layer.
+   If results are bad, identify whether the problem is the raw signal, weighting,
+   universe, construction, execution constraints, turnover, or cost.
+
+For this 5-day reversal study, the diagnosis is clear: **the signal-level result
+is most promising after robust weighting and a liquid universe filter, but the
+current implementation needs turnover control before it can be considered
+tradable.**
+
+## Next Research Directions
+
+The natural next experiments are:
+
+- Add turnover control: no-trade bands, slower rebalancing, or weight smoothing.
+- Sweep the lookback window: compare 3-day, 5-day, 10-day, and 20-day reversal.
+- Sweep liquidity filters: top 500, top 1000, top 1500 by traded amount.
+- Add position caps so no single name can dominate after normalization.
+- Compare rank weighting with volatility-scaled reversal.
+
+The most important habit is to keep the layers separate. A good alpha research
+workflow does not stop at a single performance number; it explains how the idea
+travels from hypothesis, to signal, to weights, to portfolio, to executable PnL.
+
+## Appendix: Phase Timings From This Rerun
+
+| phase | rank full (s) | rank liquid (s) |
+| --- | --- | --- |
+| alpha compute | 94.1 | 107.8 |
+| alpha eval | 93.3 | 96.9 |
+| combo combine | 21.6 | 21.7 |
+| portfolio build | 537.6 | 236.7 |
+| portfolio eval | 95.1 | 88.3 |
+| portfolio simulate | 139.6 | 139.1 |
+| total | 981.3 | 690.5 |
+
+![Phase timings](assets/reversal_5d_phase_timings.png)
+
+`portfolio build` usually dominates because it iterates per signal date and
+repairs a multi-thousand-name integer book under lot rules. The liquid run is
+faster because it carries fewer non-zero names per date.

pipeline/alpha/cli.py

@@ -64,8 +64,17 @@ def list_(alpha_modules):
     "--param", "extra_params", multiple=True,
     help="Extra alpha constructor param as name=value (repeatable)",
 )
+@click.option(
+    "--liquid-universe", is_flag=True, default=False,
+    help="Mask weights to a per-date investable universe (tradable, non-ST, "
+         "seasoned, top liquidity) before normalization",
+)
+@click.option(
+    "--universe-top-n", default=1000, type=int,
+    help="Most-liquid names kept per date when --liquid-universe is set",
+)
 def compute(data_path, alpha_name, alpha_type, output_dir, lookback, vol_window,
-            alpha_modules, extra_params):
+            alpha_modules, extra_params, liquid_universe, universe_top_n):
     """Compute one alpha from raw data and save as parquet."""
     for spec in alpha_modules:
         load_alpha_module(spec)
@@ -81,6 +90,8 @@ def compute(data_path, alpha_name, alpha_type, output_dir, lookback, vol_window,
     params = {"lookback": lookback, "vol_window": vol_window}
     params.update(_parse_params(extra_params))
 
+    universe = {"top_n": universe_top_n} if liquid_universe else None
+
     data = pd.read_parquet(data_path)
     click.echo(f"Loaded data: {len(data):,} rows from {data_path}")
 
@@ -88,6 +99,7 @@ def compute(data_path, alpha_name, alpha_type, output_dir, lookback, vol_window,
         data=data,
         alpha_name=alpha_name,
         alpha_type=alpha_type,
+        universe=universe,
         **params,
     )
 

pipeline/alpha/compute.py

@@ -25,15 +25,86 @@ def _pivot_close(df: pd.DataFrame) -> pd.DataFrame:
     return pivot.sort_index()
 
 
-def _daily_returns(close: pd.DataFrame) -> pd.DataFrame:
+def _pivot_open(df: pd.DataFrame) -> pd.DataFrame:
-    """Compute daily returns from wide close DataFrame."""
+    """Pivot data to wide format: date index, columns = symbol_id, values = open."""
-    return close.pct_change()
+    pivot = df.pivot_table(
+        index="date", columns="symbol_id", values="open", aggfunc="first"
+    )
+    return pivot.sort_index()
+
+
+def _forward_open_to_open_returns(open_: pd.DataFrame) -> pd.DataFrame:
+    """Return earned by a close-formed signal after next-open execution.
+
+    A weight formed after close on date t can first be traded at open[t+1].
+    With daily retargeting it is then held until open[t+2], so the signal-date
+    forward return is open[t+2] / open[t+1] - 1.
+    """
+    return open_.shift(-2).divide(open_.shift(-1)) - 1.0
+
+
+def investable_universe_mask(
+    data: pd.DataFrame,
+    template: pd.DataFrame,
+    *,
+    top_n: int = 1000,
+    min_history: int = 60,
+    require_tradable: bool = True,
+    exclude_st: bool = True,
+) -> pd.DataFrame:
+    """Build a per-date investable-universe mask aligned to ``template``.
+
+    A ``(date, symbol_id)`` cell is ``True`` when the name is, on that date,
+    seasoned (at least ``min_history`` prior closes), currently tradable
+    (``tradestatus == 1``), not flagged ST (``isST == 0``), and inside the
+    ``top_n`` most liquid names by trailing 20-day mean ``amount``. The mask is
+    applied to the *signal* (computed on full contiguous prices), so it
+    restricts only what is *held*, never the price history used to form the
+    signal — that keeps ``pct_change`` correct and look-ahead free.
+
+    Args:
+        data: Long DataFrame with at least ``symbol_id``, ``date``, ``close``,
+            ``amount``, ``isST``, ``tradestatus``.
+        template: Wide signal (date index × ``symbol_id`` columns) to align to.
+        top_n: Keep this many most-liquid names per date.
+        min_history: Minimum number of observed closes before a name is eligible.
+        require_tradable: Require ``tradestatus == 1`` on the date.
+        exclude_st: Drop names flagged ``isST == 1``.
+
+    Returns:
+        Boolean wide DataFrame aligned to ``template``.
+    """
+    def _wide(col: str) -> pd.DataFrame:
+        return (
+            data.pivot_table(index="date", columns="symbol_id", values=col, aggfunc="first")
+            .sort_index()
+            .reindex(index=template.index, columns=template.columns)
+        )
+
+    close = _wide("close")
+    mask = close.notna()
+
+    seasoned = close.notna().cumsum() >= min_history
+    mask &= seasoned
+
+    if exclude_st and "isST" in data.columns:
+        mask &= _wide("isST").fillna(1) == 0
+    if require_tradable and "tradestatus" in data.columns:
+        mask &= _wide("tradestatus").fillna(0) == 1
+
+    amount = _wide("amount")
+    amt_ma = amount.rolling(20, min_periods=10).mean()
+    liquid_rank = amt_ma.rank(axis=1, ascending=False)
+    mask &= liquid_rank <= top_n
+
+    return mask.fillna(False)
 
 
 def compute_alpha(
     data: pd.DataFrame,
     alpha_name: str,
     alpha_type: str,
+    universe: dict | None = None,
     **params,
 ) -> pd.DataFrame:
     """Compute alpha weights from raw data.
@@ -42,6 +113,11 @@ def compute_alpha(
         data: DataFrame with DATA_COLUMNS.
         alpha_name: Label stored in the ``alpha_name`` output column.
         alpha_type: Registry key of the alpha class (e.g. ``reversal``).
+        universe: Optional investable-universe filter. When given, the alpha's
+            raw signal is masked to the investable set (see
+            :func:`investable_universe_mask`) *before* it is turned into
+            weights, so unheld names get weight 0. Keys are forwarded as keyword
+            arguments to :func:`investable_universe_mask`.
         **params: Constructor parameters for the alpha (e.g. ``lookback``,
             ``vol_window``). Only the params the alpha's ``__init__`` accepts are
             used; extras are ignored.
@@ -54,7 +130,12 @@ def compute_alpha(
     """
     alpha = get_alpha(alpha_type, **params)
     close = _pivot_close(data)
+    if universe is None:
         weights = alpha.weights(close)
+    else:
+        signal = alpha.signal(close)
+        mask = investable_universe_mask(data, signal, **universe)
+        weights = alpha.to_weights(signal.where(mask))
 
     # Melt to long format
     weights_melted = weights.reset_index().melt(
@@ -82,8 +163,11 @@ def evaluate_alpha(alpha_df: pd.DataFrame, data_df: pd.DataFrame) -> dict:
 
     Computes return, annualized Sharpe, annualized turnover, max drawdown.
 
-    Alpha is interpreted as POSITION WEIGHTS, not predictions.
+    Alpha is interpreted as POSITION WEIGHTS, not predictions. A close-formed
-    Return on date t = sum(weight[s,t] * realized_return[s,t]) / sum(abs(weight[s,t]))
+    weight on date t is assumed tradable at open[t+1] and held until open[t+2].
+    Return on signal date t = sum(weight[s,t] * open_to_open_return[s,t]) /
+    sum(abs(weight[s,t])). This matches the execution convention without
+    crediting the new signal for the overnight gap before it can be traded.
 
     Args:
         alpha_df: DataFrame with ALPHA_COLUMNS.
@@ -93,31 +177,50 @@ def evaluate_alpha(alpha_df: pd.DataFrame, data_df: pd.DataFrame) -> dict:
         Dict with metrics: cumulative_return, sharpe_annual, turnover_annual,
         max_drawdown, hit_rate, n_dates.
     """
-    close = _pivot_close(data_df)
+    open_ = _pivot_open(data_df)
-    returns = _daily_returns(close)
+    fwd_returns_all = _forward_open_to_open_returns(open_)
 
     # Pivot alpha weights to wide format
     weights = alpha_df.pivot_table(
         index="date", columns="symbol_id", values="weight", aggfunc="first"
     ).sort_index()
 
-    # Align dates
+    # Align weights to signal dates that exist on the market calendar. Compute
-    common_dates = weights.index.intersection(returns.index)
+    # forward open-to-open returns on the full market calendar first, so sparse
+    # signal grids still earn the next available open-to-open interval instead
+    # of the next signal date.
+    common_dates = weights.index.intersection(open_.index)
     weights = weights.loc[common_dates]
-    returns = returns.loc[common_dates]
+    fwd_returns = fwd_returns_all.reindex(common_dates)
 
-    if len(common_dates) < 2:
+    if len(common_dates) < 1:
         return {
             "cumulative_return": 0.0,
             "sharpe_annual": 0.0,
             "turnover_annual": 0.0,
             "max_drawdown": 0.0,
             "hit_rate": 0.0,
-            "n_dates": len(common_dates),
+            "n_dates": 0,
         }
 
-    # Daily portfolio return = sum(w * r) / sum(|w|)  — normalized by gross exposure
+    # Daily portfolio return = sum(w_t * r_open[t+1→t+2]) / sum(|w_t|).
-    daily_returns = (weights * returns).sum(axis=1) / weights.abs().sum(axis=1)
+    # The final two signal dates have no complete next-open holding interval
+    # and are dropped below.
+    gross = weights.abs().sum(axis=1)
+    daily_returns = (
+        (weights * fwd_returns).sum(axis=1, min_count=1)
+        / gross.replace(0.0, np.nan)
+    )
+    daily_returns = daily_returns.dropna()
+    if len(daily_returns) < 2:
+        return {
+            "cumulative_return": 0.0,
+            "sharpe_annual": 0.0,
+            "turnover_annual": 0.0,
+            "max_drawdown": 0.0,
+            "hit_rate": 0.0,
+            "n_dates": int(len(daily_returns)),
+        }
 
     # Cumulative return
     cumulative_return = float((1.0 + daily_returns).prod() - 1.0)
@@ -130,7 +233,7 @@ def evaluate_alpha(alpha_df: pd.DataFrame, data_df: pd.DataFrame) -> dict:
     # Annualized turnover: avg daily turnover * 252
     # Daily turnover = sum(|w_t - w_{t-1}|) / sum(|w_{t-1}|)
     weight_change = weights.diff().abs().sum(axis=1)
-    gross_exposure = weights.abs().sum(axis=1).shift(1)
+    gross_exposure = gross.shift(1)
     daily_turnover = weight_change / gross_exposure
     turnover_annual = float(daily_turnover.mean() * 252)
 
@@ -149,5 +252,5 @@ def evaluate_alpha(alpha_df: pd.DataFrame, data_df: pd.DataFrame) -> dict:
         "turnover_annual": turnover_annual,
         "max_drawdown": max_drawdown,
         "hit_rate": hit_rate,
-        "n_dates": len(common_dates),
+        "n_dates": int(len(daily_returns)),
     }

pipeline/alpha/library/__init__.py

@@ -4,4 +4,9 @@ Importing this package imports each alpha module, which registers the alpha via
 the ``@register_alpha`` decorator. Add a new built-in by dropping a module here
 and importing it below.
 """
-from pipeline.alpha.library import momentum, reversal, reversal_vol  # noqa: F401
+from pipeline.alpha.library import (  # noqa: F401
+    momentum,
+    reversal,
+    reversal_rank,
+    reversal_vol,
+)

pipeline/alpha/library/momentum.py

@@ -15,4 +15,4 @@ class MomentumAlpha(BaseAlpha):
         self.lookback = lookback
 
     def signal(self, close: pd.DataFrame) -> pd.DataFrame:
-        return close.pct_change(self.lookback)
+        return close.pct_change(self.lookback, fill_method=None)

pipeline/alpha/library/reversal.py

@@ -15,4 +15,4 @@ class ReversalAlpha(BaseAlpha):
         self.lookback = lookback
 
     def signal(self, close: pd.DataFrame) -> pd.DataFrame:
-        return -close.pct_change(self.lookback)
+        return -close.pct_change(self.lookback, fill_method=None)

pipeline/alpha/library/reversal_rank.py

@@ -0,0 +1,33 @@
+"""Outlier-robust short-horizon reversal alpha."""
+import pandas as pd
+
+from pipeline.alpha.base import BaseAlpha
+from pipeline.alpha.registry import register_alpha
+
+
+@register_alpha
+class ReversalRankAlpha(BaseAlpha):
+    """Reversal weighted by cross-sectional rank instead of z-score.
+
+    The signal is the same trailing-return reversal as :class:`ReversalAlpha`,
+    but :meth:`to_weights` converts it with a cross-sectional rank that is then
+    demeaned. Rank weighting is bounded and monotone, so it does not dump the
+    book into a handful of extreme movers the way raw z-scoring does — the
+    failure mode that makes plain ``reversal`` collapse on the A-share universe,
+    where newly listed / post-suspension / limit-up names produce huge
+    ``pct_change`` outliers.
+    """
+
+    name = "reversal_rank"
+
+    def __init__(self, lookback: int = 5):
+        self.lookback = lookback
+
+    def signal(self, close: pd.DataFrame) -> pd.DataFrame:
+        return -close.pct_change(self.lookback, fill_method=None)
+
+    def to_weights(self, signal: pd.DataFrame) -> pd.DataFrame:
+        signal = signal.dropna(how="all")
+        ranks = signal.rank(axis=1)
+        weights = ranks.subtract(ranks.mean(axis=1), axis=0)
+        return weights.fillna(0.0)

pipeline/alpha/library/reversal_vol.py

@@ -21,6 +21,6 @@ class ReversalVolAlpha(BaseAlpha):
         self.vol_window = vol_window
 
     def signal(self, close: pd.DataFrame) -> pd.DataFrame:
-        reversal = -close.pct_change(self.lookback)
+        reversal = -close.pct_change(self.lookback, fill_method=None)
-        vol = close.pct_change().rolling(self.vol_window).std()
+        vol = close.pct_change(fill_method=None).rolling(self.vol_window).std()
         return reversal / vol

pipeline/combo/cli.py

@@ -26,8 +26,8 @@ def combo():
 def combine(alpha_paths, combo_name, method, output_dir):
     """Combine multiple alphas and save as parquet."""
     paths = [p.strip() for p in alpha_paths.split(",") if p.strip()]
-    if len(paths) < 2:
+    if len(paths) < 1:
-        click.echo("Error: --alpha-paths requires at least 2 comma-separated paths", err=True)
+        click.echo("Error: --alpha-paths requires at least 1 path", err=True)
         return
 
     result = combine_alphas(

pipeline/portfolio/construct.py

@@ -7,9 +7,9 @@ across dates (positions are stateful, unlike alphas/combos), discretizing and
 repairing each day's target into a tradable integer book.
 
 Return-convention note: weights here are *target allocations*. The research
-evaluation in :mod:`pipeline.portfolio.research` marks them close-to-close on the
+evaluation in :mod:`pipeline.portfolio.research` marks them from next open to
-*next* period (no look-ahead); the execution simulator marks the actually-filled
+the following open (no look-ahead); the execution simulator marks the
-book at the next open. See those modules for details.
+actually-filled book at the next open. See those modules for details.
 """
 
 from __future__ import annotations

pipeline/portfolio/costs.py

@@ -0,0 +1,50 @@
+"""Trading cost models for portfolio execution simulation."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Mapping
+
+import numpy as np
+
+
+class CostModel(ABC):
+    """Interface for per-name execution cost models."""
+
+    @abstractmethod
+    def compute(
+        self,
+        traded_shares: np.ndarray,
+        execution_price: np.ndarray,
+        side: np.ndarray,
+        date,
+        metadata: Mapping[str, object] | None = None,
+    ) -> np.ndarray:
+        """Return per-name trading cost in yuan."""
+
+
+@dataclass(frozen=True)
+class SimpleProportionalCostModel(CostModel):
+    """Simplified open-execution proportional cost model.
+
+    Slippage is represented as an additional cash cost. The execution price is
+    not adjusted by slippage, which avoids double-counting.
+    """
+
+    cost_bps: float = 0.0
+    slippage_bps: float = 0.0
+
+    def compute(
+        self,
+        traded_shares: np.ndarray,
+        execution_price: np.ndarray,
+        side: np.ndarray,
+        date,
+        metadata: Mapping[str, object] | None = None,
+    ) -> np.ndarray:
+        shares = np.asarray(traded_shares, dtype=np.float64)
+        price = np.asarray(execution_price, dtype=np.float64)
+        open_price = np.where(np.isfinite(price), price, 0.0)
+        trade_value = np.abs(shares * open_price)
+        return trade_value * (self.cost_bps + self.slippage_bps) / 1e4

pipeline/portfolio/research.py

@@ -6,9 +6,10 @@ trading constraints. Metrics are return / Sharpe / turnover / max-drawdown /
 convention that an alpha is a position weight, not a return predictor.
 
 Return convention (documented): the target weight formed from information at
-date ``t`` earns the *next* period's close-to-close return, i.e. weights are
+date ``t`` is assumed tradable at ``open[t+1]`` and held until ``open[t+2]``.
-shifted one day relative to realized returns, so there is no look-ahead:
+This is a costless approximation of the next-open execution path: no lots,
-``R_t = sum_i w_{i,t} · r_{i,t+1}`` normalized by gross exposure.
+constraints, or costs, but no credit for an overnight gap that the new signal
+could not have owned.
 """
 
 from __future__ import annotations
@@ -26,39 +27,46 @@ def evaluate_portfolio(positions_df: pd.DataFrame, data_df: pd.DataFrame) -> dic
     Args:
         positions_df: POSITION_COLUMNS (uses ``target_weight``; zero-gross
             construction carry dates remain flat in this research view).
-        data_df: DATA_COLUMNS (uses ``close`` for returns).
+        data_df: DATA_COLUMNS (uses ``open`` for returns).
 
     Returns:
         Dict with ``cumulative_return, sharpe_annual, turnover_annual,
         max_drawdown, fitness, hit_rate, n_dates``. No IC key.
     """
-    close = data_df.pivot_table(
+    open_ = data_df.pivot_table(
-        index="date", columns="symbol_id", values="close", aggfunc="first"
+        index="date", columns="symbol_id", values="open", aggfunc="first"
     ).sort_index()
-    returns = close.pct_change()
+    fwd = open_.shift(-2).divide(open_.shift(-1)) - 1.0
 
     weights = positions_df.pivot_table(
         index="date", columns="symbol_id", values="target_weight", aggfunc="first"
     ).sort_index()
 
-    common = weights.index.intersection(returns.index)
+    common = weights.index.intersection(open_.index)
     weights = weights.loc[common]
-    returns = returns.loc[common]
+    # Compute forward returns on the full market calendar before selecting
+    # signal dates. This preserves the next available open-to-open holding
+    # interval when the signal grid is sparser than the data grid.
+    fwd = fwd.reindex(common)
 
     empty = {
         "cumulative_return": 0.0, "sharpe_annual": 0.0, "turnover_annual": 0.0,
         "max_drawdown": 0.0, "fitness": 0.0, "hit_rate": 0.0,
         "n_dates": len(common),
     }
-    if len(common) < 3:
+    if len(common) < 1:
+        empty["n_dates"] = 0
         return empty
 
     gross = weights.abs().sum(axis=1)
-    # Weights at t earn the return from t to t+1: shift returns back by one.
+    # Weights at t earn the costless tradable interval open[t+1] -> open[t+2].
-    fwd = returns.shift(-1)
+    daily = (
-    daily = (weights * fwd).sum(axis=1) / gross.replace(0.0, np.nan)
+        (weights * fwd).sum(axis=1, min_count=1)
+        / gross.replace(0.0, np.nan)
+    )
     daily = daily.dropna()
     if len(daily) < 2:
+        empty["n_dates"] = int(len(daily))
         return empty
 
     cumulative_return = float((1.0 + daily).prod() - 1.0)

pipeline/portfolio/simulator.py

@@ -4,7 +4,8 @@ Execution model (documented convention): a position book targeted from
 information available on date ``t`` is executed at ``open[t+1]``. Trades that
 violate a :class:`~pipeline.portfolio.constraints.TradeConstraint` (suspension,
 price limit, volume cap, …) are clipped; a fully blocked buy leaves the position
-at its previous level. Realized PnL marks the *actually filled* book.
+at its previous level. Realized PnL marks the *actually filled* book. Trading
+cost defaults to a simplified open-execution proportional cash-cost model.
 
 The simulator is an ABC + a :class:`ReferenceSimulator`; constraints compose by
 intersecting their per-name signed delta bounds.
@@ -21,6 +22,7 @@ import pandas as pd
 
 from pipeline.common.schema import FILL_COLUMNS, PNL_COLUMNS
 from pipeline.portfolio.constraints import TradeConstraint
+from pipeline.portfolio.costs import CostModel, SimpleProportionalCostModel
 from pipeline.portfolio.market_rules import MarketRule, compute_limit_status
 
 logger = logging.getLogger(__name__)
@@ -65,10 +67,12 @@ class ExecutionSimulator(ABC):
     """Abstract execution layer. Subclasses define how a target gets filled."""
 
     def __init__(self, constraints: list[TradeConstraint] | None = None,
-                 cost_bps: float = 0.0, slippage_bps: float = 0.0):
+                 cost_bps: float = 0.0, slippage_bps: float = 0.0,
+                 cost_model: CostModel | None = None):
         self.constraints = constraints or []
-        self.cost_bps = cost_bps
+        self.cost_model = cost_model or SimpleProportionalCostModel(
-        self.slippage_bps = slippage_bps
+            cost_bps=cost_bps, slippage_bps=slippage_bps
+        )
 
     @abstractmethod
     def fill(self, ctx: TradeContext) -> FillResult:
@@ -104,9 +108,17 @@ class ReferenceSimulator(ExecutionSimulator):
         blocked = (traded != desired).astype(np.int64)
 
         realized = prev + traded
-        open_px = np.where(np.isfinite(ctx.slice.price), ctx.slice.price, 0.0)
+        cost = self.cost_model.compute(
-        trade_value = np.abs(traded.astype(np.float64) * open_px)
+            traded_shares=traded,
-        cost = trade_value * (self.cost_bps + self.slippage_bps) / 1e4
+            execution_price=ctx.slice.price,
+            side=np.sign(traded),
+            date=ctx.slice.date,
+            metadata={
+                "symbol_ids": ctx.slice.symbol_ids,
+                "booksize": ctx.booksize,
+                "market_slice": ctx.slice,
+            },
+        )
         return FillResult(realized, traded, cost, blocked)
 
     def run(

pyproject.toml

@@ -4,7 +4,6 @@ version = "0.1.0"
 description = "A modular Chinese A-share quant research framework (daily frequency)."
 requires-python = ">=3.10"
 dependencies = [
-    "backtrader>=1.9.76.123",
     "akshare>=1.14.0",
     "baostock>=0.8.8",
     "pandas>=2.0.0",
@@ -13,6 +12,11 @@ dependencies = [
     "pyarrow>=14.0.0",
 ]
 
+[project.optional-dependencies]
+backtrader = [
+    "backtrader>=1.9.76.123",
+]
+
 [dependency-groups]
 dev = [
     "pytest>=7.0.0",

scripts/run_reversal_rank_e2e.sh

@@ -0,0 +1,62 @@
+#!/usr/bin/env bash
+# End-to-end run of the outlier-robust reversal_rank alpha on the full
+# all-universe dataset and on a per-date liquid subset. Records per-phase
+# wall-clock time to reports/reversal_rank_timings.json.
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+DATA=data/daily_bars/all
+BOOK=10000000
+TIMINGS=reports/reversal_rank_timings.json
+mkdir -p reports
+echo "{" > "$TIMINGS"
+
+run() {  # run <json_key> <cmd...>
+  local key="$1"; shift
+  local t0 t1
+  t0=$(date +%s.%N)
+  "$@"
+  t1=$(date +%s.%N)
+  printf '  "%s": %.2f,\n' "$key" "$(echo "$t1 - $t0" | bc)" >> "$TIMINGS"
+  echo ">>> $key took $(echo "$t1 - $t0" | bc)s"
+}
+
+# ---- full all-universe, robust rank weighting ----
+run full_alpha_compute uv run python cli.py alpha compute --data-path "$DATA" \
+  --alpha-name reversal_rank_all --alpha-type reversal_rank --lookback 5 --output-dir alphas
+run full_alpha_eval uv run python cli.py alpha eval \
+  --alpha-path alphas/reversal_rank_all.pq --data-path "$DATA"
+run full_combo uv run python cli.py combo combine \
+  --alpha-paths alphas/reversal_rank_all.pq --combo-name reversal_rank_all_combo \
+  --method equal_weight --output-dir combos
+run full_portfolio_build uv run python cli.py portfolio build \
+  --weights-path combos/reversal_rank_all_combo.pq --data-path "$DATA" \
+  --booksize "$BOOK" --portfolio-name reversal_rank_all_10m --output-dir portfolio
+run full_portfolio_eval uv run python cli.py portfolio eval \
+  --positions-path portfolio/reversal_rank_all_10m.pq --data-path "$DATA"
+run full_portfolio_simulate uv run python cli.py portfolio simulate \
+  --positions-path portfolio/reversal_rank_all_10m.pq --data-path "$DATA" \
+  --constraint suspension --constraint price_limit --constraint volume_cap \
+  --cost-bps 5 --slippage-bps 5 --output-dir portfolio
+
+# ---- liquid subset (per-date investable universe), robust rank weighting ----
+run liq_alpha_compute uv run python cli.py alpha compute --data-path "$DATA" \
+  --alpha-name reversal_rank_liq --alpha-type reversal_rank --lookback 5 \
+  --liquid-universe --universe-top-n 1000 --output-dir alphas
+run liq_alpha_eval uv run python cli.py alpha eval \
+  --alpha-path alphas/reversal_rank_liq.pq --data-path "$DATA"
+run liq_combo uv run python cli.py combo combine \
+  --alpha-paths alphas/reversal_rank_liq.pq --combo-name reversal_rank_liq_combo \
+  --method equal_weight --output-dir combos
+run liq_portfolio_build uv run python cli.py portfolio build \
+  --weights-path combos/reversal_rank_liq_combo.pq --data-path "$DATA" \
+  --booksize "$BOOK" --portfolio-name reversal_rank_liq_10m --output-dir portfolio
+run liq_portfolio_eval uv run python cli.py portfolio eval \
+  --positions-path portfolio/reversal_rank_liq_10m.pq --data-path "$DATA"
+run liq_portfolio_simulate uv run python cli.py portfolio simulate \
+  --positions-path portfolio/reversal_rank_liq_10m.pq --data-path "$DATA" \
+  --constraint suspension --constraint price_limit --constraint volume_cap \
+  --cost-bps 5 --slippage-bps 5 --output-dir portfolio
+
+printf '  "_done": true\n}\n' >> "$TIMINGS"
+echo "Wrote $TIMINGS"

tests/test_alpha.py

@@ -6,7 +6,11 @@ import pandas as pd
 import pytest
 
 from pipeline.alpha.base import BaseAlpha
-from pipeline.alpha.compute import compute_alpha, evaluate_alpha
+from pipeline.alpha.compute import (
+    compute_alpha,
+    evaluate_alpha,
+    investable_universe_mask,
+)
 from pipeline.alpha.registry import (
     available_alphas,
     get_alpha,
@@ -51,7 +55,7 @@ def test_reversal_sign_matches_negative_trailing_return():
     data = _make_data()
     alpha = compute_alpha(data, "rev5", "reversal", lookback=5)
     close = data.pivot_table(index="date", columns="symbol_id", values="close").sort_index()
-    raw = -close.pct_change(5)
+    raw = -close.pct_change(5, fill_method=None)
     last = raw.index[-1]
     expected_top = raw.loc[last].idxmax()
     got = alpha[alpha["date"] == last].set_index("symbol_id")["weight"].idxmax()
@@ -74,6 +78,83 @@ def test_evaluate_alpha_keys():
         assert key in metrics
 
 
+def test_evaluate_alpha_uses_next_open_to_next_open_returns():
+    dates = pd.date_range("2024-01-01", periods=5)
+    data = pd.concat([
+        pd.DataFrame({
+            "symbol_id": "sh600000",
+            "symbol_name": "sh600000",
+            "date": dates,
+            "open": [100.0, 100.0, 100.0, 100.0, 200.0],
+            "high": [100.0, 1000.0, 1000.0, 1000.0, 1000.0],
+            "low": [100.0, 1000.0, 1000.0, 1000.0, 1000.0],
+            "close": [100.0, 1000.0, 1000.0, 1000.0, 1000.0],
+            "volume": 1_000.0,
+            "amount": 1_000.0,
+        }),
+        pd.DataFrame({
+            "symbol_id": "sz000001",
+            "symbol_name": "sz000001",
+            "date": dates,
+            "open": [100.0, 100.0, 100.0, 200.0, 200.0],
+            "high": [100.0, 10.0, 10.0, 10.0, 10.0],
+            "low": [100.0, 10.0, 10.0, 10.0, 10.0],
+            "close": [100.0, 10.0, 10.0, 10.0, 10.0],
+            "volume": 1_000.0,
+            "amount": 1_000.0,
+        }),
+    ], ignore_index=True)
+    alpha = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000", "sz000001"],
+        "date": [dates[1], dates[1], dates[2], dates[2]],
+        "alpha_name": ["toy"] * 4,
+        "weight": [-1.0, 1.0, 1.0, -1.0],
+    })
+
+    metrics = evaluate_alpha(alpha, data)
+
+    assert metrics["n_dates"] == 2
+    assert np.isclose(metrics["cumulative_return"], 1.25)
+
+
+def test_evaluate_alpha_excludes_signal_without_forward_return():
+    dates = pd.date_range("2024-01-01", periods=3)
+    data = pd.concat([
+        pd.DataFrame({
+            "symbol_id": "sh600000",
+            "symbol_name": "sh600000",
+            "date": dates,
+            "open": [100.0, 100.0, 200.0],
+            "high": [100.0, 100.0, 200.0],
+            "low": [100.0, 100.0, 200.0],
+            "close": [100.0, 100.0, 200.0],
+            "volume": 1_000.0,
+            "amount": 1_000.0,
+        }),
+        pd.DataFrame({
+            "symbol_id": "sz000001",
+            "symbol_name": "sz000001",
+            "date": dates,
+            "open": [100.0, 100.0, 100.0],
+            "high": [100.0, 100.0, 100.0],
+            "low": [100.0, 100.0, 100.0],
+            "close": [100.0, 100.0, 100.0],
+            "volume": 1_000.0,
+            "amount": 1_000.0,
+        }),
+    ], ignore_index=True)
+    alpha = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000", "sz000001"],
+        "date": [dates[0], dates[0], dates[1], dates[1]],
+        "alpha_name": ["toy"] * 4,
+        "weight": [1.0, -1.0, -1.0, 1.0],
+    })
+
+    metrics = evaluate_alpha(alpha, data)
+
+    assert metrics["n_dates"] == 1
+
+
 def test_equal_weight_is_mean_of_alphas():
     data = _make_data()
     a = compute_alpha(data, "rev", "reversal", lookback=5)
@@ -95,10 +176,25 @@ def test_combine_alphas_schema(tmp_path):
     assert (combo["combo_name"] == "eq").all()
 
 
+def test_combine_single_alpha_is_identity(tmp_path):
+    data = _make_data()
+    a = compute_alpha(data, "rev", "reversal", lookback=5)
+    a_path = tmp_path / "a.pq"
+    a.to_parquet(a_path, index=False)
+
+    combo = combine_alphas([str(a_path)], "rev_combo", method="equal_weight")
+
+    expected = a[["symbol_id", "date", "weight"]].reset_index(drop=True)
+    got = combo[["symbol_id", "date", "weight"]].reset_index(drop=True)
+    pd.testing.assert_frame_equal(got, expected)
+    assert list(combo.columns) == COMBO_COLUMNS
+    assert (combo["combo_name"] == "rev_combo").all()
+
+
 # --- registry / factory -----------------------------------------------------
 
 def test_builtins_are_registered():
-    assert {"reversal", "reversal_vol", "momentum"} <= set(available_alphas())
+    assert {"reversal", "reversal_vol", "momentum", "reversal_rank"} <= set(available_alphas())
 
 
 def test_get_alpha_filters_unaccepted_params():
@@ -163,7 +259,7 @@ def test_load_external_alpha_module(tmp_path):
                 self.span = span
 
             def signal(self, close: pd.DataFrame) -> pd.DataFrame:
-                return -close.pct_change(self.span)
+                return -close.pct_change(self.span, fill_method=None)
     '''))
 
     load_alpha_module(str(module_path))
@@ -178,3 +274,76 @@ def test_load_external_alpha_module(tmp_path):
     assert list(result.columns) == ALPHA_COLUMNS
     assert (result["alpha_name"] == "ext").all()
 
+
+# --- rank reversal + investable universe filter ------------------------------
+
+def _make_rich_data(n_days: int = 70, symbols=("sh600000", "sz000001", "sh600519", "sz300750")):
+    """Long-format data with the columns the universe filter needs."""
+    dates = pd.date_range("2024-01-01", periods=n_days)
+    rng = np.random.default_rng(1)
+    frames = []
+    for i, sym in enumerate(symbols):
+        close = 100.0 + i * 5 + np.cumsum(rng.standard_normal(n_days))
+        frames.append(pd.DataFrame({
+            "symbol_id": sym,
+            "symbol_name": sym,
+            "date": dates,
+            "open": close, "high": close, "low": close, "close": close,
+            "volume": 1_000.0,
+            "amount": (1_000.0 + i * 5_000.0) * close,  # higher i = more liquid
+            "isST": 0,
+            "tradestatus": 1,
+        }))
+    return pd.concat(frames, ignore_index=True)
+
+
+def test_reversal_rank_registered_and_bounded():
+    data = _make_data(n_days=30)
+    alpha = compute_alpha(data, "rr", "reversal_rank", lookback=5)
+    assert list(alpha.columns) == ALPHA_COLUMNS
+    # Rank-demeaned weights are per-date zero-mean and bounded by the
+    # cross-section size, never blowing up the way a z-score outlier can.
+    per_date_mean = alpha.groupby("date")["weight"].mean().abs()
+    assert (per_date_mean < 1e-9).all()
+    assert alpha["weight"].abs().max() <= len(data["symbol_id"].unique())
+
+
+def test_investable_universe_mask_excludes_st_and_suspended():
+    data = _make_rich_data()
+    # Flag one name ST throughout, and suspend another on the last date.
+    data.loc[data["symbol_id"] == "sh600000", "isST"] = 1
+    last = data["date"].max()
+    data.loc[(data["symbol_id"] == "sz000001") & (data["date"] == last), "tradestatus"] = 0
+
+    close = data.pivot_table(index="date", columns="symbol_id", values="close").sort_index()
+    mask = investable_universe_mask(data, close, top_n=10, min_history=5)
+
+    assert not mask["sh600000"].any()          # ST excluded on every date
+    assert not bool(mask.loc[last, "sz000001"])  # suspended on the last date
+    assert bool(mask.loc[last, "sh600519"])      # a normal name stays investable
+
+
+def test_compute_alpha_universe_filter_zeros_excluded_names():
+    data = _make_rich_data()
+    data.loc[data["symbol_id"] == "sh600000", "isST"] = 1
+
+    alpha = compute_alpha(
+        data, "rr_liq", "reversal_rank", lookback=5,
+        universe={"top_n": 10, "min_history": 5},
+    )
+    # The ST name is never held; an investable name is.
+    st_w = alpha.loc[alpha["symbol_id"] == "sh600000", "weight"]
+    assert (st_w.fillna(0.0) == 0.0).all()
+    assert alpha.loc[alpha["symbol_id"] == "sz300750", "weight"].abs().sum() > 0.0
+
+
+def test_universe_filter_does_not_corrupt_signal_history():
+    # Masking happens on the signal, not the price history, so weights on
+    # investable names match the unfiltered weights restricted to that set.
+    data = _make_rich_data()
+    universe = {"top_n": 2, "min_history": 5}  # keep only the 2 most liquid names
+
+    filtered = compute_alpha(data, "f", "reversal_rank", lookback=5, universe=universe)
+    held = set(filtered.loc[filtered["weight"] != 0.0, "symbol_id"].unique())
+    # The two most liquid names (highest amount) are sh600519, sz300750.
+    assert held == {"sh600519", "sz300750"}

tests/test_pipeline_invariants.py

@@ -0,0 +1,305 @@
+"""End-to-end correctness invariants for the reversal_5d pipeline (no network).
+
+Each test maps 1:1 to one of the ten review checks. Naming convention: the
+*execution date* ``d`` is the market session on which a target is actually
+filled at the open; the *signal date* ``t`` is the session whose close formed
+that target. The documented convention is ``d = next(t)`` (see
+``docs/portfolio_trading_cost_model.md``), so ``close[d-1] == close[t]``.
+"""
+
+import numpy as np
+import pandas as pd
+
+from pipeline.alpha.compute import compute_alpha, investable_universe_mask
+from pipeline.portfolio.construct import construct_positions
+from pipeline.portfolio.discretize import repair_exposure, round_to_valid_lot
+from pipeline.portfolio.market_rules import MarketRule
+from pipeline.portfolio.costs import SimpleProportionalCostModel
+from pipeline.portfolio.constraints import (
+    SuspensionConstraint,
+    VolumeCapConstraint,
+)
+from pipeline.portfolio.simulator import ReferenceSimulator
+
+
+_SYMBOLS = ("sh600000", "sz000001", "sh688981", "sz300750")
+
+
+def _panel(n_days=12, symbols=_SYMBOLS, start="2024-01-01", seed=0,
+           distinct_open=True):
+    """Contiguous long-format DATA frame with all columns the pipeline needs.
+
+    Open and close differ (so overnight vs intraday PnL terms are separable),
+    and the calendar is gap-free so each session is the next session's ``d-1``.
+    """
+    dates = pd.date_range(start, periods=n_days)
+    rng = np.random.default_rng(seed)
+    frames = []
+    for i, sym in enumerate(symbols):
+        close = np.abs(50.0 + i * 10 + np.cumsum(rng.standard_normal(n_days))) + 5.0
+        open_ = close * (0.99 + 0.02 * rng.random(n_days)) if distinct_open else close.copy()
+        preclose = np.concatenate([[close[0]], close[:-1]])
+        frames.append(pd.DataFrame({
+            "symbol_id": sym,
+            "symbol_name": sym,
+            "date": dates,
+            "open": open_,
+            "high": np.maximum(open_, close),
+            "low": np.minimum(open_, close),
+            "close": close,
+            "preclose": preclose,
+            "volume": 1_000_000.0,
+            "amount": 1_000_000.0 * close,
+            "tradestatus": 1,
+            "isST": 0,
+        }))
+    return pd.concat(frames, ignore_index=True)
+
+
+# --- 1. reversal signal uses only close[d-1] and earlier ---------------------
+
+def test_reversal_signal_does_not_peek_at_future_closes():
+    data = _panel(n_days=12)
+    base = compute_alpha(data, "rev", "reversal_rank", lookback=5)
+
+    # Perturb every close strictly AFTER an interior signal date t; the weight
+    # dated t (executed at d = t+1) must be unchanged — it may use close[t]
+    # (== close[d-1]) and earlier only.
+    t = sorted(data["date"].unique())[6]
+    future = data.copy()
+    mask = future["date"] > t
+    future.loc[mask, ["open", "high", "low", "close"]] *= 1.5
+    perturbed = compute_alpha(future, "rev", "reversal_rank", lookback=5)
+
+    b = base[base["date"] <= t].set_index(["symbol_id", "date"])["weight"]
+    p = perturbed[perturbed["date"] <= t].set_index(["symbol_id", "date"])["weight"]
+    pd.testing.assert_series_equal(b.sort_index(), p.sort_index())
+
+
+# --- 2. the executed (fill/PnL) date is the open-execution date d = next(t) --
+
+def test_fill_date_is_next_session_open_execution_date():
+    data = _panel(n_days=8)
+    weights = compute_alpha(data, "c", "reversal_rank", lookback=3)
+    weights = weights.rename(columns={"alpha_name": "combo_name"})
+    weights["combo_name"] = "c"
+    pos = construct_positions(weights, data, booksize=1e6, portfolio_name="run1")
+    fills, pnl = ReferenceSimulator(cost_bps=5, slippage_bps=5).run(pos, data)
+
+    sessions = sorted(data["date"].unique())
+    nxt = {s: sessions[i + 1] for i, s in enumerate(sessions[:-1])}
+    # Every executed date equals the session AFTER some position (signal) date.
+    pos_dates = set(pos["date"].unique())
+    exec_dates = set(pnl["date"].unique())
+    assert exec_dates == {nxt[t] for t in pos_dates if t in nxt}
+
+    # Execution price is the open of the execution date, not the signal close.
+    opn = data.pivot_table(index="date", columns="symbol_id", values="open",
+                           aggfunc="first").sort_index()
+    d = sorted(exec_dates)[1]
+    row = fills[(fills["date"] == d) & (fills["traded_shares"] != 0)].iloc[0]
+    sym = row["symbol_id"]
+    expected_cost = abs(row["traded_shares"]) * opn.loc[d, sym] * (5 + 5) / 1e4
+    assert np.isclose(row["trade_cost"], expected_cost)
+
+
+# --- 3. PnL identity: overnight(old book) + intraday(new book) - cost --------
+
+def test_daily_pnl_matches_overnight_plus_intraday_minus_cost():
+    data = _panel(n_days=8)
+    weights = compute_alpha(data, "c", "reversal_rank", lookback=3)
+    weights = weights.rename(columns={"alpha_name": "combo_name"})
+    weights["combo_name"] = "c"
+    pos = construct_positions(weights, data, booksize=1e6, portfolio_name="run1")
+    fills, pnl = ReferenceSimulator(cost_bps=5, slippage_bps=5).run(pos, data)
+
+    opn = data.pivot_table(index="date", columns="symbol_id", values="open", aggfunc="first").sort_index()
+    cls = data.pivot_table(index="date", columns="symbol_id", values="close", aggfunc="first").sort_index()
+    sessions = list(cls.index)
+
+    prev_close_of = {sessions[i]: sessions[i - 1] for i in range(1, len(sessions))}
+
+    for d in sorted(pnl["date"].unique()):
+        day = fills[fills["date"] == d]
+        prev = day.set_index("symbol_id")["prev_shares"]
+        realized = day.set_index("symbol_id")["realized_shares"]
+        cost = day["trade_cost"].sum()
+
+        intraday = float((realized * (cls.loc[d] - opn.loc[d]).reindex(realized.index)).sum())
+        # Overnight gap on the OLD book is taken from the previous *executed*
+        # date's close. With a gap-free calendar and daily execution that is the
+        # immediately preceding session; the first executed date has no prior
+        # book so the term is naturally zero (prev_shares == 0 there).
+        pc = prev_close_of.get(d)
+        if pc is not None and (prev != 0).any():
+            overnight = float((prev * (opn.loc[d] - cls.loc[pc]).reindex(prev.index)).sum())
+        else:
+            overnight = 0.0
+        expected = overnight + intraday - cost
+        got = float(pnl[pnl["date"] == d]["pnl"].iloc[0])
+        assert np.isclose(got, expected, rtol=1e-6, atol=1e-3), (d, got, expected)
+
+
+# --- 4. realized shares (not target shares) are threaded into the next day ---
+
+def test_realized_not_target_threaded_forward():
+    data = _panel(n_days=6)
+    weights = compute_alpha(data, "c", "reversal_rank", lookback=2)
+    weights = weights.rename(columns={"alpha_name": "combo_name"})
+    weights["combo_name"] = "c"
+    pos = construct_positions(weights, data, booksize=1e8, portfolio_name="run1")
+
+    # A tight volume cap forces partial fills, so realized != target on most
+    # names — exactly the case where threading target vs realized diverges.
+    fills, _ = ReferenceSimulator(
+        constraints=[VolumeCapConstraint(max_frac=1e-6)]
+    ).run(pos, data)
+
+    wide_prev = fills.pivot_table(index="date", columns="symbol_id", values="prev_shares", aggfunc="first")
+    wide_real = fills.pivot_table(index="date", columns="symbol_id", values="realized_shares", aggfunc="first")
+    exec_dates = list(wide_prev.index)
+    assert len(exec_dates) >= 2
+    # Today's prev_shares == yesterday's realized_shares for every name.
+    for a, b in zip(exec_dates[:-1], exec_dates[1:]):
+        prev_today = wide_prev.loc[b].dropna()
+        real_yest = wide_real.loc[a].reindex(prev_today.index).fillna(0.0)
+        pd.testing.assert_series_equal(
+            prev_today.astype(float), real_yest.astype(float), check_names=False
+        )
+    # And realized actually diverged from target (cap bit), so the test is real.
+    assert (fills["realized_shares"] != fills["target_shares"]).any()
+
+
+# --- 5. blocked trades create zero traded_shares and zero trade_cost ---------
+
+def test_blocked_trade_has_zero_shares_and_zero_cost():
+    data = _panel(n_days=6)
+    # Suspend one name on every session so any attempt to trade it is blocked.
+    data.loc[data["symbol_id"] == "sz000001", "tradestatus"] = 0
+    weights = compute_alpha(data, "c", "reversal_rank", lookback=2)
+    weights = weights.rename(columns={"alpha_name": "combo_name"})
+    weights["combo_name"] = "c"
+    pos = construct_positions(weights, data, booksize=1e6, portfolio_name="run1")
+
+    fills, _ = ReferenceSimulator(
+        constraints=[SuspensionConstraint()], cost_bps=5, slippage_bps=5
+    ).run(pos, data)
+
+    blocked = fills[fills["blocked"] == 1]
+    assert (blocked["traded_shares"] == 0).all()
+    assert (blocked["trade_cost"] == 0.0).all()
+    # The suspended name never trades and never accrues cost.
+    susp = fills[fills["symbol_id"] == "sz000001"]
+    assert (susp["traded_shares"] == 0).all()
+    assert (susp["trade_cost"] == 0.0).all()
+
+
+# --- 6. liquid universe uses only information known before open[d] -----------
+
+def test_investable_universe_mask_is_causal():
+    data = _panel(n_days=14)
+    close = data.pivot_table(index="date", columns="symbol_id", values="close", aggfunc="first").sort_index()
+    full = investable_universe_mask(data, close, top_n=10, min_history=3)
+
+    t = sorted(data["date"].unique())[8]
+    # Recompute the mask from data truncated at the signal date t: the mask row
+    # for t must be identical, proving it never reads dates > t (i.e. nothing
+    # from open[d=t+1] onward).
+    trunc = data[data["date"] <= t]
+    close_t = close.loc[:t]
+    mask_t = investable_universe_mask(trunc, close_t, top_n=10, min_history=3)
+    pd.testing.assert_series_equal(
+        full.loc[t].sort_index(), mask_t.loc[t].sort_index(), check_names=False
+    )
+
+
+# --- 7. cost bps is one-way per-trade (a round trip is charged twice) --------
+
+def test_cost_bps_is_one_way_per_trade():
+    model = SimpleProportionalCostModel(cost_bps=5, slippage_bps=5)
+    price = np.array([20.0])
+
+    buy = model.compute(np.array([1000]), price, np.array([1]), date=None)
+    sell = model.compute(np.array([-1000]), price, np.array([-1]), date=None)
+
+    one_way = 1000 * 20 * (5 + 5) / 1e4
+    assert np.isclose(buy[0], one_way)      # charged once on the buy leg
+    assert np.isclose(sell[0], one_way)     # charged again on the sell leg
+    # A full round trip (enter then exit) therefore costs ~2x the one-way rate.
+    assert np.isclose(buy[0] + sell[0], 2 * one_way)
+
+
+# --- 8. execution & PnL use raw tradable prices on the same scale as shares --
+
+def test_position_value_is_shares_times_raw_price():
+    data = _panel(n_days=10)
+    weights = compute_alpha(data, "c", "reversal_rank", lookback=3)
+    weights = weights.rename(columns={"alpha_name": "combo_name"})
+    weights["combo_name"] = "c"
+    pos = construct_positions(weights, data, booksize=1e6, portfolio_name="run1")
+
+    finite = pos["price"] > 0
+    # The stored value is exactly integer shares × the raw construction price —
+    # no adjusted-price factor is mixed into the share→value accounting.
+    expected = pos.loc[finite, "position_shares"] * pos.loc[finite, "price"]
+    pd.testing.assert_series_equal(
+        pos.loc[finite, "position_value"].astype(float),
+        expected.astype(float),
+        check_names=False,
+    )
+
+
+# --- 9. alpha is scale-free (adjusted prices ok); accounting uses raw units --
+
+def test_alpha_weights_invariant_to_per_symbol_price_scaling():
+    data = _panel(n_days=12)
+    base = compute_alpha(data, "rev", "reversal_rank", lookback=5)
+
+    # A qfq/hfq adjustment is (per symbol) a multiplicative rescaling of the
+    # price series; pct_change is scale-free, so the alpha weights must not move.
+    scaled = data.copy()
+    factor = {"sh600000": 2.0, "sz000001": 0.5, "sh688981": 3.0, "sz300750": 1.25}
+    for sym, f in factor.items():
+        m = scaled["symbol_id"] == sym
+        scaled.loc[m, ["open", "high", "low", "close"]] *= f
+    scaled_alpha = compute_alpha(scaled, "rev", "reversal_rank", lookback=5)
+
+    b = base.set_index(["symbol_id", "date"])["weight"].sort_index()
+    s = scaled_alpha.set_index(["symbol_id", "date"])["weight"].sort_index()
+    pd.testing.assert_series_equal(b, s)
+
+
+# --- 10. repaired book stays on valid A-share lot lattices -------------------
+
+def _on_lattice(q, min_open, increment):
+    q = np.abs(np.asarray(q, dtype=np.int64))
+    on = (q == 0) | ((q >= min_open) & ((q - min_open) % increment == 0))
+    return bool(on.all())
+
+
+def test_repair_output_stays_on_lot_lattice():
+    # Pre-2023 main board has a 100-share increment (the strongest lattice
+    # constraint); STAR uses min 200 / increment 1.
+    symbols = np.array(["sh600000", "sz000001", "sh688981", "sz300750"], dtype=object)
+    rule = MarketRule()
+    on = "2022-06-01"  # pre 2023-08-10 → main-board increment is 100
+    min_open, increment, odd_full, _ = rule.get_rules_vectorized(
+        symbols, on, np.zeros(len(symbols), dtype=bool)
+    )
+    assert min_open[0] == 100 and increment[0] == 100  # main board pre-2023
+
+    price = np.array([12.3, 8.7, 45.0, 230.0])
+    prev = np.zeros(len(symbols), dtype=np.int64)
+    q_target = np.array([3251.0, -7777.0, 640.0, -415.0])
+
+    q_round = round_to_valid_lot(q_target, prev, min_open, increment, odd_full)
+    assert _on_lattice(q_round, min_open, increment)
+
+    repaired = repair_exposure(
+        q_round, q_target, price, increment, min_open, prev, odd_full,
+        booksize=float(np.abs(q_target * price).sum()),
+    )
+    assert _on_lattice(repaired, min_open, increment)
+    # Repair never flips a name's sign relative to the rounded book.
+    nz = q_round != 0
+    assert np.all(np.sign(repaired[nz]) * np.sign(q_round[nz]) >= 0)

tests/test_portfolio.py

@@ -22,6 +22,7 @@ from pipeline.portfolio.constraints import (
     SuspensionConstraint,
     VolumeCapConstraint,
 )
+from pipeline.portfolio.costs import SimpleProportionalCostModel
 from pipeline.portfolio.simulator import (
     MarketSlice,
     ReferenceSimulator,
@@ -64,7 +65,7 @@ def _make_data(n_days: int = 40, symbols=_SYMBOLS, start="2024-01-01",
 def _make_weights(data: pd.DataFrame, name="combo") -> pd.DataFrame:
     """Demeaned per-date signed weights so the cross-section is dollar-neutral."""
     close = data.pivot_table(index="date", columns="symbol_id", values="close").sort_index()
-    raw = -close.pct_change(5)
+    raw = -close.pct_change(5, fill_method=None)
     demeaned = raw.sub(raw.mean(axis=1), axis=0)
     long = demeaned.reset_index().melt(id_vars="date", var_name="symbol_id",
                                        value_name="weight").dropna()
@@ -445,6 +446,7 @@ def test_simulator_blocked_buy_when_suspended():
     assert res.traded_shares[0] == 0
     assert res.realized_shares[0] == 0
     assert res.blocked[0] == 1
+    assert res.cost[0] == 0.0
 
 
 def test_simulator_cost_is_positive_when_trading():
@@ -458,6 +460,69 @@ def test_simulator_cost_is_positive_when_trading():
     assert np.isclose(res.cost[0], 1000 * 20 * 15 / 1e4)
 
 
+def test_simulator_cost_only_on_nonzero_realized_trades():
+    n = 2
+    sim = ReferenceSimulator(constraints=[], cost_bps=10)
+    sl = _slice(n, price=np.array([10.0, 20.0]))
+    ctx = TradeContext(np.array([100, 100], np.int64),
+                       np.array([100, 150], np.int64), sl, 1e6)
+
+    res = sim.fill(ctx)
+
+    assert res.traded_shares.tolist() == [0, 50]
+    assert res.cost[0] == 0.0
+    assert np.isclose(res.cost[1], 50 * 20 * 10 / 1e4)
+
+
+def test_simple_cost_model_adds_cost_and_slippage_without_price_adjustment():
+    model = SimpleProportionalCostModel(cost_bps=10, slippage_bps=5)
+
+    cost = model.compute(
+        traded_shares=np.array([1000, -1000]),
+        execution_price=np.array([20.0, 20.0]),
+        side=np.array([1, -1]),
+        date=dt.date(2024, 1, 2),
+    )
+
+    assert np.allclose(cost, np.array([30.0, 30.0]))
+
+
+def test_daily_pnl_cost_matches_fill_trade_cost_sum():
+    dates = pd.to_datetime(["2024-01-01", "2024-01-02"])
+    positions = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001"],
+        "date": [dates[0], dates[0]],
+        "portfolio_name": ["run1", "run1"],
+        "target_weight": [0.5, -0.5],
+        "target_value": [1000.0, -1000.0],
+        "target_shares": [100.0, -50.0],
+        "position_shares": [100, -50],
+        "position_value": [1000.0, -1000.0],
+        "price": [10.0, 20.0],
+    })
+    data = pd.DataFrame([
+        {
+            "symbol_id": sym,
+            "date": d,
+            "open": price,
+            "close": price,
+            "preclose": price,
+            "amount": 1e9,
+            "tradestatus": 1,
+            "isST": 0,
+        }
+        for d in dates
+        for sym, price in (("sh600000", 10.0), ("sz000001", 20.0))
+    ])
+
+    fills, pnl = ReferenceSimulator(cost_bps=10, slippage_bps=5).run(positions, data)
+
+    total_fill_cost = fills["trade_cost"].sum()
+    assert np.isclose(total_fill_cost, 3.0)
+    assert np.isclose(pnl["cost"].iloc[0], total_fill_cost)
+    assert np.isclose(pnl["pnl"].iloc[0], -total_fill_cost)
+
+
 # --- evaluate_portfolio ------------------------------------------------------
 
 def test_evaluate_portfolio_keys_no_ic():
@@ -470,3 +535,27 @@ def test_evaluate_portfolio_keys_no_ic():
         assert key in metrics
     assert "ic" not in metrics
     assert "rank_ic" not in metrics
+
+
+def test_evaluate_portfolio_excludes_signal_without_forward_return():
+    dates = pd.date_range("2024-01-01", periods=3)
+    data = pd.DataFrame([
+        {"symbol_id": sym, "date": d, "open": price, "close": price}
+        for d, prices in zip(dates, [(100.0, 100.0), (100.0, 100.0), (200.0, 100.0)])
+        for sym, price in zip(("sh600000", "sz000001"), prices)
+    ])
+    positions = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000", "sz000001"],
+        "date": [dates[0], dates[0], dates[1], dates[1]],
+        "portfolio_name": ["run1"] * 4,
+        "target_weight": [0.5, -0.5, -0.5, 0.5],
+        "target_value": [500.0, -500.0, -500.0, 500.0],
+        "target_shares": [5.0, -5.0, -2.5, 5.0],
+        "position_shares": [5, -5, -2, 5],
+        "position_value": [500.0, -500.0, -400.0, 500.0],
+        "price": [100.0, 100.0, 200.0, 100.0],
+    })
+
+    metrics = evaluate_portfolio(positions, data)
+
+    assert metrics["n_dates"] == 1

uv.lock

@@ -284,7 +284,6 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "akshare" },
-    { name = "backtrader" },
     { name = "baostock" },
     { name = "click" },
     { name = "matplotlib" },
@@ -293,6 +292,11 @@ dependencies = [
     { name = "pyarrow" },
 ]
 
+[package.optional-dependencies]
+backtrader = [
+    { name = "backtrader" },
+]
+
 [package.dev-dependencies]
 dev = [
     { name = "pytest" },
@@ -301,13 +305,14 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "akshare", specifier = ">=1.14.0" },
-    { name = "backtrader", specifier = ">=1.9.76.123" },
+    { name = "backtrader", marker = "extra == 'backtrader'", specifier = ">=1.9.76.123" },
     { name = "baostock", specifier = ">=0.8.8" },
     { name = "click", specifier = ">=8.0.0" },
     { name = "matplotlib", specifier = ">=3.7.0" },
     { name = "pandas", specifier = ">=2.0.0" },
     { name = "pyarrow", specifier = ">=14.0.0" },
 ]
+provides-extras = ["backtrader"]
 
 [package.metadata.requires-dev]
 dev = [{ name = "pytest", specifier = ">=7.0.0" }]