feat: add pqcat helper to inspect parquet files

A standalone tools/pqcat.py (head/tail/columns/info) wired as `cli.py pqcat`
and runnable directly via a PATH symlink, plus README docs.

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

README.md

@@ -153,6 +153,32 @@ python3 cli.py combo combine \
     --combo-name eq --method equal_weight
 ```
 
+### `pqcat` — inspect a parquet file, like `cat`
+
+Quickly dump any pipeline parquet (a single `.pq` file or a partitioned dataset
+directory) to stdout, without writing a throwaway script.
+
+| Option | Default | Description |
+| --- | --- | --- |
+| `-n, --head N` | — | Show only the first `N` rows |
+| `-t, --tail N` | — | Show only the last `N` rows |
+| `-c, --columns` | — | Comma-separated subset of columns to show |
+| `--info` | off | Show shape + dtypes instead of the rows |
+
+```bash
+python3 cli.py pqcat alphas/reversal_5d.pq                       # dump all rows
+python3 cli.py pqcat data/daily_bars/csi500 --info              # shape + dtypes
+python3 cli.py pqcat data/daily_bars/csi500 -n 10 -c symbol_id,date,close,vwap
+```
+
+**Standalone command.** `tools/pqcat.py` has no repo dependencies, so it can be
+run directly. Symlink it onto your `PATH` once and call `pqcat` from anywhere:
+
+```bash
+ln -sf "$(pwd)/tools/pqcat.py" ~/.local/bin/pqcat   # ~/.local/bin must be on PATH
+pqcat alphas/reversal_5d.pq --info
+```
+
 ## Alphas: the factory / plugin interface
 
 An **alpha** is a class that maps a wide close matrix (date index × `symbol_id`
@@ -245,6 +271,7 @@ directory yields an extra `month` (`YYYY-MM`) partition column on top of
 - `pipeline/combo/` — alpha combination → `combos/*.pq`
 - `pipeline/common/schema.py` — parquet column contracts
 - `data/downloader.py`, `data/universe.py` — baostock/akshare download + constituents
+- `tools/pqcat.py` — standalone parquet inspector (`pqcat`), also wired as `cli.py pqcat`
 - `examples/alphas/` — example external alpha(s)
 
 ## Roadmap (not yet implemented)

cli.py

@@ -14,6 +14,7 @@ import click
 from pipeline.data.cli import data
 from pipeline.alpha.cli import alpha
 from pipeline.combo.cli import combo
+from tools.pqcat import pqcat
 
 
 @click.group()
@@ -37,6 +38,7 @@ def cli(log_level):
 cli.add_command(data)
 cli.add_command(alpha)
 cli.add_command(combo)
+cli.add_command(pqcat)
 
 
 if __name__ == "__main__":

tools/__init__.py

@@ -0,0 +1 @@
+"""Standalone helper tools (not part of the data→alpha→combo pipeline)."""

tools/pqcat.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+"""CLI helper to inspect parquet files, like `cat` for `.pq`."""
+
+import click
+import pandas as pd
+
+
+@click.command(name="pqcat")
+@click.argument("path", type=click.Path(exists=True))
+@click.option("-n", "--head", "head", type=int, default=None, help="Show only the first N rows")
+@click.option("-t", "--tail", "tail", type=int, default=None, help="Show only the last N rows")
+@click.option("-c", "--columns", default=None, help="Comma-separated subset of columns to show")
+@click.option("--info", is_flag=True, help="Show shape + dtypes instead of the rows")
+def pqcat(path, head, tail, columns, info):
+    """Dump a parquet file (or partitioned dataset dir) to stdout.
+
+    PATH is a single .pq file or a Hive-partitioned dataset directory.
+    """
+    df = pd.read_parquet(path)
+
+    if columns:
+        cols = [c.strip() for c in columns.split(",") if c.strip()]
+        missing = [c for c in cols if c not in df.columns]
+        if missing:
+            raise click.ClickException(f"Columns not found: {', '.join(missing)}")
+        df = df[cols]
+
+    if info:
+        click.echo(f"path:    {path}")
+        click.echo(f"shape:   {df.shape[0]:,} rows x {df.shape[1]} cols")
+        click.echo("dtypes:")
+        for name in df.columns:
+            click.echo(f"  {name}: {df[name].dtype}")
+        return
+
+    if head is not None:
+        df = df.head(head)
+    elif tail is not None:
+        df = df.tail(tail)
+
+    with pd.option_context(
+        "display.max_rows", None if (head is None and tail is None) else len(df),
+        "display.max_columns", None,
+        "display.width", None,
+    ):
+        click.echo(df.to_string(index=False))
+
+
+if __name__ == "__main__":
+    pqcat()