# Chinese Equity Quant Research Framework

A modular Chinese A-share quant research framework. Daily frequency only (Phase 1).

It is a **decoupled, file-based pipeline**: each phase reads parquet and writes
parquet, so phases run, cache, and inspect independently.

```
  baostock (primary)                                              one weight series
  akshare  (fallback)                                             interpreted as a
        │                                                            portfolio
        ▼                                                              ▲
 ┌──────────────┐      ┌───────────────┐      ┌───────────────┐       │
 │     DATA     │      │     ALPHA     │      │     COMBO     │   ┌────┴─────┐
 │   download   │─────▶│    compute    │─────▶│    combine    │   │   EVAL   │
 │ daily bars   │      │ signal→weights│      │ merge alphas  │   │ score it │
 └──────┬───────┘      └───────┬───────┘      └───────┬───────┘   └────┬─────┘
        │                      │                      │                │
        ▼                      ▼                      ▼                │
 data/daily_bars/        alphas/*.pq            combos/*.pq            │
  {universe}/            (ALPHA_COLUMNS)        (COMBO_COLUMNS)         │
  month=YYYY-MM/*.pq            │                                      │
 (DATA_COLUMNS)                 │                                      │
        └──────── price ───────┴───────────────────────────────────────┘
                                       │
                                       ▼  (planned — not yet implemented)
              ┌ ─ ─ ─ ─ ─ ─ ┐   ┌ ─ ─ ─ ─ ─ ─ ┐   ┌ ─ ─ ─ ─ ─ ─ ─ ─ ┐
                 PORTFOLIO         BACKTEST           PAPER TRADING
              │  construct  │   │   simulate  │   │  forward / live  │  TODO
                 positions       fills + costs      execution
              └ ─ ─ ─ ─ ─ ─ ┘   └ ─ ─ ─ ─ ─ ─ ┘   └ ─ ─ ─ ─ ─ ─ ─ ─ ┘

 Each phase reads parquet and writes parquet — run, cache, and inspect
 independently. The only interface between phases is the parquet schema.
 Solid boxes are implemented; dashed boxes are on the roadmap (see TODO below).
```

Data comes from **baostock (primary)** with **akshare (fallback)**.

## Install

```bash
pip install -r requirements.txt
```

## Quick start

```bash
# 1. Download daily bars for a few symbols (writes a month-partitioned dataset).
python3 cli.py data download \
    --universe sh600000,sz000001,sh600519 \
    --start-date 2024-01-01 --end-date 2024-03-31 \
    --output-dir data/daily_bars

# 2. Compute an alpha (position weights) from that data.
#    --data-path is the dataset DIRECTORY ({output-dir}/{universe}).
python3 cli.py alpha reversal \
    --data-path "data/daily_bars/sh600000,sz000001,sh600519"

# 3. Evaluate it (return / Sharpe / turnover / drawdown).
python3 cli.py alpha eval \
    --alpha-path alphas/reversal_5d.pq \
    --data-path  "data/daily_bars/sh600000,sz000001,sh600519"

# Tests
python3 -m pytest tests/ -v        # tests/test_alpha.py is network-free; test_downloader.py hits the network
```

## CLI reference

All commands are subcommands of `python3 cli.py`. Add `--help` to any of them.

### `data download` — fetch daily bars → partitioned parquet dataset

| Option | Default | Description |
| --- | --- | --- |
| `--universe` | `csi500` | `hs300`, `csi500`, `all` (~5000 A-shares), a file path (one symbol per line), or comma-separated symbols (`sh600000,sz000001`) |
| `--start-date` | `2017-01-01` | `YYYY-MM-DD` |
| `--end-date` | today | `YYYY-MM-DD` |
| `--output-dir` | `data/daily_bars` | Root for the dataset directory |
| `--symbols` | `0` | Max symbols to download (`0` = all) |
| `--chunk-size` | `300` | Symbols per durability flush (each flush appends `.pq` files) |
| `--adjust` | `qfq` | Price adjustment: `qfq` (forward), `hfq` (backward), `none` |

Writes a **Hive-partitioned dataset** at `{output_dir}/{universe}/month=YYYY-MM/*.pq`
(one partition per calendar month). The `{universe}` directory is rebuilt from
scratch on each run. Downloads stream under a single baostock session and flush
every `--chunk-size` symbols, so memory stays bounded and a crash keeps the
partitions already written. Pass the **dataset directory** (`{output_dir}/{universe}`)
as `--data-path` to later phases — `pd.read_parquet` reads the whole partitioned
set. Symbols use the internal `sh600000` / `sz000001` form (exchange prefix + code).

### `alpha list` — show registered alpha types

```bash
python3 cli.py alpha list
python3 cli.py alpha list --alpha-module path/to/my_alpha.py   # include an external alpha
```

### `alpha compute` — alpha class → weights parquet

| Option | Default | Description |
| --- | --- | --- |
| `--data-path` | (required) | Data parquet from `data download` |
| `--alpha-name` | (required) | Label stored in the `alpha_name` column / output filename |
| `--alpha-type` | (required) | Registry key of the alpha class (see `alpha list`) |
| `--output-dir` | `alphas` | Output directory |
| `--lookback` | `5` | Lookback days (passed to alphas that accept it) |
| `--vol-window` | `20` | Volatility window (passed to alphas that accept it) |
| `--alpha-module` | — | External module(s) to import first; repeatable. Dotted path or `.py` file |
| `--param` | — | Extra constructor param as `name=value`; repeatable |

Only the params an alpha's `__init__` accepts are forwarded, so passing extras
(e.g. `--vol-window` to a reversal alpha) is harmless.

```bash
python3 cli.py alpha compute \
    --data-path <data>.pq \
    --alpha-type reversal_vol --alpha-name rv_5_20 \
    --lookback 5 --vol-window 20
```

Shortcuts for the two most common built-ins:

```bash
python3 cli.py alpha reversal     --data-path <data>.pq --lookback 5
python3 cli.py alpha reversal-vol --data-path <data>.pq --lookback 5 --vol-window 20
```

### `alpha eval` — score an alpha as a portfolio

```bash
python3 cli.py alpha eval --alpha-path alphas/<name>.pq --data-path <data>.pq
```

Interprets the weights as a portfolio and reports cumulative return, annual
Sharpe, annual turnover, max drawdown, and hit rate; also dumps
`reports/<alpha_name>_eval.json`. There is deliberately **no IC/IR** — alphas are
position weights, not return predictors.

### `combo combine` — merge several alphas into one weight

| Option | Default | Description |
| --- | --- | --- |
| `--alpha-paths` | (required) | Comma-separated alpha parquet paths (≥ 2) |
| `--combo-name` | (required) | Label stored in the `combo_name` column / output filename |
| `--method` | `equal_weight` | Combination method (see `COMBO_METHODS`) |
| `--output-dir` | `combos` | Output directory |

```bash
python3 cli.py combo combine \
    --alpha-paths alphas/reversal_5d.pq,alphas/reversal_vol_5d_20d.pq \
    --combo-name eq --method equal_weight
```

## Alphas: the factory / plugin interface

An **alpha** is a class that maps a wide close matrix (date index × `symbol_id`
columns) to **signed position weights** (positive = long, negative = short).
Every alpha subclasses `BaseAlpha` (`pipeline/alpha/base.py`) and is resolved by
name through the registry (`pipeline/alpha/registry.py`).

### Minimal alpha

```python
import pandas as pd

from pipeline.alpha.base import BaseAlpha
from pipeline.alpha.registry import register_alpha


@register_alpha
class MyAlpha(BaseAlpha):
    name = "my_alpha"                 # unique registry key (required)

    def __init__(self, lookback: int = 5):
        self.lookback = lookback      # declare whatever params you need

    def signal(self, close: pd.DataFrame) -> pd.DataFrame:
        # Raw score: wide (date × symbol_id), higher = stronger long, NaN where undefined.
        return -close.pct_change(self.lookback)
```

That is the whole contract:

- `name` — the `--alpha-type` key; must be unique.
- `signal(close)` — the only required method; return a wide DataFrame.
- `to_weights(signal)` — provided by the base class: cross-sectionally z-scores
  each date into weights (NaN → 0). **Override** it for a different scheme (rank,
  dollar-neutral caps, etc.).

### Built-in alphas

One file per alpha under `pipeline/alpha/library/`:

| `--alpha-type` | Params | Description |
| --- | --- | --- |
| `reversal` | `lookback` | Negative trailing return (oversold scores high) |
| `reversal_vol` | `lookback`, `vol_window` | Reversal scaled by trailing volatility |
| `momentum` | `lookback` | Positive trailing return |

Add a built-in by dropping a module in `pipeline/alpha/library/` and importing it
from that package's `__init__.py`.

### Using an alpha written outside this repo

Write your `@register_alpha` class in any `.py` file, then register it at runtime
with `--alpha-module` (a `.py` path or an importable dotted module). See the
worked example in `examples/alphas/mean_reversion.py`:

```bash
python3 cli.py alpha compute \
    --alpha-module examples/alphas/mean_reversion.py \
    --alpha-type mean_reversion --alpha-name mr20 \
    --param window=20 \
    --data-path <data>.pq
```

`mean_reversion` declares a `window` param (not `lookback`); `--param window=20`
supplies it and the unrelated `--lookback`/`--vol-window` defaults are ignored.

## Parquet schemas

The column contracts in `pipeline/common/schema.py` are the only interface
between phases (data is stored long/tidy):

- **data** (`DATA_COLUMNS`): `symbol_id, symbol_name, date, open, high, low, close, preclose, volume, amount, vwap, turn, pctChg, tradestatus, isST, peTTM, pbMRQ, psTTM, pcfNcfTTM`
  (`vwap` = `amount / volume` — a **raw**-price daily VWAP, *not* on the adjusted
  OHLC scale under `qfq`/`hfq`; `turn` is turnover %, `pctChg` daily % change,
  `tradestatus`/`isST` are 0/1 flags, and `peTTM`/`pbMRQ`/`psTTM`/`pcfNcfTTM` are
  baostock valuation ratios.)
- **alpha** (`ALPHA_COLUMNS`): `symbol_id, date, alpha_name, weight`
- **combo** (`COMBO_COLUMNS`): `symbol_id, date, combo_name, weight`

The data phase writes a month-partitioned dataset, so reading the dataset
directory yields an extra `month` (`YYYY-MM`) partition column on top of
`DATA_COLUMNS`; the alpha phase pivots by name and ignores it.

## Layout

- `cli.py` — entry point wiring the three phases together
- `pipeline/data/` — universe resolution + download → `data/daily_bars/{universe}/month=YYYY-MM/*.pq`
- `pipeline/alpha/` — `base.py` (`BaseAlpha`), `registry.py` (factory + plugin loader),
  `library/` (built-in alphas), `compute.py` (`compute_alpha` / `evaluate_alpha`)
- `pipeline/combo/` — alpha combination → `combos/*.pq`
- `pipeline/common/schema.py` — parquet column contracts
- `data/downloader.py`, `data/universe.py` — baostock/akshare download + constituents
- `examples/alphas/` — example external alpha(s)

## Roadmap (not yet implemented)

The pipeline currently ends at `combo`, and `alpha eval` only interprets a weight
series as a portfolio for quick scoring (return / Sharpe / turnover / drawdown).
It is **not** a true backtest — there is no transaction-cost, slippage, or
execution modeling. The following phases are planned but not built yet:

- [ ] **Portfolio construction** — turn combo weights into target positions
  (gross/net exposure caps, per-name and sector limits, capital allocation,
  rebalance schedule).
- [ ] **Backtesting** — event-driven simulation over the constructed positions
  with realistic fills, transaction costs, slippage, and borrow constraints;
  richer P&L / risk attribution than `alpha eval`.
- [ ] **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,
  and intraday VWAP. These need a tick / L1–L2 quote feed (typically a paid or
  brokerage data tier); the free daily sources here only expose daily bars, so
  this is a separate data phase rather than extra columns on the daily schema.

Until these land, treat `alpha eval` as a fast sanity check on a weight series,
not a performance estimate.