scan

Scan A-share market for stocks matching user criteria.

Arguments

$ARGUMENTS = screening criteria in natural language. If empty, ask the user.

Instructions

1. Parse criteria into pre_filter (PriceData) and filter_fn (K-line) layers
2. Create script at scanning/{name}_scan.py
3. Run and show results

Choosing scan type

• Price/volume/PE only (no K-line needed): quick_scan(pre_filter) -- instant
• Needs history (MA, MACD, patterns): scan(filter_fn, pre_filter) -- two-layer

pre_filter (Layer 1)

Receives PriceData. Key fields: p.code, p.name, p.close, p.open, p.high, p.low, p.volume, p.amount, p.lastClose, p.pe, p.totalShares, p.flowShares, p.upperLimit, p.lowerLimit

p.volume > 0 and p.close > 3 and "ST" not in p.name  # typical

filter_fn (Layer 2)

Receives (code, name, bars). Return {"score": float, "reason": str, ...} or None.

def filter_fn(code, name, bars):
    if len(bars) < 30:
        return None
    close, high, low, vol = bars_to_arrays(bars)
    # ... compute indicators (use catquant.indicators) ...
    if condition:
        return {"score": value, "reason": "description"}
    return None

scan() / quick_scan()

scan(filter_fn, pre_filter=None, universe=None, count=250,
     cycle=1440, source="facecat", max_results=20,
     sort_key="score", ascending=False, verbose=True, refresh=False)

quick_scan(pre_filter=None, max_results=50, sort_key="close", ascending=False)