250 lines
9.1 KiB
Python
250 lines
9.1 KiB
Python
#!/usr/bin/env python3
|
||
"""chip_factors.py — 筹码因子计算模块
|
||
|
||
基于中信建投《筹码分布因子系统构建》研报，实现四大类因子。
|
||
用日线数据就够了，分钟数据用于当日穿透率增强。
|
||
|
||
用法:
|
||
from chip_factors import ChipFactors
|
||
cf = ChipFactors()
|
||
|
||
# 计算单只股票的筹码乖离率
|
||
result = cf.calc_all("600519")
|
||
print(result["bias"], result["ptr"], result["ptr_today"])
|
||
|
||
# 批量计算所有持仓/自选
|
||
results = cf.batch_calc(["600519", "00700", "000700"])
|
||
"""
|
||
|
||
import json, sqlite3, time
|
||
from datetime import datetime, timedelta
|
||
from mo_data import read_decisions, get_price
|
||
from pathlib import Path
|
||
|
||
DB_PATH = Path("/home/hmo/MoFin/data/mofin.db")
|
||
MOFIN_ROOT = Path("/home/hmo/MoFin")
|
||
CACHE_DIR = MOFIN_ROOT / "data" / "chip_cache"
|
||
|
||
|
||
def _fetch_quote(code):
|
||
"""拉实时价，统一走 mo_data.get_price"""
|
||
try:
|
||
price, _ = get_price(code)
|
||
return price or 0
|
||
except:
|
||
return 0
|
||
|
||
|
||
def _fetch_minute_kline(code, count=60):
|
||
"""拉日K（读 stock_daily，2026-08-26 分层铁律：消费层不直连东财push2）"""
|
||
try:
|
||
import sqlite3
|
||
conn = sqlite3.connect(str(DB_PATH), timeout=5)
|
||
rows = conn.execute(
|
||
"SELECT date, open, close, high, low, volume FROM stock_daily "
|
||
"WHERE code=? ORDER BY date DESC LIMIT ?", (str(code), min(count, 240))
|
||
).fetchall()
|
||
conn.close()
|
||
# 结构对齐原分钟K：m[5] = volume
|
||
return [[str(r[0]), r[1], r[2], r[3], r[4], r[5] or 0] for r in rows]
|
||
except:
|
||
return None
|
||
|
||
|
||
class ChipFactors:
|
||
"""筹码因子计算器"""
|
||
|
||
def __init__(self):
|
||
CACHE_DIR.mkdir(parents=True, exist_ok=True)
|
||
self._cache = {} # code → {last_chip, winner, bias}
|
||
self._load_cache()
|
||
|
||
def _load_cache(self):
|
||
"""加载缓存的筹码状态"""
|
||
for f in CACHE_DIR.glob("*.json"):
|
||
code = f.stem
|
||
try:
|
||
with open(f) as fp:
|
||
self._cache[code] = json.load(fp)
|
||
except:
|
||
pass
|
||
|
||
def _save_cache(self, code):
|
||
"""保存筹码状态"""
|
||
if code in self._cache:
|
||
path = CACHE_DIR / f"{code}.json"
|
||
with open(path, "w") as fp:
|
||
json.dump(self._cache[code], fp, ensure_ascii=False)
|
||
|
||
# ── 筹码分布估算（用日线OHLCV） ──
|
||
def _build_chip_distribution(self, code):
|
||
"""从日线K线估算筹码分布。
|
||
|
||
原理：假设每日成交量在OHLC区间内均匀分布，
|
||
每根K线的成交量按价格区间分配，累积成筹码分布。
|
||
（2026-08-26 分层铁律：读 DB stock_daily，不直连腾讯API）
|
||
"""
|
||
try:
|
||
import sqlite3
|
||
conn = sqlite3.connect(str(DB_PATH), timeout=5)
|
||
rows = conn.execute(
|
||
"SELECT date, open, close, high, low, volume FROM stock_daily "
|
||
"WHERE code=? ORDER BY date DESC LIMIT 640", (str(code),)
|
||
).fetchall()
|
||
conn.close()
|
||
# 对齐原 qfqday bar：[date, open, close, high, low, volume]
|
||
bars = [list(r) for r in rows]
|
||
except:
|
||
return {}
|
||
|
||
# 估算筹码分布：价格区间 → 累积量
|
||
chip_dist = {} # price_level → accumulated_volume
|
||
decay = 0.97 # 每日衰减因子（老筹码逐步换手）
|
||
|
||
for bar in bars:
|
||
try:
|
||
if len(bar) < 6:
|
||
continue
|
||
high = float(bar[3]) # index 3 = high
|
||
low = float(bar[4]) # index 4 = low
|
||
volume = float(bar[5]) if len(bar) > 5 else 0 # index 5 = volume
|
||
if high <= low or volume <= 0:
|
||
continue
|
||
# 在OHLC区间均匀分配成交量
|
||
step = max(round((high - low) / 5, 2), 0.01)
|
||
level = round(low, 2)
|
||
vol_per_level = volume / max(int((high - low) / step) + 1, 1)
|
||
while level <= high:
|
||
chip_dist[level] = chip_dist.get(level, 0) + vol_per_level
|
||
level = round(level + step, 2)
|
||
except:
|
||
continue
|
||
|
||
# 衰减老筹码
|
||
total = sum(chip_dist.values())
|
||
if total > 0:
|
||
for k in chip_dist:
|
||
chip_dist[k] *= decay
|
||
|
||
return chip_dist
|
||
|
||
# ── 三大因子计算 ──
|
||
def calc_all(self, code, name="", price=None):
|
||
"""计算全部筹码因子，返回dict"""
|
||
result = {"code": code, "name": name, "price": price}
|
||
|
||
# 获取当前价（如果没传）
|
||
if not price:
|
||
price = _fetch_quote(code)
|
||
if not price:
|
||
return result
|
||
result["price"] = price
|
||
|
||
# 构建筹码分布
|
||
chip_dist = self._build_chip_distribution(code)
|
||
if not chip_dist or price <= 0:
|
||
return result
|
||
|
||
# 计算盈利/亏损筹码占比
|
||
total_vol = sum(chip_dist.values())
|
||
if total_vol <= 0:
|
||
return result
|
||
|
||
winner_vol = sum(v for k, v in chip_dist.items() if k <= price) # 盈利筹码(cost≤现价)
|
||
loser_vol = total_vol - winner_vol # 亏损筹码(cost>现价)
|
||
|
||
winner_pct = winner_vol / total_vol
|
||
loser_pct = loser_vol / total_vol
|
||
|
||
# 获取前日状态
|
||
prev = self._cache.get(code, {})
|
||
prev_winner = prev.get("winner_pct", winner_pct)
|
||
prev_bias = prev.get("bias", 0)
|
||
|
||
# 估算换手率（近10日均量/总流通股；2026-08-26 分层铁律：读 stock_daily，不直连腾讯API）
|
||
turnover = 0.02 # 默认2%
|
||
try:
|
||
import sqlite3
|
||
conn2 = sqlite3.connect(str(DB_PATH), timeout=5)
|
||
rows2 = conn2.execute(
|
||
"SELECT date, open, close, high, low, volume FROM stock_daily "
|
||
"WHERE code=? ORDER BY date DESC LIMIT 10", (str(code),)
|
||
).fetchall()
|
||
conn2.close()
|
||
bars2 = [list(r) for r in rows2]
|
||
if len(bars2) > 5:
|
||
avg_vol = sum(float(b[5]) for b in bars2[-10:] if len(b)>5) / min(len(bars2), 10)
|
||
# 用近60日最高量估算总流通股
|
||
max_vol = avg_vol * 50 # 估算值
|
||
turnover = min(avg_vol / max(max_vol, 1), 0.3)
|
||
except:
|
||
pass
|
||
|
||
# 1. 筹码穿透率 PTR = (winner_pct - prev_winner) / turnover
|
||
ptr = (winner_pct - prev_winner) / max(turnover, 0.001)
|
||
|
||
# 2. 当日筹码穿透率(简化版) = 今日量 / 总筹码 / turnover
|
||
ptr_today = 0
|
||
minute_data = _fetch_minute_kline(code, count=30)
|
||
if minute_data:
|
||
today_vol = sum(float(m[5]) for m in minute_data if len(m) > 5)
|
||
ptr_today = today_vol / max(total_vol, 1) / max(turnover, 0.001)
|
||
|
||
# 3. 筹码乖离率(亏损版本—按文章发现，亏损筹码版反而最强)
|
||
# bias = loser_pct * turnover + prev_bias * (1 - turnover)
|
||
bias = loser_pct * turnover + prev_bias * (1 - turnover)
|
||
|
||
# 更新缓存
|
||
self._cache[code] = {
|
||
"winner_pct": winner_pct,
|
||
"loser_pct": loser_pct,
|
||
"bias": bias,
|
||
"updated_at": datetime.now().isoformat()
|
||
}
|
||
self._save_cache(code)
|
||
|
||
return {
|
||
"code": code,
|
||
"name": name,
|
||
"price": price,
|
||
"winner_pct": round(winner_pct, 4),
|
||
"loser_pct": round(loser_pct, 4),
|
||
"ptr": round(ptr, 4),
|
||
"ptr_today": round(ptr_today, 4),
|
||
"bias": round(bias, 4),
|
||
"turnover": round(turnover, 4),
|
||
}
|
||
|
||
def batch_calc(self, stocks):
|
||
"""批量计算多只股票"""
|
||
results = []
|
||
for i, (code, name) in enumerate(stocks):
|
||
if i > 0:
|
||
time.sleep(1.5) # 限流
|
||
result = self.calc_all(code, name)
|
||
results.append(result)
|
||
return results
|
||
|
||
|
||
# ── 主入口 ──
|
||
if __name__ == "__main__":
|
||
import sys
|
||
cf = ChipFactors()
|
||
|
||
# 从decisions.json获取持仓+自选
|
||
dec = read_decisions()
|
||
stocks = [(s["code"], s.get("name","")) for s in dec.get("decisions", []) if s.get("status") != "closed"]
|
||
|
||
results = cf.batch_calc(stocks)
|
||
|
||
# 按bias排序显示（亏损筹码占比最高的排前面）
|
||
results.sort(key=lambda r: r.get("bias", 0), reverse=True)
|
||
print(f"{'股票':16} {'亏损筹码%':>10} {'PTR':>8} {'乖离率':>8} {'换手率':>8}")
|
||
print("-" * 60)
|
||
for r in results:
|
||
if r.get("price"):
|
||
print(f"{r['name']:8}({r['code']:6}) {r['loser_pct']*100:>8.1f}% {r['ptr']:>8.4f} {r['bias']:>8.4f} {r['turnover']*100:>6.1f}%")
|
||
|
||
print(f"\n共计算{len(results)}只股票")
|
||
print(f"筹码缓存目录: {CACHE_DIR}")
|