feat: 策略对比脚本 + 模拟结论更新
- stock_compare.py: 三策略对比(MACD/MA突破/双均线) - 贵州茅台(600519)实测: MA20突破最优, α=+4.2% - cangjie-skills/INDEX.md: 更新策略模拟结论 - 所有策略在下跌市均亏损，策略价值=减少损失
This commit is contained in:
小唯 A06
parent
ce92d29683
commit
25c0ecfdf5
|
|
@ -1,7 +1,21 @@
|
|||
# 股票投研体系 - 技能地图
|
||||
|
||||
生成时间: 2026-07-11 17:30
|
||||
状态: 理论学习完成，进入模拟交易阶段
|
||||
更新: 2026-07-12 策略模拟结论
|
||||
状态: Phase5 模拟验证进行中
|
||||
|
||||
## 策略模拟结论（2026-07-12）
|
||||
|
||||
贵州茅台(600519) 2023-01 ~ 2026-07，买入持有基准: -13.8%
|
||||
|
||||
| 策略 | 收益 | α | 最大回撤 | 交易次数 |
|
||||
|------|------|---|---------|---------|
|
||||
| MA20日突破 | -9.6% | **+4.2%** ✅ | 26.2% | 27 |
|
||||
| MACD(12,26,9) | -19.0% | -5.2% | 32.6% | 19 |
|
||||
| 双均线(5,20) | -29.3% | -15.5% | 30.7% | 16 |
|
||||
|
||||
**结论**: MA20日突破策略最优，减少亏损+跑赢大盘
|
||||
**注意**: 贵州茅台整体下跌，所有策略均亏损，策略价值在于减少损失
|
||||
|
||||
## 已蒸馏 Skill
|
||||
|
||||
|
|
@ -18,10 +32,11 @@
|
|||
## 下一阶段
|
||||
|
||||
Phase 5: 模拟交易与策略验证
|
||||
- 选择聚宽/掘金模拟平台
|
||||
- 搭建回测环境
|
||||
- 设计第一个策略：MACD金叉/死叉
|
||||
- 模拟账户运行，达到预期后实操
|
||||
- ✅ 数据源: 腾讯/ifzq K线API (前复权日K)
|
||||
- ✅ 回测引擎: 纯Python (stock_compare.py)
|
||||
- ✅ 策略对比: MACD / MA突破 / 双均线
|
||||
- ⬜ 选股范围: 扩大多只股票测试
|
||||
- ⬜ 实盘模拟: 达到预期后小仓位实操
|
||||
|
||||
## 方法论来源
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,199 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
小唯股票策略对比 — 趋势跟踪 vs MACD
|
||||
====================================
|
||||
均线突破策略(MA_Breakout): 收盘价上穿20日均线买入，下穿卖出
|
||||
|
||||
用法:
|
||||
python3 stock_compare.py <股票代码> [起始] [结束]
|
||||
"""
|
||||
|
||||
import json, sys, urllib.request
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
OUTPUT = Path.home() / ".hermes" / "stock_backtest"
|
||||
OUTPUT.mkdir(exist_ok=True)
|
||||
FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/446db983-e392-4d2c-bfb8-f9060e5df3ad"
|
||||
|
||||
|
||||
def send_feishu(msg):
|
||||
payload = json.dumps({"msg_type": "text", "content": {"text": msg}}).encode()
|
||||
req = urllib.request.Request(FEISHU_WEBHOOK, data=payload,
|
||||
headers={"Content-Type": "application/json"})
|
||||
try:
|
||||
with urllib.request.urlopen(req, timeout=10):
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def get_data(code, start, end):
|
||||
mc = f"sh{code}" if code.startswith("6") else f"sz{code}"
|
||||
url = (f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get"
|
||||
f"?_var=kline_dayqfq¶m={mc},day,{start},{end},500,qfq")
|
||||
try:
|
||||
text = urllib.request.urlopen(url, timeout=10).read().decode("utf-8")
|
||||
data = json.loads(text.replace("kline_dayqfq=", "", 1))
|
||||
qfq = (data.get("data", {}).get(mc, {}).get("qfqday") or
|
||||
data.get("data", {}).get(mc, {}).get("day") or [])
|
||||
rows = []
|
||||
for item in qfq:
|
||||
if len(item) < 6:
|
||||
continue
|
||||
try:
|
||||
rows.append({"date": item[0], "open": float(item[1]),
|
||||
"close": float(item[2]), "high": float(item[3]),
|
||||
"low": float(item[4]), "volume": float(item[5])})
|
||||
except (ValueError, IndexError):
|
||||
continue
|
||||
df = pd.DataFrame(rows)
|
||||
df["date"] = pd.to_datetime(df["date"])
|
||||
df.set_index("date", inplace=True)
|
||||
df.sort_index(inplace=True)
|
||||
return df
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def macd_backtest(df, fast=12, slow=26, sig=9, cash=100000):
|
||||
ema_f = df["close"].ewm(span=fast).mean()
|
||||
ema_s = df["close"].ewm(span=slow).mean()
|
||||
macd = ema_f - ema_s
|
||||
signal = macd.ewm(span=sig).mean()
|
||||
|
||||
shares = 0; c = cash; peak = cash; max_dd = 0
|
||||
trades = []
|
||||
for i in range(slow, len(df)):
|
||||
p = df["close"].iloc[i]
|
||||
if macd.iloc[i] > signal.iloc[i] and macd.iloc[i-1] <= signal.iloc[i-1]:
|
||||
if shares == 0:
|
||||
n = int(c / p); c -= n * p; shares = n
|
||||
trades.append(("BUY", df.index[i], n, p))
|
||||
elif macd.iloc[i] < signal.iloc[i] and macd.iloc[i-1] >= signal.iloc[i-1]:
|
||||
if shares > 0:
|
||||
c += shares * p; trades.append(("SELL", df.index[i], shares, p)); shares = 0
|
||||
peak = max(peak, c + shares * p)
|
||||
dd = (peak - (c + shares * p)) / peak * 100 if peak > 0 else 0
|
||||
max_dd = max(max_dd, dd)
|
||||
final = c + shares * df["close"].iloc[-1]
|
||||
wins = len([t for t in trades if t[0] == "SELL" and t[3] > 0])
|
||||
return dict(
|
||||
final=final, ret=(final-cash)/cash*100,
|
||||
buyhold=(df["close"].iloc[-1]-df["close"].iloc[0])/df["close"].iloc[0]*100,
|
||||
max_dd=max_dd, trades=len(trades)//2,
|
||||
winrate=wins/(len(trades)//2)*100 if trades else 0
|
||||
)
|
||||
|
||||
|
||||
def ma_breakout_backtest(df, ma_days=20, cash=100000):
|
||||
ma = df["close"].rolling(ma_days).mean()
|
||||
shares = 0; c = cash; peak = cash; max_dd = 0
|
||||
trades = []
|
||||
for i in range(ma_days, len(df)):
|
||||
p = df["close"].iloc[i]
|
||||
if df["close"].iloc[i] > ma.iloc[i] and df["close"].iloc[i-1] <= ma.iloc[i-1]:
|
||||
if shares == 0:
|
||||
n = int(c / p); c -= n * p; shares = n
|
||||
trades.append(("BUY", df.index[i], n, p))
|
||||
elif df["close"].iloc[i] < ma.iloc[i] and df["close"].iloc[i-1] >= ma.iloc[i-1]:
|
||||
if shares > 0:
|
||||
c += shares * p; trades.append(("SELL", df.index[i], shares, p)); shares = 0
|
||||
peak = max(peak, c + shares * p)
|
||||
dd = (peak - (c + shares * p)) / peak * 100 if peak > 0 else 0
|
||||
max_dd = max(max_dd, dd)
|
||||
final = c + shares * df["close"].iloc[-1]
|
||||
wins = len([t for t in trades if t[0] == "SELL" and t[3] > 0])
|
||||
return dict(
|
||||
final=final, ret=(final-cash)/cash*100,
|
||||
buyhold=(df["close"].iloc[-1]-df["close"].iloc[0])/df["close"].iloc[0]*100,
|
||||
max_dd=max_dd, trades=len(trades)//2,
|
||||
winrate=wins/(len(trades)//2)*100 if trades else 0
|
||||
)
|
||||
|
||||
|
||||
def dual_ma_backtest(df, fast=5, slow=20, cash=100000):
|
||||
"""双均线策略: 快线穿慢线金叉买，死叉卖"""
|
||||
ma_fast = df["close"].rolling(fast).mean()
|
||||
ma_slow = df["close"].rolling(slow).mean()
|
||||
shares = 0; c = cash; peak = cash; max_dd = 0
|
||||
trades = []
|
||||
for i in range(slow, len(df)):
|
||||
p = df["close"].iloc[i]
|
||||
if ma_fast.iloc[i] > ma_slow.iloc[i] and ma_fast.iloc[i-1] <= ma_slow.iloc[i-1]:
|
||||
if shares == 0:
|
||||
n = int(c / p); c -= n * p; shares = n
|
||||
trades.append(("BUY", df.index[i], n, p))
|
||||
elif ma_fast.iloc[i] < ma_slow.iloc[i] and ma_fast.iloc[i-1] >= ma_slow.iloc[i-1]:
|
||||
if shares > 0:
|
||||
c += shares * p; trades.append(("SELL", df.index[i], shares, p)); shares = 0
|
||||
peak = max(peak, c + shares * p)
|
||||
dd = (peak - (c + shares * p)) / peak * 100 if peak > 0 else 0
|
||||
max_dd = max(max_dd, dd)
|
||||
final = c + shares * df["close"].iloc[-1]
|
||||
wins = len([t for t in trades if t[0] == "SELL" and t[3] > 0])
|
||||
return dict(
|
||||
final=final, ret=(final-cash)/cash*100,
|
||||
buyhold=(df["close"].iloc[-1]-df["close"].iloc[0])/df["close"].iloc[0]*100,
|
||||
max_dd=max_dd, trades=len(trades)//2,
|
||||
winrate=wins/(len(trades)//2)*100 if trades else 0
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
if len(sys.argv) < 2:
|
||||
print("用法: python3 stock_compare.py <股票代码> [起始] [结束]")
|
||||
sys.exit(1)
|
||||
|
||||
code = sys.argv[1]
|
||||
start = sys.argv[2] if len(sys.argv) > 2 else "2023-01-01"
|
||||
end = sys.argv[3] if len(sys.argv) > 3 else datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
print(f"\n代码: {code} | {start} ~ {end}")
|
||||
df = get_data(code, start, end)
|
||||
if df is None or len(df) < 60:
|
||||
print("数据获取失败"); sys.exit(1)
|
||||
print(f"数据: {len(df)}条")
|
||||
|
||||
r1 = macd_backtest(df)
|
||||
r2 = ma_breakout_backtest(df)
|
||||
r3 = dual_ma_backtest(df)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"{'策略':<20} {'收益':>10} {'买入持有':>10} {'α':>10} {'最大回撤':>10} {'交易':>6} {'胜率':>8}")
|
||||
print(f"{'-'*60}")
|
||||
print(f"{'MACD(12,26,9)':<20} {r1['ret']:>+9.1f}% {r1['buyhold']:>+9.1f}% {r1['ret']-r1['buyhold']:>+9.1f}% {r1['max_dd']:>9.1f}% {r1['trades']:>6} {r1['winrate']:>7.0f}%")
|
||||
print(f"{'MA突破(20日)':<20} {r2['ret']:>+9.1f}% {r2['buyhold']:>+9.1f}% {r2['ret']-r2['buyhold']:>+9.1f}% {r2['max_dd']:>9.1f}% {r2['trades']:>6} {r2['winrate']:>7.0f}%")
|
||||
print(f"{'双均线(5,20)':<20} {r3['ret']:>+9.1f}% {r3['buyhold']:>+9.1f}% {r3['ret']-r3['buyhold']:>+9.1f}% {r3['max_dd']:>9.1f}% {r3['trades']:>6} {r3['winrate']:>7.0f}%")
|
||||
print(f"{'='*60}")
|
||||
|
||||
# 飞书
|
||||
msg = f"""📊 策略对比报告
|
||||
|
||||
代码: {code} | {start} ~ {end} | {len(df)}条
|
||||
|
||||
策略收益对比:
|
||||
MACD(12,26,9): {r1['ret']:+.1f}% (α={r1['ret']-r1['buyhold']:+.1f}%, 回撤{r1['max_dd']:.1f}%, {r1['trades']}笔, 胜率{r1['winrate']:.0f}%)
|
||||
MA突破(20日): {r2['ret']:+.1f}% (α={r2['ret']-r2['buyhold']:+.1f}%, 回撤{r2['max_dd']:.1f}%, {r2['trades']}笔, 胜率{r2['winrate']:.0f}%)
|
||||
双均线(5,20): {r3['ret']:+.1f}% (α={r3['ret']-r3['buyhold']:+.1f}%, 回撤{r3['max_dd']:.1f}%, {r3['trades']}笔, 胜率{r3['winrate']:.0f}%)
|
||||
|
||||
买入持有基准: {r1['buyhold']:+.1f}%
|
||||
|
||||
生成: {datetime.now().strftime('%Y-%m-%d %H:%M')}
|
||||
小唯股票投研 · 模拟阶段"""
|
||||
send_feishu(msg)
|
||||
print("\n✅ 已推送飞书")
|
||||
|
||||
# 保存
|
||||
result_file = OUTPUT / f"compare_{code}.json"
|
||||
with open(result_file, "w") as f:
|
||||
json.dump({"code": code, "start": start, "end": end,
|
||||
"macd": r1, "ma_breakout": r2, "dual_ma": r3}, f, ensure_ascii=False, indent=2, default=str)
|
||||
print(f"数据存: {result_file}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue