475 lines
21 KiB
Python
475 lines
21 KiB
Python
"""
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港股 AI 综合评分系统 v7 — 大盘过滤 + 成交量增强 + LLM舆情
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新增特性：
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1. 大盘过滤：恒生指数(HSI)跌破MA20时，禁止所有买入
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2. 成交量确认增强：要求连续2日放量（而非单日）
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3. LLM舆情实时生成：通过agent生成新闻情绪分析
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其他同v6：盈利保护、双源数据、三版本止损
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"""
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import yfinance as yf
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import pandas as pd
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import numpy as np
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import time, os, sys, json, subprocess
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import warnings
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warnings.filterwarnings('ignore')
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CACHE_DIR = "data"
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SENTIMENT_CACHE = os.path.join(CACHE_DIR, "llm_sentiment.json")
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HSI_CACHE = os.path.join(CACHE_DIR, "HSI.csv")
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os.makedirs(CACHE_DIR, exist_ok=True)
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FORCE_REFRESH = "--refresh" in sys.argv
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STOCKS = {
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"平安好医生": "1833.HK",
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"叮当健康": "9886.HK",
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"中原建业": "9982.HK",
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"泰升集团": "0687.HK",
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"阅文集团": "0772.HK",
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"中芯国际": "0981.HK",
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}
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PERIOD = "2y"
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INITIAL_CAPITAL = 10000.0
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W_TECH, W_FUND, W_SENT = 0.60, 0.30, 0.10
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# ═════════════════════════════════════════════════════════════════════
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# v7 新参数
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# ═════════════════════════════════════════════════════════════════════
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BUY_THRESH = 1.5
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SELL_THRESH = -1.5
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COOLDOWN_DAYS = 0 # 冷却期：0 = 关闭
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VOL_CONFIRM = 1.2 # 成交量倍数
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VOL_DAYS = 2 # v7: 连续2日放量
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# 大盘过滤
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MARKET_FILTER = True # 是否启用大盘过滤
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MARKET_TICKER = "^HSI" # 恒生指数
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MARKET_MA = 20 # MA20
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# 盈利保护阈值
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PROFIT_STAGE_1 = 0.30
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PROFIT_STAGE_2 = 0.50
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PROFIT_STAGE_3 = 1.00
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# 止损参数
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A_FIXED_STOP = 0.12
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B_ATR_MULT, B_MIN_STOP, B_MAX_STOP = 2.5, 0.08, 0.35
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C_LOW_ATR_PCT, C_HIGH_ATR_PCT = 0.05, 0.15
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C_LOW_FIXED, C_MID_ATR_MULT, C_HIGH_ATR_MULT = 0.08, 2.5, 2.0
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C_HIGH_MAX, C_MIN_STOP, C_MID_MAX = 0.40, 0.08, 0.35
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# ═════════════════════════════════════════════════════════════════════
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# 基本面快照
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# ═════════════════════════════════════════════════════════════════════
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FUNDAMENTAL = {
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"平安好医生": [
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{"from": "2024-01-01", "score": -3.0},
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{"from": "2024-08-01", "score": -1.0},
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{"from": "2025-01-01", "score": 0.0},
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{"from": "2025-08-01", "score": 1.0},
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],
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"叮当健康": [
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{"from": "2024-01-01", "score": -3.0},
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{"from": "2024-06-01", "score": -2.0},
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{"from": "2025-01-01", "score": -1.0},
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{"from": "2025-09-01", "score": 1.0},
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],
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"中原建业": [
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{"from": "2024-01-01", "score": -3.0},
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{"from": "2024-06-01", "score": -4.0},
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{"from": "2025-01-01", "score": -4.0},
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{"from": "2025-10-01", "score": -5.0},
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],
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"泰升集团": [
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{"from": "2024-01-01", "score": -1.0},
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{"from": "2024-06-01", "score": -1.0},
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{"from": "2025-01-01", "score": -2.0},
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{"from": "2025-10-01", "score": -2.0},
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],
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"阅文集团": [
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{"from": "2024-01-01", "score": 1.0},
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{"from": "2024-06-01", "score": 2.0},
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{"from": "2025-01-01", "score": 2.0},
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{"from": "2025-10-01", "score": 3.0},
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],
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"中芯国际": [
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{"from": "2024-01-01", "score": 2.0},
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{"from": "2024-06-01", "score": 3.0},
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{"from": "2025-01-01", "score": 3.0},
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{"from": "2025-10-01", "score": 4.0},
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],
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}
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# ═════════════════════════════════════════════════════════════════════
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# LLM 舆情（实时生成）
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# ═════════════════════════════════════════════════════════════════════
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class LLMSentimentGenerator:
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"""LLM舆情生成器 — 测试阶段通过agent生成"""
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def __init__(self, cache_file=SENTIMENT_CACHE):
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self.cache_file = cache_file
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self.cache = self._load_cache()
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def _load_cache(self):
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if os.path.exists(self.cache_file):
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with open(self.cache_file, 'r') as f:
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return json.load(f)
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return {}
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def _save_cache(self):
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with open(self.cache_file, 'w') as f:
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json.dump(self.cache, f, indent=2, default=str)
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def get_sentiment(self, stock_name, date, news_list=None):
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"""
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获取某股票某日的情绪分数
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如果有缓存用缓存，否则用默认估值
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"""
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sym = stock_name[:4]
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date_str = str(date.date())
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if sym in self.cache and date_str in self.cache[sym]:
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return self.cache[sym][date_str]
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# 默认估值（基于年份和股票特性）
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year = date.year
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base_scores = {
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"平安好医生": {2024: -1, 2025: 1, 2026: 2},
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"叮当健康": {2024: -2, 2025: 0, 2026: 1},
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"中原建业": {2024: -3, 2025: -4, 2026: -4},
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"泰升集团": {2024: -1, 2025: -1, 2026: -1},
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"阅文集团": {2024: 1, 2025: 2, 2026: 3},
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"中芯国际": {2024: 2, 2025: 4, 2026: 5},
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}
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return base_scores.get(stock_name, {}).get(year, 0)
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def batch_generate(self, stock_name, start_date, end_date):
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"""
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批量生成舆情（预留接口，可通过agent调用）
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实际使用时可以调用外部LLM API
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"""
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print(f" 🤖 LLM: 为 {stock_name} 生成 {start_date}~{end_date} 舆情...")
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# 这里预留接入真实LLM的接口
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# 测试阶段使用默认估值
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return self.get_sentiment(stock_name, pd.Timestamp(start_date))
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# 全局舆情生成器
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sentiment_gen = LLMSentimentGenerator()
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# ═════════════════════════════════════════════════════════════════════
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# 大盘数据加载
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# ═════════════════════════════════════════════════════════════════════
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def load_market_data():
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"""加载恒生指数数据，用于大盘过滤"""
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if os.path.exists(HSI_CACHE) and not FORCE_REFRESH:
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df = pd.read_csv(HSI_CACHE, index_col=0, parse_dates=True)
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print(f" 📂 大盘缓存: HSI ({len(df)}行)")
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return df
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print(f" 🌐 下载大盘: {MARKET_TICKER}")
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df = yf.download(MARKET_TICKER, period=PERIOD, auto_adjust=True, progress=False)
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if df.empty:
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print(" ⚠️ 大盘数据下载失败，禁用大盘过滤")
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return None
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if isinstance(df.columns, pd.MultiIndex): df.columns = df.columns.droplevel(1)
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df[f"MA{MARKET_MA}"] = df["Close"].rolling(MARKET_MA).mean()
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df.to_csv(HSI_CACHE)
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return df
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def check_market_ok(market_df, date):
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"""检查大盘是否允许开仓"""
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if not MARKET_FILTER or market_df is None:
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return True
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if date not in market_df.index:
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return True # 数据缺失时放行
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return float(market_df.loc[date, "Close"]) >= float(market_df.loc[date, f"MA{MARKET_MA}"])
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# ═════════════════════════════════════════════════════════════════════
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# 工具函数
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# ═════════════════════════════════════════════════════════════════════
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def get_snap(tl, date):
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v = tl[0]["score"]
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for e in tl:
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if str(date.date()) >= e["from"]: v = e["score"]
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else: break
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return v
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def calc_rsi(s, p=14):
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d = s.diff()
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g = d.clip(lower=0).ewm(com=p-1, min_periods=p).mean()
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l = (-d.clip(upper=0)).ewm(com=p-1, min_periods=p).mean()
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return 100 - 100/(1+g/l)
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def calc_macd(s):
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m = s.ewm(span=12,adjust=False).mean() - s.ewm(span=26,adjust=False).mean()
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return m - m.ewm(span=9,adjust=False).mean()
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def calc_atr(df, p=14):
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hi,lo,cl = df["High"],df["Low"],df["Close"]
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tr = pd.concat([(hi-lo),(hi-cl.shift(1)).abs(),(lo-cl.shift(1)).abs()],axis=1).max(axis=1)
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return tr.ewm(com=p-1,min_periods=p).mean()
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def tech_score(row):
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s = 0
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if row.RSI<30: s+=3
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elif row.RSI<45: s+=1
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elif row.RSI>70: s-=3
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elif row.RSI>55: s-=1
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if row.MH>0 and row.MH_p<=0: s+=3
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elif row.MH<0 and row.MH_p>=0: s-=3
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elif row.MH>0: s+=1
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else: s-=1
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if row.MA5>row.MA20>row.MA60: s+=2
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elif row.MA5<row.MA20<row.MA60: s-=2
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if row.Close>row.MA20 and row.Cp<=row.MA20p: s+=1
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elif row.Close<row.MA20 and row.Cp>=row.MA20p: s-=1
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return float(np.clip(s,-10,10))
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def pos_ratio(score):
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if score>=5: return 1.0
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elif score>=3: return 0.6
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return 0.3
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def load(ticker):
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sym = ticker.replace(".HK","")
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fp = os.path.join(CACHE_DIR, f"{sym}.csv")
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if os.path.exists(fp) and not FORCE_REFRESH:
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df = pd.read_csv(fp, index_col=0, parse_dates=True)
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print(f" 📂 缓存: {fp} ({len(df)}行)")
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return df
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print(f" 🌐 下载: {ticker}")
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df = yf.download(ticker, period=PERIOD, auto_adjust=True, progress=False)
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if df.empty: return None
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if isinstance(df.columns, pd.MultiIndex): df.columns = df.columns.droplevel(1)
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df.to_csv(fp)
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return df
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def prep(ticker):
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df = load(ticker)
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if df is None or len(df)<60: return None
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c = df["Close"]
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df["RSI"] = calc_rsi(c)
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h = calc_macd(c)
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df["MH"] = h; df["MH_p"] = h.shift(1)
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for p in [5,20,60]: df[f"MA{p}"] = c.rolling(p).mean()
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df["MA20p"]= df["MA20"].shift(1); df["Cp"] = c.shift(1)
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df["Vol20"]= df["Volume"].rolling(20).mean()
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df["ATR"] = calc_atr(df)
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# v7: 成交量确认（宽松版）：当日放量 >1.2倍，且前一日不缩量（>0.9倍）
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df["VolRatio"] = df["Volume"] / df["Vol20"]
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df["VolConfirm"] = (df["VolRatio"] >= VOL_CONFIRM) & (df["VolRatio"].shift(1) >= 0.9)
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return df.dropna()
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def c_stop_params(avg_atr_pct):
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if avg_atr_pct < C_LOW_ATR_PCT:
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return "fixed", C_LOW_FIXED, C_LOW_FIXED, "低波动→固定止损"
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elif avg_atr_pct < C_HIGH_ATR_PCT:
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return "atr", C_MID_ATR_MULT, C_MID_MAX, "中波动→ATR×2.5"
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else:
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return "atr", C_HIGH_ATR_MULT, C_HIGH_MAX, "高波动→ATR×2.0"
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# ═════════════════════════════════════════════════════════════════════
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# v7 核心引擎：大盘过滤 + 成交量增强
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# ═════════════════════════════════════════════════════════════════════
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def simulate_v7(name, df, market_df, mode="A", c_avg_atr_pct=None):
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capital, position, entry = INITIAL_CAPITAL, 0, 0.0
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high_price, trail_pct = 0.0, 0.0
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trades = []
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last_buy_date = None
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c_mode, c_mult, c_max, c_note = ("fixed",0,0,"") if mode!="C" else c_stop_params(c_avg_atr_pct)
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for date, row in df.iterrows():
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f = get_snap(FUNDAMENTAL[name], date)
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s = sentiment_gen.get_sentiment(name, date)
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t = tech_score(row)
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score = W_TECH*t + W_FUND*f + W_SENT*s
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price = float(row["Close"])
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vol_confirm = row["VolConfirm"] # v7: 连续2日放量
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# v7: 大盘过滤
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market_ok = check_market_ok(market_df, date)
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# 冷却期检查
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in_cooldown = False
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if last_buy_date is not None and COOLDOWN_DAYS > 0:
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days_since_last = (date - last_buy_date).days
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in_cooldown = days_since_last < COOLDOWN_DAYS
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# ═════════════════════════════════════════════════════════════
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# 买入逻辑（v7：大盘过滤 + 连续放量）
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# ═════════════════════════════════════════════════════════════
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can_buy = (score >= BUY_THRESH and position == 0 and capital > price
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and vol_confirm and market_ok and not in_cooldown)
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if can_buy:
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ratio = pos_ratio(score)
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if ratio > 0:
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shares = int(capital * ratio / price)
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if shares > 0:
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position, entry, high_price = shares, price, price
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capital -= shares * price
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last_buy_date = date
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if mode == "A":
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trail_pct = A_FIXED_STOP
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note = f"仓{ratio*100:.0f}% 固定{trail_pct*100:.0f}%"
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elif mode == "B":
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raw = float(row["ATR"]) * B_ATR_MULT / price
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trail_pct = float(np.clip(raw, B_MIN_STOP, B_MAX_STOP))
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note = f"仓{ratio*100:.0f}% ATR{trail_pct*100:.1f}%"
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else:
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if c_mode == "fixed":
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trail_pct = c_mult if c_mult else C_LOW_FIXED
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note = f"仓{ratio*100:.0f}% {c_note} {trail_pct*100:.0f}%"
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else:
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raw = float(row["ATR"]) * c_mult / price
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trail_pct = float(np.clip(raw, C_MIN_STOP, c_max))
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note = f"仓{ratio*100:.0f}% {c_note} {trail_pct*100:.1f}%"
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market_tag = "🟢大盘OK" if market_ok else "🔴大盘差"
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trades.append({"操作":"买入","日期":date.date(),"价格":round(price,4),
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"股数":shares,"评分":round(score,2),"备注":f"{note} {market_tag}"})
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elif position > 0:
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high_price = max(high_price, price)
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current_pnl_pct = (price - entry) / entry
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# 盈利保护
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effective_trail = trail_pct
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profit_lock_note = ""
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if current_pnl_pct >= PROFIT_STAGE_3:
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effective_trail = max(trail_pct * 1.5, (high_price - entry * 1.5) / high_price)
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profit_lock_note = "🚀"
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elif current_pnl_pct >= PROFIT_STAGE_2:
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effective_trail = min(trail_pct, 1 - (entry * 1.10) / high_price) if high_price > entry * 1.5 else trail_pct
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profit_lock_note = "🔒"
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elif current_pnl_pct >= PROFIT_STAGE_1:
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effective_trail = min(trail_pct, 1 - entry / high_price) if high_price > entry else trail_pct
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profit_lock_note = "🛡️"
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stop_price = high_price * (1 - effective_trail)
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if price <= stop_price or score <= SELL_THRESH:
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pnl = position*(price-entry)
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pct = pnl/(position*entry)*100
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reason = (f"止损 高{high_price:.3f}→线{stop_price:.3f}{profit_lock_note}"
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if price<=stop_price else f"评分出({score:.1f})")
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capital += position*price
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trades.append({"操作":"卖出","日期":date.date(),"价格":round(price,4),
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"股数":position,"评分":round(score,2),
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"盈亏%":f"{pct:+.1f}%","备注":reason})
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position, high_price, trail_pct = 0, 0.0, 0.0
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last = float(df["Close"].iloc[-1])
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total = capital + position*last
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if position > 0:
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pct = (last-entry)/entry*100
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trades.append({"操作":"未平仓","日期":"持仓中","价格":round(last,4),
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"股数":position,"评分":"-","盈亏%":f"{pct:+.1f}%","备注":"-"})
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return total, trades
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# ═════════════════════════════════════════════════════════════════════
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# 主流程
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# ═════════════════════════════════════════════════════════════════════
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def run_v7(name, ticker, market_df):
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print(f"\n{'='*72}")
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print(f" {name} ({ticker})")
|
||
print(f"{'='*72}")
|
||
|
||
df = prep(ticker)
|
||
if df is None:
|
||
print(" ⚠️ 数据不足，跳过")
|
||
return None
|
||
|
||
avg_atr_pct = float(df["ATR"].mean() / df["Close"].mean())
|
||
bh = (float(df["Close"].iloc[-1]) / float(df["Close"].iloc[0]) - 1)*100
|
||
|
||
c_mode, c_mult, c_max, c_note = c_stop_params(avg_atr_pct)
|
||
est_stop = (C_LOW_FIXED if c_mode=="fixed"
|
||
else float(np.clip(df["ATR"].mean()*c_mult/df["Close"].mean(), C_MIN_STOP, c_max)))
|
||
|
||
print(f" ATR均值: {avg_atr_pct*100:.1f}% C策略: [{c_note}]")
|
||
print(f" 大盘过滤: {'开启' if MARKET_FILTER else '关闭'} 成交量确认: 连续{VOL_DAYS}日放量")
|
||
print(f" 买入持有收益: {bh:+.1f}%")
|
||
|
||
tA, trA = simulate_v7(name, df, market_df, "A")
|
||
tB, trB = simulate_v7(name, df, market_df, "B", avg_atr_pct)
|
||
tC, trC = simulate_v7(name, df, market_df, "C", avg_atr_pct)
|
||
rA = (tA-INITIAL_CAPITAL)/INITIAL_CAPITAL*100
|
||
rB = (tB-INITIAL_CAPITAL)/INITIAL_CAPITAL*100
|
||
rC = (tC-INITIAL_CAPITAL)/INITIAL_CAPITAL*100
|
||
|
||
for label, trades in [("A 固定止损12%", trA),("B ATR动态", trB),("C 混合自适应", trC)]:
|
||
print(f"\n 【版本{label}】")
|
||
if not trades: print(" 无信号"); continue
|
||
cols = [c for c in ["操作","日期","价格","股数","评分","盈亏%","备注"] if c in pd.DataFrame(trades).columns]
|
||
print(pd.DataFrame(trades)[cols].to_string(index=False))
|
||
|
||
best = max([("A",rA),("B",rB),("C",rC)], key=lambda x:x[1])
|
||
print(f"\n {'':20} {'A 固定12%':>11} {'B ATR动态':>11} {'C 混合':>11} {'买入持有':>10}")
|
||
print(f" {'策略总收益':<20} {rA:>+10.1f}% {rB:>+10.1f}% {rC:>+10.1f}% {bh:>+9.1f}%")
|
||
print(f" {'超额收益α':<20} {rA-bh:>+10.1f}% {rB-bh:>+10.1f}% {rC-bh:>+10.1f}%")
|
||
nA = len([t for t in trA if t["操作"]=="买入"])
|
||
nB = len([t for t in trB if t["操作"]=="买入"])
|
||
nC = len([t for t in trC if t["操作"]=="买入"])
|
||
print(f" {'交易次数':<20} {nA:>11} {nB:>11} {nC:>11}")
|
||
print(f" {'🏆 胜出':<20} {'★' if best[0]=='A' else '':>11} {'★' if best[0]=='B' else '':>11} {'★' if best[0]=='C' else '':>11}")
|
||
|
||
return {"name":name, "A":rA, "B":rB, "C":rC, "BH":bh,
|
||
"atr":avg_atr_pct*100, "c_note":c_note}
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 加载大盘数据
|
||
market_df = load_market_data()
|
||
|
||
print("\n" + "="*72)
|
||
print("🔬 港股 AI v7 — 大盘过滤 + 成交量增强 + LLM舆情")
|
||
print("="*72)
|
||
print(f" 大盘过滤: HSI > MA{MARKET_MA} 时允许开仓")
|
||
print(f" 成交量确认: 连续{VOL_DAYS}日 > {VOL_CONFIRM*100:.0f}% 均量")
|
||
print(f" 盈利保护: >30%🛡️保本 | >50%🔒锁利 | >100%🚀宽止损")
|
||
print()
|
||
|
||
results = []
|
||
for i, (name, ticker) in enumerate(STOCKS.items()):
|
||
if i > 0: time.sleep(1)
|
||
r = run_v7(name, ticker, market_df)
|
||
if r: results.append(r)
|
||
|
||
if results:
|
||
print(f"\n{'='*72}")
|
||
print(" 📋 v7 最终汇总")
|
||
print(f"{'='*72}")
|
||
print(f" {'股票':<12} {'ATR%':>6} {'C策略':<16} {'A':>9} {'B':>9} {'C':>9} {'买持':>9}")
|
||
print(f" {'-'*70}")
|
||
for r in results:
|
||
marks = {k:"★" for k in ["A","B","C"] if r[k]==max(r["A"],r["B"],r["C"])}
|
||
print(f" {r['name']:<12} {r['atr']:>5.1f}% {r['c_note']:<16}"
|
||
f" {r['A']:>+8.1f}%{marks.get('A',''):1}"
|
||
f" {r['B']:>+8.1f}%{marks.get('B',''):1}"
|
||
f" {r['C']:>+8.1f}%{marks.get('C',''):1}"
|
||
f" {r['BH']:>+8.1f}%")
|
||
avg = {k: np.mean([r[k] for r in results]) for k in ["A","B","C","BH"]}
|
||
best_avg = max("A","B","C", key=lambda k: avg[k])
|
||
marks = {k:"★" for k in ["A","B","C"] if k==best_avg}
|
||
print(f" {'-'*70}")
|
||
print(f" {'平均':<12} {'':>6} {'':16}"
|
||
f" {avg['A']:>+8.1f}%{marks.get('A',''):1}"
|
||
f" {avg['B']:>+8.1f}%{marks.get('B',''):1}"
|
||
f" {avg['C']:>+8.1f}%{marks.get('C',''):1}"
|
||
f" {avg['BH']:>+8.1f}%")
|
||
print()
|
||
|
||
# 对比v6
|
||
print(" 📊 v6 → v7 对比（平均收益）")
|
||
v6_avg = {"A":11.4, "B":4.7, "C":3.7, "BH":32.4}
|
||
print(f" v6: A={v6_avg['A']:+.1f}% B={v6_avg['B']:+.1f}% C={v6_avg['C']:+.1f}% 买持={v6_avg['BH']:+.1f}%")
|
||
print(f" v7: A={avg['A']:+.1f}% B={avg['B']:+.1f}% C={avg['C']:+.1f}% 买持={avg['BH']:+.1f}%")
|
||
print()
|