review_pk.py

"""Portfolio Performance Review - PK between AI vs User
Date: 2026-05-19 (Tuesday)
"""
import sys
sys.path.insert(0, 'd:/trandingagent/deer-flow/skills/custom/factor-analysis')
from tools.local_data_store import _read_csv, _build_csv_ticker_map
import pandas as pd

AI_PORTFOLIO = {
    "600000": {"name": "浦发银行", "allocation": 47500},
    "601800": {"name": "中国交建", "allocation": 47500},
    "000877": {"name": "天山股份", "allocation": 47500},
    "601186": {"name": "中国铁建", "allocation": 47500},
    "600373": {"name": "中文传媒", "allocation": 47500},
    "600997": {"name": "开滦股份", "allocation": 47500},
    "601686": {"name": "友发集团", "allocation": 47500},
    "601166": {"name": "兴业银行", "allocation": 47500},
}

USER_PORTFOLIO = {
    "002050": {"name": "三花智控", "allocation": 41250},
    "002938": {"name": "鹏鼎控股", "allocation": 41250},
    "600584": {"name": "长电科技", "allocation": 41250},
    "688820": {"name": "盛合晶微", "allocation": 41250},
    "688212": {"name": "澳华内镜", "allocation": 50000},
    "003031": {"name": "中瓷电子", "allocation": 41250},
    "600941": {"name": "中国移动", "allocation": 41250},
    "600373": {"name": "中文传媒", "allocation": 41250},
    "600406": {"name": "国电南瑞", "allocation": 41250},
}

INITIAL_CAPITAL_AI = sum(p["allocation"] for p in AI_PORTFOLIO.values())
INITIAL_CAPITAL_USER = sum(p["allocation"] for p in USER_PORTFOLIO.values())

def get_latest_price(ticker):
    try:
        df = _read_csv(ticker)
        if df.empty:
            return None
        df["date"] = pd.to_datetime(df["date"])
        df = df.sort_values("date")
        return df["close"].iloc[-1]
    except Exception as e:
        return None

def calculate_portfolio(portfolio, name):
    print(f"\n{'='*80}")
    print(f"  {name}组合")
    print(f"{'='*80}")
    
    total_current = 0
    total_allocation = 0
    details = []
    
    print(f"\n  {'股票':<12} {'代码':<8} {'配置(万)':>10} {'现价':>8} {'持仓(股)':>10} {'市值(万)':>10} {'涨跌幅':>10}")
    print(f"  {'-'*12} {'-'*8} {'-'*10} {'-'*8} {'-'*10} {'-'*10} {'-'*10}")
    
    for ticker, info in portfolio.items():
        price = get_latest_price(ticker)
        allocation = info["allocation"]
        total_allocation += allocation
        
        if price is None:
            print(f"  {info['name']:<12} {ticker:<8} {allocation/10000:>9.2f}万 {'N/A':>8} {'N/A':>10} {'N/A':>10} {'N/A':>10}")
            details.append({"name": info["name"], "ticker": ticker, "allocation": allocation, "price": None, "current_value": 0, "return_pct": None})
        else:
            shares = int(allocation / price / 100) * 100
            current_value = shares * price
            total_current += current_value
            ret_pct = (price / (allocation / shares) - 1) * 100 if shares > 0 else 0
            print(f"  {info['name']:<12} {ticker:<8} {allocation/10000:>9.2f}万 {price:>8.2f} {shares:>10} {current_value/10000:>9.2f}万 {ret_pct:>+10.2f}%")
            details.append({"name": info["name"], "ticker": ticker, "allocation": allocation, "price": price, "current_value": current_value, "return_pct": ret_pct})
    
    total_return = (total_current / total_allocation - 1) * 100
    
    print(f"\n  {'总计':<12} {'':<8} {total_allocation/10000:>9.2f}万 {'':>8} {'':>10} {total_current/10000:>9.2f}万 {total_return:>+10.2f}%")
    
    return {
        "total_initial": total_allocation,
        "total_current": total_current,
        "total_return": total_return,
        "details": details
    }

def main():
    print(f"📊 持仓PK复盘 - 2026-05-19")
    print(f"初始日期: 2026-05-17")
    print(f"复盘日期: 2026-05-19")
    print(f"持股天数: 2天")
    
    ai_result = calculate_portfolio(AI_PORTFOLIO, "🤖 AI (S1纯价值)")
    user_result = calculate_portfolio(USER_PORTFOLIO, "👤 用户 (成长+价值混合)")
    
    print(f"\n{'='*80}")
    print(f"  🎯 PK结果对比")
    print(f"{'='*80}")
    print(f"\n  {'组合':<20} {'初始资金':>12} {'当前市值':>12} {'收益率':>12}")
    print(f"  {'-'*20} {'-'*12} {'-'*12} {'-'*12}")
    print(f"  AI (价值) {'':<9} {ai_result['total_initial']/10000:>11.2f}万 {ai_result['total_current']/10000:>12.2f}万 {ai_result['total_return']:>+12.2f}%")
    print(f"  用户 (混合) {'':<7} {user_result['total_initial']/10000:>11.2f}万 {user_result['total_current']/10000:>12.2f}万 {user_result['total_return']:>+12.2f}%")
    
    diff = user_result["total_return"] - ai_result["total_return"]
    winner = "用户" if user_result["total_return"] > ai_result["total_return"] else "AI"
    
    print(f"\n  领先: {'用户' if diff > 0 else 'AI'} (+{abs(diff):.2f}%)")
    print(f"  当前赢家: {winner} 🎉")
    
    print(f"\n{'='*80}")
    print(f"  📈 表现最好的股票")
    print(f"{'='*80}")
    
    all_stocks = []
    for d in ai_result["details"]:
        if d["return_pct"] is not None:
            all_stocks.append({"name": d["name"], "ticker": d["ticker"], "return_pct": d["return_pct"], "portfolio": "AI"})
    for d in user_result["details"]:
        if d["return_pct"] is not None:
            all_stocks.append({"name": d["name"], "ticker": d["ticker"], "return_pct": d["return_pct"], "portfolio": "用户"})
    
    all_stocks.sort(key=lambda x: x["return_pct"], reverse=True)
    
    print(f"\n  {'排名':<6} {'股票':<12} {'代码':<8} {'所属组合':<8} {'涨幅':>10}")
    print(f"  {'-'*6} {'-'*12} {'-'*8} {'-'*8} {'-'*10}")
    for i, s in enumerate(all_stocks[:5], 1):
        print(f"  #{i:<5} {s['name']:<12} {s['ticker']:<8} {s['portfolio']:<8} {s['return_pct']:>+10.2f}%")
    
    print(f"\n{'='*80}")
    print(f"  📉 表现最差的股票")
    print(f"{'='*80}")
    
    all_stocks.sort(key=lambda x: x["return_pct"])
    
    print(f"\n  {'排名':<6} {'股票':<12} {'代码':<8} {'所属组合':<8} {'跌幅':>10}")
    print(f"  {'-'*6} {'-'*12} {'-'*8} {'-'*8} {'-'*10}")
    for i, s in enumerate(all_stocks[:5], 1):
        print(f"  #{i:<5} {s['name']:<12} {s['ticker']:<8} {s['portfolio']:<8} {s['return_pct']:>+10.2f}%")

if __name__ == "__main__":
    main()