weekly_review.py

"""Weekly Portfolio Review - 2026-05-23"""
import baostock as bs
import pandas as pd
import numpy as np

lg = bs.login()

# ============================================================
# Portfolio definitions (建仓日 2026-05-15)
# ============================================================
ai_portfolio = {
    "sh.600000": {"name": "浦发银行", "weight": 4.75},
    "sh.601800": {"name": "中国交建", "weight": 4.75},
    "sz.000877": {"name": "天山股份", "weight": 4.75},
    "sh.601186": {"name": "中国铁建", "weight": 4.75},
    "sh.600373": {"name": "中文传媒", "weight": 4.75},
    "sh.600997": {"name": "开滦股份", "weight": 4.75},
    "sh.601686": {"name": "友发集团", "weight": 4.75},
    "sh.601166": {"name": "兴业银行", "weight": 4.75},
}

user_portfolio = {
    "sz.002050": {"name": "三花智控", "weight": 4.125},
    "sz.002938": {"name": "鹏鼎控股", "weight": 4.125},
    "sh.600584": {"name": "长电科技", "weight": 4.125},
    "sh.688820": {"name": "盛合晶微", "weight": 4.125},
    "sh.688212": {"name": "澳华内镜", "weight": 5.0},
    "sz.003031": {"name": "中瓷电子", "weight": 4.125},
    "sh.600941": {"name": "中国移动", "weight": 4.125},
    "sh.600373": {"name": "中文传媒", "weight": 4.125},
    "sh.600406": {"name": "国电南瑞", "weight": 4.125},
}

base_date = "2026-05-15"
current_date = "2026-05-23"

def get_prices(code, start, end):
    rs = bs.query_history_k_data_plus(code,
        "date,close,pctChg,amount",
        start_date=start, end_date=end,
        frequency="d", adjustflag="3")
    data = []
    while (rs.error_code == "0") & rs.next():
        data.append(rs.get_row_data())
    if not data:
        return None
    df = pd.DataFrame(data, columns=rs.fields)
    df["date"] = pd.to_datetime(df["date"])
    df = df.sort_values("date")
    df["close"] = pd.to_numeric(df["close"], errors="coerce")
    df["pctChg"] = pd.to_numeric(df["pctChg"], errors="coerce")
    df["amount"] = pd.to_numeric(df["amount"], errors="coerce")
    return df

# ============================================================
# Part 1: Portfolio Returns
# ============================================================
print("=" * 90)
print(f"WEEKLY PORTFOLIO REVIEW ({base_date} → {current_date})")
print("=" * 90)

for label, portfolio in [("🤖 AI (S1纯价值)", ai_portfolio), ("👤 用户 (成长+价值)", user_portfolio)]:
    total_weight = sum(v["weight"] for v in portfolio.values())
    print(f"\n{label} | 总资金: {total_weight:.2f}万")
    print(f"  {'Stock':<10} {'5/15价':>8} {'5/23价':>8} {'涨幅':>8} {'权重':>6} {'贡献':>8}")
    print(f"  {'-'*10} {'-'*8} {'-'*8} {'-'*8} {'-'*6} {'-'*8}")

    weighted_return = 0
    for code, info in portfolio.items():
        df = get_prices(code, base_date, current_date)
        if df is not None and len(df) > 0:
            base_price = df.iloc[0]["close"]
            last_price = df.iloc[-1]["close"]
            ret = (last_price / base_price - 1) * 100
            weight_pct = info["weight"] / total_weight
            contribution = ret * weight_pct
            weighted_return += contribution
            print(f"  {info['name']:<10} {base_price:>8.2f} {last_price:>8.2f} {ret:>+7.2f}% {weight_pct:>5.1%} {contribution:>+7.2f}%")
        else:
            print(f"  {info['name']:<10} {'N/A':>8} {'N/A':>8} {'N/A':>8}")

    print(f"  {'─'*50}")
    print(f"  组合加权收益率: {weighted_return:>+7.2f}%")
    print(f"  组合市值: {total_weight * (1 + weighted_return/100):.2f}万 (初始{total_weight:.2f}万)")

# ============================================================
# Part 2: Daily Performance
# ============================================================
print(f"\n{'='*90}")
print("DAILY PERFORMANCE (5/15 - 5/23)")
print("=" * 90)

for label, portfolio in [("AI", ai_portfolio), ("USER", user_portfolio)]:
    total_weight = sum(v["weight"] for v in portfolio.values())
    daily_returns = {}

    for code, info in portfolio.items():
        df = get_prices(code, base_date, current_date)
        if df is not None and len(df) > 1:
            weight_pct = info["weight"] / total_weight
            for _, row in df.iterrows():
                date_str = row["date"].strftime("%m/%d")
                if date_str not in daily_returns:
                    daily_returns[date_str] = 0
                daily_returns[date_str] += row["pctChg"] * weight_pct

    print(f"\n  {label} Daily Returns:")
    print(f"  {'Date':<8} {'Return':>8} {'Cumulative':>12}")
    cum = 0
    for date_str, ret in sorted(daily_returns.items()):
        cum += ret
        print(f"  {date_str:<8} {ret:>+7.2f}% {cum:>+10.2f}%")

# ============================================================
# Part 3: Market Index Comparison
# ============================================================
print(f"\n{'='*90}")
print("MARKET INDEX COMPARISON")
print("=" * 90)

for code, name in [("sh.000001", "上证指数"), ("sz.399006", "创业板指"), ("sh.000300", "沪深300")]:
    df = get_prices(code, base_date, current_date)
    if df is not None and len(df) > 0:
        ret = (df.iloc[-1]["close"] / df.iloc[0]["close"] - 1) * 100
        print(f"  {name}: {ret:>+7.2f}%")

# ============================================================
# Part 4: Individual Stock Technical Status
# ============================================================
print(f"\n{'='*90}")
print("INDIVIDUAL STOCK STATUS (5/23)")
print("=" * 90)

all_stocks = {}
for code, info in {**ai_portfolio, **user_portfolio}.items():
    if code not in all_stocks:
        all_stocks[code] = info

for code, info in sorted(all_stocks.items(), key=lambda x: x[1]["name"]):
    df = get_prices(code, "2026-04-20", current_date)
    if df is None or len(df) < 20:
        continue

    # RSI14
    delta = df["close"].diff()
    gain = delta.where(delta > 0, 0).rolling(14).mean()
    loss_val = (-delta.where(delta < 0, 0)).rolling(14).mean()
    rs_val = gain / loss_val.replace(0, 0.001)
    df["RSI14"] = 100 - (100 / (1 + rs_val))

    # MA
    df["MA5"] = df["close"].rolling(5).mean()
    df["MA20"] = df["close"].rolling(20).mean()

    # MACD
    df["EMA12"] = df["close"].ewm(span=12).mean()
    df["EMA26"] = df["close"].ewm(span=26).mean()
    df["DIF"] = df["EMA12"] - df["EMA26"]
    df["DEA"] = df["DIF"].ewm(span=9).mean()

    last = df.iloc[-1]
    rsi = last["RSI14"]
    macd_signal = "BULL" if last["DIF"] > last["DEA"] else "BEAR"
    ma_signal = "ABOVE" if last["close"] > last["MA20"] else "BELOW"

    # Capital flow (5d)
    df["inflow"] = df.apply(lambda r: r["amount"] if r["pctChg"] > 0 else 0, axis=1)
    df["outflow"] = df.apply(lambda r: r["amount"] if r["pctChg"] < 0 else 0, axis=1)
    net_5d = (df["inflow"].tail(5).sum() - df["outflow"].tail(5).sum()) / 1e8
    total_5d = (df["inflow"].tail(5).sum() + df["outflow"].tail(5).sum()) / 1e8
    net_ratio = (net_5d / total_5d * 100) if total_5d > 0 else 0

    # Return since 5/15
    base_df = get_prices(code, base_date, current_date)
    if base_df is not None and len(base_df) > 0:
        ret = (base_df.iloc[-1]["close"] / base_df.iloc[0]["close"] - 1) * 100
    else:
        ret = 0

    portfolio = "AI" if code in ai_portfolio else ""
    portfolio2 = "USER" if code in user_portfolio else ""
    port_label = "/".join(filter(None, [portfolio, portfolio2]))

    print(f"  {info['name']:<10} [{port_label:>8}] Price:{last['close']:>8.2f} Ret:{ret:>+6.2f}% RSI:{rsi:>5.1f} MACD:{macd_signal:>4} MA20:{ma_signal:>5} Flow5d:{net_ratio:>+6.1f}%")

bs.logout()