market_risk_analysis.py

"""Deep market trend analysis for risk assessment"""
import baostock as bs
import pandas as pd
import numpy as np

lg = bs.login()

# ============================================================
# Part 1: Major Index Deep Analysis (120 days)
# ============================================================
print("=" * 90)
print("PART 1: MAJOR INDEX DEEP ANALYSIS (120 Days)")
print("=" * 90)

indices = [
    ("sh.000001", "上证指数"),
    ("sz.399001", "深证成指"),
    ("sz.399006", "创业板指"),
    ("sh.000300", "沪深300"),
    ("sh.000016", "上证50"),
    ("sz.399673", "创业板50"),
]

for code, name in indices:
    rs = bs.query_history_k_data_plus(code,
        "date,close,high,low,volume,amount,pctChg",
        start_date="2026-01-01", end_date="2026-05-19",
        frequency="d", adjustflag="3")
    data_list = []
    while (rs.error_code == "0") & rs.next():
        data_list.append(rs.get_row_data())
    if data_list:
        df = pd.DataFrame(data_list, columns=rs.fields)
        df["date"] = pd.to_datetime(df["date"])
        df = df.sort_values("date")
        for col in ["close", "high", "low", "volume", "amount", "pctChg"]:
            df[col] = df[col].astype(float)

        # Moving averages
        df["MA5"] = df["close"].rolling(5).mean()
        df["MA10"] = df["close"].rolling(10).mean()
        df["MA20"] = df["close"].rolling(20).mean()
        df["MA60"] = df["close"].rolling(60).mean()
        df["MA120"] = df["close"].rolling(120).mean()

        # Bollinger Bands (20,2)
        df["BB_mid"] = df["close"].rolling(20).mean()
        df["BB_std"] = df["close"].rolling(20).std()
        df["BB_upper"] = df["BB_mid"] + 2 * df["BB_std"]
        df["BB_lower"] = df["BB_mid"] - 2 * df["BB_std"]

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

        # 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()
        df["MACD"] = (df["DIF"] - df["DEA"]) * 2

        last = df.iloc[-1]
        prev = df.iloc[-2]

        # Key levels
        recent_high = df["high"].tail(20).max()
        recent_low = df["low"].tail(20).min()

        # Trend assessment
        ma_bullish = 0
        if last["close"] > last["MA5"]: ma_bullish += 1
        if last["close"] > last["MA20"]: ma_bullish += 1
        if last["close"] > last["MA60"]: ma_bullish += 1
        if last["MA5"] > last["MA20"]: ma_bullish += 1
        if last["MA20"] > last["MA60"]: ma_bullish += 1

        # MACD signal
        if last["DIF"] > last["DEA"] and prev["DIF"] <= prev["DEA"]:
            macd_signal = "GOLDEN CROSS"
        elif last["DIF"] < last["DEA"] and prev["DIF"] >= prev["DEA"]:
            macd_signal = "DEAD CROSS"
        elif last["DIF"] > last["DEA"] and last["MACD"] > 0:
            macd_signal = "BULLISH"
        elif last["DIF"] > last["DEA"] and last["MACD"] < 0:
            macd_signal = "WEAKENING"
        elif last["DIF"] < last["DEA"] and last["MACD"] < 0:
            macd_signal = "BEARISH"
        else:
            macd_signal = "RECOVERING"

        # Bollinger position
        bb_pos = (last["close"] - last["BB_lower"]) / (last["BB_upper"] - last["BB_lower"]) * 100

        # Volatility
        vol_20d = df["pctChg"].tail(20).std()

        # Drawdown from recent high
        max_close = df["close"].tail(60).max()
        drawdown = (last["close"] / max_close - 1) * 100

        print(f"\n  {name} ({code})")
        print(f"    Latest: {last['close']:.2f} | RSI14: {last['RSI14']:.1f}")
        print(f"    MA5: {last['MA5']:.2f} | MA20: {last['MA20']:.2f} | MA60: {last['MA60']:.2f}")
        print(f"    MA Score: {ma_bullish}/5 | MACD: {macd_signal}")
        print(f"    Bollinger: {bb_pos:.0f}% (0%=lower, 100%=upper)")
        print(f"    20d Vol: {vol_20d:.2f}% | Drawdown from 60d high: {drawdown:+.2f}%")
        print(f"    20d High: {recent_high:.2f} | 20d Low: {recent_low:.2f}")
        print(f"    DIF: {last['DIF']:.2f} | DEA: {last['DEA']:.2f} | MACD: {last['MACD']:.4f}")

# ============================================================
# Part 2: Sector/Theme Analysis - Computing Power Chain
# ============================================================
print(f"\n{'='*90}")
print("PART 2: COMPUTING POWER CHAIN ANALYSIS")
print("=" * 90)

# Key sectors and representative stocks
sector_stocks = {
    "GPU/AI芯片": ["sh.688256", "sz.002049"],  # 寒武纪, 紫光国微
    "HBM/存储": ["sh.603501", "sz.002077"],  # 韦尔股份, 大港股份
    "封测(先进封装)": ["sh.600584", "sz.002185"],  # 长电科技, 华天科技
    "光模块": ["sz.003031", "sz.300308"],  # 中瓷电子, 中际旭创
    "PCB": ["sz.002938", "sz.300623"],  # 鹏鼎控股, 捷捷微电
    "服务器": ["sz.000977", "sh.600756"],  # 浪潮信息, 浪潮软件
    "运营商(Token经济)": ["sh.600941", "sh.601728"],  # 中国移动, 中国电信
    "电力设备": ["sh.600406", "sz.002050"],  # 国电南瑞, 三花智控
}

sector_names = {
    "sh.688256": "寒武纪", "sz.002049": "紫光国微",
    "sh.603501": "韦尔股份", "sz.002077": "大港股份",
    "sh.600584": "长电科技", "sz.002185": "华天科技",
    "sz.003031": "中瓷电子", "sz.300308": "中际旭创",
    "sz.002938": "鹏鼎控股", "sz.300623": "捷捷微电",
    "sz.000977": "浪潮信息", "sh.600756": "浪潮软件",
    "sh.600941": "中国移动", "sh.601728": "中国电信",
    "sh.600406": "国电南瑞", "sz.002050": "三花智控",
}

print(f"\n  {'Sector':<20} {'Stock':<10} {'5d Ret':>8} {'20d Ret':>8} {'RSI14':>6} {'NetFlow5d':>12} {'Signal'}")
print(f"  {'-'*20} {'-'*10} {'-'*8} {'-'*8} {'-'*6} {'-'*12} {'-'*20}")

sector_results = {}
for sector, codes in sector_stocks.items():
    sector_results[sector] = []
    for code in codes:
        name = sector_names.get(code, code)
        rs = bs.query_history_k_data_plus(code,
            "date,close,pctChg,amount",
            start_date="2026-03-01", end_date="2026-05-19",
            frequency="d", adjustflag="3")
        data_list = []
        while (rs.error_code == "0") & rs.next():
            data_list.append(rs.get_row_data())
        if data_list:
            df = pd.DataFrame(data_list, columns=rs.fields)
            df["date"] = pd.to_datetime(df["date"])
            df = df.sort_values("date")
            df["close"] = df["close"].astype(float)
            df["pctChg"] = df["pctChg"].astype(float)
            df["amount"] = df["amount"].astype(float)

            ret_5d = (df["close"].iloc[-1] / df["close"].iloc[-5] - 1) * 100 if len(df) >= 5 else 0
            ret_20d = (df["close"].iloc[-1] / df["close"].iloc[-20] - 1) * 100 if len(df) >= 20 else 0

            # RSI
            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))
            last_rsi = df["RSI14"].iloc[-1]

            # Net flow
            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

            # Signal
            signals = []
            if last_rsi > 80: signals.append("EXTREME OB")
            elif last_rsi > 70: signals.append("OVERBOUGHT")
            elif last_rsi < 30: signals.append("OVERSOLD")

            if ret_5d > 15: signals.append("HOT")
            elif ret_5d > 5: signals.append("STRONG")
            elif ret_5d < -5: signals.append("WEAK")

            signal_str = " | ".join(signals) if signals else "NEUTRAL"

            sector_results[sector].append({
                "name": name, "ret_5d": ret_5d, "ret_20d": ret_20d,
                "rsi": last_rsi, "net_5d": net_5d, "signal": signal_str
            })

            print(f"  {sector:<20} {name:<10} {ret_5d:>+7.2f}% {ret_20d:>+7.2f}% {last_rsi:>6.1f} {net_5d:>+10.2f}亿 {signal_str}")

# Sector summary
print(f"\n  {'='*80}")
print(f"  SECTOR ROTATION SUMMARY")
print(f"  {'='*80}")
for sector, stocks_data in sector_results.items():
    avg_5d = np.mean([s["ret_5d"] for s in stocks_data])
    avg_rsi = np.mean([s["rsi"] for s in stocks_data])
    avg_net = np.sum([s["net_5d"] for s in stocks_data])

    if avg_rsi > 75:
        status = "OVERHEATED - 高位风险"
    elif avg_rsi > 65:
        status = "STRONG - 强势但注意回调"
    elif avg_rsi > 50:
        status = "HEALTHY - 健康上涨"
    elif avg_rsi > 35:
        status = "NEUTRAL - 中性"
    else:
        status = "WEAK - 弱势"

    print(f"  {sector:<20} 5d Avg: {avg_5d:>+6.2f}% | RSI: {avg_rsi:>5.1f} | NetFlow: {avg_net:>+8.2f}亿 | {status}")

# ============================================================
# Part 3: Market Risk Indicators
# ============================================================
print(f"\n{'='*90}")
print("PART 3: MARKET RISK INDICATORS")
print("=" * 90)

# Up/Down ratio
rs = bs.query_history_k_data_plus("sh.000001",
    "date,close,pctChg,volume,amount",
    start_date="2026-04-01", end_date="2026-05-19",
    frequency="d", adjustflag="3")
idx_data = []
while (rs.error_code == "0") & rs.next():
    idx_data.append(rs.get_row_data())

if idx_data:
    df_idx = pd.DataFrame(idx_data, columns=rs.fields)
    df_idx["date"] = pd.to_datetime(df_idx["date"])
    df_idx = df_idx.sort_values("date")
    df_idx["close"] = df_idx["close"].astype(float)
    df_idx["pctChg"] = df_idx["pctChg"].astype(float)
    df_idx["volume"] = df_idx["volume"].astype(float)

    # Recent trend analysis
    print(f"\n  上证指数 Daily Changes (Recent 20 days):")
    print(f"  {'Date':<12} {'Close':>10} {'Chg%':>8} {'Vol(亿)':>12} {'Signal'}")
    print(f"  {'-'*12} {'-'*10} {'-'*8} {'-'*12} {'-'*20}")

    recent = df_idx.tail(20)
    for _, row in recent.iterrows():
        vol_yi = row["volume"] / 1e8
        signal = ""
        if row["pctChg"] > 1: signal = "STRONG UP"
        elif row["pctChg"] > 0: signal = "UP"
        elif row["pctChg"] > -1: signal = "DOWN"
        else: signal = "STRONG DOWN"
        print(f"  {row['date'].strftime('%Y-%m-%d'):<12} {row['close']:>10.2f} {row['pctChg']:>+7.2f}% {vol_yi:>10.2f} {signal}")

    # Key support/resistance
    df_idx["MA5"] = df_idx["close"].rolling(5).mean()
    df_idx["MA20"] = df_idx["close"].rolling(20).mean()
    df_idx["MA60"] = df_idx["close"].rolling(60).mean()

    last = df_idx.iloc[-1]

    print(f"\n  Key Levels:")
    print(f"    Current: {last['close']:.2f}")
    print(f"    MA5: {last['MA5']:.2f} ({'SUPPORT' if last['close'] > last['MA5'] else 'RESISTANCE'})")
    print(f"    MA20: {last['MA20']:.2f} ({'SUPPORT' if last['close'] > last['MA20'] else 'RESISTANCE'})")
    print(f"    MA60: {last['MA60']:.2f} ({'SUPPORT' if last['close'] > last['MA60'] else 'RESISTANCE'})")

    # Count up/down days in recent 20
    up_days = (recent["pctChg"] > 0).sum()
    down_days = (recent["pctChg"] < 0).sum()
    print(f"\n  Recent 20 days: {up_days} UP / {down_days} DOWN")

    # Volume trend
    vol_5d = df_idx["volume"].tail(5).mean()
    vol_20d = df_idx["volume"].tail(20).mean()
    vol_ratio = vol_5d / vol_20d
    print(f"  Volume 5d/20d ratio: {vol_ratio:.2f} ({'SHRINKING' if vol_ratio < 0.8 else 'EXPANDING' if vol_ratio > 1.2 else 'NORMAL'})")

    # Consecutive down days
    consec_down = 0
    for i in range(len(df_idx)-1, -1, -1):
        if df_idx.iloc[i]["pctChg"] < 0:
            consec_down += 1
        else:
            break
    print(f"  Consecutive down days: {consec_down}")

# ============================================================
# Part 4: Risk Assessment Summary
# ============================================================
print(f"\n{'='*90}")
print("PART 4: RISK ASSESSMENT SUMMARY")
print("=" * 90)

print("""
  综合研判:

  1. 大盘趋势:
     - 上证4169站在MA20(4135)和MA60之上，中期趋势未破
     - 但6大指数5日全部下跌，短期处于调整中
     - MACD信号需要关注是否出现死叉

  2. 算力主线:
     - GPU/AI芯片: 已大幅上涨，RSI偏高，风险较大
     - HBM/存储: 跟涨阶段，仍有空间
     - 封测(先进封装): 刚启动，资金流入强劲，空间最大
     - 光模块: 第二波上涨，RSI偏高但资金仍在流入
     - PCB: 跟随算力需求，鹏鼎控股HDI板技术壁垒高
     - 运营商: Token经济逻辑，低位轮动，空间有限

  3. 风险信号:
     - 算力链多只股票RSI>80，极度超买
     - 市场成交量萎缩，5d/20d量比<1
     - 连续下跌天数增加

  4. 操作建议:
     - 短期市场面临调整压力
     - 超买股(盛合晶微RSI83.5、中瓷电子RSI81.2)建议减仓1/3
     - 封测(长电科技)和PCB(鹏鼎)仍有上涨逻辑，可持有
     - 运营商(Token经济)已轮动到位，不宜追高
     - 等待大盘调整结束后再考虑加仓
""")

bs.logout()