stock_detail_analysis.py

"""Check daily capital flow for Loongson & Hygon, and find similar AIoT chip stocks"""
import baostock as bs
import pandas as pd
import numpy as np

lg = bs.login()

# ============================================================
# Part 1: Loongson & Hygon daily capital flow (recent 10 days)
# ============================================================
print("=" * 90)
print("PART 1: DAILY CAPITAL FLOW - LOONGSON & HYGON (Recent 10 Days)")
print("=" * 90)

for code, name in [("sh.688047", "龙芯中科"), ("sh.688041", "海光信息")]:
    rs = bs.query_history_k_data_plus(code,
        "date,close,open,high,low,volume,amount,pctChg",
        start_date="2026-05-06", 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", "open", "high", "low", "volume", "amount", "pctChg"]:
            df[col] = df[col].astype(float)

        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)
        df["net_flow"] = (df["inflow"] - df["outflow"]) / 1e8
        df["total_flow"] = (df["inflow"] + df["outflow"]) / 1e8
        df["net_ratio"] = (df["net_flow"] / df["total_flow"] * 100)

        # RSI6
        delta = df["close"].diff()
        gain6 = delta.where(delta > 0, 0).rolling(6).mean()
        loss6 = (-delta.where(delta < 0, 0)).rolling(6).mean()
        rs6 = gain6 / loss6.replace(0, 0.001)
        df["RSI6"] = 100 - (100 / (1 + rs6))

        print(f"\n  {name} ({code}) - Daily Capital Flow")
        print(f"  {'Date':<12} {'Close':>8} {'Chg%':>7} {'Vol(万)':>10} {'NetFlow(亿)':>12} {'NetRatio':>10} {'RSI6':>6} {'Signal'}")
        print(f"  {'-'*12} {'-'*8} {'-'*7} {'-'*10} {'-'*12} {'-'*10} {'-'*6} {'-'*20}")

        for _, row in df.iterrows():
            signal = "INFLOW" if row["net_ratio"] > 10 else "INFLOW+" if row["net_ratio"] > 0 else "OUTFLOW" if row["net_ratio"] < -10 else "OUTFLOW-" if row["net_ratio"] < 0 else "FLAT"
            rsi6 = row.get("RSI6", 0)
            print(f"  {row['date'].strftime('%Y-%m-%d'):<12} {row['close']:>8.2f} {row['pctChg']:>+6.2f}% {row['volume']/1e4:>10.0f} {row['net_flow']:>+10.2f} {row['net_ratio']:>+8.1f}% {rsi6:>6.1f} {signal}")

        # Check last 2 days
        last2 = df.tail(2)
        both_inflow = all(last2["net_ratio"] > 0)
        print(f"\n  Last 2 days both positive inflow: {'YES' if both_inflow else 'NO'}")
        print(f"  5/18: NetFlow={last2.iloc[0]['net_flow']:+.2f}亿 ({last2.iloc[0]['net_ratio']:+.1f}%)")
        print(f"  5/19: NetFlow={last2.iloc[1]['net_flow']:+.2f}亿 ({last2.iloc[1]['net_ratio']:+.1f}%)")

        # Volume trend: compare 5/18-19 vs 5/14-15
        recent2_vol = df.tail(2)["volume"].mean()
        prev2_vol = df.iloc[-6:-4]["volume"].mean() if len(df) >= 6 else recent2_vol
        vol_change = (recent2_vol / prev2_vol - 1) * 100 if prev2_vol > 0 else 0
        print(f"  Volume trend: Recent 2d avg vs Previous 2d avg: {vol_change:+.1f}% ({'缩量' if vol_change < -20 else '放量' if vol_change > 20 else '持平'})")

        # Price stabilization check
        recent2_range = (df.tail(2)["high"].max() - df.tail(2)["low"].min()) / df.tail(2)["close"].mean() * 100
        prev2_range = (df.iloc[-6:-4]["high"].max() - df.iloc[-6:-4]["low"].min()) / df.iloc[-6:-4]["close"].mean() * 100 if len(df) >= 6 else recent2_range
        print(f"  Price volatility: Recent 2d={recent2_range:.1f}% vs Previous 2d={prev2_range:.1f}% ({'波动收窄=企稳' if recent2_range < prev2_range else '波动扩大'})")

# ============================================================
# Part 2: Edge-side AI Chip Stocks (similar to Rockchip)
# ============================================================
print(f"\n{'='*90}")
print("PART 2: EDGE-SIDE AI CHIP STOCKS (Similar to Rockchip)")
print("=" * 90)

# Key edge-side AI / AIoT chip stocks
edge_ai_stocks = [
    ("sh.603893", "瑞芯微", "AIoT SoC"),
    ("sz.300782", "卓胜微", "射频芯片"),
    ("sh.688396", "华润微", "功率半导体"),
    ("sz.300458", "全志科技", "智能应用处理器SoC"),
    ("sh.688521", "芯原股份", "芯片IP"),
    ("sz.300474", "景嘉微", "GPU芯片"),
    ("sh.688012", "中微半导", "MCU芯片"),
    ("sh.688256", "寒武纪", "AI芯片"),
    ("sz.002371", "北方华创", "半导体设备"),
    ("sh.688981", "中芯国际", "晶圆代工"),
    ("sz.300223", "北京君正", "存储+处理器"),
    ("sh.688049", "晶晨股份", "智能机顶盒SoC"),
    ("sz.300613", "富瀚微", "安防芯片"),
    ("sh.688008", "澜起科技", "内存接口芯片"),
    ("sz.300053", "欧比特", "宇航芯片+AI"),
]

print(f"\n  {'Stock':<10} {'Sector':<16} {'Price':>8} {'5d%':>7} {'20d%':>7} {'RSI14':>6} {'NetFlow5d':>12} {'MACD':>10} {'Score'}")
print(f"  {'-'*10} {'-'*16} {'-'*8} {'-'*7} {'-'*7} {'-'*6} {'-'*12} {'-'*10} {'-'*6}")

results = []
for code, name, sector in edge_ai_stocks:
    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 not data_list:
        print(f"  {name:<10} {sector:<16} {'N/A':>8}")
        continue

    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)

    last_close = df["close"].iloc[-1]
    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

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

    # 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_dif = df["DIF"].iloc[-1]
    last_dea = df["DEA"].iloc[-1]
    prev_dif = df["DIF"].iloc[-2]
    prev_dea = df["DEA"].iloc[-2]

    if last_dif > last_dea and prev_dif <= prev_dea:
        macd_signal = "GOLDEN"
    elif last_dif > last_dea:
        macd_signal = "BULL"
    elif last_dif < last_dea and prev_dif >= prev_dea:
        macd_signal = "DEAD"
    else:
        macd_signal = "BEAR"

    # Capital 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
    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

    # Quick score
    score = 0
    if ret_5d > 5: score += 2
    elif ret_5d > 0: score += 1
    elif ret_5d < -5: score -= 2
    else: score -= 1

    if 40 <= last_rsi <= 65: score += 2
    elif 30 <= last_rsi <= 70: score += 1
    elif last_rsi > 80: score -= 1

    if net_ratio > 20: score += 2
    elif net_ratio > 0: score += 1
    elif net_ratio < -20: score -= 2
    else: score -= 1

    if macd_signal in ("GOLDEN", "BULL"): score += 2
    elif macd_signal == "DEAD": score -= 2
    elif macd_signal == "BEAR": score -= 1

    results.append({
        "name": name, "sector": sector, "price": last_close,
        "ret_5d": ret_5d, "ret_20d": ret_20d, "rsi": last_rsi,
        "net_5d": net_5d, "net_ratio": net_ratio,
        "macd": macd_signal, "score": score
    })

    print(f"  {name:<10} {sector:<16} {last_close:>8.2f} {ret_5d:>+6.2f}% {ret_20d:>+6.2f}% {last_rsi:>6.1f} {net_5d:>+10.2f}亿 {macd_signal:>10} {score:>+3d}")

# Sort by score
print(f"\n  {'='*80}")
print(f"  RANKING BY SCORE (Edge-side AI Chips)")
print(f"  {'='*80}")
results.sort(key=lambda x: x["score"], reverse=True)
for i, r in enumerate(results, 1):
    if r["score"] >= 3:
        tag = "STRONG BUY"
    elif r["score"] >= 1:
        tag = "BUY"
    elif r["score"] >= -1:
        tag = "HOLD"
    else:
        tag = "AVOID"
    print(f"  #{i:2d} {r['name']:<10} {r['sector']:<16} Score:{r['score']:>+3d} RSI:{r['rsi']:.0f} 5d:{r['ret_5d']:+.1f}% Flow:{r['net_ratio']:+.0f}% → {tag}")

bs.logout()