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README.md

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+# 美股模拟投资系统
+
+基于 [daily_stock_analysis](https://github.com/ZhuLinsen/daily_stock_analysis) 的技术分析体系。
+
+## 文件说明
+
+| 文件 | 说明 |
+|------|------|
+| `portfolio.py` | 基础操作：买入/卖出/查看持仓 |
+| `monitor.py` | 实时行情监控（终端刷新） |
+| `stock_screener.py` | 技术面选股器（扫描69只美股评分排序） |
+| `auto_trader.py` | 自动交易Bot（每日定时分析+虚拟买卖） |
+| `my_portfolio.json` | 当前持仓数据 |
+| `trade_log.json` | 历史交易日志 |
+| `daily_reports/` | 每日分析报告 |
+
+## 日常使用
+
+```bash
+cd ~/stock-sim
+
+# 手动操作
+python3 portfolio.py show           # 看持仓
+python3 portfolio.py buy AAPL 5000  # 买入
+python3 portfolio.py sell TSLA 10   # 卖出
+
+# 选股
+python3 stock_screener.py           # 技术面筛选Top20
+
+# 自动交易Bot
+python3 auto_trader.py              # 立即执行一次
+python3 auto_trader.py --daemon     # 后台定时(每日21:35北京时间)
+python3 auto_trader.py --history    # 查看历史记录
+
+# AI深度分析（需要LLMBox）
+cd ~/daily_stock_analysis
+python3 main.py --stocks AAPL,PLTR --force-run --no-notify
+```
+
+## 评分体系（100分制）
+
+| 维度 | 满分 | 说明 |
+|------|------|------|
+| 趋势 | 30 | MA5>MA10>MA20 多头排列 |
+| 乖离率 | 20 | 接近MA5不追高 |
+| 量能 | 15 | 缩量回调最优 |
+| MACD | 15 | 金叉/多头 |
+| RSI | 10 | 超卖反弹/强势 |
+| 支撑 | 10 | 均线支撑有效 |
+
+## Bot 交易规则
+
+- 评分>=60 且"买入"信号 → 自动买入
+- 评分<30 或"卖出"信号 → 自动卖出
+- 空头排列且<45 → 减半仓
+- 单只最多占总资产20%
+- 最多持有8只
+- 每日最多用30%现金买入

auto_trader.py

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+#!/usr/bin/env python3
+"""
+美股自动交易Bot — 每日定时运行
+1. 技术面筛选69只美股
+2. AI深度分析Top候选
+3. 自动虚拟买卖（用portfolio.py）
+4. 记录每日操作和收益到日志
+
+用法:
+  python3 auto_trader.py              # 立即执行一次
+  python3 auto_trader.py --daemon     # 后台定时运行(每个交易日21:35北京时间)
+  python3 auto_trader.py --backtest   # 查看历史操作记录
+"""
+
+import json
+import os
+import sys
+import subprocess
+import time
+import warnings
+from datetime import datetime, timedelta
+from pathlib import Path
+
+warnings.filterwarnings("ignore")
+
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+# ── 路径 ──
+BASE_DIR = Path(__file__).parent
+PORTFOLIO_FILE = BASE_DIR / "my_portfolio.json"
+TRADE_LOG = BASE_DIR / "trade_log.json"
+DAILY_REPORT_DIR = BASE_DIR / "daily_reports"
+DAILY_REPORT_DIR.mkdir(exist_ok=True)
+
+# ── 交易参数 ──
+MAX_POSITION_PCT = 0.20       # 单只最多占总资产20%
+MIN_BUY_SCORE = 60            # 最低买入评分
+SELL_SCORE_THRESHOLD = 30     # 低于此分卖出
+MAX_HOLDINGS = 8              # 最多持有8只
+DAILY_BUY_BUDGET_PCT = 0.30   # 每天最多用30%现金买入
+
+# ── 候选池 ──
+US_STOCKS = [
+    "AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "TSLA",
+    "AMD", "AVGO", "QCOM", "INTC", "MU", "MRVL", "ARM", "SMCI", "TSM",
+    "CRM", "ORCL", "ADBE", "NOW", "SNOW", "PLTR", "NET", "DDOG", "CRWD",
+    "COST", "WMT", "TGT", "NKE", "SBUX", "MCD", "PEP", "KO",
+    "JPM", "GS", "MS", "BAC", "V", "MA", "AXP",
+    "LLY", "UNH", "JNJ", "PFE", "ABBV", "MRK", "BMY",
+    "XOM", "CVX", "SLB", "OXY",
+    "BA", "CAT", "DE", "GE", "LMT", "COIN", "UBER", "ABNB",
+    "SHOP", "PYPL", "ROKU", "SNAP", "PINS", "RBLX",
+]
+
+
+# ══════════════════════════════════════════════════════════
+#  技术分析（与 stock_screener.py 相同的评分体系）
+# ══════════════════════════════════════════════════════════
+
+def calc_macd(close, fast=12, slow=26, signal=9):
+    ema_fast = close.ewm(span=fast, adjust=False).mean()
+    ema_slow = close.ewm(span=slow, adjust=False).mean()
+    dif = ema_fast - ema_slow
+    dea = dif.ewm(span=signal, adjust=False).mean()
+    return dif, dea
+
+def calc_rsi(close, period=12):
+    delta = close.diff()
+    gain = delta.where(delta > 0, 0).rolling(period).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(period).mean()
+    rs = gain / loss
+    return (100 - 100 / (1 + rs)).fillna(50)
+
+def analyze_stock(symbol):
+    """技术分析评分，返回 dict 或 None"""
+    try:
+        df = yf.Ticker(symbol).history(period="6mo", auto_adjust=True)
+        if df is None or len(df) < 60:
+            return None
+
+        close = df["Close"]
+        volume = df["Volume"]
+        last = len(df) - 1
+        price = float(close.iloc[last])
+
+        ma5 = float(close.rolling(5).mean().iloc[last])
+        ma10 = float(close.rolling(10).mean().iloc[last])
+        ma20 = float(close.rolling(20).mean().iloc[last])
+
+        # 趋势（30分）
+        if ma5 > ma10 > ma20:
+            prev_idx = max(0, last - 5)
+            prev_s = (float(close.rolling(5).mean().iloc[prev_idx]) - float(close.rolling(20).mean().iloc[prev_idx])) / float(close.rolling(20).mean().iloc[prev_idx]) * 100
+            curr_s = (ma5 - ma20) / ma20 * 100
+            if curr_s > prev_s and curr_s > 5:
+                trend_score, trend = 30, "强势多头"
+            else:
+                trend_score, trend = 26, "多头排列"
+        elif ma5 > ma10:
+            trend_score, trend = 18, "弱势多头"
+        elif ma5 < ma10 < ma20:
+            trend_score, trend = 4, "空头排列"
+        else:
+            trend_score, trend = 12, "盘整"
+
+        # 乖离率（20分）
+        bias = (price - ma5) / ma5 * 100 if ma5 > 0 else 0
+        if bias < 0 and bias > -3:
+            bias_score = 20
+        elif bias < 0 and bias > -5:
+            bias_score = 16
+        elif bias < 0:
+            bias_score = 8
+        elif bias < 2:
+            bias_score = 18
+        elif bias < 5:
+            bias_score = 14
+        else:
+            bias_score = 4
+
+        # 量能（15分）
+        vol_avg = float(volume.iloc[-6:-1].mean())
+        vol_ratio = float(volume.iloc[last]) / vol_avg if vol_avg > 0 else 1
+        prev_close = float(close.iloc[last - 1])
+        chg = (price - prev_close) / prev_close * 100
+        if vol_ratio >= 1.5:
+            vol_score = 12 if chg > 0 else 0
+        elif vol_ratio <= 0.7:
+            vol_score = 15 if chg <= 0 else 6
+        else:
+            vol_score = 10
+
+        # MACD（15分）
+        dif, dea = calc_macd(close)
+        macd_dif, macd_dea = float(dif.iloc[last]), float(dea.iloc[last])
+        prev_diff = float(dif.iloc[last-1]) - float(dea.iloc[last-1])
+        curr_diff = macd_dif - macd_dea
+        golden = prev_diff <= 0 and curr_diff > 0
+        if golden and macd_dif > 0:
+            macd_score, macd_label = 15, "零轴上金叉"
+        elif golden:
+            macd_score, macd_label = 12, "金叉"
+        elif prev_diff >= 0 and curr_diff < 0:
+            macd_score, macd_label = 0, "死叉"
+        elif macd_dif > 0 and macd_dea > 0:
+            macd_score, macd_label = 8, "多头"
+        elif macd_dif < 0 and macd_dea < 0:
+            macd_score, macd_label = 2, "空头"
+        else:
+            macd_score, macd_label = 5, "中性"
+
+        # RSI（10分）
+        rsi = float(calc_rsi(close, 12).iloc[last])
+        if rsi > 70: rsi_score = 0
+        elif rsi > 60: rsi_score = 8
+        elif rsi >= 40: rsi_score = 5
+        elif rsi >= 30: rsi_score = 3
+        else: rsi_score = 10
+
+        # 支撑（10分）
+        sup_score = 0
+        if abs(price - ma5) / ma5 <= 0.02 and price >= ma5: sup_score += 5
+        if abs(price - ma10) / ma10 <= 0.02 and price >= ma10: sup_score += 5
+
+        total = trend_score + bias_score + vol_score + macd_score + rsi_score + sup_score
+
+        # 信号
+        if total >= 75 and trend in ("强势多头", "多头排列"):
+            signal = "强烈买入"
+        elif total >= 60 and trend in ("强势多头", "多头排列", "弱势多头"):
+            signal = "买入"
+        elif total >= 45:
+            signal = "持有"
+        elif total >= 30:
+            signal = "观望"
+        elif trend in ("空头排列",):
+            signal = "卖出"
+        else:
+            signal = "观望"
+
+        return {
+            "symbol": symbol, "price": price, "score": total, "signal": signal,
+            "trend": trend, "bias": bias, "macd": macd_label, "rsi": rsi,
+            "ma5": ma5, "ma10": ma10, "ma20": ma20,
+        }
+    except Exception:
+        return None
+
+
+# ══════════════════════════════════════════════════════════
+#  交易引擎
+# ══════════════════════════════════════════════════════════
+
+def load_portfolio():
+    if PORTFOLIO_FILE.exists():
+        with open(PORTFOLIO_FILE) as f:
+            pf = json.load(f)
+        if "transactions" not in pf:
+            pf["transactions"] = []
+        return pf
+    return {"cash": 100000, "holdings": {}, "transactions": []}
+
+def save_portfolio(pf):
+    with open(PORTFOLIO_FILE, "w") as f:
+        json.dump(pf, f, indent=2, ensure_ascii=False)
+
+def load_trade_log():
+    if TRADE_LOG.exists():
+        with open(TRADE_LOG) as f:
+            return json.load(f)
+    return []
+
+def save_trade_log(log):
+    with open(TRADE_LOG, "w") as f:
+        json.dump(log, f, indent=2, ensure_ascii=False)
+
+def execute_buy(pf, symbol, price, amount):
+    """虚拟买入"""
+    shares = int(amount / price)
+    if shares <= 0:
+        return None
+    cost = shares * price
+    if cost > pf["cash"]:
+        return None
+    pf["cash"] -= cost
+    if symbol in pf["holdings"]:
+        old = pf["holdings"][symbol]
+        total_shares = old["shares"] + shares
+        old["avg_cost"] = (old["avg_cost"] * old["shares"] + cost) / total_shares
+        old["shares"] = total_shares
+    else:
+        pf["holdings"][symbol] = {"shares": shares, "avg_cost": price}
+    pf["transactions"].append({
+        "type": "buy", "symbol": symbol, "shares": shares,
+        "price": price, "time": datetime.now().isoformat()
+    })
+    return {"symbol": symbol, "shares": shares, "price": price, "cost": cost}
+
+def execute_sell(pf, symbol, price, shares=None):
+    """虚拟卖出"""
+    if symbol not in pf["holdings"]:
+        return None
+    h = pf["holdings"][symbol]
+    sell_shares = shares or h["shares"]
+    sell_shares = min(sell_shares, h["shares"])
+    revenue = sell_shares * price
+    pf["cash"] += revenue
+    pnl = (price - h["avg_cost"]) * sell_shares
+    h["shares"] -= sell_shares
+    if h["shares"] <= 0:
+        del pf["holdings"][symbol]
+    pf["transactions"].append({
+        "type": "sell", "symbol": symbol, "shares": sell_shares,
+        "price": price, "pnl": round(pnl, 2), "time": datetime.now().isoformat()
+    })
+    return {"symbol": symbol, "shares": sell_shares, "price": price, "revenue": revenue, "pnl": pnl}
+
+def is_us_trading_day():
+    """检查今天是否是美股交易日"""
+    try:
+        from zoneinfo import ZoneInfo
+    except ImportError:
+        from backports.zoneinfo import ZoneInfo
+    et = datetime.now(ZoneInfo("America/New_York"))
+    return et.weekday() < 5  # 简化：不考虑节假日
+
+
+# ══════════════════════════════════════════════════════════
+#  每日策略执行
+# ══════════════════════════════════════════════════════════
+
+def run_daily_strategy():
+    """每日策略核心"""
+    today = datetime.now().strftime("%Y-%m-%d")
+    print(f"\n{'='*60}")
+    print(f"  🤖 自动交易Bot — {today}")
+    print(f"{'='*60}")
+
+    # 1. 扫描全部股票
+    print(f"\n  📡 正在扫描 {len(US_STOCKS)} 只美股...")
+    results = []
+    with ThreadPoolExecutor(max_workers=8) as pool:
+        futures = {pool.submit(analyze_stock, s): s for s in US_STOCKS}
+        for f in as_completed(futures):
+            r = f.result()
+            if r:
+                results.append(r)
+    results.sort(key=lambda x: x["score"], reverse=True)
+    print(f"  ✅ 扫描完成，{len(results)} 只有效")
+
+    # 2. 加载持仓
+    pf = load_portfolio()
+    total_assets = pf["cash"]
+    for sym, info in pf["holdings"].items():
+        # 用最新价更新
+        match = next((r for r in results if r["symbol"] == sym), None)
+        if match:
+            total_assets += match["price"] * info["shares"]
+        else:
+            total_assets += info["avg_cost"] * info["shares"]
+
+    print(f"\n  💼 当前资产: ${total_assets:,.0f}  现金: ${pf['cash']:,.0f}  持仓: {len(pf['holdings'])}只")
+
+    trades_today = []
+
+    # 3. 卖出逻辑：持仓中评分低的
+    print(f"\n  📉 检查卖出信号...")
+    for sym in list(pf["holdings"].keys()):
+        match = next((r for r in results if r["symbol"] == sym), None)
+        if match:
+            if match["score"] < SELL_SCORE_THRESHOLD or match["signal"] == "卖出":
+                result = execute_sell(pf, sym, match["price"])
+                if result:
+                    print(f"     🔴 卖出 {sym} x{result['shares']}股 @ ${result['price']:.2f}  盈亏: ${result['pnl']:+,.2f}")
+                    trades_today.append({"action": "SELL", **result})
+            elif match["score"] < 45 and match["trend"] in ("空头排列",):
+                # 空头排列减半仓
+                half = pf["holdings"][sym]["shares"] // 2
+                if half > 0:
+                    result = execute_sell(pf, sym, match["price"], half)
+                    if result:
+                        print(f"     🟡 减仓 {sym} x{result['shares']}股 @ ${result['price']:.2f}  盈亏: ${result['pnl']:+,.2f}")
+                        trades_today.append({"action": "REDUCE", **result})
+        else:
+            print(f"     ⚠️  {sym} 数据获取失败，保持持仓")
+
+    # 4. 买入逻辑：选评分最高的买入信号
+    print(f"\n  📈 检查买入信号...")
+    buy_budget = pf["cash"] * DAILY_BUY_BUDGET_PCT
+    buy_candidates = [r for r in results
+                      if r["score"] >= MIN_BUY_SCORE
+                      and r["signal"] in ("买入", "强烈买入")
+                      and r["symbol"] not in pf["holdings"]]
+
+    bought_count = 0
+    for r in buy_candidates:
+        if len(pf["holdings"]) >= MAX_HOLDINGS:
+            print(f"     ⏸️  已持有{MAX_HOLDINGS}只，不再买入")
+            break
+        if buy_budget < 1000:
+            print(f"     ⏸️  今日买入预算用完")
+            break
+
+        # 单只限额
+        max_amount = total_assets * MAX_POSITION_PCT
+        amount = min(buy_budget, max_amount, pf["cash"])
+        if amount < 500:
+            break
+
+        result = execute_buy(pf, r["symbol"], r["price"], amount)
+        if result:
+            print(f"     🟢 买入 {r['symbol']} x{result['shares']}股 @ ${result['price']:.2f} = ${result['cost']:,.0f}  (评分{r['score']})")
+            buy_budget -= result["cost"]
+            trades_today.append({"action": "BUY", "score": r["score"], **result})
+            bought_count += 1
+
+    if not trades_today:
+        print(f"     🔵 今日无操作")
+
+    # 5. 保存
+    save_portfolio(pf)
+
+    # 6. 计算总收益
+    total_now = pf["cash"]
+    holding_details = []
+    for sym, info in pf["holdings"].items():
+        match = next((r for r in results if r["symbol"] == sym), None)
+        cur_price = match["price"] if match else info["avg_cost"]
+        mkt = cur_price * info["shares"]
+        pnl = (cur_price - info["avg_cost"]) * info["shares"]
+        pct = (cur_price / info["avg_cost"] - 1) * 100
+        total_now += mkt
+        score = match["score"] if match else 0
+        holding_details.append({
+            "symbol": sym, "shares": info["shares"], "avg_cost": info["avg_cost"],
+            "price": cur_price, "mkt": mkt, "pnl": pnl, "pct": pct, "score": score
+        })
+
+    total_pnl = total_now - 100000
+    total_pct = total_pnl / 100000 * 100
+
+    # 7. 显示持仓
+    print(f"\n  {'─'*60}")
+    print(f"  📊 持仓明细:")
+    for h in sorted(holding_details, key=lambda x: x["score"], reverse=True):
+        sign = "+" if h["pnl"] >= 0 else ""
+        print(f"     {h['symbol']:<6} {h['shares']:>5}股  成本${h['avg_cost']:.2f}  现价${h['price']:.2f}  {sign}${h['pnl']:,.0f}({sign}{h['pct']:.1f}%)  评分{h['score']}")
+
+    sign = "+" if total_pnl >= 0 else ""
+    print(f"\n  💰 现金: ${pf['cash']:,.2f}")
+    print(f"  💼 总资产: ${total_now:,.2f}  总盈亏: {sign}${total_pnl:,.2f} ({sign}{total_pct:.2f}%)")
+
+    # 8. 保存日报
+    daily_report = {
+        "date": today,
+        "total_assets": round(total_now, 2),
+        "cash": round(pf["cash"], 2),
+        "total_pnl": round(total_pnl, 2),
+        "total_pct": round(total_pct, 2),
+        "holdings": len(pf["holdings"]),
+        "trades": trades_today,
+        "top5_scores": [{"symbol": r["symbol"], "score": r["score"], "signal": r["signal"]} for r in results[:5]],
+        "portfolio": holding_details,
+    }
+
+    report_file = DAILY_REPORT_DIR / f"report_{today}.json"
+    with open(report_file, "w") as f:
+        json.dump(daily_report, f, indent=2, ensure_ascii=False)
+
+    # 9. 追加交易日志
+    trade_log = load_trade_log()
+    trade_log.append(daily_report)
+    save_trade_log(trade_log)
+
+    print(f"\n  📝 日报已保存: {report_file}")
+    print(f"{'='*60}\n")
+
+    return daily_report
+
+
+def show_history():
+    """显示历史操作记录"""
+    log = load_trade_log()
+    if not log:
+        print("  暂无交易记录")
+        return
+
+    print(f"\n  📊 交易历史 ({len(log)}个交易日)")
+    print(f"  {'─'*60}")
+    print(f"  {'日期':<12} {'总资产':>12} {'盈亏':>10} {'涨幅':>8} {'持仓':>4} {'交易':>4}")
+    print(f"  {'─'*60}")
+
+    for day in log:
+        sign = "+" if day["total_pnl"] >= 0 else ""
+        n_trades = len(day.get("trades", []))
+        print(f"  {day['date']:<12} ${day['total_assets']:>10,.2f} {sign}${day['total_pnl']:>8,.0f} {sign}{day['total_pct']:>6.2f}% {day['holdings']:>4} {n_trades:>4}")
+
+    latest = log[-1]
+    print(f"  {'─'*60}")
+    sign = "+" if latest["total_pnl"] >= 0 else ""
+    print(f"  最新: ${latest['total_assets']:,.2f}  {sign}${latest['total_pnl']:,.2f} ({sign}{latest['total_pct']:.2f}%)")
+
+
+def daemon_mode():
+    """后台定时运行模式"""
+    try:
+        from zoneinfo import ZoneInfo
+    except ImportError:
+        from backports.zoneinfo import ZoneInfo
+
+    print("  🤖 自动交易Bot已启动（后台模式）")
+    print("  ⏰ 每个交易日 21:35 (北京时间) 自动执行")
+    print("  按 Ctrl+C 停止\n")
+
+    while True:
+        try:
+            bj = datetime.now(ZoneInfo("Asia/Shanghai"))
+
+            # 检查是否到了执行时间 (21:35 北京时间 = 开盘后5分钟)
+            if bj.hour == 21 and bj.minute == 35:
+                if is_us_trading_day():
+                    print(f"\n  ⏰ {bj.strftime('%Y-%m-%d %H:%M')} 触发交易...")
+                    run_daily_strategy()
+                else:
+                    print(f"  📅 {bj.strftime('%Y-%m-%d')} 非交易日，跳过")
+                # 等到下一分钟避免重复
+                time.sleep(60)
+            else:
+                # 每30秒检查一次
+                time.sleep(30)
+
+        except KeyboardInterrupt:
+            print("\n\n  👋 Bot已停止\n")
+            break
+        except Exception as e:
+            print(f"\n  ⚠️ 出错: {e}，60秒后重试...")
+            time.sleep(60)
+
+
+if __name__ == "__main__":
+    if "--daemon" in sys.argv:
+        daemon_mode()
+    elif "--backtest" in sys.argv or "--history" in sys.argv:
+        show_history()
+    else:
+        run_daily_strategy()

daily_reports/report_2026-03-20.json

@@ -0,0 +1,139 @@
+{
+  "date": "2026-03-20",
+  "total_assets": 100000.0,
+  "cash": 28385.48,
+  "total_pnl": -0.0,
+  "total_pct": -0.0,
+  "holdings": 6,
+  "trades": [
+    {
+      "action": "REDUCE",
+      "symbol": "AAPL",
+      "shares": 40,
+      "price": 247.99000549316406,
+      "revenue": 9919.600219726562,
+      "pnl": 0.00021972656213620212
+    },
+    {
+      "action": "REDUCE",
+      "symbol": "NVDA",
+      "shares": 57,
+      "price": 172.6999969482422,
+      "revenue": 9843.899826049805,
+      "pnl": -0.00017395019466448502
+    },
+    {
+      "action": "REDUCE",
+      "symbol": "MSFT",
+      "shares": 26,
+      "price": 381.8699951171875,
+      "revenue": 9928.619873046875,
+      "pnl": -0.0001269531251182343
+    },
+    {
+      "action": "REDUCE",
+      "symbol": "TSLA",
+      "shares": 27,
+      "price": 367.9599914550781,
+      "revenue": 9934.91976928711,
+      "pnl": -0.00023071289007248197
+    },
+    {
+      "action": "BUY",
+      "score": 71,
+      "symbol": "MS",
+      "shares": 74,
+      "price": 161.47000122070312,
+      "cost": 11948.780090332031
+    }
+  ],
+  "top5_scores": [
+    {
+      "symbol": "MS",
+      "score": 71,
+      "signal": "买入"
+    },
+    {
+      "symbol": "PLTR",
+      "score": 69,
+      "signal": "买入"
+    },
+    {
+      "symbol": "NET",
+      "score": 69,
+      "signal": "买入"
+    },
+    {
+      "symbol": "PFE",
+      "score": 69,
+      "signal": "买入"
+    },
+    {
+      "symbol": "CVX",
+      "score": 69,
+      "signal": "买入"
+    }
+  ],
+  "portfolio": [
+    {
+      "symbol": "AAPL",
+      "shares": 40,
+      "avg_cost": 247.99,
+      "price": 247.99000549316406,
+      "mkt": 9919.600219726562,
+      "pnl": 0.00021972656213620212,
+      "pct": 2.215074812461637e-06,
+      "score": 36
+    },
+    {
+      "symbol": "GOOGL",
+      "shares": 66,
+      "avg_cost": 301.0,
+      "price": 301.0,
+      "mkt": 19866.0,
+      "pnl": 0.0,
+      "pct": 0.0,
+      "score": 45
+    },
+    {
+      "symbol": "NVDA",
+      "shares": 58,
+      "avg_cost": 172.7,
+      "price": 172.6999969482422,
+      "mkt": 10016.599822998047,
+      "pnl": -0.00017700195246561634,
+      "pct": -1.7670861662821835e-06,
+      "score": 35
+    },
+    {
+      "symbol": "MSFT",
+      "shares": 26,
+      "avg_cost": 381.87,
+      "price": 381.8699951171875,
+      "mkt": 9928.619873046875,
+      "pnl": -0.0001269531251182343,
+      "pct": -1.2786583125645734e-06,
+      "score": 34
+    },
+    {
+      "symbol": "TSLA",
+      "shares": 27,
+      "avg_cost": 367.96,
+      "price": 367.9599914550781,
+      "mkt": 9934.91976928711,
+      "pnl": -0.00023071289007248197,
+      "pct": -2.322242054209056e-06,
+      "score": 42
+    },
+    {
+      "symbol": "MS",
+      "shares": 74,
+      "avg_cost": 161.47000122070312,
+      "price": 161.47000122070312,
+      "mkt": 11948.780090332031,
+      "pnl": 0.0,
+      "pct": 0.0,
+      "score": 71
+    }
+  ]
+}

monitor.py

@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+"""
+美股持仓监控 — 盘中每分钟刷新，盘外自动等待
+用法: python3 monitor.py [刷新间隔秒数，默认60]
+"""
+
+import json
+import os
+import sys
+import time
+from datetime import datetime, timedelta
+
+import yfinance as yf
+
+PORTFOLIO_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "my_portfolio.json")
+INITIAL_CASH = 100_000.0
+REFRESH_SEC = int(sys.argv[1]) if len(sys.argv) > 1 else 60
+
+
+def load_portfolio():
+    with open(PORTFOLIO_FILE) as f:
+        return json.load(f)
+
+
+def get_prices(symbols):
+    """批量获取价格（比逐个快很多）"""
+    prices = {}
+    if not symbols:
+        return prices
+    tickers = yf.Tickers(" ".join(symbols))
+    for sym in symbols:
+        try:
+            info = tickers.tickers[sym].fast_info
+            p = info.get("lastPrice") or info.get("last_price")
+            if p is None:
+                hist = tickers.tickers[sym].history(period="1d")
+                p = hist["Close"].iloc[-1] if not hist.empty else 0
+            prices[sym] = round(float(p), 2)
+        except Exception:
+            prices[sym] = 0
+    return prices
+
+
+def is_market_open():
+    """判断美股是否开盘（美东时间 周一-周五 9:30-16:00）"""
+    try:
+        from zoneinfo import ZoneInfo
+    except ImportError:
+        from backports.zoneinfo import ZoneInfo
+
+    et = datetime.now(ZoneInfo("America/New_York"))
+    # 周末
+    if et.weekday() >= 5:
+        return False, et
+    # 盘前/盘后
+    market_open = et.replace(hour=9, minute=30, second=0, microsecond=0)
+    market_close = et.replace(hour=16, minute=0, second=0, microsecond=0)
+    return market_open <= et <= market_close, et
+
+
+def time_to_next_open():
+    """计算距离下次开盘的时间"""
+    try:
+        from zoneinfo import ZoneInfo
+    except ImportError:
+        from backports.zoneinfo import ZoneInfo
+
+    et = datetime.now(ZoneInfo("America/New_York"))
+    # 找到下一个工作日的9:30
+    target = et.replace(hour=9, minute=30, second=0, microsecond=0)
+
+    if et.weekday() < 5 and et < target:
+        # 今天是工作日且还没开盘
+        pass
+    else:
+        # 找下一个工作日
+        days_ahead = 1
+        while True:
+            target += timedelta(days=1)
+            if target.weekday() < 5:
+                break
+            days_ahead += 1
+        target = target.replace(hour=9, minute=30, second=0, microsecond=0)
+
+    diff = target - et
+    return diff
+
+
+def clear_screen():
+    os.system("clear" if os.name != "nt" else "cls")
+
+
+def display(portfolio, prices, et_now, is_open):
+    clear_screen()
+    status = "🟢 开盘中" if is_open else "🔴 已休市"
+    print(f"""
+╔══════════════════════════════════════════════════════════════╗
+║  📊 美股模拟投资 — 实时监控          {status}            ║
+║  美东时间: {et_now.strftime('%Y-%m-%d %H:%M:%S'):<20}  刷新间隔: {REFRESH_SEC}秒       ║
+╠══════════════════════════════════════════════════════════════╣""")
+
+    total_market = 0
+    total_cost = 0
+
+    if portfolio["holdings"]:
+        print(f"║  {'股票':<7} {'股数':>5} {'成本':>9} {'现价':>9} {'市值':>11} {'盈亏':>11} {'涨跌':>7} ║")
+        print(f"║  {'─'*62} ║")
+
+        for sym in sorted(portfolio["holdings"]):
+            info = portfolio["holdings"][sym]
+            price = prices.get(sym, info["avg_cost"])
+            mkt = info["shares"] * price
+            cost = info["shares"] * info["avg_cost"]
+            pnl = mkt - cost
+            pct = (pnl / cost * 100) if cost > 0 else 0
+            sign = "+" if pnl >= 0 else ""
+            color_pnl = f"{sign}{pnl:,.0f}"
+            color_pct = f"{sign}{pct:.1f}%"
+
+            total_market += mkt
+            total_cost += cost
+
+            print(f"║  {sym:<7} {info['shares']:>5} {info['avg_cost']:>9.2f} {price:>9.2f} {mkt:>11,.2f} {color_pnl:>11} {color_pct:>7} ║")
+    else:
+        print("║  （空仓）                                                      ║")
+
+    total_assets = portfolio["cash"] + total_market
+    total_pnl = total_assets - INITIAL_CASH
+    total_pct = (total_pnl / INITIAL_CASH * 100)
+    sign = "+" if total_pnl >= 0 else ""
+
+    print(f"║  {'─'*62} ║")
+    print(f"║  💰 现金:    ${portfolio['cash']:>11,.2f}                                ║")
+    print(f"║  📈 持仓:    ${total_market:>11,.2f}                                ║")
+    print(f"║  💼 总资产:  ${total_assets:>11,.2f}    总盈亏: {sign}${total_pnl:>10,.2f} ({sign}{total_pct:.1f}%) ║")
+    print(f"╚══════════════════════════════════════════���═══════════════════╝")
+
+    if not is_open:
+        delta = time_to_next_open()
+        hours = int(delta.total_seconds() // 3600)
+        mins = int((delta.total_seconds() % 3600) // 60)
+        print(f"\n  ⏳ 距离下次开盘: {hours}小时{mins}分钟（休市期间每5分钟刷新一次）")
+
+    print(f"\n  按 Ctrl+C 退出")
+
+
+def main():
+    print("  🚀 启动监控中...")
+
+    while True:
+        try:
+            portfolio = load_portfolio()
+            symbols = list(portfolio["holdings"].keys())
+            prices = get_prices(symbols)
+            is_open, et_now = is_market_open()
+
+            display(portfolio, prices, et_now, is_open)
+
+            # 盘中按设定间隔刷新，盘外每5分钟刷一次
+            wait = REFRESH_SEC if is_open else 300
+            time.sleep(wait)
+
+        except KeyboardInterrupt:
+            print("\n\n  👋 监控已停止\n")
+            break
+        except Exception as e:
+            print(f"\n  ⚠️ 出错: {e}，{REFRESH_SEC}秒后重试...")
+            time.sleep(REFRESH_SEC)
+
+
+if __name__ == "__main__":
+    main()

my_portfolio.json

@@ -0,0 +1,113 @@
+{
+  "cash": 28385.47959777832,
+  "holdings": {
+    "AAPL": {
+      "shares": 40,
+      "avg_cost": 247.99
+    },
+    "GOOGL": {
+      "shares": 66,
+      "avg_cost": 301.0
+    },
+    "NVDA": {
+      "shares": 58,
+      "avg_cost": 172.7
+    },
+    "MSFT": {
+      "shares": 26,
+      "avg_cost": 381.87
+    },
+    "TSLA": {
+      "shares": 27,
+      "avg_cost": 367.96
+    },
+    "MS": {
+      "shares": 74,
+      "avg_cost": 161.47000122070312
+    }
+  },
+  "history": [
+    {
+      "action": "BUY",
+      "symbol": "AAPL",
+      "shares": 80,
+      "price": 247.99,
+      "total": 19839.2,
+      "time": "2026-03-20 22:59:24"
+    },
+    {
+      "action": "BUY",
+      "symbol": "GOOGL",
+      "shares": 66,
+      "price": 301.0,
+      "total": 19866.0,
+      "time": "2026-03-20 22:59:28"
+    },
+    {
+      "action": "BUY",
+      "symbol": "NVDA",
+      "shares": 115,
+      "price": 172.7,
+      "total": 19860.5,
+      "time": "2026-03-20 22:59:33"
+    },
+    {
+      "action": "BUY",
+      "symbol": "MSFT",
+      "shares": 52,
+      "price": 381.87,
+      "total": 19857.24,
+      "time": "2026-03-20 22:59:35"
+    },
+    {
+      "action": "BUY",
+      "symbol": "TSLA",
+      "shares": 54,
+      "price": 367.96,
+      "total": 19869.84,
+      "time": "2026-03-20 22:59:36"
+    }
+  ],
+  "created": "2026-03-20 22:59:24",
+  "transactions": [
+    {
+      "type": "sell",
+      "symbol": "AAPL",
+      "shares": 40,
+      "price": 247.99000549316406,
+      "pnl": 0.0,
+      "time": "2026-03-20T23:58:37.436602"
+    },
+    {
+      "type": "sell",
+      "symbol": "NVDA",
+      "shares": 57,
+      "price": 172.6999969482422,
+      "pnl": -0.0,
+      "time": "2026-03-20T23:58:37.436621"
+    },
+    {
+      "type": "sell",
+      "symbol": "MSFT",
+      "shares": 26,
+      "price": 381.8699951171875,
+      "pnl": -0.0,
+      "time": "2026-03-20T23:58:37.436629"
+    },
+    {
+      "type": "sell",
+      "symbol": "TSLA",
+      "shares": 27,
+      "price": 367.9599914550781,
+      "pnl": -0.0,
+      "time": "2026-03-20T23:58:37.436634"
+    },
+    {
+      "type": "buy",
+      "symbol": "MS",
+      "shares": 74,
+      "price": 161.47000122070312,
+      "time": "2026-03-20T23:58:37.436644"
+    }
+  ]
+}

portfolio.py

@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""
+美股模拟投资工具
+- 初始资金: $100,000
+- 用真实股价买卖
+- 随时查看持仓和收益
+"""
+
+import json
+import os
+import sys
+from datetime import datetime
+
+import yfinance as yf
+
+PORTFOLIO_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "my_portfolio.json")
+INITIAL_CASH = 100_000.0
+
+
+def load_portfolio():
+    """加载投资组合"""
+    if os.path.exists(PORTFOLIO_FILE):
+        with open(PORTFOLIO_FILE) as f:
+            return json.load(f)
+    # 初始化: 10万美刀现金，空持仓
+    portfolio = {
+        "cash": INITIAL_CASH,
+        "holdings": {},  # {"AAPL": {"shares": 10, "avg_cost": 150.0}, ...}
+        "history": [],   # 交易记录
+        "created": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+    }
+    save_portfolio(portfolio)
+    return portfolio
+
+
+def save_portfolio(portfolio):
+    with open(PORTFOLIO_FILE, "w") as f:
+        json.dump(portfolio, f, indent=2, ensure_ascii=False)
+
+
+def get_price(symbol):
+    """获取股票当前价格"""
+    ticker = yf.Ticker(symbol)
+    info = ticker.fast_info
+    price = info.get("lastPrice") or info.get("last_price")
+    if price is None:
+        hist = ticker.history(period="1d")
+        if hist.empty:
+            return None
+        price = hist["Close"].iloc[-1]
+    return round(float(price), 2)
+
+
+def buy(symbol, amount_usd):
+    """用指定金额买入股票（按当前价自动算股数）"""
+    symbol = symbol.upper()
+    price = get_price(symbol)
+    if price is None:
+        print(f"  ❌ 找不到 {symbol} 的价格，请检查股票代码")
+        return
+
+    portfolio = load_portfolio()
+    amount_usd = float(amount_usd)
+
+    if amount_usd > portfolio["cash"]:
+        print(f"  ❌ 现金不足！可用: ${portfolio['cash']:,.2f}，想买: ${amount_usd:,.2f}")
+        return
+
+    shares = int(amount_usd / price)  # 买整数股
+    if shares == 0:
+        print(f"  ❌ 金额太少，{symbol} 当前 ${price}，至少需要 ${price}")
+        return
+
+    cost = shares * price
+    portfolio["cash"] -= cost
+
+    if symbol in portfolio["holdings"]:
+        old = portfolio["holdings"][symbol]
+        total_shares = old["shares"] + shares
+        total_cost = old["shares"] * old["avg_cost"] + cost
+        portfolio["holdings"][symbol] = {
+            "shares": total_shares,
+            "avg_cost": round(total_cost / total_shares, 2),
+        }
+    else:
+        portfolio["holdings"][symbol] = {
+            "shares": shares,
+            "avg_cost": price,
+        }
+
+    portfolio["history"].append({
+        "action": "BUY",
+        "symbol": symbol,
+        "shares": shares,
+        "price": price,
+        "total": round(cost, 2),
+        "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+    })
+
+    save_portfolio(portfolio)
+    print(f"  ✅ 买入 {symbol} x {shares}股 @ ${price} = ${cost:,.2f}")
+    print(f"     剩余现金: ${portfolio['cash']:,.2f}")
+
+
+def sell(symbol, shares=None):
+    """卖出股票（默认全部卖出）"""
+    symbol = symbol.upper()
+    portfolio = load_portfolio()
+
+    if symbol not in portfolio["holdings"]:
+        print(f"  ❌ 你没有持有 {symbol}")
+        return
+
+    holding = portfolio["holdings"][symbol]
+    if shares is None:
+        shares = holding["shares"]
+    else:
+        shares = int(shares)
+
+    if shares > holding["shares"]:
+        print(f"  ❌ 只有 {holding['shares']}股，不能卖 {shares}股")
+        return
+
+    price = get_price(symbol)
+    if price is None:
+        print(f"  ❌ 获取 {symbol} 价格失败")
+        return
+
+    revenue = shares * price
+    profit = (price - holding["avg_cost"]) * shares
+    portfolio["cash"] += revenue
+
+    if shares == holding["shares"]:
+        del portfolio["holdings"][symbol]
+    else:
+        portfolio["holdings"][symbol]["shares"] -= shares
+
+    portfolio["history"].append({
+        "action": "SELL",
+        "symbol": symbol,
+        "shares": shares,
+        "price": price,
+        "total": round(revenue, 2),
+        "profit": round(profit, 2),
+        "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+    })
+
+    save_portfolio(portfolio)
+    sign = "+" if profit >= 0 else ""
+    print(f"  ✅ 卖出 {symbol} x {shares}股 @ ${price} = ${revenue:,.2f}")
+    print(f"     盈亏: {sign}${profit:,.2f}")
+    print(f"     剩余现金: ${portfolio['cash']:,.2f}")
+
+
+def show():
+    """查看持仓和收益"""
+    portfolio = load_portfolio()
+    print()
+    print("=" * 60)
+    print("  📊 我的美股模拟投资组合")
+    print("=" * 60)
+
+    total_market_value = 0
+    total_cost = 0
+
+    if portfolio["holdings"]:
+        print(f"\n  {'股票':<8} {'股数':>6} {'成本价':>10} {'现价':>10} {'市值':>12} {'盈亏':>12} {'涨跌%':>8}")
+        print("  " + "-" * 68)
+
+        for symbol, info in sorted(portfolio["holdings"].items()):
+            price = get_price(symbol)
+            if price is None:
+                price = info["avg_cost"]  # fallback
+
+            market_val = info["shares"] * price
+            cost_val = info["shares"] * info["avg_cost"]
+            profit = market_val - cost_val
+            pct = (profit / cost_val * 100) if cost_val > 0 else 0
+            sign = "+" if profit >= 0 else ""
+
+            total_market_value += market_val
+            total_cost += cost_val
+
+            print(f"  {symbol:<8} {info['shares']:>6} {info['avg_cost']:>10.2f} {price:>10.2f} {market_val:>12,.2f} {sign}{profit:>11,.2f} {sign}{pct:>7.1f}%")
+    else:
+        print("\n  （空仓，还没买任何股票）")
+
+    total_assets = portfolio["cash"] + total_market_value
+    total_profit = total_assets - INITIAL_CASH
+    total_pct = (total_profit / INITIAL_CASH * 100)
+    sign = "+" if total_profit >= 0 else ""
+
+    print()
+    print("  " + "-" * 68)
+    print(f"  💰 现金:           ${portfolio['cash']:>12,.2f}")
+    print(f"  📈 持仓市值:       ${total_market_value:>12,.2f}")
+    print(f"  💼 总资产:         ${total_assets:>12,.2f}")
+    print(f"  📊 总盈亏:         {sign}${total_profit:>11,.2f} ({sign}{total_pct:.1f}%)")
+    print("=" * 60)
+    print()
+
+
+def history():
+    """查看交易记录"""
+    portfolio = load_portfolio()
+    if not portfolio["history"]:
+        print("  还没有交易记录")
+        return
+
+    print(f"\n  {'时间':<20} {'操作':<5} {'股票':<8} {'股数':>6} {'价格':>10} {'金额':>12}")
+    print("  " + "-" * 65)
+    for tx in portfolio["history"][-20:]:  # 最近20条
+        print(f"  {tx['time']:<20} {tx['action']:<5} {tx['symbol']:<8} {tx['shares']:>6} {tx['price']:>10.2f} ${tx['total']:>11,.2f}")
+    print()
+
+
+def reset():
+    """重置投资组合"""
+    if os.path.exists(PORTFOLIO_FILE):
+        os.remove(PORTFOLIO_FILE)
+    load_portfolio()
+    print("  🔄 已重置！初始资金 $100,000.00")
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("""
+  美股模拟投资工具 💹
+  ──────────────────────────────────
+  用法:
+    python portfolio.py show          查看持仓和收益
+    python portfolio.py buy AAPL 10000   用$10000买入AAPL
+    python portfolio.py sell AAPL        全部卖出AAPL
+    python portfolio.py sell AAPL 5      卖出5股AAPL
+    python portfolio.py history       查看交易记录
+    python portfolio.py reset         重置（重新开始）
+
+  示例: 把10万分散投资
+    python portfolio.py buy AAPL 20000
+    python portfolio.py buy GOOGL 20000
+    python portfolio.py buy MSFT 20000
+    python portfolio.py buy NVDA 20000
+    python portfolio.py buy TSLA 20000
+""")
+        return
+
+    cmd = sys.argv[1].lower()
+
+    if cmd == "show":
+        show()
+    elif cmd == "buy":
+        if len(sys.argv) < 4:
+            print("  用法: python portfolio.py buy <股票代码> <金额>")
+            return
+        buy(sys.argv[2], sys.argv[3])
+    elif cmd == "sell":
+        if len(sys.argv) < 3:
+            print("  用法: python portfolio.py sell <股票代码> [股数]")
+            return
+        shares = int(sys.argv[3]) if len(sys.argv) > 3 else None
+        sell(sys.argv[2], shares)
+    elif cmd == "history":
+        history()
+    elif cmd == "reset":
+        reset()
+    else:
+        print(f"  未知命令: {cmd}")
+
+
+if __name__ == "__main__":
+    main()

stock_screener.py

@@ -0,0 +1,385 @@
+#!/usr/bin/env python3
+"""
+美股智能选股器 — 基于 daily_stock_analysis 项目的技术分析体系
+参考: https://github.com/ZhuLinsen/daily_stock_analysis
+
+评分维度（100分制）：
+  - 趋势排列 30分：MA5>MA10>MA20 多头排列
+  - 乖离率   20分：接近 MA5 不追高
+  - 量能形态 15分：缩量回调最佳
+  - MACD     15分：金叉/多头
+  - RSI      10分：超卖反弹/强势
+  - 支撑     10分：均线支撑有效
+
+用法: python3 stock_screener.py
+"""
+
+import sys
+import warnings
+warnings.filterwarnings("ignore")
+
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from datetime import datetime, timedelta
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+# ── 候选股票池 ──────────────────────────────────────────
+# 科技/半导体/AI 热门 + 大盘蓝筹 + 消费 + 医药 + 能源 + 金融
+US_STOCKS = [
+    # 科技巨头
+    "AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "TSLA",
+    # 半导体/AI
+    "AMD", "AVGO", "QCOM", "INTC", "MU", "MRVL", "ARM", "SMCI", "TSM",
+    # 软件/云
+    "CRM", "ORCL", "ADBE", "NOW", "SNOW", "PLTR", "NET", "DDOG", "CRWD",
+    # 消费/零售
+    "COST", "WMT", "TGT", "NKE", "SBUX", "MCD", "PEP", "KO",
+    # 金融
+    "JPM", "GS", "MS", "BAC", "V", "MA", "AXP",
+    # 医药/生物
+    "LLY", "UNH", "JNJ", "PFE", "ABBV", "MRK", "BMY",
+    # 能源
+    "XOM", "CVX", "SLB", "OXY",
+    # 其他热门
+    "BA", "CAT", "DE", "GE", "LMT", "COIN", "MARA", "RIVN", "UBER", "ABNB",
+    "SQ", "SHOP", "PYPL", "ROKU", "SNAP", "PINS", "RBLX", "U",
+]
+
+
+# ── 技术分析核心（复刻 daily_stock_analysis 的 StockTrendAnalyzer） ──
+
+def calc_macd(close, fast=12, slow=26, signal=9):
+    ema_fast = close.ewm(span=fast, adjust=False).mean()
+    ema_slow = close.ewm(span=slow, adjust=False).mean()
+    dif = ema_fast - ema_slow
+    dea = dif.ewm(span=signal, adjust=False).mean()
+    bar = (dif - dea) * 2
+    return dif, dea, bar
+
+
+def calc_rsi(close, period=12):
+    delta = close.diff()
+    gain = delta.where(delta > 0, 0).rolling(period).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(period).mean()
+    rs = gain / loss
+    return (100 - 100 / (1 + rs)).fillna(50)
+
+
+def analyze_one(symbol):
+    """对单只股票做完整技术分析，返回 dict 或 None"""
+    try:
+        tk = yf.Ticker(symbol)
+        df = tk.history(period="6mo", auto_adjust=True)
+        if df is None or len(df) < 60:
+            return None
+
+        df = df.reset_index()
+        close = df["Close"]
+        volume = df["Volume"]
+
+        # ── 均线 ──
+        ma5  = close.rolling(5).mean()
+        ma10 = close.rolling(10).mean()
+        ma20 = close.rolling(20).mean()
+        ma60 = close.rolling(60).mean()
+
+        last = len(df) - 1
+        price = float(close.iloc[last])
+        m5, m10, m20, m60 = (
+            float(ma5.iloc[last]),
+            float(ma10.iloc[last]),
+            float(ma20.iloc[last]),
+            float(ma60.iloc[last]),
+        )
+
+        # ── 趋势判断（30分）──
+        trend_score = 0
+        trend_label = ""
+        if m5 > m10 > m20:
+            # 检查间距是否在扩大
+            prev_idx = max(0, last - 5)
+            prev_spread = (float(ma5.iloc[prev_idx]) - float(ma20.iloc[prev_idx])) / float(ma20.iloc[prev_idx]) * 100
+            curr_spread = (m5 - m20) / m20 * 100
+            if curr_spread > prev_spread and curr_spread > 5:
+                trend_score = 30
+                trend_label = "强势多头"
+            else:
+                trend_score = 26
+                trend_label = "多头排列"
+        elif m5 > m10 and m10 <= m20:
+            trend_score = 18
+            trend_label = "弱势多头"
+        elif abs(m5 - m10) / m10 < 0.01 and abs(m10 - m20) / m20 < 0.01:
+            trend_score = 12
+            trend_label = "盘整"
+        elif m5 < m10 and m10 >= m20:
+            trend_score = 8
+            trend_label = "弱势空头"
+        elif m5 < m10 < m20:
+            prev_idx = max(0, last - 5)
+            prev_spread = (float(ma20.iloc[prev_idx]) - float(ma5.iloc[prev_idx])) / float(ma5.iloc[prev_idx]) * 100
+            curr_spread = (m20 - m5) / m5 * 100
+            if curr_spread > prev_spread and curr_spread > 5:
+                trend_score = 0
+                trend_label = "强势空头"
+            else:
+                trend_score = 4
+                trend_label = "空头排列"
+        else:
+            trend_score = 12
+            trend_label = "盘整"
+
+        # ── 乖离率（20分）──
+        bias_ma5 = (price - m5) / m5 * 100 if m5 > 0 else 0
+        bias_score = 0
+        bias_label = ""
+        BIAS_THRESHOLD = 5.0
+        if bias_ma5 < 0:
+            if bias_ma5 > -3:
+                bias_score = 20
+                bias_label = f"回踩买点({bias_ma5:+.1f}%)"
+            elif bias_ma5 > -5:
+                bias_score = 16
+                bias_label = f"回踩MA5({bias_ma5:+.1f}%)"
+            else:
+                bias_score = 8
+                bias_label = f"偏离过大({bias_ma5:+.1f}%)"
+        elif bias_ma5 < 2:
+            bias_score = 18
+            bias_label = f"贴近MA5({bias_ma5:+.1f}%)"
+        elif bias_ma5 < BIAS_THRESHOLD:
+            bias_score = 14
+            bias_label = f"略高({bias_ma5:+.1f}%)"
+        else:
+            bias_score = 4
+            bias_label = f"追高危险({bias_ma5:+.1f}%)"
+
+        # ── 量能（15分）──
+        vol_5d_avg = float(volume.iloc[-6:-1].mean())
+        vol_ratio = float(volume.iloc[last]) / vol_5d_avg if vol_5d_avg > 0 else 1
+        prev_close = float(close.iloc[last - 1])
+        price_chg = (price - prev_close) / prev_close * 100
+
+        vol_score = 0
+        vol_label = ""
+        if vol_ratio >= 1.5:
+            if price_chg > 0:
+                vol_score = 12
+                vol_label = "放量上涨"
+            else:
+                vol_score = 0
+                vol_label = "放量下跌"
+        elif vol_ratio <= 0.7:
+            if price_chg > 0:
+                vol_score = 6
+                vol_label = "缩量上涨"
+            else:
+                vol_score = 15
+                vol_label = "缩量回调"
+        else:
+            vol_score = 10
+            vol_label = "量能正常"
+
+        # ── MACD（15分）──
+        dif, dea, bar = calc_macd(close)
+        macd_dif = float(dif.iloc[last])
+        macd_dea = float(dea.iloc[last])
+        prev_diff = float(dif.iloc[last - 1]) - float(dea.iloc[last - 1])
+        curr_diff = macd_dif - macd_dea
+
+        macd_score = 0
+        macd_label = ""
+        is_golden = prev_diff <= 0 and curr_diff > 0
+        is_death = prev_diff >= 0 and curr_diff < 0
+
+        if is_golden and macd_dif > 0:
+            macd_score = 15
+            macd_label = "零轴上金叉"
+        elif is_golden:
+            macd_score = 12
+            macd_label = "金叉"
+        elif float(dif.iloc[last - 1]) <= 0 and macd_dif > 0:
+            macd_score = 10
+            macd_label = "上穿零轴"
+        elif is_death:
+            macd_score = 0
+            macd_label = "死叉"
+        elif macd_dif > 0 and macd_dea > 0:
+            macd_score = 8
+            macd_label = "多头"
+        elif macd_dif < 0 and macd_dea < 0:
+            macd_score = 2
+            macd_label = "空头"
+        else:
+            macd_score = 5
+            macd_label = "中性"
+
+        # ── RSI（10分）──
+        rsi_12 = float(calc_rsi(close, 12).iloc[last])
+        rsi_score = 0
+        rsi_label = ""
+        if rsi_12 > 70:
+            rsi_score = 0
+            rsi_label = f"超买({rsi_12:.0f})"
+        elif rsi_12 > 60:
+            rsi_score = 8
+            rsi_label = f"强势({rsi_12:.0f})"
+        elif rsi_12 >= 40:
+            rsi_score = 5
+            rsi_label = f"中性({rsi_12:.0f})"
+        elif rsi_12 >= 30:
+            rsi_score = 3
+            rsi_label = f"弱势({rsi_12:.0f})"
+        else:
+            rsi_score = 10
+            rsi_label = f"超卖({rsi_12:.0f})"
+
+        # ── 支撑（10分）──
+        support_score = 0
+        support_label = ""
+        ma5_dist = abs(price - m5) / m5 if m5 > 0 else 1
+        ma10_dist = abs(price - m10) / m10 if m10 > 0 else 1
+        supports = []
+        if ma5_dist <= 0.02 and price >= m5:
+            support_score += 5
+            supports.append("MA5")
+        if ma10_dist <= 0.02 and price >= m10:
+            support_score += 5
+            supports.append("MA10")
+        support_label = "+".join(supports) if supports else "无"
+
+        # ── 总分 ──
+        total = trend_score + bias_score + vol_score + macd_score + rsi_score + support_score
+
+        # ── 信号 ──
+        if total >= 75 and trend_label in ("强势多头", "多头排列"):
+            signal = "强烈买入"
+        elif total >= 60 and trend_label in ("强势多头", "多头排列", "弱势多头"):
+            signal = "买入"
+        elif total >= 45:
+            signal = "持有"
+        elif total >= 30:
+            signal = "观望"
+        elif trend_label in ("空头排列", "强势空头"):
+            signal = "强烈卖出"
+        else:
+            signal = "卖出"
+
+        # 20日涨跌幅
+        price_20d_ago = float(close.iloc[max(0, last - 20)])
+        chg_20d = (price - price_20d_ago) / price_20d_ago * 100
+
+        return {
+            "symbol": symbol,
+            "price": price,
+            "total": total,
+            "signal": signal,
+            "trend": f"{trend_label}({trend_score})",
+            "bias": f"{bias_label}({bias_score})",
+            "volume": f"{vol_label}({vol_score})",
+            "macd": f"{macd_label}({macd_score})",
+            "rsi": f"{rsi_label}({rsi_score})",
+            "support": f"{support_label}({support_score})",
+            "chg_20d": chg_20d,
+            "vol_ratio": vol_ratio,
+            "trend_label": trend_label,
+        }
+    except Exception as e:
+        return None
+
+
+def main():
+    print("\n  🔍 美股智能选股器 (基于 daily_stock_analysis 技术分析体系)")
+    print("  " + "=" * 62)
+    print(f"  📅 分析日期: {datetime.now().strftime('%Y-%m-%d %H:%M')}")
+    print(f"  📊 候选池: {len(US_STOCKS)} 只美股")
+    print(f"  ⏳ 正在获取数据并分析（约30秒）...\n")
+
+    results = []
+    failed = []
+
+    with ThreadPoolExecutor(max_workers=8) as pool:
+        futures = {pool.submit(analyze_one, sym): sym for sym in US_STOCKS}
+        done_count = 0
+        for future in as_completed(futures):
+            done_count += 1
+            sym = futures[future]
+            r = future.result()
+            if r:
+                results.append(r)
+            else:
+                failed.append(sym)
+            # 进度
+            if done_count % 10 == 0 or done_count == len(US_STOCKS):
+                sys.stdout.write(f"\r  进度: {done_count}/{len(US_STOCKS)}")
+                sys.stdout.flush()
+
+    print("\n")
+
+    if not results:
+        print("  ❌ 未获取到任何数据")
+        return
+
+    # 按总分降序
+    results.sort(key=lambda x: x["total"], reverse=True)
+
+    # ── 输出Top推荐 ──
+    print("  ╔══════════════════════════════════════════════════════════════════════════════╗")
+    print("  ║                    📈 明日选股推荐（按综合评分排序）                        ║")
+    print("  ╠══════════════════════════════════════════════════════════════════════════════╣")
+    print(f"  ║  {'排名':>2} {'代码':<6} {'现价':>8} {'评分':>4} {'信号':<8} {'趋势':<12} {'乖离率':<16} {'MACD':<10} {'RSI':<10} ║")
+    print(f"  ║  {'─'*74} ║")
+
+    for i, r in enumerate(results[:20]):
+        rank = i + 1
+        sig = r["signal"]
+        # 信号颜色标记
+        if "买入" in sig:
+            sig_mark = f"🟢{sig}"
+        elif "卖" in sig:
+            sig_mark = f"🔴{sig}"
+        else:
+            sig_mark = f"🟡{sig}"
+
+        print(f"  ║  {rank:>2}. {r['symbol']:<6} ${r['price']:>7.2f} {r['total']:>3}分 {sig_mark:<10} {r['trend']:<12} {r['bias']:<16} {r['macd']:<10} {r['rsi']:<10} ║")
+
+    print("  ╚══════════════════════════════════════════════════════════════════════════════╝")
+
+    # ── 强烈买入 ──
+    strong_buys = [r for r in results if r["signal"] == "强烈买入"]
+    buys = [r for r in results if r["signal"] == "买入"]
+
+    print(f"\n  🎯 总结")
+    print(f"  {'─'*60}")
+
+    if strong_buys:
+        print(f"\n  🟢🟢 强烈买入信号（{len(strong_buys)}只）:")
+        for r in strong_buys:
+            print(f"     {r['symbol']:>6} @ ${r['price']:.2f}  评分{r['total']}  {r['trend']}  {r['macd']}  {r['rsi']}")
+            print(f"            20日涨幅: {r['chg_20d']:+.1f}%  量比: {r['vol_ratio']:.2f}")
+    else:
+        print(f"\n  ⚠️  当前无强烈买入信号")
+
+    if buys:
+        print(f"\n  🟢 买入信号（{len(buys)}只）:")
+        for r in buys:
+            print(f"     {r['symbol']:>6} @ ${r['price']:.2f}  评分{r['total']}  {r['trend']}  {r['macd']}  {r['rsi']}")
+
+    # ── 空头警告（已持仓的） ──
+    held = ["AAPL", "GOOGL", "MSFT", "NVDA", "TSLA"]
+    print(f"\n  📋 你的持仓状态:")
+    for r in results:
+        if r["symbol"] in held:
+            icon = "🟢" if "买" in r["signal"] else ("🔴" if "卖" in r["signal"] else "🟡")
+            print(f"     {icon} {r['symbol']:>6}  评分{r['total']}  {r['signal']}  {r['trend']}  {r['macd']}  20日涨幅{r['chg_20d']:+.1f}%")
+
+    if failed:
+        print(f"\n  ⚠️  {len(failed)}只获取失败: {', '.join(failed[:10])}")
+
+    print(f"\n  ⚠️  免责声明: 技术分析仅供参考，不构成投资建议，入市有风险！")
+    print()
+
+
+if __name__ == "__main__":
+    main()

trade_log.json

@@ -0,0 +1,141 @@
+[
+  {
+    "date": "2026-03-20",
+    "total_assets": 100000.0,
+    "cash": 28385.48,
+    "total_pnl": -0.0,
+    "total_pct": -0.0,
+    "holdings": 6,
+    "trades": [
+      {
+        "action": "REDUCE",
+        "symbol": "AAPL",
+        "shares": 40,
+        "price": 247.99000549316406,
+        "revenue": 9919.600219726562,
+        "pnl": 0.00021972656213620212
+      },
+      {
+        "action": "REDUCE",
+        "symbol": "NVDA",
+        "shares": 57,
+        "price": 172.6999969482422,
+        "revenue": 9843.899826049805,
+        "pnl": -0.00017395019466448502
+      },
+      {
+        "action": "REDUCE",
+        "symbol": "MSFT",
+        "shares": 26,
+        "price": 381.8699951171875,
+        "revenue": 9928.619873046875,
+        "pnl": -0.0001269531251182343
+      },
+      {
+        "action": "REDUCE",
+        "symbol": "TSLA",
+        "shares": 27,
+        "price": 367.9599914550781,
+        "revenue": 9934.91976928711,
+        "pnl": -0.00023071289007248197
+      },
+      {
+        "action": "BUY",
+        "score": 71,
+        "symbol": "MS",
+        "shares": 74,
+        "price": 161.47000122070312,
+        "cost": 11948.780090332031
+      }
+    ],
+    "top5_scores": [
+      {
+        "symbol": "MS",
+        "score": 71,
+        "signal": "买入"
+      },
+      {
+        "symbol": "PLTR",
+        "score": 69,
+        "signal": "买入"
+      },
+      {
+        "symbol": "NET",
+        "score": 69,
+        "signal": "买入"
+      },
+      {
+        "symbol": "PFE",
+        "score": 69,
+        "signal": "买入"
+      },
+      {
+        "symbol": "CVX",
+        "score": 69,
+        "signal": "买入"
+      }
+    ],
+    "portfolio": [
+      {
+        "symbol": "AAPL",
+        "shares": 40,
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