Initial commit: stock analysis backend and prototype UI.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/portfolio.py

@@ -0,0 +1,176 @@
+"""持仓与盈亏归因（移动加权平均成本法）。"""
+from __future__ import annotations
+
+from collections import defaultdict
+
+from sqlalchemy import select
+
+from db import get_session
+from models import Trade, StockMetric, DailyQuote, Security, IndexDaily
+
+
+def _current_prices(codes):
+    px = {}
+    if not codes:
+        return px
+    with get_session() as s:
+        for m in s.execute(select(StockMetric).where(StockMetric.code.in_(codes))).scalars():
+            px[m.code] = m.close
+        missing = [c for c in codes if c not in px]
+        for c in missing:
+            row = s.execute(select(DailyQuote.close).where(DailyQuote.code == c)
+                            .order_by(DailyQuote.date.desc()).limit(1)).scalar()
+            if row:
+                px[c] = float(row)
+    return px
+
+
+def compute():
+    with get_session() as s:
+        trades = s.execute(select(Trade).order_by(Trade.date, Trade.id)).scalars().all()
+
+    pos = defaultdict(lambda: {"qty": 0, "cost": 0.0, "name": ""})   # 持仓
+    realized = defaultdict(float)        # 各股已实现盈亏
+    by_reason = defaultdict(float)       # 按理由归因（已实现）
+    by_emotion = defaultdict(float)
+    closed_sells = []                    # 每笔卖出的盈亏（胜率用）
+
+    for t in trades:
+        p = pos[t.code]
+        p["name"] = t.name or p["name"]
+        if t.side == "buy":
+            p["cost"] += t.price * t.qty + t.fee
+            p["qty"] += t.qty
+        else:  # sell
+            if p["qty"] <= 0:
+                continue
+            avg = p["cost"] / p["qty"] if p["qty"] else 0.0
+            qty = min(t.qty, p["qty"])
+            pnl = (t.price - avg) * qty - t.fee
+            realized[t.code] += pnl
+            by_reason[t.reason or "未标注"] += pnl
+            by_emotion[t.emotion or "未标注"] += pnl
+            closed_sells.append(pnl)
+            p["cost"] -= avg * qty
+            p["qty"] -= qty
+
+    codes = [c for c, v in pos.items() if v["qty"] > 0]
+    px = _current_prices(codes)
+
+    holdings, mkt_val, hold_cost, unreal = [], 0.0, 0.0, 0.0
+    for c in codes:
+        v = pos[c]
+        avg = v["cost"] / v["qty"] if v["qty"] else 0.0
+        cur = px.get(c, avg)
+        mv = cur * v["qty"]
+        u = (cur - avg) * v["qty"]
+        mkt_val += mv; hold_cost += v["cost"]; unreal += u
+        holdings.append({"code": c, "name": v["name"], "qty": v["qty"],
+                         "avg_cost": round(avg, 3), "cur": round(cur, 3),
+                         "market_value": round(mv, 2), "unrealized": round(u, 2),
+                         "unrealized_pct": round((cur / avg - 1) * 100, 2) if avg else 0.0})
+    holdings.sort(key=lambda x: x["unrealized"], reverse=True)
+
+    total_realized = sum(realized.values())
+    wins = sum(1 for x in closed_sells if x > 0)
+    win_rate = round(wins / len(closed_sells) * 100, 1) if closed_sells else 0.0
+
+    # 盈亏归因：按个股（已实现+浮动）
+    attr_codes = defaultdict(float)
+    for c, r in realized.items():
+        attr_codes[c] += r
+    for h in holdings:
+        attr_codes[h["code"]] += h["unrealized"]
+    name_map = {h["code"]: h["name"] for h in holdings}
+    name_map.update({c: pos[c]["name"] for c in realized})
+    by_stock = sorted([{"code": c, "name": name_map.get(c, c), "pnl": round(v, 2)}
+                       for c, v in attr_codes.items()], key=lambda x: x["pnl"], reverse=True)
+
+    return {
+        "summary": {
+            "market_value": round(mkt_val, 2), "hold_cost": round(hold_cost, 2),
+            "unrealized": round(unreal, 2), "realized": round(total_realized, 2),
+            "total_pnl": round(unreal + total_realized, 2),
+            "positions": len(holdings), "closed_trades": len(closed_sells), "win_rate": win_rate,
+        },
+        "holdings": holdings,
+        "attribution": {
+            "by_stock": by_stock,
+            "by_reason": sorted([{"key": k, "pnl": round(v, 2)} for k, v in by_reason.items()], key=lambda x: x["pnl"], reverse=True),
+            "by_emotion": sorted([{"key": k, "pnl": round(v, 2)} for k, v in by_emotion.items()], key=lambda x: x["pnl"], reverse=True),
+        },
+    }
+
+
+def equity_curve():
+    """重建每日资金曲线：累计盈亏(已实现+浮动) + 投入本金净值，并对比沪深300。"""
+    with get_session() as s:
+        trades = s.execute(select(Trade).order_by(Trade.date, Trade.id)).scalars().all()
+        if not trades:
+            return {"ok": False, "msg": "暂无交易记录"}
+        codes = list({t.code for t in trades})
+        # 各股日线 {code: {date: close}}
+        px = defaultdict(dict)
+        start = min(t.date for t in trades)
+        for c in codes:
+            for d, close in s.execute(select(DailyQuote.date, DailyQuote.close)
+                                      .where(DailyQuote.code == c, DailyQuote.date >= start)
+                                      .order_by(DailyQuote.date)).all():
+                px[c][d] = float(close)
+        # 交易日序列（用沪深300的日期轴）
+        idx = s.execute(select(IndexDaily.date, IndexDaily.close)
+                        .where(IndexDaily.code == "sh000300", IndexDaily.date >= start)
+                        .order_by(IndexDaily.date)).all()
+
+    days = [d for d, _ in idx] or sorted({d for c in px for d in px[c]})
+    idx_map = {d: float(c) for d, c in idx}
+
+    # 按日推进
+    from collections import defaultdict as dd
+    pos = dd(lambda: {"qty": 0, "cost": 0.0})
+    realized = 0.0
+    ti = 0
+    last_px = {}
+    dates, equity, invested_curve, bench = [], [], [], []
+    base_idx = None
+    max_invested = 0.0
+    for d in days:
+        # 应用当天及之前所有交易
+        while ti < len(trades) and trades[ti].date <= d:
+            t = trades[ti]; p = pos[t.code]
+            if t.side == "buy":
+                p["cost"] += t.price * t.qty + t.fee; p["qty"] += t.qty
+            else:
+                if p["qty"] > 0:
+                    avg = p["cost"] / p["qty"]; qty = min(t.qty, p["qty"])
+                    realized += (t.price - avg) * qty - t.fee
+                    p["cost"] -= avg * qty; p["qty"] -= qty
+            ti += 1
+        unreal = 0.0
+        invested = 0.0
+        for c, p in pos.items():
+            if p["qty"] <= 0:
+                continue
+            close = px[c].get(d, last_px.get(c))
+            if close is None:
+                continue
+            last_px[c] = close
+            avg = p["cost"] / p["qty"]
+            unreal += (close - avg) * p["qty"]
+            invested += p["cost"]
+        max_invested = max(max_invested, invested, 1.0)
+        dates.append(d.isoformat())
+        equity.append(round(realized + unreal, 2))
+        invested_curve.append(round(invested, 2))
+        if d in idx_map:
+            base_idx = base_idx or idx_map[d]
+            bench.append(round(idx_map[d] / base_idx, 4))
+        else:
+            bench.append(bench[-1] if bench else 1.0)
+
+    # 净值：以累计最大投入为基准
+    nav = [round(1 + e / max_invested, 4) for e in equity]
+    return {"ok": True, "dates": dates, "equity": equity, "invested": invested_curve,
+            "nav": nav, "bench": bench,
+            "final_pnl": equity[-1] if equity else 0.0,
+            "max_invested": round(max_invested, 2)}