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