dekun
8ebad6e8a2
Calendar shows daily closed trade count and PnL with emotion-day highlighting; day click loads review-first trade list. Use exchange-only entry average and improve vnpy position sync after CTP reconnect.
569 lines
19 KiB
Python
569 lines
19 KiB
Python
# Copyright (c) 2025-2026 马建军. All rights reserved.
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# 专有软件 — 未经授权禁止复制、传播、转售。
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# 严禁用于：带单/代客理财、向他人推荐期货品种或买卖建议、融资配资等业务。
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# 详见 LICENSE.zh-CN.txt 与 docs/软件购买与使用协议.md
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"""交易统计计算与缓存结构。"""
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from __future__ import annotations
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import calendar
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import json
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import threading
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from datetime import date, datetime
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from typing import Any, Optional
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from zoneinfo import ZoneInfo
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from db_conn import commit_retry, execute_retry
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_stats_refresh_lock = threading.Lock()
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TZ = ZoneInfo("Asia/Shanghai")
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STATS_VIEWS = [
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{"key": "by_time", "label": "按时间统计"},
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{"key": "by_week", "label": "周统计"},
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{"key": "by_month", "label": "月统计"},
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{"key": "by_symbol", "label": "按品种统计"},
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{"key": "by_fee", "label": "按手续费统计"},
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{"key": "by_direction", "label": "按方向统计"},
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{"key": "by_trade_type", "label": "按交易类型统计"},
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{"key": "by_emotion", "label": "情绪单统计"},
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]
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BREAKDOWN_COLUMNS = [
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{"key": "label", "label": "维度"},
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{"key": "count", "label": "交易次数"},
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{"key": "wins", "label": "盈利笔数"},
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{"key": "losses", "label": "亏损笔数"},
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{"key": "win_rate", "label": "胜率(%)"},
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{"key": "avg_profit", "label": "平均盈利"},
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{"key": "avg_loss", "label": "平均亏损"},
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{"key": "profit_loss_ratio", "label": "盈亏比"},
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{"key": "total_fee", "label": "累计手续费"},
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{"key": "total_net", "label": "净盈亏合计"},
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{"key": "max_loss", "label": "最大亏损"},
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{"key": "max_profit", "label": "最大盈利"},
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]
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def _parse_dt(value: str) -> Optional[datetime]:
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if not value:
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return None
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text = value.strip().replace(" ", "T")
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try:
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return datetime.fromisoformat(text)
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except ValueError:
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return None
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def _row_dict(row) -> dict:
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return dict(row) if row is not None else {}
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def _net_pnl(row: dict) -> float:
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if row.get("pnl_net") is not None:
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return float(row["pnl_net"])
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pnl = float(row.get("pnl") or 0)
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fee = float(row.get("fee") or 0)
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return round(pnl - fee, 2)
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def _fee(row: dict) -> float:
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return float(row.get("fee") or 0)
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def _margin_pct(pnl_net: float, margin: Optional[float]) -> Optional[float]:
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if margin and margin > 0:
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return round(pnl_net / margin * 100, 2)
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return None
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def _agg_group(rows: list[dict], key_fn) -> list[dict]:
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groups: dict[str, list[dict]] = {}
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for row in rows:
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key = key_fn(row) or "未知"
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groups.setdefault(key, []).append(row)
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result = []
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for label, items in sorted(groups.items(), key=lambda x: x[0]):
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result.append(_agg_metrics(label, items))
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return result
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def _agg_metrics(label: str, items: list[dict]) -> dict:
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nets = [_net_pnl(r) for r in items]
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wins = [n for n in nets if n > 0]
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losses = [n for n in nets if n < 0]
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count = len(items)
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win_cnt = len(wins)
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loss_cnt = len(losses)
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avg_profit = round(sum(wins) / len(wins), 2) if wins else 0.0
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avg_loss = round(sum(losses) / len(losses), 2) if losses else 0.0
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pl_ratio = round(avg_profit / abs(avg_loss), 2) if wins and losses and avg_loss != 0 else 0.0
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total_fee = round(sum(_fee(r) for r in items), 2)
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total_net = round(sum(nets), 2)
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max_loss = round(min(nets), 2) if nets else 0.0
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max_profit = round(max(nets), 2) if nets else 0.0
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win_rate = round(win_cnt / count * 100, 2) if count else 0.0
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return {
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"label": label,
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"count": count,
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"wins": win_cnt,
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"losses": loss_cnt,
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"win_rate": win_rate,
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"avg_profit": avg_profit,
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"avg_loss": avg_loss,
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"profit_loss_ratio": pl_ratio,
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"total_fee": total_fee,
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"total_net": total_net,
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"max_loss": max_loss,
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"max_profit": max_profit,
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}
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def _max_consecutive_losses(nets: list[float]) -> int:
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streak = 0
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best = 0
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for n in nets:
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if n < 0:
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streak += 1
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best = max(best, streak)
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else:
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streak = 0
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return best
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def _max_drawdown(nets: list[float], initial_capital: float) -> tuple[float, float]:
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equity = initial_capital
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peak = initial_capital
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max_dd = 0.0
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max_dd_pct = 0.0
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for n in nets:
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equity += n
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if equity > peak:
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peak = equity
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dd = peak - equity
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if dd > max_dd:
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max_dd = dd
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if peak > 0:
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pct = dd / peak * 100
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if pct > max_dd_pct:
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max_dd_pct = pct
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return round(max_dd, 2), round(max_dd_pct, 2)
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def fetch_trade_rows(conn) -> list[dict]:
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rows = conn.execute(
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"SELECT * FROM trade_logs ORDER BY close_time ASC, id ASC"
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).fetchall()
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return [_row_dict(r) for r in rows]
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def fetch_review_rows(conn) -> list[dict]:
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rows = conn.execute(
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"SELECT * FROM review_records ORDER BY close_time ASC, id ASC"
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).fetchall()
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return [_row_dict(r) for r in rows]
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def compute_summary(trades: list[dict], reviews: list[dict], live_capital: float) -> dict:
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nets = [_net_pnl(t) for t in trades]
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count = len(trades)
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wins = [n for n in nets if n > 0]
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losses = [n for n in nets if n < 0]
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win_cnt = len(wins)
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loss_cnt = len(losses)
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avg_profit = round(sum(wins) / len(wins), 2) if wins else 0.0
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avg_loss = round(sum(losses) / len(losses), 2) if losses else 0.0
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pl_ratio = round(avg_profit / abs(avg_loss), 2) if wins and losses and avg_loss != 0 else 0.0
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total_fee = round(sum(_fee(t) for t in trades) + sum(_fee(r) for r in reviews), 2)
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max_loss_amt = round(min(nets), 2) if nets else 0.0
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max_profit_amt = round(max(nets), 2) if nets else 0.0
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margins_loss = [
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_margin_pct(_net_pnl(t), t.get("margin"))
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for t in trades
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if _net_pnl(t) < 0 and t.get("margin")
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]
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margins_profit = [
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_margin_pct(_net_pnl(t), t.get("margin"))
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for t in trades
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if _net_pnl(t) > 0 and t.get("margin")
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]
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max_loss_pct = round(min(margins_loss), 2) if margins_loss else 0.0
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max_profit_pct = round(max(margins_profit), 2) if margins_profit else 0.0
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consec_loss = _max_consecutive_losses(nets)
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max_dd, max_dd_pct = _max_drawdown(nets, live_capital)
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emotion_cnt = sum(1 for r in reviews if r.get("is_emotion"))
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review_cnt = len(reviews)
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denom = count if count else review_cnt
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emotion_ratio = round(emotion_cnt / denom * 100, 2) if denom else 0.0
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return {
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"total_trades": count,
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"win_rate": round(win_cnt / count * 100, 2) if count else 0.0,
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"avg_profit": avg_profit,
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"avg_loss": avg_loss,
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"profit_loss_ratio": pl_ratio,
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"consecutive_losses": consec_loss,
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"max_drawdown": max_dd,
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"max_drawdown_pct": max_dd_pct,
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"max_loss_amount": max_loss_amt,
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"max_loss_pct": max_loss_pct,
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"max_profit_amount": max_profit_amt,
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"max_profit_pct": max_profit_pct,
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"total_fee": total_fee,
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"emotion_count": emotion_cnt,
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"emotion_ratio": emotion_ratio,
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"review_count": review_cnt,
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"win_count": win_cnt,
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"loss_count": loss_cnt,
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}
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def compute_breakdowns(trades: list[dict], reviews: list[dict]) -> dict[str, dict]:
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def day_key(row: dict) -> str:
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dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
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return dt.date().isoformat() if dt else "未知"
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def week_key(row: dict) -> str:
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dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
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if not dt:
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return "未知"
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iso = dt.isocalendar()
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return f"{iso.year}-W{iso.week:02d}"
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def month_key(row: dict) -> str:
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dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
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return dt.strftime("%Y-%m") if dt else "未知"
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def symbol_key(row: dict) -> str:
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return row.get("symbol_name") or row.get("symbol") or "未知"
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def direction_key(row: dict) -> str:
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d = row.get("direction") or ""
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return "做多" if d == "long" else ("做空" if d == "short" else d or "未知")
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def type_key(row: dict) -> str:
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return row.get("monitor_type") or "未知"
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by_fee_rows = []
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fee_groups = {}
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for t in trades:
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key = symbol_key(t)
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fee_groups.setdefault(key, []).append(t)
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for label, items in sorted(fee_groups.items()):
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row = _agg_metrics(label, items)
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row["avg_fee"] = round(row["total_fee"] / row["count"], 2) if row["count"] else 0.0
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by_fee_rows.append(row)
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emotion_trades = [r for r in reviews if r.get("is_emotion")]
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non_emotion = [r for r in reviews if not r.get("is_emotion")]
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emotion_rows = [
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_agg_metrics("情绪单", emotion_trades),
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_agg_metrics("非情绪单", non_emotion),
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]
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fee_columns = BREAKDOWN_COLUMNS + [{"key": "avg_fee", "label": "平均手续费"}]
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return {
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"by_time": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, day_key)},
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"by_week": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, week_key)},
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"by_month": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, month_key)},
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"by_symbol": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, symbol_key)},
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"by_fee": {"columns": fee_columns, "rows": by_fee_rows},
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"by_direction": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, direction_key)},
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"by_trade_type": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, type_key)},
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"by_emotion": {"columns": BREAKDOWN_COLUMNS, "rows": emotion_rows},
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}
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def build_all_stats(conn, live_capital: float = 0.0) -> dict:
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trades = fetch_trade_rows(conn)
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reviews = fetch_review_rows(conn)
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summary = compute_summary(trades, reviews, live_capital)
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breakdowns = compute_breakdowns(trades, reviews)
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return {
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"updated_at": datetime.now(TZ).isoformat(timespec="seconds"),
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"summary": summary,
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"views": STATS_VIEWS,
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"breakdowns": breakdowns,
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}
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def save_stats_cache(conn, data: dict) -> None:
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execute_retry(
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conn,
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"""INSERT INTO stats_cache (key, data_json, updated_at)
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VALUES ('all', ?, ?)
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ON CONFLICT(key) DO UPDATE SET data_json=excluded.data_json, updated_at=excluded.updated_at""",
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(json.dumps(data, ensure_ascii=False), data["updated_at"]),
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)
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commit_retry(conn)
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def load_stats_cache(conn) -> Optional[dict]:
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row = conn.execute(
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"SELECT data_json FROM stats_cache WHERE key='all'"
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).fetchone()
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if not row:
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return None
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try:
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return json.loads(row["data_json"])
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except json.JSONDecodeError:
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return None
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def refresh_stats_cache(conn, live_capital: float = 0.0) -> dict:
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with _stats_refresh_lock:
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data = build_all_stats(conn, live_capital)
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save_stats_cache(conn, data)
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return data
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def _norm_symbol(symbol: str) -> str:
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s = (symbol or "").strip().lower()
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if "." in s:
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s = s.split(".")[0]
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return s
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def _close_day_key(row: dict) -> str:
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dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
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return dt.date().isoformat() if dt else ""
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def _close_ts(row: dict) -> float:
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dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
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return dt.timestamp() if dt else 0.0
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def _direction_label(direction: str) -> str:
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if direction == "long":
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return "做多"
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if direction == "short":
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return "做空"
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return direction or ""
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def _index_reviews_by_day_sym(reviews: list[dict]) -> dict[tuple[str, str], list[dict]]:
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index: dict[tuple[str, str], list[dict]] = {}
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for review in reviews:
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day = _close_day_key(review)
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if not day:
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continue
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sym = _norm_symbol(review.get("symbol") or "")
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index.setdefault((day, sym), []).append(review)
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return index
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def _review_match_score(trade: dict, review: dict) -> float:
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score = abs(_close_ts(trade) - _close_ts(review))
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lots_t = trade.get("lots")
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lots_r = review.get("lots")
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if lots_t is not None and lots_r is not None and float(lots_t) != float(lots_r):
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score += 86400.0
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entry_t = trade.get("entry_price")
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entry_r = review.get("entry_price")
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if entry_t is not None and entry_r is not None and abs(float(entry_t) - float(entry_r)) > 0.01:
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score += 3600.0
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return score
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def _find_review_for_trade(
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trade: dict,
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review_index: dict[tuple[str, str], list[dict]],
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used_review_ids: set[int],
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) -> Optional[dict]:
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day = _close_day_key(trade)
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sym = _norm_symbol(trade.get("symbol") or "")
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candidates = [
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r for r in review_index.get((day, sym), [])
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if r.get("id") not in used_review_ids
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]
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if not candidates:
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return None
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return min(candidates, key=lambda r: _review_match_score(trade, r))
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def _format_day_entry(
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*,
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trade: Optional[dict] = None,
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review: Optional[dict] = None,
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source: str,
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) -> dict:
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row = review if source == "review" and review else trade or review or {}
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symbol = row.get("symbol") or ""
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pnl_net = _net_pnl(row)
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tags = (row.get("behavior_tags") or "").strip()
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is_emotion = bool(row.get("is_emotion"))
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return {
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"source": source,
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"trade_id": trade.get("id") if trade else None,
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"review_id": review.get("id") if review else None,
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"symbol": row.get("symbol_name") or symbol,
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"symbol_code": symbol,
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"direction": _direction_label(row.get("direction") or ""),
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"lots": row.get("lots"),
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"entry_price": row.get("entry_price"),
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"close_price": row.get("close_price"),
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"stop_loss": row.get("stop_loss"),
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"take_profit": row.get("take_profit"),
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"open_time": row.get("open_time") or "",
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"close_time": row.get("close_time") or "",
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"pnl": row.get("pnl"),
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"fee": row.get("fee"),
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"pnl_net": pnl_net,
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"result": row.get("result") if trade else None,
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"monitor_type": row.get("monitor_type") if trade else None,
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"is_emotion": is_emotion,
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"behavior_tags": tags,
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"open_type": row.get("open_type") if review else None,
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"exit_trigger": row.get("exit_trigger") if review else None,
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"exit_supplement": row.get("exit_supplement") if review else None,
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"holding_duration": row.get("holding_duration") if review else None,
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"initial_pnl": row.get("initial_pnl") if review else None,
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"actual_pnl": row.get("actual_pnl") if review else None,
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"timeframe": row.get("timeframe") if review else None,
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"notes": row.get("notes") if review else None,
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"screenshot": row.get("screenshot") if review else None,
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}
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def build_day_detail(trades: list[dict], reviews: list[dict], day: str) -> list[dict]:
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day_trades = [t for t in trades if _close_day_key(t) == day]
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day_reviews = [r for r in reviews if _close_day_key(r) == day]
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review_index = _index_reviews_by_day_sym(day_reviews)
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used_review_ids: set[int] = set()
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|
items: list[dict] = []
|
|
|
|
for trade in day_trades:
|
|
review = _find_review_for_trade(trade, review_index, used_review_ids)
|
|
if review:
|
|
used_review_ids.add(int(review["id"]))
|
|
items.append(_format_day_entry(trade=trade, review=review, source="review"))
|
|
else:
|
|
items.append(_format_day_entry(trade=trade, source="trade"))
|
|
|
|
for review in day_reviews:
|
|
if int(review.get("id") or 0) in used_review_ids:
|
|
continue
|
|
items.append(_format_day_entry(review=review, source="review"))
|
|
|
|
items.sort(key=lambda x: _close_ts(x), reverse=True)
|
|
return items
|
|
|
|
|
|
def build_calendar_month(trades: list[dict], reviews: list[dict], year: int, month: int) -> dict:
|
|
review_index = _index_reviews_by_day_sym(reviews)
|
|
day_map: dict[str, dict] = {}
|
|
matched_review_ids: dict[str, set[int]] = {}
|
|
|
|
for trade in trades:
|
|
dt = _parse_dt(trade.get("close_time") or "")
|
|
if not dt or dt.year != year or dt.month != month:
|
|
continue
|
|
day = dt.date().isoformat()
|
|
bucket = day_map.setdefault(
|
|
day,
|
|
{
|
|
"date": day,
|
|
"count": 0,
|
|
"total_net": 0.0,
|
|
"review_count": 0,
|
|
"emotion_count": 0,
|
|
"has_emotion": False,
|
|
},
|
|
)
|
|
bucket["count"] += 1
|
|
used = matched_review_ids.setdefault(day, set())
|
|
review = _find_review_for_trade(trade, review_index, used)
|
|
if review:
|
|
rid = int(review["id"])
|
|
used.add(rid)
|
|
bucket["total_net"] = round(bucket["total_net"] + _net_pnl(review), 2)
|
|
bucket["review_count"] += 1
|
|
if review.get("is_emotion"):
|
|
bucket["emotion_count"] += 1
|
|
bucket["has_emotion"] = True
|
|
else:
|
|
bucket["total_net"] = round(bucket["total_net"] + _net_pnl(trade), 2)
|
|
|
|
for review in reviews:
|
|
if not review.get("is_emotion"):
|
|
continue
|
|
day = _close_day_key(review)
|
|
if not day:
|
|
continue
|
|
try:
|
|
dt = date.fromisoformat(day)
|
|
except ValueError:
|
|
continue
|
|
if dt.year != year or dt.month != month:
|
|
continue
|
|
bucket = day_map.setdefault(
|
|
day,
|
|
{
|
|
"date": day,
|
|
"count": 0,
|
|
"total_net": 0.0,
|
|
"review_count": 0,
|
|
"emotion_count": 0,
|
|
"has_emotion": False,
|
|
},
|
|
)
|
|
bucket["has_emotion"] = True
|
|
rid = int(review.get("id") or 0)
|
|
if rid and rid not in matched_review_ids.get(day, set()):
|
|
bucket["emotion_count"] += 1
|
|
|
|
_, last_day = calendar.monthrange(year, month)
|
|
days = []
|
|
for d in range(1, last_day + 1):
|
|
iso = date(year, month, d).isoformat()
|
|
if iso in day_map:
|
|
row = day_map[iso]
|
|
row["total_net"] = round(row["total_net"], 2)
|
|
days.append(row)
|
|
else:
|
|
days.append(
|
|
{
|
|
"date": iso,
|
|
"count": 0,
|
|
"total_net": 0.0,
|
|
"review_count": 0,
|
|
"emotion_count": 0,
|
|
"has_emotion": False,
|
|
}
|
|
)
|
|
|
|
return {
|
|
"year": year,
|
|
"month": month,
|
|
"days": days,
|
|
"weekday_start": date(year, month, 1).weekday(),
|
|
}
|
|
|
|
|
|
def get_calendar_month(conn, year: int, month: int) -> dict:
|
|
trades = fetch_trade_rows(conn)
|
|
reviews = fetch_review_rows(conn)
|
|
return build_calendar_month(trades, reviews, year, month)
|
|
|
|
|
|
def get_calendar_day(conn, day: str) -> dict:
|
|
trades = fetch_trade_rows(conn)
|
|
reviews = fetch_review_rows(conn)
|
|
items = build_day_detail(trades, reviews, day)
|
|
total_net = round(sum(float(i.get("pnl_net") or 0) for i in items), 2)
|
|
emotion_count = sum(1 for i in items if i.get("is_emotion"))
|
|
return {
|
|
"date": day,
|
|
"count": len(items),
|
|
"total_net": total_net,
|
|
"emotion_count": emotion_count,
|
|
"items": items,
|
|
}
|