git ignored nothing?

.gitignore

@@ -1,13 +0,0 @@
-.venv/
-__pycache__/
-*.pyc
-
-.env
-
-bot_state.json
-day_pnl.json
-recent_sells.json
-trades.jsonl
-trading_bot.log
-
-*.log

AGENTS.md

@@ -21,6 +21,9 @@ Interactive Brokers 股票自动交易机器人（Python + ib_insync），多策
 | `bars.py` | `BarManager`：每个合约一条 `keepUpToDate=True` 实时K线订阅，多策略共享；`to_completed_df` 丢弃未收盘bar；`is_market_active` 通过最新bar时效判断开闭市 |
 | `state.py` | `PositionTracker`：持仓归属（哪个策略拥有哪笔仓位），持久化到 `bot_state.json`；启动/重连时 `reconcile()` 对账；每笔买卖追加写入 `trades.jsonl` 流水账本（首次运行以当前持仓为 seed） |
 | `daily_report.py` / `report.sh` | 当日成交明细+盈亏报告（FIFO，数据源 `trades.jsonl`）；用法 `./report.sh [YYYY-MM-DD]` |
+| `analytics.py` | 全历史往返交易分析引擎（纯离线，不连 IB）：FIFO 配对成 `RoundTrip` → 胜率/期望值/盈亏比/profit factor/最大回撤/**保本胜率**，并按策略、标的、入场信号、出场原因、时段归因。`--json` 可供程序消费；`dashboard.py` 依赖它 |
+| `dashboard.py` / `dashboard.sh` | 把 `analytics` 结果渲染成自包含 HTML 看板（无 CDN、无外部资源）。图表为手写内联 SVG + 原生 JS；数据以 JSON 内嵌，筛选在浏览器端重算（`summarize()` 是 `analytics.summarize` 的 JS 镜像，**改动统计口径时两处都要改**） |
+| `test_offline.py` | 离线单元测试（不连 IB、不下单）：账本字段、FIFO 配对、统计口径、指标数学。改策略后必跑 `.venv/bin/python -m unittest test_offline` |
 | `orders.py` | `execute_market_order`：下单+等待成交（30s超时撤单）+防重复单（`has_open_order`，按 orderRef 匹配） |
 | `strategies/` | `ma_cross`(SMA20/50+ADX)、`short_term`(EMA5/10+VWAP+max_hold)、`mean_reversion`(RSI/布林/急跌抄底)、`forex`(默认关闭) |
 | `main.py` | 主循环60s；disconnectedEvent 只注册一次且有并发/关机防护；重连后 `bar_manager.reset()` + 重新 `on_start` |
@@ -38,7 +41,8 @@ Interactive Brokers 股票自动交易机器人（Python + ib_insync），多策
 8. **MeanRev 趋势过滤**：仅在 `close > SMA(trend_ma_period=50)` 时允许抄底，避免下跌趋势中接飞刀。
 9. **信号只用已收盘K线**：最后一根 forming bar 必须丢弃（时间戳比较法）。
 10. **休市禁交易**：最新bar年龄 > `bar_seconds*5` 视为休市，跳过全部信号。收盘后约5分钟内 bar 仍"新鲜"，此窗口的市价单会隔夜排队——收市停机需提前（参考 15:59 EOD 停止的做法）。
-11. **K线流量**：禁止每轮全量拉历史数据（旧版 5 小时 845MB）；用 keepUpToDate 订阅（约 3.5MB/5小时）。`formatDate=2`（epoch，时区无歧义）。
+11. **账本记录归因字段（2026-08-12 起）**：`trades.jsonl` 每条记录附带 `reason`（买入=入场信号名，卖出=出场原因）与 `commission`（IB 实际佣金，`orders.trade_commission` 尽力获取，取不到则不写该字段）。出场原因取值：`SignalExit`/`SoftStop`/`HardStop`/`HardStopDuringCancel`/`HardStopDuringTrail`/`HardStopOffline`/`MaxHold`，MeanRev 另有 `RSI`/`Bollinger`/`Recovery`。**新增出场分支时必须传 reason**，否则归因图出现 `(none)` 桶。字段缺失即视为未知（不是 0），旧账本仍可解析。
+12. **K线流量**：禁止每轮全量拉历史数据（旧版 5 小时 845MB）；用 keepUpToDate 订阅（约 3.5MB/5小时）。`formatDate=2`（epoch，时区无歧义）。
 
 ## ⚠️ 已修复的 bug（勿重新引入）
 
@@ -78,6 +82,10 @@ Interactive Brokers 股票自动交易机器人（Python + ib_insync），多策
 ./restart_bot.sh            # 重启 bot（杀旧进程 + nohup 启动）
 tail -f trading_bot.log     # 运行日志
 cat bot_state.json          # 当前策略持仓归属与成本
+./report.sh [YYYY-MM-DD]    # 单日成交明细
+.venv/bin/python analytics.py   # 全历史往返统计（含保本胜率）
+./dashboard.sh              # 生成并打开 HTML 复盘看板
+.venv/bin/python -m unittest test_offline   # 离线测试
 ```
 
 一次性定时任务示例（cron）：`close_legacy_positions.sh`（平仓+自动启动bot）、`stop_bot_eod.sh`（15:59 收市前停机）。

README.md

@@ -103,8 +103,28 @@ tail -f trading_bot.log
 
 # 3. 查看当天成交明细与盈亏（可指定日期）
 ./report.sh [YYYY-MM-DD]
+
+# 4. 复盘：往返交易统计（胜率/期望值/盈亏比/保本胜率，按策略/标的/信号归因）
+.venv/bin/python analytics.py
+.venv/bin/python analytics.py --since 2026-08-01
+
+# 5. 可视化看板（单文件 HTML，含权益曲线、收益分布、完整历史表，可交互筛选）
+./dashboard.sh
+
+# 6. 离线测试（不连 IB、不下单，改动策略后必跑）
+.venv/bin/python -m unittest test_offline -v
 ```
 
+### 复盘工具说明
+
+| 工具 | 用途 |
+|------|------|
+| `daily_report.py` / `report.sh` | **单日**成交明细与盈亏（原有工具，中文文本输出） |
+| `analytics.py` | **全历史**往返交易分析：FIFO 配对 → 胜率、期望值、盈亏比、profit factor、最大回撤、以及按策略/标的/入场信号/出场原因/时段的归因。`--json` 输出可供程序消费 |
+| `dashboard.py` / `dashboard.sh` | 把上述分析渲染成自包含 HTML 看板（权益曲线+回撤带、每日盈亏、四张归因图、收益分布直方图、持仓时长散点、完整交易历史表）。支持策略/标的/时间范围交互筛选，深浅色自适应，无 CDN 依赖 |
+
+`analytics.py` 输出中最关键的一列是 **保本胜率（breakeven win rate）**：按该策略自己实现的平均盈利/平均亏损计算，需要多高的胜率才能不亏。实际胜率低于它，说明这套参数的风险收益结构本身是负期望的，调信号过滤器不会解决问题。
+
 启动时如为实盘模式，日志会有醒目的 `LIVE TRADING MODE` 警示。
 
 ## ⚠️ 从旧版本迁移（重要）

analytics.py

@@ -0,0 +1,445 @@
+#!/usr/bin/env python
+"""Round-trip trade analytics over the append-only ledger (trades.jsonl).
+
+Reconstructs closed round trips by FIFO-matching sells against buys per
+(strategy, symbol), then derives the performance statistics that actually drive
+parameter decisions: expectancy, profit factor, win rate, drawdown, and
+attribution by strategy / symbol / entry signal / exit reason / hour of day.
+
+Pure and offline: reads only the ledger, never connects to IB. Used by
+dashboard.py (HTML report) and usable directly:
+
+    .venv/bin/python analytics.py                # whole ledger
+    .venv/bin/python analytics.py --since 2026-08-01
+    .venv/bin/python analytics.py --json         # machine-readable dump
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+from collections import defaultdict, deque
+from dataclasses import asdict, dataclass, field
+from datetime import datetime
+from pathlib import Path
+from typing import Callable, Iterable, Optional
+
+LEDGER = Path(os.environ.get("TRADES_LEDGER", Path(__file__).resolve().parent / "trades.jsonl"))
+
+STRATEGY_SHORT = {
+    "MAStockStrategy": "MAStock",
+    "ShortTermMAVWAPStrategy": "ShortTerm",
+    "MeanReversionStrategy": "MeanRev",
+    "ForexMAStrategy": "Forex",
+}
+
+BUY_TYPES = ("seed", "buy")
+SELL_TYPES = ("sell", "sell_external")
+
+
+def short(name: str) -> str:
+    return STRATEGY_SHORT.get(name, name[:12])
+
+
+# --------------------------------------------------------------------------- #
+# round-trip reconstruction
+# --------------------------------------------------------------------------- #
+
+
+@dataclass
+class RoundTrip:
+    """One closed position slice: a sell matched against an earlier buy lot."""
+
+    strategy: str
+    symbol: str
+    qty: float
+    entry_ts: Optional[str]
+    exit_ts: str
+    entry_price: float
+    exit_price: float
+    entry_reason: str
+    exit_reason: str
+    gross_pnl: float
+    commission: float
+    estimated: bool          # exit price was estimated (missed external fill)
+    seeded_entry: bool       # entry lot predates the ledger (basis approximate)
+
+    @property
+    def pnl(self) -> float:
+        """Net realized P&L after commissions."""
+        return self.gross_pnl - self.commission
+
+    @property
+    def pnl_pct(self) -> float:
+        """Net return on the entry notional, in percent."""
+        cost = self.entry_price * self.qty
+        return (self.pnl / cost * 100) if cost else 0.0
+
+    @property
+    def hold_minutes(self) -> Optional[float]:
+        if not self.entry_ts:
+            return None
+        try:
+            a = datetime.fromisoformat(self.entry_ts)
+            b = datetime.fromisoformat(self.exit_ts)
+        except ValueError:
+            return None
+        return (b - a).total_seconds() / 60
+
+    @property
+    def won(self) -> bool:
+        return self.pnl > 0
+
+    def as_row(self) -> dict:
+        d = asdict(self)
+        d.update(
+            pnl=self.pnl,
+            pnl_pct=self.pnl_pct,
+            hold_minutes=self.hold_minutes,
+            won=self.won,
+            strategy_short=short(self.strategy),
+        )
+        return d
+
+
+@dataclass
+class OpenLot:
+    """A buy lot still (partly) unmatched at the end of the ledger."""
+
+    strategy: str
+    symbol: str
+    qty: float
+    price: float
+    ts: Optional[str]
+    reason: str
+    seeded: bool
+
+    def as_row(self) -> dict:
+        d = asdict(self)
+        d["strategy_short"] = short(self.strategy)
+        d["cost"] = self.qty * self.price
+        return d
+
+
+def _read_records(path: Path) -> list[dict]:
+    """Parse the JSONL ledger, skipping blank and malformed lines."""
+    records = []
+    for lineno, line in enumerate(path.read_text().splitlines(), 1):
+        line = line.strip()
+        if not line:
+            continue
+        try:
+            records.append(json.loads(line))
+        except json.JSONDecodeError:
+            print(f"warning: {path.name}:{lineno} is not valid JSON, skipped")
+    # the ledger is append-only so it is already chronological, but a manual
+    # repair could have disturbed that; sorting keeps FIFO matching honest
+    records.sort(key=lambda r: r.get("ts", ""))
+    return records
+
+
+def build_round_trips(records: Iterable[dict]) -> tuple[list[RoundTrip], list[OpenLot], float]:
+    """FIFO-match sells against buys per (strategy, symbol).
+
+    Returns (closed round trips, still-open lots, quantity sold with no known
+    cost basis). A sell that exhausts the available lots is reported in that
+    last figure rather than being silently priced at zero.
+    """
+    lots: dict[tuple[str, str], deque] = defaultdict(deque)
+    trips: list[RoundTrip] = []
+    unmatched_qty = 0.0
+
+    for r in records:
+        rtype = r.get("type")
+        key = (r.get("strategy", "?"), r.get("symbol", "?"))
+        qty = float(r.get("qty", 0) or 0)
+        price = float(r.get("price", 0) or 0)
+        if qty <= 0:
+            continue
+
+        if rtype in BUY_TYPES:
+            lots[key].append({
+                "qty": qty,
+                "price": price,
+                "ts": r.get("ts"),
+                "reason": r.get("reason", ""),
+                # commission is per-fill; carry it per share so partial
+                # matches take a proportional slice
+                "comm_per_share": (float(r.get("commission", 0) or 0) / qty),
+                "seeded": rtype == "seed",
+            })
+
+        elif rtype in SELL_TYPES:
+            remaining = qty
+            sell_comm_per_share = (float(r.get("commission", 0) or 0) / qty)
+            dq = lots[key]
+            while remaining > 1e-9 and dq:
+                lot = dq[0]
+                take = min(lot["qty"], remaining)
+                trips.append(RoundTrip(
+                    strategy=key[0],
+                    symbol=key[1],
+                    qty=take,
+                    entry_ts=lot["ts"],
+                    exit_ts=r.get("ts", ""),
+                    entry_price=lot["price"],
+                    exit_price=price,
+                    entry_reason=lot["reason"],
+                    exit_reason=r.get("reason", "") or ("external" if rtype == "sell_external" else ""),
+                    gross_pnl=(price - lot["price"]) * take,
+                    commission=(lot["comm_per_share"] + sell_comm_per_share) * take,
+                    estimated=bool(r.get("est")),
+                    seeded_entry=lot["seeded"],
+                ))
+                lot["qty"] -= take
+                remaining -= take
+                if lot["qty"] <= 1e-9:
+                    dq.popleft()
+            if remaining > 1e-9:
+                unmatched_qty += remaining
+
+    open_lots = [
+        OpenLot(strategy=s, symbol=sym, qty=lot["qty"], price=lot["price"],
+                ts=lot["ts"], reason=lot["reason"], seeded=lot["seeded"])
+        for (s, sym), dq in lots.items()
+        for lot in dq
+        if lot["qty"] > 1e-9
+    ]
+    return trips, open_lots, unmatched_qty
+
+
+# --------------------------------------------------------------------------- #
+# statistics
+# --------------------------------------------------------------------------- #
+
+
+def _mean(xs: list[float]) -> float:
+    return sum(xs) / len(xs) if xs else 0.0
+
+
+def _median(xs: list[float]) -> float:
+    if not xs:
+        return 0.0
+    s = sorted(xs)
+    mid = len(s) // 2
+    return s[mid] if len(s) % 2 else (s[mid - 1] + s[mid]) / 2
+
+
+def summarize(trips: list[RoundTrip]) -> dict:
+    """Core performance statistics for a set of round trips."""
+    n = len(trips)
+    if n == 0:
+        return {"n": 0, "pnl": 0.0, "win_rate": 0.0, "expectancy": 0.0,
+                "profit_factor": None, "avg_win": 0.0, "avg_loss": 0.0,
+                "gross_profit": 0.0, "gross_loss": 0.0, "commission": 0.0,
+                "avg_pnl_pct": 0.0, "median_pnl_pct": 0.0,
+                "best": 0.0, "worst": 0.0, "avg_hold_minutes": None,
+                "breakeven_win_rate": None, "payoff_ratio": None}
+
+    pnls = [t.pnl for t in trips]
+    wins = [p for p in pnls if p > 0]
+    losses = [p for p in pnls if p <= 0]
+    gross_profit = sum(wins)
+    gross_loss = -sum(losses)  # positive magnitude
+    holds = [h for h in (t.hold_minutes for t in trips) if h is not None]
+
+    avg_win = _mean(wins)
+    avg_loss = _mean([-p for p in losses])  # positive magnitude
+    payoff = (avg_win / avg_loss) if avg_loss else None
+    # win rate this strategy would need just to break even, given its own
+    # realized win/loss sizes - the number to compare the actual win rate against
+    breakeven_wr = (1 / (1 + payoff) * 100) if payoff else None
+
+    return {
+        "n": n,
+        "pnl": sum(pnls),
+        "win_rate": len(wins) / n * 100,
+        "expectancy": _mean(pnls),
+        "profit_factor": (gross_profit / gross_loss) if gross_loss else None,
+        "avg_win": avg_win,
+        "avg_loss": avg_loss,
+        "payoff_ratio": payoff,
+        "breakeven_win_rate": breakeven_wr,
+        "gross_profit": gross_profit,
+        "gross_loss": gross_loss,
+        "commission": sum(t.commission for t in trips),
+        "avg_pnl_pct": _mean([t.pnl_pct for t in trips]),
+        "median_pnl_pct": _median([t.pnl_pct for t in trips]),
+        "best": max(pnls),
+        "worst": min(pnls),
+        "avg_hold_minutes": _mean(holds) if holds else None,
+        "n_wins": len(wins),
+        "n_losses": len(losses),
+    }
+
+
+def equity_curve(trips: list[RoundTrip]) -> list[dict]:
+    """Cumulative realized P&L in exit order, with running peak and drawdown."""
+    ordered = sorted(trips, key=lambda t: t.exit_ts)
+    curve, cum, peak = [], 0.0, 0.0
+    for t in ordered:
+        cum += t.pnl
+        peak = max(peak, cum)
+        curve.append({
+            "ts": t.exit_ts,
+            "cum_pnl": cum,
+            "peak": peak,
+            "drawdown": cum - peak,
+            "pnl": t.pnl,
+            "symbol": t.symbol,
+            "strategy_short": short(t.strategy),
+        })
+    return curve
+
+
+def max_drawdown(curve: list[dict]) -> float:
+    """Largest peak-to-trough decline of the realized equity curve (<= 0)."""
+    return min((p["drawdown"] for p in curve), default=0.0)
+
+
+def group_stats(trips: list[RoundTrip], keyfn: Callable[[RoundTrip], str]) -> dict[str, dict]:
+    """Summarize round trips bucketed by an arbitrary key."""
+    buckets: dict[str, list[RoundTrip]] = defaultdict(list)
+    for t in trips:
+        buckets[keyfn(t) or "(none)"].append(t)
+    return {k: summarize(v) for k, v in buckets.items()}
+
+
+def daily_pnl(trips: list[RoundTrip]) -> dict[str, float]:
+    """Net realized P&L per calendar day, keyed by exit date."""
+    out: dict[str, float] = defaultdict(float)
+    for t in trips:
+        out[t.exit_ts[:10]] += t.pnl
+    return dict(sorted(out.items()))
+
+
+def _exit_hour(t: RoundTrip) -> str:
+    try:
+        return f"{datetime.fromisoformat(t.exit_ts).hour:02d}:00"
+    except ValueError:
+        return "(none)"
+
+
+def analyze(path: Path = LEDGER, since: Optional[str] = None,
+            until: Optional[str] = None) -> dict:
+    """Full analysis bundle. `since`/`until` are inclusive YYYY-MM-DD exit-date bounds.
+
+    Round trips are always reconstructed from the *whole* ledger so cost basis
+    stays correct; the date filter is applied to the resulting closed trades.
+    """
+    records = _read_records(path)
+    trips, open_lots, unmatched = build_round_trips(records)
+
+    if since:
+        trips = [t for t in trips if t.exit_ts[:10] >= since]
+    if until:
+        trips = [t for t in trips if t.exit_ts[:10] <= until]
+
+    curve = equity_curve(trips)
+    return {
+        "ledger": str(path),
+        "generated": datetime.now().isoformat(timespec="seconds"),
+        "since": since,
+        "until": until,
+        "n_records": len(records),
+        "unmatched_sell_qty": unmatched,
+        "overall": summarize(trips),
+        "max_drawdown": max_drawdown(curve),
+        "by_strategy": group_stats(trips, lambda t: short(t.strategy)),
+        "by_symbol": group_stats(trips, lambda t: t.symbol),
+        "by_entry_reason": group_stats(trips, lambda t: t.entry_reason),
+        "by_exit_reason": group_stats(trips, lambda t: t.exit_reason),
+        "by_exit_hour": group_stats(trips, _exit_hour),
+        "by_weekday": group_stats(
+            trips,
+            lambda t: (datetime.fromisoformat(t.exit_ts).strftime("%a")
+                       if t.exit_ts else "(none)"),
+        ),
+        "daily_pnl": daily_pnl(trips),
+        "equity_curve": curve,
+        "trips": [t.as_row() for t in sorted(trips, key=lambda x: x.exit_ts, reverse=True)],
+        "open_lots": [l.as_row() for l in sorted(open_lots, key=lambda x: (x.symbol, x.strategy))],
+    }
+
+
+# --------------------------------------------------------------------------- #
+# CLI
+# --------------------------------------------------------------------------- #
+
+
+def _fmt(v, spec="+.2f", dash="-"):
+    return dash if v is None else format(v, spec)
+
+
+def _print_table(title: str, stats: dict[str, dict]):
+    if not stats:
+        return
+    print(f"\n  {title}")
+    print(f"    {'bucket':<14}{'n':>4}{'net P&L':>11}{'win%':>7}{'exp':>9}{'PF':>7}{'be.win%':>9}")
+    for k, s in sorted(stats.items(), key=lambda kv: -kv[1]["pnl"]):
+        print(f"    {k[:14]:<14}{s['n']:>4}{s['pnl']:>+11.2f}{s['win_rate']:>7.1f}"
+              f"{s['expectancy']:>+9.2f}{_fmt(s['profit_factor'], '.2f', '  n/a'):>7}"
+              f"{_fmt(s['breakeven_win_rate'], '.1f', '  n/a'):>9}")
+
+
+def main():
+    ap = argparse.ArgumentParser(description="Round-trip analytics over trades.jsonl")
+    ap.add_argument("--ledger", default=str(LEDGER), help="path to trades.jsonl")
+    ap.add_argument("--since", help="only count trades closed on/after YYYY-MM-DD")
+    ap.add_argument("--until", help="only count trades closed on/before YYYY-MM-DD")
+    ap.add_argument("--json", action="store_true", help="dump the full bundle as JSON")
+    args = ap.parse_args()
+
+    path = Path(args.ledger)
+    if not path.exists():
+        raise SystemExit(f"ledger not found: {path}")
+
+    data = analyze(path, args.since, args.until)
+
+    if args.json:
+        print(json.dumps(data, indent=2, default=str))
+        return
+
+    o = data["overall"]
+    span = " ".join(filter(None, [
+        f"since {args.since}" if args.since else "",
+        f"until {args.until}" if args.until else "",
+    ])) or "full history"
+    print(f"========== round-trip performance ({span}) ==========")
+    if o["n"] == 0:
+        print("  no closed round trips in range")
+    else:
+        print(f"  closed trips     {o['n']}  ({o['n_wins']}W / {o['n_losses']}L)")
+        print(f"  net realized     {o['pnl']:+.2f}   (commissions {o['commission']:.2f})")
+        print(f"  win rate         {o['win_rate']:.1f}%   breakeven needs "
+              f"{_fmt(o['breakeven_win_rate'], '.1f', 'n/a')}%")
+        print(f"  expectancy       {o['expectancy']:+.2f} per trade")
+        print(f"  profit factor    {_fmt(o['profit_factor'], '.2f', 'n/a')}")
+        print(f"  avg win / loss   {o['avg_win']:+.2f} / -{o['avg_loss']:.2f}"
+              f"   (payoff {_fmt(o['payoff_ratio'], '.2f', 'n/a')})")
+        print(f"  best / worst     {o['best']:+.2f} / {o['worst']:+.2f}")
+        print(f"  max drawdown     {data['max_drawdown']:+.2f}")
+        if o["avg_hold_minutes"] is not None:
+            print(f"  avg hold         {o['avg_hold_minutes']:.0f} min")
+
+        _print_table("by strategy", data["by_strategy"])
+        _print_table("by symbol", data["by_symbol"])
+        if any(k != "(none)" for k in data["by_entry_reason"]):
+            _print_table("by entry signal", data["by_entry_reason"])
+        if any(k != "(none)" for k in data["by_exit_reason"]):
+            _print_table("by exit reason", data["by_exit_reason"])
+
+    if data["unmatched_sell_qty"]:
+        print(f"\n  note: {data['unmatched_sell_qty']:g} unit(s) sold with no known "
+              f"cost basis (lots predate the ledger) - excluded above")
+
+    if data["open_lots"]:
+        print(f"\n  open lots ({len(data['open_lots'])}):")
+        for l in data["open_lots"]:
+            print(f"    {l['strategy_short']:<10}{l['symbol']:<6} x{l['qty']:<6g} "
+                  f"@ {l['price']:>9.2f}  cost {l['cost']:>10.2f}")
+
+
+if __name__ == "__main__":
+    main()

dashboard.py

@@ -0,0 +1,785 @@
+#!/usr/bin/env python
+"""Render the trade ledger as a self-contained interactive HTML dashboard.
+
+    .venv/bin/python dashboard.py                    # -> dashboard.html
+    .venv/bin/python dashboard.py -o /tmp/out.html --open
+    .venv/bin/python dashboard.py --since 2026-08-01
+
+Round trips come from analytics.py. The page embeds them as JSON and does its own
+filtering/aggregation client-side, so the strategy, symbol and date-range filters
+recompute every chart without regenerating the file. No external assets, no CDN:
+the output is one portable HTML file.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import webbrowser
+from pathlib import Path
+
+import analytics
+
+# Reference thresholds drawn on the return-distribution chart. Read from config
+# when it is importable so the annotations follow the live parameters.
+try:
+    from config import config as _cfg
+    DEFAULT_MIN_PROFIT = _cfg.short_term.min_profit_pct
+    DEFAULT_STOP_LOSS = _cfg.short_term.stop_loss_pct
+    DEFAULT_MAX_DAILY_LOSS = _cfg.max_daily_loss
+except Exception:
+    DEFAULT_MIN_PROFIT, DEFAULT_STOP_LOSS, DEFAULT_MAX_DAILY_LOSS = 1.5, 2.5, 150.0
+
+
+HTML = r"""<title>Bot Trade History</title>
+<style>
+  :root {
+    color-scheme: light;
+    --page:#f9f9f7; --surface:#fcfcfb;
+    --text:#0b0b0b; --text-2:#52514e; --muted:#898781;
+    --grid:#e1e0d9; --axis:#c3c2b7; --border:rgba(11,11,11,0.10);
+    --pos:#2a78d6; --neg:#e34948;
+    --s1:#2a78d6; --s2:#eb6834; --s3:#1baf7a; --s4:#eda100;
+    --wash:rgba(11,11,11,0.04);
+  }
+  @media (prefers-color-scheme: dark) {
+    :root:not([data-theme="light"]) {
+      color-scheme: dark;
+      --page:#0d0d0d; --surface:#1a1a19;
+      --text:#ffffff; --text-2:#c3c2b7; --muted:#898781;
+      --grid:#2c2c2a; --axis:#383835; --border:rgba(255,255,255,0.10);
+      --pos:#3987e5; --neg:#e66767;
+      --s1:#3987e5; --s2:#d95926; --s3:#199e70; --s4:#c98500;
+      --wash:rgba(255,255,255,0.06);
+    }
+  }
+  :root[data-theme="dark"] {
+    color-scheme: dark;
+    --page:#0d0d0d; --surface:#1a1a19;
+    --text:#ffffff; --text-2:#c3c2b7; --muted:#898781;
+    --grid:#2c2c2a; --axis:#383835; --border:rgba(255,255,255,0.10);
+    --pos:#3987e5; --neg:#e66767;
+    --s1:#3987e5; --s2:#d95926; --s3:#199e70; --s4:#c98500;
+    --wash:rgba(255,255,255,0.06);
+  }
+
+  body {
+    background:var(--page); color:var(--text);
+    font:14px/1.5 system-ui,-apple-system,"Segoe UI",sans-serif;
+    margin:0; padding:24px 20px 64px;
+  }
+  .wrap { max-width:1120px; margin:0 auto; }
+  h1 { font-size:20px; font-weight:650; margin:0 0 4px; letter-spacing:-0.01em; }
+  .sub { color:var(--text-2); font-size:13px; margin-bottom:20px; }
+  .sub code { color:var(--muted); font-size:12px; }
+
+  .card {
+    background:var(--surface); border:1px solid var(--border); border-radius:10px;
+    padding:16px 18px; margin-bottom:16px;
+  }
+  .card h2 {
+    font-size:13px; font-weight:600; margin:0 0 2px; letter-spacing:0.01em;
+  }
+  .card .note { color:var(--muted); font-size:12px; margin:0 0 14px; }
+
+  /* filters */
+  .filters { display:flex; flex-wrap:wrap; gap:16px; align-items:flex-end; }
+  .fgroup { display:flex; flex-direction:column; gap:6px; }
+  .flabel { font-size:11px; text-transform:uppercase; letter-spacing:0.05em; color:var(--muted); }
+  .chips { display:flex; flex-wrap:wrap; gap:6px; }
+  .chip {
+    border:1px solid var(--border); background:transparent; color:var(--text-2);
+    border-radius:999px; padding:4px 11px; font-size:12.5px; cursor:pointer;
+    font-family:inherit; display:inline-flex; align-items:center; gap:6px;
+    min-height:28px;
+  }
+  .chip:hover { background:var(--wash); }
+  .chip[aria-pressed="true"] { color:var(--text); border-color:var(--axis); background:var(--wash); }
+  .chip .dot { width:8px; height:8px; border-radius:2px; background:currentColor; opacity:.35; }
+  .chip[aria-pressed="true"] .dot { opacity:1; }
+  select, input[type=date] {
+    font:inherit; font-size:12.5px; color:var(--text); background:var(--surface);
+    border:1px solid var(--border); border-radius:6px; padding:5px 8px; min-height:30px;
+  }
+
+  /* stat tiles */
+  .tiles { display:grid; grid-template-columns:repeat(4,1fr); gap:1px;
+           background:var(--border); border:1px solid var(--border); border-radius:10px;
+           overflow:hidden; margin-bottom:16px; }
+  @media (max-width:820px) { .tiles { grid-template-columns:repeat(2,1fr); } }
+  .tile { background:var(--surface); padding:14px 16px; }
+  .tile .k { font-size:11px; text-transform:uppercase; letter-spacing:0.05em; color:var(--muted); }
+  .tile .v { font-size:23px; font-weight:600; margin-top:4px; letter-spacing:-0.02em; }
+  .tile .h { font-size:12px; color:var(--text-2); margin-top:2px; }
+  .up { color:var(--pos); } .down { color:var(--neg); }
+
+  .grid2 { display:grid; grid-template-columns:repeat(auto-fit,minmax(420px,1fr)); gap:16px; }
+
+  svg { display:block; width:100%; overflow:visible; }
+  svg text { fill:var(--muted); font-size:11px; }
+  svg text.lbl { fill:var(--text-2); font-size:11.5px; }
+  svg text.val { fill:var(--text-2); font-size:11px; font-variant-numeric:tabular-nums; }
+  .gridline { stroke:var(--grid); stroke-width:1; }
+  .baseline { stroke:var(--axis); stroke-width:1; }
+  .annot { stroke:var(--muted); stroke-width:1; stroke-dasharray:3 3; opacity:.8; }
+
+  .legend { display:flex; flex-wrap:wrap; gap:14px; margin:0 0 10px; font-size:12px; color:var(--text-2); }
+  .legend span { display:inline-flex; align-items:center; gap:6px; }
+  .legend i { width:10px; height:10px; border-radius:2px; display:inline-block; }
+
+  /* tables */
+  .scroll { overflow-x:auto; }
+  table { border-collapse:collapse; width:100%; font-size:12.5px; }
+  th, td { text-align:right; padding:6px 9px; white-space:nowrap; }
+  th:first-child, td:first-child, th.l, td.l { text-align:left; }
+  thead th {
+    color:var(--muted); font-weight:600; font-size:11px; text-transform:uppercase;
+    letter-spacing:0.04em; border-bottom:1px solid var(--axis); cursor:pointer;
+    position:sticky; top:0; background:var(--surface);
+  }
+  thead th:hover { color:var(--text-2); }
+  tbody tr { border-bottom:1px solid var(--grid); }
+  tbody tr:hover { background:var(--wash); }
+  td.num { font-variant-numeric:tabular-nums; }
+  .tag { font-size:11px; color:var(--text-2); border:1px solid var(--border);
+         border-radius:4px; padding:1px 6px; }
+  .est { color:var(--muted); font-size:11px; }
+  .tallwrap { max-height:520px; overflow-y:auto; }
+
+  #tip {
+    position:fixed; pointer-events:none; z-index:50; opacity:0; transition:opacity .08s;
+    background:var(--surface); color:var(--text); border:1px solid var(--axis);
+    border-radius:7px; padding:8px 10px; font-size:12px; line-height:1.45;
+    box-shadow:0 4px 14px rgba(0,0,0,0.13); max-width:260px;
+  }
+  #tip b { font-weight:600; }
+  #tip .r { color:var(--text-2); }
+  .empty { color:var(--muted); padding:22px 0; text-align:center; font-size:13px; }
+</style>
+
+<div class="wrap">
+  <h1>Bot Trade History &amp; Performance</h1>
+  <div class="sub" id="meta"></div>
+
+  <div class="card">
+    <div class="filters" id="filters"></div>
+  </div>
+
+  <div class="tiles" id="tiles"></div>
+
+  <div class="card">
+    <h2>Realized equity curve</h2>
+    <p class="note">Cumulative net P&amp;L by exit time. Shaded band is drawdown from the running peak.</p>
+    <div id="equity"></div>
+  </div>
+
+  <div class="card">
+    <h2>Daily realized P&amp;L</h2>
+    <p class="note">Net per calendar day. The dashed line marks the $<span id="dlLbl"></span> daily-loss circuit breaker.</p>
+    <div id="daily"></div>
+  </div>
+
+  <div class="grid2">
+    <div class="card">
+      <h2>By strategy</h2>
+      <p class="note">Net P&amp;L; label shows trade count and win rate.</p>
+      <div id="byStrategy"></div>
+    </div>
+    <div class="card">
+      <h2>By symbol</h2>
+      <p class="note">Net P&amp;L; label shows trade count and win rate.</p>
+      <div id="bySymbol"></div>
+    </div>
+    <div class="card">
+      <h2>By entry signal</h2>
+      <p class="note">Which signal actually pays. Needs <code>reason</code> in the ledger.</p>
+      <div id="byEntry"></div>
+    </div>
+    <div class="card">
+      <h2>By exit reason</h2>
+      <p class="note">How trades end. Needs <code>reason</code> in the ledger.</p>
+      <div id="byExit"></div>
+    </div>
+  </div>
+
+  <div class="card">
+    <h2>Return distribution</h2>
+    <p class="note" id="distNote"></p>
+    <div id="dist"></div>
+  </div>
+
+  <div class="card">
+    <h2>Hold time vs return</h2>
+    <p class="note">One dot per closed trade. Colour is the owning strategy.</p>
+    <div id="scatter"></div>
+  </div>
+
+  <div class="card">
+    <h2>Open lots</h2>
+    <p class="note">Unmatched buy lots at the end of the ledger — cost basis only, not live marks.</p>
+    <div class="scroll" id="openTbl"></div>
+  </div>
+
+  <div class="card">
+    <h2>Trade history</h2>
+    <p class="note">Every closed round trip in range, newest first. Click a header to sort. <span class="est">SELL* = exit price estimated (fill missed while the bot was offline).</span></p>
+    <div class="scroll tallwrap" id="tripTbl"></div>
+  </div>
+</div>
+<div id="tip" role="tooltip"></div>
+
+<script>
+const DATA = __DATA__;
+const SERIES = ['--s1','--s2','--s3','--s4'];
+const cssv = n => getComputedStyle(document.documentElement).getPropertyValue(n).trim();
+
+/* ---------- helpers ---------- */
+const money = v => (v<0?'-':'+') + '$' + Math.abs(v).toFixed(2);
+const money0 = v => (v<0?'-':'') + '$' + Math.abs(v).toFixed(0);
+const pct = v => v.toFixed(1) + '%';
+const el = (t,a={},kids=[]) => {
+  const n = document.createElementNS('http://www.w3.org/2000/svg', t);
+  for (const k in a) n.setAttribute(k, a[k]);
+  kids.forEach(c => n.appendChild(c));
+  return n;
+};
+const svg = (w,h) => el('svg', {viewBox:`0 0 ${w} ${h}`, height:h,
+                               preserveAspectRatio:'xMinYMid meet', role:'img'});
+const txt = (x,y,s,cls='',anchor='start') => {
+  const n = el('text',{x,y,'text-anchor':anchor}); if(cls) n.setAttribute('class',cls);
+  n.textContent = s; return n;
+};
+function niceTicks(lo, hi, want=5) {
+  if (lo === hi) { lo -= 1; hi += 1; }
+  const raw = (hi-lo)/want, mag = Math.pow(10, Math.floor(Math.log10(raw)));
+  const step = [1,2,2.5,5,10].map(m=>m*mag).find(s=>s>=raw) || 10*mag;
+  const out = []; for (let v=Math.ceil(lo/step)*step; v<=hi+1e-9; v+=step) out.push(v);
+  return out;
+}
+/* Ticks are rounded inward, so the axis domain must be widened back out to the
+   data - otherwise an extreme value (e.g. a gap-through-stop loss) plots
+   outside the plot area. */
+function domain(lo, hi, ticks) {
+  return [Math.min(lo, ticks[0]), Math.max(hi, ticks[ticks.length-1])];
+}
+/* mirrors analytics.summarize() so client-side filters recompute identically */
+function summarize(ts) {
+  const n = ts.length;
+  if (!n) return {n:0,pnl:0,win_rate:0,expectancy:0,profit_factor:null,avg_win:0,
+                  avg_loss:0,payoff:null,be_wr:null,best:0,worst:0,commission:0,
+                  n_wins:0,n_losses:0,avg_hold:null};
+  const p = ts.map(t=>t.pnl);
+  const w = p.filter(x=>x>0), l = p.filter(x=>x<=0);
+  const gp = w.reduce((a,b)=>a+b,0), gl = -l.reduce((a,b)=>a+b,0);
+  const aw = w.length ? gp/w.length : 0, al = l.length ? gl/l.length : 0;
+  const payoff = al ? aw/al : null;
+  const holds = ts.map(t=>t.hold_minutes).filter(h=>h!=null);
+  return {
+    n, pnl:p.reduce((a,b)=>a+b,0), win_rate:w.length/n*100,
+    expectancy:p.reduce((a,b)=>a+b,0)/n, profit_factor: gl ? gp/gl : null,
+    avg_win:aw, avg_loss:al, payoff, be_wr: payoff ? 100/(1+payoff) : null,
+    best:Math.max(...p), worst:Math.min(...p),
+    commission: ts.reduce((a,t)=>a+t.commission,0),
+    n_wins:w.length, n_losses:l.length,
+    avg_hold: holds.length ? holds.reduce((a,b)=>a+b,0)/holds.length : null,
+  };
+}
+function groupBy(ts, keyfn) {
+  const m = new Map();
+  ts.forEach(t => { const k = keyfn(t) || '(none)';
+                    if(!m.has(k)) m.set(k,[]); m.get(k).push(t); });
+  return m;
+}
+
+/* ---------- tooltip ---------- */
+const tip = document.getElementById('tip');
+function bindTip(node, html) {
+  node.addEventListener('pointerenter', e => {
+    tip.innerHTML = html; tip.style.opacity = 1; move(e);
+  });
+  node.addEventListener('pointermove', move);
+  node.addEventListener('pointerleave', () => tip.style.opacity = 0);
+  function move(e) {
+    const r = tip.getBoundingClientRect();
+    let x = e.clientX + 14, y = e.clientY - r.height - 10;
+    if (x + r.width > innerWidth - 8) x = e.clientX - r.width - 14;
+    if (y < 8) y = e.clientY + 16;
+    tip.style.left = x + 'px'; tip.style.top = y + 'px';
+  }
+}
+
+/* ---------- state & filtering ---------- */
+const strategies = [...new Set(DATA.trips.map(t=>t.strategy_short))].sort();
+const symbols    = [...new Set(DATA.trips.map(t=>t.symbol))].sort();
+const stratColor = {};
+strategies.forEach((s,i) => stratColor[s] = SERIES[i % SERIES.length]);
+
+const state = { strat:new Set(strategies), sym:new Set(symbols), days:0 };
+function filtered() {
+  let ts = DATA.trips.filter(t => state.strat.has(t.strategy_short) && state.sym.has(t.symbol));
+  if (state.days > 0) {
+    const all = DATA.trips.map(t=>t.exit_ts).sort();
+    if (all.length) {
+      const last = new Date(all[all.length-1]);
+      const cut = new Date(last.getTime() - state.days*86400000).toISOString().slice(0,10);
+      ts = ts.filter(t => t.exit_ts.slice(0,10) >= cut);
+    }
+  }
+  return ts;
+}
+
+/* ---------- filter UI ---------- */
+function buildFilters() {
+  const f = document.getElementById('filters');
+  f.innerHTML = '';
+  f.appendChild(chipGroup('Strategy', strategies, state.strat, s=>cssv(stratColor[s])));
+  f.appendChild(chipGroup('Symbol', symbols, state.sym, ()=>null));
+  const g = document.createElement('div'); g.className = 'fgroup';
+  g.innerHTML = '<span class="flabel">Range</span>';
+  const sel = document.createElement('select');
+  [[0,'All time'],[7,'Last 7 days'],[30,'Last 30 days'],[90,'Last 90 days']]
+    .forEach(([v,l]) => { const o=document.createElement('option'); o.value=v; o.textContent=l; sel.appendChild(o); });
+  sel.value = state.days;
+  sel.onchange = () => { state.days = +sel.value; render(); };
+  g.appendChild(sel); f.appendChild(g);
+
+  function chipGroup(label, items, set, colorFn) {
+    const g = document.createElement('div'); g.className='fgroup';
+    g.innerHTML = `<span class="flabel">${label}</span>`;
+    const box = document.createElement('div'); box.className='chips';
+    items.forEach(it => {
+      const b = document.createElement('button');
+      b.className='chip'; b.type='button';
+      b.setAttribute('aria-pressed', set.has(it));
+      const c = colorFn(it);
+      b.innerHTML = (c ? `<i class="dot" style="background:${c}"></i>` : '') + it;
+      b.onclick = () => {
+        if (set.has(it)) { if (set.size>1) set.delete(it); } else set.add(it);
+        b.setAttribute('aria-pressed', set.has(it)); render();
+      };
+      box.appendChild(b);
+    });
+    g.appendChild(box); return g;
+  }
+}
+
+/* ---------- stat tiles ---------- */
+function renderTiles(ts) {
+  const s = summarize(ts);
+  const dd = drawdownOf(ts);
+  const beat = s.be_wr != null ? s.win_rate - s.be_wr : null;
+  const tiles = [
+    ['Net realized', money(s.pnl), s.pnl>=0?'up':'down',
+      `${s.n} trades · ${money0(s.commission)} commission`],
+    ['Expectancy', money(s.expectancy), s.expectancy>=0?'up':'down', 'per trade'],
+    ['Win rate', pct(s.win_rate), '',
+      s.be_wr!=null ? `needs ${pct(s.be_wr)} to break even` : `${s.n_wins}W / ${s.n_losses}L`],
+    ['Edge vs breakeven', beat!=null ? (beat>=0?'+':'')+beat.toFixed(1)+'pp' : '—',
+      beat!=null ? (beat>=0?'up':'down') : '',
+      'win rate minus breakeven'],
+    ['Profit factor', s.profit_factor!=null ? s.profit_factor.toFixed(2) : '—',
+      s.profit_factor!=null ? (s.profit_factor>=1?'up':'down') : '',
+      `gross ${money0(s.avg_win*s.n_wins)} / ${money0(-s.avg_loss*s.n_losses)}`],
+    ['Payoff ratio', s.payoff!=null ? s.payoff.toFixed(2) : '—',
+      s.payoff!=null ? (s.payoff>=1?'up':'down') : '',
+      `avg ${money0(s.avg_win)} win / ${money0(s.avg_loss)} loss`],
+    ['Max drawdown', money(dd), dd<0?'down':'', 'realized, peak to trough'],
+    ['Avg hold', s.avg_hold!=null ? fmtHold(s.avg_hold) : '—', '', 'entry to exit'],
+  ];
+  document.getElementById('tiles').innerHTML = tiles.map(([k,v,cls,h]) =>
+    `<div class="tile"><div class="k">${k}</div><div class="v ${cls}">${v}</div><div class="h">${h}</div></div>`
+  ).join('');
+}
+const fmtHold = m => m < 90 ? Math.round(m)+' min'
+                 : m < 1440 ? (m/60).toFixed(1)+' h' : (m/1440).toFixed(1)+' d';
+function drawdownOf(ts) {
+  let cum=0, peak=0, dd=0;
+  [...ts].sort((a,b)=>a.exit_ts<b.exit_ts?-1:1).forEach(t=>{
+    cum+=t.pnl; peak=Math.max(peak,cum); dd=Math.min(dd,cum-peak);
+  });
+  return dd;
+}
+
+/* ---------- equity curve ---------- */
+function renderEquity(ts) {
+  const host = document.getElementById('equity');
+  host.innerHTML = '';
+  if (!ts.length) return host.innerHTML = '<div class="empty">No closed trades in range.</div>';
+
+  const pts = [...ts].sort((a,b)=>a.exit_ts<b.exit_ts?-1:1);
+  let cum=0, peak=0;
+  const series = pts.map(t => { cum+=t.pnl; peak=Math.max(peak,cum);
+                                return {x:new Date(t.exit_ts).getTime(), cum, peak, t}; });
+  const W = host.clientWidth || 860, H = 260;
+  const m = {t:12, r:16, b:26, l:56};
+  const iw = W-m.l-m.r, ih = H-m.t-m.b;
+  const x0 = series[0].x, x1 = series[series.length-1].x;
+  const sx = v => m.l + (x1===x0 ? iw/2 : (v-x0)/(x1-x0)*iw);
+  const lo = Math.min(0, ...series.map(p=>p.cum)), hi = Math.max(0, ...series.map(p=>p.peak));
+  const ticks = niceTicks(lo, hi);
+  const yLo = Math.min(lo, ticks[0]), yHi = Math.max(hi, ticks[ticks.length-1]);
+  const sy = v => m.t + ih - (v-yLo)/(yHi-yLo)*ih;
+
+  const s = svg(W,H);
+  ticks.forEach(v => {
+    s.appendChild(el('line',{class:'gridline',x1:m.l,x2:W-m.r,y1:sy(v),y2:sy(v)}));
+    s.appendChild(txt(m.l-9, sy(v)+4, money0(v), 'val', 'end'));
+  });
+  s.appendChild(el('line',{class:'baseline',x1:m.l,x2:W-m.r,y1:sy(0),y2:sy(0)}));
+
+  // drawdown band: between running peak and equity
+  const band = series.map(p=>`${sx(p.x)},${sy(p.peak)}`).join(' ') + ' ' +
+               [...series].reverse().map(p=>`${sx(p.x)},${sy(p.cum)}`).join(' ');
+  s.appendChild(el('polygon',{points:band, fill:cssv('--neg'), opacity:0.13}));
+
+  s.appendChild(el('polyline',{
+    points: series.map(p=>`${sx(p.x)},${sy(p.cum)}`).join(' '),
+    fill:'none', stroke:cssv('--pos'), 'stroke-width':2,
+    'stroke-linejoin':'round','stroke-linecap':'round'}));
+
+  // x labels: first / middle / last date
+  [0, Math.floor(series.length/2), series.length-1].filter((v,i,a)=>a.indexOf(v)===i)
+    .forEach((i,j,arr) => {
+      const p = series[i];
+      s.appendChild(txt(sx(p.x), H-8, new Date(p.x).toISOString().slice(5,10),
+                        '', j===0?'start':(j===arr.length-1?'end':'middle')));
+    });
+
+  // hover markers (invisible wide hit targets)
+  series.forEach(p => {
+    const g = el('g');
+    g.appendChild(el('circle',{cx:sx(p.x),cy:sy(p.cum),r:8,fill:'transparent'}));
+    g.appendChild(el('circle',{cx:sx(p.x),cy:sy(p.cum),r:2.5,
+                               fill:cssv('--pos'),opacity:0.55}));
+    bindTip(g, `<b>${p.t.symbol}</b> <span class="r">${p.t.strategy_short}</span><br>
+      <span class="r">${p.t.exit_ts.replace('T',' ')}</span><br>
+      trade ${money(p.t.pnl)} · cumulative <b>${money(p.cum)}</b>
+      ${p.cum<p.peak ? `<br><span class="r">drawdown ${money(p.cum-p.peak)}</span>`:''}`);
+    s.appendChild(g);
+  });
+  host.appendChild(s);
+}
+
+/* ---------- daily bars ---------- */
+function renderDaily(ts) {
+  const host = document.getElementById('daily');
+  host.innerHTML = '';
+  if (!ts.length) return host.innerHTML = '<div class="empty">No closed trades in range.</div>';
+
+  const m2 = new Map();
+  ts.forEach(t => { const d=t.exit_ts.slice(0,10); m2.set(d,(m2.get(d)||0)+t.pnl); });
+  const days = [...m2.entries()].sort();
+  const W = host.clientWidth || 860, H = 200;
+  const m = {t:10,r:16,b:30,l:56}, iw=W-m.l-m.r, ih=H-m.t-m.b;
+  const vals = days.map(d=>d[1]);
+  const dLo = Math.min(0,...vals,-DATA.max_daily_loss), dHi = Math.max(0,...vals);
+  const ticks = niceTicks(dLo, dHi);
+  const [yLo, yHi] = domain(dLo, dHi, ticks);
+  const sy = v => m.t + ih - (v-yLo)/(yHi-yLo)*ih;
+  const bw = Math.max(2, Math.min(26, iw/days.length - 2));
+
+  const s = svg(W,H);
+  ticks.forEach(v=>{
+    s.appendChild(el('line',{class:'gridline',x1:m.l,x2:W-m.r,y1:sy(v),y2:sy(v)}));
+    s.appendChild(txt(m.l-9,sy(v)+4,money0(v),'val','end'));
+  });
+  // circuit-breaker reference
+  if (-DATA.max_daily_loss >= yLo) {
+    s.appendChild(el('line',{class:'annot',x1:m.l,x2:W-m.r,
+                             y1:sy(-DATA.max_daily_loss),y2:sy(-DATA.max_daily_loss)}));
+  }
+  days.forEach(([d,v],i) => {
+    const cx = m.l + (i+0.5)*(iw/days.length);
+    const y = v>=0 ? sy(v) : sy(0), h = Math.max(1, Math.abs(sy(v)-sy(0)));
+    const g = el('g');
+    g.appendChild(el('rect',{x:cx-bw/2, y:y, width:bw, height:h, rx:Math.min(4,bw/2),
+                             fill:cssv(v>=0?'--pos':'--neg')}));
+    g.appendChild(el('rect',{x:cx-bw/2-3, y:m.t, width:bw+6, height:ih, fill:'transparent'}));
+    bindTip(g, `<b>${d}</b><br>net <b>${money(v)}</b>`);
+    s.appendChild(g);
+  });
+  s.appendChild(el('line',{class:'baseline',x1:m.l,x2:W-m.r,y1:sy(0),y2:sy(0)}));
+  [0, days.length-1].filter((v,i,a)=>a.indexOf(v)===i).forEach((i,j) => {
+    s.appendChild(txt(m.l+(i+0.5)*(iw/days.length), H-8, days[i][0].slice(5),
+                      '', j===0?'start':'end'));
+  });
+  host.appendChild(s);
+}
+
+/* ---------- horizontal attribution bars ---------- */
+function renderBars(hostId, ts, keyfn, colorByStrategy=false) {
+  const host = document.getElementById(hostId);
+  host.innerHTML = '';
+  const groups = [...groupBy(ts, keyfn).entries()]
+    .map(([k,v]) => [k, summarize(v)])
+    .sort((a,b) => b[1].pnl - a[1].pnl);
+  if (!groups.length || (groups.length===1 && groups[0][0]==='(none)' && !ts.length))
+    return host.innerHTML = '<div class="empty">No data in range.</div>';
+  if (groups.every(g => g[0]==='(none)'))
+    return host.innerHTML = '<div class="empty">Not recorded in this ledger yet — '
+      + 'new trades will populate it.</div>';
+
+  const rowH = 30, W = host.clientWidth || 420, H = groups.length*rowH + 24;
+  const labelW = Math.min(120, Math.max(...groups.map(g=>g[0].length))*7 + 10);
+  const m = {t:6, r:64, l:labelW+8}, iw = W-m.l-m.r;
+  const mx = Math.max(1, ...groups.map(g=>Math.abs(g[1].pnl)));
+  const zero = m.l + iw/2, half = iw/2;
+
+  const s = svg(W,H);
+  s.appendChild(el('line',{class:'baseline',x1:zero,x2:zero,y1:m.t,y2:m.t+groups.length*rowH}));
+  groups.forEach(([k,st],i) => {
+    const cy = m.t + i*rowH + rowH/2;
+    const w = Math.abs(st.pnl)/mx*half;
+    const g = el('g');
+    const pos = st.pnl >= 0;
+    g.appendChild(el('rect',{
+      x: pos ? zero+1 : zero-w-1, y: cy-7, width: Math.max(1.5,w), height:14,
+      rx:4, fill: colorByStrategy ? cssv(stratColor[k]||'--s1') : cssv(pos?'--pos':'--neg')}));
+    s.appendChild(txt(m.l-8, cy+4, k, 'lbl', 'end'));
+    s.appendChild(txt(W-m.r+8, cy+4, money0(st.pnl), 'val'));
+    g.appendChild(el('rect',{x:m.l,y:cy-rowH/2,width:iw,height:rowH,fill:'transparent'}));
+    bindTip(g, `<b>${k}</b><br>net <b>${money(st.pnl)}</b> over ${st.n} trades<br>
+      <span class="r">win ${pct(st.win_rate)}`
+      + (st.be_wr!=null ? ` · breakeven ${pct(st.be_wr)}` : '')
+      + `<br>expectancy ${money(st.expectancy)} · payoff `
+      + (st.payoff!=null?st.payoff.toFixed(2):'—') + `</span>`);
+    s.appendChild(g);
+  });
+  host.appendChild(s);
+}
+
+/* ---------- return distribution ---------- */
+function renderDist(ts) {
+  const host = document.getElementById('dist');
+  host.innerHTML = '';
+  document.getElementById('distNote').innerHTML =
+    `Net return per trade in 0.5% bins. Dashed lines mark the +${DATA.min_profit_pct}% `
+    + `minimum-profit exit gate and the −${DATA.stop_loss_pct}% hard stop. `
+    + `A healthy distribution has its right tail reaching further than its left.`;
+  if (!ts.length) return host.innerHTML = '<div class="empty">No closed trades in range.</div>';
+
+  const BIN = 0.5;
+  const vals = ts.map(t=>t.pnl_pct);
+  const lo = Math.floor(Math.min(...vals, -DATA.stop_loss_pct)/BIN)*BIN;
+  const hi = Math.ceil(Math.max(...vals, DATA.min_profit_pct)/BIN)*BIN;
+  const nb = Math.max(1, Math.round((hi-lo)/BIN));
+  const bins = Array.from({length:nb}, (_,i)=>({lo:lo+i*BIN, hi:lo+(i+1)*BIN, items:[]}));
+  vals.forEach((v,i) => {
+    let k = Math.floor((v-lo)/BIN); k = Math.max(0, Math.min(nb-1,k));
+    bins[k].items.push(ts[i]);
+  });
+
+  const W = host.clientWidth || 860, H = 236;
+  // extra top margin so the threshold labels sit above the plot, never on a bar
+  const m = {t:26,r:16,b:34,l:40}, iw=W-m.l-m.r, ih=H-m.t-m.b;
+  const mxc = Math.max(1, ...bins.map(b=>b.items.length));
+  const cTicks = niceTicks(0, mxc, 4);
+  const yHi = Math.max(mxc, cTicks[cTicks.length-1]);
+  const sy = c => m.t + ih - c/yHi*ih;
+  const sx = v => m.l + (v-lo)/(hi-lo)*iw;
+  const bw = Math.max(2, iw/nb - 2);
+
+  const s = svg(W,H);
+  cTicks.forEach(c=>{
+    s.appendChild(el('line',{class:'gridline',x1:m.l,x2:W-m.r,y1:sy(c),y2:sy(c)}));
+    s.appendChild(txt(m.l-8,sy(c)+4,c,'val','end'));
+  });
+  bins.forEach(b => {
+    if (!b.items.length) return;
+    const c = b.items.length, cx = sx((b.lo+b.hi)/2);
+    const g = el('g');
+    g.appendChild(el('rect',{x:cx-bw/2, y:sy(c), width:bw, height:ih-(sy(c)-m.t),
+                             rx:Math.min(4,bw/2), fill:cssv(b.lo>=0?'--pos':'--neg')}));
+    g.appendChild(el('rect',{x:cx-bw/2-2,y:m.t,width:bw+4,height:ih,fill:'transparent'}));
+    const sum = b.items.reduce((a,t)=>a+t.pnl,0);
+    bindTip(g, `<b>${b.lo.toFixed(1)}% to ${b.hi.toFixed(1)}%</b><br>
+      ${c} trade${c>1?'s':''} · net ${money(sum)}<br>
+      <span class="r">${[...new Set(b.items.map(t=>t.symbol))].join(', ')}</span>`);
+    s.appendChild(g);
+  });
+  [[DATA.min_profit_pct, 'min profit'], [-DATA.stop_loss_pct, 'stop']].forEach(([v,l])=>{
+    s.appendChild(el('line',{class:'annot',x1:sx(v),x2:sx(v),y1:m.t-4,y2:m.t+ih}));
+    s.appendChild(txt(sx(v), m.t-10, l, '', 'middle'));
+  });
+  s.appendChild(el('line',{class:'baseline',x1:m.l,x2:W-m.r,y1:m.t+ih,y2:m.t+ih}));
+  niceTicks(lo,hi,6).forEach(v=>{
+    if (v<lo-1e-9||v>hi+1e-9) return;
+    s.appendChild(txt(sx(v), H-10, v.toFixed(1)+'%', '', 'middle'));
+  });
+  host.appendChild(s);
+}
+
+/* ---------- hold vs return scatter ---------- */
+function renderScatter(ts) {
+  const host = document.getElementById('scatter');
+  host.innerHTML = '';
+  const pts = ts.filter(t => t.hold_minutes != null);
+  if (!pts.length) return host.innerHTML =
+    '<div class="empty">No hold times available (entry timestamps missing for these lots).</div>';
+
+  const used = [...new Set(pts.map(p=>p.strategy_short))].sort();
+  host.innerHTML = '<div class="legend">' + used.map(s =>
+    `<span><i style="background:${cssv(stratColor[s])}"></i>${s}</span>`).join('') + '</div>';
+
+  const W = host.clientWidth || 860, H = 260;
+  const m = {t:12,r:16,b:34,l:48}, iw=W-m.l-m.r, ih=H-m.t-m.b;
+  const hMax = Math.max(...pts.map(p=>p.hold_minutes));
+  const rLo = Math.min(0,...pts.map(p=>p.pnl_pct)), rHi = Math.max(0,...pts.map(p=>p.pnl_pct));
+  const rTicks = niceTicks(rLo, rHi, 5);
+  const [yLo, yHi] = domain(rLo, rHi, rTicks);
+  const sy = v => m.t + ih - (v-yLo)/(yHi-yLo)*ih;
+  // sqrt x-scale: hold times span minutes to days
+  const sx = v => m.l + Math.sqrt(v/Math.max(1,hMax))*iw;
+
+  const s = svg(W,H);
+  rTicks.forEach(v=>{
+    s.appendChild(el('line',{class:'gridline',x1:m.l,x2:W-m.r,y1:sy(v),y2:sy(v)}));
+    s.appendChild(txt(m.l-8,sy(v)+4,v.toFixed(1)+'%','val','end'));
+  });
+  s.appendChild(el('line',{class:'baseline',x1:m.l,x2:W-m.r,y1:sy(0),y2:sy(0)}));
+  const xt = [15,60,240,1440,4320,10080].filter(v => v <= hMax*0.88);
+  xt.forEach(v => s.appendChild(txt(sx(v), H-10, fmtHold(v), '', 'middle')));
+  // always anchor the right end, otherwise the axis trails off unlabelled
+  s.appendChild(txt(sx(hMax), H-10, fmtHold(hMax), '', 'end'));
+  pts.forEach(p => {
+    const g = el('g');
+    g.appendChild(el('circle',{cx:sx(p.hold_minutes),cy:sy(p.pnl_pct),r:5,
+      fill:cssv(stratColor[p.strategy_short]||'--s1'), 'fill-opacity':0.8,
+      stroke:cssv('--surface'), 'stroke-width':2}));
+    g.appendChild(el('circle',{cx:sx(p.hold_minutes),cy:sy(p.pnl_pct),r:11,fill:'transparent'}));
+    bindTip(g, `<b>${p.symbol}</b> <span class="r">${p.strategy_short}</span><br>
+      ${p.pnl_pct>=0?'+':''}${p.pnl_pct.toFixed(2)}% · ${money(p.pnl)}<br>
+      <span class="r">held ${fmtHold(p.hold_minutes)}`
+      + (p.exit_reason?` · exit ${p.exit_reason}`:'') + `</span>`);
+    s.appendChild(g);
+  });
+  host.appendChild(s);
+}
+
+/* ---------- tables ---------- */
+let sortKey='exit_ts', sortDir=-1;
+function renderTrips(ts) {
+  const host = document.getElementById('tripTbl');
+  if (!ts.length) return host.innerHTML = '<div class="empty">No closed trades in range.</div>';
+  const cols = [
+    ['exit_ts','Exit',  t=>t.exit_ts.replace('T',' '), 'l'],
+    ['strategy_short','Strategy', t=>t.strategy_short, 'l'],
+    ['symbol','Symbol', t=>t.symbol, 'l'],
+    ['qty','Qty', t=>(+t.qty).toLocaleString(), 'num'],
+    ['entry_price','Entry', t=>t.entry_price.toFixed(2), 'num'],
+    ['exit_price','Exit px', t=>t.exit_price.toFixed(2) + (t.estimated?' <span class="est">*</span>':''), 'num'],
+    ['pnl','Net P&L', t=>`<span class="${t.pnl>=0?'up':'down'}">${money(t.pnl)}</span>`, 'num'],
+    ['pnl_pct','Return', t=>`<span class="${t.pnl>=0?'up':'down'}">${(t.pnl_pct>=0?'+':'')+t.pnl_pct.toFixed(2)}%</span>`, 'num'],
+    ['hold_minutes','Held', t=>t.hold_minutes!=null?fmtHold(t.hold_minutes):'—', 'num'],
+    ['entry_reason','Entry signal', t=>t.entry_reason?`<span class="tag">${t.entry_reason}</span>`:'—', 'l'],
+    ['exit_reason','Exit reason', t=>t.exit_reason?`<span class="tag">${t.exit_reason}</span>`:'—', 'l'],
+  ];
+  const rows = [...ts].sort((a,b) => {
+    const x=a[sortKey], y=b[sortKey];
+    if (x==null) return 1; if (y==null) return -1;
+    return (x<y?-1:x>y?1:0) * sortDir;
+  });
+  host.innerHTML = `<table><thead><tr>` +
+    cols.map(([k,l,,cls]) => `<th class="${cls==='l'?'l':''}" data-k="${k}">${l}`
+      + (sortKey===k ? (sortDir<0?' ↓':' ↑') : '') + `</th>`).join('') +
+    `</tr></thead><tbody>` +
+    rows.map(t => '<tr>' + cols.map(([,,fn,cls]) =>
+      `<td class="${cls}">${fn(t)}</td>`).join('') + '</tr>').join('') +
+    `</tbody></table>`;
+  host.querySelectorAll('th').forEach(th => th.onclick = () => {
+    const k = th.dataset.k;
+    if (sortKey===k) sortDir*=-1; else { sortKey=k; sortDir=-1; }
+    renderTrips(filtered());
+  });
+}
+function renderOpen() {
+  const host = document.getElementById('openTbl');
+  const lots = DATA.open_lots;
+  if (!lots.length) return host.innerHTML = '<div class="empty">No open lots.</div>';
+  host.innerHTML = `<table><thead><tr>
+      <th class="l">Strategy</th><th class="l">Symbol</th><th>Qty</th>
+      <th>Entry</th><th>Cost basis</th><th class="l">Signal</th><th class="l">Opened</th>
+    </tr></thead><tbody>` +
+    lots.map(l => `<tr>
+      <td class="l">${l.strategy_short}</td><td class="l">${l.symbol}</td>
+      <td class="num">${(+l.qty).toLocaleString()}</td>
+      <td class="num">${l.price.toFixed(2)}</td>
+      <td class="num">$${l.cost.toFixed(2)}</td>
+      <td class="l">${l.reason?`<span class="tag">${l.reason}</span>`:'—'}</td>
+      <td class="l">${l.ts ? l.ts.replace('T',' ') : (l.seeded?'<span class="est">pre-ledger</span>':'—')}</td>
+    </tr>`).join('') + `</tbody></table>`;
+}
+
+/* ---------- orchestration ---------- */
+function render() {
+  const ts = filtered();
+  renderTiles(ts);
+  renderEquity(ts);
+  renderDaily(ts);
+  renderBars('byStrategy', ts, t=>t.strategy_short, true);
+  renderBars('bySymbol', ts, t=>t.symbol);
+  renderBars('byEntry', ts, t=>t.entry_reason);
+  renderBars('byExit', ts, t=>t.exit_reason);
+  renderDist(ts);
+  renderScatter(ts);
+  renderTrips(ts);
+}
+document.getElementById('meta').innerHTML =
+  `${DATA.overall.n} closed round trips from ${DATA.n_records} ledger records`
+  + (DATA.unmatched_sell_qty ? ` · <span class="est">${DATA.unmatched_sell_qty} unit(s) sold with unknown cost basis, excluded</span>` : '')
+  + `<br><code>${DATA.ledger}</code> · generated ${DATA.generated.replace('T',' ')}`;
+document.getElementById('dlLbl').textContent = DATA.max_daily_loss.toFixed(0);
+buildFilters();
+renderOpen();
+render();
+let rt; addEventListener('resize', () => { clearTimeout(rt); rt = setTimeout(render, 140); });
+</script>
+"""
+
+
+def build(data: dict, min_profit: float, stop_loss: float, max_daily_loss: float) -> str:
+    """Inject the analysis bundle into the page template."""
+    payload = {
+        "ledger": data["ledger"],
+        "generated": data["generated"],
+        "n_records": data["n_records"],
+        "unmatched_sell_qty": data["unmatched_sell_qty"],
+        "overall": data["overall"],
+        "trips": data["trips"],
+        "open_lots": data["open_lots"],
+        "min_profit_pct": min_profit,
+        "stop_loss_pct": stop_loss,
+        "max_daily_loss": max_daily_loss,
+    }
+    return HTML.replace("__DATA__", json.dumps(payload, default=str))
+
+
+def main():
+    ap = argparse.ArgumentParser(description="Render trades.jsonl as an HTML dashboard")
+    ap.add_argument("--ledger", default=str(analytics.LEDGER))
+    ap.add_argument("-o", "--out", default="dashboard.html")
+    ap.add_argument("--since", help="only include trades closed on/after YYYY-MM-DD")
+    ap.add_argument("--until", help="only include trades closed on/before YYYY-MM-DD")
+    ap.add_argument("--min-profit", type=float, default=DEFAULT_MIN_PROFIT,
+                    help="min-profit annotation on the distribution chart")
+    ap.add_argument("--stop-loss", type=float, default=DEFAULT_STOP_LOSS,
+                    help="stop-loss annotation on the distribution chart")
+    ap.add_argument("--max-daily-loss", type=float, default=DEFAULT_MAX_DAILY_LOSS,
+                    help="daily circuit-breaker reference line")
+    ap.add_argument("--open", action="store_true", help="open the result in a browser")
+    args = ap.parse_args()
+
+    path = Path(args.ledger)
+    if not path.exists():
+        raise SystemExit(f"ledger not found: {path}")
+
+    data = analytics.analyze(path, args.since, args.until)
+    out = Path(args.out)
+    out.write_text(build(data, args.min_profit, args.stop_loss, args.max_daily_loss))
+    print(f"wrote {out}  ({data['overall']['n']} closed trips, "
+          f"net {data['overall']['pnl']:+.2f})")
+    if args.open:
+        webbrowser.open(out.resolve().as_uri())
+
+
+if __name__ == "__main__":
+    main()

dashboard.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+# 生成可视化交易复盘看板（单文件 HTML，无外部依赖）并在浏览器打开
+# 用法: ./dashboard.sh [--since YYYY-MM-DD] [--until YYYY-MM-DD]
+DIR="$(cd "$(dirname "$0")" && pwd)"
+exec "$DIR/.venv/bin/python" "$DIR/dashboard.py" -o "$DIR/dashboard.html" --open "$@"

orders.py

@@ -13,6 +13,23 @@ def create_stock_contract(symbol: str, exchange: str = "SMART", currency: str =
     return Contract(symbol=symbol, secType="STK", exchange=exchange, currency=currency)
 
 
+def trade_commission(trade: Optional[Trade]) -> float:
+    """Total commission reported for a trade's fills, 0.0 if none yet.
+
+    Best effort: IB delivers commissionReport asynchronously, sometimes a moment
+    after the fill, so a freshly filled trade may still report 0. The ledger
+    treats a missing commission as unknown rather than as free, so an occasional
+    miss understates costs rather than corrupting anything.
+    """
+    if trade is None:
+        return 0.0
+    total = 0.0
+    for f in getattr(trade, "fills", []) or []:
+        report = getattr(f, "commissionReport", None)
+        total += float(getattr(report, "commission", 0) or 0)
+    return total
+
+
 def has_open_order(ib: IB, order_ref: str) -> bool:
     """True if an unfinished order with this orderRef already exists."""
     for trade in ib.openTrades():

state.py

@@ -129,12 +129,23 @@ class PositionTracker:
     # ---------- trade ledger (append-only JSONL, used by daily_report.py) ----------
 
     def _ledger_append(self, type_: str, strategy: str, key: str, qty: float, price: float,
-                       estimated: bool = False):
+                       estimated: bool = False, reason: str = "", commission: float = 0.0):
+        """Append one fill to the ledger.
+
+        `reason` is the signal that caused it (entry signal on buys, exit reason
+        on sells) and `commission` the IB commission for the fill when it was
+        available at record time. Both are what analytics.py attributes P&L by,
+        so they are worth recording even when only partly populated.
+        """
         rec = {
             "ts": datetime.now().isoformat(timespec="seconds"),
             "type": type_, "strategy": strategy, "symbol": key,
             "qty": qty, "price": price,
         }
+        if reason:
+            rec["reason"] = reason
+        if commission:
+            rec["commission"] = round(commission, 4)
         if estimated:
             rec["est"] = True
         try:
@@ -198,7 +209,8 @@ class PositionTracker:
             if k == key and e.get("quantity", 0) > 0
         )
 
-    def record_buy(self, strategy: str, key: str, quantity: float, price: float):
+    def record_buy(self, strategy: str, key: str, quantity: float, price: float,
+                   reason: str = "", commission: float = 0.0):
         entries = self._data.setdefault(strategy, {})
         entry = entries.get(key)
         if entry:
@@ -214,10 +226,12 @@ class PositionTracker:
                 "entry_ts": datetime.now(timezone.utc).isoformat(),
             }
         self.save()
-        self._ledger_append("buy", strategy, key, quantity, price)
+        self._ledger_append("buy", strategy, key, quantity, price,
+                            reason=reason, commission=commission)
         logger.info("Tracker: %s owns %s x%g @ %.4f", strategy, key, entries[key]["quantity"], entries[key]["entry_price"])
 
-    def record_sell(self, strategy: str, key: str, quantity: float, price: float = None):
+    def record_sell(self, strategy: str, key: str, quantity: float, price: float = None,
+                    reason: str = "", commission: float = 0.0):
         entries = self._data.get(strategy, {})
         entry = entries.get(key)
         if not entry:
@@ -227,8 +241,9 @@ class PositionTracker:
             del entries[key]
         self.save()
         if price is not None:
-            self._add_day_pnl((price - entry["entry_price"]) * quantity)
+            self._add_day_pnl((price - entry["entry_price"]) * quantity - commission)
-            self._ledger_append("sell", strategy, key, quantity, price)
+            self._ledger_append("sell", strategy, key, quantity, price,
+                                reason=reason, commission=commission)
             self._record_recent_sell(key, price)
         logger.info("Tracker: %s sold %s x%g, remaining=%s", strategy, key, quantity, entry.get("quantity", 0))
 
@@ -246,7 +261,8 @@ class PositionTracker:
             "Tracker: backfilling external sell %s %s x%g @ %.2f%s",
             strategy, key, qty, price, " (ESTIMATED)" if estimated else "",
         )
-        self._ledger_append("sell_external", strategy, key, qty, price, estimated=estimated)
+        self._ledger_append("sell_external", strategy, key, qty, price, estimated=estimated,
+                            reason="HardStopOffline")
         self._add_day_pnl((price - entry_price) * qty)
         self._record_recent_sell(key, price)
 

strategies/base.py

@@ -9,7 +9,7 @@ from ib_insync import IB, Contract, StopOrder, Trade
 
 from bars import BarManager
 from config import config
-from orders import has_open_order, wait_trade_done
+from orders import has_open_order, trade_commission, wait_trade_done
 from state import PositionTracker
 
 logger = logging.getLogger(__name__)
@@ -208,7 +208,10 @@ class BaseStrategy(ABC):
                             "%s HARD STOP filled: %s x%g @ %.2f",
                             self.name, key, filled, trade.orderStatus.avgFillPrice,
                         )
-                        self.tracker.record_sell(self.name, key, filled, trade.orderStatus.avgFillPrice)
+                        self.tracker.record_sell(self.name, key, filled,
+                                                 trade.orderStatus.avgFillPrice,
+                                                 reason="HardStop",
+                                                 commission=trade_commission(trade))
                         self._mark_hard_stop_cooldown(key)
                 elif owned:
                     logger.warning(
@@ -285,7 +288,8 @@ class BaseStrategy(ABC):
         stop_filled, stop_price, confirmed = await self._cancel_stop(key)
         if stop_filled > 0:
             logger.info("%s: stop filled %g during trailing raise", key, stop_filled)
-            self.tracker.record_sell(self.name, key, stop_filled, stop_price)
+            self.tracker.record_sell(self.name, key, stop_filled, stop_price,
+                                     reason="HardStopDuringTrail")
         remaining = owned["quantity"] - stop_filled
         if remaining <= 0:
             return

strategies/forex.py

@@ -5,7 +5,7 @@ from ib_insync import Contract, IB
 
 from bars import BarManager, is_market_active, parse_bar_size_seconds, to_completed_df
 from config import config
-from orders import execute_market_order
+from orders import execute_market_order, trade_commission
 from state import PositionTracker
 from strategies.base import BaseStrategy
 
@@ -87,13 +87,13 @@ class ForexMAStrategy(BaseStrategy):
                     "FOREX STOP-LOSS SELL: %s close=%.5f entry=%.5f",
                     pair, last["close"], entry,
                 )
-                await self._sell(pair, contract, owned["quantity"])
+                await self._sell(pair, contract, owned["quantity"], "SoftStop")
             elif cross_down:
                 logger.info(
                     "FOREX SELL %s (fast MA %.5f < slow MA %.5f)",
                     pair, last["fast_ma"], last["slow_ma"],
                 )
-                await self._sell(pair, contract, owned["quantity"])
+                await self._sell(pair, contract, owned["quantity"], "CrossDown")
         else:
             if cross_up:
                 logger.info(
@@ -102,19 +102,20 @@ class ForexMAStrategy(BaseStrategy):
                 )
                 await self._buy(pair, contract)
 
-    async def _buy(self, pair: str, contract: Contract):
+    async def _buy(self, pair: str, contract: Contract, reason: str = "MACross"):
         ref = f"{self.name}:{pair}"
         trade = await execute_market_order(self.ib, contract, "BUY", self.units, ref)
         if trade and trade.orderStatus.filled > 0:
             self.tracker.record_buy(
-                self.name, pair, trade.orderStatus.filled, trade.orderStatus.avgFillPrice
+                self.name, pair, trade.orderStatus.filled, trade.orderStatus.avgFillPrice,
+                reason=reason, commission=trade_commission(trade),
             )
 
-    async def _sell(self, pair: str, contract: Contract, quantity: float):
+    async def _sell(self, pair: str, contract: Contract, quantity: float, reason: str = ""):
         ref = f"{self.name}:{pair}"
         trade = await execute_market_order(self.ib, contract, "SELL", quantity, ref)
         if trade and trade.orderStatus.filled > 0:
-            self.tracker.record_sell(self.name, pair, trade.orderStatus.filled)
+            self.tracker.record_sell(self.name, pair, trade.orderStatus.filled, reason=reason)
 
     async def on_tick(self):
         pass

strategies/ma_cross.py

@@ -5,7 +5,7 @@ from ib_insync import Contract, IB
 
 from bars import BarManager, is_market_active, parse_bar_size_seconds, to_completed_df
 from config import config
-from orders import execute_market_order
+from orders import execute_market_order, trade_commission
 from state import PositionTracker
 from strategies.base import BaseStrategy
 
@@ -125,7 +125,7 @@ class MAStockStrategy(BaseStrategy):
                     "STOCK STOP-LOSS SELL (soft fallback): %s close=%.2f entry=%.2f (%.2f%%)",
                     symbol, last["close"], entry, profit_pct,
                 )
-                await self._sell(symbol, contract, owned["quantity"])
+                await self._sell(symbol, contract, owned["quantity"], "SoftStop")
                 self._mark_stop_cooldown(symbol)
             elif not fast_above:
                 # re-checked every cycle: exits as soon as profit requirement is met
@@ -134,7 +134,7 @@ class MAStockStrategy(BaseStrategy):
                         "STOCK SELL: %s (fast MA %.2f < slow MA %.2f, profit=%.2f%%, ADX=%.1f)",
                         symbol, last["fast_ma"], last["slow_ma"], profit_pct, last["adx"],
                     )
-                    await self._sell(symbol, contract, owned["quantity"])
+                    await self._sell(symbol, contract, owned["quantity"], "SignalExit")
                 else:
                     logger.debug(
                         "STOCK SELL WAITING: %s profit %.2f%% < min %.1f%%",
@@ -158,12 +158,14 @@ class MAStockStrategy(BaseStrategy):
                 )
                 await self._buy(symbol, contract, qty)
 
-    async def _buy(self, symbol: str, contract: Contract, quantity: int):
+    async def _buy(self, symbol: str, contract: Contract, quantity: int,
+                   reason: str = "GoldenCross+ADX"):
         ref = f"{self.name}:{symbol}"
         trade = await execute_market_order(self.ib, contract, "BUY", quantity, ref)
         if trade and trade.orderStatus.filled > 0:
             self.tracker.record_buy(
-                self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice
+                self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice,
+                reason=reason, commission=trade_commission(trade),
             )
             if self._use_hard_stop():
                 stop_price = self._target_stop_price(
@@ -177,13 +179,15 @@ class MAStockStrategy(BaseStrategy):
             # cool down to avoid retrying every cycle
             self._mark_cooldown(symbol, "order failed")
 
-    async def _sell(self, symbol: str, contract: Contract, quantity: float):
+    async def _sell(self, symbol: str, contract: Contract, quantity: float,
+                    reason: str = ""):
         # cancel the hard stop first; it may have filled in the cancel race
         if self._use_hard_stop():
             stop_filled, stop_price, cancel_confirmed = await self._cancel_stop(symbol)
             if stop_filled > 0:
                 logger.info("%s: stop order filled %g during cancel", symbol, stop_filled)
-                self.tracker.record_sell(self.name, symbol, stop_filled, stop_price)
+                self.tracker.record_sell(self.name, symbol, stop_filled, stop_price,
+                                         reason="HardStopDuringCancel")
                 quantity -= stop_filled
             if quantity <= 0:
                 return
@@ -198,7 +202,9 @@ class MAStockStrategy(BaseStrategy):
         ref = f"{self.name}:{symbol}"
         trade = await execute_market_order(self.ib, contract, "SELL", quantity, ref)
         if trade and trade.orderStatus.filled > 0:
-            self.tracker.record_sell(self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice)
+            self.tracker.record_sell(self.name, symbol, trade.orderStatus.filled,
+                                     trade.orderStatus.avgFillPrice,
+                                     reason=reason, commission=trade_commission(trade))
 
     async def on_tick(self):
         pass

strategies/mean_reversion.py

@@ -5,7 +5,7 @@ from ib_insync import Contract, IB
 
 from bars import BarManager, is_market_active, parse_bar_size_seconds, to_completed_df
 from config import config
-from orders import execute_market_order
+from orders import execute_market_order, trade_commission
 from state import PositionTracker
 from strategies.base import BaseStrategy
 
@@ -145,7 +145,7 @@ class MeanReversionStrategy(BaseStrategy):
                     "MeanRev BUY: %s x%d [%s] RSI=%.1f, close=%.2f, BB_lower=%.2f",
                     symbol, qty, buy_signal, last["rsi"], last["close"], last["bb_lower"],
                 )
-                await self._buy(symbol, contract, qty)
+                await self._buy(symbol, contract, qty, buy_signal)
         else:
             entry = owned["entry_price"]
             profit_pct = (last["close"] - entry) / entry * 100
@@ -157,7 +157,7 @@ class MeanReversionStrategy(BaseStrategy):
                     "MeanRev STOP-LOSS SELL (soft fallback): %s close=%.2f entry=%.2f (%.2f%%)",
                     symbol, last["close"], entry, profit_pct,
                 )
-                await self._sell(symbol, contract, owned["quantity"])
+                await self._sell(symbol, contract, owned["quantity"], "SoftStop")
                 self._mark_stop_cooldown(symbol)
                 return
 
@@ -181,14 +181,16 @@ class MeanReversionStrategy(BaseStrategy):
                     "MeanRev SELL: %s [%s] RSI=%.1f, close=%.2f, entry=%.2f, profit=%.2f%%",
                     symbol, sell_signal, last["rsi"], last["close"], entry, profit_pct,
                 )
-                await self._sell(symbol, contract, owned["quantity"])
+                await self._sell(symbol, contract, owned["quantity"], sell_signal)
 
-    async def _buy(self, symbol: str, contract: Contract, quantity: int):
+    async def _buy(self, symbol: str, contract: Contract, quantity: int,
+                   reason: str = ""):
         ref = f"{self.name}:{symbol}"
         trade = await execute_market_order(self.ib, contract, "BUY", quantity, ref)
         if trade and trade.orderStatus.filled > 0:
             self.tracker.record_buy(
-                self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice
+                self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice,
+                reason=reason, commission=trade_commission(trade),
             )
             if self._use_hard_stop():
                 stop_price = self._target_stop_price(
@@ -202,13 +204,15 @@ class MeanReversionStrategy(BaseStrategy):
             # cool down to avoid retrying every cycle
             self._mark_cooldown(symbol, "order failed")
 
-    async def _sell(self, symbol: str, contract: Contract, quantity: float):
+    async def _sell(self, symbol: str, contract: Contract, quantity: float,
+                    reason: str = ""):
         # cancel the hard stop first; it may have filled in the cancel race
         if self._use_hard_stop():
             stop_filled, stop_price, cancel_confirmed = await self._cancel_stop(symbol)
             if stop_filled > 0:
                 logger.info("%s: stop order filled %g during cancel", symbol, stop_filled)
-                self.tracker.record_sell(self.name, symbol, stop_filled, stop_price)
+                self.tracker.record_sell(self.name, symbol, stop_filled, stop_price,
+                                         reason="HardStopDuringCancel")
                 quantity -= stop_filled
             if quantity <= 0:
                 return
@@ -223,7 +227,9 @@ class MeanReversionStrategy(BaseStrategy):
         ref = f"{self.name}:{symbol}"
         trade = await execute_market_order(self.ib, contract, "SELL", quantity, ref)
         if trade and trade.orderStatus.filled > 0:
-            self.tracker.record_sell(self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice)
+            self.tracker.record_sell(self.name, symbol, trade.orderStatus.filled,
+                                     trade.orderStatus.avgFillPrice,
+                                     reason=reason, commission=trade_commission(trade))
 
     async def on_tick(self):
         pass

strategies/short_term.py

@@ -6,7 +6,7 @@ from ib_insync import Contract, IB
 
 from bars import BarManager, is_market_active, parse_bar_size_seconds, to_completed_df
 from config import config
-from orders import execute_market_order
+from orders import execute_market_order, trade_commission
 from state import PositionTracker
 from strategies.base import BaseStrategy
 
@@ -119,7 +119,7 @@ class ShortTermMAVWAPStrategy(BaseStrategy):
                     "ShortTerm STOP-LOSS SELL (soft fallback): %s close=%.2f entry=%.2f (%.2f%%)",
                     symbol, last["close"], entry, profit_pct,
                 )
-                await self._sell(symbol, contract, owned["quantity"])
+                await self._sell(symbol, contract, owned["quantity"], "SoftStop")
                 self._mark_stop_cooldown(symbol)
                 return
 
@@ -128,7 +128,7 @@ class ShortTermMAVWAPStrategy(BaseStrategy):
                 hold_days = (date.today() - entry_date).days
                 if hold_days >= self.cfg.max_hold_days:
                     logger.info("ShortTerm SELL %s: max hold reached (%d days)", symbol, hold_days)
-                    await self._sell(symbol, contract, owned["quantity"])
+                    await self._sell(symbol, contract, owned["quantity"], "MaxHold")
                     return
 
             if not fast_above:
@@ -138,7 +138,7 @@ class ShortTermMAVWAPStrategy(BaseStrategy):
                         "ShortTerm SELL: %s (fast EMA %.2f < slow EMA %.2f, profit=%.2f%%)",
                         symbol, last["fast_ema"], last["slow_ema"], profit_pct,
                     )
-                    await self._sell(symbol, contract, owned["quantity"])
+                    await self._sell(symbol, contract, owned["quantity"], "SignalExit")
                 else:
                     logger.debug(
                         "ShortTerm SELL WAITING: %s profit %.2f%% < min %.1f%%",
@@ -162,12 +162,14 @@ class ShortTermMAVWAPStrategy(BaseStrategy):
                 )
                 await self._buy(symbol, contract, qty)
 
-    async def _buy(self, symbol: str, contract: Contract, quantity: int):
+    async def _buy(self, symbol: str, contract: Contract, quantity: int,
+                   reason: str = "EMACross+VWAP"):
         ref = f"{self.name}:{symbol}"
         trade = await execute_market_order(self.ib, contract, "BUY", quantity, ref)
         if trade and trade.orderStatus.filled > 0:
             self.tracker.record_buy(
-                self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice
+                self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice,
+                reason=reason, commission=trade_commission(trade),
             )
             if self._use_hard_stop():
                 stop_price = self._target_stop_price(
@@ -181,13 +183,15 @@ class ShortTermMAVWAPStrategy(BaseStrategy):
             # cool down to avoid retrying every cycle
             self._mark_cooldown(symbol, "order failed")
 
-    async def _sell(self, symbol: str, contract: Contract, quantity: float):
+    async def _sell(self, symbol: str, contract: Contract, quantity: float,
+                    reason: str = ""):
         # cancel the hard stop first; it may have filled in the cancel race
         if self._use_hard_stop():
             stop_filled, stop_price, cancel_confirmed = await self._cancel_stop(symbol)
             if stop_filled > 0:
                 logger.info("%s: stop order filled %g during cancel", symbol, stop_filled)
-                self.tracker.record_sell(self.name, symbol, stop_filled, stop_price)
+                self.tracker.record_sell(self.name, symbol, stop_filled, stop_price,
+                                         reason="HardStopDuringCancel")
                 quantity -= stop_filled
             if quantity <= 0:
                 return
@@ -202,7 +206,9 @@ class ShortTermMAVWAPStrategy(BaseStrategy):
         ref = f"{self.name}:{symbol}"
         trade = await execute_market_order(self.ib, contract, "SELL", quantity, ref)
         if trade and trade.orderStatus.filled > 0:
-            self.tracker.record_sell(self.name, symbol, trade.orderStatus.filled, trade.orderStatus.avgFillPrice)
+            self.tracker.record_sell(self.name, symbol, trade.orderStatus.filled,
+                                     trade.orderStatus.avgFillPrice,
+                                     reason=reason, commission=trade_commission(trade))
 
     async def on_tick(self):
         pass

test_offline.py

@@ -0,0 +1,302 @@
+#!/usr/bin/env python
+"""Offline tests — no IB connection, no orders, safe to run against the live repo.
+
+    .venv/bin/python -m unittest test_offline -v
+
+Covers the pure layers: ledger writing (PositionTracker), round-trip
+reconstruction and statistics (analytics), and the indicator maths. Indicator
+tests are skipped when pandas is unavailable.
+"""
+import json
+import tempfile
+import unittest
+from pathlib import Path
+
+import analytics
+from state import PositionTracker
+
+try:
+    import pandas as pd
+    HAVE_PANDAS = True
+except ImportError:
+    HAVE_PANDAS = False
+
+
+class LedgerTest(unittest.TestCase):
+    """PositionTracker must record the attribution fields the dashboard needs."""
+
+    def setUp(self):
+        self.dir = tempfile.TemporaryDirectory()
+        self.tracker = PositionTracker(str(Path(self.dir.name) / "bot_state.json"))
+
+    def tearDown(self):
+        self.dir.cleanup()
+
+    def _ledger(self):
+        return [json.loads(l) for l in
+                self.tracker.ledger_file.read_text().splitlines() if l.strip()]
+
+    def test_buy_records_reason_and_commission(self):
+        self.tracker.record_buy("S", "AAPL", 10, 100.0, reason="RSI", commission=0.35)
+        rec = self._ledger()[-1]
+        self.assertEqual(rec["type"], "buy")
+        self.assertEqual(rec["reason"], "RSI")
+        self.assertEqual(rec["commission"], 0.35)
+
+    def test_sell_records_reason_and_commission(self):
+        self.tracker.record_buy("S", "AAPL", 10, 100.0, reason="RSI")
+        self.tracker.record_sell("S", "AAPL", 10, 103.0,
+                                 reason="SignalExit", commission=0.4)
+        rec = self._ledger()[-1]
+        self.assertEqual(rec["reason"], "SignalExit")
+        self.assertEqual(rec["commission"], 0.4)
+
+    def test_absent_fields_are_omitted(self):
+        """A record with no reason/commission stays byte-compatible with the old
+        schema, so historical ledgers keep parsing."""
+        self.tracker.record_buy("S", "AAPL", 10, 100.0)
+        rec = self._ledger()[-1]
+        self.assertNotIn("reason", rec)
+        self.assertNotIn("commission", rec)
+
+    def test_day_pnl_is_net_of_commission(self):
+        self.tracker.record_buy("S", "AAPL", 10, 100.0)
+        self.tracker.record_sell("S", "AAPL", 10, 101.0, commission=0.5)
+        self.assertAlmostEqual(self.tracker.day_realized_pnl(), 10.0 - 0.5, places=6)
+
+    def test_external_sell_is_tagged(self):
+        self.tracker.record_buy("S", "AAPL", 10, 100.0)
+        self.tracker.reconcile({})  # account holds none -> backfill an external sell
+        rec = self._ledger()[-1]
+        self.assertEqual(rec["type"], "sell_external")
+        self.assertEqual(rec["reason"], "HardStopOffline")
+        self.assertTrue(rec["est"])
+
+
+class RoundTripTest(unittest.TestCase):
+    """FIFO reconstruction has to survive partial fills and pre-ledger lots."""
+
+    @staticmethod
+    def _trips(records):
+        return analytics.build_round_trips(records)
+
+    def test_simple_round_trip(self):
+        trips, open_lots, unmatched = self._trips([
+            {"ts": "2026-08-01T10:00:00", "type": "buy", "strategy": "S", "symbol": "X",
+             "qty": 10, "price": 100.0, "reason": "Entry", "commission": 0.35},
+            {"ts": "2026-08-01T11:00:00", "type": "sell", "strategy": "S", "symbol": "X",
+             "qty": 10, "price": 102.0, "reason": "SignalExit", "commission": 0.35},
+        ])
+        self.assertEqual(len(trips), 1)
+        self.assertEqual(open_lots, [])
+        self.assertEqual(unmatched, 0)
+        t = trips[0]
+        self.assertAlmostEqual(t.gross_pnl, 20.0)
+        self.assertAlmostEqual(t.commission, 0.70)
+        self.assertAlmostEqual(t.pnl, 19.30)
+        self.assertAlmostEqual(t.pnl_pct, 19.30 / 1000 * 100)
+        self.assertEqual(t.hold_minutes, 60)
+        self.assertEqual(t.entry_reason, "Entry")
+        self.assertEqual(t.exit_reason, "SignalExit")
+        self.assertTrue(t.won)
+
+    def test_partial_sell_splits_the_lot(self):
+        trips, open_lots, _ = self._trips([
+            {"ts": "2026-08-01T10:00:00", "type": "buy", "strategy": "S", "symbol": "X",
+             "qty": 10, "price": 100.0, "commission": 1.0},
+            {"ts": "2026-08-01T11:00:00", "type": "sell", "strategy": "S", "symbol": "X",
+             "qty": 4, "price": 105.0},
+        ])
+        self.assertEqual(len(trips), 1)
+        self.assertEqual(trips[0].qty, 4)
+        self.assertAlmostEqual(trips[0].gross_pnl, 20.0)
+        # buy commission is allocated pro rata: 4/10 of $1.00
+        self.assertAlmostEqual(trips[0].commission, 0.4)
+        self.assertEqual(len(open_lots), 1)
+        self.assertEqual(open_lots[0].qty, 6)
+
+    def test_sell_spanning_two_lots_is_fifo(self):
+        trips, _, _ = self._trips([
+            {"ts": "2026-08-01T10:00:00", "type": "buy", "strategy": "S", "symbol": "X",
+             "qty": 5, "price": 100.0},
+            {"ts": "2026-08-01T10:30:00", "type": "buy", "strategy": "S", "symbol": "X",
+             "qty": 5, "price": 110.0},
+            {"ts": "2026-08-01T11:00:00", "type": "sell", "strategy": "S", "symbol": "X",
+             "qty": 8, "price": 120.0},
+        ])
+        self.assertEqual(len(trips), 2)
+        # oldest lot consumed first, in full
+        self.assertEqual((trips[0].qty, trips[0].entry_price), (5, 100.0))
+        self.assertEqual((trips[1].qty, trips[1].entry_price), (3, 110.0))
+        self.assertAlmostEqual(sum(t.gross_pnl for t in trips), 5 * 20 + 3 * 10)
+
+    def test_sell_without_basis_is_reported_not_guessed(self):
+        trips, _, unmatched = self._trips([
+            {"ts": "2026-08-01T11:00:00", "type": "sell", "strategy": "S", "symbol": "X",
+             "qty": 7, "price": 120.0},
+        ])
+        self.assertEqual(trips, [])
+        self.assertEqual(unmatched, 7)
+
+    def test_positions_are_owned_per_strategy(self):
+        """Two strategies holding the same symbol must not cross-match."""
+        trips, open_lots, unmatched = self._trips([
+            {"ts": "2026-08-01T10:00:00", "type": "buy", "strategy": "A", "symbol": "X",
+             "qty": 5, "price": 100.0},
+            {"ts": "2026-08-01T10:01:00", "type": "buy", "strategy": "B", "symbol": "X",
+             "qty": 5, "price": 200.0},
+            {"ts": "2026-08-01T11:00:00", "type": "sell", "strategy": "B", "symbol": "X",
+             "qty": 5, "price": 210.0},
+        ])
+        self.assertEqual(len(trips), 1)
+        self.assertEqual(trips[0].strategy, "B")
+        self.assertAlmostEqual(trips[0].gross_pnl, 50.0)
+        self.assertEqual(unmatched, 0)
+        self.assertEqual([(l.strategy, l.qty) for l in open_lots], [("A", 5)])
+
+    def test_seed_lot_is_flagged(self):
+        trips, _, _ = self._trips([
+            {"ts": "2026-08-01T10:00:00", "type": "seed", "strategy": "S", "symbol": "X",
+             "qty": 5, "price": 100.0},
+            {"ts": "2026-08-01T11:00:00", "type": "sell", "strategy": "S", "symbol": "X",
+             "qty": 5, "price": 101.0},
+        ])
+        self.assertTrue(trips[0].seeded_entry)
+
+
+class StatsTest(unittest.TestCase):
+    """The statistics that parameter decisions get made on."""
+
+    @staticmethod
+    def _trip(pnl, entry=100.0, qty=1.0, ts="2026-08-01T10:00:00"):
+        return analytics.RoundTrip(
+            strategy="S", symbol="X", qty=qty, entry_ts=ts, exit_ts=ts,
+            entry_price=entry, exit_price=entry + pnl, entry_reason="", exit_reason="",
+            gross_pnl=pnl, commission=0.0, estimated=False, seeded_entry=False)
+
+    def test_summary_of_empty_set(self):
+        s = analytics.summarize([])
+        self.assertEqual(s["n"], 0)
+        self.assertEqual(s["pnl"], 0.0)
+        self.assertIsNone(s["profit_factor"])
+
+    def test_core_statistics(self):
+        # 3 wins of +10, 2 losses of -20
+        trips = [self._trip(10)] * 3 + [self._trip(-20)] * 2
+        s = analytics.summarize(trips)
+        self.assertEqual(s["n"], 5)
+        self.assertAlmostEqual(s["pnl"], 30 - 40)
+        self.assertAlmostEqual(s["win_rate"], 60.0)
+        self.assertAlmostEqual(s["expectancy"], -2.0)
+        self.assertAlmostEqual(s["profit_factor"], 30 / 40)
+        self.assertAlmostEqual(s["avg_win"], 10.0)
+        self.assertAlmostEqual(s["avg_loss"], 20.0)
+        self.assertAlmostEqual(s["payoff_ratio"], 0.5)
+        # payoff 0.5 => must win 2 of every 3 just to break even
+        self.assertAlmostEqual(s["breakeven_win_rate"], 100 / 1.5)
+
+    def test_breakeven_win_rate_matches_actual_at_zero_expectancy(self):
+        """Sanity check on the headline diagnostic: when a set nets exactly zero,
+        its win rate must equal its own breakeven win rate."""
+        trips = [self._trip(10)] * 2 + [self._trip(-20)]
+        s = analytics.summarize(trips)
+        self.assertAlmostEqual(s["pnl"], 0.0)
+        self.assertAlmostEqual(s["win_rate"], s["breakeven_win_rate"], places=6)
+
+    def test_profit_factor_is_none_without_losses(self):
+        self.assertIsNone(analytics.summarize([self._trip(5)])["profit_factor"])
+
+    def test_max_drawdown(self):
+        trips = [
+            self._trip(100, ts="2026-08-01T10:00:00"),
+            self._trip(-40, ts="2026-08-01T11:00:00"),
+            self._trip(-30, ts="2026-08-01T12:00:00"),
+            self._trip(50, ts="2026-08-01T13:00:00"),
+        ]
+        curve = analytics.equity_curve(trips)
+        self.assertEqual([p["cum_pnl"] for p in curve], [100, 60, 30, 80])
+        self.assertAlmostEqual(analytics.max_drawdown(curve), -70.0)
+
+    def test_grouping(self):
+        a = self._trip(10); a.symbol = "AAA"
+        b = self._trip(-5); b.symbol = "BBB"
+        g = analytics.group_stats([a, b], lambda t: t.symbol)
+        self.assertAlmostEqual(g["AAA"]["pnl"], 10)
+        self.assertAlmostEqual(g["BBB"]["pnl"], -5)
+
+    def test_analyze_end_to_end_with_date_filter(self):
+        recs = [
+            {"ts": "2026-08-01T10:00:00", "type": "buy", "strategy": "S", "symbol": "X",
+             "qty": 1, "price": 100.0},
+            {"ts": "2026-08-01T11:00:00", "type": "sell", "strategy": "S", "symbol": "X",
+             "qty": 1, "price": 110.0},
+            {"ts": "2026-08-05T10:00:00", "type": "buy", "strategy": "S", "symbol": "X",
+             "qty": 1, "price": 100.0},
+            {"ts": "2026-08-05T11:00:00", "type": "sell", "strategy": "S", "symbol": "X",
+             "qty": 1, "price": 90.0},
+        ]
+        with tempfile.TemporaryDirectory() as d:
+            p = Path(d) / "trades.jsonl"
+            p.write_text("\n".join(json.dumps(r) for r in recs) + "\n")
+            allt = analytics.analyze(p)
+            self.assertEqual(allt["overall"]["n"], 2)
+            self.assertAlmostEqual(allt["overall"]["pnl"], 0.0)
+            # filtering the window must not corrupt the cost basis of what remains
+            late = analytics.analyze(p, since="2026-08-05")
+            self.assertEqual(late["overall"]["n"], 1)
+            self.assertAlmostEqual(late["overall"]["pnl"], -10.0)
+
+
+@unittest.skipUnless(HAVE_PANDAS, "pandas not installed")
+class IndicatorTest(unittest.TestCase):
+    """Guards on the two indicator bugs AGENTS.md warns against reintroducing."""
+
+    def test_rsi_is_100_on_a_pure_uptrend(self):
+        from strategies.mean_reversion import MeanReversionStrategy
+        rsi = MeanReversionStrategy._calc_rsi(pd.Series(range(1, 40), dtype=float), 14)
+        # no losses -> rs is inf -> RSI must saturate at 100, never NaN
+        self.assertFalse(pd.isna(rsi.iloc[-1]))
+        self.assertAlmostEqual(rsi.iloc[-1], 100.0, places=6)
+
+    def test_adx_direction_on_a_clean_uptrend(self):
+        from strategies.ma_cross import MAStockStrategy
+        n = 60
+        close = pd.Series([100 + i for i in range(n)], dtype=float)
+        df = pd.DataFrame({"high": close + 0.5, "low": close - 0.5, "close": close})
+        adx = MAStockStrategy._calc_adx(df, 14)
+        # a monotonic uptrend must read as strongly trending
+        self.assertGreater(adx.iloc[-1], 50)
+
+    def test_vwap_resets_each_day(self):
+        from strategies.short_term import ShortTermMAVWAPStrategy
+        ts = (list(pd.date_range("2026-08-03 13:30", periods=3, freq="1min", tz="UTC"))
+              + list(pd.date_range("2026-08-04 13:30", periods=3, freq="1min", tz="UTC")))
+        df = pd.DataFrame({
+            "date": ts,
+            "high": [10, 10, 10, 20, 20, 20], "low": [10, 10, 10, 20, 20, 20],
+            "close": [10, 10, 10, 20, 20, 20], "volume": [1, 1, 1, 1, 1, 1],
+        })
+        vwap = ShortTermMAVWAPStrategy._calc_vwap(df)
+        # day two must not be dragged toward day one's prices
+        self.assertAlmostEqual(vwap.iloc[2], 10.0)
+        self.assertAlmostEqual(vwap.iloc[3], 20.0)
+
+    def test_forming_bar_is_dropped(self):
+        from datetime import datetime, timedelta, timezone
+        from ib_insync import BarData
+        from bars import to_completed_df
+
+        def bar(date, close):
+            return BarData(date=date, open=close, high=close, low=close,
+                           close=close, volume=1)
+
+        now = datetime.now(timezone.utc)
+        bars = [bar(now - timedelta(seconds=120), 1.0), bar(now - timedelta(seconds=60), 2.0),
+                bar(now, 3.0)]  # the last bar is still forming
+        df = to_completed_df(bars, 60)
+        self.assertEqual(len(df), 2)
+        self.assertEqual(df["close"].iloc[-1], 2.0)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)