feat: weekly execution tallies — midday-entry net + market_exit post-run
Read-only nightly-scheduler job (Friday-gated): buckets realized P&L by entry time (open/late-morning/midday) and, for each market_exit close, computes the post-exit run-up capturable under a breakeven stop (0 if price undercuts entry first; intrabar ties count as stopped). Emits a weekly_tally event + Telegram summary. Evidence accumulator for the dead no_new_entries_after cutoff and the weakness-exit winner-clipping question — no live trading path touched. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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momentum devandClaude Fable 5 committed
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@@ -569,7 +569,7 @@ services:
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command:
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- /bin/sh
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- -c
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- "echo \"[scheduler] up; fires 20:00 ET Mon-Fri\"\nwhile true; do\n if [ \"$$(date +%H:%M)\" = \"20:00\" ] && [ \"$$(date +%u)\" -le 5 ]; then\n python -m research.premarket_eval || echo \"[scheduler] eval FAILED\"\n python -m research.ingestion.market_context || echo \"[scheduler] market_context FAILED\"\n python -m research.eod_reconcile || echo \"[scheduler] reconcile FAILED\"\n python eod_replay.py || echo \"[scheduler] eod_replay FAILED\"\n python -m research.labeler build || echo \"[scheduler] labeler build FAILED\"\n python -m research.labeler build-tod1 || echo \"[scheduler] labeler build-tod1 FAILED\"\n python nightly_tune.py || echo \"[scheduler] nightly_tune FAILED\"\n python -m research.regime build-features || echo \"[scheduler] regime build FAILED\"\n python -m research.regime score --model threshold_v1 || echo \"[scheduler] regime threshold FAILED\"\n python -m research.regime score --model meta_v1 || echo \"[scheduler] regime meta FAILED\"\n python -m research.experiments run || echo \"[scheduler] experiments run FAILED\"\n python -m research.experiments adjudicate || echo \"[scheduler] experiments adjudicate FAILED\"\n sleep 120\n fi\n sleep 20\ndone\n"
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- "echo \"[scheduler] up; fires 20:00 ET Mon-Fri\"\nwhile true; do\n if [ \"$$(date +%H:%M)\" = \"20:00\" ] && [ \"$$(date +%u)\" -le 5 ]; then\n python -m research.premarket_eval || echo \"[scheduler] eval FAILED\"\n python -m research.ingestion.market_context || echo \"[scheduler] market_context FAILED\"\n python -m research.eod_reconcile || echo \"[scheduler] reconcile FAILED\"\n python eod_replay.py || echo \"[scheduler] eod_replay FAILED\"\n python -m research.labeler build || echo \"[scheduler] labeler build FAILED\"\n python -m research.labeler build-tod1 || echo \"[scheduler] labeler build-tod1 FAILED\"\n python nightly_tune.py || echo \"[scheduler] nightly_tune FAILED\"\n python -m research.regime build-features || echo \"[scheduler] regime build FAILED\"\n python -m research.regime score --model threshold_v1 || echo \"[scheduler] regime threshold FAILED\"\n python -m research.regime score --model meta_v1 || echo \"[scheduler] regime meta FAILED\"\n python -m research.experiments run || echo \"[scheduler] experiments run FAILED\"\n python -m research.experiments adjudicate || echo \"[scheduler] experiments adjudicate FAILED\"\n python -m research.weekly_tallies || echo \"[scheduler] weekly_tallies FAILED\"\n sleep 120\n fi\n sleep 20\ndone\n"
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environment:
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ALPACA_API_KEY: <FILL-ME-ALPACA-KEY>
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ALPACA_BACKFILL_FEED: sip
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@@ -233,6 +233,9 @@ services:
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# newly-logged fires, then adjudicate against pre-registered bars.
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python -m research.experiments run || echo "[scheduler] experiments run FAILED"
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python -m research.experiments adjudicate || echo "[scheduler] experiments adjudicate FAILED"
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# Weekly execution tallies (read-only): midday-entry net + market_exit
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# post-run. Script self-gates to Friday; other nights it no-ops.
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python -m research.weekly_tallies || echo "[scheduler] weekly_tallies FAILED"
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sleep 120
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fi
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sleep 20
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@@ -0,0 +1,206 @@
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"""Weekly execution tallies — two standing stats the 2026-07-24 trade review flagged.
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READ-ONLY: queries the event store + minute bars, emits one summary event, optional
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Telegram. Never touches live trading state.
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MIDDAY NET — realized P&L bucketed by ENTRY time (open ≤10:30 ET, late-morning
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≤12:00, midday after). Accumulating evidence for wiring the dead
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no_new_entries_after cutoff: if midday stays net-negative for a few
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weeks, wire it; until then just count.
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EXIT POST-RUN — for each market_exit (weakness exit) close: how much run-up was
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CAPTURABLE after the exit, i.e. the high reached before price first
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dipped below entry (a breakeven stop survives until then; a dip below
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entry first means the "money left" was never reachable → 0).
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Entry time = first order_submitted event for (day, symbol); re-entries on the same
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symbol/day share that first submit time (close enough for a bucket tally).
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CLI: python -m research.weekly_tallies [--days 7] [--force]
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Runs only on Friday (ET) unless --force — the nightly scheduler chain calls it daily.
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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from datetime import datetime, time, timedelta, timezone
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from zoneinfo import ZoneInfo
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ET = ZoneInfo("America/New_York")
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OPEN_CUTOFF = time(10, 30)
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MIDDAY_CUTOFF = time(12, 0)
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def _bucket(entry_et: time) -> str:
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if entry_et <= OPEN_CUTOFF:
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return "open"
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if entry_et <= MIDDAY_CUTOFF:
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return "late_morning"
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return "midday"
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def capturable_left_pct(bars: list[dict], exit_ts_et: datetime,
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entry_price: float, exit_price: float) -> float:
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"""Run-up (% of exit price) to the highest high after exit BEFORE the first bar
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whose low undercuts entry price. ``bars`` are chronological dicts with tz-aware
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ET ``ts``, ``high``, ``low``. Dip below entry before any new high -> 0.0.
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Intrabar order is unknowable from minute bars, so a bar that both undercuts
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entry and makes a new high counts as stopped first (pessimistic on purpose —
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'capturable' must not depend on winning an intrabar coin flip)."""
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best = exit_price
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for b in bars:
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if b["ts"] <= exit_ts_et:
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continue
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if b["low"] < entry_price:
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break
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if b["high"] > best:
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best = b["high"]
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return max(0.0, (best - exit_price) / exit_price * 100.0)
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def compute_tallies(closes: list[dict], bars_for) -> dict:
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"""Pure core. ``closes``: dicts with symbol, day (date), entry_ts_et /
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exit_ts_et (tz-aware datetimes), entry_price, exit_price, exit_reason,
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realized_pnl. ``bars_for(symbol, day)`` -> chronological bar dicts
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(tz-aware ET ts, high, low). Returns the tally dict."""
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buckets: dict[str, dict] = {k: {"n": 0, "net_pnl": 0.0}
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for k in ("open", "late_morning", "midday")}
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post_runs: list[dict] = []
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for c in closes:
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b = _bucket(c["entry_ts_et"].timetz().replace(tzinfo=None))
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buckets[b]["n"] += 1
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buckets[b]["net_pnl"] = round(buckets[b]["net_pnl"] + c["realized_pnl"], 2)
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if c["exit_reason"] == "market_exit":
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left = capturable_left_pct(bars_for(c["symbol"], c["day"]),
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c["exit_ts_et"], c["entry_price"], c["exit_price"])
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post_runs.append({"symbol": c["symbol"], "day": str(c["day"]),
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"pnl": c["realized_pnl"], "left_pct": round(left, 1)})
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clipped = [p for p in post_runs if p["left_pct"] >= 5.0]
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return {
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"n_trades": len(closes),
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"entry_buckets": buckets,
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"market_exit_post_runs": post_runs,
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"n_market_exits": len(post_runs),
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"n_clipped_ge_5pct": len(clipped),
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"median_left_pct": (sorted(p["left_pct"] for p in post_runs)[len(post_runs) // 2]
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if post_runs else 0.0),
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}
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def _load_closes(con, since_et: datetime) -> list[dict]:
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"""events.timestamp is stored ET-naive (matches the live loop's wall clock)."""
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rows = con.execute(
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"SELECT timestamp, payload_json FROM events "
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"WHERE event_type = 'position_closed' AND timestamp >= ? ORDER BY timestamp",
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[since_et.replace(tzinfo=None)],
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).fetchall()
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submits = con.execute(
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"SELECT timestamp, payload_json FROM events "
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"WHERE event_type = 'order_submitted' AND timestamp >= ? ORDER BY timestamp",
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[since_et.replace(tzinfo=None)],
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).fetchall()
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first_submit: dict[tuple, datetime] = {}
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for ts, pj in submits:
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p = pj if isinstance(pj, dict) else json.loads(pj or "{}")
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key = (ts.date(), p.get("symbol"))
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first_submit.setdefault(key, ts)
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closes = []
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for ts, pj in rows:
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p = pj if isinstance(pj, dict) else json.loads(pj or "{}")
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sym = p.get("symbol")
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if not sym or p.get("realized_pnl") is None:
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continue
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sub = first_submit.get((ts.date(), sym)) or ts
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closes.append({
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"symbol": sym, "day": ts.date(),
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"entry_ts_et": sub.replace(tzinfo=ET),
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"exit_ts_et": ts.replace(tzinfo=ET),
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"entry_price": float(p.get("entry_price") or 0),
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"exit_price": float(p.get("exit_price") or 0),
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"exit_reason": p.get("exit_reason") or "",
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"realized_pnl": float(p["realized_pnl"]),
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})
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return closes
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def _bars_for(con):
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"""minute_bars.timestamp is stored UTC-naive — convert to tz-aware ET."""
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def fetch(symbol: str, day) -> list[dict]:
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rows = con.execute(
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"SELECT timestamp, high, low FROM minute_bars "
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"WHERE symbol = ? AND session_date = ? AND is_regular_hours "
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"ORDER BY timestamp",
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[symbol, day],
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).fetchall()
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return [{"ts": t.replace(tzinfo=timezone.utc).astimezone(ET),
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"high": float(h), "low": float(l)} for t, h, l in rows]
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return fetch
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def _summary_text(t: dict) -> str:
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eb = t["entry_buckets"]
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lines = [f"Weekly tallies ({t['n_trades']} trades):"]
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for k in ("open", "late_morning", "midday"):
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lines.append(f" entry {k}: {eb[k]['n']} trades net ${eb[k]['net_pnl']:+.0f}")
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lines.append(f" market_exit post-run: {t['n_market_exits']} exits, "
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f"{t['n_clipped_ge_5pct']} clipped >=5% capturable, "
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f"median left {t['median_left_pct']:.1f}%")
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return "\n".join(lines)
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def run(con, store, *, days: int = 7, now_et: datetime | None = None, notify=None) -> dict:
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from storage.event_schema import EventMode, RiskRuleTriggeredEvent
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now_et = now_et or datetime.now(ET)
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since = now_et - timedelta(days=days)
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closes = _load_closes(con, since)
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tallies = compute_tallies(closes, _bars_for(con))
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msg = _summary_text(tallies)
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store.emit(RiskRuleTriggeredEvent(
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timestamp=datetime.now(), mode=EventMode.PAPER,
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correlation_id="weekly_tallies", message=msg.splitlines()[0],
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rule_type="weekly_tally", rule_value=float(tallies["n_trades"]),
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current_state=tallies, action_taken="reported",
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))
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if notify is not None:
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try:
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notify(f"\U0001F4CA {msg}")
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except Exception: # noqa: BLE001 — an alert failure must not break the job
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pass
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print(f"[weekly_tallies] {msg}", flush=True)
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return tallies
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def main() -> int:
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from dotenv import load_dotenv
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load_dotenv()
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from runtime.env_overrides import load as _load_overrides
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_load_overrides()
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parser = argparse.ArgumentParser()
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parser.add_argument("--days", type=int, default=7)
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parser.add_argument("--force", action="store_true",
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help="run even when it is not Friday ET")
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args = parser.parse_args()
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now_et = datetime.now(ET)
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if now_et.weekday() != 4 and not args.force:
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print("[weekly_tallies] not Friday — skipping (use --force to override)", flush=True)
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return 0
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from storage.db import get_connection
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from storage.event_store import EventStore
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con = get_connection(os.environ.get("WATCHER_EVENT_DB_PATH", "momentum"))
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store = EventStore(os.environ.get("WATCHER_EVENT_DB_PATH", "momentum"))
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try:
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from runtime.notifier import send_telegram
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except Exception: # noqa: BLE001
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send_telegram = None
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run(con, store, days=args.days, now_et=now_et, notify=send_telegram)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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@@ -0,0 +1,64 @@
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"""Pure-core tests for research.weekly_tallies (no DB, no broker)."""
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from datetime import date, datetime
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from zoneinfo import ZoneInfo
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from research.weekly_tallies import capturable_left_pct, compute_tallies
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ET = ZoneInfo("America/New_York")
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DAY = date(2026, 7, 24)
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def _ts(h, m):
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return datetime(2026, 7, 24, h, m, tzinfo=ET)
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def _bar(h, m, high, low):
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return {"ts": _ts(h, m), "high": high, "low": low}
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def _close(sym, entry_hm, exit_hm, entry_px, exit_px, reason, pnl):
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return {
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"symbol": sym, "day": DAY,
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"entry_ts_et": _ts(*entry_hm), "exit_ts_et": _ts(*exit_hm),
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"entry_price": entry_px, "exit_price": exit_px,
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"exit_reason": reason, "realized_pnl": pnl,
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}
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def test_capturable_left_clean_runup():
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# ADVB-like: after exit at 7.0 the stock runs to 9.4 without undercutting entry
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bars = [_bar(9, 45, 7.2, 6.9), _bar(10, 0, 8.0, 7.1), _bar(15, 44, 9.4, 8.8)]
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left = capturable_left_pct(bars, _ts(9, 41), entry_price=6.73, exit_price=7.0)
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assert round(left, 1) == round((9.4 - 7.0) / 7.0 * 100, 1)
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def test_capturable_left_zero_when_dip_below_entry_first():
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# WLDS-like: dips below entry before the big run -> BE stop dies, nothing capturable
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bars = [_bar(10, 10, 2.8, 2.55), _bar(15, 36, 4.35, 2.6)]
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left = capturable_left_pct(bars, _ts(10, 2), entry_price=2.73, exit_price=2.78)
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assert left == 0.0
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def test_capturable_ignores_bars_before_exit():
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bars = [_bar(9, 30, 99.0, 1.0), _bar(11, 0, 3.0, 2.9)]
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left = capturable_left_pct(bars, _ts(10, 0), entry_price=2.0, exit_price=2.5)
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assert round(left, 1) == 20.0
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def test_compute_tallies_buckets_and_post_runs():
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closes = [
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_close("AAA", (9, 40), (9, 50), 5.0, 5.5, "stop_loss", 200.0),
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_close("BBB", (11, 0), (11, 30), 3.0, 3.1, "stop_loss", 50.0),
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_close("CCC", (12, 47), (15, 40), 15.76, 15.36, "market_exit", -370.0),
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]
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# recovery above entry without undercutting it -> capturable run-up
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bars = {"CCC": [_bar(15, 50, 16.5, 15.8)]}
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t = compute_tallies(closes, lambda sym, day: bars.get(sym, []))
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assert t["n_trades"] == 3
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assert t["entry_buckets"]["open"] == {"n": 1, "net_pnl": 200.0}
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assert t["entry_buckets"]["late_morning"] == {"n": 1, "net_pnl": 50.0}
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assert t["entry_buckets"]["midday"] == {"n": 1, "net_pnl": -370.0}
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assert t["n_market_exits"] == 1
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run = t["market_exit_post_runs"][0]
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assert run["symbol"] == "CCC" and run["left_pct"] > 0
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