From 1e9f2dc4fb8f04396623bdfe876d60d74dd938ab Mon Sep 17 00:00:00 2001 From: Dennis Thiessen Date: Fri, 17 Jul 2026 13:21:06 +0200 Subject: [PATCH] feat: add five-session post-stop reentry lockdown --- app/routers/trades.py | 2 + app/services/alert_service.py | 1 + app/services/backtest_service.py | 253 +- app/services/rr_scanner_service.py | 13 +- app/services/trade_policy.py | 68 + .../src/components/signals/BacktestPanel.tsx | 3 + frontend/src/lib/types.ts | 10 +- reports/gate-protected-stop-20260717.json | 3173 +++ .../post-stop-cooldown-sweep-20260717.json | 17494 ++++++++++++++++ reports/post-stop-reentry-20260717.json | 14691 +++++++++++++ scripts/run_gate_protected_stop_study.py | 422 + scripts/run_post_stop_reentry_study.py | 541 + tests/unit/test_backtest_service.py | 169 + tests/unit/test_rr_scanner_preservation.py | 93 + 14 files changed, 36919 insertions(+), 14 deletions(-) create mode 100644 app/services/trade_policy.py create mode 100644 reports/gate-protected-stop-20260717.json create mode 100644 reports/post-stop-cooldown-sweep-20260717.json create mode 100644 reports/post-stop-reentry-20260717.json create mode 100644 scripts/run_gate_protected_stop_study.py create mode 100644 scripts/run_post_stop_reentry_study.py diff --git a/app/routers/trades.py b/app/routers/trades.py index e1b1e8c..7c6470e 100644 --- a/app/routers/trades.py +++ b/app/routers/trades.py @@ -36,6 +36,7 @@ async def list_trade_setups( recommended_action=recommended_action, live_recommendation=True, exclude_open_trade_tickers=True, + exclude_reentry_lockdown_tickers=True, ) data = [] @@ -98,6 +99,7 @@ async def get_ticker_trade_setups( db, symbol=symbol, live_recommendation=True, + exclude_reentry_lockdown_tickers=True, ) data = [] for row in rows: diff --git a/app/services/alert_service.py b/app/services/alert_service.py index 828fad0..0928c6c 100644 --- a/app/services/alert_service.py +++ b/app/services/alert_service.py @@ -282,6 +282,7 @@ async def _qualified_setups(db: AsyncSession) -> list[dict]: db, live_recommendation=True, exclude_open_trade_tickers=True, + exclude_reentry_lockdown_tickers=True, ) config = await get_activation_config(db) return [s for s in setups if setup_qualifies(SimpleNamespace(**s), config)] diff --git a/app/services/backtest_service.py b/app/services/backtest_service.py index 6af5805..485765a 100644 --- a/app/services/backtest_service.py +++ b/app/services/backtest_service.py @@ -94,6 +94,7 @@ from app.services.scoring_service import ( compute_technical_from_arrays, ) from app.services.sr_service import detect_gate_target_ladder, detect_sr_levels +from app.services.trade_policy import REENTRY_LOCKDOWN_SESSIONS logger = logging.getLogger(__name__) @@ -991,6 +992,68 @@ def _replay_and_signals( ) +def _replay_candidates_for_period( + symbol: str, + columns: tuple, + config: dict, + activation: dict, + benchmark_closes: dict[date, float] | None, + start_date: date, +) -> list[dict]: + """Slim picklable replay used by local event studies. + + Unlike the full report worker it skips factor-series construction and only + evaluates setup dates on or after ``start_date``. + """ + date_ords, opens, highs, lows, closes, volumes = columns + bars = [ + SimpleNamespace( + date=date.fromordinal(o), open=op, high=hi, low=lo, close=cl, volume=vo + ) + for o, op, hi, lo, cl, vo in zip( + date_ords, opens, highs, lows, closes, volumes + ) + ] + candidates: list[dict] = [] + for i in range(MIN_LOOKBACK - 1, len(bars) - HORIZON, STEP_DAYS): + if bars[i].date < start_date: + continue + window = bars[: i + 1] + window_closes = [float(r.close) for r in window] + window_dates = [r.date for r in window] + residual_momentum = _residual_momentum_12_1( + window_dates, + window_closes, + len(window) - 1, + benchmark_closes, + ) + vol_6m = _realized_vol_6m(window_closes, len(window) - 1) + iso = bars[i].date.isocalendar() + for setup in _window_setups(window, config, activation): + if setup["direction"] != "long": + continue + candidates.append({ + "symbol": symbol, + "date": bars[i].date.isoformat(), + "iso_week": (iso[0], iso[1]), + "direction": "long", + "entry": setup["entry"], + "stop": setup["stop"], + "target": setup["target"], + "rr": setup["rr"], + "confidence": setup["confidence"], + "primary_prob": setup["primary_prob"], + "best_prob": setup["best_prob"], + "momentum": setup["momentum"], + "residual_momentum": residual_momentum, + "vol_6m": vol_6m, + "meets_core": setup["meets_core"], + "action": setup["action"], + "risk_level": setup["risk_level"], + }) + return candidates + + def _backtest_worker_count() -> int: """How many worker processes to replay tickers across. Capped to cpu_count-1 so a core stays free for the web server; 1 means sequential.""" @@ -1293,9 +1356,17 @@ def _simulate_portfolio( max_positions: int = SIM_MAX_POSITIONS, risk_per_trade: float = SIM_RISK_PER_TRADE, atr_trail_multiplier: float = ATR_TRAIL_MULTIPLIER, + reentry_cooldown_days: int = 0, + initial_stop_refresh_fn: ( + Callable[[str, int, float, dict, Any], float | None] | None + ) = None, + post_stop_reentry_fn: ( + Callable[[str, int, dict, Any], dict | None] | None + ) = None, start_date: date | None = None, end_date: date | None = None, include_curve: bool = False, + include_trades: bool = False, ) -> dict | None: """Replay the qualified setups as ONE capital-constrained book and report portfolio economics from the daily equity curve (return, CAGR, drawdown, @@ -1309,7 +1380,14 @@ def _simulate_portfolio( runs the ATR trail *and* the S/R take-profit together — the trade ends at whichever comes first. Stops fill at the worse of stop or open (gaps modeled); positions still open at the end are closed at their last mark. - Returns None when there is nothing to trade. + ``reentry_cooldown_days`` blocks a ticker for that many market sessions + after an initial-stop loss. Profitable trailing-stop exits do not trigger + it. ``initial_stop_refresh_fn`` may supply a lower, point-in-time valid long + stop when the active initial stop is touched; the replacement is still + checked against the same bar. ``post_stop_reentry_fn`` turns an initial + stop-out into a stateful episode and is the only path by which that ticker + can re-enter until the callback emits a new candidate. Returns None when + there is nothing to trade. """ if qualified_fn is None: def _default_qualified(c: dict) -> bool: @@ -1362,6 +1440,14 @@ def _simulate_portfolio( curve: list[tuple[int, float]] = [] trades: list[dict] = [] skipped_full = 0 + skipped_cooldown = 0 + cooldown_until_index: dict[str, int] = {} + stop_refresh_attempts = 0 + stop_refreshes = 0 + stop_refresh_same_bar_hits = 0 + post_stop_states: dict[str, dict] = {} + post_stop_events = 0 + reentry_events: list[dict] = [] technical_cache: dict[tuple[str, int], float | None] = {} atr_cache: dict[tuple[str, int], float | None] = {} @@ -1426,7 +1512,7 @@ def _simulate_portfolio( atr_cache[key] = None return atr_cache[key] - def _close_trade(sym: str, fill: float, reason: str) -> None: + def _close_trade(sym: str, fill: float, reason: str) -> dict: nonlocal cash pos = positions.pop(sym) proceeds = pos["shares"] * fill @@ -1434,16 +1520,29 @@ def _simulate_portfolio( cash += proceeds - cost risk = pos["entry"] - pos["initial_stop"] trades.append({ + "symbol": sym, + "entry_ord": pos["entry_ord"], + "exit_ord": o, + "entry": pos["entry"], + "initial_stop": pos["initial_stop"], + "active_stop": pos["stop"], + "fill": fill, "pnl": proceeds - pos["shares"] * pos["entry"] - cost - pos["entry_cost"], "r": (fill - pos["entry"]) / risk if risk > 0 else 0.0, "hold": pos["bars_held"], "reason": reason, + "stop_refreshes": pos["stop_refreshes"], + "is_reentry": pos["is_reentry"], + "reentry_wait_sessions": pos["reentry_wait_sessions"], + "transaction_cost": pos["entry_cost"] + cost, }) + return pos def _marked_equity() -> float: return cash + sum(p["shares"] * p["last_close"] for p in positions.values()) - for o in calendar: + cooldown_days = max(0, int(reentry_cooldown_days)) + for calendar_index, o in enumerate(calendar): # 1) exits on today's bars (stop intraday, target intraday, time at close) for sym in list(positions): pos = positions[sym] @@ -1460,8 +1559,42 @@ def _simulate_portfolio( if pos["stop"] > pos["initial_stop"] + 1e-9 else "stop" ) - _close_trade(sym, min(pos["stop"], bar.open), reason) - continue + survived_refresh = False + if reason == "stop" and initial_stop_refresh_fn is not None: + stop_refresh_attempts += 1 + refreshed_stop = initial_stop_refresh_fn( + sym, o, float(pos["stop"]), pos, bar + ) + if ( + refreshed_stop is not None + and 0 < float(refreshed_stop) < pos["stop"] - 1e-9 + ): + pos["stop"] = float(refreshed_stop) + pos["stop_refreshes"] += 1 + stop_refreshes += 1 + if bar.low > pos["stop"]: + survived_refresh = True + else: + stop_refresh_same_bar_hits += 1 + if not survived_refresh: + fill = min(pos["stop"], bar.open) + closed_pos = _close_trade(sym, fill, reason) + if reason == "stop" and cooldown_days: + cooldown_until_index[sym] = calendar_index + cooldown_days + if reason == "stop" and post_stop_reentry_fn is not None: + post_stop_events += 1 + post_stop_states[sym] = { + "stop_ord": o, + "stop_calendar_index": calendar_index, + "stop_day_high": float(bar.high), + "stop_day_low": float(bar.low), + "stop_day_close": float(bar.close), + "exit_fill": float(fill), + "previous_entry": float(closed_pos["entry"]), + "previous_stop": float(closed_pos["initial_stop"]), + "gate_went_unqualified": False, + } + continue if exit_policy in ("target", "atr_trail3_target") and pos["target"] and bar.high >= pos["target"]: _close_trade(sym, pos["target"], "target") continue @@ -1493,8 +1626,29 @@ def _simulate_portfolio( # 2) entries at today's close, best momentum first equity = _marked_equity() + fixed_todays = list(entries_by_ord.get(o, ())) + reentry_todays: list[dict] = [] + if post_stop_reentry_fn is not None: + fixed_todays = [ + candidate + for candidate in fixed_todays + if candidate["symbol"] not in post_stop_states + ] + for sym, state in list(post_stop_states.items()): + bar = _bar(sym, o) + if bar is None: + continue + state["sessions_since_stop"] = ( + calendar_index - state["stop_calendar_index"] + ) + candidate = post_stop_reentry_fn(sym, o, state, bar) + if candidate is None: + continue + tagged = dict(candidate) + tagged["_post_stop_reentry"] = True + reentry_todays.append(tagged) todays = sorted( - entries_by_ord.get(o, ()), + fixed_todays + reentry_todays, key=lambda c: c.get(ranking_key) or 0.0, reverse=True, ) @@ -1502,6 +1656,9 @@ def _simulate_portfolio( sym = c["symbol"] if sym in positions: continue + if calendar_index < cooldown_until_index.get(sym, -1): + skipped_cooldown += 1 + continue if len(positions) >= max_positions: skipped_full += 1 continue @@ -1518,9 +1675,23 @@ def _simulate_portfolio( continue entry_cost = shares * entry * COST_PER_SIDE cash -= shares * entry + entry_cost + is_reentry = bool(c.get("_post_stop_reentry")) + reentry_wait_sessions: int | None = None + if is_reentry: + state = post_stop_states.pop(sym, None) + if state is not None: + reentry_wait_sessions = int(state["sessions_since_stop"]) + reentry_events.append({ + "symbol": sym, + "stop_ord": state["stop_ord"], + "reentry_ord": o, + "wait_sessions": reentry_wait_sessions, + "reason": c.get("_reentry_reason"), + }) positions[sym] = { "shares": shares, "entry": entry, + "entry_ord": o, "initial_stop": stop, "stop": stop, "target": float(c["target"]) if c.get("target") else None, @@ -1528,6 +1699,9 @@ def _simulate_portfolio( "bars_held": 0, "last_close": entry, "highest_close": entry, + "stop_refreshes": 0, + "is_reentry": is_reentry, + "reentry_wait_sessions": reentry_wait_sessions, } equity = _marked_equity() @@ -1659,6 +1833,42 @@ def _simulate_portfolio( result["equity_curve"] = curve_payload if benchmark_payload is not None: result["benchmark_curve"] = benchmark_payload + if cooldown_days: + result["reentry_cooldown_days"] = cooldown_days + result["skipped_cooldown"] = skipped_cooldown + if initial_stop_refresh_fn is not None: + result["stop_refresh_attempts"] = stop_refresh_attempts + result["stop_refreshes"] = stop_refreshes + result["stop_refresh_same_bar_hits"] = stop_refresh_same_bar_hits + if post_stop_reentry_fn is not None: + result["post_stop_events"] = post_stop_events + result["post_stop_reentries"] = len(reentry_events) + result["post_stop_states_open_at_end"] = len(post_stop_states) + result["reentry_events"] = [ + { + **{ + key: value + for key, value in event.items() + if key not in {"stop_ord", "reentry_ord"} + }, + "stop_date": date.fromordinal(event["stop_ord"]).isoformat(), + "reentry_date": date.fromordinal(event["reentry_ord"]).isoformat(), + } + for event in reentry_events + ] + if include_trades: + result["trade_details"] = [ + { + **{ + key: value + for key, value in trade.items() + if key not in {"entry_ord", "exit_ord"} + }, + "entry_date": date.fromordinal(trade["entry_ord"]).isoformat(), + "exit_date": date.fromordinal(trade["exit_ord"]).isoformat(), + } + for trade in trades + ] return result @@ -2019,13 +2229,15 @@ PORTFOLIO_MONITOR_STRATEGIES: tuple[dict, ...] = ( }, { "strategy": PRODUCTION_PORTFOLIO_STRATEGY, - "label": "Production: residual/high-vol 80/20 + 3x ATR trail", + "label": "Production: residual/high-vol 80/20 + 3x ATR trail + 5-session lockdown", "description": ( "The live strategy: production activation gate and Admin exit policy " - "as currently configured, 80/20 residual/high-vol rank." + "as currently configured, 80/20 residual/high-vol rank, and a " + "five-session re-entry lockdown after an initial-stop exit." ), "entry_variant": "residual80_highvol_blend80_20_fixed10", "exit_policy": "atr_trail3", + "reentry_lockdown_sessions": REENTRY_LOCKDOWN_SESSIONS, # The production row replays what the platform actually does right now: # the live qualification flag (runtime Admin activation settings) and the # live Admin exit policy, instead of the frozen research-variant gate. @@ -2149,6 +2361,9 @@ def _min_rr_sweep( exit_policy = str(strategy["exit_policy"]) row_hold_days = hold_days trail_multiplier = ATR_TRAIL_MULTIPLIER + reentry_lockdown_sessions = int( + strategy.get("reentry_lockdown_sessions", 0) + ) if strategy.get("use_live_config") and live_exit_policy is not None: exit_policy = LIVE_EXIT_MODE_TO_SIM.get( str(live_exit_policy.get("mode", "atr_trailing")), "atr_trail3" @@ -2188,6 +2403,7 @@ def _min_rr_sweep( max_positions=int(entry_cfg["max_positions"]), risk_per_trade=float(entry_cfg["risk_per_trade"]), atr_trail_multiplier=trail_multiplier, + reentry_cooldown_days=reentry_lockdown_sessions, start_date=sweep_start, ) if sim is None: @@ -2212,6 +2428,7 @@ def _min_rr_sweep( "live_qualified_setups": live_qualified, "reproduces_production_gate": reproduces, "exit_policy": exit_policy, + "reentry_lockdown_sessions": reentry_lockdown_sessions, "entries_from": sweep_start.isoformat() if sweep_start else None, "window": "out-of-sample (test)" if sweep_start else "full history (in-sample)", "rows": rows, @@ -2267,6 +2484,9 @@ def _holdout_evaluation( exit_policy = str(strategy["exit_policy"]) row_hold_days = hold_days trail_multiplier = ATR_TRAIL_MULTIPLIER + reentry_lockdown_sessions = int( + strategy.get("reentry_lockdown_sessions", 0) + ) if strategy.get("use_live_config") and live_exit_policy is not None: exit_policy = LIVE_EXIT_MODE_TO_SIM.get( str(live_exit_policy.get("mode", "atr_trailing")), "atr_trail3" @@ -2296,6 +2516,7 @@ def _holdout_evaluation( max_positions=int(entry_cfg["max_positions"]), risk_per_trade=float(entry_cfg["risk_per_trade"]), atr_trail_multiplier=trail_multiplier, + reentry_cooldown_days=reentry_lockdown_sessions, start_date=start, end_date=end, include_curve=True, @@ -2307,6 +2528,7 @@ def _holdout_evaluation( return { "split_date": split.isoformat(), "strategy": strategy["strategy"], + "reentry_lockdown_sessions": reentry_lockdown_sessions, "rows": rows, "note": ( "Train = entries before the split; test = entries on/after it. The two " @@ -2339,6 +2561,9 @@ def _portfolio_monitor( # policy. The overlay opts into this deliberately so only ordering # changes relative to the production row. use_live = bool(strategy.get("use_live_config")) + reentry_lockdown_sessions = int( + strategy.get("reentry_lockdown_sessions", 0) + ) exit_policy = str(strategy["exit_policy"]) row_hold_days = hold_days trail_multiplier = ATR_TRAIL_MULTIPLIER @@ -2367,6 +2592,7 @@ def _portfolio_monitor( max_positions=int(entry_cfg["max_positions"]), risk_per_trade=float(entry_cfg["risk_per_trade"]), atr_trail_multiplier=trail_multiplier, + reentry_cooldown_days=reentry_lockdown_sessions, start_date=start, include_curve=True, ) @@ -2381,6 +2607,7 @@ def _portfolio_monitor( "ranking_key": ranking_key, "exit_policy": exit_policy, "live_exit_mode": live_exit_mode, + "reentry_lockdown_sessions": reentry_lockdown_sessions, "lookback": lookback["lookback"], "lookback_label": lookback["label"], **sim, @@ -2393,6 +2620,9 @@ def _portfolio_monitor( "label": s["label"], "description": s["description"], "is_production": bool(s.get("is_production")), + "reentry_lockdown_sessions": int( + s.get("reentry_lockdown_sessions", 0) + ), } for s in strategies ], @@ -2405,7 +2635,8 @@ def _portfolio_monitor( "Portfolio monitor runs supported named strategies across cached lookbacks. " "The structural overlay appears only in its explicit research arm and changes " "ordering, not production qualification. Local snapshot backtests remain the " - "research surface for broad variant sweeps." + "research surface for broad variant sweeps. The production row applies the " + "same five-session post-initial-stop re-entry lockdown as the live setup list." ), } @@ -2618,7 +2849,8 @@ def _build_recommendation(report: dict) -> dict: if production_row is not None: headline = ( "Production baseline: residual/high-vol 80/20 entry rank with a " - "3x ATR trailing exit and 30-trading-day max hold." + "3x ATR trailing exit, 30-trading-day max hold, and 5-session " + "re-entry lockdown after an initial stop." ) if ( production_row.get("cagr_pct") is not None @@ -2994,6 +3226,7 @@ async def run_backtest( "target_model": target_model, "target_model_label": BACKTEST_TARGET_MODELS[target_model], "is_production_target_model": target_model == PRODUCTION_GTL_TARGET_MODEL, + "production_reentry_lockdown_sessions": REENTRY_LOCKDOWN_SESSIONS, }, "activation": activation, "overall_qualified": _bucket_stats(qualified), diff --git a/app/services/rr_scanner_service.py b/app/services/rr_scanner_service.py index af86f3a..78c9388 100644 --- a/app/services/rr_scanner_service.py +++ b/app/services/rr_scanner_service.py @@ -29,6 +29,7 @@ from app.models.trade_setup import TradeSetup from app.services.indicator_service import _extract_ohlcv, compute_atr from app.services.price_service import query_ohlcv from app.services.sr_service import detect_gate_target_ladder +from app.services.trade_policy import get_reentry_lockdown_ticker_ids from app.services.recommendation_service import ( _risk_level_from_conflicts, build_recommendation_snapshot, @@ -771,6 +772,7 @@ async def get_trade_setups( symbol: str | None = None, live_recommendation: bool = False, exclude_open_trade_tickers: bool = False, + exclude_reentry_lockdown_tickers: bool = False, ) -> list[dict]: """Get latest stored trade setups, optionally filtered. @@ -794,15 +796,20 @@ async def get_trade_setups( stmt = stmt.where(TradeSetup.confidence_score >= min_confidence) if recommended_action is not None and not live_recommendation: stmt = stmt.where(TradeSetup.recommended_action == recommended_action) + excluded_ticker_ids: set[int] = set() if exclude_open_trade_tickers: open_trade_result = await db.execute( select(PaperTrade.ticker_id) .where(PaperTrade.status == "open") .distinct() ) - open_ticker_ids = {ticker_id for ticker_id, in open_trade_result.all()} - if open_ticker_ids: - stmt = stmt.where(~TradeSetup.ticker_id.in_(open_ticker_ids)) + excluded_ticker_ids.update( + ticker_id for ticker_id, in open_trade_result.all() + ) + if exclude_reentry_lockdown_tickers: + excluded_ticker_ids.update(await get_reentry_lockdown_ticker_ids(db)) + if excluded_ticker_ids: + stmt = stmt.where(~TradeSetup.ticker_id.in_(excluded_ticker_ids)) stmt = stmt.order_by(TradeSetup.detected_at.desc(), TradeSetup.id.desc()) diff --git a/app/services/trade_policy.py b/app/services/trade_policy.py new file mode 100644 index 0000000..b29dd0d --- /dev/null +++ b/app/services/trade_policy.py @@ -0,0 +1,68 @@ +"""Shared live/backtest trading-policy constants and availability checks.""" + +from __future__ import annotations + +from datetime import date, datetime, time, timezone + +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.models.ohlcv import OHLCVRecord +from app.models.paper_trade import PaperTrade + +# A ticker stopped at its initial stop may qualify again immediately, but the +# July 2026 event study showed that waiting five market sessions materially +# improved the production book. The stop session is wait_session=0; the first +# permitted re-entry is wait_session=5, provided the normal gate still passes. +REENTRY_LOCKDOWN_SESSIONS = 5 + + +async def get_reentry_lockdown_ticker_ids( + db: AsyncSession, + *, + as_of: date | None = None, + sessions: int = REENTRY_LOCKDOWN_SESSIONS, +) -> set[int]: + """Ticker ids still inside the post-initial-stop market-session lockdown. + + The market calendar is derived from stored OHLCV dates, not calendar days. + A stop on session D is released once five later stored sessions exist. Only + an initial-stop close (``close_reason == "stop"``) starts the lockdown; + trailing, target, time, and manual exits do not. + """ + sessions = max(0, int(sessions)) + if sessions == 0: + return set() + + session_cutoff = as_of or datetime.now(timezone.utc).date() + session_result = await db.execute( + select(OHLCVRecord.date) + .where(OHLCVRecord.date <= session_cutoff) + .distinct() + .order_by(OHLCVRecord.date.desc()) + .limit(sessions) + ) + recent_sessions = [row[0] for row in session_result.all()] + if not recent_sessions: + return set() + + # Stops on or after the oldest of the latest N sessions have fewer than N + # later completed sessions. Once that oldest session rolls forward, the + # corresponding stop automatically leaves the result set. + lockdown_threshold = min(recent_sessions) + threshold_start = datetime.combine( + lockdown_threshold, + time.min, + tzinfo=timezone.utc, + ) + result = await db.execute( + select(PaperTrade.ticker_id) + .where( + PaperTrade.status == "closed", + PaperTrade.close_reason == "stop", + PaperTrade.closed_at.is_not(None), + PaperTrade.closed_at >= threshold_start, + ) + .distinct() + ) + return {ticker_id for ticker_id, in result.all()} diff --git a/frontend/src/components/signals/BacktestPanel.tsx b/frontend/src/components/signals/BacktestPanel.tsx index d1cf8ca..0b87c84 100644 --- a/frontend/src/components/signals/BacktestPanel.tsx +++ b/frontend/src/components/signals/BacktestPanel.tsx @@ -321,6 +321,9 @@ export function BacktestPanel() {

Avg hold {fmtDays(monitorRun.avg_hold_days)} · Best {fmtR(monitorRun.best_trade_r)} / Worst{' '} {fmtR(monitorRun.worst_trade_r)} · Avg P&L per trade {fmtMoney(monitorRun.avg_trade_pnl)} + {monitorRun.reentry_lockdown_sessions ? ( + <> · Re-entry lockdown {monitorRun.reentry_lockdown_sessions} market sessions after initial stop + ) : null}

{monitorRun.yearly_returns && monitorRun.yearly_returns.length > 0 && ( diff --git a/frontend/src/lib/types.ts b/frontend/src/lib/types.ts index 04b4283..978bea9 100644 --- a/frontend/src/lib/types.ts +++ b/frontend/src/lib/types.ts @@ -357,13 +357,20 @@ export interface BacktestPortfolioMonitorRun extends BacktestPortfolioPolicy { is_production: boolean; entry_variant: string; exit_policy: string; + reentry_lockdown_sessions?: number; lookback: string; lookback_label: string; } export interface BacktestPortfolioMonitor { production_strategy: string; - strategies: { strategy: string; label: string; description: string; is_production: boolean }[]; + strategies: { + strategy: string; + label: string; + description: string; + is_production: boolean; + reentry_lockdown_sessions?: number; + }[]; lookbacks: { lookback: string; label: string }[]; runs: BacktestPortfolioMonitorRun[]; note?: string; @@ -402,6 +409,7 @@ export interface BacktestReport { target_model?: 'production_gtl' | 'structural_sr'; target_model_label?: string; is_production_target_model?: boolean; + production_reentry_lockdown_sessions?: number; }; overall_qualified: BacktestBucket; overall_all: BacktestBucket; diff --git a/reports/gate-protected-stop-20260717.json b/reports/gate-protected-stop-20260717.json new file mode 100644 index 0000000..14331cb --- /dev/null +++ b/reports/gate-protected-stop-20260717.json @@ -0,0 +1,3173 @@ +{ + "generated_at": "2026-07-17T10:32:13.434934+02:00", + "snapshot": "C:\\Workspace\\signal-platform\\backtest_snapshots\\prod.sqlite", + "period_start": "2024-07-01", + "tickers": 505, + "entry_candidates": 39867, + "qualified_candidates": 484, + "params": { + "entry_cadence_days": 5, + "setup_stop_atr_multiplier": 1.5, + "exit_policy": "atr_trail3", + "exit_atr_multiplier": 3.0, + "hold_days": 30, + "momentum_percentile_floor": 80.0, + "gate_refresh_information_cutoff": "previous close" + }, + "arms": [ + { + "arm": "baseline", + "starting_capital": 10000.0, + "final_equity": 26150.52, + "total_return_pct": 161.5, + "cagr_pct": 61.7, + "max_drawdown_pct": 11.8, + "sharpe": 2.4, + "trades": 162, + "win_rate": 42.6, + "avg_trade_pnl": 99.69, + "best_trade_r": 12.87, + "worst_trade_r": -2.56, + "best_trade_pnl": 2583.37, + "worst_trade_pnl": -429.69, + "avg_hold_days": 16.6, + "exit_reasons": { + "stop": 65, + "time": 43, + "trailing_stop": 54 + }, + "skipped_book_full": 5, + "spy_return_pct": 36.6, + "yearly_returns": [ + { + "year": 2024, + "return_pct": 24.5 + }, + { + "year": 2025, + "return_pct": 56.0 + }, + { + "year": 2026, + "return_pct": 34.6 + } + ], + "start_date": "2024-07-01", + "end_date": "2026-07-02" + }, + { + "arm": "cooldown_5", + "starting_capital": 10000.0, + "final_equity": 27013.52, + "total_return_pct": 170.1, + "cagr_pct": 64.3, + "max_drawdown_pct": 11.8, + "sharpe": 2.51, + "trades": 163, + "win_rate": 42.3, + "avg_trade_pnl": 104.38, + "best_trade_r": 12.87, + "worst_trade_r": -2.56, + "best_trade_pnl": 2668.62, + "worst_trade_pnl": -430.34, + "avg_hold_days": 16.6, + "exit_reasons": { + "stop": 67, + "time": 43, + "trailing_stop": 53 + }, + "skipped_book_full": 5, + "spy_return_pct": 36.6, + "yearly_returns": [ + { + "year": 2024, + "return_pct": 23.7 + }, + { + "year": 2025, + "return_pct": 57.5 + }, + { + "year": 2026, + "return_pct": 38.7 + } + ], + "start_date": "2024-07-01", + "end_date": "2026-07-02", + "reentry_cooldown_days": 5, + "skipped_cooldown": 5 + }, + { + "arm": "cooldown_10", + "starting_capital": 10000.0, + "final_equity": 25830.23, + "total_return_pct": 158.3, + "cagr_pct": 60.7, + "max_drawdown_pct": 11.0, + "sharpe": 2.37, + "trades": 154, + "win_rate": 39.6, + "avg_trade_pnl": 102.79, + "best_trade_r": 12.87, + "worst_trade_r": -2.56, + "best_trade_pnl": 2539.73, + "worst_trade_pnl": -451.67, + "avg_hold_days": 15.9, + "exit_reasons": { + "stop": 66, + "time": 37, + "trailing_stop": 51 + }, + "skipped_book_full": 5, + "spy_return_pct": 36.6, + "yearly_returns": [ + { + "year": 2024, + "return_pct": 23.2 + }, + { + "year": 2025, + "return_pct": 54.6 + }, + { + "year": 2026, + "return_pct": 35.5 + } + ], + "start_date": "2024-07-01", + "end_date": "2026-07-02", + "reentry_cooldown_days": 10, + "skipped_cooldown": 15 + }, + { + "arm": "gate_protected_stop", + "starting_capital": 10000.0, + "final_equity": 27792.18, + "total_return_pct": 177.9, + "cagr_pct": 66.7, + "max_drawdown_pct": 11.8, + "sharpe": 2.53, + "trades": 154, + "win_rate": 44.2, + "avg_trade_pnl": 115.53, + "best_trade_r": 12.87, + "worst_trade_r": -2.56, + "best_trade_pnl": 2720.83, + "worst_trade_pnl": -455.88, + "avg_hold_days": 17.4, + "exit_reasons": { + "stop": 57, + "time": 42, + "trailing_stop": 55 + }, + "skipped_book_full": 5, + "spy_return_pct": 36.6, + "yearly_returns": [ + { + "year": 2024, + "return_pct": 32.6 + }, + { + "year": 2025, + "return_pct": 55.0 + }, + { + "year": 2026, + "return_pct": 35.2 + } + ], + "start_date": "2024-07-01", + "end_date": "2026-07-02", + 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"stop_date": "2026-05-06", + "gate_asof_date": "2026-05-05", + "momentum_percentile": 94.01, + "gate_core_passed": false, + "gate_passed": false, + "active_stop": 57.1896, + "replacement_stop": null, + "lower_stop": false, + "same_bar_survives": false, + "replacement_risk_r": null + }, + { + "symbol": "NEM", + "stop_date": "2026-05-19", + "gate_asof_date": "2026-05-18", + "momentum_percentile": 96.81, + "gate_core_passed": false, + "gate_passed": false, + "active_stop": 106.9009, + "replacement_stop": null, + "lower_stop": false, + "same_bar_survives": false, + "replacement_risk_r": null + }, + { + "symbol": "MRNA", + "stop_date": "2026-05-19", + "gate_asof_date": "2026-05-18", + "momentum_percentile": 96.41, + "gate_core_passed": false, + "gate_passed": false, + "active_stop": 47.3288, + "replacement_stop": null, + "lower_stop": false, + "same_bar_survives": false, + "replacement_risk_r": null + }, + { + "symbol": "APA", + "stop_date": "2026-05-26", + "gate_asof_date": "2026-05-22", + "momentum_percentile": 97.6, + "gate_core_passed": false, + "gate_passed": false, + "active_stop": 37.5838, + "replacement_stop": null, + "lower_stop": false, + "same_bar_survives": false, + "replacement_risk_r": null + }, + { + "symbol": "CVS", + "stop_date": "2026-05-26", + "gate_asof_date": "2026-05-22", + "momentum_percentile": 80.64, + "gate_core_passed": false, + "gate_passed": false, + "active_stop": 92.303, + "replacement_stop": null, + "lower_stop": false, + "same_bar_survives": false, + "replacement_risk_r": null + }, + { + "symbol": "OXY", + "stop_date": "2026-05-27", + "gate_asof_date": "2026-05-26", + "momentum_percentile": 92.02, + "gate_core_passed": false, + "gate_passed": false, + "active_stop": 56.7075, + "replacement_stop": null, + "lower_stop": false, + "same_bar_survives": false, + "replacement_risk_r": null + } + ] + }, + "note": "The gate-protected arm recalculates the gate at an initial-stop touch using only data available through the previous close. It accepts only a lower stop from a newly valid long setup and checks that replacement against the same bar. It does not cancel stops using the later same-day close." +} diff --git a/reports/post-stop-cooldown-sweep-20260717.json b/reports/post-stop-cooldown-sweep-20260717.json new file mode 100644 index 0000000..32f1a86 --- /dev/null +++ b/reports/post-stop-cooldown-sweep-20260717.json @@ -0,0 +1,17494 @@ +{ + "generated_at": "2026-07-17T12:15:02.918220+02:00", + "snapshot": "C:\\Workspace\\signal-platform\\backtest_snapshots\\prod.sqlite", + "period_start": "2024-07-01", + "tickers": 505, + "entry_candidates": 39867, + "qualified_candidates": 484, + "params": { + "initial_entry_cadence_days": 5, + "post_stop_evaluation_cadence_days": 1, + "setup_stop_atr_multiplier": 1.5, + "exit_policy": "atr_trail3", + "exit_atr_multiplier": 3.0, + "hold_days": 30, + "cost_per_side_pct": 0.1, + "momentum_percentile_floor": 80.0, + "reclaim_atr_buffer": 0.25, + "cooldown_sessions": [ + 3, + 5, + 7, + 10 + ] + }, + "arms": [ + { + "arm": "immediate", + "starting_capital": 10000.0, + "final_equity": 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Only tickers stopped at their initial stop switch to daily evaluation, which isolates next-day/same-episode re-entry churn. A cooldown of N sessions permits the first re-entry at wait_sessions=N. Gate reset requires at least one unqualified daily close before requalification. The reclaim arm alternatively accepts a close above stop-day high + 0.25 ATR only when the new setup stop is above the prior stop." +} diff --git a/reports/post-stop-reentry-20260717.json b/reports/post-stop-reentry-20260717.json new file mode 100644 index 0000000..6e098be --- /dev/null +++ b/reports/post-stop-reentry-20260717.json @@ -0,0 +1,14691 @@ +{ + "generated_at": "2026-07-17T11:26:27.212863+02:00", + "snapshot": "C:\\Workspace\\signal-platform\\backtest_snapshots\\prod.sqlite", + "period_start": "2024-07-01", + "tickers": 505, + "entry_candidates": 39867, + "qualified_candidates": 484, + "params": { + "initial_entry_cadence_days": 5, + "post_stop_evaluation_cadence_days": 1, + "setup_stop_atr_multiplier": 1.5, + "exit_policy": "atr_trail3", + "exit_atr_multiplier": 3.0, + "hold_days": 30, + "cost_per_side_pct": 0.1, + "momentum_percentile_floor": 80.0, + "reclaim_atr_buffer": 0.25 + }, + "arms": [ + { + "arm": "immediate", + "starting_capital": 10000.0, + "final_equity": 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weekly replay cadence. Only tickers stopped at their initial stop switch to daily evaluation, which isolates next-day/same-episode re-entry churn. Gate reset requires at least one unqualified daily close before requalification. The reclaim arm alternatively accepts a close above stop-day high + 0.25 ATR only when the new setup stop is above the prior stop." +} diff --git a/scripts/run_gate_protected_stop_study.py b/scripts/run_gate_protected_stop_study.py new file mode 100644 index 0000000..fcf2197 --- /dev/null +++ b/scripts/run_gate_protected_stop_study.py @@ -0,0 +1,422 @@ +"""Targeted offline study of a gate-conditioned initial-stop refresh. + +The study replays production entries only for the requested period. Whenever +an initial stop is touched, it rebuilds that ticker's setup using bars through +the previous close and recomputes the production momentum gate across the whole +historical universe. If the gate still passes and the new setup has a lower +valid stop, the simulator adopts it and checks it against the same day's low. + +This is causal: no value from the stop day's eventual close is used to cancel +an intraday stop. The snapshot is read-only and no live settings are changed. +""" + +from __future__ import annotations + +import argparse +import asyncio +import bisect +import json +import multiprocessing +import os +import sys +from collections import Counter +from concurrent.futures import ProcessPoolExecutor, as_completed +from datetime import date, datetime +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + + +def _sqlite_url(path: Path) -> str: + return f"sqlite+aiosqlite:///{path.resolve().as_posix()}" + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("snapshot") + parser.add_argument("--start-date", default="2024-07-01") + parser.add_argument("--workers", type=int, default=6) + parser.add_argument("--out", default=None) + parser.add_argument("--quiet", action="store_true") + return parser.parse_args() + + +def _default_output_path() -> Path: + stamp = datetime.now().strftime("%Y%m%d-%H%M%S") + return Path("reports") / f"gate-protected-stop-{stamp}.json" + + +class GateStopRefresher: + """Point-in-time gate and replacement-stop calculator for stop events.""" + + def __init__( + self, + prices: dict[str, tuple], + recommendation_config: dict, + activation: dict, + benchmark_closes: dict[date, float], + ) -> None: + from app.services import backtest_service as bt + + self.bt = bt + self.prices = prices + self.recommendation_config = recommendation_config + self.activation = activation + self.benchmark_closes = benchmark_closes + self.threshold = float(activation.get("min_momentum_percentile", 80.0)) + self.dates = { + symbol: [date.fromordinal(value) for value in columns[0]] + for symbol, columns in prices.items() + } + self.index_of = { + symbol: {value: index for index, value in enumerate(columns[0])} + for symbol, columns in prices.items() + } + self.percentile_cache: dict[int, dict[str, float]] = {} + self.setup_cache: dict[tuple[str, int], dict | None] = {} + self.events: list[dict[str, Any]] = [] + + def _momentum_percentiles(self, asof_ord: int) -> dict[str, float]: + cached = self.percentile_cache.get(asof_ord) + if cached is not None: + return cached + + values: dict[str, float] = {} + for symbol, columns in self.prices.items(): + idx = bisect.bisect_right(columns[0], asof_ord) - 1 + if idx < 252: + continue + closes = columns[4] + value = self.bt._residual_momentum_12_1( + self.dates[symbol], closes, idx, self.benchmark_closes + ) + if value is None and closes[idx - 252] > 0: + value = closes[idx - 21] / closes[idx - 252] - 1.0 + if value is not None: + values[symbol] = float(value) + + ordered = sorted(values, key=lambda symbol: values[symbol]) + denominator = len(ordered) - 1 + percentiles = { + symbol: (rank / denominator * 100.0) if denominator > 0 else 100.0 + for rank, symbol in enumerate(ordered) + } + self.percentile_cache[asof_ord] = percentiles + return percentiles + + def _long_setup(self, symbol: str, asof_idx: int) -> dict | None: + columns = self.prices[symbol] + asof_ord = columns[0][asof_idx] + key = (symbol, asof_ord) + if key in self.setup_cache: + return self.setup_cache[key] + + records = [ + SimpleNamespace( + date=date.fromordinal(o), + open=op, + high=high, + low=low, + close=close, + volume=volume, + ) + for o, op, high, low, close, volume in zip( + columns[0][: asof_idx + 1], + columns[1][: asof_idx + 1], + columns[2][: asof_idx + 1], + columns[3][: asof_idx + 1], + columns[4][: asof_idx + 1], + columns[5][: asof_idx + 1], + ) + ] + setups = self.bt._window_setups( + records, self.recommendation_config, self.activation + ) + setup = next((row for row in setups if row["direction"] == "long"), None) + self.setup_cache[key] = setup + return setup + + def __call__( + self, + symbol: str, + stop_ord: int, + active_stop: float, + position: dict, + bar: Any, + ) -> float | None: + columns = self.prices[symbol] + stop_idx = self.index_of[symbol].get(stop_ord) + if stop_idx is None: + stop_idx = bisect.bisect_left(columns[0], stop_ord) + asof_idx = stop_idx - 1 + if asof_idx < self.bt.MIN_LOOKBACK - 1: + return None + + asof_ord = columns[0][asof_idx] + setup = self._long_setup(symbol, asof_idx) + momentum_pct = self._momentum_percentiles(asof_ord).get(symbol) + gate_passed = bool( + setup is not None + and self.bt._momentum_qualifies( + { + "meets_core": setup["meets_core"], + "direction": "long", + self.bt.PRODUCTION_PERCENTILE_KEY: momentum_pct, + }, + self.threshold, + ) + ) + new_stop = float(setup["stop"]) if gate_passed and setup is not None else None + lower_stop = bool(new_stop is not None and new_stop < active_stop - 1e-9) + original_risk = float(position["entry"] - position["initial_stop"]) + replacement_risk_r = ( + (float(position["entry"]) - new_stop) / original_risk + if lower_stop and original_risk > 0 and new_stop is not None + else None + ) + self.events.append({ + "symbol": symbol, + "stop_date": date.fromordinal(stop_ord).isoformat(), + "gate_asof_date": date.fromordinal(asof_ord).isoformat(), + "momentum_percentile": round(momentum_pct, 2) + if momentum_pct is not None + else None, + "gate_core_passed": bool(setup and setup["meets_core"]), + "gate_passed": gate_passed, + "active_stop": round(active_stop, 4), + "replacement_stop": round(new_stop, 4) if new_stop is not None else None, + "lower_stop": lower_stop, + "same_bar_survives": bool(lower_stop and bar.low > new_stop), + "replacement_risk_r": round(replacement_risk_r, 3) + if replacement_risk_r is not None + else None, + }) + return new_stop + + +def _arm(label: str, sim: dict) -> dict: + trade_details = sim.pop("trade_details", None) + row = {"arm": label, **sim} + if trade_details is not None: + row["trade_details"] = trade_details + return row + + +def _rescued_trade_summary(trades: list[dict]) -> dict: + rescued = [trade for trade in trades if trade.get("stop_refreshes", 0) > 0] + rs = [float(trade["r"]) for trade in rescued] + return { + "trades": len(rescued), + "wins": sum(value > 0 for value in rs), + "win_rate": round(sum(value > 0 for value in rs) / len(rs) * 100.0, 1) + if rs + else None, + "avg_r": round(sum(rs) / len(rs), 3) if rs else None, + "total_r": round(sum(rs), 2) if rs else None, + "worst_r": round(min(rs), 2) if rs else None, + "best_r": round(max(rs), 2) if rs else None, + "exit_reasons": dict(Counter(trade["reason"] for trade in rescued)), + } + + +async def _main() -> None: + args = _parse_args() + snapshot = Path(args.snapshot) + if not snapshot.exists(): + raise SystemExit(f"Snapshot not found: {snapshot}") + try: + start_date = date.fromisoformat(args.start_date) + except ValueError as exc: + raise SystemExit("--start-date must use YYYY-MM-DD") from exc + + os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1" + os.environ["BACKTEST_ALLOW_SPAWN"] = "1" + + from app.models.ticker import Ticker + from app.services import backtest_service as bt + from app.services.admin_service import get_activation_config + from app.services.paper_trade_service import get_exit_policy + from app.services.recommendation_service import get_recommendation_config + + engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True) + Session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + try: + async with Session() as db: + recommendation_config = await get_recommendation_config(db) + activation = await get_activation_config(db) + exit_config = await get_exit_policy(db) + benchmark_closes = await bt._load_benchmark_closes_for_backtest( + db, days=None, refresh=False + ) + ticker_result = await db.execute(select(Ticker).order_by(Ticker.symbol)) + symbols = [ticker.symbol for ticker in ticker_result.scalars().all()] + prices: dict[str, tuple] = {} + for index, symbol in enumerate(symbols, 1): + columns = await bt._fetch_columns(db, symbol) + if columns is not None: + prices[symbol] = columns + if not args.quiet and index % 50 == 0: + print(f"loaded prices: {index}/{len(symbols)}", flush=True) + finally: + await engine.dispose() + + candidates: list[dict] = [] + workers = max(1, min(int(args.workers), multiprocessing.cpu_count() - 1)) + context = multiprocessing.get_context("spawn") + with ProcessPoolExecutor(max_workers=workers, mp_context=context) as pool: + futures = { + pool.submit( + bt._replay_candidates_for_period, + symbol, + columns, + recommendation_config, + activation, + benchmark_closes, + start_date, + ): symbol + for symbol, columns in prices.items() + } + for index, future in enumerate(as_completed(futures), 1): + candidates.extend(future.result()) + if not args.quiet and index % 25 == 0: + print(f"replayed tickers: {index}/{len(futures)}", flush=True) + + bt._assign_momentum_percentiles(candidates) + bt._assign_residual_momentum_percentiles(candidates) + bt._assign_low_volatility_percentiles(candidates) + bt._assign_activation_momentum_percentiles(candidates) + bt._assign_residual_high_vol_blend(candidates) + threshold = float(activation.get("min_momentum_percentile", 80.0)) + for candidate in candidates: + candidate["qualified"] = bt._momentum_qualifies(candidate, threshold) + + strategy = next( + row for row in bt.PORTFOLIO_MONITOR_STRATEGIES if row.get("is_production") + ) + entry_config = bt._entry_variant_config(str(strategy["entry_variant"])) + if entry_config is None: + raise RuntimeError("Production entry configuration missing") + exit_policy = bt.LIVE_EXIT_MODE_TO_SIM.get( + str(exit_config.get("mode", "atr_trailing")), "atr_trail3" + ) + hold_days = int(exit_config.get("hold_days", max(bt.TIME_EXIT_DAYS))) + trail_multiplier = float( + exit_config.get("atr_multiplier", bt.ATR_TRAIL_MULTIPLIER) + ) + sim_kwargs = { + "qualified_fn": None, + "ranking_key": str( + entry_config.get("ranking_key") or entry_config["percentile_key"] + ), + "max_positions": int(entry_config["max_positions"]), + "risk_per_trade": float(entry_config["risk_per_trade"]), + "atr_trail_multiplier": trail_multiplier, + "start_date": start_date, + } + + baseline = bt._simulate_portfolio( + candidates, prices, benchmark_closes, exit_policy, hold_days, **sim_kwargs + ) + cooldown_5 = bt._simulate_portfolio( + candidates, + prices, + benchmark_closes, + exit_policy, + hold_days, + reentry_cooldown_days=5, + **sim_kwargs, + ) + cooldown_10 = bt._simulate_portfolio( + candidates, + prices, + benchmark_closes, + exit_policy, + hold_days, + reentry_cooldown_days=10, + **sim_kwargs, + ) + refresher = GateStopRefresher( + prices, recommendation_config, activation, benchmark_closes + ) + gate_protected = bt._simulate_portfolio( + candidates, + prices, + benchmark_closes, + exit_policy, + hold_days, + initial_stop_refresh_fn=refresher, + include_trades=True, + **sim_kwargs, + ) + if any(row is None for row in (baseline, cooldown_5, cooldown_10, gate_protected)): + raise RuntimeError("A study arm produced no trades") + + gate_trades = list(gate_protected.get("trade_details") or []) + event_counts = Counter() + for event in refresher.events: + event_counts["stop_touches"] += 1 + if event["gate_passed"]: + event_counts["gate_passed"] += 1 + if event["lower_stop"]: + event_counts["lower_stop"] += 1 + if event["same_bar_survives"]: + event_counts["same_bar_survives"] += 1 + + report = { + "generated_at": datetime.now().astimezone().isoformat(), + "snapshot": str(snapshot.resolve()), + "period_start": start_date.isoformat(), + "tickers": len(prices), + "entry_candidates": len(candidates), + "qualified_candidates": sum(bool(row["qualified"]) for row in candidates), + "params": { + "entry_cadence_days": bt.STEP_DAYS, + "setup_stop_atr_multiplier": bt.ATR_MULTIPLIER, + "exit_policy": exit_policy, + "exit_atr_multiplier": trail_multiplier, + "hold_days": hold_days, + "momentum_percentile_floor": threshold, + "gate_refresh_information_cutoff": "previous close", + }, + "arms": [ + _arm("baseline", baseline), + _arm("cooldown_5", cooldown_5), + _arm("cooldown_10", cooldown_10), + _arm("gate_protected_stop", gate_protected), + ], + "gate_stop_events": { + **dict(event_counts), + "unique_symbols": len({event["symbol"] for event in refresher.events}), + "rescued_trade_outcomes": _rescued_trade_summary(gate_trades), + "events": refresher.events, + }, + "note": ( + "The gate-protected arm recalculates the gate at an initial-stop touch " + "using only data available through the previous close. It accepts only " + "a lower stop from a newly valid long setup and checks that replacement " + "against the same bar. It does not cancel stops using the later same-day close." + ), + } + output = Path(args.out) if args.out else _default_output_path() + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + + print(f"Report written: {output}") + for arm in report["arms"]: + print( + f"{arm['arm']}: Sharpe {arm['sharpe']}, CAGR {arm['cagr_pct']}%, " + f"DD {arm['max_drawdown_pct']}%, trades {arm['trades']}" + ) + print(f"gate stop events: {dict(event_counts)}") + print(f"rescued outcomes: {report['gate_stop_events']['rescued_trade_outcomes']}") + + +if __name__ == "__main__": + asyncio.run(_main()) diff --git a/scripts/run_post_stop_reentry_study.py b/scripts/run_post_stop_reentry_study.py new file mode 100644 index 0000000..daffba9 --- /dev/null +++ b/scripts/run_post_stop_reentry_study.py @@ -0,0 +1,541 @@ +"""Offline event study for stateful post-stop re-entry policies. + +Initial entries keep the validated weekly production cadence. After an initial +stop, the affected ticker is evaluated on every subsequent daily close. This +isolates the exact churn problem without changing the rest of the portfolio. +All arms retain the hard stop, production position sizing, 3x ATR trail, and +round-trip transaction costs. +""" + +from __future__ import annotations + +import argparse +import asyncio +import bisect +import json +import multiprocessing +import os +import pickle +import sys +from collections import Counter +from concurrent.futures import ProcessPoolExecutor, as_completed +from datetime import date, datetime +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +RECLAIM_ATR_BUFFER = 0.25 + + +def _sqlite_url(path: Path) -> str: + return f"sqlite+aiosqlite:///{path.resolve().as_posix()}" + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("snapshot") + parser.add_argument("--start-date", default="2024-07-01") + parser.add_argument("--workers", type=int, default=6) + parser.add_argument("--out", default=None) + parser.add_argument( + "--candidate-cache", + default=None, + help="Optional pickle cache for the expensive weekly candidate replay.", + ) + parser.add_argument("--quiet", action="store_true") + parser.add_argument( + "--cooldowns", + type=int, + nargs="+", + default=None, + help=( + "Run an immediate baseline plus the given cooldown lengths instead " + "of the gate-reset policy study (for example: 3 5 7 10)." + ), + ) + return parser.parse_args() + + +def _default_output_path() -> Path: + stamp = datetime.now().strftime("%Y%m%d-%H%M%S") + return Path("reports") / f"post-stop-reentry-{stamp}.json" + + +class DailySetupEngine: + """Point-in-time daily setup and universe-rank cache.""" + + def __init__( + self, + prices: dict[str, tuple], + recommendation_config: dict, + activation: dict, + benchmark_closes: dict[date, float], + ) -> None: + from app.services import backtest_service as bt + + self.bt = bt + self.prices = prices + self.recommendation_config = recommendation_config + self.activation = activation + self.benchmark_closes = benchmark_closes + self.threshold = float(activation.get("min_momentum_percentile", 80.0)) + self.dates = { + symbol: [date.fromordinal(value) for value in columns[0]] + for symbol, columns in prices.items() + } + self.index_of = { + symbol: {value: index for index, value in enumerate(columns[0])} + for symbol, columns in prices.items() + } + self.rank_cache: dict[int, dict[str, tuple[float, float]]] = {} + self.candidate_cache: dict[tuple[str, int], dict | None] = {} + self.atr_cache: dict[tuple[str, int], float | None] = {} + + @staticmethod + def _percentiles(values: dict[str, float]) -> dict[str, float]: + ordered = sorted(values, key=lambda symbol: values[symbol]) + denominator = len(ordered) - 1 + return { + symbol: (rank / denominator * 100.0) if denominator > 0 else 100.0 + for rank, symbol in enumerate(ordered) + } + + def _ranks(self, asof_ord: int) -> dict[str, tuple[float, float]]: + cached = self.rank_cache.get(asof_ord) + if cached is not None: + return cached + + momentum_values: dict[str, float] = {} + volatility_values: dict[str, float] = {} + for symbol, columns in self.prices.items(): + idx = bisect.bisect_right(columns[0], asof_ord) - 1 + if idx < 0: + continue + closes = columns[4] + if idx >= 252: + momentum = self.bt._residual_momentum_12_1( + self.dates[symbol], closes, idx, self.benchmark_closes + ) + if momentum is None and closes[idx - 252] > 0: + momentum = closes[idx - 21] / closes[idx - 252] - 1.0 + if momentum is not None: + momentum_values[symbol] = float(momentum) + volatility = self.bt._realized_vol_6m(closes, idx) + if volatility is not None: + volatility_values[symbol] = float(volatility) + + momentum_pct = self._percentiles(momentum_values) + volatility_pct = self._percentiles(volatility_values) + ranks = { + symbol: (momentum_pct[symbol], volatility_pct.get(symbol, 0.0)) + for symbol in momentum_pct + } + self.rank_cache[asof_ord] = ranks + return ranks + + def atr(self, symbol: str, asof_ord: int) -> float | None: + key = (symbol, asof_ord) + if key in self.atr_cache: + return self.atr_cache[key] + columns = self.prices[symbol] + idx = self.index_of[symbol].get(asof_ord) + if idx is None: + idx = bisect.bisect_right(columns[0], asof_ord) - 1 + if idx < 0: + self.atr_cache[key] = None + return None + try: + value = self.bt.compute_atr( + columns[2][: idx + 1], + columns[3][: idx + 1], + columns[4][: idx + 1], + )["atr"] + result = float(value) if value and value > 0 else None + except Exception: + result = None + self.atr_cache[key] = result + return result + + def candidate(self, symbol: str, asof_ord: int) -> dict | None: + key = (symbol, asof_ord) + if key in self.candidate_cache: + cached = self.candidate_cache[key] + return dict(cached) if cached is not None else None + + columns = self.prices[symbol] + idx = self.index_of[symbol].get(asof_ord) + if idx is None or idx < self.bt.MIN_LOOKBACK - 1: + self.candidate_cache[key] = None + return None + records = [ + SimpleNamespace( + date=date.fromordinal(o), + open=op, + high=high, + low=low, + close=close, + volume=volume, + ) + for o, op, high, low, close, volume in zip( + columns[0][: idx + 1], + columns[1][: idx + 1], + columns[2][: idx + 1], + columns[3][: idx + 1], + columns[4][: idx + 1], + columns[5][: idx + 1], + ) + ] + setups = self.bt._window_setups( + records, self.recommendation_config, self.activation + ) + setup = next((row for row in setups if row["direction"] == "long"), None) + rank = self._ranks(asof_ord).get(symbol) + gate_passed = bool( + setup is not None + and rank is not None + and self.bt._momentum_qualifies( + { + "meets_core": setup["meets_core"], + "direction": "long", + self.bt.PRODUCTION_PERCENTILE_KEY: rank[0], + }, + self.threshold, + ) + ) + if not gate_passed or setup is None or rank is None: + self.candidate_cache[key] = None + return None + + strategy_rank = ( + rank[0] * self.bt.STRATEGY_RANK_MOMENTUM_WEIGHT + + rank[1] * (1.0 - self.bt.STRATEGY_RANK_MOMENTUM_WEIGHT) + ) + candidate = { + "symbol": symbol, + "date": date.fromordinal(asof_ord).isoformat(), + "direction": "long", + "entry": float(setup["entry"]), + "stop": float(setup["stop"]), + "target": float(setup["target"]), + "qualified": True, + self.bt.PRODUCTION_PERCENTILE_KEY: rank[0], + self.bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY: strategy_rank, + } + self.candidate_cache[key] = candidate + return dict(candidate) + + +class ReentryPolicy: + def __init__(self, name: str, engine: DailySetupEngine) -> None: + self.name = name + self.engine = engine + self.checks = 0 + self.gate_passes = 0 + self.emitted = Counter() + + def __call__( + self, + symbol: str, + asof_ord: int, + state: dict, + bar: Any, + ) -> dict | None: + self.checks += 1 + if "reentry_trigger" not in state: + stop_atr = self.engine.atr(symbol, state["stop_ord"]) + state["reentry_trigger"] = ( + state["stop_day_high"] + RECLAIM_ATR_BUFFER * stop_atr + if stop_atr is not None + else state["stop_day_high"] + ) + + candidate = self.engine.candidate(symbol, asof_ord) + if candidate is None: + state["gate_went_unqualified"] = True + return None + self.gate_passes += 1 + + reason: str | None = None + sessions = int(state["sessions_since_stop"]) + if self.name == "immediate": + reason = "gate_still_or_again_qualified" + elif self.name.startswith("cooldown_"): + cooldown_sessions = int(self.name.removeprefix("cooldown_")) + if sessions >= cooldown_sessions: + reason = f"{cooldown_sessions}_session_cooldown_complete" + elif self.name == "gate_reset": + if state["gate_went_unqualified"]: + reason = "gate_failed_then_requalified" + elif self.name == "gate_reset_or_reclaim": + if state["gate_went_unqualified"]: + reason = "gate_failed_then_requalified" + elif ( + bar.close > state["reentry_trigger"] + and float(candidate["stop"]) > state["previous_stop"] + ): + reason = "price_reclaim_with_improved_stop" + else: + raise ValueError(f"Unknown re-entry policy: {self.name}") + + if reason is None: + return None + emitted = dict(candidate) + emitted["_reentry_reason"] = reason + self.emitted[reason] += 1 + return emitted + + def summary(self) -> dict: + return { + "daily_checks": self.checks, + "qualified_checks": self.gate_passes, + "emitted_by_reason": dict(self.emitted), + } + + +def _trade_summary(trades: list[dict]) -> dict: + reentries = [trade for trade in trades if trade.get("is_reentry")] + waits = [ + int(trade["reentry_wait_sessions"]) + for trade in reentries + if trade.get("reentry_wait_sessions") is not None + ] + return { + "transaction_cost": round( + sum(float(trade["transaction_cost"]) for trade in trades), 2 + ), + "reentry_trades": len(reentries), + "same_day_reentries": sum(wait == 0 for wait in waits), + "next_day_reentries": sum(wait == 1 for wait in waits), + "reentries_within_5_sessions": sum(wait <= 5 for wait in waits), + "avg_reentry_wait_sessions": round(sum(waits) / len(waits), 1) + if waits + else None, + "reentry_win_rate": round( + sum(float(trade["pnl"]) > 0 for trade in reentries) + / len(reentries) + * 100.0, + 1, + ) + if reentries + else None, + "reentry_total_pnl": round( + sum(float(trade["pnl"]) for trade in reentries), 2 + ), + } + + +async def _main() -> None: + args = _parse_args() + snapshot = Path(args.snapshot) + if not snapshot.exists(): + raise SystemExit(f"Snapshot not found: {snapshot}") + try: + start_date = date.fromisoformat(args.start_date) + except ValueError as exc: + raise SystemExit("--start-date must use YYYY-MM-DD") from exc + + os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1" + os.environ["BACKTEST_ALLOW_SPAWN"] = "1" + + from app.models.ticker import Ticker + from app.services import backtest_service as bt + from app.services.admin_service import get_activation_config + from app.services.paper_trade_service import get_exit_policy + from app.services.recommendation_service import get_recommendation_config + + engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True) + Session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + try: + async with Session() as db: + recommendation_config = await get_recommendation_config(db) + activation = await get_activation_config(db) + exit_config = await get_exit_policy(db) + benchmark_closes = await bt._load_benchmark_closes_for_backtest( + db, days=None, refresh=False + ) + ticker_result = await db.execute(select(Ticker).order_by(Ticker.symbol)) + symbols = [ticker.symbol for ticker in ticker_result.scalars().all()] + prices: dict[str, tuple] = {} + for index, symbol in enumerate(symbols, 1): + columns = await bt._fetch_columns(db, symbol) + if columns is not None: + prices[symbol] = columns + if not args.quiet and index % 50 == 0: + print(f"loaded prices: {index}/{len(symbols)}", flush=True) + finally: + await engine.dispose() + + cache_path = Path(args.candidate_cache) if args.candidate_cache else None + snapshot_stat = snapshot.stat() + cache_key = { + "snapshot": str(snapshot.resolve()), + "snapshot_size": snapshot_stat.st_size, + "snapshot_mtime_ns": snapshot_stat.st_mtime_ns, + "start_date": start_date.isoformat(), + } + candidates: list[dict] + if cache_path is not None and cache_path.exists(): + with cache_path.open("rb") as handle: + cached_replay = pickle.load(handle) # noqa: S301 - trusted local cache + if cached_replay.get("key") != cache_key: + raise SystemExit(f"Candidate cache does not match this run: {cache_path}") + candidates = list(cached_replay["candidates"]) + if not args.quiet: + print(f"loaded candidate cache: {cache_path}", flush=True) + else: + candidates = [] + workers = max(1, min(int(args.workers), multiprocessing.cpu_count() - 1)) + context = multiprocessing.get_context("spawn") + with ProcessPoolExecutor(max_workers=workers, mp_context=context) as pool: + futures = { + pool.submit( + bt._replay_candidates_for_period, + symbol, + columns, + recommendation_config, + activation, + benchmark_closes, + start_date, + ): symbol + for symbol, columns in prices.items() + } + for index, future in enumerate(as_completed(futures), 1): + candidates.extend(future.result()) + if not args.quiet and index % 25 == 0: + print(f"replayed tickers: {index}/{len(futures)}", flush=True) + if cache_path is not None: + cache_path.parent.mkdir(parents=True, exist_ok=True) + with cache_path.open("wb") as handle: + pickle.dump( + {"key": cache_key, "candidates": candidates}, + handle, + protocol=pickle.HIGHEST_PROTOCOL, + ) + if not args.quiet: + print(f"wrote candidate cache: {cache_path}", flush=True) + + bt._assign_momentum_percentiles(candidates) + bt._assign_residual_momentum_percentiles(candidates) + bt._assign_low_volatility_percentiles(candidates) + bt._assign_activation_momentum_percentiles(candidates) + bt._assign_residual_high_vol_blend(candidates) + threshold = float(activation.get("min_momentum_percentile", 80.0)) + for candidate in candidates: + candidate["qualified"] = bt._momentum_qualifies(candidate, threshold) + + strategy = next( + row for row in bt.PORTFOLIO_MONITOR_STRATEGIES if row.get("is_production") + ) + entry_config = bt._entry_variant_config(str(strategy["entry_variant"])) + if entry_config is None: + raise RuntimeError("Production entry configuration missing") + exit_policy = bt.LIVE_EXIT_MODE_TO_SIM.get( + str(exit_config.get("mode", "atr_trailing")), "atr_trail3" + ) + hold_days = int(exit_config.get("hold_days", max(bt.TIME_EXIT_DAYS))) + trail_multiplier = float( + exit_config.get("atr_multiplier", bt.ATR_TRAIL_MULTIPLIER) + ) + sim_kwargs = { + "ranking_key": str( + entry_config.get("ranking_key") or entry_config["percentile_key"] + ), + "max_positions": int(entry_config["max_positions"]), + "risk_per_trade": float(entry_config["risk_per_trade"]), + "atr_trail_multiplier": trail_multiplier, + "start_date": start_date, + "include_trades": True, + } + + daily_engine = DailySetupEngine( + prices, recommendation_config, activation, benchmark_closes + ) + if args.cooldowns is None: + policy_names = ( + "immediate", + "cooldown_5", + "gate_reset", + "gate_reset_or_reclaim", + ) + else: + cooldowns = sorted(set(args.cooldowns)) + if any(value < 1 for value in cooldowns): + raise SystemExit("--cooldowns values must be positive integers") + policy_names = ("immediate", *(f"cooldown_{value}" for value in cooldowns)) + + arms: list[dict] = [] + for policy_name in policy_names: + policy = ReentryPolicy(policy_name, daily_engine) + sim = bt._simulate_portfolio( + candidates, + prices, + benchmark_closes, + exit_policy, + hold_days, + post_stop_reentry_fn=policy, + **sim_kwargs, + ) + if sim is None: + raise RuntimeError(f"Policy {policy_name} produced no trades") + trades = list(sim.pop("trade_details")) + arms.append({ + "arm": policy_name, + **sim, + "turnover": _trade_summary(trades), + "policy": policy.summary(), + "trade_details": trades, + }) + + output = Path(args.out) if args.out else _default_output_path() + report = { + "generated_at": datetime.now().astimezone().isoformat(), + "snapshot": str(snapshot.resolve()), + "period_start": start_date.isoformat(), + "tickers": len(prices), + "entry_candidates": len(candidates), + "qualified_candidates": sum(bool(row["qualified"]) for row in candidates), + "params": { + "initial_entry_cadence_days": bt.STEP_DAYS, + "post_stop_evaluation_cadence_days": 1, + "setup_stop_atr_multiplier": bt.ATR_MULTIPLIER, + "exit_policy": exit_policy, + "exit_atr_multiplier": trail_multiplier, + "hold_days": hold_days, + "cost_per_side_pct": bt.COST_PER_SIDE * 100.0, + "momentum_percentile_floor": threshold, + "reclaim_atr_buffer": RECLAIM_ATR_BUFFER, + "cooldown_sessions": cooldowns if args.cooldowns is not None else None, + }, + "arms": arms, + "note": ( + "Initial opportunities retain the validated weekly replay cadence. " + "Only tickers stopped at their initial stop switch to daily evaluation, " + "which isolates next-day/same-episode re-entry churn. A cooldown of N " + "sessions permits the first re-entry at wait_sessions=N. Gate reset " + "requires at least one unqualified daily close before requalification. " + "The reclaim arm alternatively accepts a close above stop-day high + " + "0.25 ATR only when the new setup stop is above the prior stop." + ), + } + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + + print(f"Report written: {output}") + for arm in arms: + turnover = arm["turnover"] + print( + f"{arm['arm']}: Sharpe {arm['sharpe']}, CAGR {arm['cagr_pct']}%, " + f"DD {arm['max_drawdown_pct']}%, trades {arm['trades']}, " + f"reentries {turnover['reentry_trades']}, fees ${turnover['transaction_cost']}" + ) + + +if __name__ == "__main__": + asyncio.run(_main()) diff --git a/tests/unit/test_backtest_service.py b/tests/unit/test_backtest_service.py index 7a31ebd..5231327 100644 --- a/tests/unit/test_backtest_service.py +++ b/tests/unit/test_backtest_service.py @@ -575,6 +575,170 @@ class TestSimulatePortfolio: assert sim["trades"] == 1 assert sim["worst_trade_r"] == pytest.approx(-2.0) # (90 − 100) / 5 + def test_initial_stop_cooldown_blocks_immediate_reentry(self): + closes = [100.0, 94.0, 96.0] + prices = {"AAA": _sim_prices(self.ORD, closes)} + candidates = [ + _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=120.0), + _sim_cand("AAA", self.ORD + 1, entry=94.0, stop=89.0, target=110.0), + ] + + baseline = bt._simulate_portfolio(candidates, prices, None, "hold", 30) + cooldown = bt._simulate_portfolio( + candidates, + prices, + None, + "hold", + 30, + reentry_cooldown_days=5, + ) + + assert baseline is not None and baseline["trades"] == 2 + assert cooldown is not None and cooldown["trades"] == 1 + assert cooldown["skipped_cooldown"] == 1 + assert cooldown["reentry_cooldown_days"] == 5 + + def test_production_monitor_applies_live_reentry_lockdown(self, monkeypatch): + def fake_simulator(*_args, **kwargs): + return { + "trades": 0, + "applied_reentry_lockdown": kwargs.get("reentry_cooldown_days", 0), + } + + monkeypatch.setattr(bt, "_simulate_portfolio", fake_simulator) + market_ord = date(2026, 7, 1).toordinal() + prices = {"AAA": ([market_ord], [], [], [], [], [])} + + monitor = bt._portfolio_monitor([], prices, None, 30) + production_rows = [ + row for row in monitor["runs"] if row["is_production"] + ] + comparison_rows = [ + row for row in monitor["runs"] if not row["is_production"] + ] + + assert production_rows + assert all( + row["reentry_lockdown_sessions"] == bt.REENTRY_LOCKDOWN_SESSIONS + and row["applied_reentry_lockdown"] == bt.REENTRY_LOCKDOWN_SESSIONS + for row in production_rows + ) + assert comparison_rows + assert all( + row["reentry_lockdown_sessions"] == 0 + and row["applied_reentry_lockdown"] == 0 + for row in comparison_rows + ) + + def test_initial_stop_can_refresh_lower_and_survive_same_bar(self): + closes = [100.0, 94.0, 96.0] + prices = {"AAA": _sim_prices(self.ORD, closes)} + candidate = _sim_cand( + "AAA", self.ORD, entry=100.0, stop=95.0, target=120.0 + ) + + sim = bt._simulate_portfolio( + [candidate], + prices, + None, + "hold", + 2, + initial_stop_refresh_fn=lambda *_: 90.0, + include_trades=True, + ) + + assert sim is not None + assert sim["stop_refresh_attempts"] == 1 + assert sim["stop_refreshes"] == 1 + assert sim["stop_refresh_same_bar_hits"] == 0 + assert sim["exit_reasons"] == {"time": 1} + assert sim["trade_details"][0]["stop_refreshes"] == 1 + + def test_refreshed_stop_is_checked_against_same_bar(self): + ords = list(range(self.ORD, self.ORD + 2)) + prices = { + "AAA": ( + ords, + [100.0, 94.0], + [101.0, 96.0], + [99.0, 89.0], + [100.0, 94.0], + [1, 1], + ) + } + candidate = _sim_cand( + "AAA", self.ORD, entry=100.0, stop=95.0, target=120.0 + ) + + sim = bt._simulate_portfolio( + [candidate], + prices, + None, + "hold", + 30, + initial_stop_refresh_fn=lambda *_: 90.0, + ) + + assert sim is not None + assert sim["stop_refresh_same_bar_hits"] == 1 + assert sim["worst_trade_r"] == pytest.approx(-2.0) + + def test_post_stop_state_suppresses_same_episode_candidate(self): + closes = [100.0, 94.0, 96.0] + prices = {"AAA": _sim_prices(self.ORD, closes)} + candidates = [ + _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=120.0), + _sim_cand("AAA", self.ORD + 1, entry=94.0, stop=89.0, target=110.0), + ] + + sim = bt._simulate_portfolio( + candidates, + prices, + None, + "hold", + 30, + post_stop_reentry_fn=lambda *_: None, + ) + + assert sim is not None + assert sim["trades"] == 1 + assert sim["post_stop_events"] == 1 + assert sim["post_stop_reentries"] == 0 + assert sim["post_stop_states_open_at_end"] == 1 + + def test_post_stop_callback_can_reenter_same_day(self): + closes = [100.0, 94.0, 96.0] + prices = {"AAA": _sim_prices(self.ORD, closes)} + initial = _sim_cand( + "AAA", self.ORD, entry=100.0, stop=95.0, target=120.0 + ) + + def immediate_reentry(sym, current_ord, _state, bar): + return _sim_cand( + sym, + current_ord, + entry=bar.close, + stop=bar.close - 5.0, + target=bar.close + 15.0, + ) + + sim = bt._simulate_portfolio( + [initial], + prices, + None, + "hold", + 30, + post_stop_reentry_fn=immediate_reentry, + include_trades=True, + ) + + assert sim is not None + assert sim["trades"] == 2 + assert sim["post_stop_reentries"] == 1 + assert sim["reentry_events"][0]["wait_sessions"] == 0 + assert sim["trade_details"][1]["is_reentry"] is True + assert sim["trade_details"][1]["reentry_wait_sessions"] == 0 + def test_sma50_policy_exits_on_close_break(self): closes = [100.0] * 56 + [90.0, 91.0] prices = {"AAA": _sim_prices(self.ORD, closes)} @@ -722,6 +886,7 @@ def test_build_recommendation_prefers_production_monitor_headline(): }) assert rec["headline"] is not None assert "3x ATR trailing exit" in rec["headline"] + assert "5-session re-entry lockdown" in rec["headline"] assert any(item["topic"] == "production" for item in rec["items"]) @@ -870,6 +1035,10 @@ async def test_run_backtest_smoke(session): assert report["params"]["cost_per_side_pct"] == pytest.approx(bt.COST_PER_SIDE * 100) assert report["params"]["target_model"] == bt.PRODUCTION_GTL_TARGET_MODEL assert report["params"]["is_production_target_model"] is True + assert ( + report["params"]["production_reentry_lockdown_sessions"] + == bt.REENTRY_LOCKDOWN_SESSIONS + ) assert "net_avg_r" in report["overall_all"] # ablation baseline reproduces the qualified set exactly, and every row diff --git a/tests/unit/test_rr_scanner_preservation.py b/tests/unit/test_rr_scanner_preservation.py index b271a4c..3631553 100644 --- a/tests/unit/test_rr_scanner_preservation.py +++ b/tests/unit/test_rr_scanner_preservation.py @@ -607,6 +607,99 @@ async def test_get_trade_setups_can_exclude_tickers_with_open_paper_trades( assert [row["symbol"] for row in ticker_rows] == ["OPENQ"] +@pytest.mark.asyncio +async def test_get_trade_setups_applies_five_session_initial_stop_lockdown( + db_session: AsyncSession, +): + now = datetime.now(timezone.utc) + today = now.date() + if await db_session.get(User, 1) is None: + db_session.add( + User(id=1, username="u", password_hash="x", role="user", has_access=True) + ) + await db_session.flush() + + blocked = Ticker(symbol="STOP4") + released = Ticker(symbol="STOP5") + trailing = Ticker(symbol="TRAILQ") + db_session.add_all([blocked, released, trailing]) + await db_session.flush() + + # Six synthetic stored market sessions D0..D5. A stop on D0 has five + # later sessions and is released; a stop on D1 has only four and is not. + market_sessions = [today - timedelta(days=offset) for offset in range(5, -1, -1)] + for market_date in market_sessions: + db_session.add( + OHLCVRecord( + ticker_id=blocked.id, + date=market_date, + open=100.0, + high=101.0, + low=99.0, + close=100.0, + volume=1_000, + ) + ) + + for ticker in (blocked, released, trailing): + db_session.add( + TradeSetup( + ticker_id=ticker.id, + direction="long", + entry_price=100.0, + stop_loss=95.0, + target=115.0, + rr_ratio=3.0, + composite_score=80.0, + detected_at=now, + ) + ) + + def closed_trade(ticker: Ticker, closed_on: date, reason: str) -> PaperTrade: + return PaperTrade( + user_id=1, + ticker_id=ticker.id, + direction="long", + entry_price=100.0, + shares=10.0, + stop_loss=95.0, + target=115.0, + status="closed", + opened_at=datetime.combine( + closed_on - timedelta(days=1), datetime.min.time(), tzinfo=timezone.utc + ), + close_price=95.0, + closed_at=datetime.combine( + closed_on, datetime.min.time(), tzinfo=timezone.utc + ), + close_reason=reason, + ) + + db_session.add_all( + [ + closed_trade(blocked, market_sessions[1], "stop"), + closed_trade(released, market_sessions[0], "stop"), + closed_trade(trailing, market_sessions[-1], "trailing"), + ] + ) + await db_session.flush() + + default_symbols = { + row["symbol"] for row in await get_trade_setups(db_session) + } + assert {"STOP4", "STOP5", "TRAILQ"}.issubset(default_symbols) + + available_symbols = { + row["symbol"] + for row in await get_trade_setups( + db_session, + exclude_reentry_lockdown_tickers=True, + ) + } + assert "STOP4" not in available_symbols + assert {"STOP5", "TRAILQ"}.issubset(available_symbols) + + async def _seed_stale_setup_with_current_scores(db_session: AsyncSession) -> TradeSetup: """Stored setup frozen at scan time (conf 82, neutral) vs. current context (bullish sentiment, composite 96) that yields live confidence 97.