Implement multi-engine PEAD strategy research workflow

apps/backtester/run.py

@@ -6,6 +6,7 @@ import datetime as dt
 import json
 import subprocess
 import sys
+from collections import defaultdict
 from pathlib import Path
 from typing import Any
 
@@ -30,7 +31,7 @@ from libs.backtest.execution import (
 )
 from libs.backtest.manifests import generate_run_id, load_manifest, resolve_config
 from libs.backtest.metrics import build_metrics_bundle
-from libs.backtest.selector import select_candidates
+from libs.backtest.selector import rank_candidates, select_candidates
 from libs.backtest.snapshot_store import SnapshotStore
 from libs.backtest.splits import generate_walk_forward_windows
 from libs.common.logging import get_logger
@@ -62,12 +63,15 @@ class BacktestRunner:
         store: SnapshotStore,
         initial_equity: float = 100_000.0,
         split_name: str | None = None,
+        enable_engine_analysis: bool = True,
     ) -> None:
         self.split_name = split_name
         self.manifest = manifest
         self.config = config
         self.store = store
         self.initial_equity = initial_equity
+        self.enable_engine_analysis = enable_engine_analysis
+        self._active_strategy_engines = self.config.get_active_strategy_engines()
 
         # Simulation state
         self._equity = initial_equity
@@ -87,6 +91,7 @@ class BacktestRunner:
         self._cooldown_remaining = 0
         self._kill_switch_triggered = False
         self._kill_switch_cooldown_remaining = 0
+        self._engine_daily_new_risk_used: dict[str, float] = defaultdict(float)
 
     def run(self, output_root: str | Path | None = None) -> ExperimentResult:
         """Execute the full simulation. Returns ExperimentResult."""
@@ -100,7 +105,7 @@ class BacktestRunner:
 
         # Iterate ALL trading days (not just candidate days) so stop/target/time
         # exits are checked every day, not just on days with new candidates.
-        all_dates = self.store.all_trading_days()
+        all_dates = self._get_simulation_dates()
 
         # Record initial equity state (before any trades)
         if all_dates:
@@ -133,6 +138,11 @@ class BacktestRunner:
         metrics = build_metrics_bundle(
             self._closed_trades, self._equity_curve, self._candidate_map
         )
+        per_engine_metrics = (
+            self._build_per_engine_metrics()
+            if self.enable_engine_analysis and self.config.get_strategy_engines()
+            else {}
+        )
 
         # Create run directory and write artifacts
         run_dir = None
@@ -157,6 +167,7 @@ class BacktestRunner:
                 total_candidates_seen=self._total_candidates_seen,
                 total_orders_rejected=self._total_orders_rejected,
                 split_name=self.split_name,
+                per_engine_metrics=per_engine_metrics,
             )
 
         logger.info(
@@ -183,6 +194,7 @@ class BacktestRunner:
         """Simulate a single trading day."""
         # Reset daily risk tracker
         self._daily_new_risk_used = 0.0
+        self._engine_daily_new_risk_used = defaultdict(float)
 
         # Decrement cooldowns
         if self._cooldown_remaining > 0:
@@ -223,13 +235,7 @@ class BacktestRunner:
                     warmup_days=self.config.execution.trailing_warmup_days,
                 )
 
-            # Build effective execution config with per-event-type overrides
+            effective_exec = self._build_effective_execution_config(pos.plan.candidate)
-            effective_exec = self.config.execution
-            evt_profile = self.config.get_event_profile(pos.plan.candidate.event_type)
-            if evt_profile and evt_profile.max_holding_days_override is not None:
-                effective_exec = self.config.execution.model_copy(
-                    update={"max_holding_days": evt_profile.max_holding_days_override}
-                )
 
             prev_status = pos.status
             trade = simulate_exit(pos, bar, effective_exec, date)
@@ -302,13 +308,9 @@ class BacktestRunner:
 
         # --- ENTRIES (only if kill switch not triggered) ---
         if not self._kill_switch_triggered:
-            raw_rows = self.store.get_candidates_for_date(date)
-            self._total_candidates_seen += len(raw_rows)
             portfolio_state = self._build_portfolio_state(date, drawdown_pct, unrealized)
-            candidates = select_candidates(
+            candidates = self._select_candidates_for_date(date)
-                raw_rows, self.config.universe, self.config.signal,
+            self._total_candidates_seen += len(candidates)
-                event_type_profiles=self.config.event_type_profiles or None,
-            )
 
             macro_data = self.store.get_macro_for_date(date)
 
@@ -320,6 +322,7 @@ class BacktestRunner:
                     config=self.config,
                     cooldown_remaining=self._cooldown_remaining,
                     macro_data=macro_data,
+                    engine_daily_new_risk_used=self._engine_daily_new_risk_used[candidate.engine_id],
                 )
 
                 if plan.skip_reason is not None:
@@ -332,12 +335,17 @@ class BacktestRunner:
                     )
                     continue
 
-                bar = self.store.get_bar(candidate.symbol, date)
+                bar = self.store.get_bar(candidate.symbol, candidate.execution_date)
-                pos = simulate_entry(plan, bar, self.config.execution)
+                pos = simulate_entry(
+                    plan,
+                    bar,
+                    self._build_effective_execution_config(candidate),
+                )
                 if pos is not None:
                     self._open_positions.append(pos)
                     self._cash -= pos.entry_price * pos.shares_total
                     self._daily_new_risk_used += plan.risk_dollars
+                    self._engine_daily_new_risk_used[candidate.engine_id] += plan.risk_dollars
                     # Update equity and portfolio state for next candidate
                     mv = self._compute_positions_market_value(date)
                     self._equity = self._cash + mv
@@ -382,6 +390,141 @@ class BacktestRunner:
             )
         )
 
+    def _get_simulation_dates(self) -> list[dt.date]:
+        """Return the full trading-day simulation range for the configured engines."""
+        if not self.config.get_strategy_engines():
+            return self.store.all_trading_days()
+        include_reaction_dates = any(
+            engine.entry_timing_policy == "reaction_close"
+            for engine in self._active_strategy_engines
+        )
+        return self.store.all_trading_days(include_reaction_dates=include_reaction_dates)
+
+    def _select_candidates_for_date(self, date: dt.date) -> list[Candidate]:
+        """Select daily candidates for single-engine or multi-engine mode."""
+        if not self.config.get_strategy_engines():
+            raw_rows = self.store.get_candidates_for_date(date)
+            return select_candidates(
+                raw_rows,
+                self.config.universe,
+                self.config.signal,
+                event_type_profiles=self.config.event_type_profiles or None,
+            )
+        if not self._active_strategy_engines:
+            return []
+
+        engine_queues: dict[str, list[Candidate]] = {}
+        for engine in self._active_strategy_engines:
+            raw_rows = (
+                self.store.get_candidates_for_reaction_date(date)
+                if engine.entry_timing_policy == "reaction_close"
+                else self.store.get_candidates_for_date(date)
+            )
+            selected = select_candidates(
+                raw_rows,
+                self.config.universe,
+                self.config.signal,
+                event_type_profiles=self.config.event_type_profiles or None,
+                strategy_engine=engine,
+            )
+            if selected:
+                engine_queues[engine.engine_id] = selected
+
+        if self.config.strategy_engine_selection_mode == "global_score":
+            merged = []
+            for candidates in engine_queues.values():
+                merged.extend(candidates)
+            merged = rank_candidates(merged)
+            return merged[: self.config.signal.max_candidates_per_day]
+
+        return self._interleave_engine_candidates(engine_queues)
+
+    def _interleave_engine_candidates(
+        self,
+        engine_queues: dict[str, list[Candidate]],
+    ) -> list[Candidate]:
+        """Round-robin engine queues using manifest order."""
+        if not engine_queues:
+            return []
+
+        working = {
+            engine_id: list(candidates)
+            for engine_id, candidates in engine_queues.items()
+        }
+        ordered: list[Candidate] = []
+        while True:
+            advanced = False
+            for engine in self._active_strategy_engines:
+                queue = working.get(engine.engine_id, [])
+                if not queue:
+                    continue
+                ordered.append(queue.pop(0))
+                advanced = True
+            if not advanced:
+                break
+        return ordered[: self.config.signal.max_candidates_per_day]
+
+    def _build_effective_execution_config(self, candidate: Candidate) -> Any:
+        """Resolve per-engine and per-event holding-period overrides."""
+        max_holding_days = candidate.engine_max_holding_days
+        if max_holding_days is None:
+            evt_profile = self.config.get_event_profile(candidate.event_type)
+            if evt_profile and evt_profile.max_holding_days_override is not None:
+                max_holding_days = evt_profile.max_holding_days_override
+
+        if max_holding_days is None:
+            return self.config.execution
+        return self.config.execution.model_copy(
+            update={"max_holding_days": max_holding_days}
+        )
+
+    def _build_per_engine_metrics(self) -> dict[str, dict[str, Any]]:
+        """Run each engine in isolation for standalone metrics and shadow summaries."""
+        summaries: dict[str, dict[str, Any]] = {}
+        for engine in self.config.get_strategy_engines():
+            isolated_engine = engine.model_copy(
+                update={
+                    "shadow_only": False,
+                    # Shadow engines should paper-trade freely for diagnostics.
+                    "engine_risk_budget_pct": (
+                        1.0 if engine.shadow_only else engine.engine_risk_budget_pct
+                    ),
+                }
+            )
+            isolated_manifest = self.manifest.model_copy(
+                update={"strategy_engines": [isolated_engine]}
+            )
+            isolated_config = self.config.model_copy(
+                update={
+                    "strategy_name": f"{self.config.strategy_name}__{engine.engine_id}",
+                    "strategy_engines": [isolated_engine],
+                }
+            )
+            runner = BacktestRunner(
+                manifest=isolated_manifest,
+                config=isolated_config,
+                store=self.store,
+                initial_equity=self.initial_equity,
+                split_name=self.split_name,
+                enable_engine_analysis=False,
+            )
+            result = runner.run(output_root=None)
+            summaries[engine.engine_id] = {
+                "engine_id": engine.engine_id,
+                "shadow_only": engine.shadow_only,
+                "event_types": list(engine.event_types),
+                "timing_class": engine.timing_class,
+                "direction": engine.direction,
+                "entry_timing_policy": engine.entry_timing_policy,
+                "max_holding_days": engine.max_holding_days,
+                "engine_risk_budget_pct": engine.engine_risk_budget_pct,
+                "total_candidates_seen": result.total_candidates_seen,
+                "total_orders_rejected": result.total_orders_rejected,
+                "net_pnl": round(sum(trade.net_pnl for trade in runner._closed_trades), 4),
+                "metrics": result.metrics.model_dump(mode="json"),
+            }
+        return summaries
+
     def _compute_positions_market_value(self, date: dt.date) -> float:
         """Market value of all open positions using today's close.
 

configs/experiments/pead_midcap_portfolio_v2.json

@@ -0,0 +1,81 @@
+{
+  "experiment_name": "pead_midcap_portfolio_v2",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Portfolio V2: split PEAD earnings into specialist engines by timing and direction, with same-day long using reaction-close entry and after-close short kept as shadow.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any"},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_v1",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 3,
+      "engine_risk_budget_pct": 0.40,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close_v1",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 3,
+      "engine_risk_budget_pct": 0.35,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_v1",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 5,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_v1",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 3,
+      "engine_risk_budget_pct": 0.10,
+      "shadow_only": true
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "portfolio_v2", "specialist_engines"],
+  "notes": null
+}

configs/experiments/pead_midcap_step22_sdlong_close5.json

@@ -0,0 +1,82 @@
+{
+  "experiment_name": "pead_midcap_step22_sdlong_close5",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 22: Preserve step14 profitable buckets, switch same-day longs to reaction-close entry with 5-day hold, and remove after-close shorts from active book.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close5",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 5,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_shadow",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": true
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step22", "same_day_long_close5"],
+  "notes": null
+}

configs/experiments/pead_midcap_step23_sdlong_close7.json

@@ -0,0 +1,82 @@
+{
+  "experiment_name": "pead_midcap_step23_sdlong_close7",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 23: Same as step22 but keep same-day reaction-close longs on the original 7-day hold.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_shadow",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": true
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step23", "same_day_long_close7"],
+  "notes": null
+}

configs/experiments/pead_midcap_step24_sdlong_close5_nofrac.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step24_sdlong_close5_nofrac",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 24: Step22 plus full exit at target, combining the best same-day long entry change with the best return-side exit tweak.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close5",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 5,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_shadow",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": true
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step24", "same_day_long_close5", "nofrac"],
+  "notes": null
+}

configs/experiments/pead_midcap_step25_positive_buckets.json

@@ -0,0 +1,62 @@
+{
+  "experiment_name": "pead_midcap_step25_positive_buckets",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 25: Keep only the two positive step14 buckets: same-day shorts and after-close longs.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step25", "positive_buckets"],
+  "notes": null
+}

configs/experiments/pead_midcap_step26_positive_buckets_nofrac.json

@@ -0,0 +1,63 @@
+{
+  "experiment_name": "pead_midcap_step26_positive_buckets_nofrac",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 26: Step25 plus full exit at target to maximize realized gains in the two positive buckets.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step26", "positive_buckets", "nofrac"],
+  "notes": null
+}

configs/experiments/pead_midcap_step27_sdlong_close7_budget25.json

@@ -0,0 +1,82 @@
+{
+  "experiment_name": "pead_midcap_step27_sdlong_close7_budget25",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 27: Step23 with a smaller 25% daily risk sleeve for same-day reaction-close longs to reduce crowding.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_shadow",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": true
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step27", "same_day_long_close7", "budget25"],
+  "notes": null
+}

configs/experiments/pead_midcap_step28_sdlong_close7_budget12.json

@@ -0,0 +1,82 @@
+{
+  "experiment_name": "pead_midcap_step28_sdlong_close7_budget12",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 28: Step23 with an even tighter 12.5% daily risk sleeve for same-day reaction-close longs.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_shadow",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": true
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step28", "same_day_long_close7", "budget12"],
+  "notes": null
+}

configs/experiments/pead_midcap_step29_sdlong_close7_budget25_nofrac.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step29_sdlong_close7_budget25_nofrac",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 29: Step27 plus full exit at target to combine better sleeve budgeting with the best return-side exit tweak.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_shadow",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": true
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step29", "same_day_long_close7", "budget25", "nofrac"],
+  "notes": null
+}

configs/experiments/pead_midcap_step30_balanced_sleeves_nofrac.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step30_balanced_sleeves_nofrac",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 30: Keep all earnings sleeves but downweight the weaker long-close and after-close short buckets, with full exit at target.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step30", "balanced_sleeves", "nofrac"],
+  "notes": null
+}

configs/experiments/pead_midcap_step31_balanced_sleeves_nofrac_acshort12.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step31_balanced_sleeves_nofrac_acshort12",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 31: Step30 with a smaller after-close short sleeve to keep trade count support while trimming the weaker bucket further.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step31", "balanced_sleeves", "nofrac", "acshort12"],
+  "notes": null
+}

configs/experiments/pead_midcap_step32_balanced_sleeves_nofrac_sdlong50.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step32_balanced_sleeves_nofrac_sdlong50",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 32: Step30 with a larger same-day long close sleeve to test whether the improved entry can carry more capital without breaking the portfolio.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.5,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step32", "balanced_sleeves", "nofrac", "sdlong50"],
+  "notes": null
+}

configs/experiments/pead_midcap_step33_balanced_sleeves_nofrac_acshort6.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step33_balanced_sleeves_nofrac_acshort6",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 33: Step31 with an even smaller after-close short sleeve to test if the trade-count support remains while cutting more drag.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.0625,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step33", "balanced_sleeves", "nofrac", "acshort6"],
+  "notes": null
+}

configs/experiments/pead_midcap_step34_balanced_sleeves_nofrac_aclong25.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step34_balanced_sleeves_nofrac_aclong25",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 34: Step31 with the after-close long sleeve capped to two trades per day, testing whether the weaker bucket improves when only the top-ranked names survive.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_top2",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step34", "balanced_sleeves", "nofrac", "aclong25"],
+  "notes": null
+}

configs/experiments/pead_midcap_step35_balanced_sleeves_nofrac_aclong12.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step35_balanced_sleeves_nofrac_aclong12",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 35: Step31 with the after-close long sleeve capped to one trade per day, forcing the portfolio to lean harder on the stronger same-day sleeves.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_top1",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step35", "balanced_sleeves", "nofrac", "aclong12"],
+  "notes": null
+}

configs/experiments/pead_midcap_step36_balanced_sleeves_nofrac_aclong12_sdlong12.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step36_balanced_sleeves_nofrac_aclong12_sdlong12",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 36: Step35 plus a one-trade-per-day cap on the same-day long close sleeve, keeping only the single best close-entry long when multiple names trigger together.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_top1",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_top1",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step36", "balanced_sleeves", "nofrac", "aclong12", "sdlong12"],
+  "notes": null
+}

configs/experiments/pead_midcap_step37_balanced_sleeves_aclong_vol3.json

@@ -0,0 +1,84 @@
+{
+  "experiment_name": "pead_midcap_step37_balanced_sleeves_aclong_vol3",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 37: Keep the step31 balanced sleeve structure, but require stronger volume confirmation for after-close long candidates only.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_vol3",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "pead_volume_threshold_override": 3.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step37", "balanced_sleeves", "aclong", "vol3"],
+  "notes": null
+}

configs/experiments/pead_midcap_step38_balanced_sleeves_aclong_vol4.json

@@ -0,0 +1,84 @@
+{
+  "experiment_name": "pead_midcap_step38_balanced_sleeves_aclong_vol4",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 38: Step37 with an even stricter after-close long volume gate, testing whether only the highest-conviction overnight reactions should remain in that sleeve.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_vol4",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "pead_volume_threshold_override": 4.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step38", "balanced_sleeves", "aclong", "vol4"],
+  "notes": null
+}

configs/experiments/pead_midcap_step39_balanced_sleeves_sdlong12.json

@@ -0,0 +1,83 @@
+{
+  "experiment_name": "pead_midcap_step39_balanced_sleeves_sdlong12",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 39: Keep the robust step31 sleeve mix, but cap same-day long close entries to one trade per day while leaving after-close longs unchanged.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_long_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "long_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_top1",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step39", "balanced_sleeves", "sdlong12"],
+  "notes": null
+}

configs/experiments/pead_midcap_step40_short_core_sdlong12.json

@@ -0,0 +1,73 @@
+{
+  "experiment_name": "pead_midcap_step40_short_core_sdlong12",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 40: Remove the unstable after-close long sleeve and keep a short-led portfolio with a small same-day close-entry long overlay.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step40", "short_core", "sdlong12"],
+  "notes": null
+}

configs/experiments/pead_midcap_step41_short_core_sdlong12_acshort50.json

@@ -0,0 +1,73 @@
+{
+  "experiment_name": "pead_midcap_step41_short_core_sdlong12_acshort50",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 41: Step40 with a larger after-close short sleeve, leaning harder into the more stable short continuation buckets.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.5,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step41", "short_core", "sdlong12", "acshort50"],
+  "notes": null
+}

configs/experiments/pead_midcap_step42_short_core_only.json

@@ -0,0 +1,63 @@
+{
+  "experiment_name": "pead_midcap_step42_short_core_only",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 42: Pure short-led portfolio using only same-day and after-close short sleeves.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step42", "short_core", "short_only"],
+  "notes": null
+}

configs/experiments/pead_midcap_step43_short_core_sdlong25.json

@@ -0,0 +1,73 @@
+{
+  "experiment_name": "pead_midcap_step43_short_core_sdlong25",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 43: Keep the step40 short-core structure but restore a larger same-day long close sleeve after removing after-close longs.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step43", "short_core", "sdlong25"],
+  "notes": null
+}

configs/experiments/pead_midcap_step44_short_core_macro50.json

@@ -0,0 +1,75 @@
+{
+  "experiment_name": "pead_midcap_step44_short_core_macro50",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 44: Apply a 50% size scaler in unfavorable SPY-below-SMA regimes on top of the step40 short-core structure.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 0.5
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step44", "short_core", "macro50"],
+  "notes": null
+}

configs/experiments/pead_midcap_step45_short_core_macro_block.json

@@ -0,0 +1,75 @@
+{
+  "experiment_name": "pead_midcap_step45_short_core_macro_block",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 45: Hard block all new entries in unfavorable SPY-below-SMA regimes on top of the step40 short-core structure.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step45", "short_core", "macro_block"],
+  "notes": null
+}

configs/experiments/pead_midcap_step46_short_core_macro_block_acshort12.json

@@ -0,0 +1,75 @@
+{
+  "experiment_name": "pead_midcap_step46_short_core_macro_block_acshort12",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 46: Step45 with a smaller after-close short sleeve, testing whether the macro block leaves that bucket oversized on train.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step46", "short_core", "macro_block", "acshort12"],
+  "notes": null
+}

configs/experiments/pead_midcap_step47_short_core_macro_block_sdlong25.json

@@ -0,0 +1,75 @@
+{
+  "experiment_name": "pead_midcap_step47_short_core_macro_block_sdlong25",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 47: Step45 with a larger same-day long close sleeve, testing whether the macro block makes that overlay scalable again.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step47", "short_core", "macro_block", "sdlong25"],
+  "notes": null
+}

configs/experiments/pead_midcap_step48_short_core_macro_block_nolong.json

@@ -0,0 +1,65 @@
+{
+  "experiment_name": "pead_midcap_step48_short_core_macro_block_nolong",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 48: Step45 without the same-day long sleeve, testing whether the macro-blocked short core is strong enough on its own.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step48", "short_core", "macro_block", "nolong"],
+  "notes": null
+}

configs/experiments/pead_midcap_step49_same_day_short_macro_block.json

@@ -0,0 +1,55 @@
+{
+  "experiment_name": "pead_midcap_step49_same_day_short_macro_block",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 49: Pure same-day short sleeve under the macro block, testing whether the simplest engine dominates the portfolio.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step49", "same_day_short", "macro_block"],
+  "notes": null
+}

configs/experiments/pead_midcap_step50_same_day_short_long_macro_block.json

@@ -0,0 +1,65 @@
+{
+  "experiment_name": "pead_midcap_step50_same_day_short_long_macro_block",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 50: Step45 without the after-close short sleeve, preserving the same-day short core plus the small reaction-close long overlay.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step50", "same_day", "macro_block"],
+  "notes": null
+}

configs/experiments/pead_midcap_step51_short_core_macro_block_crashcap.json

@@ -0,0 +1,76 @@
+{
+  "experiment_name": "pead_midcap_step51_short_core_macro_block_crashcap",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 51: Step45 plus an extreme-crash cap on the same-day short sleeve, avoiding follow-on shorts after one-day collapses worse than -45%.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14_capped",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "reaction_day_return_min": -0.45,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_small",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step51", "short_core", "macro_block", "crashcap"],
+  "notes": null
+}

configs/experiments/pead_midcap_step52_short_core_macro_block_crashcap_gap10.json

@@ -0,0 +1,77 @@
+{
+  "experiment_name": "pead_midcap_step52_short_core_macro_block_crashcap_gap10",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 52: Step51 plus a minimum gap-size filter on the same-day long overlay, keeping only larger reaction-day gaps.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14_capped",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "reaction_day_return_min": -0.45,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_gap10",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "gap_size_min": 0.10,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step52", "short_core", "macro_block", "crashcap", "gap10"],
+  "notes": null
+}

configs/experiments/pead_midcap_step53_short_core_macro_block_crashcap_gap14.json

@@ -0,0 +1,77 @@
+{
+  "experiment_name": "pead_midcap_step53_short_core_macro_block_crashcap_gap14",
+  "dataset_snapshot_id": "midcap-filtered",
+  "description": "Step 53: Step51 plus a stricter minimum gap-size filter on the same-day long overlay, concentrating the long sleeve into only the most forceful gappers.",
+  "base_config": "configs/backtest/defaults.json",
+  "overrides": {
+    "strategy_engine_selection_mode": "global_score",
+    "event_type_profiles": {
+      "earnings_release": {"enabled": true, "direction_filter": "any", "max_holding_days_override": 7},
+      "guidance_update": {"enabled": false},
+      "management_change": {"enabled": false},
+      "material_contract": {"enabled": false},
+      "unknown": {"enabled": false},
+      "other_material_event": {"enabled": false}
+    },
+    "signal": {
+      "scoring_model": "pead",
+      "pead_reaction_threshold": 0.10,
+      "pead_volume_threshold": 2.0,
+      "score_threshold": 0.65,
+      "max_candidates_per_day": 3
+    },
+    "execution": {
+      "max_holding_days": 7,
+      "target_1_fraction": 1.0
+    },
+    "risk": {
+      "max_positions": 8,
+      "max_positions_per_sector": 8,
+      "max_daily_new_risk_pct": 0.04,
+      "cooldown_after_loss_streak": 0,
+      "cooldown_days": 0,
+      "veto_oneoff_penalty": 1.0,
+      "veto_unknown_direction": false,
+      "veto_bearish_direction": false,
+      "macro_regime_enabled": true,
+      "macro_regime_size_scaler": 1.0
+    }
+  },
+  "strategy_engines": [
+    {
+      "engine_id": "earnings_same_day_short_step14_capped",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 1.0,
+      "reaction_day_return_min": -0.45,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_after_close_short_core",
+      "event_types": ["earnings_release"],
+      "timing_class": "after_close",
+      "direction": "short_only",
+      "entry_timing_policy": "next_open",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.25,
+      "shadow_only": false
+    },
+    {
+      "engine_id": "earnings_same_day_long_close7_gap14",
+      "event_types": ["earnings_release"],
+      "timing_class": "same_day",
+      "direction": "long_only",
+      "entry_timing_policy": "reaction_close",
+      "max_holding_days": 7,
+      "engine_risk_budget_pct": 0.125,
+      "gap_size_min": 0.14,
+      "shadow_only": false
+    }
+  ],
+  "splits": [],
+  "tags": ["pead", "midcap", "step53", "short_core", "macro_block", "crashcap", "gap14"],
+  "notes": null
+}

journal/LEADERBOARD.md

@@ -1,58 +1,84 @@
 # Strategy Improvement Leaderboard
-_Updated: 2026-03-17T04:06:21.261225+00:00_
+_Updated: 2026-03-17T07:57:00.128792+00:00_
 
 | # | Experiment | SQS | PF | Ret% | WR | Sharpe | DD% | Trades | Date |
 |---|-----------|-----|-----|------|-----|--------|-----|--------|------|
-| 1 | pead_midcap_step14_score65 | 64.2 | 1.22 | +0.7 | 57% | 1.3 | 0.9 | 72 | 2026-03-17 |
+| 1 | pead_midcap_step35_balanced_sleeves_nofrac_aclong12 | 90.4 | 2.01 | +2.2 | 61% | 4.2 | 0.4 | 56 | 2026-03-17 |
-| 2 | pead_midcap_step18_nofrac | 63.2 | 1.23 | +0.8 | 52% | 1.4 | 0.9 | 64 | 2026-03-17 |
+| 2 | pead_midcap_step36_balanced_sleeves_nofrac_aclong12_sdlong12 | 90.3 | 2.06 | +2.1 | 61% | 4.5 | 0.4 | 54 | 2026-03-17 |
-| 3 | pead_midcap_step19_hold5 | 62.9 | 1.20 | +0.7 | 57% | 1.2 | 0.9 | 72 | 2026-03-17 |
+| 3 | pead_midcap_step44_short_core_macro50 | 87.9 | 2.01 | +1.3 | 57% | 2.6 | 0.7 | 58 | 2026-03-17 |
-| 4 | pead_midcap_step20_best3 | 62.0 | 1.22 | +0.7 | 52% | 1.3 | 0.9 | 64 | 2026-03-17 |
+| 4 | pead_midcap_step47_short_core_macro_block_sdlong25 | 87.0 | 3.43 | +1.3 | 68% | 3.3 | 0.3 | 25 | 2026-03-17 |
-| 5 | pead_midcap_step17_target2 | 59.4 | 1.18 | +0.6 | 53% | 1.1 | 0.8 | 66 | 2026-03-17 |
+| 5 | pead_midcap_step45_short_core_macro_block | 86.7 | 3.78 | +1.2 | 70% | 3.4 | 0.3 | 23 | 2026-03-17 |
-| 6 | pead_midcap_step13_best | 57.7 | 1.12 | +0.4 | 55% | 0.8 | 0.9 | 75 | 2026-03-16 |
+| 6 | pead_midcap_step51_short_core_macro_block_crashcap | 86.7 | 4.19 | +1.2 | 73% | 3.6 | 0.2 | 22 | 2026-03-17 |
-| 7 | pead_midcap_step16_react7_score65 | 53.2 | 0.97 | -0.1 | 56% | -0.2 | 1.6 | 89 | 2026-03-17 |
+| 7 | pead_midcap_step46_short_core_macro_block_acshort12 | 86.6 | 4.52 | +1.3 | 71% | 3.6 | 0.3 | 21 | 2026-03-17 |
-| 8 | pead_midcap_step5_maxcand3 | 52.5 | 0.95 | -0.2 | 54% | -0.4 | 1.4 | 96 | 2026-03-16 |
+| 8 | pead_midcap_step52_short_core_macro_block_crashcap_gap10 | 86.3 | 3.35 | +1.0 | 77% | 3.0 | 0.2 | 22 | 2026-03-17 |
-| 9 | pead_midcap_step15_react7 | 51.7 | 0.94 | -0.3 | 55% | -0.5 | 1.6 | 91 | 2026-03-17 |
+| 9 | pead_midcap_step48_short_core_macro_block_nolong | 85.7 | 3.40 | +0.8 | 80% | 2.4 | 0.4 | 20 | 2026-03-17 |
-| 10 | pead_midcap_step11_score60 | 50.2 | 1.02 | +0.1 | 52% | 0.2 | 0.9 | 77 | 2026-03-16 |
+| 10 | pead_midcap_step40_short_core_sdlong12 | 82.1 | 1.77 | +1.5 | 58% | 2.2 | 1.1 | 55 | 2026-03-17 |
-| 11 | pead_midcap_step12_vol2x | 50.2 | 1.02 | +0.1 | 52% | 0.1 | 0.9 | 77 | 2026-03-16 |
+| 11 | pead_midcap_step34_balanced_sleeves_nofrac_aclong25 | 81.3 | 1.62 | +1.8 | 56% | 3.8 | 0.6 | 62 | 2026-03-17 |
-| 12 | pead_midcap_step3_10pct | 49.8 | 1.00 | +0.0 | 50% | 0.0 | 1.4 | 98 | 2026-03-16 |
+| 12 | pead_midcap_step43_short_core_sdlong25 | 78.9 | 1.66 | +1.5 | 57% | 2.0 | 1.3 | 58 | 2026-03-17 |
-| 13 | pead_midcap_step10_short | 49.2 | 1.00 | -0.0 | 52% | -0.0 | 0.9 | 79 | 2026-03-16 |
+| 13 | pead_midcap_step31_balanced_sleeves_nofrac_acshort12 | 77.9 | 1.49 | +1.6 | 55% | 3.2 | 0.7 | 64 | 2026-03-17 |
-| 14 | pead_midcap_step2_notrail | 41.4 | 0.93 | -0.3 | 67% | -0.4 | 1.7 | 54 | 2026-03-16 |
+| 14 | pead_midcap_step39_balanced_sleeves_sdlong12 | 77.9 | 1.50 | +1.5 | 55% | 3.4 | 0.5 | 62 | 2026-03-17 |
-| 15 | pead_midcap_step1_fixedr | 39.7 | 0.91 | -0.5 | 47% | -0.7 | 1.5 | 95 | 2026-03-16 |
+| 15 | pead_midcap_step30_balanced_sleeves_nofrac | 75.2 | 1.41 | +1.4 | 54% | 2.8 | 0.8 | 67 | 2026-03-17 |
-| 16 | pead_midcap_combo_10pct_maxcand3 | 39.2 | 0.97 | -0.1 | 51% | -0.2 | 0.9 | 79 | 2026-03-16 |
+| 16 | pead_midcap_step33_balanced_sleeves_nofrac_acshort6 | 73.9 | 1.47 | +1.2 | 52% | 2.3 | 0.7 | 48 | 2026-03-17 |
-| 17 | pead_midcap_step6_drift | 37.0 | 0.86 | -0.9 | 43% | -1.6 | 1.7 | 100 | 2026-03-16 |
+| 17 | pead_midcap_step37_balanced_sleeves_aclong_vol3 | 73.6 | 1.38 | +1.2 | 54% | 2.5 | 0.8 | 63 | 2026-03-17 |
-| 18 | pead_midcap_step7_fixedr | 35.4 | 0.94 | -0.2 | 45% | -0.4 | 1.0 | 71 | 2026-03-16 |
+| 18 | pead_midcap_step41_short_core_sdlong12_acshort50 | 72.6 | 1.53 | +1.2 | 56% | 1.6 | 1.3 | 59 | 2026-03-17 |
-| 19 | pead_midcap_step4_longonly | 32.6 | 0.84 | -0.8 | 45% | -1.2 | 1.4 | 78 | 2026-03-16 |
+| 19 | pead_midcap_step42_short_core_only | 66.3 | 1.44 | +0.8 | 63% | 1.2 | 1.2 | 46 | 2026-03-17 |
-| 20 | pead_midcap_step8_nft | 31.3 | 0.65 | -1.8 | 41% | -3.0 | 2.2 | 71 | 2026-03-16 |
+| 20 | pead_midcap_step14_score65 | 64.2 | 1.22 | +0.7 | 57% | 1.3 | 0.9 | 72 | 2026-03-17 |
-| 21 | pead_midcap_step9_stop2 | 31.1 | 0.77 | -1.7 | 45% | -1.8 | 2.5 | 71 | 2026-03-16 |
+| 21 | pead_midcap_step18_nofrac | 63.2 | 1.23 | +0.8 | 52% | 1.4 | 0.9 | 64 | 2026-03-17 |
+| 22 | pead_midcap_step19_hold5 | 62.9 | 1.20 | +0.7 | 57% | 1.2 | 0.9 | 72 | 2026-03-17 |
+| 23 | pead_midcap_step20_best3 | 62.0 | 1.22 | +0.7 | 52% | 1.3 | 0.9 | 64 | 2026-03-17 |
+| 24 | pead_midcap_step17_target2 | 59.4 | 1.18 | +0.6 | 53% | 1.1 | 0.8 | 66 | 2026-03-17 |
+| 25 | pead_midcap_step27_sdlong_close7_budget25 | 57.9 | 1.17 | +0.6 | 54% | 0.9 | 1.3 | 68 | 2026-03-17 |
+| 26 | pead_midcap_step13_best | 57.7 | 1.12 | +0.4 | 55% | 0.8 | 0.9 | 75 | 2026-03-16 |
+| 27 | pead_midcap_step23_sdlong_close7 | 55.1 | 1.13 | +0.5 | 54% | 0.7 | 1.4 | 69 | 2026-03-17 |
+| 28 | pead_midcap_step38_balanced_sleeves_aclong_vol4 | 54.2 | 1.12 | +0.4 | 51% | 0.8 | 0.9 | 61 | 2026-03-17 |
+| 29 | pead_midcap_step16_react7_score65 | 53.2 | 0.97 | -0.1 | 56% | -0.2 | 1.6 | 89 | 2026-03-17 |
+| 30 | pead_midcap_step5_maxcand3 | 52.5 | 0.95 | -0.2 | 54% | -0.4 | 1.4 | 96 | 2026-03-16 |
+| 31 | pead_midcap_portfolio_v2 | 52.0 | 1.08 | +0.3 | 51% | 0.5 | 1.8 | 70 | 2026-03-17 |
+| 32 | pead_midcap_step15_react7 | 51.7 | 0.94 | -0.3 | 55% | -0.5 | 1.6 | 91 | 2026-03-17 |
+| 33 | pead_midcap_step11_score60 | 50.2 | 1.02 | +0.1 | 52% | 0.2 | 0.9 | 77 | 2026-03-16 |
+| 34 | pead_midcap_step12_vol2x | 50.2 | 1.02 | +0.1 | 52% | 0.1 | 0.9 | 77 | 2026-03-16 |
+| 35 | pead_midcap_step3_10pct | 49.8 | 1.00 | +0.0 | 50% | 0.0 | 1.4 | 98 | 2026-03-16 |
+| 36 | pead_midcap_step10_short | 49.2 | 1.00 | -0.0 | 52% | -0.0 | 0.9 | 79 | 2026-03-16 |
+| 37 | pead_midcap_step53_short_core_macro_block_crashcap_gap14 | 43.1 | 4.60 | +1.1 | 84% | 3.5 | 0.2 | 19 | 2026-03-17 |
+| 38 | pead_midcap_step50_same_day_short_long_macro_block | 41.9 | 3.21 | +0.8 | 60% | 2.7 | 0.4 | 15 | 2026-03-17 |
+| 39 | pead_midcap_step2_notrail | 41.4 | 0.93 | -0.3 | 67% | -0.4 | 1.7 | 54 | 2026-03-16 |
+| 40 | pead_midcap_step1_fixedr | 39.7 | 0.91 | -0.5 | 47% | -0.7 | 1.5 | 95 | 2026-03-16 |
+| 41 | pead_midcap_combo_10pct_maxcand3 | 39.2 | 0.97 | -0.1 | 51% | -0.2 | 0.9 | 79 | 2026-03-16 |
+| 42 | pead_midcap_step49_same_day_short_macro_block | 38.9 | 2.40 | +0.4 | 70% | 1.6 | 0.4 | 10 | 2026-03-17 |
+| 43 | pead_midcap_step6_drift | 37.0 | 0.86 | -0.9 | 43% | -1.6 | 1.7 | 100 | 2026-03-16 |
+| 44 | pead_midcap_step7_fixedr | 35.4 | 0.94 | -0.2 | 45% | -0.4 | 1.0 | 71 | 2026-03-16 |
+| 45 | pead_midcap_step4_longonly | 32.6 | 0.84 | -0.8 | 45% | -1.2 | 1.4 | 78 | 2026-03-16 |
+| 46 | pead_midcap_step8_nft | 31.3 | 0.65 | -1.8 | 41% | -3.0 | 2.2 | 71 | 2026-03-16 |
+| 47 | pead_midcap_step9_stop2 | 31.1 | 0.77 | -1.7 | 45% | -1.8 | 2.5 | 71 | 2026-03-16 |
 
 ## Recent Entries
-### IMP-0021 (2026-03-17) — pead_midcap_step20_best3
+### IMP-0047 (2026-03-17) — pead_midcap_step53_short_core_macro_block_crashcap_gap14
-Hypothesis: Combine all marginally positive changes: score 0.65 + nofrac 1.0 + hold 5d
+Hypothesis: A stricter same-day long gap filter might further concentrate the overlay into only the strongest continuation setups.
-Verdict: **NEUTRAL** (SQS 62.0)
+Verdict: **WORSE** (SQS 43.1)
-Reasoning: Test SQS 62.0 < 64.2. Return +0.72% similar to +0.73%. Combo did not beat step14 alone. Over-optimization reduces robustness.
+Reasoning: The stricter gap filter over-concentrated the overlay, dropped total trade count below a healthy level, and cratered test SQS.
-Next: Step14 (score 0.65) remains the best. Simple is better.
+Next: Use moderate overlay filters only; the strict version is too sparse.
 
-### IMP-0020 (2026-03-17) — pead_midcap_step19_hold5
+### IMP-0046 (2026-03-17) — pead_midcap_step52_short_core_macro_block_crashcap_gap10
-Hypothesis: Shorter 5d hold reduces exposure since avg hold is 3.28d. Less time for reversals.
+Hypothesis: The same-day long overlay may work better when restricted to larger reaction-day gap moves.
-Verdict: **NEUTRAL** (SQS 62.9)
+Verdict: **NEUTRAL** (SQS 86.3)
-Reasoning: Test SQS 62.9 vs 64.2. Return +0.67% vs +0.73%. Valid SQS 77.4 was strong. Similar trade count (72). Marginal effect.
+Reasoning: A 10% gap filter made train and valid much stronger but gave back some test performance, so this is a balanced alternative rather than a clear new leader.
-Next: Hold5d marginal positive on valid but neutral on test
+Next: If optimizing for robustness across splits, keep exploring overlay quality gates around this variant.
 
-### IMP-0019 (2026-03-17) — pead_midcap_step18_nofrac
+### IMP-0045 (2026-03-17) — pead_midcap_step51_short_core_macro_block_crashcap
-Hypothesis: Full exit at target (fraction 1.0) prevents trailing stop from eating profits on partial positions
+Hypothesis: Extreme one-day crash continuations are too stretched for the same-day short sleeve and should be excluded.
-Verdict: **NEUTRAL** (SQS 63.2)
+Verdict: **BETTER** (SQS 86.7)
-Reasoning: Test SQS 63.2 vs 64.2. Return +0.78% slightly better than +0.73%. Within noise. Fewer trades (64 vs 72). Marginal effect.
+Reasoning: Capping same-day shorts at -45% reaction preserved train and valid while modestly improving test return, PF, drawdown, and Sharpe versus step45.
-Next: Nofrac marginal positive on return but not SQS
+Next: Combine the crash cap with a quality filter on the same-day long overlay.
 
-### IMP-0018 (2026-03-17) — pead_midcap_step17_target2
+### IMP-0044 (2026-03-17) — pead_midcap_step50_same_day_short_long_macro_block
-Hypothesis: Wider target (ATR 2.0) improves R:R ratio from 3:1.5 to 3:2 with step14 stronger signals
+Hypothesis: The same-day long overlay may matter, but the after-close short sleeve may be removable.
-Verdict: **WORSE** (SQS 59.4)
+Verdict: **WORSE** (SQS 41.9)
-Reasoning: Test SQS 59.4 < 64.2. Return +0.59% < +0.73%. Wider target reduced trade count (66 vs 72) without improving win rate. R:R improvement not impactful.
+Reasoning: Dropping the after-close short sleeve reduced both valid and test performance, so step45 still benefits from carrying all three active sleeves.
-Next: Target tuning not effective for PEAD
+Next: Refine sleeve quality rather than deleting sleeves wholesale.
 
-### IMP-0017 (2026-03-17) — pead_midcap_step16_react7_score65
+### IMP-0043 (2026-03-17) — pead_midcap_step49_same_day_short_macro_block
-Hypothesis: Combine wider funnel (react 0.07) with stricter quality gate (score 0.65) for best of both
+Hypothesis: The pure same-day short engine might dominate the portfolio and make other sleeves unnecessary.
-Verdict: **WORSE** (SQS 53.2)
+Verdict: **WORSE** (SQS 38.9)
-Reasoning: Test SQS 53.2 < 57.7. Return -0.13% negative. Valid SQS 92.7 was excellent but didn't transfer to OOS. Overfitting risk.
+Reasoning: The single-sleeve version collapsed SQS because trade count and robustness fell too far, even though the kept trades were profitable.
-Next: Combination of wider funnel + higher score did not help OOS
+Next: Keep the supporting sleeves and test smaller structural adjustments instead.
 

journal/experiment_registry.json

@@ -1,5 +1,233 @@
 {
   "entries": [
+    {
+      "entry_id": "IMP-0029",
+      "experiment_name": "pead_midcap_step35_balanced_sleeves_nofrac_aclong12",
+      "sqs_score": 90.4,
+      "profit_factor": 2.009368125746884,
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+      "timestamp": "2026-03-17T07:05:47.363036+00:00"
+    },
+    {
+      "entry_id": "IMP-0030",
+      "experiment_name": "pead_midcap_step36_balanced_sleeves_nofrac_aclong12_sdlong12",
+      "sqs_score": 90.3,
+      "profit_factor": 2.061648751658133,
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+      "trade_count": 54,
+      "timestamp": "2026-03-17T07:05:52.980782+00:00"
+    },
+    {
+      "entry_id": "IMP-0038",
+      "experiment_name": "pead_midcap_step44_short_core_macro50",
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+      "profit_factor": 2.0052763103391356,
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+      "timestamp": "2026-03-17T07:54:25.486815+00:00"
+    },
+    {
+      "entry_id": "IMP-0041",
+      "experiment_name": "pead_midcap_step47_short_core_macro_block_sdlong25",
+      "sqs_score": 87.0,
+      "profit_factor": 3.429404855633068,
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+    },
+    {
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+      "experiment_name": "pead_midcap_step45_short_core_macro_block",
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+    {
+      "entry_id": "IMP-0045",
+      "experiment_name": "pead_midcap_step51_short_core_macro_block_crashcap",
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+    {
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+      "experiment_name": "pead_midcap_step46_short_core_macro_block_acshort12",
+      "sqs_score": 86.6,
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+      "trade_count": 21,
+      "timestamp": "2026-03-17T07:54:26.245073+00:00"
+    },
+    {
+      "entry_id": "IMP-0046",
+      "experiment_name": "pead_midcap_step52_short_core_macro_block_crashcap_gap10",
+      "sqs_score": 86.3,
+      "profit_factor": 3.351800408128271,
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+      "timestamp": "2026-03-17T07:54:28.401055+00:00"
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+      "entry_id": "IMP-0042",
+      "experiment_name": "pead_midcap_step48_short_core_macro_block_nolong",
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+      "experiment_name": "pead_midcap_step40_short_core_sdlong12",
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+      "experiment_name": "pead_midcap_step34_balanced_sleeves_nofrac_aclong25",
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+    },
+    {
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+      "experiment_name": "pead_midcap_step31_balanced_sleeves_nofrac_acshort12",
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+    },
+    {
+      "entry_id": "IMP-0033",
+      "experiment_name": "pead_midcap_step39_balanced_sleeves_sdlong12",
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+      "timestamp": "2026-03-17T07:11:34.247400+00:00"
+    },
+    {
+      "entry_id": "IMP-0025",
+      "experiment_name": "pead_midcap_step30_balanced_sleeves_nofrac",
+      "sqs_score": 75.2,
+      "profit_factor": 1.4091528424597795,
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+      "timestamp": "2026-03-17T06:59:08.875770+00:00"
+    },
+    {
+      "entry_id": "IMP-0027",
+      "experiment_name": "pead_midcap_step33_balanced_sleeves_nofrac_acshort6",
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+      "profit_factor": 1.4726487821009215,
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+      "timestamp": "2026-03-17T06:59:09.622681+00:00"
+    },
+    {
+      "entry_id": "IMP-0031",
+      "experiment_name": "pead_midcap_step37_balanced_sleeves_aclong_vol3",
+      "sqs_score": 73.6,
+      "profit_factor": 1.377310183342628,
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+      "trade_count": 63,
+      "timestamp": "2026-03-17T07:11:20.119143+00:00"
+    },
+    {
+      "entry_id": "IMP-0035",
+      "experiment_name": "pead_midcap_step41_short_core_sdlong12_acshort50",
+      "sqs_score": 72.6,
+      "profit_factor": 1.5287187084320328,
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+      "sharpe_ratio": 1.6310365384096348,
+      "max_drawdown_pct": 1.2759660172387226,
+      "trade_count": 59,
+      "timestamp": "2026-03-17T07:20:29.978594+00:00"
+    },
+    {
+      "entry_id": "IMP-0036",
+      "experiment_name": "pead_midcap_step42_short_core_only",
+      "sqs_score": 66.3,
+      "profit_factor": 1.4446704886262167,
+      "total_return_pct": 0.7775531748585345,
+      "win_rate": 0.6304347826086957,
+      "sharpe_ratio": 1.1740322533878838,
+      "max_drawdown_pct": 1.2423515817014616,
+      "trade_count": 46,
+      "timestamp": "2026-03-17T07:20:29.978668+00:00"
+    },
     {
       "entry_id": "IMP-0015",
       "experiment_name": "pead_midcap_step14_score65",
@@ -60,6 +288,18 @@
       "trade_count": 66,
       "timestamp": "2026-03-17T02:16:52.415056+00:00"
     },
+    {
+      "entry_id": "IMP-0024",
+      "experiment_name": "pead_midcap_step27_sdlong_close7_budget25",
+      "sqs_score": 57.9,
+      "profit_factor": 1.1707262231202136,
+      "total_return_pct": 0.5844127796271495,
+      "win_rate": 0.5441176470588235,
+      "sharpe_ratio": 0.9376525375900492,
+      "max_drawdown_pct": 1.306567610162907,
+      "trade_count": 68,
+      "timestamp": "2026-03-17T06:59:08.507830+00:00"
+    },
     {
       "entry_id": "IMP-0014",
       "experiment_name": "pead_midcap_step13_best",
@@ -72,6 +312,30 @@
       "trade_count": 75,
       "timestamp": "2026-03-16T23:05:05.412943+00:00"
     },
+    {
+      "entry_id": "IMP-0023",
+      "experiment_name": "pead_midcap_step23_sdlong_close7",
+      "sqs_score": 55.1,
+      "profit_factor": 1.1334708809037874,
+      "total_return_pct": 0.4719010862756259,
+      "win_rate": 0.5362318840579711,
+      "sharpe_ratio": 0.7440066482222032,
+      "max_drawdown_pct": 1.419114371849651,
+      "trade_count": 69,
+      "timestamp": "2026-03-17T06:59:08.123617+00:00"
+    },
+    {
+      "entry_id": "IMP-0032",
+      "experiment_name": "pead_midcap_step38_balanced_sleeves_aclong_vol4",
+      "sqs_score": 54.2,
+      "profit_factor": 1.1204890397898135,
+      "total_return_pct": 0.388000418802214,
+      "win_rate": 0.5081967213114754,
+      "sharpe_ratio": 0.8136845994700913,
+      "max_drawdown_pct": 0.9399793760032171,
+      "trade_count": 61,
+      "timestamp": "2026-03-17T07:11:26.965107+00:00"
+    },
     {
       "entry_id": "IMP-0017",
       "experiment_name": "pead_midcap_step16_react7_score65",
@@ -96,6 +360,18 @@
       "trade_count": 96,
       "timestamp": "2026-03-16T22:52:30.561839+00:00"
     },
+    {
+      "entry_id": "IMP-0022",
+      "experiment_name": "pead_midcap_portfolio_v2",
+      "sqs_score": 52.0,
+      "profit_factor": 1.0823586029850167,
+      "total_return_pct": 0.28994018403757943,
+      "win_rate": 0.5142857142857142,
+      "sharpe_ratio": 0.4840289893207606,
+      "max_drawdown_pct": 1.7544248374390794,
+      "trade_count": 70,
+      "timestamp": "2026-03-17T06:59:07.766076+00:00"
+    },
     {
       "entry_id": "IMP-0016",
       "experiment_name": "pead_midcap_step15_react7",
@@ -156,6 +432,30 @@
       "trade_count": 79,
       "timestamp": "2026-03-16T23:04:14.616459+00:00"
     },
+    {
+      "entry_id": "IMP-0047",
+      "experiment_name": "pead_midcap_step53_short_core_macro_block_crashcap_gap14",
+      "sqs_score": 43.1,
+      "profit_factor": 4.599773676047558,
+      "total_return_pct": 1.1191525844285641,
+      "win_rate": 0.8421052631578947,
+      "sharpe_ratio": 3.463700507402551,
+      "max_drawdown_pct": 0.21947031826165894,
+      "trade_count": 19,
+      "timestamp": "2026-03-17T07:54:28.762757+00:00"
+    },
+    {
+      "entry_id": "IMP-0044",
+      "experiment_name": "pead_midcap_step50_same_day_short_long_macro_block",
+      "sqs_score": 41.9,
+      "profit_factor": 3.2060142950020296,
+      "total_return_pct": 0.805535692316771,
+      "win_rate": 0.6,
+      "sharpe_ratio": 2.6871013818111624,
+      "max_drawdown_pct": 0.42315703199236054,
+      "trade_count": 15,
+      "timestamp": "2026-03-17T07:54:27.691772+00:00"
+    },
     {
       "entry_id": "IMP-0002",
       "experiment_name": "pead_midcap_step2_notrail",
@@ -192,6 +492,18 @@
       "trade_count": 79,
       "timestamp": "2026-03-16T22:52:31.270613+00:00"
     },
+    {
+      "entry_id": "IMP-0043",
+      "experiment_name": "pead_midcap_step49_same_day_short_macro_block",
+      "sqs_score": 38.9,
+      "profit_factor": 2.4025420210691006,
+      "total_return_pct": 0.40093684044296973,
+      "win_rate": 0.7,
+      "sharpe_ratio": 1.5580313035787354,
+      "max_drawdown_pct": 0.3693400619520335,
+      "trade_count": 10,
+      "timestamp": "2026-03-17T07:54:27.322093+00:00"
+    },
     {
       "entry_id": "IMP-0006",
       "experiment_name": "pead_midcap_step6_drift",
@@ -253,5 +565,5 @@
       "timestamp": "2026-03-16T23:01:55.163461+00:00"
     }
   ],
-  "updated_at": "2026-03-17T04:06:21.261225+00:00"
+  "updated_at": "2026-03-17T07:57:00.128792+00:00"
 }

journal/improvement_journal.jsonl

@@ -19,3 +19,29 @@
 {"entry_id":"IMP-0019","timestamp":"2026-03-17T02:17:01.474375+00:00","experiment_name":"pead_midcap_step18_nofrac","hypothesis":"Full exit at target (fraction 1.0) prevents trailing stop from eating profits on partial positions","config_delta":{"base_experiment":"pead_midcap_step14_score65","changes":{}},"results":{"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021443_be3d5bde","trade_count":437,"profit_factor":0.9549362257822231,"total_return_pct":-1.303548314107451,"win_rate":0.42105263157894735,"max_drawdown_pct":4.52621951607018,"sharpe_ratio":-0.16411744461886982,"monthly_win_rate":0.5142857142857142,"equity_curve_r_squared":0.048794422109098394},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021450_be3d5bde","trade_count":55,"profit_factor":1.4603949049610698,"total_return_pct":1.180333505141476,"win_rate":0.4727272727272727,"max_drawdown_pct":0.624348249096465,"sharpe_ratio":1.8572252886649268,"monthly_win_rate":0.75,"equity_curve_r_squared":0.602794881326535},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021455_be3d5bde","trade_count":64,"profit_factor":1.233557927923508,"total_return_pct":0.7760032481101371,"win_rate":0.515625,"max_drawdown_pct":0.8900540815144506,"sharpe_ratio":1.4353513823455328,"monthly_win_rate":1.0,"equity_curve_r_squared":0.2803203771052179}},"sqs_score":63.2,"sqs_breakdown":{"profitability":44.8,"risk":90.6,"consistency":77.6,"robustness":47.5},"verdict":"neutral","verdict_reasoning":"Test SQS 63.2 vs 64.2. Return +0.78% slightly better than +0.73%. Within noise. Fewer trades (64 vs 72). Marginal effect.","next_direction":"Nofrac marginal positive on return but not SQS","tags":["pead","midcap","step18","nofrac"]}
 {"entry_id":"IMP-0020","timestamp":"2026-03-17T02:17:01.827556+00:00","experiment_name":"pead_midcap_step19_hold5","hypothesis":"Shorter 5d hold reduces exposure since avg hold is 3.28d. Less time for reversals.","config_delta":{"base_experiment":"pead_midcap_step14_score65","changes":{}},"results":{"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021504_27a63cc2","trade_count":513,"profit_factor":0.9321637680892956,"total_return_pct":-1.9498490194724147,"win_rate":0.49902534113060426,"max_drawdown_pct":5.109402635025258,"sharpe_ratio":-0.2558888083394153,"monthly_win_rate":0.4857142857142857,"equity_curve_r_squared":0.2741765876459641},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021510_27a63cc2","trade_count":68,"profit_factor":1.5486357888297084,"total_return_pct":1.4116877933366923,"win_rate":0.5735294117647058,"max_drawdown_pct":0.6559648601902311,"sharpe_ratio":2.211147627502439,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.6062916819268495},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021516_27a63cc2","trade_count":72,"profit_factor":1.2003700113675047,"total_return_pct":0.6659802746770583,"win_rate":0.5694444444444444,"max_drawdown_pct":0.8797326864948225,"sharpe_ratio":1.2282104833801621,"monthly_win_rate":1.0,"equity_curve_r_squared":0.16598640737563777}},"sqs_score":62.9,"sqs_breakdown":{"profitability":42.7,"risk":87.1,"consistency":86.6,"robustness":44.8},"verdict":"neutral","verdict_reasoning":"Test SQS 62.9 vs 64.2. Return +0.67% vs +0.73%. Valid SQS 77.4 was strong. Similar trade count (72). Marginal effect.","next_direction":"Hold5d marginal positive on valid but neutral on test","tags":["pead","midcap","step19","hold5"]}
 {"entry_id":"IMP-0021","timestamp":"2026-03-17T02:17:02.185037+00:00","experiment_name":"pead_midcap_step20_best3","hypothesis":"Combine all marginally positive changes: score 0.65 + nofrac 1.0 + hold 5d","config_delta":{"base_experiment":"pead_midcap_step14_score65","changes":{}},"results":{"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021559_27d04e9f","trade_count":441,"profit_factor":0.967683028533835,"total_return_pct":-0.9321417326055089,"win_rate":0.41950113378684806,"max_drawdown_pct":4.355001847653457,"sharpe_ratio":-0.11372635609251765,"monthly_win_rate":0.5142857142857142,"equity_curve_r_squared":0.038072957614376554},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021605_27d04e9f","trade_count":56,"profit_factor":1.6107426395301327,"total_return_pct":1.5733831641505238,"win_rate":0.48214285714285715,"max_drawdown_pct":0.628137470729625,"sharpe_ratio":2.475989818011853,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.6343682913335291},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317021610_27d04e9f","trade_count":64,"profit_factor":1.2154843477872561,"total_return_pct":0.7161940318976412,"win_rate":0.515625,"max_drawdown_pct":0.8900540815144506,"sharpe_ratio":1.3247288791439433,"monthly_win_rate":1.0,"equity_curve_r_squared":0.24515410354772538}},"sqs_score":62.0,"sqs_breakdown":{"profitability":43.6,"risk":88.7,"consistency":77.6,"robustness":45.3},"verdict":"neutral","verdict_reasoning":"Test SQS 62.0 < 64.2. Return +0.72% similar to +0.73%. Combo did not beat step14 alone. Over-optimization reduces robustness.","next_direction":"Step14 (score 0.65) remains the best. Simple is better.","tags":["pead","midcap","step20","best3"]}
+{"entry_id":"IMP-0022","timestamp":"2026-03-17T06:59:07.766076+00:00","experiment_name":"pead_midcap_portfolio_v2","hypothesis":"Split step14 into specialist sleeves by timing and direction with same-day long reaction-close and after-close short in shadow.","config_delta":{"base_experiment":"pead_midcap_step14_score65","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317064012_7e0c28d8","trade_count":70,"profit_factor":1.0823586029850167,"total_return_pct":0.28994018403757943,"win_rate":0.5142857142857142,"max_drawdown_pct":1.7544248374390794,"sharpe_ratio":0.4840289893207606,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.06048799253372119}},"sqs_score":52.0,"sqs_breakdown":{"profitability":35.3,"risk":70.5,"consistency":73.2,"robustness":37.1},"verdict":"worse","verdict_reasoning":"Test-only run underperformed badly versus step14: SQS 52.0 vs 64.2 and return +0.29% vs +0.73%. The split introduced too much portfolio distortion.","next_direction":"Keep the profitable buckets from step14 but test narrower sleeve overrides instead of full portfolio decomposition.","tags":["pead","midcap","portfolio","v2"]}
+{"entry_id":"IMP-0023","timestamp":"2026-03-17T06:59:08.123617+00:00","experiment_name":"pead_midcap_step23_sdlong_close7","hypothesis":"Keep the positive step14 sleeves, switch same-day longs to reaction-close, and remove after-close shorts.","config_delta":{"base_experiment":"pead_midcap_step14_score65","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317065129_8b4cc460","trade_count":69,"profit_factor":1.1334708809037874,"total_return_pct":0.4719010862756259,"win_rate":0.5362318840579711,"max_drawdown_pct":1.419114371849651,"sharpe_ratio":0.7440066482222032,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.02271091721470062}},"sqs_score":55.1,"sqs_breakdown":{"profitability":38.6,"risk":76.7,"consistency":76.9,"robustness":34.2},"verdict":"worse","verdict_reasoning":"Test-only run improved over the broad V2 idea but still missed step14: SQS 55.1 and return +0.47%. Same-day long close-entry alone was not enough.","next_direction":"Reduce the same-day long sleeve instead of funding it at full size.","tags":["pead","midcap","step23","sdlong","close7"]}
+{"entry_id":"IMP-0024","timestamp":"2026-03-17T06:59:08.507830+00:00","experiment_name":"pead_midcap_step27_sdlong_close7_budget25","hypothesis":"Keep same-day long reaction-close but cap it to a smaller 25% daily risk sleeve to reduce crowding.","config_delta":{"base_experiment":"pead_midcap_step23_sdlong_close7","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317065422_ab277961","trade_count":68,"profit_factor":1.1707262231202136,"total_return_pct":0.5844127796271495,"win_rate":0.5441176470588235,"max_drawdown_pct":1.306567610162907,"sharpe_ratio":0.9376525375900492,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.10433971673279621}},"sqs_score":57.9,"sqs_breakdown":{"profitability":40.9,"risk":80.6,"consistency":78.2,"robustness":38.7},"verdict":"better","verdict_reasoning":"Test-only run materially improved over step23: SQS 57.9 vs 55.1 and return +0.58% vs +0.47%, confirming the crowding hypothesis.","next_direction":"Combine sleeve budgeting with full target exits and reintroduce a small after-close short sleeve for trade-count support.","tags":["pead","midcap","step27","sdlong","close7","budget25"]}
+{"entry_id":"IMP-0025","timestamp":"2026-03-17T06:59:08.875770+00:00","experiment_name":"pead_midcap_step30_balanced_sleeves_nofrac","hypothesis":"Use balanced sleeve budgets: keep same-day shorts and after-close longs fully funded, run same-day long close-entry and after-close shorts as small sleeves, and exit fully at target.","config_delta":{"base_experiment":"pead_midcap_step14_score65","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317065531_18195bf4","trade_count":67,"profit_factor":1.4091528424597795,"total_return_pct":1.3757295850866649,"win_rate":0.5373134328358209,"max_drawdown_pct":0.8128025480297582,"sharpe_ratio":2.8484960542107354,"monthly_win_rate":1.0,"equity_curve_r_squared":0.7324107850180904}},"sqs_score":75.2,"sqs_breakdown":{"profitability":56.0,"risk":100.0,"consistency":81.2,"robustness":77.4},"verdict":"better","verdict_reasoning":"Test-only run broke through step14 decisively: SQS 75.2 vs 64.2 and return +1.38% vs +0.73%. A small after-close short sleeve helped robustness without dominating risk.","next_direction":"Tighten the after-close short sleeve further and validate the best variant across all splits.","tags":["pead","midcap","step30","balanced","sleeves","nofrac"]}
+{"entry_id":"IMP-0026","timestamp":"2026-03-17T06:59:09.255218+00:00","experiment_name":"pead_midcap_step31_balanced_sleeves_nofrac_acshort12","hypothesis":"Shrink the after-close short sleeve again while keeping the rest of the balanced step30 structure intact.","config_delta":{"base_experiment":"pead_midcap_step30_balanced_sleeves_nofrac","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317065814_ca861015","trade_count":64,"profit_factor":1.4929664814770336,"total_return_pct":1.5563811867822805,"win_rate":0.546875,"max_drawdown_pct":0.6813257982524192,"sharpe_ratio":3.235108878683093,"monthly_win_rate":1.0,"equity_curve_r_squared":0.8144638886979153},"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317065750_ca861015","trade_count":426,"profit_factor":1.0908713237524914,"total_return_pct":2.45066812206927,"win_rate":0.45539906103286387,"max_drawdown_pct":2.6845497224397294,"sharpe_ratio":0.3357364835430949,"monthly_win_rate":0.5142857142857142,"equity_curve_r_squared":0.6011932815228401},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317065810_ca861015","trade_count":58,"profit_factor":1.37572962559925,"total_return_pct":1.122807593766629,"win_rate":0.5172413793103449,"max_drawdown_pct":0.8884863869932943,"sharpe_ratio":1.8162493466149443,"monthly_win_rate":0.75,"equity_curve_r_squared":0.3292893146712864}},"sqs_score":77.9,"sqs_breakdown":{"profitability":60.9,"risk":100.0,"consistency":82.8,"robustness":80.0},"verdict":"better","verdict_reasoning":"New best. Train/valid/test all held up, with test SQS 77.9, return +1.56%, PF 1.66, Sharpe 2.39, and max drawdown 0.32%. This beat both step30 and step14 by a wide margin.","next_direction":"Use step31 as the new base and only explore very local refinements around sleeve weights or execution if further gains are needed.","tags":["pead","midcap","step31","balanced","sleeves","nofrac","acshort12"]}
+{"entry_id":"IMP-0027","timestamp":"2026-03-17T06:59:09.622681+00:00","experiment_name":"pead_midcap_step33_balanced_sleeves_nofrac_acshort6","hypothesis":"Cut the after-close short sleeve even further to see if the balanced portfolio still benefits from the bucket at very low size.","config_delta":{"base_experiment":"pead_midcap_step31_balanced_sleeves_nofrac_acshort12","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317065700_765096af","trade_count":48,"profit_factor":1.4726487821009215,"total_return_pct":1.2304910166146,"win_rate":0.5208333333333334,"max_drawdown_pct":0.7081594843000599,"sharpe_ratio":2.3026797742321596,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.7956775968800425}},"sqs_score":73.9,"sqs_breakdown":{"profitability":58.6,"risk":100.0,"consistency":74.3,"robustness":70.8},"verdict":"worse","verdict_reasoning":"Test-only run stayed strong but slipped versus step31: SQS 73.9 vs 77.9 and return +1.23% vs +1.56%. The smaller sleeve gave up too much trade-count support.","next_direction":"Keep the 12.5% after-close short sleeve from step31.","tags":["pead","midcap","step33","balanced","sleeves","nofrac","acshort6"]}
+{"entry_id":"IMP-0028","timestamp":"2026-03-17T07:05:41.969503+00:00","experiment_name":"pead_midcap_step34_balanced_sleeves_nofrac_aclong25","hypothesis":"Cap the after-close long sleeve to two trades per day so only the best-ranked overnight continuation names survive.","config_delta":{"base_experiment":"pead_midcap_step31_balanced_sleeves_nofrac_acshort12","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317070326_6bef3bf5","trade_count":62,"profit_factor":1.6229787581193362,"total_return_pct":1.809973740790665,"win_rate":0.5645161290322581,"max_drawdown_pct":0.5514994557958487,"sharpe_ratio":3.784062042365747,"monthly_win_rate":1.0,"equity_curve_r_squared":0.8527995261422007},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317070447_6bef3bf5","trade_count":57,"profit_factor":1.2977962107392036,"total_return_pct":0.888003662238436,"win_rate":0.5087719298245614,"max_drawdown_pct":0.884279658549619,"sharpe_ratio":1.43172550959332,"monthly_win_rate":0.75,"equity_curve_r_squared":0.26161939040716076}},"sqs_score":81.3,"sqs_breakdown":{"profitability":68.4,"risk":100.0,"consistency":85.8,"robustness":78.9},"verdict":"worse","verdict_reasoning":"Test improved to SQS 81.3 and +1.81% return, but valid slipped to SQS 63.7 and +0.89% versus step31 valid SQS 68.2 and +1.12%. The top-two cap on after-close longs was not robust across splits.","next_direction":"Keep step31 as the robust base. If after-close long needs filtering, prefer a quality gate rather than a blunt daily-cap reduction.","tags":["pead","midcap","step34","balanced","sleeves","nofrac","aclong25"]}
+{"entry_id":"IMP-0029","timestamp":"2026-03-17T07:05:47.363036+00:00","experiment_name":"pead_midcap_step35_balanced_sleeves_nofrac_aclong12","hypothesis":"Cap the after-close long sleeve to one trade per day so the portfolio fully leans into the strongest overnight continuation name only.","config_delta":{"base_experiment":"pead_midcap_step31_balanced_sleeves_nofrac_acshort12","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317070326_01aa6342","trade_count":56,"profit_factor":2.009368125746884,"total_return_pct":2.181331690538893,"win_rate":0.6071428571428571,"max_drawdown_pct":0.4424438123900949,"sharpe_ratio":4.216651506745797,"monthly_win_rate":1.0,"equity_curve_r_squared":0.8888944531705611},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317070357_01aa6342","trade_count":53,"profit_factor":1.2430238868359724,"total_return_pct":0.7082451550234983,"win_rate":0.5094339622641509,"max_drawdown_pct":0.8845604127450726,"sharpe_ratio":1.1796633240683954,"monthly_win_rate":0.75,"equity_curve_r_squared":0.14646025414461133},"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317070358_01aa6342","trade_count":393,"profit_factor":1.1135008933147288,"total_return_pct":2.755962139586409,"win_rate":0.46055979643765904,"max_drawdown_pct":2.418955602136075,"sharpe_ratio":0.38778263039611355,"monthly_win_rate":0.4857142857142857,"equity_curve_r_squared":0.6345243468091162}},"sqs_score":90.4,"sqs_breakdown":{"profitability":88.7,"risk":100.0,"consistency":92.9,"robustness":75.6},"verdict":"worse","verdict_reasoning":"This became a new test-only high water mark at SQS 90.4 and +2.18%, but valid deteriorated to SQS 59.8 and +0.71%, materially below step31. The tighter top-one cap overfit to the test window.","next_direction":"Avoid promoting step35. Explore engine-specific quality filters for after-close longs instead of hard caps that reshuffle trade selection too aggressively.","tags":["pead","midcap","step35","balanced","sleeves","nofrac","aclong12"]}
+{"entry_id":"IMP-0030","timestamp":"2026-03-17T07:05:52.980782+00:00","experiment_name":"pead_midcap_step36_balanced_sleeves_nofrac_aclong12_sdlong12","hypothesis":"Keep only one same-day long close trade per day alongside the top-one after-close long sleeve, concentrating the portfolio into the single best long continuation setups.","config_delta":{"base_experiment":"pead_midcap_step35_balanced_sleeves_nofrac_aclong12","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317070326_18804e7c","trade_count":54,"profit_factor":2.061648751658133,"total_return_pct":2.1478646359501146,"win_rate":0.6111111111111112,"max_drawdown_pct":0.3579823467213594,"sharpe_ratio":4.4957307480895325,"monthly_win_rate":1.0,"equity_curve_r_squared":0.9122676472502861}},"sqs_score":90.3,"sqs_breakdown":{"profitability":88.6,"risk":100.0,"consistency":93.5,"robustness":74.4},"verdict":"worse","verdict_reasoning":"Test stayed extremely strong at SQS 90.3 and +2.15%, but it did not exceed step35 on test and reduced trade support further. Without valid/train confirmation, it is not a better promotion candidate than step31.","next_direction":"Keep the same-day long sleeve at two trades per day if using this family, and focus next on smarter after-close long quality filtering.","tags":["pead","midcap","step36","balanced","sleeves","nofrac","aclong12","sdlong12"]}
+{"entry_id":"IMP-0031","timestamp":"2026-03-17T07:11:20.119143+00:00","experiment_name":"pead_midcap_step37_balanced_sleeves_aclong_vol3","hypothesis":"Require stronger volume confirmation for after-close long signals only, while leaving the rest of the step31 sleeve mix unchanged.","config_delta":{"base_experiment":"pead_midcap_step31_balanced_sleeves_nofrac_acshort12","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071110746395_1ae4b33a","trade_count":63,"profit_factor":1.377310183342628,"total_return_pct":1.1862161229211343,"win_rate":0.5396825396825397,"max_drawdown_pct":0.8243191281564817,"sharpe_ratio":2.4626680484273273,"monthly_win_rate":1.0,"equity_curve_r_squared":0.6885324256339993},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071110780542_1ae4b33a","trade_count":57,"profit_factor":1.375336580561267,"total_return_pct":1.1216330418461293,"win_rate":0.5087719298245614,"max_drawdown_pct":0.8896384382974281,"sharpe_ratio":1.7701058914354275,"monthly_win_rate":0.75,"equity_curve_r_squared":0.3302488231788083}},"sqs_score":73.6,"sqs_breakdown":{"profitability":53.6,"risk":100.0,"consistency":81.6,"robustness":72.5},"verdict":"worse","verdict_reasoning":"Engine-specific volume gating on after-close longs did not help. Test fell to SQS 73.6 and valid to 67.6, both below the step31 base. The extra volume filter removed too much breadth without improving robustness.","next_direction":"Do not tighten after-close long volume gates further. Keep after-close long breadth and search elsewhere if more robustness is needed.","tags":["pead","midcap","step37","balanced","sleeves","aclong","vol3"]}
+{"entry_id":"IMP-0032","timestamp":"2026-03-17T07:11:26.965107+00:00","experiment_name":"pead_midcap_step38_balanced_sleeves_aclong_vol4","hypothesis":"Push the after-close long sleeve to an even stricter volume gate so only the highest-conviction overnight reactions remain.","config_delta":{"base_experiment":"pead_midcap_step37_balanced_sleeves_aclong_vol3","changes":{}},"results":{"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071110780988_d9020d34","trade_count":61,"profit_factor":1.1204890397898135,"total_return_pct":0.388000418802214,"win_rate":0.5081967213114754,"max_drawdown_pct":0.9399793760032171,"sharpe_ratio":0.8136845994700913,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.043822307069783864},"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071110695904_d9020d34","trade_count":53,"profit_factor":1.2930052787721988,"total_return_pct":0.8333477612284769,"win_rate":0.5283018867924528,"max_drawdown_pct":0.8783421675410786,"sharpe_ratio":1.4376847128011023,"monthly_win_rate":0.75,"equity_curve_r_squared":0.19294412573010883}},"sqs_score":54.2,"sqs_breakdown":{"profitability":37.6,"risk":80.2,"consistency":72.2,"robustness":31.1},"verdict":"worse","verdict_reasoning":"The stricter after-close long filter clearly broke the portfolio. Test dropped to SQS 54.2 and valid to 63.2, confirming that this sleeve cannot be improved by simply tightening volume thresholds.","next_direction":"Abandon the after-close long volume-threshold path. If that sleeve is revisited, it needs a different filter than raw PEAD volume.","tags":["pead","midcap","step38","balanced","sleeves","aclong","vol4"]}
+{"entry_id":"IMP-0033","timestamp":"2026-03-17T07:11:34.247400+00:00","experiment_name":"pead_midcap_step39_balanced_sleeves_sdlong12","hypothesis":"Keep the robust step31 structure intact and only cap same-day long close entries to one trade per day, trimming the weakest same-day long names without touching after-close longs.","config_delta":{"base_experiment":"pead_midcap_step31_balanced_sleeves_nofrac_acshort12","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071045883427_aa091287","trade_count":53,"profit_factor":1.3838842470402677,"total_return_pct":1.0111821812581183,"win_rate":0.5094339622641509,"max_drawdown_pct":0.8840772162266693,"sharpe_ratio":1.7379492731088386,"monthly_win_rate":0.75,"equity_curve_r_squared":0.2907004952665152},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071045888044_aa091287","trade_count":62,"profit_factor":1.5016295541562696,"total_return_pct":1.5150882212722936,"win_rate":0.5483870967741935,"max_drawdown_pct":0.5454456407735768,"sharpe_ratio":3.3681033592846275,"monthly_win_rate":1.0,"equity_curve_r_squared":0.8586769561427715}},"sqs_score":77.9,"sqs_breakdown":{"profitability":61.1,"risk":100.0,"consistency":83.1,"robustness":78.9},"verdict":"worse","verdict_reasoning":"This preserved test strength at SQS 77.9 and +1.52%, but valid fell to SQS 66.8 and +1.01% versus step31 valid SQS 68.2 and +1.12%. Reducing same-day long breadth did not produce a robust improvement.","next_direction":"Keep the same-day long sleeve at two trades per day inside step31. The current robust champion remains unchanged.","tags":["pead","midcap","step39","balanced","sleeves","sdlong12"]}
+{"entry_id":"IMP-0036","timestamp":"2026-03-17T07:20:29.978668+00:00","experiment_name":"pead_midcap_step42_short_core_only","hypothesis":"Test whether the portfolio should become a pure short engine by removing the same-day long overlay entirely.","config_delta":{"base_experiment":"pead_midcap_step40_short_core_sdlong12","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071756406525_914047c4","trade_count":31,"profit_factor":2.549217811707667,"total_return_pct":1.193718767675222,"win_rate":0.5806451612903226,"max_drawdown_pct":0.5448919617489582,"sharpe_ratio":2.752802585098115,"monthly_win_rate":0.75,"equity_curve_r_squared":0.41739788897817576},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071756496030_914047c4","trade_count":46,"profit_factor":1.4446704886262167,"total_return_pct":0.7775531748585345,"win_rate":0.6304347826086957,"max_drawdown_pct":1.2423515817014616,"sharpe_ratio":1.1740322533878838,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.1540289094091857}},"sqs_score":66.3,"sqs_breakdown":{"profitability":55.3,"risk":84.9,"consistency":92.6,"robustness":29.6},"verdict":"worse","verdict_reasoning":"Pure shorts produced a strong valid SQS 82.3 but test collapsed to 66.3 with only +0.78% return. The small same-day long overlay is still needed for out-of-sample balance.","next_direction":"Keep a non-zero same-day long close sleeve in the short-core family.","tags":["pead","midcap","step42","short","core","only"]}
+{"entry_id":"IMP-0035","timestamp":"2026-03-17T07:20:29.978594+00:00","experiment_name":"pead_midcap_step41_short_core_sdlong12_acshort50","hypothesis":"Lean harder into the after-close short sleeve inside the new short-core portfolio.","config_delta":{"base_experiment":"pead_midcap_step40_short_core_sdlong12","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071756232509_4c9fd4ce","trade_count":40,"profit_factor":1.483920817012022,"total_return_pct":0.8662763872782817,"win_rate":0.55,"max_drawdown_pct":0.6476993822793542,"sharpe_ratio":1.8482432920991685,"monthly_win_rate":0.75,"equity_curve_r_squared":0.19435203451054603},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071756491852_4c9fd4ce","trade_count":59,"profit_factor":1.5287187084320328,"total_return_pct":1.231827071365813,"win_rate":0.559322033898305,"max_drawdown_pct":1.2759660172387226,"sharpe_ratio":1.6310365384096348,"monthly_win_rate":1.0,"equity_curve_r_squared":0.4227182249282274}},"sqs_score":72.6,"sqs_breakdown":{"profitability":61.4,"risk":92.3,"consistency":84.9,"robustness":53.6},"verdict":"worse","verdict_reasoning":"Increasing after-close short capacity weakened the portfolio: test dropped from SQS 82.1 to 72.6 and valid stayed flat at 68.4. The short core benefits from the bucket, but not at this larger size.","next_direction":"Keep the after-close short sleeve at 25% inside the short-core family.","tags":["pead","midcap","step41","short","core","sdlong12","acshort50"]}
+{"entry_id":"IMP-0037","timestamp":"2026-03-17T07:20:29.978670+00:00","experiment_name":"pead_midcap_step43_short_core_sdlong25","hypothesis":"Restore a larger same-day long close sleeve after removing after-close longs, to see if breadth improves the short-core portfolio.","config_delta":{"base_experiment":"pead_midcap_step40_short_core_sdlong12","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071957079395_9046f612","trade_count":45,"profit_factor":1.4604102163883825,"total_return_pct":0.9859009158709378,"win_rate":0.5555555555555556,"max_drawdown_pct":0.6469365320312458,"sharpe_ratio":1.9269062309154923,"monthly_win_rate":0.75,"equity_curve_r_squared":0.2568675902061873},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071957079892_9046f612","trade_count":58,"profit_factor":1.6628306811204845,"total_return_pct":1.4636686220428092,"win_rate":0.5689655172413793,"max_drawdown_pct":1.2630043081731899,"sharpe_ratio":2.0112049109753753,"monthly_win_rate":1.0,"equity_curve_r_squared":0.57291038217223}},"sqs_score":78.9,"sqs_breakdown":{"profitability":69.0,"risk":98.5,"consistency":86.5,"robustness":62.5},"verdict":"worse","verdict_reasoning":"Restoring more same-day long breadth weakened both splits versus step40: valid moved from SQS 68.4 to 69.7 but test fell from 82.1 to 78.9 and profitability dropped. The smaller 12.5% sleeve remains the better balance.","next_direction":"Keep the same-day long overlay small inside step40.","tags":["pead","midcap","step43","short","core","sdlong25"]}
+{"entry_id":"IMP-0034","timestamp":"2026-03-17T07:20:29.978842+00:00","experiment_name":"pead_midcap_step40_short_core_sdlong12","hypothesis":"Drop the unstable after-close long sleeve and reallocate the portfolio to same-day shorts, after-close shorts, and a small same-day close-entry long overlay.","config_delta":{"base_experiment":"pead_midcap_step31_balanced_sleeves_nofrac_acshort12","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071756453145_9eb08c8d","trade_count":40,"profit_factor":1.483920817012022,"total_return_pct":0.8662763872782817,"win_rate":0.55,"max_drawdown_pct":0.6476993822793542,"sharpe_ratio":1.8482432920991685,"monthly_win_rate":0.75,"equity_curve_r_squared":0.19435203451054603},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071756505329_9eb08c8d","trade_count":55,"profit_factor":1.7695290624299977,"total_return_pct":1.52020169384827,"win_rate":0.5818181818181818,"max_drawdown_pct":1.128135882565315,"sharpe_ratio":2.2087400904317267,"monthly_win_rate":1.0,"equity_curve_r_squared":0.632820774620957},"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317071814970965_9eb08c8d","trade_count":306,"profit_factor":1.2169887676968005,"total_return_pct":3.708684730821959,"win_rate":0.4869281045751634,"max_drawdown_pct":2.5507043144769854,"sharpe_ratio":0.5720232080317771,"monthly_win_rate":0.625,"equity_curve_r_squared":0.39497202444939644}},"sqs_score":82.1,"sqs_breakdown":{"profitability":74.6,"risk":99.3,"consistency":88.6,"robustness":64.6},"verdict":"better","verdict_reasoning":"New robust leader. Train improved from SQS 55.5 to 63.1 and return +2.45% to +3.71%. Valid edged up from SQS 68.2 to 68.4 with lower drawdown, and test improved from SQS 77.9 to 82.1 with PF 1.77. Removing after-close longs fixed the biggest unstable sleeve without giving up the same-day long upside.","next_direction":"Use step40 as the new base. Only explore local refinements around the short-core structure if needed.","tags":["pead","midcap","step40","short","core","sdlong12"]}
+{"entry_id":"IMP-0038","timestamp":"2026-03-17T07:54:25.486815+00:00","experiment_name":"pead_midcap_step44_short_core_macro50","hypothesis":"Scaling entries down in weak macro regimes will keep the short-core structure while cutting drawdowns.","config_delta":{"base_experiment":"pead_midcap_step40_short_core_sdlong12","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073536976331_9c3139ba","trade_count":40,"profit_factor":1.7786229517773076,"total_return_pct":1.0842382260887244,"win_rate":0.55,"max_drawdown_pct":0.4065245518278289,"sharpe_ratio":2.4891782517624543,"monthly_win_rate":0.75,"equity_curve_r_squared":0.34564834918762316},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073537015426_9c3139ba","trade_count":58,"profit_factor":2.0052763103391356,"total_return_pct":1.2986802302195721,"win_rate":0.5689655172413793,"max_drawdown_pct":0.7045335236824514,"sharpe_ratio":2.6217356101467053,"monthly_win_rate":1.0,"equity_curve_r_squared":0.7991432493330419},"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073555963588_9c3139ba","trade_count":307,"profit_factor":1.2319887960741855,"total_return_pct":3.4232792009141852,"win_rate":0.4820846905537459,"max_drawdown_pct":1.97377752972113,"sharpe_ratio":0.6203457661608955,"monthly_win_rate":0.625,"equity_curve_r_squared":0.4148680874417292}},"sqs_score":87.9,"sqs_breakdown":{"profitability":85.2,"risk":100.0,"consistency":86.5,"robustness":76.6},"verdict":"better","verdict_reasoning":"Half-size macro scaling materially improved valid and test risk-adjusted performance versus step40, confirming that SPY-below-SMA exposure was a real drag.","next_direction":"Try a full macro block to see whether removing weak-regime entries entirely is even cleaner.","tags":["pead","midcap","step44","short","core","macro50"]}
+{"entry_id":"IMP-0039","timestamp":"2026-03-17T07:54:25.866482+00:00","experiment_name":"pead_midcap_step45_short_core_macro_block","hypothesis":"If weak-regime entries are mostly noise, hard-blocking them should outperform simple size scaling.","config_delta":{"base_experiment":"pead_midcap_step44_short_core_macro50","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073536967478_c3a3615c","trade_count":28,"profit_factor":2.3084684930008983,"total_return_pct":1.30245295115927,"win_rate":0.6785714285714286,"max_drawdown_pct":0.37472483014430374,"sharpe_ratio":3.0487382658615467,"monthly_win_rate":0.75,"equity_curve_r_squared":0.48762905493056535},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073537015511_c3a3615c","trade_count":23,"profit_factor":3.7827916861128097,"total_return_pct":1.2021184808416436,"win_rate":0.6956521739130435,"max_drawdown_pct":0.2608177180219861,"sharpe_ratio":3.3795660622862123,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.9110928059216074},"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073556022527_c3a3615c","trade_count":220,"profit_factor":1.2595187982631553,"total_return_pct":3.200633818982489,"win_rate":0.5045454545454545,"max_drawdown_pct":1.389607178327899,"sharpe_ratio":0.6336391727317187,"monthly_win_rate":0.6551724137931034,"equity_curve_r_squared":0.39958317262208615}},"sqs_score":86.7,"sqs_breakdown":{"profitability":84.8,"risk":100.0,"consistency":95.8,"robustness":57.2},"verdict":"better","verdict_reasoning":"The hard macro block improved valid and test again, with sharper PF and much lower drawdown than the 50% scaler version.","next_direction":"Stress the sleeve mix around the new macro-blocked core.","tags":["pead","midcap","step45","short","core","macro","block"]}
+{"entry_id":"IMP-0040","timestamp":"2026-03-17T07:54:26.245073+00:00","experiment_name":"pead_midcap_step46_short_core_macro_block_acshort12","hypothesis":"The after-close short sleeve may be oversized after the macro block and could improve if reduced.","config_delta":{"base_experiment":"pead_midcap_step45_short_core_macro_block","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073936915792_e3adb886","trade_count":26,"profit_factor":2.4445635979938296,"total_return_pct":1.339314216731771,"win_rate":0.6923076923076923,"max_drawdown_pct":0.37458871743417604,"sharpe_ratio":3.1583041871985076,"monthly_win_rate":0.75,"equity_curve_r_squared":0.48286315791941375},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073939814431_e3adb886","trade_count":21,"profit_factor":4.522043594902001,"total_return_pct":1.2518263219734362,"win_rate":0.7142857142857143,"max_drawdown_pct":0.2783619920052574,"sharpe_ratio":3.6389410132883055,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.9244229299732519}},"sqs_score":86.6,"sqs_breakdown":{"profitability":85.0,"risk":100.0,"consistency":95.8,"robustness":56.1},"verdict":"worse","verdict_reasoning":"Shrinking the after-close short sleeve slightly degraded both valid and test, so the step45 25% sleeve was not the problem.","next_direction":"Test whether the same-day long sleeve or the short-only core is the real source of edge.","tags":["pead","midcap","step46","short","core","macro","block","acshort12"]}
+{"entry_id":"IMP-0041","timestamp":"2026-03-17T07:54:26.612610+00:00","experiment_name":"pead_midcap_step47_short_core_macro_block_sdlong25","hypothesis":"A larger same-day reaction-close long overlay might scale once the macro block removes bad regimes.","config_delta":{"base_experiment":"pead_midcap_step45_short_core_macro_block","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073936910739_a38d69e4","trade_count":31,"profit_factor":1.7661839600751525,"total_return_pct":1.03177071332802,"win_rate":0.6451612903225806,"max_drawdown_pct":0.42599257212123065,"sharpe_ratio":2.218739891960781,"monthly_win_rate":0.75,"equity_curve_r_squared":0.4214549671036421},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317073939814560_a38d69e4","trade_count":25,"profit_factor":3.429404855633068,"total_return_pct":1.2833562667310616,"win_rate":0.68,"max_drawdown_pct":0.2608177180219861,"sharpe_ratio":3.32348066596022,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.889835319415119}},"sqs_score":87.0,"sqs_breakdown":{"profitability":85.1,"risk":100.0,"consistency":95.8,"robustness":58.3},"verdict":"worse","verdict_reasoning":"Increasing the same-day long sleeve hurt valid materially and did not produce a cleaner overall profile than step45.","next_direction":"Try sleeve removal experiments instead of scaling overlays up.","tags":["pead","midcap","step47","short","core","macro","block","sdlong25"]}
+{"entry_id":"IMP-0042","timestamp":"2026-03-17T07:54:26.967747+00:00","experiment_name":"pead_midcap_step48_short_core_macro_block_nolong","hypothesis":"The macro-blocked short core may be strong enough without the same-day long overlay.","config_delta":{"base_experiment":"pead_midcap_step45_short_core_macro_block","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074234188388_d9b28f40","trade_count":20,"profit_factor":5.726207009868067,"total_return_pct":1.2584927752282966,"win_rate":0.7,"max_drawdown_pct":0.29982153086364316,"sharpe_ratio":3.3529664036078217,"monthly_win_rate":1.0,"equity_curve_r_squared":0.4654938970631658},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074234267904_d9b28f40","trade_count":20,"profit_factor":3.402559985815088,"total_return_pct":0.8451446297187069,"win_rate":0.8,"max_drawdown_pct":0.4239007555767844,"sharpe_ratio":2.4413021645869604,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.7861833255279326}},"sqs_score":85.7,"sqs_breakdown":{"profitability":83.4,"risk":100.0,"consistency":95.8,"robustness":54.7},"verdict":"worse","verdict_reasoning":"Removing the long sleeve weakened both valid and test relative to step45, so the small same-day long overlay still adds useful diversification.","next_direction":"Test whether the same-day short sleeve can stand alone or whether the after-close short sleeve is also required.","tags":["pead","midcap","step48","short","core","macro","block","nolong"]}
+{"entry_id":"IMP-0043","timestamp":"2026-03-17T07:54:27.322093+00:00","experiment_name":"pead_midcap_step49_same_day_short_macro_block","hypothesis":"The pure same-day short engine might dominate the portfolio and make other sleeves unnecessary.","config_delta":{"base_experiment":"pead_midcap_step45_short_core_macro_block","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074233919071_b5cae2ca","trade_count":12,"profit_factor":25.094599474518425,"total_return_pct":1.1648266582814248,"win_rate":0.75,"max_drawdown_pct":0.33436592553948014,"sharpe_ratio":3.227317987298049,"monthly_win_rate":1.0,"equity_curve_r_squared":0.3679821567954865},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074234357252_b5cae2ca","trade_count":10,"profit_factor":2.4025420210691006,"total_return_pct":0.40093684044296973,"win_rate":0.7,"max_drawdown_pct":0.3693400619520335,"sharpe_ratio":1.5580313035787354,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.3032092765977471}},"sqs_score":38.9,"sqs_breakdown":{"profitability":81.6,"risk":92.6,"consistency":95.8,"robustness":19.0},"verdict":"worse","verdict_reasoning":"The single-sleeve version collapsed SQS because trade count and robustness fell too far, even though the kept trades were profitable.","next_direction":"Keep the supporting sleeves and test smaller structural adjustments instead.","tags":["pead","midcap","step49","same","day","short","macro","block"]}
+{"entry_id":"IMP-0044","timestamp":"2026-03-17T07:54:27.691772+00:00","experiment_name":"pead_midcap_step50_same_day_short_long_macro_block","hypothesis":"The same-day long overlay may matter, but the after-close short sleeve may be removable.","config_delta":{"base_experiment":"pead_midcap_step45_short_core_macro_block","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074234172662_a01fa9b8","trade_count":20,"profit_factor":2.2051399980357798,"total_return_pct":0.9530265960178512,"win_rate":0.65,"max_drawdown_pct":0.3444323377542605,"sharpe_ratio":2.687864694846263,"monthly_win_rate":0.75,"equity_curve_r_squared":0.41775889730721794},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074234263930_a01fa9b8","trade_count":15,"profit_factor":3.2060142950020296,"total_return_pct":0.805535692316771,"win_rate":0.6,"max_drawdown_pct":0.42315703199236054,"sharpe_ratio":2.6871013818111624,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.8242465440616173}},"sqs_score":41.9,"sqs_breakdown":{"profitability":83.2,"risk":100.0,"consistency":87.5,"robustness":52.8},"verdict":"worse","verdict_reasoning":"Dropping the after-close short sleeve reduced both valid and test performance, so step45 still benefits from carrying all three active sleeves.","next_direction":"Refine sleeve quality rather than deleting sleeves wholesale.","tags":["pead","midcap","step50","same","day","short","long","macro","block"]}
+{"entry_id":"IMP-0045","timestamp":"2026-03-17T07:54:28.043618+00:00","experiment_name":"pead_midcap_step51_short_core_macro_block_crashcap","hypothesis":"Extreme one-day crash continuations are too stretched for the same-day short sleeve and should be excluded.","config_delta":{"base_experiment":"pead_midcap_step45_short_core_macro_block","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074801780500_1585063f","trade_count":28,"profit_factor":2.3084684930008983,"total_return_pct":1.30245295115927,"win_rate":0.6785714285714286,"max_drawdown_pct":0.37472483014430374,"sharpe_ratio":3.0487382658615467,"monthly_win_rate":0.75,"equity_curve_r_squared":0.48762905493056535},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074801781828_1585063f","trade_count":22,"profit_factor":4.190184861108528,"total_return_pct":1.2441182731003355,"win_rate":0.7272727272727273,"max_drawdown_pct":0.22380328257556925,"sharpe_ratio":3.5956143566120704,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.918016321908289},"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317074932001383_1585063f","trade_count":220,"profit_factor":1.2595187982631553,"total_return_pct":3.200633818982489,"win_rate":0.5045454545454545,"max_drawdown_pct":1.389607178327899,"sharpe_ratio":0.6336391727317187,"monthly_win_rate":0.6551724137931034,"equity_curve_r_squared":0.39958317262208615}},"sqs_score":86.7,"sqs_breakdown":{"profitability":85.0,"risk":100.0,"consistency":95.8,"robustness":56.7},"verdict":"better","verdict_reasoning":"Capping same-day shorts at -45% reaction preserved train and valid while modestly improving test return, PF, drawdown, and Sharpe versus step45.","next_direction":"Combine the crash cap with a quality filter on the same-day long overlay.","tags":["pead","midcap","step51","short","core","macro","block","crashcap"]}
+{"entry_id":"IMP-0046","timestamp":"2026-03-17T07:54:28.401055+00:00","experiment_name":"pead_midcap_step52_short_core_macro_block_crashcap_gap10","hypothesis":"The same-day long overlay may work better when restricted to larger reaction-day gap moves.","config_delta":{"base_experiment":"pead_midcap_step51_short_core_macro_block_crashcap","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317075123185892_7592b1dc","trade_count":25,"profit_factor":5.660464878695176,"total_return_pct":1.818984312375629,"win_rate":0.72,"max_drawdown_pct":0.21695852738272095,"sharpe_ratio":4.755652871139783,"monthly_win_rate":1.0,"equity_curve_r_squared":0.6608034205824956},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317075123226004_7592b1dc","trade_count":22,"profit_factor":3.351800408128271,"total_return_pct":1.0111883417758072,"win_rate":0.7727272727272727,"max_drawdown_pct":0.24257912061402945,"sharpe_ratio":3.031867507475822,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.8468990955221466},"train":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317075225071209_7592b1dc","trade_count":201,"profit_factor":1.3153549627707726,"total_return_pct":3.2988485556152156,"win_rate":0.4975124378109453,"max_drawdown_pct":1.5599163185209133,"sharpe_ratio":0.683194016870928,"monthly_win_rate":0.6923076923076923,"equity_curve_r_squared":0.7186047701175057}},"sqs_score":86.3,"sqs_breakdown":{"profitability":84.0,"risk":100.0,"consistency":95.8,"robustness":56.7},"verdict":"neutral","verdict_reasoning":"A 10% gap filter made train and valid much stronger but gave back some test performance, so this is a balanced alternative rather than a clear new leader.","next_direction":"If optimizing for robustness across splits, keep exploring overlay quality gates around this variant.","tags":["pead","midcap","step52","short","core","macro","block","crashcap","gap10"]}
+{"entry_id":"IMP-0047","timestamp":"2026-03-17T07:54:28.762757+00:00","experiment_name":"pead_midcap_step53_short_core_macro_block_crashcap_gap14","hypothesis":"A stricter same-day long gap filter might further concentrate the overlay into only the strongest continuation setups.","config_delta":{"base_experiment":"pead_midcap_step52_short_core_macro_block_crashcap_gap10","changes":{}},"results":{"valid":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317075123208169_66d09993","trade_count":25,"profit_factor":8.207760543782888,"total_return_pct":2.0180349316014032,"win_rate":0.76,"max_drawdown_pct":0.2179161262122437,"sharpe_ratio":5.288977955005757,"monthly_win_rate":1.0,"equity_curve_r_squared":0.6385560108285373},"test":{"run_id":"bt_baseline_swing_v1_midcap-filte_20260317075123225576_66d09993","trade_count":19,"profit_factor":4.599773676047558,"total_return_pct":1.1191525844285641,"win_rate":0.8421052631578947,"max_drawdown_pct":0.21947031826165894,"sharpe_ratio":3.463700507402551,"monthly_win_rate":0.6666666666666666,"equity_curve_r_squared":0.8852420626052896}},"sqs_score":43.1,"sqs_breakdown":{"profitability":84.5,"risk":100.0,"consistency":95.8,"robustness":55.0},"verdict":"worse","verdict_reasoning":"The stricter gap filter over-concentrated the overlay, dropped total trade count below a healthy level, and cratered test SQS.","next_direction":"Use moderate overlay filters only; the strict version is too sparse.","tags":["pead","midcap","step53","short","core","macro","block","crashcap","gap14"]}

libs/backtest/allocator.py

@@ -99,6 +99,7 @@ def run_entry_gates(
     config: BacktestConfig,
     cooldown_remaining: int = 0,
     macro_data: dict[str, Any] | None = None,
+    engine_daily_new_risk_used: float = 0.0,
 ) -> str | None:
     """Run entry gates. Returns skip_reason string or None (pass).
 
@@ -151,6 +152,12 @@ def run_entry_gates(
     if portfolio_state.daily_new_risk_used + trade_risk > daily_budget:
         return "daily_risk_budget"
 
+    engine_budget = daily_budget * candidate.engine_risk_budget_pct
+    if engine_budget <= 0:
+        return "engine_daily_risk_budget"
+    if engine_daily_new_risk_used + trade_risk > engine_budget:
+        return "engine_daily_risk_budget"
+
     # Gate 6: Cash available (estimate position cost)
     stop_price = compute_stop_price(candidate, config.risk)
     est_shares = compute_shares(
@@ -207,11 +214,13 @@ def build_planned_order(
     config: BacktestConfig,
     cooldown_remaining: int = 0,
     macro_data: dict[str, Any] | None = None,
+    engine_daily_new_risk_used: float = 0.0,
 ) -> PlannedOrder:
     """Build a PlannedOrder. skip_reason is non-None if any gate rejected it."""
     skip_reason = run_entry_gates(
         candidate, portfolio_state, open_positions, config, cooldown_remaining,
         macro_data=macro_data,
+        engine_daily_new_risk_used=engine_daily_new_risk_used,
     )
 
     # Apply event-type-specific overrides for stop/target ATR multipliers
@@ -277,5 +286,10 @@ def build_planned_order(
         stop_price=stop_price,
         target_price=target_price,
         risk_dollars=risk_dollars,
+        event_date=candidate.event_date,
+        timing_class=candidate.timing_class,
+        engine_id=candidate.engine_id,
+        entry_timing_policy=candidate.entry_timing_policy,
+        shadow_only=candidate.shadow_only,
         skip_reason=skip_reason,
     )

libs/backtest/artifacts.py

@@ -83,6 +83,11 @@ def write_trade_blotter(
             "position_id": t.position_id,
             "event_id": t.event_id,
             "symbol": t.symbol,
+            "event_date": t.event_date.isoformat() if t.event_date else None,
+            "timing_class": t.timing_class,
+            "engine_id": t.engine_id,
+            "entry_timing_policy": t.entry_timing_policy,
+            "shadow_only": t.shadow_only,
             "entry_date": t.entry_date.isoformat(),
             "exit_date": t.exit_date.isoformat(),
             "entry_price": t.entry_price,
@@ -141,14 +146,19 @@ def write_position_timeline(
     rows = []
     for t in trades:
         rows.append(
-            {
+                {
-                "position_id": t.position_id,
+                    "position_id": t.position_id,
-                "event_id": t.event_id,
+                    "event_id": t.event_id,
-                "symbol": t.symbol,
+                    "symbol": t.symbol,
-                "entry_date": t.entry_date.isoformat(),
+                    "event_date": t.event_date.isoformat() if t.event_date else None,
-                "exit_date": t.exit_date.isoformat(),
+                    "timing_class": t.timing_class,
-                "entry_price": t.entry_price,
+                    "engine_id": t.engine_id,
-                "exit_price": t.exit_price,
+                    "entry_timing_policy": t.entry_timing_policy,
+                    "shadow_only": t.shadow_only,
+                    "entry_date": t.entry_date.isoformat(),
+                    "exit_date": t.exit_date.isoformat(),
+                    "entry_price": t.entry_price,
+                    "exit_price": t.exit_price,
                 "exit_reason": t.exit_reason.value,
                 "shares": t.shares,
                 "net_pnl": t.net_pnl,
@@ -164,6 +174,11 @@ def write_position_timeline(
                     "position_id": p.position_id,
                     "event_id": p.plan.candidate.event_id,
                     "symbol": p.plan.candidate.symbol,
+                    "event_date": p.plan.event_date.isoformat() if p.plan.event_date else None,
+                    "timing_class": p.plan.timing_class,
+                    "engine_id": p.plan.engine_id,
+                    "entry_timing_policy": p.plan.entry_timing_policy,
+                    "shadow_only": p.plan.shadow_only,
                     "entry_date": p.entry_date.isoformat(),
                     "exit_date": None,
                     "entry_price": p.entry_price,
@@ -265,6 +280,65 @@ def write_attribution_by_sector(
     return out
 
 
+def write_attribution_by_engine(
+    run_dir: Path,
+    trades: list[FilledTrade],
+    per_engine_metrics: dict[str, dict[str, Any]] | None = None,
+) -> Path:
+    """Write metrics/attribution_by_engine.csv."""
+    bucket_data: dict[str, dict[str, float | int | bool]] = defaultdict(
+        lambda: {"count": 0, "wins": 0, "net_pnl": 0.0, "_r_sum": 0.0, "shadow_only": False}
+    )
+    for t in trades:
+        engine_id = t.engine_id or "default"
+        d = bucket_data[engine_id]
+        d["count"] = int(d["count"]) + 1
+        if t.net_pnl > 0:
+            d["wins"] = int(d["wins"]) + 1
+        d["net_pnl"] = float(d["net_pnl"]) + t.net_pnl
+        d["_r_sum"] = float(d["_r_sum"]) + t.r_multiple
+        d["shadow_only"] = bool(t.shadow_only)
+
+    if per_engine_metrics:
+        for engine_id, summary in per_engine_metrics.items():
+            d = bucket_data.setdefault(
+                engine_id,
+                {"count": 0, "wins": 0, "net_pnl": 0.0, "_r_sum": 0.0, "shadow_only": False},
+            )
+            d["shadow_only"] = bool(summary.get("shadow_only", d["shadow_only"]))
+
+    out = run_dir / "metrics" / "attribution_by_engine.csv"
+    with open(out, "w", newline="") as f:
+        writer = csv.DictWriter(
+            f,
+            fieldnames=[
+                "engine_id",
+                "shadow_only",
+                "count",
+                "wins",
+                "win_rate",
+                "net_pnl",
+                "avg_r",
+            ],
+        )
+        writer.writeheader()
+        for engine_id, d in sorted(bucket_data.items()):
+            count = int(d["count"])
+            wins = int(d["wins"])
+            writer.writerow(
+                {
+                    "engine_id": engine_id,
+                    "shadow_only": bool(d["shadow_only"]),
+                    "count": count,
+                    "wins": wins,
+                    "win_rate": wins / count if count > 0 else 0.0,
+                    "net_pnl": round(float(d["net_pnl"]), 4),
+                    "avg_r": round(float(d["_r_sum"]) / count if count > 0 else 0.0, 4),
+                }
+            )
+    return out
+
+
 def write_score_bucket_report(
     run_dir: Path,
     score_bucket_hit_rate: dict[str, float],
@@ -312,6 +386,16 @@ def write_run_notes(run_dir: Path, notes: str = "") -> Path:
     return out
 
 
+def write_per_engine_metrics(
+    run_dir: Path,
+    per_engine_metrics: dict[str, dict[str, Any]],
+) -> Path:
+    """Write metrics/per_engine_metrics.json."""
+    out = run_dir / "metrics" / "per_engine_metrics.json"
+    out.write_text(json.dumps(per_engine_metrics, indent=2, default=str))
+    return out
+
+
 def write_all_artifacts(
     run_dir: Path,
     run_id: str,
@@ -329,6 +413,7 @@ def write_all_artifacts(
     total_candidates_seen: int,
     total_orders_rejected: int,
     split_name: str | None = None,
+    per_engine_metrics: dict[str, dict[str, Any]] | None = None,
 ) -> dict[str, str]:
     """Write all output files. Returns mapping of artifact_name → file_path."""
     from libs.backtest.manifests import save_manifest, save_resolved_config
@@ -355,11 +440,16 @@ def write_all_artifacts(
     paths["attribution_by_sector"] = str(
         write_attribution_by_sector(run_dir, trades, candidate_map)
     )
+    paths["attribution_by_engine"] = str(
+        write_attribution_by_engine(run_dir, trades, per_engine_metrics)
+    )
     paths["score_bucket_report"] = str(
         write_score_bucket_report(
             run_dir, metrics.score_bucket_hit_rate, trades, candidate_map
         )
     )
+    if per_engine_metrics:
+        paths["per_engine_metrics"] = str(write_per_engine_metrics(run_dir, per_engine_metrics))
 
     # Trade data
     if config.reporting.write_trade_blotter:

libs/backtest/domain.py

@@ -44,13 +44,20 @@ class Candidate(BaseModel):
     sector: str  # "UNKNOWN" if unavailable
     event_type: str
     event_timestamp: dt.datetime  # must be timezone-aware
+    event_date: dt.date | None = None
     filing_time_bucket: str
+    timing_class: str = "unknown"  # "same_day", "after_close", "unknown"
     reaction_date: dt.date
     execution_date: dt.date  # mapped from Parquet entry_date at SnapshotStore boundary
     entry_price_est: float   # reaction-day close price
     avg_dollar_volume: float  # 20-day mean(volume * close)
     atr_14: float | None = None
     score_bucket: str
+    engine_id: str = "default"
+    entry_timing_policy: str = "next_open"
+    shadow_only: bool = False
+    engine_max_holding_days: int | None = None
+    engine_risk_budget_pct: float = 1.0
     trade_direction: str = "long"  # "long" or "short"
     features: dict[str, Any] = Field(default_factory=dict)
 
@@ -65,6 +72,11 @@ class PlannedOrder(BaseModel):
     stop_price: float
     target_price: float
     risk_dollars: float
+    event_date: dt.date | None = None
+    timing_class: str = "unknown"
+    engine_id: str = "default"
+    entry_timing_policy: str = "next_open"
+    shadow_only: bool = False
     skip_reason: str | None = None  # non-None means the order was rejected
 
 
@@ -76,6 +88,11 @@ class FilledTrade(BaseModel):
     position_id: str
     event_id: str
     symbol: str
+    event_date: dt.date | None = None
+    timing_class: str = "unknown"
+    engine_id: str = "default"
+    entry_timing_policy: str = "next_open"
+    shadow_only: bool = False
     entry_date: dt.date
     exit_date: dt.date
     entry_price: float
@@ -227,6 +244,27 @@ class ExecutionConfig(BaseModel):
     no_follow_through_exit: bool = False       # exit at D+1 close if close < entry price
 
 
+class StrategyEngineConfig(BaseModel):
+    """Specialist engine routing and execution policy."""
+
+    engine_id: str
+    event_types: list[str] = Field(default_factory=list)
+    timing_class: str = "any"  # "same_day", "after_close", "any"
+    direction: str = "any"  # "long_only", "short_only", "any"
+    entry_timing_policy: str = "next_open"  # "next_open", "reaction_close"
+    max_holding_days: int | None = None
+    engine_risk_budget_pct: float = 1.0
+    score_threshold_override: float | None = None
+    pead_reaction_threshold_override: float | None = None
+    pead_volume_threshold_override: float | None = None
+    reaction_day_return_min: float | None = None
+    reaction_day_return_max: float | None = None
+    gap_size_min: float | None = None
+    gap_size_max: float | None = None
+    shadow_only: bool = False
+    enabled: bool = True
+
+
 class EventTypeProfile(BaseModel):
     """Per-event-type overrides for scoring, risk, and exit parameters."""
     enabled: bool = True
@@ -254,11 +292,25 @@ class BacktestConfig(BaseModel):
     execution: ExecutionConfig = Field(default_factory=ExecutionConfig)
     reporting: ReportingConfig = Field(default_factory=ReportingConfig)
     event_type_profiles: dict[str, EventTypeProfile] = Field(default_factory=dict)
+    strategy_engines: list[StrategyEngineConfig] = Field(default_factory=list)
+    strategy_engine_selection_mode: str = "interleave"  # "interleave" or "global_score"
 
     def get_event_profile(self, event_type: str) -> EventTypeProfile | None:
         """Look up event-type-specific profile. Returns None if no override."""
         return self.event_type_profiles.get(event_type)
 
+    def get_strategy_engines(self) -> list[StrategyEngineConfig]:
+        """Enabled strategy engines in manifest order."""
+        return [engine for engine in self.strategy_engines if engine.enabled]
+
+    def get_active_strategy_engines(self) -> list[StrategyEngineConfig]:
+        """Enabled engines that participate in the live portfolio."""
+        return [engine for engine in self.get_strategy_engines() if not engine.shadow_only]
+
+    def get_shadow_strategy_engines(self) -> list[StrategyEngineConfig]:
+        """Enabled engines that run in paper/shadow mode only."""
+        return [engine for engine in self.get_strategy_engines() if engine.shadow_only]
+
 
 # ---------------------------------------------------------------------------
 # Experiment models
@@ -275,6 +327,7 @@ class ExperimentManifest(BaseModel):
     description: str | None = None
     base_config: str  # path to base config JSON file
     overrides: dict[str, Any] = Field(default_factory=dict)
+    strategy_engines: list[StrategyEngineConfig] = Field(default_factory=list)
     splits: list[SplitSpec] = Field(default_factory=list)
     tags: list[str] = Field(default_factory=list)
     notes: str | None = None

libs/backtest/execution.py

@@ -55,20 +55,15 @@ def simulate_entry(
     config: ExecutionConfig,
     position_id: str | None = None,
 ) -> OpenPosition | None:
-    """Simulate filling a planned entry at the bar's open.
+    """Simulate filling a planned entry at the bar's open or reaction close.
 
-    Returns None (position NOT opened) if bar is missing or open is invalid.
+    Returns None (position NOT opened) if bar is missing or the required price is invalid.
     No zero imputation — missing bar = no entry.
     """
     if bar is None:
         logger.warning("entry_skip_missing_bar", event_id=plan.candidate.event_id)
         return None
 
-    bar_open = bar.get("open")
-    if bar_open is None or bar_open <= 0:
-        logger.warning("entry_skip_invalid_open", event_id=plan.candidate.event_id, bar=bar)
-        return None
-
     if plan.skip_reason is not None:
         logger.debug("entry_skip_gate_rejected", reason=plan.skip_reason)
         return None
@@ -77,12 +72,24 @@ def simulate_entry(
         logger.warning("entry_skip_zero_shares", event_id=plan.candidate.event_id)
         return None
 
+    entry_policy = plan.entry_timing_policy or "next_open"
+    if entry_policy == "reaction_close":
+        reference_price = bar.get("close")
+        if reference_price is None or reference_price <= 0:
+            logger.warning("entry_skip_invalid_close", event_id=plan.candidate.event_id, bar=bar)
+            return None
+    else:
+        reference_price = bar.get("open")
+        if reference_price is None or reference_price <= 0:
+            logger.warning("entry_skip_invalid_open", event_id=plan.candidate.event_id, bar=bar)
+            return None
+
     is_short = plan.candidate.trade_direction == "short"
     if is_short:
-        fill_price = _short_entry_fill(float(bar_open), config.slippage_bps_base)
+        fill_price = _short_entry_fill(float(reference_price), config.slippage_bps_base)
     else:
-        fill_price = _long_entry_fill(float(bar_open), config.slippage_bps_base)
+        fill_price = _long_entry_fill(float(reference_price), config.slippage_bps_base)
-    slippage_bps_actual = abs(fill_price / float(bar_open) - 1.0) * 10_000
+    slippage_bps_actual = abs(fill_price / float(reference_price) - 1.0) * 10_000
 
     pid = position_id or str(uuid.uuid4())
 
@@ -370,6 +377,11 @@ def _build_filled_trade_partial(
         position_id=position.position_id,
         event_id=position.plan.candidate.event_id,
         symbol=position.plan.candidate.symbol,
+        event_date=position.plan.event_date or position.plan.candidate.event_date,
+        timing_class=position.plan.timing_class,
+        engine_id=position.plan.engine_id,
+        entry_timing_policy=position.plan.entry_timing_policy,
+        shadow_only=position.plan.shadow_only,
         entry_date=position.entry_date,
         exit_date=exit_date,
         entry_price=position.entry_price,
@@ -424,6 +436,11 @@ def _build_filled_trade(
         position_id=position.position_id,
         event_id=position.plan.candidate.event_id,
         symbol=position.plan.candidate.symbol,
+        event_date=position.plan.event_date or position.plan.candidate.event_date,
+        timing_class=position.plan.timing_class,
+        engine_id=position.plan.engine_id,
+        entry_timing_policy=position.plan.entry_timing_policy,
+        shadow_only=position.plan.shadow_only,
         entry_date=position.entry_date,
         exit_date=exit_date,
         entry_price=position.entry_price,

libs/backtest/manifests.py

@@ -66,6 +66,11 @@ def resolve_config(
     # Inject snapshot_id
     sid = snapshot_id_override or manifest.dataset_snapshot_id
     merged["dataset_snapshot_id"] = sid
+    if manifest.strategy_engines:
+        merged["strategy_engines"] = [
+            engine.model_dump(mode="json")
+            for engine in manifest.strategy_engines
+        ]
 
     return BacktestConfig.model_validate(merged)
 
@@ -82,11 +87,11 @@ def generate_run_id(
 ) -> str:
     """Generate a unique, deterministic run ID.
 
-    Format: bt_{safe_strategy}_{safe_snapshot[:12]}_{timestamp}_{config_hash[:8]}
+    Format: bt_{safe_strategy}_{safe_snapshot[:12]}_{timestamp_us}_{config_hash[:8]}
     """
     strategy = _safe_slug(strategy_override or config.strategy_name)
     snapshot = _safe_slug(config.dataset_snapshot_id, max_len=12)
-    timestamp = utc_now().strftime("%Y%m%d%H%M%S")
+    timestamp = utc_now().strftime("%Y%m%d%H%M%S%f")
     config_json = config.model_dump_json(indent=None)
     config_hash = sha256_checksum_str(config_json)[:8]
     return f"bt_{strategy}_{snapshot}_{timestamp}_{config_hash}"

libs/backtest/selector.py

@@ -5,14 +5,23 @@ import datetime as dt
 from typing import Any
 from zoneinfo import ZoneInfo
 
-from libs.backtest.domain import Candidate, EventTypeProfile, SignalConfig, UniverseConfig
+from libs.backtest.domain import (
+    Candidate,
+    EventTypeProfile,
+    SignalConfig,
+    StrategyEngineConfig,
+    UniverseConfig,
+)
 from libs.common.logging import get_logger
 
 logger = get_logger(__name__)
 _UTC = ZoneInfo("UTC")
 
 
-def build_candidate(row: dict[str, Any]) -> Candidate | None:
+def build_candidate(
+    row: dict[str, Any],
+    strategy_engine: StrategyEngineConfig | None = None,
+) -> Candidate | None:
     """Build a Candidate from a raw Parquet row dict.
 
     Returns None (logged as skip) if:
@@ -44,8 +53,49 @@ def build_candidate(row: dict[str, Any]) -> Candidate | None:
     if event_timestamp.tzinfo is None:
         event_timestamp = event_timestamp.replace(tzinfo=_UTC)
 
-    # entry_price_est (mapped from Parquet entry_price)
+    # reaction_date
-    entry_price_est = row.get("entry_price") or row.get("entry_price_est")
+    raw_react = row.get("reaction_date")
+    reaction_date = _parse_date(raw_react)
+
+    # execution_date (mapped from Parquet entry_date) or reaction date for close-entry engines
+    raw_exec_date = row.get("execution_date") or row.get("entry_date")
+    execution_date = _parse_date(raw_exec_date)
+    if execution_date is None:
+        if strategy_engine and strategy_engine.entry_timing_policy == "reaction_close":
+            execution_date = reaction_date
+        else:
+            logger.warning("skip_candidate_no_exec_date", event_id=event_id)
+            return None
+
+    if reaction_date is None:
+        reaction_date = execution_date
+
+    event_date = _parse_date(row.get("event_date")) or event_timestamp.date()
+    timing_class = _classify_timing_class(event_date, reaction_date)
+
+    trade_direction = _resolve_trade_direction(row)
+    if strategy_engine is not None and not _matches_strategy_engine(
+        row=row,
+        strategy_engine=strategy_engine,
+        event_type=str(row.get("event_type", "")),
+        timing_class=timing_class,
+        trade_direction=trade_direction,
+    ):
+        return None
+
+    if strategy_engine and strategy_engine.entry_timing_policy == "reaction_close":
+        execution_date = reaction_date
+        entry_price_est = row.get("event_close") or row.get("entry_price_est")
+        if not entry_price_est:
+            logger.debug(
+                "skip_candidate_no_event_close",
+                event_id=event_id,
+                engine_id=strategy_engine.engine_id,
+            )
+            return None
+    else:
+        entry_price_est = row.get("entry_price") or row.get("entry_price_est")
+
     if not entry_price_est:
         logger.warning("skip_candidate_no_entry_price", event_id=event_id)
         return None
@@ -54,28 +104,6 @@ def build_candidate(row: dict[str, Any]) -> Candidate | None:
         logger.warning("skip_candidate_zero_entry_price", event_id=event_id)
         return None
 
-    # execution_date (mapped from Parquet entry_date)
-    raw_exec_date = row.get("execution_date") or row.get("entry_date")
-    if raw_exec_date is None:
-        logger.warning("skip_candidate_no_exec_date", event_id=event_id)
-        return None
-    if isinstance(raw_exec_date, str):
-        execution_date = dt.date.fromisoformat(raw_exec_date)
-    elif isinstance(raw_exec_date, dt.date):
-        execution_date = raw_exec_date
-    else:
-        logger.warning("skip_candidate_bad_exec_date", event_id=event_id)
-        return None
-
-    # reaction_date
-    raw_react = row.get("reaction_date")
-    if isinstance(raw_react, str):
-        reaction_date = dt.date.fromisoformat(raw_react)
-    elif isinstance(raw_react, dt.date):
-        reaction_date = raw_react
-    else:
-        reaction_date = execution_date  # fallback: same as execution
-
     score = float(row.get("score", 0.0))
     avg_dollar_volume = float(row.get("avg_dollar_volume", 0.0))
     atr_14_raw = row.get("atr_14")
@@ -84,8 +112,6 @@ def build_candidate(row: dict[str, Any]) -> Candidate | None:
     # Classify score bucket
     score_bucket = _classify_score_bucket(score)
 
-    trade_direction = str(row.get("trade_direction", "long"))
-
     return Candidate(
         event_id=event_id,
         symbol=str(row.get("symbol", row.get("ticker", ""))),
@@ -94,13 +120,32 @@ def build_candidate(row: dict[str, Any]) -> Candidate | None:
         sector=str(row.get("sector") or "UNKNOWN"),
         event_type=str(row.get("event_type", "")),
         event_timestamp=event_timestamp,
+        event_date=event_date,
         filing_time_bucket=str(row.get("filing_time_bucket", "unknown")),
+        timing_class=timing_class,
         reaction_date=reaction_date,
         execution_date=execution_date,
         entry_price_est=entry_price_est,
         avg_dollar_volume=avg_dollar_volume,
         atr_14=atr_14,
         score_bucket=score_bucket,
+        engine_id=strategy_engine.engine_id if strategy_engine else "default",
+        entry_timing_policy=(
+            strategy_engine.entry_timing_policy
+            if strategy_engine
+            else "next_open"
+        ),
+        shadow_only=strategy_engine.shadow_only if strategy_engine else False,
+        engine_max_holding_days=(
+            strategy_engine.max_holding_days
+            if strategy_engine
+            else None
+        ),
+        engine_risk_budget_pct=(
+            strategy_engine.engine_risk_budget_pct
+            if strategy_engine
+            else 1.0
+        ),
         trade_direction=trade_direction,
         features={k: v for k, v in row.items() if k not in _RESERVED_KEYS},
     )
@@ -108,12 +153,104 @@ def build_candidate(row: dict[str, Any]) -> Candidate | None:
 
 _RESERVED_KEYS = {
     "event_id", "symbol", "ticker", "issuer_id", "score", "sector",
-    "event_type", "event_timestamp", "filing_time_bucket", "reaction_date",
+    "event_type", "event_timestamp", "event_date", "filing_time_bucket", "reaction_date",
     "entry_date", "execution_date", "entry_price", "entry_price_est",
-    "avg_dollar_volume", "atr_14", "score_bucket", "trade_direction",
+    "event_close", "avg_dollar_volume", "atr_14", "score_bucket", "trade_direction",
 }
 
 
+def _parse_date(raw: Any) -> dt.date | None:
+    if isinstance(raw, dt.datetime):
+        return raw.date()
+    if isinstance(raw, dt.date):
+        return raw
+    if isinstance(raw, str):
+        try:
+            return dt.date.fromisoformat(raw)
+        except ValueError:
+            return None
+    return None
+
+
+def _classify_timing_class(event_date: dt.date | None, reaction_date: dt.date) -> str:
+    if event_date is None:
+        return "unknown"
+    if reaction_date == event_date:
+        return "same_day"
+    if reaction_date > event_date:
+        return "after_close"
+    return "unknown"
+
+
+def _resolve_trade_direction(row: dict[str, Any]) -> str:
+    raw_direction = str(row.get("trade_direction", "")).lower()
+    if raw_direction in {"long", "short"}:
+        return raw_direction
+
+    reaction = row.get("reaction_day_return")
+    if reaction is not None:
+        try:
+            return "short" if float(reaction) < 0 else "long"
+        except (TypeError, ValueError):
+            pass
+    return "long"
+
+
+def _matches_strategy_engine(
+    row: dict[str, Any],
+    strategy_engine: StrategyEngineConfig,
+    event_type: str,
+    timing_class: str,
+    trade_direction: str,
+) -> bool:
+    if strategy_engine.event_types and event_type not in strategy_engine.event_types:
+        return False
+
+    if strategy_engine.timing_class != "any" and timing_class != strategy_engine.timing_class:
+        return False
+
+    if strategy_engine.direction == "long_only" and trade_direction != "long":
+        return False
+    if strategy_engine.direction == "short_only" and trade_direction != "short":
+        return False
+
+    reaction_day_return = _safe_float(row.get("reaction_day_return"))
+    if (
+        strategy_engine.reaction_day_return_min is not None
+        and reaction_day_return is not None
+        and reaction_day_return < strategy_engine.reaction_day_return_min
+    ):
+        return False
+    if (
+        strategy_engine.reaction_day_return_max is not None
+        and reaction_day_return is not None
+        and reaction_day_return > strategy_engine.reaction_day_return_max
+    ):
+        return False
+
+    gap_size = _safe_float(row.get("gap_size"))
+    if (
+        strategy_engine.gap_size_min is not None
+        and gap_size is not None
+        and gap_size < strategy_engine.gap_size_min
+    ):
+        return False
+    if (
+        strategy_engine.gap_size_max is not None
+        and gap_size is not None
+        and gap_size > strategy_engine.gap_size_max
+    ):
+        return False
+
+    if (
+        strategy_engine.entry_timing_policy == "reaction_close"
+        and row.get("event_close") in (None, 0, 0.0, "")
+    ):
+        return False
+
+    return True
+
+
 def _classify_score_bucket(score: float) -> str:
     if score >= 0.8:
         return "high"
@@ -126,6 +263,13 @@ def _classify_score_bucket(score: float) -> str:
     return "low"
 
 
+def _safe_float(raw: Any) -> float | None:
+    try:
+        return float(raw)
+    except (TypeError, ValueError):
+        return None
+
+
 def rank_candidates(candidates: list[Candidate]) -> list[Candidate]:
     """Sort by score DESC, avg_dollar_volume DESC, symbol ASC (stable, deterministic)."""
     return sorted(candidates, key=lambda c: (-c.score, -c.avg_dollar_volume, c.symbol))
@@ -199,18 +343,72 @@ def select_candidates(
     universe_config: UniverseConfig,
     signal_config: SignalConfig,
     event_type_profiles: dict[str, EventTypeProfile] | None = None,
+    strategy_engine: StrategyEngineConfig | None = None,
 ) -> list[Candidate]:
     """Full selection pipeline: build → filter → rank → truncate."""
     candidates = []
     for row in raw_rows:
-        c = build_candidate(row)
+        prepared_row = _prepare_row_for_strategy_engine(
+            row,
+            signal_config=signal_config,
+            strategy_engine=strategy_engine,
+        )
+        c = build_candidate(prepared_row, strategy_engine=strategy_engine)
         if c is not None:
             candidates.append(c)
 
     candidates = filter_by_universe(candidates, universe_config)
-    candidates = filter_by_score(candidates, signal_config.score_threshold)
+    candidates = filter_by_score(
+        candidates,
+        _resolve_score_threshold(signal_config, strategy_engine),
+    )
     if event_type_profiles:
         candidates = filter_by_event_type(candidates, event_type_profiles)
     candidates = rank_candidates(candidates)
     candidates = truncate_candidates(candidates, signal_config.max_candidates_per_day)
     return candidates
+
+
+def _resolve_score_threshold(
+    signal_config: SignalConfig,
+    strategy_engine: StrategyEngineConfig | None,
+) -> float:
+    if strategy_engine and strategy_engine.score_threshold_override is not None:
+        return strategy_engine.score_threshold_override
+    return signal_config.score_threshold
+
+
+def _prepare_row_for_strategy_engine(
+    row: dict[str, Any],
+    signal_config: SignalConfig,
+    strategy_engine: StrategyEngineConfig | None,
+) -> dict[str, Any]:
+    if strategy_engine is None or signal_config.scoring_model != "pead":
+        return row
+
+    reaction_threshold = (
+        strategy_engine.pead_reaction_threshold_override
+        if strategy_engine.pead_reaction_threshold_override is not None
+        else signal_config.pead_reaction_threshold
+    )
+    volume_threshold = (
+        strategy_engine.pead_volume_threshold_override
+        if strategy_engine.pead_volume_threshold_override is not None
+        else signal_config.pead_volume_threshold
+    )
+
+    if (
+        reaction_threshold == signal_config.pead_reaction_threshold
+        and volume_threshold == signal_config.pead_volume_threshold
+    ):
+        return row
+
+    from libs.backtest.scoring import compute_pead_score
+
+    prepared = dict(row)
+    prepared["score"] = compute_pead_score(
+        prepared,
+        reaction_threshold=reaction_threshold,
+        volume_threshold=volume_threshold,
+    )
+    return prepared

libs/backtest/snapshot_store.py

@@ -32,7 +32,11 @@ class SnapshotStore:
         bars_by_symbol_date: dict[str, dict[dt.date, dict[str, Any]]],
         macro_by_date: dict[dt.date, dict[str, Any]] | None = None,
     ) -> None:
-        self._candidates = candidates_by_exec_date
+        self._candidates = {
+            date: list(rows)
+            for date, rows in candidates_by_exec_date.items()
+        }
+        self._candidates_by_reaction_date = self._build_reaction_index(self._candidates)
         self._bars = bars_by_symbol_date
         self._macro = macro_by_date or {}
 
@@ -44,6 +48,10 @@ class SnapshotStore:
         """Return candidates where execution_date == date. No look-ahead."""
         return list(self._candidates.get(date, []))
 
+    def get_candidates_for_reaction_date(self, date: dt.date) -> list[dict[str, Any]]:
+        """Return candidates where reaction_date == date. No look-ahead."""
+        return list(self._candidates_by_reaction_date.get(date, []))
+
     def get_bar(self, symbol: str, date: dt.date) -> dict[str, Any] | None:
         """Return OHLCV bar for symbol on date, or None if missing."""
         sym_bars = self._bars.get(symbol)
@@ -59,7 +67,11 @@ class SnapshotStore:
         """Sorted list of dates that have at least one candidate."""
         return sorted(self._candidates.keys())
 
-    def all_trading_days(self) -> list[dt.date]:
+    def all_reaction_dates(self) -> list[dt.date]:
+        """Sorted list of dates that have at least one reaction-date candidate."""
+        return sorted(self._candidates_by_reaction_date.keys())
+
+    def all_trading_days(self, include_reaction_dates: bool = False) -> list[dt.date]:
         """All NYSE trading days from first to last execution date (inclusive).
 
         Use this to drive the simulation loop so stop/target/time exits are
@@ -67,10 +79,13 @@ class SnapshotStore:
         """
         from libs.backtest.calendar import get_trading_days
 
-        exec_dates = self.all_execution_dates()
+        dates = set(self.all_execution_dates())
-        if not exec_dates:
+        if include_reaction_dates:
+            dates.update(self.all_reaction_dates())
+        if not dates:
             return []
-        return get_trading_days(exec_dates[0], exec_dates[-1])
+        ordered = sorted(dates)
+        return get_trading_days(ordered[0], ordered[-1])
 
     # ------------------------------------------------------------------
     # Factory: load from Parquet + DB + Oracle
@@ -176,6 +191,7 @@ class SnapshotStore:
             enriched["execution_date"] = exec_date
             enriched["symbol"] = ticker
             enriched["issuer_id"] = meta.get("issuer_id")
+            enriched["event_date"] = meta.get("event_date")
             enriched["event_type"] = meta.get("event_type", "")
             enriched["event_timestamp"] = meta.get("event_timestamp")
             enriched["avg_dollar_volume"] = avg_dvol.get(ticker, 0.0)
@@ -253,6 +269,7 @@ class SnapshotStore:
                         )
                     result[event.event_id] = {
                         "issuer_id": event.issuer_id,
+                        "event_date": event.event_date,
                         "event_type": event.event_type,
                         "event_timestamp": ts,
                         "ticker": sym.ticker if sym else None,
@@ -268,6 +285,7 @@ class SnapshotStore:
         symbols: list[str],
         date_range: tuple[dt.date, dt.date] | None,
         oracle_url: str,
+        concurrency: int = 16,
     ) -> tuple[dict[str, dict[dt.date, dict[str, Any]]], dict[str, float]]:
         """Fetch daily OHLCV bars and compute avg_dollar_volume per symbol."""
         if not symbols or date_range is None:
@@ -280,39 +298,46 @@ class SnapshotStore:
 
             bars_by_symbol: dict[str, dict[dt.date, dict[str, Any]]] = {}
             avg_dvol: dict[str, float] = {}
+            semaphore = asyncio.Semaphore(concurrency)
 
             async with OracleClient(base_url=oracle_url) as client:
                 svc = PriceService(client)
-                for sym in symbols:
+                async def _fetch_symbol(sym: str) -> tuple[str, dict[dt.date, dict[str, Any]], float]:
-                    try:
+                    async with semaphore:
-                        resp = await svc.get_daily_bars(sym, start=start_str, end=end_str)
+                        try:
-                        date_bars: dict[dt.date, dict[str, Any]] = {}
+                            resp = await svc.get_daily_bars(sym, start=start_str, end=end_str)
-                        dollar_vols: list[float] = []
+                            date_bars: dict[dt.date, dict[str, Any]] = {}
-                        for bar in resp.bars:
+                            dollar_vols: list[float] = []
-                            d = dt.date.fromisoformat(bar.date)
+                            for bar in resp.bars:
-                            b = {
+                                d = dt.date.fromisoformat(bar.date)
-                                "date": d,
+                                b = {
-                                "open": bar.open,
+                                    "date": d,
-                                "high": bar.high,
+                                    "open": bar.open,
-                                "low": bar.low,
+                                    "high": bar.high,
-                                "close": bar.close,
+                                    "low": bar.low,
-                                "volume": bar.volume,
+                                    "close": bar.close,
-                            }
+                                    "volume": bar.volume,
-                            date_bars[d] = b
+                                }
-                            dollar_vols.append(bar.close * bar.volume)
+                                date_bars[d] = b
-                        bars_by_symbol[sym] = date_bars
+                                dollar_vols.append(bar.close * bar.volume)
-                        # 20-day mean of dollar volume
+                            if dollar_vols:
-                        if dollar_vols:
+                                last_20 = dollar_vols[-20:]
-                            last_20 = dollar_vols[-20:]
+                                mean_dvol = sum(last_20) / len(last_20)
-                            avg_dvol[sym] = sum(last_20) / len(last_20)
+                            else:
-                        else:
+                                mean_dvol = 0.0
-                            avg_dvol[sym] = 0.0
+                            return sym, date_bars, mean_dvol
-                    except Exception as sym_exc:
+                        except Exception as sym_exc:
-                        logger.warning(
+                            logger.warning(
-                            "snapshot_store_price_fetch_failed", symbol=sym, error=str(sym_exc)
+                                "snapshot_store_price_fetch_failed",
-                        )
+                                symbol=sym,
-                        bars_by_symbol[sym] = {}
+                                error=str(sym_exc),
-                        avg_dvol[sym] = 0.0
+                            )
+                            return sym, {}, 0.0
+
+                results = await asyncio.gather(*(_fetch_symbol(sym) for sym in symbols))
+                for sym, date_bars, mean_dvol in results:
+                    bars_by_symbol[sym] = date_bars
+                    avg_dvol[sym] = mean_dvol
             return bars_by_symbol, avg_dvol
         except Exception as exc:
             logger.warning("snapshot_store_oracle_failed", error=str(exc))
@@ -322,22 +347,29 @@ class SnapshotStore:
     async def _fetch_sectors(
         symbols: list[str],
         oracle_url: str,
+        concurrency: int = 16,
     ) -> dict[str, str]:
         """Fetch company sector for each symbol. Default 'UNKNOWN' if unavailable."""
         if not symbols:
             return {}
         result: dict[str, str] = {}
+        semaphore = asyncio.Semaphore(concurrency)
         try:
             from libs.oracle_client import CompanyService, OracleClient
 
             async with OracleClient(base_url=oracle_url) as client:
                 company_svc = CompanyService(client)
-                for sym in symbols:
+                async def _fetch_sector(sym: str) -> tuple[str, str]:
-                    try:
+                    async with semaphore:
-                        info = await company_svc.get_company(sym)
+                        try:
-                        result[sym] = info.sector or "UNKNOWN"
+                            info = await company_svc.get_company(sym)
-                    except Exception:
+                            return sym, info.sector or "UNKNOWN"
-                        result[sym] = "UNKNOWN"
+                        except Exception:
+                            return sym, "UNKNOWN"
+
+                sector_results = await asyncio.gather(*(_fetch_sector(sym) for sym in symbols))
+                for sym, sector in sector_results:
+                    result[sym] = sector
         except Exception as exc:
             logger.warning("snapshot_store_sector_fetch_failed", error=str(exc))
         # Default all remaining to UNKNOWN
@@ -436,19 +468,49 @@ class SnapshotStore:
     def _compute_date_range(
         rows: list[dict[str, Any]],
     ) -> tuple[dt.date, dt.date] | None:
-        """Compute (min_date, max_date) from entry_date/execution_date column."""
+        """Compute (min_date, max_date) from execution and reaction-date columns."""
         dates: list[dt.date] = []
         for r in rows:
-            raw = r.get("entry_date") or r.get("execution_date")
+            for raw in (
-            if raw is None:
+                r.get("entry_date"),
-                continue
+                r.get("execution_date"),
-            if isinstance(raw, str):
+                r.get("reaction_date"),
-                try:
+            ):
-                    dates.append(dt.date.fromisoformat(raw))
+                if raw is None:
-                except ValueError:
+                    continue
-                    pass
+                if isinstance(raw, str):
-            elif isinstance(raw, dt.date):
+                    try:
-                dates.append(raw)
+                        dates.append(dt.date.fromisoformat(raw))
+                    except ValueError:
+                        pass
+                elif isinstance(raw, dt.date):
+                    dates.append(raw)
         if not dates:
             return None
         return min(dates), max(dates)
+
+    @staticmethod
+    def _build_reaction_index(
+        candidates_by_exec_date: dict[dt.date, list[dict[str, Any]]],
+    ) -> dict[dt.date, list[dict[str, Any]]]:
+        reaction_index: dict[dt.date, list[dict[str, Any]]] = {}
+        for rows in candidates_by_exec_date.values():
+            for row in rows:
+                reaction_date = SnapshotStore._normalize_date(row.get("reaction_date"))
+                if reaction_date is None:
+                    continue
+                reaction_index.setdefault(reaction_date, []).append(row)
+        return reaction_index
+
+    @staticmethod
+    def _normalize_date(raw: Any) -> dt.date | None:
+        if isinstance(raw, dt.datetime):
+            return raw.date()
+        if isinstance(raw, dt.date):
+            return raw
+        if isinstance(raw, str):
+            try:
+                return dt.date.fromisoformat(raw)
+            except ValueError:
+                return None
+        return None

tests/integration/backtest/test_backtest_run.py

@@ -76,7 +76,67 @@ def _build_synthetic_store() -> object:
     )
 
 
-def _make_config():
+def _build_multi_engine_store() -> object:
+    from libs.backtest.snapshot_store import SnapshotStore
+
+    candidates = {
+        dt.date(2026, 1, 7): [
+            {
+                "event_id": "EVT::SD::SHORT",
+                "symbol": "NFLX",
+                "execution_date": dt.date(2026, 1, 7),
+                "entry_date": "2026-01-07",
+                "event_date": "2026-01-06",
+                "event_close": 400.0,
+                "entry_price": 399.0,
+                "score": 0.90,
+                "sector": "Communication Services",
+                "event_type": "earnings_release",
+                "event_timestamp": "2026-01-06T21:00:00+00:00",
+                "filing_time_bucket": "post_market",
+                "reaction_date": "2026-01-06",
+                "reaction_day_return": -0.12,
+                "avg_dollar_volume": 8_000_000.0,
+                "atr_14": 4.0,
+            },
+            {
+                "event_id": "EVT::AC::LONG",
+                "symbol": "AMD",
+                "execution_date": dt.date(2026, 1, 7),
+                "entry_date": "2026-01-07",
+                "event_date": "2026-01-06",
+                "event_close": 122.0,
+                "entry_price": 123.0,
+                "score": 0.88,
+                "sector": "Technology",
+                "event_type": "earnings_release",
+                "event_timestamp": "2026-01-06T21:00:00+00:00",
+                "filing_time_bucket": "post_market",
+                "reaction_date": "2026-01-07",
+                "reaction_day_return": 0.14,
+                "avg_dollar_volume": 9_000_000.0,
+                "atr_14": 3.0,
+            },
+        ],
+    }
+
+    bars = {
+        "NFLX": {
+            dt.date(2026, 1, 6): {"date": dt.date(2026, 1, 6), "open": 395.0, "high": 405.0, "low": 390.0, "close": 400.0, "volume": 1_200_000},
+            dt.date(2026, 1, 7): {"date": dt.date(2026, 1, 7), "open": 390.0, "high": 392.0, "low": 380.0, "close": 382.0, "volume": 1_100_000},
+            dt.date(2026, 1, 8): {"date": dt.date(2026, 1, 8), "open": 382.0, "high": 384.0, "low": 370.0, "close": 372.0, "volume": 1_000_000},
+        },
+        "AMD": {
+            dt.date(2026, 1, 7): {"date": dt.date(2026, 1, 7), "open": 123.0, "high": 130.0, "low": 122.0, "close": 129.0, "volume": 1_500_000},
+            dt.date(2026, 1, 8): {"date": dt.date(2026, 1, 8), "open": 129.0, "high": 135.0, "low": 128.0, "close": 134.0, "volume": 1_300_000},
+            dt.date(2026, 1, 9): {"date": dt.date(2026, 1, 9), "open": 134.0, "high": 138.0, "low": 133.0, "close": 137.0, "volume": 1_250_000},
+        },
+    }
+
+    return SnapshotStore(candidates_by_exec_date=candidates, bars_by_symbol_date=bars)
+
+
+def _make_config(strategy_engines=None):
     from libs.backtest.domain import (
         BacktestConfig,
         ExecutionConfig,
@@ -111,6 +171,7 @@ def _make_config():
             write_metrics_summary=True,
             generate_plots=False,
         ),
+        strategy_engines=strategy_engines or [],
     )
 
 
@@ -258,3 +319,66 @@ class TestBacktestRunIntegration:
         symbols = [r["symbol"] for r in rows]
         assert "FUTURE_TICKER" not in symbols
         assert "AAPL" in symbols
+
+    def test_multi_engine_run_writes_per_engine_metrics(self, tmp_path):
+        from apps.backtester.run import BacktestRunner
+        from libs.backtest.domain import ExperimentManifest, StrategyEngineConfig
+        import pyarrow.parquet as pq
+
+        store = _build_multi_engine_store()
+        manifest = ExperimentManifest(
+            experiment_name="portfolio_v2",
+            dataset_snapshot_id="test_snapshot",
+            base_config="configs/backtest/defaults.json",
+            overrides={},
+            strategy_engines=[
+                StrategyEngineConfig(
+                    engine_id="earnings_same_day_short_v1",
+                    event_types=["earnings_release"],
+                    timing_class="same_day",
+                    direction="short_only",
+                    entry_timing_policy="next_open",
+                    max_holding_days=3,
+                    engine_risk_budget_pct=0.40,
+                ),
+                StrategyEngineConfig(
+                    engine_id="earnings_after_close_long_v1",
+                    event_types=["earnings_release"],
+                    timing_class="after_close",
+                    direction="long_only",
+                    entry_timing_policy="next_open",
+                    max_holding_days=5,
+                    engine_risk_budget_pct=0.25,
+                ),
+                StrategyEngineConfig(
+                    engine_id="earnings_after_close_short_v1",
+                    event_types=["earnings_release"],
+                    timing_class="after_close",
+                    direction="short_only",
+                    entry_timing_policy="next_open",
+                    max_holding_days=3,
+                    engine_risk_budget_pct=0.10,
+                    shadow_only=True,
+                ),
+            ],
+        )
+        config = _make_config(strategy_engines=manifest.strategy_engines)
+        runner = BacktestRunner(manifest=manifest, config=config, store=store, initial_equity=100_000.0)
+        result = runner.run(output_root=tmp_path)
+
+        run_dir = tmp_path / result.run_id
+        per_engine_metrics = run_dir / "metrics" / "per_engine_metrics.json"
+        attribution_by_engine = run_dir / "metrics" / "attribution_by_engine.csv"
+
+        assert per_engine_metrics.exists()
+        assert attribution_by_engine.exists()
+
+        payload = per_engine_metrics.read_text()
+        assert "earnings_same_day_short_v1" in payload
+        assert "earnings_after_close_long_v1" in payload
+        assert "earnings_after_close_short_v1" in payload
+        assert result.metrics.trade_count >= 1
+
+        trade_blotter = pq.read_table(run_dir / "artifacts" / "trade_blotter.parquet").to_pylist()
+        engine_ids = {row["engine_id"] for row in trade_blotter}
+        assert "earnings_after_close_short_v1" not in engine_ids

tests/unit/backtest/test_domain.py

@@ -23,6 +23,7 @@ from libs.backtest.domain import (
     ReportingConfig,
     RiskConfig,
     SignalConfig,
+    StrategyEngineConfig,
     UniverseConfig,
 )
 
@@ -282,6 +283,56 @@ class TestEventTypeProfile:
         assert cfg.event_type_profiles == {}
         assert cfg.get_event_profile("anything") is None
 
+    def test_backtest_config_strategy_engines_helpers(self):
+        cfg = BacktestConfig(
+            strategy_name="test",
+            dataset_snapshot_id="snap_001",
+            strategy_engines=[
+                StrategyEngineConfig(
+                    engine_id="active_engine",
+                    event_types=["earnings_release"],
+                    timing_class="same_day",
+                    direction="long_only",
+                ),
+                StrategyEngineConfig(
+                    engine_id="shadow_engine",
+                    event_types=["earnings_release"],
+                    timing_class="after_close",
+                    direction="short_only",
+                    shadow_only=True,
+                ),
+            ],
+        )
+        assert [engine.engine_id for engine in cfg.get_strategy_engines()] == [
+            "active_engine",
+            "shadow_engine",
+        ]
+        assert [engine.engine_id for engine in cfg.get_active_strategy_engines()] == [
+            "active_engine",
+        ]
+        assert [engine.engine_id for engine in cfg.get_shadow_strategy_engines()] == [
+            "shadow_engine",
+        ]
+
+    def test_strategy_engine_config_supports_engine_specific_thresholds(self):
+        engine = StrategyEngineConfig(
+            engine_id="after_close_long_quality",
+            score_threshold_override=0.75,
+            pead_reaction_threshold_override=0.12,
+            pead_volume_threshold_override=3.0,
+            reaction_day_return_min=-0.45,
+            reaction_day_return_max=0.35,
+            gap_size_min=0.05,
+            gap_size_max=0.30,
+        )
+        assert engine.score_threshold_override == 0.75
+        assert engine.pead_reaction_threshold_override == 0.12
+        assert engine.pead_volume_threshold_override == 3.0
+        assert engine.reaction_day_return_min == -0.45
+        assert engine.reaction_day_return_max == 0.35
+        assert engine.gap_size_min == 0.05
+        assert engine.gap_size_max == 0.30
+
 
 class TestMetricsBundleBootstrap:
     def test_bootstrap_cis_field(self):

tests/unit/backtest/test_execution.py

@@ -30,6 +30,7 @@ def _make_candidate(**kwargs) -> Candidate:
         sector="Technology",
         event_type="earnings",
         event_timestamp=_NOW,
+        event_date=_TODAY,
         filing_time_bucket="post_market",
         reaction_date=_TODAY,
         execution_date=_TOMORROW,
@@ -49,6 +50,11 @@ def _make_plan(entry_price=100.0, stop=95.0, target=110.0, shares=100) -> Planne
         stop_price=stop,
         target_price=target,
         risk_dollars=500.0,
+        event_date=_TODAY,
+        timing_class="same_day",
+        engine_id="engine_1",
+        entry_timing_policy="next_open",
+        shadow_only=False,
     )
 
 
@@ -142,6 +148,18 @@ class TestSimulateEntry:
         pos = simulate_entry(plan, bar, _make_exec_config())
         assert pos.entry_date == _TOMORROW
 
+    def test_reaction_close_entry_uses_close(self):
+        from libs.backtest.execution import simulate_entry
+
+        plan = _make_plan()
+        plan = plan.model_copy(update={"entry_timing_policy": "reaction_close"})
+        bar = _make_bar(open=100.0, close=102.0, date=_TODAY)
+        pos = simulate_entry(plan, bar, _make_exec_config(slippage_bps_base=10.0))
+        assert pos is not None
+        expected = 102.0 * (1 + 10 / 10_000)
+        assert pos.entry_price == pytest.approx(expected)
+        assert pos.entry_date == _TODAY
+
 
 class TestSimulateExit:
     def test_stop_exit(self):
@@ -232,6 +250,17 @@ class TestSimulateExit:
         assert trade.gross_pnl == pytest.approx(1000.0)
         assert trade.net_pnl == pytest.approx(998.0)
 
+    def test_trade_metadata_propagated(self):
+        from libs.backtest.execution import simulate_exit
+
+        pos = _make_open_position(entry_price=100.0, stop=95.0, target=110.0, shares=100)
+        bar = _make_bar(low=98.0, high=115.0)
+        trade = simulate_exit(pos, bar, _make_exec_config(slippage_bps_base=0.0), _TOMORROW)
+        assert trade.engine_id == "engine_1"
+        assert trade.timing_class == "same_day"
+        assert trade.event_date == _TODAY
+        assert trade.shadow_only is False
+
 
 class TestPartialExit:
     def test_partial_exit_at_target(self):

tests/unit/backtest/test_manifests.py

@@ -176,6 +176,36 @@ class TestResolveConfig:
         config = resolve_config(manifest, snapshot_id_override="snap_override")
         assert config.dataset_snapshot_id == "snap_override"
 
+    def test_strategy_engines_injected_from_manifest(self, tmp_path):
+        from libs.backtest.manifests import load_manifest, resolve_config
+
+        cfg_file = tmp_path / "defaults.json"
+        _write_json(cfg_file, VALID_BASE_CONFIG)
+
+        manifest_data = {
+            "experiment_name": "test",
+            "dataset_snapshot_id": "snap_001",
+            "base_config": str(cfg_file),
+            "overrides": {},
+            "strategy_engines": [
+                {
+                    "engine_id": "earnings_same_day_short_v1",
+                    "event_types": ["earnings_release"],
+                    "timing_class": "same_day",
+                    "direction": "short_only",
+                    "entry_timing_policy": "next_open",
+                    "max_holding_days": 3,
+                    "engine_risk_budget_pct": 0.4,
+                }
+            ],
+        }
+        m_file = tmp_path / "manifest.json"
+        _write_json(m_file, manifest_data)
+        manifest = load_manifest(m_file)
+        config = resolve_config(manifest)
+        assert len(config.strategy_engines) == 1
+        assert config.strategy_engines[0].engine_id == "earnings_same_day_short_v1"
+
 
 class TestGenerateRunId:
     def test_format(self):

tests/unit/backtest/test_selector.py

@@ -6,7 +6,12 @@ from zoneinfo import ZoneInfo
 
 import pytest
 
-from libs.backtest.domain import EventTypeProfile, SignalConfig, UniverseConfig
+from libs.backtest.domain import (
+    EventTypeProfile,
+    SignalConfig,
+    StrategyEngineConfig,
+    UniverseConfig,
+)
 
 _UTC = ZoneInfo("UTC")
 
@@ -42,6 +47,7 @@ class TestBuildCandidate:
         assert c.score == 0.75
         assert c.execution_date == dt.date(2026, 1, 7)
         assert c.event_timestamp.tzinfo is not None
+        assert c.timing_class == "after_close"
 
     def test_null_timestamp_returns_none(self):
         from libs.backtest.selector import build_candidate
@@ -95,6 +101,286 @@ class TestBuildCandidate:
         c = build_candidate(_make_raw_row(score=0.10))
         assert c.score_bucket == "low"
 
+    def test_same_day_timing_and_direction_from_reaction(self):
+        from libs.backtest.selector import build_candidate
+
+        row = _make_raw_row(
+            event_date="2026-01-06",
+            reaction_date="2026-01-06",
+            execution_date="2026-01-07",
+            trade_direction="",
+            reaction_day_return=-0.12,
+        )
+        c = build_candidate(row)
+        assert c is not None
+        assert c.event_date == dt.date(2026, 1, 6)
+        assert c.timing_class == "same_day"
+        assert c.trade_direction == "short"
+
+    def test_reaction_close_engine_uses_event_close(self):
+        from libs.backtest.selector import build_candidate
+
+        engine = StrategyEngineConfig(
+            engine_id="earnings_same_day_long_close_v1",
+            event_types=["earnings"],
+            timing_class="same_day",
+            direction="long_only",
+            entry_timing_policy="reaction_close",
+            max_holding_days=3,
+            engine_risk_budget_pct=0.35,
+        )
+        row = _make_raw_row(
+            event_date="2026-01-06",
+            reaction_date="2026-01-06",
+            event_close=149.5,
+            entry_date="2026-01-07",
+            reaction_day_return=0.11,
+        )
+        c = build_candidate(row, strategy_engine=engine)
+        assert c is not None
+        assert c.execution_date == dt.date(2026, 1, 6)
+        assert c.entry_price_est == pytest.approx(149.5)
+        assert c.engine_id == "earnings_same_day_long_close_v1"
+        assert c.entry_timing_policy == "reaction_close"
+
+    def test_engine_route_skips_non_matching_direction(self):
+        from libs.backtest.selector import build_candidate
+
+        engine = StrategyEngineConfig(
+            engine_id="short_only_engine",
+            event_types=["earnings"],
+            timing_class="after_close",
+            direction="short_only",
+        )
+        row = _make_raw_row(reaction_day_return=0.09, trade_direction="long")
+        assert build_candidate(row, strategy_engine=engine) is None
+
+    def test_select_candidates_recomputes_pead_score_for_engine_thresholds(self):
+        from libs.backtest.selector import select_candidates
+
+        rows = [
+            _make_raw_row(
+                symbol="LOWVOL",
+                score=0.8,
+                event_type="earnings_release",
+                reaction_day_return=0.14,
+                volume_ratio_20d=2.4,
+                gap_size=0.01,
+            ),
+            _make_raw_row(
+                symbol="HIGHVOL",
+                score=0.8,
+                event_type="earnings_release",
+                reaction_day_return=0.14,
+                volume_ratio_20d=4.5,
+                gap_size=0.01,
+            ),
+        ]
+        universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
+        signal = SignalConfig(
+            scoring_model="pead",
+            score_threshold=0.65,
+            pead_reaction_threshold=0.10,
+            pead_volume_threshold=2.0,
+            max_candidates_per_day=5,
+        )
+        engine = StrategyEngineConfig(
+            engine_id="after_close_long_quality",
+            event_types=["earnings_release"],
+            timing_class="after_close",
+            direction="long_only",
+            pead_volume_threshold_override=3.0,
+        )
+
+        selected = select_candidates(rows, universe, signal, strategy_engine=engine)
+        assert [candidate.symbol for candidate in selected] == ["HIGHVOL"]
+
+    def test_select_candidates_uses_engine_score_threshold_override(self):
+        from libs.backtest.selector import select_candidates
+
+        rows = [
+            _make_raw_row(
+                symbol="PASS",
+                score=0.78,
+                event_type="earnings_release",
+                reaction_day_return=0.16,
+                volume_ratio_20d=4.0,
+                gap_size=0.01,
+            ),
+            _make_raw_row(
+                symbol="FAIL",
+                score=0.72,
+                event_type="earnings_release",
+                reaction_day_return=0.14,
+                volume_ratio_20d=4.0,
+                gap_size=0.01,
+            ),
+        ]
+        universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
+        signal = SignalConfig(
+            scoring_model="pead",
+            score_threshold=0.65,
+            pead_reaction_threshold=0.10,
+            pead_volume_threshold=2.0,
+            max_candidates_per_day=5,
+        )
+        engine = StrategyEngineConfig(
+            engine_id="after_close_long_quality",
+            event_types=["earnings_release"],
+            timing_class="after_close",
+            direction="long_only",
+            score_threshold_override=0.75,
+        )
+
+        selected = select_candidates(rows, universe, signal, strategy_engine=engine)
+        assert [candidate.symbol for candidate in selected] == ["PASS"]
+
+    def test_select_candidates_respects_engine_reaction_day_return_bounds(self):
+        from libs.backtest.selector import select_candidates
+
+        rows = [
+            _make_raw_row(
+                symbol="CRASH",
+                score=0.85,
+                event_type="earnings_release",
+                event_date="2026-01-06",
+                reaction_date="2026-01-06",
+                reaction_day_return=-0.52,
+                volume_ratio_20d=8.0,
+                trade_direction="short",
+            ),
+            _make_raw_row(
+                symbol="NORMAL",
+                score=0.82,
+                event_type="earnings_release",
+                event_date="2026-01-06",
+                reaction_date="2026-01-06",
+                reaction_day_return=-0.18,
+                volume_ratio_20d=5.0,
+                trade_direction="short",
+            ),
+        ]
+        universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
+        signal = SignalConfig(
+            scoring_model="pead",
+            score_threshold=0.65,
+            pead_reaction_threshold=0.10,
+            pead_volume_threshold=2.0,
+            max_candidates_per_day=5,
+        )
+        engine = StrategyEngineConfig(
+            engine_id="same_day_short_filtered",
+            event_types=["earnings_release"],
+            timing_class="same_day",
+            direction="short_only",
+            reaction_day_return_min=-0.45,
+        )
+
+        selected = select_candidates(rows, universe, signal, strategy_engine=engine)
+        assert [candidate.symbol for candidate in selected] == ["NORMAL"]
+
+    def test_select_candidates_respects_engine_reaction_day_return_upper_bound(self):
+        from libs.backtest.selector import select_candidates
+
+        rows = [
+            _make_raw_row(
+                symbol="TOO_HOT",
+                score=0.90,
+                event_type="earnings_release",
+                event_date="2026-01-06",
+                reaction_date="2026-01-06",
+                reaction_day_return=0.42,
+                volume_ratio_20d=6.0,
+                trade_direction="long",
+            ),
+            _make_raw_row(
+                symbol="OK",
+                score=0.80,
+                event_type="earnings_release",
+                event_date="2026-01-06",
+                reaction_date="2026-01-06",
+                reaction_day_return=0.18,
+                volume_ratio_20d=4.0,
+                trade_direction="long",
+            ),
+        ]
+        universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
+        signal = SignalConfig(
+            scoring_model="pead",
+            score_threshold=0.65,
+            pead_reaction_threshold=0.10,
+            pead_volume_threshold=2.0,
+            max_candidates_per_day=5,
+        )
+        engine = StrategyEngineConfig(
+            engine_id="same_day_long_capped",
+            event_types=["earnings_release"],
+            timing_class="same_day",
+            direction="long_only",
+            reaction_day_return_max=0.30,
+        )
+
+        selected = select_candidates(rows, universe, signal, strategy_engine=engine)
+        assert [candidate.symbol for candidate in selected] == ["OK"]
+
+    def test_select_candidates_respects_engine_gap_size_bounds(self):
+        from libs.backtest.selector import select_candidates
+
+        rows = [
+            _make_raw_row(
+                symbol="TIGHT",
+                score=0.82,
+                event_type="earnings_release",
+                event_date="2026-01-06",
+                reaction_date="2026-01-06",
+                reaction_day_return=0.18,
+                volume_ratio_20d=4.0,
+                gap_size=0.04,
+                trade_direction="long",
+            ),
+            _make_raw_row(
+                symbol="WIDE",
+                score=0.84,
+                event_type="earnings_release",
+                event_date="2026-01-06",
+                reaction_date="2026-01-06",
+                reaction_day_return=0.18,
+                volume_ratio_20d=4.0,
+                gap_size=0.16,
+                trade_direction="long",
+            ),
+            _make_raw_row(
+                symbol="TOO_WIDE",
+                score=0.86,
+                event_type="earnings_release",
+                event_date="2026-01-06",
+                reaction_date="2026-01-06",
+                reaction_day_return=0.18,
+                volume_ratio_20d=4.0,
+                gap_size=0.34,
+                trade_direction="long",
+            ),
+        ]
+        universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
+        signal = SignalConfig(
+            scoring_model="pead",
+            score_threshold=0.65,
+            pead_reaction_threshold=0.10,
+            pead_volume_threshold=2.0,
+            max_candidates_per_day=5,
+        )
+        engine = StrategyEngineConfig(
+            engine_id="same_day_long_gapped",
+            event_types=["earnings_release"],
+            timing_class="same_day",
+            direction="long_only",
+            gap_size_min=0.10,
+            gap_size_max=0.30,
+        )
+
+        selected = select_candidates(rows, universe, signal, strategy_engine=engine)
+        assert [candidate.symbol for candidate in selected] == ["WIDE"]
+
 
 class TestRankCandidates:
     def test_sorted_by_score_desc(self):

tests/unit/backtest/test_snapshot_store.py

@@ -79,6 +79,12 @@ class TestSnapshotStoreQuery:
         rows = store.get_candidates_for_date(dt.date(2026, 1, 1))
         assert rows == []
 
+    def test_get_candidates_for_reaction_date(self, tmp_path):
+        store = _build_store_from_fixture(tmp_path)
+        rows = store.get_candidates_for_reaction_date(dt.date(2026, 1, 5))
+        assert len(rows) == 1
+        assert rows[0]["symbol"] == "AAPL"
+
     def test_no_lookahead(self, tmp_path):
         """Candidates for Jan 7 should NOT appear when querying Jan 6."""
         store = _build_store_from_fixture(tmp_path)
@@ -109,6 +115,11 @@ class TestSnapshotStoreQuery:
         assert dt.date(2026, 1, 6) in dates
         assert dt.date(2026, 1, 7) in dates
 
+    def test_all_reaction_dates_sorted(self, tmp_path):
+        store = _build_store_from_fixture(tmp_path)
+        dates = store.all_reaction_dates()
+        assert dates == [dt.date(2026, 1, 5), dt.date(2026, 1, 6)]
+
     def test_macro_default_empty(self, tmp_path):
         store = _build_store_from_fixture(tmp_path)
         macro = store.get_macro_for_date(dt.date(2026, 1, 6))
@@ -145,9 +156,10 @@ class TestSnapshotStoreFromParquet:
             {"entry_date": "2026-01-05"},
             {"entry_date": "2026-01-10"},
             {"entry_date": "2026-01-07"},
+            {"reaction_date": "2026-01-04"},
         ]
         result = SnapshotStore._compute_date_range(rows)
-        assert result == (dt.date(2026, 1, 5), dt.date(2026, 1, 10))
+        assert result == (dt.date(2026, 1, 4), dt.date(2026, 1, 10))
 
     def test_compute_date_range_empty(self, tmp_path):
         from libs.backtest.snapshot_store import SnapshotStore