Research: S/R levels, the target exit, and the entry gate

Investigated whether our support/resistance detection follows best practice
and whether we actually use it that way. Three findings, all backed by runs
against the prod snapshot and written up in docs/research/sr-levels-and-exits.md:

- The S/R target must NOT become an exit. Honoring it as a take-profit on top
  of the 3x ATR trail drops Sharpe 2.04 -> 1.47 and halves CAGR. Win rate rises
  (37.5% -> 40.0%), which is the tell: it truncates the right tail where
  momentum's edge lives.
- The clear-air fallback (synthesize a 3xATR target where no resistance exists,
  so 52-week-high breakouts stop being vetoed) looked strictly better in-sample
  (Sharpe 2.04 -> 2.07, CAGR 50.4% -> 62.3%, DD 21.4% -> 20.1%) but FAILED a
  real out-of-sample holdout: on entries after 2024-07-01 it is worse on Sharpe
  (2.78 -> 2.45) and Calmar, better only on raw CAGR. Not shipped.
- The detector itself is weak vs best practice (POC/VAH/VAL computed then
  discarded, HVN = any above-mean bin, 1.48x volume double-counting, "touch"
  counts pass-throughs, no round numbers), but its only causal path to P&L is
  the entry gate. Fix it for the displayed levels, not for returns.

Method note: nested lookback windows are NOT out-of-sample. The in-sample result
was clean, large, and consistent across five windows, and still did not survive
a proper entry-date split.

All research paths are off by default and the default report is unchanged:
  BACKTEST_RESEARCH_EXITS=1        take-profit exit rows
  BACKTEST_ATR_TARGET_FALLBACK=k   synthetic k*ATR target when S/R offers none
  BACKTEST_FALLBACK_CLEAR_AIR_ONLY=1  restrict that to genuinely clear air
  BACKTEST_HOLDOUT_SPLIT=YYYY-MM-DD   train/test split by entry date

Also fixes two reproducibility holes found while reconciling our local baseline
against the live report:

- create_backtest_snapshot.py now copies paper_% settings. The production
  monitor row replays the runtime exit policy via get_exit_policy(); without
  those keys a snapshot silently falls back to code defaults, so a live-tuned
  exit would never be reflected.
- Migration 020 drops activation_min_expected_value and
  activation_min_target_probability. Both are orphans of the June EV-gate
  redesign, read by no code path, but prod carries min_target_probability = 50.0
  which implies a probability floor that is not enforced (the real floor is the
  20% constant in qualification.py).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

scripts/create_backtest_snapshot.py

@@ -1,9 +1,9 @@
 """Create a minimal local SQLite snapshot for offline backtest research.
 
 Copies only the data required by app.services.backtest_service.run_backtest:
-tickers, OHLCV bars, SPY benchmark closes, and activation/recommendation
-settings. Other system settings are intentionally skipped to avoid copying
-secrets into local snapshot files.
+tickers, OHLCV bars, SPY benchmark closes, and the activation / recommendation /
+paper-exit settings the run reads. Other system settings are intentionally
+skipped to avoid copying secrets into local snapshot files.
 """
 
 from __future__ import annotations
@@ -144,6 +144,12 @@ async def _main() -> None:
                     where=or_(
                         SystemSetting.key.like("activation_%"),
                         SystemSetting.key.like("recommendation_%"),
+                        # The production portfolio-monitor row replays the RUNTIME
+                        # exit policy via get_exit_policy(). Without these keys a
+                        # snapshot silently falls back to the code defaults, so a
+                        # live-tuned exit would not be reflected — the snapshot run
+                        # would disagree with prod and give no hint why.
+                        SystemSetting.key.like("paper_%"),
                     ),
                 ),
                 "benchmark_prices": await _copy_table(source, dest, BenchmarkPrice, batch_size=args.batch_size),