chore: repo hygiene — stale gitignore entries, finished plans, superseded research, doc drift

Removed (all recoverable from git history):
- llm_provider_switch_plan.txt: 2026-08-08 DeepSeek/Anthropic multi-provider plan that
  was never implemented (llm_client.py is DeepSeek-only, no `anthropic` anywhere)
- docs/superpowers/specs/2026-07-21-centralized-config-design.md: spec of the
  config.json migration that merged the same day; its plan sibling was already
  deleted in b936d44. app_config's docstring pointer goes with it.
- scripts/compare_oanda_tiingo.py: Phase 0 recent-tail smoke test, superseded by the
  Phase 1/2 study scripts (result recorded in the test plan)
- backtest/research/fetch_oanda_m15.py + oanda_td_divergence.py + its by-hour CSV:
  the 2026-07-24 Oanda-vs-Twelve-Data study, superseded by the 2026-08-14 feed
  divergence report (which keeps a one-line citation)
- backtest/research/burst_profile.py and hour_analysis.py: superseded by burst_map.py
  and book_report.py
- backtest/research/panel_payload_drytest_2026-08-15.py: one-off live DeepSeek probe;
  its .jsonl evidence stays

Added to git: the three 2026-08-14 research reports (feed divergence, re-pin
verification, signal parity), their CSV, and docs/data_source_change_test_plan.md -
CLAUDE.md cites them as standing evidence and their driver scripts were already
committed. The test plan no longer refers to auto-execution.

Hygiene:
- .gitignore: drop oanda_state.json (daemon removed 2026-09-03) and the vanished
  backtest/trailgrid dirs; ignore .coverage
- docs/architecture_diagram.py only writes the PNG when run as a script (it used
  to plot on import); test box refreshed to 461 tests / 31 modules; PNG regenerated
  (the committed one still drew the removed daemon)
- backtest/instructions_for_backtest.txt: manual-journal head replaced by the
  actual reporting checklist; the 2026-09-06 addendum is unchanged
- doc drift: README/publisher list all six published files; news_summary.php says
  4 h (not 24 h) and drops the pre-id sha1 fallback whose window closed;
  analyze_signal_log lists six lenses and --scan-outcomes; agent_panel cost model
  says 4 requests by default; tiingo comment cites the NY-anchored weekend;
  Module 21 title no longer says "execution lifecycle"

Suite: 461 passed, 0 failed.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0156YhogLytzj9P2iH1skGbL

backtest/instructions_for_backtest.txt

@@ -1,61 +1,21 @@
-Note down entery and exit Why we took trade?
-What were each indicator signal on that specific time when we took trade
-How many indicators agreed at trade time
-Always backtest using engine.py and signal_aggregator.py
-Dont take trades on weekends and Fridays
-Keep TP small 1.3 Reward ratio
-At the end of test make table of results and print all values you observed.
-Trade context
-
-Time of day and trading session (Asian/London/NY/overlap)
-Day of week (track Mon/Tue/Wed/Thu performance separately)
-Market regime at entry: trending, ranging, or choppy (use ATR or ADX)
-Volatility level (ATR value or ATR percentile vs recent average)
-Pair/instrument traded
-News proximity (high-impact news within 30 min before/after entry)
-
-Trade execution details
-
-Spread at entry in pips
-Slippage on entry and exit (planned vs actual fill)
-Time in trade (minutes/hours from entry to exit)
-Maximum Adverse Excursion (MAE): how far against you it went
-Maximum Favorable Excursion (MFE): how far in profit before pullback
-
-Risk metrics
-
-Max consecutive losses (streak)
-Max consecutive wins (streak)
-Expectancy: (Win% x Avg Win) minus (Loss% x Avg Loss)
-Expectancy per hour in trade
-Recovery factor: Total R divided by Max DD
-
-Indicator/signal analysis
-
-Each indicator's standalone win rate when it agreed with the trade
-Confluence level performance (2 vs 3 vs 4+ indicators agreeing)
-Which indicator disagreed on losing trades (find the "canary")
-
-Decision quality
-
-Rate each setup A/B/C at entry, before knowing outcome
-Verify A-grade setups actually win more often
-Track system trades vs discretionary deviations separately
-
-Process notes
-
-Screenshot or chart snapshot at entry and exit
-Emotional state (calm, FOMO, revenge, tired)
-What would have happened if you held longer or exited sooner
-
-Aggregate analysis at end of backtest
-
-Equity curve, not just total R
-Performance breakdown by session, day, volatility regime
-Win rate over rolling 20-trade windows (check for edge decay)
-
-Critical extra
-"Do not trade" log: setups flagged but skipped, and what happened after
+Backtest reporting checklist — ForexBot2 (XAU/USD)
+==================================================
+Every backtest report must record:
+- The exact command line (window, --primary-tf, --spread, and any --set /
+  --quality / --strategies / --confirm-tfs overrides) and the "Backend:" log line.
+- Trades, total R, win rate, profit factor and max drawdown for the whole
+  window AND for both halves, at both cost conventions ($0.65 measured-Oanda
+  headline, $0.30 cross-book comparability) — CLAUDE.md rule 1.
+- Per trade (the exported CSV already carries these): entry/exit time and
+  price, exit reason, R multiple, MFE/MAE, entry reasons, and every
+  indicator's signal / value / agreement at decision time.
+- Breakdowns by session/hour, weekday, regime and volatility regime
+  (backtest/research/book_report.py) and, for any candidate change, the
+  removed/added/shifted trade diff against the control book
+  (backtest/research/book_diff.py).
+- The md5 of the exported book whenever the run is a parity or regression check.
+Replays go through the real signal code (analyze_from_dataframes) — never a
+re-implementation.
 
 
 --- 2026-09-06: decision-time feature export ---