Bug: pending candidate DB records were only swept inside the "has open
positions" branch of run_eod_exit, so a server restart mid-day (ORB
detection ran, no breakouts, no positions) left candidates as "pending"
forever.
Fix: move the in-memory and DB pending sweep to run unconditionally before
the positions check.
Test: TestEodDbSweep verifies both code paths (no-position + in-memory).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Tests the three structural guards that were previously untested:
- Rolling loss filter: skip logic, boundary, window slicing, date exclusion
- Circuit breaker: 25% drawdown halts session
- max_simultaneous_entries: blocks new entries when at cap
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Delete v7.360-v7.363 experiment configs (rotation/momentum tests)
- Remove _schedule_momentum_breakout_candidates() from backtester run.py
- Remove MomentumBreakoutConfig from domain.py
- Delete momentum_calendar.py, momentum_screener.py, build_momentum_calendar.py
- Delete data/momentum_calendar/ parquet data
Valid period performance was -31.36% vs +152.4% baseline — sleeve is not viable
without walk-forward validation. Abandoning for now.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Backtester (run.py):
- cash_available = (self._cash + parking_value) * multiplier caused trades to be
approved even when self._cash ≈ 0 (all money in SGOV/QQQ). Trades executed
by deducting from self._cash → negative cash (phantom money).
- Fix: after simulate_entry, if self._cash < actual trade cost and parking exists,
call _liquidate_parking_for_cash(shortfall) before deducting from cash.
- Verified: 2022-2026 backtest with qqqm_low_dd shows 0 cash_negative events.
Live engine (engine.py):
- Add _parking_liquidate_for_event(): frees parking cash to fund event entries.
SGOV (virtual) reduces entry_value in DB; QQQM/QQQ sells real shares via broker.
- Both entry loops (engines mode + flat/reaction_close mode) now attempt parking
liquidation when plan.skip_reason == "insufficient_cash" before giving up.
Also includes prior session work (accumulated since last commit):
- 6 novel parking gate signals: VRP, Market Temperature, Hurst exponent, Rolling
Kurtosis, Return Autocorrelation, SPY-QQQ Correlation (composite risk score v2)
- QQQM parking symbol support (lower expense ratio vs QQQ)
- Snapshot auto-refresh + bar extension cache (pickle) to avoid 10-min re-fetches
- Bar extension clamps to last market-closed date (ET 4PM check)
- fithia2 refresh command; --no-refresh flag for paper backtest
- Paper backtest macro extension beyond last event date (parking-only periods)
- parking_state DB schema: 7 new columns (peak_price, gate_in_sgov,
committed_target, pending_target, pending_days, sgov_entry_value, sold_today)
- Live engine: target confirmation (2-day), top-up drawdown gate, trailing stop,
SGOV interest accrual, full 6-signal gate evaluation
- New PARKING_PRESETS: qqqm_low_dd, composite_v2, vv_24_vrp8, vt_24_t13, etc.
- Web GUI / CLI result parity fix (Oracle URL via get_settings().stock_oracle_url)
- Force-close uses last_exec_date (has bar data); parking liquidates at last_date
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Builds a full synthetic market data pipeline to test strategies against
12 diverse market regimes (bull/bear/crash/chop/rotation/liquidity drought)
that may not exist in historical data. Computes Regime Robustness Score (RRS)
to detect overfitting and environment-specific fragility.
- libs/backtest/scenarios/: price_gen, macro_gen, event_gen, coupling,
store_builder, scenarios (12 pre-built), robustness (RRS)
- apps/scenario/cli.py: `fithia2 scenario-test` with Rich output
- apps/tracker/cli.py: scenario-test command routing
- tests/: 83 unit tests across 3 new test files
- docs/scenario_test.md: usage guide and result interpretation
- docs/research_workflow_and_handoff.md: Step 5.5 scenario test added
Fix: no_signal scenario uses drift=0% (was +10%) for fair signal integrity scoring.
Fix: synthetic candidates now carry macro_vix/macro_hy_spread from macro_by_date
to pass selector engine filters.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ReconciliationReport dataclass tracking orphaned/ghost positions and stale orders
- _cancel_stale_orders(): cancel leftover open orders at daily run start
- _reconcile_positions(): detect Alpaca vs local state mismatches; auto-close ghost positions with RECONCILED exit reason
- _verify_order_fill(): poll broker up to 2s to confirm market order fill before saving state
- _check_kill_switch(): activate and persist kill switch at 25% drawdown; blocks new entries
- run_daily() and _process_entries() wired with all safety checks
- 18 unit tests covering all reconciliation scenarios
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Track experiment cycles with SQS scoring (0-100), JSONL journal, and
auto-generated leaderboard to prevent duplicate experiments and enable
data-driven strategy decisions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove 5 non-alpha features (earnings surprise, risk penalty, parse confidence,
direction clarity, LM sentiment) from composite score to eliminate double-counting
with hard gates and noise sources. Redistribute weights to 5 alpha features.
Add default-deny for unknown event types, no-follow-through early exit (D+1),
kill switch log-only mode, macro regime size scaler. Remove SUE gate (Gate 8).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Flip scoring weights so event/document quality is primary signal (55%)
and market confirmation is secondary (35%). Add research mode with
kill-switch cooldown/reset, veto gates for bad events, reduced portfolio
risk, and 4 diagnostic analysis scripts.
Phase A: Research mode kill-switch reset, risk reduction (0.5%/trade,
max 4 positions), bullish-only direction for all event types.
Phase B: 2 new sub-scorers (parse_confidence, direction_clarity),
4 veto gates (oneoff risk, parse confidence, unknown/bearish direction).
Phase C: signal_quality, event_type_decomposition, kill_switch_impact,
concurrent_position analysis scripts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace naive abs(reaction_day_return) fallback with a composite score
from 4 market microstructure features available at entry time:
1. Reaction quality (35%) — moderate positive return (PEAD zone) is
ideal; extreme positives penalized as "priced in"
2. Close strength (30%) — close near session high = buyers won
3. Volume conviction (20%) — 1.2-2x is healthy; >3x is exhaustion
4. Gap quality (15%) — small positive gap = orderly strength
Real data results (14 events, b1868603 snapshot):
- Score filters out 6 of 10 losers (DDOG -11.7%, META -9.1%, etc.)
- With threshold 0.5: return -2.63% → +0.27%, drawdown 4.24% → 0.86%
- Profit factor 0.44 → 1.16 (turns profitable)
- MSFT loss (-8.7%) is macro-driven, not predictable from stock features
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add `think: False` and `num_ctx: 8192` to Ollama payload:
Qwen3.5 extended thinking mode generated 1300+ internal reasoning
tokens before each response, adding 30-60s latency per LLM call.
Disabling it reduces parse time from 600s timeout to ~13s.
- Rewrite OllamaClient to use sync httpx.Client inside asyncio.to_thread():
Async httpx inside an active asyncpg SQLAlchemy session context on
Python 3.13 hung indefinitely. Synchronous httpx in a thread pool
completely isolates Ollama I/O from the asyncio event loop.
- Fix filing_poller to set issuer_id/symbol_id on Document records:
Missing FK caused feature_builder to reject all events with
event_no_symbol warning. Now looks up IssuerMaster/SymbolMaster
by ticker before creating Document rows.
- Update test_llm_client to mock _sync_call instead of _client attr.
- Raise ollama_timeout default to 600s for large document processing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Oracle 서비스 어댑터 5개를 실제 API 포맷에 맞게 수정
- 모든 경로에 /api/v1/ prefix 추가
- price: data[] → bars 매핑, volume float→int
- filings: accession_number→accession_no, total_count→total
- financial: financial_data[] → periods, period_date 파싱
- finra: entries[] → data 매핑
- fred: data.observations 언패킹, value string→float (버그 수정 포함)
- fixtures 6개를 실제 Oracle 응답 포맷으로 전면 교체
- 통합 테스트에서 httpx_mock 완전 제거 → 실제 Oracle 직접 호출
- 신규 단위 테스트 3개 파일 추가 (logging, fred_service, llm_parser_stub)
- test_retries.py에 exhaustion 테스트 추가
- test_oracle_client.py에 connection/timeout/no-ctx 테스트 추가
- Phase 1/2 testing_checklist.md 실제 구현 기준으로 전면 재작성
- 전체 114 tests pass (unit 100 + replay 5 + integration 9)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- filing_poller: add --start-date/--end-date CLI args for historical backfill
(defaults to 7 days ago when omitted)
- OracleClient.get/post: apply with_retry(max_attempts=3) so transient
connection errors, timeouts, and 5xx responses are automatically retried
with exponential backoff (0.1s→0.2s→fail)
- financial_features: new compute_financial_features() extracting latest_eps,
latest_gross_margin, latest_operating_margin, eps_growth_qoq,
revenue_growth_qoq from FinancialDataResponse
- feature_builder: wire FinancialService into build_features_for_event(),
persisting financial_v1 FeatureSnapshot (non-fatal if unavailable)
- tests: 94 pass (81→89 unit + 5 replay); +8 new tests covering financial
features and retry success path
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
I Luk Kim