Files

Gahow Wang a9683b6d1f chore: remove pre-lab research and dead strategies; index them in research/ARCHIVE.md

Removed (79 files, all in history; research/ARCHIVE.md lists each with its
purpose and the commit to restore it from):
- root factor_*.py mining scripts (2026-04) and yearly_sweep.py
- scripts/run_daily.{py,sh} (superseded by the monitor service; no crontab)
- strategies no registry, test or live book uses: trend_rider_v8,
  adaptive_blend, cross_asset_momentum, long_hedged, ls_momentum
- 55 research/*.py scripts from 2026-04/05 written before research/lab,
  the us-alpha design spec, and the 7 tests that only covered them

Kept research/permanent_yearly.py: bridge.py and precompute_backtest.py
import equity_with_cashflows. CLAUDE.md gains the archive note and the
command lines of the studies committed in the previous commit.

Checks: 99 unit tests OK (125 before, minus the 26 removed with their
modules), all 157 remaining modules import, 94 registry strategies build,
bridge.py list OK, web 23 tests OK and build OK.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

2026-10-09 22:20:59 -07:00

8.3 KiB

Raw Blame History

Removed research and legacy code

Removed on 2026-10-09 in the repo clean-up. Every file is in git history; restore one with

git show 92a2a58:<path> > <path>

Nothing live (trader.py, bridge.py, backtest_runner.py, precompute_backtest.py, tests, research/lab) imported any of them. Their conclusions that still matter are in CLAUDE.md and the research/lab docstrings.

Root factor-mining scripts (2026-04, before the research/lab harness; superseded by research/lab/factor_scan.py on the PIT panels)

file	purpose
`factor_backtest.py`	Backtest best factor combinations with yearly return breakdown.
`factor_deep_analysis.py`	Deep factor analysis — orthogonality, proper correlations, residual alpha.
`factor_final_check.py`	Final robustness check on champions from the discovery loop.
`factor_loop.py`	Iterative Factor Discovery Loop.
`factor_real_backtest.py`	Factor research v2 — Portfolio-first approach.
`factor_research.py`	Factor Research Script — Professional QR-style factor mining.
`factor_robustness.py`	Robustness checks for winning factor strategies.
`factor_yearly_fresh.py`	Rebalancing frequency comparison: daily (1d) vs weekly (5d) vs biweekly (10d) vs monthly (21d).
`factor_yearly_report.py`	Yearly ROI report for champion strategies vs SPY, starting from $10,000.

Old runners superseded by the monitor service, precompute_backtest.py and research/lab/evaluate.py

file	purpose
`yearly_sweep.py`	Run all US strategies and report yearly performance vs SPY.
`scripts/run_daily.py`	Daily live-trading update for all registered strategies.
`scripts/run_daily.sh`	cron wrapper for scripts/run_daily.py

Strategies no registry, test or live book used

file	purpose
`strategies/trend_rider_v8.py`	TrendRider V8 — V7 + TMF risk-off upgrade.
`strategies/adaptive_blend.py`	PIT-Compliant Adaptive Multi-Strategy Blend — Sharpe 1.5+ target.
`strategies/cross_asset_momentum.py`	Cross-asset time-series momentum strategy (ETF-only, inherently PIT).
`strategies/long_hedged.py`	Market-hedged long-only stock portfolio.
`strategies/ls_momentum.py`	Industry-neutral long/short momentum on the S&P 500.

research/*.py from 2026-04/05 (written before the research/lab harness of PIT panels, next-open fills and preregistration; the questions that still matter were redone there)

file	purpose
`docs/superpowers/specs/2026-04-17-us-alpha-research-design.md`	design spec for research/us_alpha_*
`research/alpha_factors.py`	Alpha factor library — price-only, academically motivated, with a rolling-IC
`research/alpha_research.py`	Professional QR-style factor research on the PIT S&P 500 universe.
`research/dca_simulation.py`	DCA simulation: $10,000 initial + $5,000 every Feb & Aug from 2017.
`research/event_factors.py`	(no docstring)
`research/factor_optimize.py`	Factor-level optimization on the point-in-time S&P 500 universe.
`research/fetch_historical.py`	Fetch price history for all tickers that were ever S&P 500 members — including
`research/interaction_alpha.py`	Interaction / multiplicative factor strategy.
`research/ls_momentum_eval.py`	Evaluate the industry-neutral L/S momentum strategy with realistic costs.
`research/new_frameworks_eval.py`	New frameworks: Vol trading (#2) + Event-driven simplified (#4).
`research/optimize.py`	End-to-end optimization study for the US recovery+momentum strategy family,
`research/pit_backtest.py`	Point-in-time backtest runner.
`research/pit_comparison.py`	PIT-compliant backtest: mask prices to historical S&P 500 membership.
`research/pit_optimization.py`	PIT-compliant strategy optimization.
`research/regime_filters.py`	(no docstring)
`research/regime_stock_picker_eval.py`	Does V3 regime timing + S&P 500 stock picking improve over either alone?
`research/run_interaction.py`	Evaluate interaction/ensemble strategies on 1/3/5/10y PIT windows with a
`research/sharpe_blend.py`	PIT-compliant Sharpe 1.5+ blend: V5 ETF timing + PIT stock-picking + cross-asset momentum.
`research/single_stock_swing.py`	Single-stock swing trading: adapt V3/V7 concepts to 20 famous stocks.
`research/smart_dca_eval.py`	Smart DCA Strategy Evaluation — comprehensive comparison of DCA approaches.
`research/sota_ranking.py`	Rank all top strategies head-to-head on the same 10-year PIT-safe data.
`research/strategies_plus.py`	Optimization variants of RecoveryMomentumStrategy.
`research/strategy_final_report.py`	FINAL REPORT: Strategy improvement results — 10-year yearly backtest.
`research/strategy_improvement_eval.py`	Comprehensive strategy improvement evaluation.
`research/strategy_improvement_r2.py`	Round 2: Strategy improvement iteration.
`research/strategy_improvement_r3.py`	Round 3: Signal-level ensemble and enhanced factor combo.
`research/strategy_improvement_r4.py`	Round 4 - Final iteration: Optimize the winning EnhFC strategy.
`research/strategy_risk_managed_eval.py`	Risk-Managed Ensemble Strategy Evaluation.
`research/strategy_risk_managed_r2.py`	Round 2: Risk-Managed Ensemble with DD-reactive approach.
`research/strategy_sharpe_boost.py`	Sharpe boost research: blend pure momentum into the Ensemble signal.
`research/strategy_sharpe_boost_v2.py`	Sharpe boost v2: Dispersion-adaptive exposure + momentum blend.
`research/strategy_sharpe_boost_v3.py`	Sharpe boost v3: Concentration + rebalance frequency + trailing alpha.
`research/strategy_sharpe_boost_v4.py`	Sharpe boost v4: Long holding period (42d rebal) is the key lever.
`research/strategy_sharpe_boost_v5.py`	Sharpe boost v5: Fine-tune DD dampener on top of the Sharpe 1.52 config.
`research/strategy_sweep.py`	Unified 3/5/10-year PIT backtest for every production strategy.
`research/trade_analysis.py`	Trade-level analysis of SharpeBoostedEnsembleStrategy.
`research/trend_rider_p0.py`	P0 robustness validation for TrendRiderV3.
`research/trend_rider_robustness.py`	Robustness analysis for TrendRiderV3.
`research/trend_rider_v5_eval.py`	Evaluate TrendRiderV5 vs V3 baseline and benchmarks.
`research/trend_rider_v6_eval.py`	Evaluate TrendRiderV6 vs V5 baseline.
`research/us_alpha_pipeline.py`	(no docstring)
`research/us_alpha_report.py`	(no docstring)
`research/us_combo_sweep.py`	(no docstring)
`research/us_fundamentals.py`	(no docstring)
`research/us_universe.py`	(no docstring)
`research/v5_drawdown_trace.py`	Trace where V3/V5 maximum drawdowns occur and what holdings they had.
`research/v5_p0_validate.py`	P0 validation for TrendRiderV5 — walk-forward + bootstrap.
`research/v6_voltarget.py`	Vol-targeting overlay on V5/V6 blends — tests if dynamic exposure scaling
`research/v7_breakthrough_eval.py`	Three directions to break V7+VT36's ceiling (61% Ann, Sharpe 1.89).
`research/v7_breakthrough_fixed.py`	Fixed re-run for Directions B and C based on review feedback.
`research/v7_literature_alpha.py`	Literature-informed alpha research: can we beat V7+VT36?
`research/v7_parameter_sweep.py`	V7 parameter sweep: vol-target range + adaptive profit-take variants.
`research/v7_synthetic_leverage_eval.py`	Direction 2: V7 regime + synthetic 2x/3x leveraged individual stocks.
`research/v7_three_ideas_eval.py`	Test three structural improvements to V7+VT36 identified by independent review.
`research/v7_trade_audit.py`	V7+VT36 full trade log: regime changes, instrument switches, PT events,

Tests of the removed research modules

file	tested
`tests/test_alpha_signals.py`	research/event_factors.py, research/regime_filters.py
`tests/test_fetch_historical.py`	research/fetch_historical.py
`tests/test_trend_rider_robustness.py`	research/trend_rider_robustness.py
`tests/test_us_alpha_pipeline.py`	research/us_alpha_pipeline.py, research/us_alpha_report.py
`tests/test_us_combo_sweep.py`	research/us_combo_sweep.py
`tests/test_us_fundamentals.py`	research/us_fundamentals.py
`tests/test_us_universe.py`	research/us_universe.py

Kept on purpose: research/permanent_yearly.py (bridge.py and precompute_backtest.py import its equity_with_cashflows).

8.3 KiB Raw Blame History