# Removed research and legacy code

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

```bash
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## Root factor-mining scripts (2026-04, before the research/lab harness; superseded by research/lab/factor_scan.py on the PIT panels)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## Old runners superseded by the monitor service, precompute_backtest.py and research/lab/evaluate.py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`).