fix lint issues in research suites

Author: Pigbibi

research/backtest_cash_buffer_branch_daily_overlay.py

@@ -17,11 +17,10 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_growth_pullback_suite as gp
-import backtest_growth_pullback_v1_2_geometry_repair as v12
-import backtest_stock_alpha_suite as suite
-import backtest_stock_alpha_v1_1_spec_lock as v11
-import backtest_stock_alpha_v1_robustness as robust
+import backtest_growth_pullback_suite as gp  # noqa: E402
+import backtest_growth_pullback_v1_2_geometry_repair as v12  # noqa: E402
+import backtest_stock_alpha_suite as suite  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"
@@ -913,7 +912,7 @@ def write_summary_markdown(
         f"- sector_cap={float(baseline_payload['sector_cap']):.0%}",
         f"- hold_bonus={float(baseline_payload['hold_bonus']):.2f}",
         f"- benchmark={baseline_payload['benchmark_symbol']}",
-        f"- non-rebalance days=no-op baseline; overlay only changes exposure / existing-name weights / BOXX",
+        "- non-rebalance days=no-op baseline; overlay only changes exposure / existing-name weights / BOXX",
         "",
         "## Overlay families tested",
         "- daily_portfolio_throttle_overlay",
@@ -1030,7 +1029,6 @@ def main() -> None:
     best_portfolio_row = family_scores.loc[family_scores["family"] == "daily_portfolio_throttle_overlay"].iloc[0]
     best_name_row = family_scores.loc[family_scores["family"] == "daily_name_level_trim_overlay"].iloc[0]
 
-    combo_rows: pd.DataFrame | None = None
     combo_candidate: OverlayStrategyConfig | None = None
     if overlay_has_incremental_value(best_portfolio_row, baseline_oos_row) and overlay_has_incremental_value(best_name_row, baseline_oos_row):
         portfolio_cfg = next(candidate for candidate in portfolio_candidates if candidate.name == str(best_portfolio_row["strategy"]))
@@ -1041,7 +1039,6 @@ def main() -> None:
         combo_candidate_rows = candidate_rows_from_artifacts(combo_candidate.name, combo_candidate.family, combo_artifacts, benchmark_returns, cost_bps_values=COST_LEVELS)
         all_rows_df = pd.concat([all_rows_df, pd.DataFrame(combo_candidate_rows)], ignore_index=True)
         family_scores = build_family_selection_table(all_rows_df, oos_benchmark_cagr=qqq_oos_cagr)
-        combo_rows = family_scores.loc[family_scores["strategy"] == combo_candidate.name].copy()
 
     # qqq_plus_current_default reference
     qqq_rows = build_reference_rows_from_existing_results(

research/backtest_growth_pullback_suite.py

@@ -15,7 +15,7 @@
 import json
 import math
 import sys
-from dataclasses import dataclass, replace
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Iterable, Mapping
 
@@ -26,9 +26,9 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_stock_alpha_suite as suite
-import backtest_stock_alpha_v1_1_spec_lock as v11
-import backtest_stock_alpha_v1_robustness as robust
+import backtest_stock_alpha_suite as suite  # noqa: E402
+import backtest_stock_alpha_v1_1_spec_lock as v11  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"
@@ -1157,8 +1157,6 @@ def build_recommendation(
     broad_best = family_best_df.loc[family_best_df["family"] == "broad_growth_leadership_pullback"].iloc[0] if (family_best_df["family"] == "broad_growth_leadership_pullback").any() else None
     tech_best = family_best_df.loc[family_best_df["family"] == "tech_heavy_pullback"].iloc[0] if (family_best_df["family"] == "tech_heavy_pullback").any() else None
     crypto_best = family_best_df.loc[family_best_df["family"] == "crypto_equity_theme_pullback"].iloc[0] if (family_best_df["family"] == "crypto_equity_theme_pullback").any() else None
-    trend_best = family_best_df.loc[family_best_df["family"] == "trend_only_control"].iloc[0] if (family_best_df["family"] == "trend_only_control").any() else None
-    naive_best = family_best_df.loc[family_best_df["family"] == "naive_dip_buy_control"].iloc[0] if (family_best_df["family"] == "naive_dip_buy_control").any() else None
 
     if default_row["family"] == "broad_growth_leadership_pullback":
         main_line = "broad_growth_leadership_pullback"

research/backtest_growth_pullback_v1_1_spec_lock.py

@@ -9,7 +9,6 @@
 import sys
 from dataclasses import replace
 from pathlib import Path
-from typing import Iterable
 
 import numpy as np
 import pandas as pd
@@ -18,10 +17,9 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_growth_pullback_suite as gp
-import backtest_stock_alpha_v1_1_spec_lock as v11
-import backtest_stock_alpha_v1_robustness as robust
-import backtest_stock_alpha_suite as suite
+import backtest_growth_pullback_suite as gp  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
+import backtest_stock_alpha_suite as suite  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"
@@ -398,7 +396,6 @@ def build_local_plateau(local_oos_rows: pd.DataFrame, center_row: pd.Series) ->
     frame["within_100bps"] = frame["abs_delta_oos_cagr_vs_center"] <= 0.010
     frame["within_200bps"] = frame["abs_delta_oos_cagr_vs_center"] <= 0.020
     local_only = frame.loc[frame["strategy"] != str(center_row["strategy"])].copy()
-    denom = max(len(local_only), 1)
     summary = {
         "variant_count_ex_center": int(len(local_only)),
         "local_plateau_50bps_share": float(local_only["within_50bps"].mean()) if not local_only.empty else float("nan"),
@@ -510,8 +507,6 @@ def write_markdown_report(
     comparison_df_5bps: pd.DataFrame,
     recommendation: dict[str, object],
 ) -> None:
-    center_occ = occupancy_summary_df.loc[occupancy_summary_df["strategy"] == center_config.name].iloc[0]
-    stable_occ = occupancy_summary_df.loc[occupancy_summary_df["strategy"] == str(stable_result_5bps["strategy"])].iloc[0]
     lines = [
         "# growth_pullback_systematic_v1.1 spec lock",
         "",
@@ -639,7 +634,6 @@ def main() -> None:
     local_oos_main = local_df.loc[(local_df["cost_bps_one_way"] == MAIN_COST_BPS) & (local_df["period"] == "OOS Sample")].copy()
     local_plateau_df, local_plateau_summary = build_local_plateau(local_oos_main, center_oos)
 
-    center_full = extract_period_row(local_df, center_config.name, MAIN_COST_BPS, "Full Sample")
     local_summary_rows = []
     for strategy_name in sorted(local_df["strategy"].unique()):
         full_row = extract_period_row(local_df, strategy_name, MAIN_COST_BPS, "Full Sample")

research/backtest_growth_pullback_v1_2_geometry_repair.py

@@ -18,10 +18,10 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_growth_pullback_suite as gp
-import backtest_growth_pullback_v1_1_spec_lock as v11g
-import backtest_stock_alpha_v1_robustness as robust
-import backtest_stock_alpha_suite as suite
+import backtest_growth_pullback_suite as gp  # noqa: E402
+import backtest_growth_pullback_v1_1_spec_lock as v11g  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
+import backtest_stock_alpha_suite as suite  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"
@@ -672,7 +672,6 @@ def main() -> None:
         deployment_summary_rows.append(summarize_deployment(monthly))
 
     candidate_df = pd.DataFrame(candidate_rows)
-    deployment_monthly_df = pd.concat(deployment_monthly_frames, ignore_index=True)
     deployment_summary_df = pd.DataFrame(deployment_summary_rows)
 
     reference_rows_df, reference_artifacts = gp.build_reference_rows(context, COST_LEVELS)

research/backtest_growth_pullback_v1_3_spec_normalization.py

@@ -8,7 +8,6 @@
 import sys
 from dataclasses import replace
 from pathlib import Path
-from typing import Iterable
 
 import numpy as np
 import pandas as pd
@@ -17,9 +16,9 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_growth_pullback_suite as gp
-import backtest_growth_pullback_v1_2_geometry_repair as v12
-import backtest_stock_alpha_v1_robustness as robust
+import backtest_growth_pullback_suite as gp  # noqa: E402
+import backtest_growth_pullback_v1_2_geometry_repair as v12  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"

research/backtest_growth_pullback_v1_4_cash_buffer_refinement.py

@@ -18,10 +18,10 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_growth_pullback_suite as gp
-import backtest_growth_pullback_v1_2_geometry_repair as v12
-import backtest_growth_pullback_v1_3_spec_normalization as v13
-import backtest_stock_alpha_v1_robustness as robust
+import backtest_growth_pullback_suite as gp  # noqa: E402
+import backtest_growth_pullback_v1_2_geometry_repair as v12  # noqa: E402
+import backtest_growth_pullback_v1_3_spec_normalization as v13  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"

research/backtest_growth_pullback_v1_5_freeze_review.py

@@ -16,11 +16,11 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_growth_pullback_suite as gp
-import backtest_growth_pullback_v1_2_geometry_repair as v12
-import backtest_growth_pullback_v1_3_spec_normalization as v13
-import backtest_growth_pullback_v1_4_cash_buffer_refinement as v14
-import backtest_stock_alpha_v1_robustness as robust
+import backtest_growth_pullback_suite as gp  # noqa: E402
+import backtest_growth_pullback_v1_2_geometry_repair as v12  # noqa: E402
+import backtest_growth_pullback_v1_3_spec_normalization as v13  # noqa: E402
+import backtest_growth_pullback_v1_4_cash_buffer_refinement as v14  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"

research/backtest_stock_alpha_suite.py

@@ -18,7 +18,7 @@
 import json
 import math
 import sys
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Iterable, Mapping
 

research/backtest_stock_alpha_v1_1_spec_lock.py

@@ -13,7 +13,6 @@
 
 import argparse
 import json
-import math
 import sys
 from dataclasses import replace
 from pathlib import Path
@@ -26,8 +25,8 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_stock_alpha_suite as suite
-import backtest_stock_alpha_v1_robustness as robust
+import backtest_stock_alpha_suite as suite  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"

research/backtest_stock_alpha_v1_1b_candidate_gate.py

@@ -27,9 +27,9 @@
 if str(SCRIPT_DIR) not in sys.path:
     sys.path.insert(0, str(SCRIPT_DIR))
 
-import backtest_stock_alpha_suite as suite
-import backtest_stock_alpha_v1_1_spec_lock as v11
-import backtest_stock_alpha_v1_robustness as robust
+import backtest_stock_alpha_suite as suite  # noqa: E402
+import backtest_stock_alpha_v1_1_spec_lock as v11  # noqa: E402
+import backtest_stock_alpha_v1_robustness as robust  # noqa: E402
 
 
 DEFAULT_RESULTS_DIR = SCRIPT_DIR / "results"
@@ -591,7 +591,7 @@ def main() -> None:
         & (candidate_gate_df["period"] == "OOS Sample")
     ].iloc[0]
     neighbor_oos_rows_5bps = candidate_gate_df.loc[
-        (candidate_gate_df["is_neighbor"] == True)
+        (candidate_gate_df["is_neighbor"])
         & (candidate_gate_df["cost_bps_one_way"] == MAIN_COST_BPS)
         & (candidate_gate_df["period"] == "OOS Sample")
     ].copy()