research: add strategy evaluation and exploration scripts

Add 28 research scripts covering DCA simulation, momentum evaluation,
Sharpe optimization, trend rider analysis, and US fundamentals exploration.

research/trend_rider_v5_eval.py

@@ -0,0 +1,150 @@
+"""Evaluate TrendRiderV5 vs V3 baseline and benchmarks.
+
+Run:
+    uv run python -m research.trend_rider_v5_eval
+"""
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+from dataclasses import asdict
+from itertools import product
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from research.trend_rider_robustness import (
+    buy_hold_weights,
+    evaluate_weights,
+    load_price_panel,
+    portfolio_returns,
+)
+from strategies.permanent import TrendRiderV3
+from strategies.trend_rider_v5 import TrendRiderV5
+
+
+IS_START = "2015-01-02"
+IS_END = "2020-12-31"
+OOS_START = "2021-01-01"
+OOS_END = "2026-05-07"
+FULL_START = IS_START
+FULL_END = OOS_END
+
+
+def _fmt(x: float) -> str:
+    return f"{x * 100:7.2f}%"
+
+
+def print_eval(label: str, ev) -> None:
+    print(
+        f"  {label:<32s}  "
+        f"CAGR {_fmt(ev.cagr)}  Vol {_fmt(ev.volatility)}  "
+        f"Sharpe {ev.sharpe:5.2f}  MDD {_fmt(ev.max_drawdown)}  "
+        f"Calmar {ev.calmar:5.2f}  X {ev.final_multiple:6.2f}  "
+        f"Sw {ev.switches:4d}  Turn {ev.avg_daily_turnover*100:5.2f}%"
+    )
+
+
+def evaluate_panel(name: str, weights: pd.DataFrame, prices: pd.DataFrame,
+                   start: str, end: str, transaction_cost: float = 0.001):
+    return evaluate_weights(name, weights, prices[weights.columns],
+                            transaction_cost=transaction_cost,
+                            start=start, end=end)
+
+
+def annual_returns_table(weights_map: dict, prices: pd.DataFrame,
+                         transaction_cost: float = 0.001) -> pd.DataFrame:
+    out = {}
+    for name, w in weights_map.items():
+        rets = portfolio_returns(w, prices[w.columns], transaction_cost=transaction_cost)
+        rets = rets[(rets.index >= FULL_START) & (rets.index <= FULL_END)]
+        out[name] = (1.0 + rets).groupby(rets.index.year).prod() - 1.0
+    return pd.DataFrame(out)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--transaction-cost", type=float, default=0.001)
+    parser.add_argument("--out-dir", default="data")
+    parser.add_argument("--vol-target", type=float, default=0.30)
+    args = parser.parse_args()
+
+    os.makedirs(args.out_dir, exist_ok=True)
+    prices = load_price_panel()
+    print(f"Panel: {prices.index.min().date()} to {prices.index.max().date()}, {prices.shape[1]} cols")
+
+    candidates = {
+        "V3 default":              TrendRiderV3(),
+        "V5 default":              TrendRiderV5(),
+        # Tighter panic detection
+        "V5 panic 1.4 / 3%":       TrendRiderV5(
+            panic_vol_ratio=1.4, panic_peak_drop_pct=0.03
+        ),
+        "V5 panic 1.5 / 3.5%":     TrendRiderV5(
+            panic_vol_ratio=1.5, panic_peak_drop_pct=0.035
+        ),
+        "V5 panic 1.8 / 5%":       TrendRiderV5(
+            panic_vol_ratio=1.8, panic_peak_drop_pct=0.05
+        ),
+        # Combine panic + harder promote
+        "V5 panic+conserv":        TrendRiderV5(
+            promote_thresholds=(0.45, 0.70),
+            demote_thresholds=(0.35, 0.55),
+            panic_vol_ratio=1.4, panic_peak_drop_pct=0.03,
+        ),
+        # No panic at all (pure conviction)
+        "V5 no panic":             TrendRiderV5(
+            panic_vol_ratio=99.0, panic_peak_drop_pct=0.99
+        ),
+    }
+
+    weights_map = {}
+    print("\n=== Generating signals ===")
+    for name, strat in candidates.items():
+        weights_map[name] = strat.generate_signals(prices)
+
+    print("\n=== FULL period (2015-01 → 2026-05) ===")
+    rows = []
+    for name, w in weights_map.items():
+        ev = evaluate_panel(name, w, prices, FULL_START, FULL_END, args.transaction_cost)
+        rows.append(asdict(ev) | {"name": name})
+        print_eval(name, ev)
+    spy_w = buy_hold_weights(prices, "SPY")
+    qqq_w = buy_hold_weights(prices, "QQQ")
+    bench = {
+        "SPY B&H": evaluate_panel("SPY B&H", spy_w, prices, FULL_START, FULL_END, 0.0),
+        "QQQ B&H": evaluate_panel("QQQ B&H", qqq_w, prices, FULL_START, FULL_END, 0.0),
+    }
+    for name, ev in bench.items():
+        print_eval(name, ev)
+
+    print("\n=== IS (2015 → 2020) ===")
+    for name, w in weights_map.items():
+        ev = evaluate_panel(name, w, prices, IS_START, IS_END, args.transaction_cost)
+        print_eval(name, ev)
+    for name, w in [("SPY B&H", spy_w), ("QQQ B&H", qqq_w)]:
+        ev = evaluate_panel(name, w, prices, IS_START, IS_END, 0.0)
+        print_eval(name, ev)
+
+    print("\n=== OOS (2021 → 2026-05) ===")
+    for name, w in weights_map.items():
+        ev = evaluate_panel(name, w, prices, OOS_START, OOS_END, args.transaction_cost)
+        print_eval(name, ev)
+    for name, w in [("SPY B&H", spy_w), ("QQQ B&H", qqq_w)]:
+        ev = evaluate_panel(name, w, prices, OOS_START, OOS_END, 0.0)
+        print_eval(name, ev)
+
+    print("\n=== Annual returns ===")
+    annual = annual_returns_table(weights_map, prices, args.transaction_cost)
+    annual = annual.applymap(lambda x: f"{x*100:6.1f}%")
+    print(annual.to_string())
+
+    pd.DataFrame(rows).to_csv(os.path.join(args.out_dir, "v5_eval_full.csv"), index=False)
+    annual.to_csv(os.path.join(args.out_dir, "v5_eval_annual.csv"))
+
+
+if __name__ == "__main__":
+    main()