Gahow Wang
2c19e5efe5
Register two live strategies from the 2026-09-29 US research: - lowrisk_top20: idiosyncratic vol + low beta, top 20, monthly, buffer 2 (new `low_risk` signal in strategies/robust_momentum.py) - us_three_sleeve: 1/3 each of overnight_recovery_top10, lowrisk_top20 and trend_rider_v7_vt24 in one account (strategies/sleeve_portfolio.py, a mixed stock+ETF strategy). Sleeve shares drift with their returns and reset at a month start once any is >25% off its share. Research (research/lab, S&P 500 PIT, next-open fills): - rotation.py / persistence.py: walk-forward factor weighting (factor momentum, rolling IC, market state) and last-year's-winner selection do not beat a fixed composite - us_sweep.py: holdings count, z/vol weights, dynamic N, tranching and market overlays (Faber SMA, de-risk vol target, statistical jump model, momentum state); engine.py gains zvol weighting and dyn_n - fetch_us_fundamentals.py / fundamentals.py / fund_eval.py: SEC EDGAR point-in-time fundamentals (first-filed values) and factor tests - trend_rider_long.py / us_final.py: 2010-2026 TrendRider curves and the 3-sleeve check (2011-2026 Sharpe 1.21, CAGR 21.9%, MaxDD -23%) Web-UI engine, 2017-09..2026-09, $2/order: us_three_sleeve CAGR 28.9%, Sharpe 1.49, MaxDD -24.8%. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
"""
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TrendRider equity curves from 2010 (the live ETF panel starts in 2016), for
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the US portfolio research (research/lab/us_final.py).
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PYTHONPATH=. uv run python -m research.lab.trend_rider_long
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Downloads the TrendRider ETF basket from 2008 ticker by ticker (batched
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Yahoo requests fail intermittently) and runs each variant through the live
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book simulation ($2 per order on $10k, 5 bp, 10% resize band). Writes
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data/research/tr_long.pkl: {"equity": {name: Series}, "close": DataFrame}.
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A ticker that still fails is left out; the strategy then picks among the
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rest of its basket (in the 2026-09-29 run DBC was missing, so the risk-off
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basket was GLD alone).
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"""
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import os
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import pickle
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import time
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import warnings
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import pandas as pd
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import yfinance as yf
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from main import simulate_live_book, strategy_positions
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from research.lab.fetch_us import _proxy_env
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from strategies.permanent import ETF_UNIVERSE
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from trader import RESIZE_BAND, STRATEGY_REGISTRY
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warnings.filterwarnings("ignore")
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OUT = os.path.join("data", "research", "tr_long.pkl")
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VARIANTS = ["trend_rider_v7_vt24", "trend_rider_v7_vt36", "trend_rider_v5_vt30"]
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BLOCKS = [("2010-03-01", "2013-02-28"), ("2013-03-01", "2016-09-26"),
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("2016-09-27", "2021-09-26"), ("2021-09-27", "2026-09-25"), ("2010-03-01", "2026-09-25")]
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def fetch(tickers: list[str], start: str = "2008-01-01") -> tuple[pd.DataFrame, pd.DataFrame]:
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close, opens = {}, {}
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for t in tickers:
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for attempt in range(5):
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try:
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h = yf.Ticker(t).history(start=start, auto_adjust=True)
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if len(h):
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close[t], opens[t] = h["Close"], h["Open"]
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break
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except Exception as exc: # noqa: BLE001 — retry transient Yahoo errors
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print(f" {t}: {exc}")
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time.sleep(3 * (attempt + 1))
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c, o = pd.DataFrame(close), pd.DataFrame(opens)
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c.index = c.index.tz_localize(None).normalize()
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o.index = c.index
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return c, o
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def main() -> None:
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_proxy_env()
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tickers = sorted(set(ETF_UNIVERSE) | {"SPY", "SHY"})
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close, opens = fetch(tickers)
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print("missing:", sorted(set(tickers) - set(close.columns)))
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out = {}
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for name in VARIANTS:
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held = strategy_positions(STRATEGY_REGISTRY[name](), close, open_data=opens)
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eq, _ = simulate_live_book(held, close, 10_000, 0.0005, 2.0, 0, 0, 50.0, RESIZE_BAND)
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out[name] = eq
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r = eq.pct_change().dropna()
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for a, b in BLOCKS:
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x = r.loc[a:b]
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e = (1 + x).cumprod()
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print(f"{name} {a[:4]}-{b[:4]} cagr {(e.iloc[-1] ** (252 / len(x)) - 1) * 100:5.1f}% "
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f"sh {x.mean() / x.std() * 252 ** 0.5:.2f} mdd {((e / e.cummax()) - 1).min() * 100:5.1f}%")
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with open(OUT, "wb") as fh:
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pickle.dump({"equity": out, "close": close}, fh)
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if __name__ == "__main__":
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main()
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