""" Strategy summaries for the Best Ideas command center UI. Static copy + optional metrics from ``RenTech/data/logs/*_meta.json``. """ from __future__ import annotations import json from pathlib import Path from typing import Any _REPO = Path(__file__).resolve().parents[2] LOGS = _REPO / "RenTech" / "data" / "logs" STRATEGY_DEFINITIONS: dict[str, dict[str, Any]] = { "spy_theta": { "token": "lit4 + vrp", "role": "SPY options (Theta margin + MTM)", "summary": ( "Literature four (S055, S057, S059, S089) plus 4-regime VRP on SPY Theta chains. " "One margin simulation: overlapping sleeves, daily MTM on open legs." ), "rules": [ "VRP: no PMCC, VIX-scale R2 contracts, term-structure gate, overlap slices=2", "Lit sleeves: one position at a time per spec, bid/ask execution", "Headline risk: max_drawdown_frac_mtm on combined book", ], "runner": "python -m RenTech.strategy_stack.diverse_theta_strategies_v1.evaluate_theta_margin", "artifacts": [ "RenTech/data/logs/lit_stack_vrp_margin_daily.csv", "RenTech/data/logs/lit_stack_vrp_margin_meta.json", ], "meta_path": LOGS / "lit_stack_vrp_margin_meta.json", }, "vxx_regime": { "token": "vxx_regime_stack", "role": "VXX regime stack (6 sleeves)", "summary": ( "Dynamic VXX Regime Strategy Stack: six regime strategies summed on one capital base " "(contango, steep contango, backwardation, etc.)." ), "rules": [ "Approach B: sum daily_pnl_mtm_usd across sleeves", "Preload chains for MTM; combine-only rebuild from sleeve CSVs", ], "runner": "RenTech/strategy_stack/run_vxx_regime_mtm_report.py", "artifacts": [ "RenTech/data/logs/vxx_regime_mtm_2016_2026_dynamic_vxx_regime_stack_daily_mtm.csv", "RenTech/data/logs/vxx_regime_mtm_2016_2026_dynamic_vxx_regime_stack_meta.json", ], "meta_path": LOGS / "vxx_regime_mtm_2016_2026_dynamic_vxx_regime_stack_meta.json", }, "vxx_long_call": { "token": "vxx_long_call", "role": "VXX tail hedge", "summary": "Long OTM VXX calls sized to ~5% of stacked-book PnL contribution (not full 1% audit sizing).", "rules": ["10% OTM calls", "Combined via fund weight × quarterly NAV scale"], "runner": "RenTech/strategy_stack/run_vxx_long_call_daily.py", "artifacts": ["RenTech/data/logs/vxx_long_call_standard_daily.csv"], "meta_path": None, }, "macro_aw": { "token": "macro_aw", "role": "Macro all-weather options", "summary": ( "Eight ETF option sleeves (TLT, USO, DBC, GLD, …) equal-weight in combine. " "Low equity beta complement to SPY theta." ), "rules": ["Equal-weight portfolio curve", "Theta 15:45 chunks per underlying"], "runner": "RenTech/strategy_stack/macro_aw_options_portfolio.py", "artifacts": [ "RenTech/data/logs/macro_aw_options_portfolio_eq_daily.csv", "RenTech/data/logs/macro_aw_options_portfolio_eq_trades.csv", ], "meta_path": LOGS / "macro_aw_options_portfolio_eq_meta.json", }, "sector_momentum": { "token": "sector_momentum", "role": "SPDR sector rotation", "summary": "12−1 month momentum on SPDR sectors; hold top-3 names, monthly rebalance.", "rules": ["Rank by trailing momentum", "Long sector ETFs, no short"], "runner": "RenTech/strategy_stack/run_sector_momentum_standard.py", "artifacts": [ "RenTech/data/logs/sector_momentum_standard_daily.csv", "RenTech/data/logs/sector_momentum_standard_rebalances.csv", ], "meta_path": LOGS / "sector_momentum_standard_meta.json", }, "tactical_aw": { "token": "tactical_aw", "role": "Tactical all-weather (macro ETFs)", "summary": ( "Bridgewater-style static weights on SPY/TLT/IEF/GLD/DBC with per-sleeve trend + momentum gates. " "Production variant: TLT/IEF baseline ×0.7 (bond70 sweep winner)." ), "rules": [ "Invest baseline weight when close > SMA(200) and 12−1 momentum > 0", "Else cash at ~4% annual yield; weights execute next day", "bond_baseline_mult=0.7 on TLT and IEF", ], "runner": "RenTech/strategy_stack/run_tactical_all_weather_standard.py", "artifacts": [ "RenTech/data/logs/tactical_aw_standard_daily.csv", "RenTech/data/logs/tactical_aw_standard_allocations.csv", "RenTech/data/logs/tactical_aw_variant_sweep_best.json", ], "meta_path": LOGS / "tactical_aw_standard_meta.json", "config_path": LOGS / "tactical_aw_variant_sweep_best.json", }, "tsmom": { "token": "tsmom", "role": "Managed futures / TSMOM", "summary": ( "8-asset time-series momentum (SPY, EFA, EEM, TLT, IEF, GLD, DBC, UUP). " "3/6/12m signal blend, vol-normalized, monthly rebalance. Low SPY beta." ), "rules": ["Long/short by trend sign", "Vol targeting per asset"], "runner": "RenTech/strategy_stack/run_tsmom_managed_futures.py", "artifacts": ["RenTech/data/logs/tsmom_managed_futures_daily.csv"], "meta_path": LOGS / "tsmom_managed_futures_meta.json", }, } def _read_json(path: Path | None) -> dict[str, Any] | None: if path is None or not path.is_file(): return None try: raw = json.loads(path.read_text(encoding="utf-8")) return raw if isinstance(raw, dict) else None except json.JSONDecodeError: return None def _normalize_standalone_meta(meta: dict[str, Any] | None) -> dict[str, Any] | None: if not meta: return None out: dict[str, Any] = {} tr = meta.get("total_return_pct") if tr is not None: out["total_return_pct"] = float(tr) cagr = meta.get("cagr_pct") if cagr is not None: out["cagr_pct"] = float(cagr) sh = meta.get("sharpe") or meta.get("portfolio_sharpe_daily_mtm") if sh is not None: out["sharpe"] = float(sh) dd = meta.get("max_drawdown_pct") if dd is None and meta.get("max_drawdown_frac_mtm") is not None: dd = float(meta["max_drawdown_frac_mtm"]) * 100.0 if dd is not None: out["max_drawdown_pct"] = float(dd) if meta.get("start") or meta.get("first_day"): out["start"] = str(meta.get("start") or meta.get("first_day")) if meta.get("end") or meta.get("last_day"): out["end"] = str(meta.get("end") or meta.get("last_day")) return out or None def build_strategy_summaries( portfolio: dict[str, Any], combine_meta: dict[str, Any] | None = None, ) -> dict[str, Any]: """ Build ordered strategy cards for the command center. Merges static definitions, standalone backtest meta, and in-fund attribution. """ combine_meta = combine_meta or {} fund_weights = portfolio.get("fund_weights") or {} sleeves = portfolio.get("sleeves") or {} yearly = portfolio.get("yearly") or [] book_blurb = ( "Fund-mode 2.2× gross, quarterly NAV-sized sleeves. " "Daily sleeve PnL scales from $100k standalone backtests: " "notional = weight × fund_scale × quarter-open NAV." ) if combine_meta.get("combine_mode"): book_blurb = ( f"{combine_meta.get('combine_mode')} · " f"fund_scale={combine_meta.get('fund_scale', '2.2')} · " f"nav_rebalance={combine_meta.get('fund_nav_rebalance', 'quarterly')}. " + book_blurb ) strategies: list[dict[str, Any]] = [] keys = sorted( fund_weights.keys(), key=lambda k: float(fund_weights.get(k, 0)), reverse=True, ) for key in keys: defn = STRATEGY_DEFINITIONS.get(key, {}) sl = sleeves.get(key, {}) meta = _normalize_standalone_meta(_read_json(defn.get("meta_path"))) cfg_raw = _read_json(defn.get("config_path")) config = (cfg_raw or {}).get("config") if cfg_raw else None if config is None and cfg_raw and "bond_baseline_mult" in str(cfg_raw): config = cfg_raw.get("config", cfg_raw) strategies.append( { "id": key, "label": sl.get("label") or defn.get("label", key), "token": defn.get("token", key), "role": defn.get("role", ""), "summary": defn.get("summary", ""), "rules": list(defn.get("rules") or []), "runner": defn.get("runner", ""), "artifacts": list(defn.get("artifacts") or []), "weight": float(fund_weights[key]), "weight_pct": float(fund_weights[key]) * 100.0, "config": config, "variant_name": (cfg_raw or {}).get("best_variant") if cfg_raw else None, "standalone": meta, "in_fund": { "notional_usd": sl.get("notional_usd"), "mean_notional_usd": sl.get("mean_notional_usd"), "total_pnl_usd": sl.get("total_pnl_usd"), "return_on_start_capital_pct": sl.get("return_on_start_capital_pct"), "pct_of_fund_pnl": sl.get("pct_of_fund_pnl"), "pnl_today_usd": sl.get("pnl_today_usd"), }, } ) return { "book_title": "Best Ideas — 7-sleeve fund (reference backtest)", "book_blurb": book_blurb, "window": { "start": portfolio.get("start_date"), "end": portfolio.get("end_date"), "as_of": portfolio.get("date"), }, "headline": { "total_return_pct": portfolio.get("total_return_pct"), "cagr_pct": portfolio.get("cagr_pct"), "sharpe": portfolio.get("sharpe"), "max_drawdown_pct": portfolio.get("max_drawdown_pct"), "fund_nav_usd": portfolio.get("fund_nav_usd"), "capital_usd": portfolio.get("capital_usd"), }, "yearly": yearly, "strategies": strategies, "regenerate_combine": ( "cd /Users/robzingale/trading_bot && PYTHONUNBUFFERED=1 .venv/bin/python " "RenTech/strategy_stack/combine_best_ideas_stack.py " "--start 2016-01-04 --end 2026-04-02 --capital 100000 " "--mtm --fund-mode --fund-scale 2.2 " "--with-vxx-long-call --with-macro-aw --with-sector-momentum " "--with-tactical-aw --with-tsmom " "--lit-mtm-daily RenTech/data/logs/lit_stack_vrp_margin_daily.csv " "--out-prefix RenTech/data/logs/best_ideas_stack_10dd" ), "regenerate_snapshot": ( "cd /Users/robzingale/trading_bot && PYTHONUNBUFFERED=1 " ".venv/bin/python -m RenTech.monitor.build_command_center_snapshot --ibkr" ), }