Strategy: policy_conditional_ma_50_200_6m

2 variant(s) documented · earliest 2026-09-11 · latest 2026-09-11.

Variant	Generated	Registered	Verdict
`fast=50, slow=200, lookback_months=6, series_id=FEDFUNDS, vintage_policy=latest…`	2026-09-11T22:27:46+00:00	✓	FAIL
`fast=50, slow=200, lookback_months=6, series_id=TB3MS, vintage_policy=latest_vi…`	2026-09-11T22:57:37+00:00	✓	FAIL

Variant 1

Generated: 2026-09-11T22:27:46+00:00
Source report: 20260911_222746_policy_conditional_ma_50_200_6m.md
Registered: ✓ — 2026-09-11T22:27:46+00:00 (walk_forward_passed=False)

FAIL

policy_conditional_ma_50_200_6m variant 1 return and Sharpe comparison

Experiment

Period: 2015-01-02 to 2026-08-14 (2921 sessions)
Execution rule: close-derived weights are applied one session later
Transaction costs: 5.00 bps per unit of turnover
Cash return: 0%; taxes, slippage and market impact: not modeled
Parameters: fast=50, slow=200, lookback_months=6, series_id=FEDFUNDS, vintage_policy=latest_vintage_date_lte_trading_day

Monetary-policy data provenance and action rule

Source: FRED ALFRED API (live, authenticated point-in-time API)
Series: FEDFUNDS (monthly Effective Federal Funds Rate)
Vendor vintage inventory: 364 total; the requested newest-200 response returned 200 from 2010-07-06 to 2026-09-01
Backtest snapshots: 150 distinct vintages from 2014-06-02 to 2026-08-03; latest available observation periods span 2014-05-01 to 2026-07-01
Live API use: 151 calls against a 200-call client budget (one vintage inventory plus one observation snapshot per required vintage; cached for full-sample and walk-forward reuse)
Point-in-time policy: backward merge_asof on publication/vintage date; only vintage_date <= trading day can match, and pre-first-vintage dates remain missing
Frozen action rule: apply existing 50/200 MA weights only when the current as-known policy rate is below the rate known 6 calendar months earlier; otherwise cash
Missing current or trailing readings raise PolicyRateDataUnavailable; no revised-history or unconditional fallback is allowed

Data provenance

Source: market-data-hub (adj_close; live rows included: False)
Requested symbols: SPY, QQQ
Validated common panel: 2015-01-02 to 2026-08-14; 2921 rows
Database: C:\Users\Administrator\Documents\GitHub\market-data-hub\market_data.duckdb

Symbol	Last date	Status	Coverage score	Stalled
QQQ	2026-08-14 00:00:00	ok	97.31	False
SPY	2026-08-14 00:00:00	ok	97.33	False

Research rationale

A slow trend filter may reduce severe drawdowns, but after costs it need not beat passive exposure.

For each ETF, hold it when its 50-day moving average is above its 200-day average; equal-weight active ETFs; otherwise cash.

Sources:

A Quantitative Approach to Tactical Asset Allocation — Motivates a simple trend-following tactical allocation rule across asset classes.
A Quantitative Approach to Faber's Tactical Asset Allocation — Warns that attractive rule-based results require statistical caution and bootstrap-style robustness tests.
Momentum Trading, Return Chasing, and Predictable Crashes — Documents high historical momentum returns alongside material and sometimes predictable crash risk.

Known risks:

whipsaw and repeated transaction costs in sideways markets
parameter/data-mining sensitivity
cash return is modeled as zero in the MVP
one historical sample is not evidence of future profitability

Net backtest metrics

Metric	Strategy	Ungated MA	Benchmark
Cumulative return	60.40%	318.82%	500.62%
CAGR	4.16%	13.15%	16.73%
Annualized volatility	12.93%	16.74%	19.44%
Annualized Sharpe (rf=0)	0.38	0.82	0.89
Maximum drawdown	-30.86%	-30.86%	-30.86%
Annualized turnover	155.29%	189.80%	8.63%
Transaction-cost drag (sum of daily rates)	0.90%	1.10%	0.05%

Walk-forward / out-of-sample validation

Parameters were frozen before the chronological split. Each window is non-overlapping and backtested independently.

Window	Role	Period	Sessions	Return	CAGR	Volatility	Sharpe	Max drawdown	Benchmark return	Benchmark Sharpe	Return delta	Sharpe delta
development	development	2015-01-02 to 2021-12-15	1752	44.83%	5.47%	13.32%	0.47	-30.86%	227.51%	1.00	-182.68%	-0.53
oos_1	out_of_sample	2021-12-16 to 2026-08-14	1169	17.37%	3.51%	11.54%	0.36	-20.78%	86.52%	0.77	-69.15%	-0.41

Pass criterion: in every OOS window, strategy cumulative return and annualized Sharpe must each be at least the corresponding benchmark value.

Walk-forward verdict: FAIL: degrades out of sample; the frozen strategy trails the benchmark on return or Sharpe in at least one OOS window.

Giudizio / conclusion

Full-sample comparison: FAIL on this sample: lower net return and Sharpe than the benchmark.

Final validation judgment: FAIL: degrades out of sample; the frozen strategy trails the benchmark on return or Sharpe in at least one OOS window.

This remains historical research, not evidence of tradability or a recommendation. Promotion still requires parameter-robustness and multiple-testing checks plus a live paper period.

Ecosystem review

LazyFin: Do not recreate hierarchical portfolio optimization in LazyAlpha; the line was moved/retired from LazyFin and belongs to LazyPortfolio. (C:\Users\Administrator\Documents\GitHub\LazyFin\docs\status.md)
investmentcommittee: Keep B0/P0/Sa/PH/PF as methodological reference: same data, folds, costs and rebalance rules for honest comparisons; do not clone it 1:1. (C:\Users\Administrator\Documents\GitHub\investment-process-top-down-etf.md)

Variant 2

Generated: 2026-09-11T22:57:37+00:00
Source report: 20260911_225737_policy_conditional_ma_50_200_6m.md
Registered: ✓ — 2026-09-11T22:57:37+00:00 (walk_forward_passed=False)