Methodology Summary
Compressed trust-boundary reference. Guide is the canonical manual; this page is the shorter derivative version.
Use this when you want the compact trust ceiling first. For page-by-page instructions, definitions, and the full workflow manual, use Guide. For staged theory and study tracks, use Learn.
| Topic | Current implementation | What it does not establish |
|---|---|---|
| Signal timing | Signals are computed from daily bars and shown as LONG / SHORT / FLAT with persistence-based strength. | No execution latency model, no intraday fill guarantee. |
| Backtest return model | Position on bar t is applied to forward return t→t+1. Commission and slippage are deducted via turnover. | No market impact model, no partial fills, no borrow fees. |
| Out-of-sample | Optional terminal holdout split reports OOS metrics on the last X% of observations. | Still only one terminal holdout. It does not establish regime invariance or protect against repeated tuning on the same idea. |
| Walk-forward validation | Sequential holdout folds now include a purge gap between train and test windows and report fold count, average Sharpe, worst-fold Sharpe, and positive-fold ratio. | Still lighter than combinatorial purged CV, nested tuning, and full live execution validation. |
| Benchmark | Default is same-ticker buy-and-hold; you can override with a single ticker or a weighted basket such as SPY:0.60, IEF:0.40 on the backtest, compare, and portfolio pages. | Still not a full optimizer: no risk-parity constructor, no automatic factor benchmark builder, and no benchmark search/ranking workflow yet. |
| Paper scoreboards | Scoreboards rank forward-tracked paper strategies and portfolios with governed forward scores that scale for live-record maturity, modeled frictions, validation quality, and owner caps. | Still not a brokerage-linked live track record, and start-date selection remains a product-layer rather than broker-audited constraint. |
Data Sources
Free keeps the narrow reliability contract: core liquid symbols only. Pro unlocks extended best-effort symbols and weighted benchmark baskets, not blanket long-tail coverage.
Past performance does not guarantee future results. Backtests are model outputs conditional on data quality, cost assumptions, and implementation details. They do not establish regime robustness, crowding resilience, or live execution quality.
Alphrex is still an evolving product. Paper tracking and scoreboards are implemented at the product layer, but broker integration, hardened code sandboxing, and a full production-database rollout remain incomplete.