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Research profile / Archived system

Cross-asset OU multibot

A multi-system extension of cross-asset mean reversion. The archive allows comparison of configurations without treating a retrospective replay as live performance.

Archived systemImplementation privateNo current deployment claim
§Selected results
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Different systems.
Different outcomes.

Selected positive-return simulations, not live performance and not the complete research record. Ordered by source-reported Sharpe where available; other ratios are unranked. S17 and the deployed OU configuration are omitted from this view.

  1. Not Sharpe-rankedOU Top 5Reported CAGR / bar vol: 1.72
Source-reported JSON · unvalidated

OU Top 5

1 Jan 2026 — 21 Aug 2026
Costs excluded

Annualised return
Strategy+39.15%
S&P 500+19.61%
Strategy cumulative: +23.48% · 233 calendar daysGeometric extrapolation · not a forecast
Return / risk ratios
Strategy1.72
S&P 5001.39
Strategy: Reported CAGR / bar vol · NOT conventional SharpeS&P 500: daily Sharpe · √252 · risk-free rate 0
Maximum drawdown
Strategy-25.74%
S&P 500-9.10%
Strategy: based on saved observationsS&P 500: daily closes, including the pre-window baseline · not intraday lows

Strategy vs S&P 500

Matching window · annualised returns
OU Top 5+39.15%
S&P 500+19.61%

S&P 500 cumulative: +12.11% · Cumulative gap: +11.37 percentage points above the index.

USD price return · dividends excluded. Recomputed from Yahoo Finance / ^GSPC ↗.

Read this comparison carefully. This is a market reference, not risk-matched alpha: assets, leverage and costs differ. The index excludes dividends; a total-return benchmark would differ. Strategy inputs are source-reported, not independently reconstructed.

Monthly return · partial first / last months
2026-01+7.55%
2026-02-1.51%
2026-03-13.71%
2026-04+10.43%
2026-05+6.15%
2026-06+18.67%
2026-07-10.48%
2026-08+8.49%

Separate configuration / paper proxy. No standalone raw returns ledger available for this archived summary.

Calculation & evidence Formulas, sources & conventions

Strategy evidence

Configuration:Pre-recalibration configuration; distinct from the deployed configuration.

CORRECTED: source run_lane05.py lines 75-79 divides bar-count-annualised compounded return by annualised bar volatility. Its reported 1.72 is NOT conventional Sharpe. The window is a pre-recalibration configuration, not the deployed configuration.

Source:05_optimised_top5_prerecal.json · Archived simulation — not live performance. Source files are retained privately; strategy parameters and execution logic are not published here.

Benchmark & annualisation

Daily observed trading-close returns: mean / sample standard deviation × √252; risk-free rate 0. USD price index, dividends excluded. Drawdown uses closing prices including the pre-window baseline, not intraday lows.

160 observed daily returns. Maximum drawdown peak: 27 Jan 2026; trough: 30 Mar 2026.

Baseline: 2025-12-31 close (6845.50); endpoint: 2026-08-21 close (7674.37). The last close before the window covers the opening holiday. Annualisation uses the same 233-day window as the strategy.

Annualised return = [(1 + cumulative return / 100)^(365 / days) − 1] × 100. Days include both stated dates, using UTC calendar days. Sub-year rates extrapolate the observed period; they are not earned one-year returns. Source-reported inputs remain unvalidated after annualisation. Monthly bars remain actual period returns, not annualised.

Selected results are not a combined portfolio or a complete track record. The ordering is by reported Sharpe values, not a harmonised performance ranking; costs, sample conventions and selection methods differ. OU and Gold use compounded-return/volatility ratios and are not Sharpe-ranked. Recomputing ledger arithmetic does not establish out-of-sample validity or an executable edge.

The research question

When is a relationship economically meaningful rather than a coincidence in the sample?

A multi-system extension of cross-asset mean reversion. The archive allows comparison of configurations without treating a retrospective replay as live performance.

The technical work

Spread construction, statistical relationship tests, rolling formation windows and portfolio-level implementation.

The challenge is to connect the economic idea to a precise, reviewable experiment. Data transformations, the moment of a decision and the interpretation of the resulting return series all belong to that specification.

What to challenge

These are evaluation considerations for this domain, not a checklist presented as already passed.

  • Stability after the formation period
  • Both hedge legs aligned by timestamp
  • Transaction costs, financing and borrow where relevant
  • Selection of pairs kept separate from evaluation

The data boundary

Price histories for both legs, corporate-action or contract adjustments where applicable, and defensible execution-cost estimates.

Descriptions are intentionally high-level. Parameters, execution rules and non-public source data are not published here. Withholding an implementation is different from overstating its evidence.

The interesting question is not whether a backtest looks good. It is what would have to be true for the result to mean something.

For the broader framework, see validation. For the engineering context, see the research stack.

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