We ran a few scans using aggressive filtering criteria in order to produce baskets of raw pairs we could back test. The focus was on achieving high levels of normality pair-by-pair.
No economic sense checking was performed on the results – we just wanted to test the power of ArbMaker’s statistical filters and run what they came up with against a uniform, untweaked and non-customized set of default back test criteria.
The results (which are total, not annualised returns – a simple 5 pair sample leaves significant “dead money” date blanks between trades):
Such an in-sample is, of course, too small to draw earth-shattering conclusions from but our main goals here are to:
- Show how ArbMaker’s stat filters blow away the at least 80% chaff users would otherwise be wrestling with and harvest a core list of pairs for analysis.
- Demo how even the default back test criteria, linked to multi chart factors, churn out profitable entry and exit points.
- Reveal some of the portfolio-level features we plan to roll out in Q1 next year that will make this kind of testing easy to do.
- Suggest that, run right, stat arb builds-in very stable hedges and reduces draw down risk.
The pair combinations used are listed above and all the raw data shown can be pulled from ArbMaker for replication purposes.