This article analyzes the combination of risk premiums for TIPS and crude oil. Based on data as of October 9, 2026, the 10-year TIPS real yield is 2.91%, up 116 basis points from the same period last year, and the 10-year breakeven inflation rate is 2.33%. This dual-asset portfolio can enhance expected returns with virtually no additional risk, while also offering educational value for dynamic position management.
Read originalQuantocracy has released its latest roundup of quantitative research links as of October 10, 2026, featuring four related research pieces. It notes that the 30-year U.S. Treasury yield closed at 5.60% in the first week of October, the highest level since 2002; the remaining content covers research discussions on momentum portfolio development, pre-market patterns, and market stop-loss hunting.
Read originalIn the 10th installment of his 2026 portfolio optimization series, Rob Carver conducts rolling out-of-sample backtests to compare four return pooling schemes. The test sample consists of 50 instruments randomly selected from 214 available instruments, with in-sample lengths of 5, 10, and 20 years and out-of-sample lengths of 1 and 5 years. The optimization uses shrinkage methods with an SR shrinkage of 0.5 and a correlation shrinkage of 0.75, while also incorporating the reverse rule of each trading rule, with only positive SR rules retained for optimization.
Read originalThis article is the eighth installment in the Portfolio Optimization Series. The author proposes using the Euclidean distance of SR vectors across multiple trading rules for each instrument to determine whether to combine cross-instrument returns, calibrates 95% critical values for different historical lengths using random data, and then obtains pseudo-instruments through hierarchical merging. The author compares three approaches—full merging, algorithmic merging, and no merging—using in-sample backtests across 214 futures instruments. The results show inconsistent out-of-sample performance, with longer sample data favoring no merging. The author ultimately concludes that the algorithm is highly complex and the grouping results lack intuitive logic, so it will not be adopted for now, and future research will explore simpler merging rules.
Read originalThis article introduces a method for automatically identifying structural breakpoints in the Sharpe ratio of trading rules, using independent t-tests to compare mean differences across subsamples, recursively splitting the sample while requiring each subsample to retain at least 5 years of data. The authors provide complete Python implementation code, using the momentum4 rule for CORN futures as an example, with the algorithm automatically identifying August 26, 1982 as the breakpoint.
Read originalThe author examines the effect of equal-weighted averaging of portfolio weights generated by different shrinkage methods in the Portfolio Optimization Series, comparing 9 shrinkage parameter combinations with 6 weight averaging combinations, totaling 15 approaches. The backtests use randomly selected 9 trading rules and random instruments, with 1-, 5-, and 10-year in-sample periods and 1- and 5-year out-of-sample periods, each group containing 4,000 random portfolios.
Read originalThis article is the fifth in a statistical arbitrage series for independent traders, focusing on the triangulated statistical arbitrage method. Using the energy sector spread network as an example, the author explains how to cross-validate multiple overlapping spreads to identify genuinely mispriced individual stocks, rather than treating deviations in a single spread as a direct trading signal.
Read originalThis article introduces a triangulated statistical arbitrage approach. Using 8 spread groups constructed from 6 energy stocks as an example, the z-score signal of each spread is decomposed into opposite votes for the two individual stocks, which are then aggregated into long-short signals at the individual stock level. This allows direct trading of undervalued or overvalued stocks, replacing the traditional pairs-trading model of hedging both legs simultaneously. The approach can improve capital efficiency, reduce transaction costs, and increase signal utilization, while achieving variance control similar to pairs trading through a long-short basket portfolio. The article will further explain how to evaluate the quality of aggregated signals.
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