Quantocracy 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 a trading lab at the Robinhood Summit, the author used Nike’s (NKE) after-hours earnings release on October 1, 2025, as a backdrop to demonstrate how to express a specific directional view using vertical spreads. At the time, NKE traded around $36, and the $36 straddle implied an expected move of about $3 (8%). The author chose to buy the 38.5/39 call vertical for a premium of $0.12, which would pay $0.50 if the stock closed above $39 at expiration, for a risk-reward ratio of roughly 3:1.
Read originalThis article is the sixth part of a series on statistical arbitrage for independent traders. It explains the three core modules of triangulated statistical arbitrage and their resource allocation priorities. High-quality pair screening is the core foundation: using 24 months of historical data, the top 10% of pairs are screened monthly and divided into 5 quality tiers, with the best tier achieving a Sharpe ratio of 1.6 and the worst only 0.42. The author discloses only the general methodology and does not reveal specific implementation details.
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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