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Adaptive Factor Evaluation for a Multi-Asset Perpetual Futures Strategy

Article FMZ digest · Author: 发明者量化-小小梦

Summary

APFF is a framework for ranking a changing universe of crypto perpetual futures and combining interpretable signals into a long-short portfolio. Its seed factors cover momentum, short-term reversal, funding, premium, and open-interest behavior. Cross-sectional ranks put different signals on a common scale; volatility scaling, symbol caps, and a limited BTC hedge help manage exposures. Decisions and factor evaluations run on slower schedules than market-data collection.

The central research process records forward returns, waits for samples to mature, and updates factor budgets gradually. New candidates pass through observation and small-weight trials before gaining more capital. The article describes engineering checks and simulated runtime scenarios, but explicitly says these do not establish long-term returns or predictive power. It also identifies limitations, including incomplete historical point-in-time universe data, simulated premium proxies that differ from production data, and execution costs and risk controls that still need further work. Independent out-of-sample evidence remains necessary.

Key ideas

  • APFF ranks multiple perpetual contracts by combining normalized cross-sectional scores from five seed factor families.
  • Volatility scaling, per-symbol exposure caps, and a limited BTC hedge address risks that equal long and short notionals do not remove.
  • Factor performance is evaluated using forward returns, with mature samples informing gradual budget changes.
  • New factors move through observation and low-weight trials before receiving larger allocations.
  • Engineering checks and simulated execution tests do not demonstrate long-term profitability or factor predictive power.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.