financial risk engineers

Abstract: Testing by betting is a powerful framework that recasts hypothesis testing as a betting game. A popular approach uses an $F$-weighted portfolio with a predefined prior $F$. For suitable priors, this approach has recently been shown to achieve the optimal expected rejection time as the error probability goes to zero at a fixed alternative. Yet, the prior $F$ changes how the portfolio test behaves near the null. This motivates the question: Which prior should we use in testing-by-betting algorithms? We study bounded scalar payoffs and introduce an efficiency measure that allows the error probability and alternative to vary jointly. Under mild assumptions, we prove that efficiency equals a logarithmic decay index of the prior near zero. This gives a necessary and sufficient condition for optimality: Beta priors have infinite efficiency, whereas suitable Robbins-type priors attain efficiency 1. As an application, we consider testing a specified mean of a bounded random variable and describe an implementation of the portfolio algorithm with almost-linear computational complexity.

Abstract: Cryptocurrency swing trading spans mismatched timescales: positions remain active while market and event risk can change within minutes. However, many predictive and large language model (LLM)-based trading systems assess risk after strategy generation or run both functions at the same cadence, leaving proposals unconditioned by current risk and exposure weakly governed between revisions. We propose SwingGuard, a dual-timescale, long-only multi-agent framework for risk-conditioned strategy generation. At the strategy timescale, specialized analysts synthesize point-in-time technical, sentiment, on-chain, and macroeconomic evidence through bullish-bearish deliberation. The resulting evidence and current risk context are then combined by a Portfolio Architect to form a target position. At the governance timescale, a five-minute Risk Controller performs conditional diagnosis, maintains persistent risk states with staged recovery, audits the proposal, and constrains execution until the next strategy revision. This design allows risk to shape the feasible strategy before proposal finalization while continuing to govern the active position afterward. Evaluations on Bitcoin (BTC) and Ether (ETH) show that SwingGuard preserves positive returns after transaction costs while maintaining shallow drawdowns and strong risk-adjusted performance on both assets. The exposure-capped joint portfolio achieves a Sharpe ratio of 2.98, compared with 2.79 for an otherwise identical Post-hoc Risk variant. Structured traces further link risk evidence and state transitions to strategy constraints and executed position changes, providing an auditable framework for asynchronous cryptocurrency swing trading. Overall, the results indicate that conditioning strategy formation on current risk, together with persistent high-frequency governance, improves risk-adjusted performance without sacrificing auditability.