A quantitative-finance benchmark for AI coding agents.
Agents run their own experiments on real LSEG FX market data. Every number comes with an error bar.
M.Sc. Statistics
Status
Release 0, updated 2026-10-18. No tasks and no runs yet.
LEADERBOARD
No runs yet.
TASKS
No tasks yet.
METHOD
σ-Bench rests on three parts:
Experiment loops. Agents run their own loops of hypothesis, experiment and evaluation, as in Dream-RSI
[1]. A run is scored by how far its result improves and how much of that survives on hidden
data, not by pass@k.
Data. Trade-by-trade CME FX futures from LSEG, with trade size and aggressor side, sampled as dollar bars
[2] instead of 1-minute bars.
Tasks. Statistical inference on real data: martingale tests, optimal stopping, Markov regimes and
meta-labeling.
Agents never receive credentials or live data access. They work on frozen data snapshots.
PRE-REGISTERED EXPERIMENTS
None yet.
LIMITATIONS
One author. I write the tasks, solutions and verifiers myself, so the set reflects my view of quant work
and my conventions.
Private tasks. Tasks built on LSEG data cannot be published. Methods and aggregate results are public; raw
prices never are.
LOG
2026-10-15
Concept written.
2026-10-16
LSEG data access confirmed. sigmabench.dev registered.
2026-10-17
Release 0: this page, no runs yet. The benchmark is built on experiment loops with
real FX data.
2026-10-18
Dataset fixed: trade-by-trade CME FX futures, sampled as dollar bars.
REFERENCES
[1] T. Zheng et al. Dream-RSI: Recursive Self-Improvement through Evolving Worlds.
arXiv:2609.14858, 2026.
[2] M. López de Prado. Advances in Financial Machine Learning. Wiley, 2018.