Peer-style research on broker execution, latency arbitrage, and market microstructure, from a developer who has built execution software since 2000.
This is the research library of BJF Trading Group. Each paper takes a claim that traders usually argue about- broker fairness, whether an arbitrage edge survives, when it pays to trade- and settles it with data and a reproducible method. Boris Fesenko (Ontario, Canada) authored the work, and it’s mirrored on open academic repositories so it can be cited and checked. Start with the abstracts below, then read the full papers.
Proposes an open, reproducible index that turns a trader’s own trade and tick logs into a single comparable execution-quality score. It combines fill rate, slippage symmetry, rejection behavior, and execution latency, so two brokers can be ranked on evidence rather than reputation. Includes the full methodology and open-source reference implementation.
Also on: arXiv, ResearchGate, Academia.edu.
Quantifies how added execution latency and asymmetric fills turn a positive-expectancy latency-arbitrage backtest into a losing strategy live, with nothing wrong in the trading logic. Models the execution-time budget at which an edge survives and shows why perfect-fill backtests systematically overstate arbitrage performance.
Also on: Zenodo, ResearchGate.
Derives how slippage distributions, symmetric versus asymmetric, translate into expected cost per trade, and how one-sided fills quietly erase the edge of high-frequency strategies. Shows how to detect execution manipulation from a trade log by testing the symmetry of the slippage distribution rather than its average.
Also on: Academia.edu, ResearchGate, SSRN
Measures how long price-feed and execution inefficiencies persist before they are competed away or filtered by broker risk systems, and what drives their decay. Provides a realistic picture of how durable an arbitrage edge is in 2026, and why speed and broker selection matter more than the raw size of the gap.
Also on: MPRA, ResearchGate, Dev
Analyzes intraday variation in spread, liquidity, and opportunity to identify the hours where a given strategy has the strongest edge. Turns the vague advice to trade the active sessions into a measurable, per-strategy trading-window map based on real data.
Also on: OSF, ResearchGate.
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