orders are the atom of financial markets everything else is downstream Microsoft Research built an AI that starts there MarS simulates markets from the order level up behavior emerges the way it does in real markets and the people building the next generation of trading systems need to understand this - article below

The same schema serving all market regimes simultaneously is a calibration compromise. That's the problem Mixture of Schemas solves. Different market conditions have completely different signal structures. The fields that dominate one regime carry near zero weight in another. A schema built to serve all conditions at once serves none of them optimally. Mixture of Experts solved this exact problem for language models and it's the architecture behind the dominant frontier models today The idea is elegant. Don't force one model to handle everything. Route each input to a specialist calibrated for that specific condition. Mixture of Schemas applies the same principle to typed decision systems. Each market regime gets a specialist schema calibrated to its own signal structure. A lightweight router selects the right one each cycle. At regime boundaries outputs blend proportionally to router confidence instead of hard switching, keeping transitions smooth. The architecture requires zero changes to the execution layer. From downstream it looks identical to a single schema system, just with substantially better calibration. What makes this genuinely interesting is the latency result. Specialist schemas are smaller. Fewer fields means faster decisions. The router overhead gets more than offset by the speed of smaller specialists. Better calibration and faster decisions from the same engine simultaneously. The broader implication reaches well beyond market making. Any typed decision system where the optimal feature set shifts with input type is a candidate for this architecture.

Jev decision layer + hftbacktest is the most complete open source market making stack I've seen queue position, feed latency, order latency, full L2/L3 order book reconstruction, all modeled properly your backtest should reflect your strategies exactly. this is how you actually get there

This might be one of the most serious open-source RL trading frameworks out there. Test strategies, tune hyperparameters, run rigorous experiments and see exactly where your edge breaks down. And the article below is what serious algo trading engineering looks like.

Good quant infrastructure is hard to find. Zipline is a proper event-driven backtesting engine, built to simulate how markets actually behave and clean enough that your strategy stays the focus. The kind of tool that keeps coming up for a reason.

Open Byte open-sourced a full AI trading OS and made it free to self-host. AI research → strategy → backtest → execution, all in one stack you fully control. The kind of infrastructure that used to require stitching together five different tools.

There is a Python library that lets you backtest, simulate a broker, and analyze Sharpe and SQN all in the same framework. Backtrader. Been around for nearly a decade, widely used across the algo trading community, and still one of the most complete frameworks for learning strategy testing cleanly. Not actively maintained anymore, but for understanding the fundamentals, it remains one of the best starting points out there.

this is what the quant stack looks like when AI actually does the work the agent handles the full research pipeline: data, valuation models, analysis, in minutes this is where research is heading