Monetize smaller flow
Compete for eligible tickets that do not justify manual attention, with explicit coverage, cost and profitability criteria.
Algorithmic trading for the sell side
Turn smaller trades into a scalable profit engine.
Put an algorithmic trader to work on smaller flow. Free your traders for bigger tickets, complex risk and client relationships. Give the same engine mandates to rebalance the book, exit positions and pursue your desk’s objectives.
Built on Large Event Model (LEM) technology: better predictions become useful when they improve the decision the desk actually makes.
Deploy a trader, not just another signal
Work the opportunities that do not justify manual attention.
Without an algorithm for smaller trades, a desk must spend scarce trader time on modest tickets or leave business unanswered. Small opportunities can add up when the economics work at scale.
Mises connects prediction to trading policy and approved execution. Evaluate the opportunity, choose whether and where to quote, and act within a mandate—not simply deliver another mark for a trader to interpret.
One trader, multiple mandates
Not just RFQ response. An objective-driven trader.
Rebalance the book
Work toward inventory and risk targets set by the desk. Reduce concentrations or recycle available risk capacity.
Exit positions
Work down unwanted holdings within price, urgency and liquidity constraints, balancing execution opportunity with market impact.
Pursue your objective
Acquire desired inventory, accumulate axes or prioritize selected client flow. The desk sets priorities, eligible quotes and orders, and when a trader takes over.
Your flow powers your trader
Use the transaction you are actually considering.
Learn from RFQs, quotes, covers, passes and traded-away inquiries—not just completed trades. Your counterparty context, axes and inventory complement public market signals.
Condition the decision on the bond, intended size, side and venue context at decision time. Use price uncertainty and fill estimates where the desk’s data supports them, rather than one generic mark for every trade.
Desk data + mandate → Predict outcomes → Select quote, order or pass → Risk checks + approved execution
Convert predictions into a trading policy
Compare candidate actions against expected economics after fill likelihood, adverse selection, inventory impact, transaction costs and hedging costs. Balance urgency, price and market impact. Quote, adjust or pass within the desk’s explicit mandate.
Wide uncertainty, stale inputs or a limit breach should trigger review or prevent quoting. Automation is bounded by your controls—not a replacement for them.
The dealer sets the mandate
Your economics. Your controls. Your scorecard.
Define the economics
Set the eligible universe, target outcomes, quote boundaries, inventory appetite and the costs that count against performance.
Retain control
Keep risk limits, permissions, supervision, human override and the ability to stop quoting. Agree escalation and exception handling before launch.
Use approved infrastructure
Scope deployment within your environment, connected to approved market-data, RFQ and execution systems. Integrations and operating responsibilities are agreed per engagement.
Measure what matters
Track net P&L after costs, trader time freed, inventory outcomes, coverage and operating reliability—not model accuracy alone.
A measured deployment path
Replay. Shadow. Launch within limits.
- 01 / Define and replayStart with smaller flow or one inventory mandate. Agree the baseline, costs and success criteria, then evaluate on held-out desk history.
- 02 / Run in shadowGenerate decisions without trading. Check inputs, latency, limits and quote behavior against live flow.
- 03 / Launch within limitsBegin with a bounded mandate. Monitor fills, markouts, inventory and realized economics before expanding.
Start with one desk, one mandate and one scorecard. A paid pilot should establish the integrations, control framework and economic evidence needed to decide whether to broaden the deployment.
One technology, different products
From market intelligence to action.
AXOR supplies production corporate-bond pricing intelligence. Hayek is a customer-trained predictive model built around proprietary event history. Mises packages predictions, trading policy and execution integration into an algorithmic trading workflow. LEMs.ai is the broader platform for outcome forecasts and expected-value business decisions.
All share the goal of helping customers make more money through more accurate future-event probabilities. Each has a different deployment scope; evidence of model accuracy is not evidence of realized trading P&L.
Why Mises?
Prices make economic calculation possible.
Named for Ludwig von Mises, one of the most forceful advocates of the indispensable role of market prices. At the heart of his criticism of communism and socialist central planning was the economic calculation problem: without private ownership and exchange of the means of production, planners lack the market prices needed to compare alternative uses of scarce resources.
In his 1920 essay, Economic Calculation in the Socialist Commonwealth, Mises argued that this is not simply a shortage of data. It is the absence of prices formed through market exchange.
Our product takes its name from that insight: prices are essential to disciplined decisions about capital and risk. Mises brings event-driven intelligence to a dealer’s pricing and trading decisions within real markets—not as a substitute for the market’s price-discovery process.
Deployment scope and integrations are agreed per engagement. Profit potential is not a guarantee. Validate net results after costs, adverse selection and inventory risk before expanding the mandate.