Evaluation platform for financial agents

Evaluate and optimize your AI agents for financial decisions making.

I want to evaluate my ai agent

Million-dollar questions... unanswered

Is my AI ready to trade and make money?

Can AI generate durable and additive alpha over traditional strategies?

My agent was trading so well before April - what happened!?

How can we systematically backtest and evaluate AI agents?

Should AI run my portfolio like a hedge fund or index?

Which investment strategy is implementatble with the current state of AI?

Which model/harness is best for investing? Is Claude > GPT?

How can we quantify impact from each iteration of the strategy and agent?

Finance knowhow, meet eval science

To answer those questions, you need an evaluation framework and platform that is strategy-specific and agent-agnostic.

finance

Finance knowhow

  • Implementable investment strategies
  • Systematic research pipeline
  • Financial data knowledge
+
ai

Eval science

  • Controlled agent simulation
  • Benchmark optimization pipeline
  • Scalability-driven and open sourcing

built by ex-BlackRock PMs and quants

flfintel.

can provide the answers

the difference

Financial agent eval is different and non-negotiable

When coding agents fail, you can retry right away. When financial agents fail, you find out after losing money.

Typical agentic evals
fintel.
unit
Task — finish an instruction in a sandbox
Decision — emit views at a point-in-time (PIT)
environment
Container — filesystem is the world
PIT-controlled information access
scoring
Tests / reward when the task completes
Investment performance metrics computed post-run
coupling
Tasks scored independently
Decisions are coupled across investment universe and horizons
benchmark
Shared, generalizable datasets
Investment strategies are highly specialized

Own your benchmark evaluation pipeline

Specify your strategy, connect an agent, and run the evaluation in minutes.

Try the platform now