The Training Ground for Financial AI Agents

The Problem: Where Do You Train Your AI Agents?

You cannot train AI agents on live market data. It is too expensive, too slow, and mistakes cost real money. But historical data alone is not enough - your agents only learn from scenarios that have already happened.

What happens when your AI encounters a flash crash it has never seen? A liquidity crisis outside its training distribution? A correlation regime that did not exist in your backtest window?

Your AI needs to train on markets that could exist, not just markets that have existed. It needs a sandbox where it can fail safely, iterate rapidly, and explore the full distribution of possible futures.

Use Cases

• Autonomous Trading Strategy Development: Let AI agents iterate on trading strategies in simulated markets, learning from millions of synthetic scenarios
• LLM-Based Financial Analysis: Ground financial reasoning agents in realistic market data without exposing them to production systems
• Reinforcement Learning for Trading Bots: Provide diverse training environments with controllable reward signals and market dynamics
• AI Risk Assessment Agents: Train risk models on tail scenarios and stress conditions that rarely appear in historical data
• Multi-Agent Market Simulation: Run agent-vs-agent simulations to study emergent behavior and market dynamics

Example: AI Agent Trading Session

// Your AI agent requests a specific market environment
const scenario = await mcp.call("generate_market_scenario", {
  base_volatility: 0.4,
  trend: "bearish",
  liquidity_stress: true,
  correlation_regime: "risk-off",
  duration_hours: 24
});

// Stream realistic L2 order book data
const orderbook = await mcp.call("stream_orderbook", {
  scenario_id: scenario.id,
  symbol: "ETH-USD",
  update_frequency_ms: 100
});

// Agent executes trades against the simulated book
const result = await mcp.call("simulate_trade", {
  scenario_id: scenario.id,
  symbol: "ETH-USD",
  side: "sell",
  size: 10.0,
  order_type: "limit",
  price: orderbook.best_bid - 0.50
});

// Evaluate performance, iterate on strategy
console.log(`Fill price: ${result.avg_fill_price}`);
console.log(`Slippage: ${result.slippage_bps} bps`);
console.log(`Market impact: ${result.market_impact}`);

Methods That Power Agent Training