Alpha Migration: Training LLMs on Synthetic Markets Before Live Deployment

TL;DR: Can LLMs learn trading skills in sandbox markets (e.g., crypto) before graduating to stocks? We'll pre-train agents in high-volatility synthetic environments and test if knowledge transfer accelerates live adaptation.

Research Question: Does multi-market pre-training (e.g., crypto + prediction markets) improve LLM generalization to unseen asset classes in LiveTradeBench?

Hypothesis: Agents fine-tuned on diverse market regimes will adapt 40% faster to new markets (e.g., transitioning from Polymarket to forex).

Experiment Plan: - Setup: Create synthetic markets (crypto, commodities) mirroring LiveTradeBench’s structure but with added noise (per Heublein et al.’s GNSS discrepancies).

• Data: Pre-train LLMs on 3 synthetic markets, then deploy in LiveTradeBench’s U.S. stocks.
• Metrics: Time-to-stable returns (TTSR) and cross-market transfer efficiency.
• Expected Outcome: Multi-market agents will show superior early performance in live deployments.

References: ['Heublein, L., et al. (2024). Evaluation of ML Methods for GNSS Interference with Real-World Data Discrepancies. ION GNSS+.', 'Yu, H., et al. (2025). LiveTradeBench: Seeking Real-World Alpha with Large Language Models.']