An AI-oriented quantitative investment research platform covering the full quant pipeline — data, feature engineering, ML model training, backtesting, and portfolio optimization.
QuantAI is an AI-oriented quantitative investment research platform covering the full quant pipeline from raw data ingestion to live trading. It handles data wrangling, feature engineering, ML model training, backtesting, and portfolio optimization in one coherent framework. Its RD-Agent module takes it further: an LLM that autonomously generates, tests, and deploys trading strategies. Cited in NeurIPS and ICML papers, it is the benchmark platform for institutional quant research.
Quant research teams at hedge funds and asset managers routinely spend 70% of their engineering resources on infrastructure — data pipelines, backtesting environments, portfolio construction frameworks — before writing a single line of alpha research. Every firm rebuilds the same foundations. When deep learning and reinforcement learning emerged as viable strategy generation tools, the infrastructure gap widened further: PyTorch training loops, realistic backtesting with transaction costs, and live deployment are four different engineering domains that rarely share a unified interface.
QuantAI provides a unified research infrastructure that eliminates the rebuild problem. The data layer normalizes time-series financial data from 14+ sources (Yahoo Finance, Alpaca, Binance, and more). The model layer supports supervised ML, deep learning, and reinforcement learning (A2C, DDPG, PPO, SAC, TD3) through a common training interface. The backtesting engine applies realistic transaction cost models, slippage, and position constraints. For forward-looking teams, the RD-Agent module automates the research loop itself: an LLM generates strategy hypotheses, QuantAI executes backtests, and the agent iterates toward statistically significant alpha — without human intervention at each step.
Tell us about your problem. We'll tell you honestly how we'd approach it — and whether we're the right team.