This is a step‑by‑step playbook for building a robot‑driven investment fund. Instead of human stock pickers, you design and deploy computer programs that systematically search for patterns in market data and trade automatically.
Explains how to go from idea to operating quantitative hedge fund: designing trading strategies, building data and research infrastructure, implementing algorithmic execution, managing risk, and setting up the business/legal structure so the fund can scale beyond discretionary, manual trading.
In quantitative hedge funds, defensibility typically comes from proprietary data pipelines, unique alpha signals/models, superior execution technology, and an integrated research–to–production workflow that is hard to replicate quickly.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Market data throughput and latency, plus the cost/complexity of reliably running backtests and live trading at scale with strict risk controls.
Early Majority
This guide focuses on end‑to‑end fund creation—investment strategy, research stack, execution, and business setup—rather than only on building trading models. It is more of a holistic playbook than a pure modeling tutorial.