Quant Trading Onboarding
Phase 3 of the quantitative learning path. You've learned markets and the math. Now it's time to get hands on the keyboard. Python, the Arithmion SDK, your first strategy, and your first backtest.
8 articles ~48 min
Concepts
- What Quant Trading Actually Is (And Isn't) . What quant trading actually is (and isn't). The four flavors, why most retail quants lose, and the brutal math.
- What Is Backtesting, Really? . A backtest simulates how a strategy would have performed historically. Every backtest is a lie; the question is which lies you've eliminated.
- Five Ways Backtests Lie . The five (and a bonus sixth) ways to lie with backtests: lookahead, survivorship, cost amnesia, overfit, shorting impossible inventory, regime change.
- Vectorized vs Event-Driven Backtests . Vectorized is fast and subtly wrong. Event-driven is slow and correct. Know when to use which.
Tools
- Python for Quant: Skip the Boring Parts . Skip the parts that don't matter. The 20% of numpy/pandas that does 80% of quant work.
- Arithmion SDK Quickstart . Installation, your first data fetch, and your first backtest, all in under 30 lines.
- Your Data Is Wrong. How to Fix It. . Free vs paid data, and the three traps (survivorship, lookahead, corporate actions) that break everyone.