Dispatches from the intersection of mathematics, engineering, and finance.
Should your algorithm trade intraday or hold overnight? We break down the massive architectural differences in data handling, risk, and execution logic."
Providing liquidity is profitable but risky. Learn the basics of inventory risk, skewing quotes, and coding a simple market maker in Python."
Skills, degrees, and projects you need to land a role at a top quant firm.
Separating marketing fluff from mathematical reality in financial ML.
Kill switches, fat-finger checks, and survival mechanisms in high-speed markets.
Exploring LSTMs and Attention Mechanisms for non-linear pattern recognition.
Zerodha vs. Dhan vs. Fyers. Latency benchmarks and stability scores.
The reality of quantitative investing. Why drawdowns are feature, not a bug.
A starter guide to Pandas and Backtrader for aspiring quants.