algorithmic-trading
7 free lessons tagged algorithmic-trading across AI, Business. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Market Making: Inventory, Adverse Selection, and What RL Adds
A market maker quotes both sides and profits from the spread, but every fill leaves an unwanted position and the counterparties who trade most eagerly are the ones who know something. This lesson simulates the inventory-skew trade-off, showing a 63 percent cut in exposure for 2.5 percent of profit, and locates where a learned policy genuinely helps.
Reward Design: Where Execution Agents Go Wrong
An agent optimises the reward you wrote, not the objective you meant, and in execution the gap between those is unusually easy to open. This lesson computes how a mis-sized penalty makes leaving part of the order unexecuted rationally optimal, and covers the shaping that provably does not change the policy.
Framing Execution as a Markov Decision Process
A static execution schedule is an open-loop policy: it commits to a plan before seeing anything. This lesson formulates execution as an MDP so the plan can react, and computes the ceiling on what any adaptive policy could win, which turns out to collapse as market impact grows.
Costs, Capacity, and a Protocol You Can Trust
The edge that survives statistics still has to survive trading. Spread, market impact and the square-root law, why every strategy has a capital ceiling, and the research protocol that makes a backtest worth believing.
Selection Bias and the Deflated Sharpe Ratio
The statistical core of backtest overfitting: why the best of many trials is inflated even when nothing works, how much to discount it, and why finance needs a far higher significance bar than the usual one.
The Biases That Break It Before Statistics
Look-ahead bias, survivorship bias, and point-in-time data. The errors that make a backtest wrong as a simulation, independent of any statistical question about whether the edge is real.
What a Backtest Actually Claims
A backtest is not a measurement of the past, it is a counterfactual about a world that never happened. Getting precise about that claim explains every way backtests mislead.