Every serious quant trader is likely using AI now to research and build models. Mastering how your coding agent propagates knowledge you build up is essential.

One of the most fundamental decisions that aspiring algorithmic developers make is that of which platform to choose. This decision is more consequential than most developers realise.

1. Almgren, R., & Chriss, N. (1999). Optimal execution of portfolio transactions. Journal of Risk, 3(2), 5--39.

The current state of retail backtesting practice is characterised by a significant gap between the apparent sophistication of the tools and the actual rigour of the analysis they produce.

Based on the foregoing analysis, I propose the following minimum standards for a backtest to be considered credible evidence of a potentially viable trading strategy.

Novice and retail trading system developers suffer from what might be termed the signal isolation blindspot: an overwhelming focus on a single dimension of the problem, generating a profitable entry…

Off-the-shelf backtesting frameworks are not neutral tools.

Python's dominance in retail algorithmic trading is a sociological phenomenon, not the result of a deliberate technical evaluation.

Traders who are aware of the overfitting problem described in the preceding section often turn to a family of validation techniques (Monte Carlo simulation, synthetic data generation, and…

Harvey, Liu, and Zhu (2016) demonstrated that the threshold for statistical significance in backtested strategies must be adjusted for the number of strategies tested.

Every OHLC bar, regardless of its timeframe, is a lossy compression of the true price path. A bar records the open, the high, the low, and the close, but nothing about the trajectory between them.

The most fundamental question in any backtest is deceptively simple: would this trade have been filled, and at what price?

A backtest is only as reliable as the data on which it is built, and the quality of freely available and even commercially distributed historical market data is far worse than most traders realise.

The barriers to entry for quantitative trading research have fallen dramatically over the past decade.