Machine Learning for Investment Strategy A structured learning record

This page documents what participants actually work through inside the bexazaa programme - the tools covered, the structure of sessions, and the range of outcomes learners report after completing the full path.

6 Learning modules

340+ Learners across Ireland

Live Group & private sessions

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Format Live online - group cohorts and one-to-one sessions

Duration 12 weeks, flexible pacing available

Level Intermediate - basic Python assumed

Region Remote access across all of Ireland

Language English

What the programme covers

The curriculum moves from statistical foundations into applied ML - regression models, time-series classification, and feature engineering for financial data. Participants spend roughly 40% of session time writing and reviewing actual code against real market datasets.

Sessions are structured so that group cohorts tackle shared problem sets while individual learners follow a path shaped around their own portfolio context. Both tracks converge at the same technical depth; the difference is in pacing and the type of feedback you receive.

Instructors are practitioners who currently work in quantitative analysis. They bring current problems into sessions rather than relying on textbook scenarios, which keeps the material grounded in what markets actually look like right now.

Six areas of technical focus

Time-Series Analysis

Working with OHLCV data, rolling statistics, and autocorrelation - the practical groundwork before any model is trained.

Feature Engineering

Translating raw price and volume into signals that models can actually learn from - momentum, volatility ratios, cross-asset correlations.

Supervised Classification

Random forests and gradient boosting applied to directional prediction - including how to avoid the lookahead bias that breaks most backtests.

Portfolio Optimisation

Mean-variance frameworks extended with ML-derived return forecasts - understanding where the math holds and where it breaks down in practice.

Risk & Validation

Walk-forward validation, Sharpe ratio decomposition, and stress-testing model output against historical drawdown periods.

Deployment Basics

Packaging a trained model into a reusable pipeline - scheduling retraining, logging predictions, and monitoring for distribution shift.

Weeks of structured content

Live instructor sessions per cohort

Hands-on projects with real data

1:6

Maximum instructor-to-learner ratio in group sessions

What learners report after completing

Participants consistently mention two things: they can now read a backtest critically rather than just running it, and they have a working codebase they actually understand rather than copied from a tutorial.

Several learners from regional areas - Galway, Limerick, Sligo - have noted that remote access made the difference for them. The live session format preserves the back-and-forth that self-paced video cannot replicate.

Ability to build and validate ML pipelines on financial data independently
Clearer understanding of where models fail and why
Confidence to adapt the techniques to new asset classes
Ongoing access to the learner community after the programme ends

Aoife Ní Bhriain Equity analyst, Cork

★★★★★

"The feature engineering module alone changed how I approach data preparation. The instructor caught a lookahead error in my code that I'd been carrying for months without noticing."

Tomáš Dvořáček Independent trader, Dublin

★★★★★

"I've done other ML courses. This one was different because the instructors pushed back on my assumptions rather than just confirming that my model looked good."

Siobhán Ó Maolalaidh Risk associate, Galway

★★★★

★

"The walk-forward validation section took me two attempts to fully follow, but the instructor was patient and the group sessions helped me see where my understanding had gaps."

Interested in joining the next cohort?

The programme runs in structured cohorts with limited places per group. Individual sessions can be arranged independently of the cohort schedule.

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