Financial Machine Learning

It is extraordinarily difficult to succeed in financial machine learning. That’s why we started, a financial machine learning SaaS that computes the Probability of Profit for your next investment.

We highlight some of the difficulties of financial ML below. and how can help your investment management practice overcome them.

Financial data scrubbing

This may seem like a task for plumbers, but is actually the crucial first step in successful financial machine learning, and it takes surprisingly deep domain knowledge to accomplish. For example, do you know that many fundamental stocks databases have look-ahead bias? How would you find out if your news sentiment data is sensible? With years of financial and data science experience behind us, we can help you find out.

See Part 1 of Dr. Chan and Dr. Hunter’s Lifecycle of Trading Strategy Development course to learn about detecting and fixing myriad problems of financial data. has a team of financial data scientists that can do that for you.

Features engineering and selection

We can help identify, combine, select and rank variables that affect the returns of an investment strategy.

See Dr. Chan’s talk, Toronto, September 2018. has a team of quantitative strategists that can do that for you.

Predicting “non-reflexive”* targets

If returns can be predicted, returns will change in response to the prediction. On the other hand, if weather can be predicted, weather will not change in response. Yet accurate weather prediction can benefit agricultural futures traders. can help identify and predict non-reflexive targets (e.g. earnings, same-store sales, etc.) that cannot be arbitraged away. E.g. We already successfully predicted the Non-Farm Payroll Surprises using alternative data.

* Reflexivity is a term used by George Soros to describe the effects of arbitrage activities on the financial markets and the general economy.

Capital allocation and risk management via “meta-labeling”*

Determine Probability of Profit for a trade through machine learning models, and allocate capital and manage risks accordingly. These are part of “quantamental strategies“: applying machine learning to help discretionary or fundamental investment managers quantify and systematize their ideas, factors, and knowledge, done without the necessity of disclosing the fundamental strategy to the consultant.

See Dr. Chan’s Quant World Canada talkToronto, November, 2018.

Predicting the Probability of Profit is exactly what SaaS does!

* Meta-labeling is a term coined by Dr. López de Prado in his book “Advances in Financial Machine Learning”.

Market simulations for performance evaluation

Using neural networks to capture essential market patterns, and generate realistic simulations for trading strategies evaluation and risk assessment.

See Dr. Chan’s QuantCon keynote speech, New York, 2018.

In Summary

Our Research Articles and Presentations

“What is the probability of profit of your next trade? (Introducing” explains in details how you can use our SaaS to improve your existing investment or trading strategy.

Flirting with Models with Dr. Ernest Chan : a talk about Machine Learning applied to finance and investment

The best way to select features?“, preprint by Xin Man and Ernest Chan comparing MDA, LIME, and SHAP feature selection methods in machine learning.

US nonfarm employment prediction using RIWI Corp. alternative data“, working paper by Radu Ciobanu and Ernest Chan on using alternative data to predict NFP numbers.

Financial Data Science and Machine Learning” webinar at, October 2019.

What we learned from Kaggle Two-Sigma News Sentiment competition?“, UNICOM conference keynote speech, London, June 2019.

Is News Sentiment Still Adding Alpha?“, blog post on on our News Sentiment research.

Why features importance should only be computed in validation set“, an open source kernel in Kaggle Two-Sigma competition.

What to do before machine learning?“, Quantopian webinar, October, 2018. (Video.)

FX Order Flow as a Predictor” at Friedberg Mercantile Group event in Toronto, May 2018.

Optimizing Trading Strategies Without Overfitting”, keynote speech at QuantCon, New York, April 2018.

Applying machine learning techniques to everyday strategies” at Grupo L&S conference, Brazil, November 2017.

Enhancing Statistical Significance of Backtests” at QuantCon, New York, April 2017.

The Peculiarity of Volatility” at QuantCon, New York, April 2016.

Optimal Order Types” at Market Microstructure, Liquidity and Automated Trading conference, London, July 2015.

Beware of Low Frequency Data” at QuantCon, New York, March 2015.

Factor Models in Practice” at Society of Technical Analysts, London, Oct 2014.

Backtesting and Its Pitfalls”. Market Technician Association meeting, London, Apr 2013.

How to Succeed in the Quant Trading Business”. York University Schulich School of Business Professional Seminar Series. January, 2013.

Discovering Risk Indicators in the FX Markets” at Quant Invest Canada conference in Toronto, Canada, October 2012.