Applying Machine Learning & Science to Trading Decisions

This is a past event

275 people went


6:00 - 6:30 PM - Networking

6:30 - 8 PM - Talk and Q&A

Title & Abstract :

- Applying Machine Learning and Science to Trading Decisions

From the view point of a Quantitative Global Macro Investor, the speaker will go over:

- Why investors and traders use stop-levels instead of hypothesis tests to exit trades?

- Why are the best trading strategies discovered in the search for the surprisingly mundane?

- Why searching for profitable strategies leads to bad strategies (and it’s not because of overfitting)?

This talk will take a clear look into trade sizing covering Kelly Criterion to the fat tails of the Sharpe Ratio and Correlations that no one talks about in the world of finance and investing.

The talk is open to all audiences with an interest in investing. Working examples (IPython/Jupyter Notebooks) will be provided to all that attend and who want to roll up their sleeves and do some python (doing math is always encouraged but optional).

The speaker would love to learn more about what parts of the investment process our members are excited about to tailor the presentation material better. The speaker has created a short survey for members who are interested in participating.

Click Here

to take part in the study.

About Speaker:

Sean Kruzel is the Founder and CEO of Astrocyte Research, Inc. Prior to launching his company, Sean worked for large financial institutions and hedge funds where he collaborated in the area of Quantitative Global Macro Strategy and worked as an analyst in fixed income relative-value setting .

As CEO of Astrocyte Research, he is currently working towards creating data-driven analysis and frameworks for investors using a combination of industry best practices, novel sources of information and state of the art computation infrastructure. Sean holds a double degree in Mathematics and Economics from Massachusetts Institute of Technology.