📈 About Dataset Daily

Data, sample code, and analytics on crypto, stocks, industry deep dives, and more.

💸 Weekly Agenda 💸

📊 Data Science Tuesday: on Tuesdays we send a free newsletter to both paying and non-paying subscribers. This edition covers news from our field, funding rounds, book lists, and a Data Science job board.

💻 Sample Code Wednesday: on Wednesdays we explore sample code that you can copy or improve for your own work and projects.

🏭 Industry Dive Thursday: on Thursdays we dive deep into an industry or sector of the economy and find interesting stats to produce unique insights.

📈 Market Review Friday: on Fridays we review the portfolio we trade, announce any big trades, and review market performance each week.

To receive Wednesday through Friday posts you can subscribe for $15/mo or $140/yr.

🌏 Thesis

As an investor, I believe a data-driven approach is the best way to source, trade, and execute in Finance. Conversely, as a Data Scientist, I believe markets give us insights and predictive power into the world around us. Not only do they provide a signal on current trends, in markets hide signals for thinking about possible future scenarios.

To invest or trade equities, crypto, or any asset is to try to uncover a view about the world that you think is more “correct” than the average view. To discover alpha is to find some insight about the world that most others have failed to uncover.

That’s why we take a varied approach on this newsletter…

  • Being skilled with data presents one with a potential edge in markets.

  • Being interested in markets presents one with a potential edge in life.

One can argue that to understand a certain company or sector we can go purely quantitative and only dig into the fundamental data. Sure. But to understand trends in that sector and the impacts of external market forces on that sector, we must become intimately familiar with how that company or sector functions, past the numbers.

Data will help us find where to look, domain knowledge will turn that data into explainable findings.

An example…

Imagine we take the performance of a motor and chart its output month over month.

We see that every 11th month it starts to struggle and by the 12th month its output almost looks anomalous. So we decide to replace it with another motor on that 12th month. The data showed us this insight. Job done? No.

What we may not realize is that the 11th and 12th months on this chart are the coldest months of the year. During the coldest months the motor produces less output because of Physics. By replacing it we’re wasting money on a new motor that won’t outperform.

Maybe we could have dodged this scenario by putting better labels on our chart.

But the real solution to this problem would have been simply asking someone with knowledge on how these things operate if they had any insight into this behavior. They might give us this very important insight and even point out that the output isn’t even that much lower, our scales just make it seem so.

Without this extra insight the data is useless.

We can make up a million similar examples in markets where Finance professionals make lofty claims about industries of which they have no idea. Can someone who has never stepped foot on a manufacturing floor give commentary on the industry? Yes, albeit limited.

That’s why we do our best on this newsletter to deeply explore the data and uncover insights we would have otherwise missed.

If you want to follow along with our weekly analysis and studies you can subscribe to the newsletter for $15/mo or $140/yr.


About the writer: Luke Posey is the founder and lead writer of Dataset Daily. He is passionate about building and investing in great data-driven products. He’s the co-founder of Spawner.ai, an analytics company building data tools for traders and investors. He started his career as a Machine Learning Engineer after studying Electrical Engineering and currently resides in St. Louis, Missouri.

You can follow his work on Twitter and his long-form writing on Medium.

Until next time.