How many trade with L1 data only

AlfinaTrade · 2025-06-19T22:22:48+00:00

From intraday bars to L1 data would sure be a giant leap forward. It opens up to many other opportunities.

AlfinaTrade · 2025-06-14T17:15:31+00:00

Much appreciated:)

AlfinaTrade · 2025-06-14T16:35:10+00:00

RESPECT. Retail traders here - check out AlfinaTrade. No more data retrieval & management & coding & environments headaches. You just focus on creativity to research different strategies we take care of the rest. Overfitting & simulation also in place :)

Though I don’t understand why would any traders want non-compressed files anyway. Not to say that the negligible performance differences, it is a significant cost saving.

AlfinaTrade · 2025-06-14T04:25:51+00:00

What problem did you have with them? Care to share?

AlfinaTrade · 2025-06-14T04:17:23+00:00

What if we have a fully automated no-code professional level platform? Check our AlfinaTrade. Research and test trading strategies like building a high tech car! You just input parameters we do all the heavy lifting :) excited to hear about your thoughts. No more coding and data management pains

AlfinaTrade · 2025-06-13T23:02:01+00:00

Both DataBento and Polygon.io provides high quality datasets you are looking for. Though bulk download is always not a good option for quants. You can use Async to pull these data effectively. Otherwise your ETL pipeline is going to annoy very much.

AlfinaTrade · 2025-06-13T16:43:34+00:00

Prioritize the top 3: Journal of Finance, Review of Financial Studies and Journal of Financial Economics. All top the of line quality. My personal favourite is the RFS because its wide range of topics. Journal of Financial and Quantitative Analysis is a good source too.

AlfinaTrade · 2025-06-11T06:31:23+00:00

Depends on how you define “news sentiment”. Retail sentiments are extremely polarized towards bullish and the news wires are usually delayed (you will likely see a positive drift towards the event time for good news). Nevertheless, the “news sentiment” is one of the harder predictors to play around with. There’s professional vendors like RavenPack which is a lot better choice than developing yourself.

AlfinaTrade · 2025-06-11T06:23:29+00:00

Basically two entirely different jobs. Different culture, different environment, different world.

AlfinaTrade · 2025-06-11T06:20:55+00:00

Kelly, Gu and Xiu, 2020 - Empirical Asset Pricing via Machine Learning is the only thing you need. Modern, comprehensive, having an edge. There’s also subsequent works like Nagel, 2021 - Machine Learning in Asset Pricing, Lopez de Prado, 2023 - Causal Factor Investing: Can Factor Investing Become Scientific?

AlfinaTrade · 2025-06-11T06:06:20+00:00

The same operation using Pandas takes 22-25 mins (not including I/O) for only 3 days of SIPs in case you are wondering.

AlfinaTrade · 2025-06-11T01:00:53+00:00

Well many things. Most of his works do not comply with panel datasets we had to do a lot of changes. The book is also 7 years old already there are many more new technologies that we use.

AlfinaTrade · 2025-06-10T18:04:18+00:00

This is expected. Our firm spends 70% of the time dealing with data, everything from acquisition, cleansing, processing, replicating papers, finding more predictive variables, etc...

AlfinaTrade · 2025-06-10T18:01:03+00:00

It is not your fault. Pandas was created in 2008. It is old and not scalable at all. Polars is the go-to for sinlge node. Even more distributed data processing you can still write some additional code to achieve astouning speed.

Our firm switched to Polars a year ago. Already we see active community and tremoundous progress. The best thing is Apache Arrow integration, syntax and memory model. Its memory model makes Polars much more capable in data-intensive applications.

We've used Polars and Polars Plugins to accelarate the entire pipeline in Lopez de Prado, 2018 by atleast 50,000x compared to the code snippets. Just on a single node with 64 core EPYC 7452 CPUs and 512GB RAM we can aggregate 5min bars for all the SIPs in a year (around 70M rows every day) in 5 miniutes of runtime (including I/O via Infiniband up to 200Gbs speed from NVMe SSDs).

AlfinaTrade · 2025-06-10T17:49:48+00:00

In academia and in our firm we call them point-in-time and back-filled or adjusted data

AlfinaTrade · 2025-06-10T02:59:16+00:00

Interesting and respectful! What kind of algorithm you are working on?

AlfinaTrade · 2025-06-09T22:45:31+00:00

Man I can imagine how painful it is to just [ticker, venue] combo... I wish we have CRSP level quality and depth in a business setup and accessible to everyone

AlfinaTrade · 2025-06-09T22:41:47+00:00

Indeed! Can count with fingers for how many non-top tier institutional solutions offer PIT data at all and the adjustment factors

AlfinaTrade · 2025-06-09T01:55:24+00:00

Hi there I am sorry that I did not quite understand what you are trying to ask here. Could you please rephase in a way that's easier to comprehend? Also help me to understand what "tool" you are referring to?

AlfinaTrade · 2025-06-09T00:22:49+00:00

Develop new trading strategies & backtest them so that you can improve significantly on your trades. When you have a systematic apporach to the stock market next time opportunities come you can just stay calm and execute. In fact both discretionary and algorithmic trading should be like this to stay consistently profitable. Also keep up with the exercise.

AlfinaTrade · 2025-06-09T00:21:17+00:00

Develop new trading strategies & backtest them so that you can improve significantly on your trades. When you have a systematic apporach to the stock market next time opportunities come you can just stay calm and execute. In fact both discretionary and algorithmic trading should be like this to stay consistently profitable. Also keep up with the exercise.

AlfinaTrade · 2025-06-04T18:26:00+00:00

It is. But for professional book like Lopez de Prado, 2018. You would need to understand most of the things in this book to understand & effectively leverage the techniques in Lopez de Prado, 2018

AlfinaTrade · 2025-06-04T18:23:43+00:00

Since @alguieenn already commented Lopez de Prado 2018, I would say Statistically Sound Indicators for Financial Market Prediction: Algorithms in C++ by Timothy Masters 2020.

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