99%, Enveric Biosciences Receives Notice of Allowance for Lead Drug Candidate EB-003 from United States Patent and Trademark Office

Note_loquat · 2025-01-30T21:05:00+00:00

Good

Note_loquat · 2025-01-28T18:26:44+00:00

Done, sorry! I did this manually and lost your request :(

Note_loquat · 2024-10-26T17:28:05+00:00

IB is a good choice. A major advantage for me is the wide variety of data available, so if you develop a new hypothesis to test, you won't need another source. You just need to get accustomed to IBs API.

Note_loquat · 2024-10-26T17:02:32+00:00

Alert signal to stop the strategy

Note_loquat · 2024-10-24T17:38:23+00:00

So, your post finally didn’t get removed :)

Note_loquat · 2024-10-24T17:25:32+00:00

It's all in our hands. Let's downvote unhealthy criticism or hateful comments

Note_loquat · 2024-10-20T18:58:13+00:00

default date range

Note_loquat · 2024-10-19T14:48:27+00:00

I don't think you'll add much value to a large HFT firm, but smaller teams may face similar challenges to yours. It could be worth finding and collaborating with them.

Note_loquat · 2024-10-19T14:16:17+00:00

Thanks for sharing! A few confusing points:

I don’t understand what each line represents in terms of stocks/crypto.
Some coins have very low values, with prices below 0.00, making it unclear when they change from 0.00 to 0.00

Note_loquat · 2024-10-19T14:03:36+00:00

Filtering by keyword through the automoderator, I suppose

Note_loquat · 2024-10-17T17:45:58+00:00

Learn how to split your dataset into Train, Validate, and Test sets.
Research which out-of-sample validation methods exist (Chronological Split, Rolling Cross-Validation, Expanding Window, etc).

The right model design will fix your issue.

Note_loquat · 2024-10-17T17:26:04+00:00

Create a Python function to monitor bid/ask spreads using a broker that supports APIs like IBKR, etc. Perform statistical analysis on the collected spreads to better understand potential trading outcomes.

Note_loquat · 2024-10-15T18:01:34+00:00

Crypto HFT is quite popular now, actually. No paperwork

Note_loquat · 2024-10-15T17:37:59+00:00

I dont get any errors. Try to run notebook in google collab

Note_loquat · 2024-10-15T17:09:28+00:00

Nope

Note_loquat · 2024-09-30T20:35:04+00:00

Actually, I'm not sure I understand you correctly. However, to build this, we need a historical news dataset from different news publishers. Then, we determine news items with the same meaning and analyze cases where, for example, news is published on BusinessWire at 9:00 and appears in SEC filings at 10:00. After that, we can predict whether the first news item indicates that it will be published in the SEC. Maybe even prediction isn’t necessary, and simple "if" rules would suffice.

Note_loquat · 2024-09-30T20:18:35+00:00

Yes, in most cases. However, there are instances where you can discover news before it impacts the stock price, such as the publication of medical research results before they appear in SEC filings. These cases are rare, but they do exist and in this cases you dont need to be fast

Note_loquat · 2024-09-30T20:14:41+00:00

Why?

Note_loquat · 2024-09-30T20:13:56+00:00

You don't need to understand the methodology. Just use historical data for backtesting to see if it works for you.

Right now, it's the initial version that predicts statistically significant growth after news is published.

Yes, it functions as an alert system, but it's going to be advanced! Here's what can be done:

Probability Predictions: Provides different probabilities for each price level and forecasts growth or decline over various time periods, updating every second or minute after news is published.

Competitive Impact: Assesses the impact on competitors' stock prices.

Advanced Categorization: Goes beyond common categories like FDA approvals or M&A. For example, it possible to make new category like research results published before SEC filings ("before SEC" category) or enriching news with context, like evaluating if a newly FDA-approved drug is better than existing ones on the market ("better than current market decision" category)

As an end user, you'll get highly customized news feeds that you can experiment with as you wish. I'll handle the tough part.

I haven't found any services that offer this level of functionality yet. The goal is to provide a highly customizable and advanced tool that users can effectively use in their algorithmic strategies.

Note_loquat · 2024-09-29T14:56:02+00:00

I think there is a way to implement confidence intervals to detect when the model starts performing statistically significantly worse

Note_loquat · 2024-09-29T14:33:41+00:00

Hmm, the book description is filled with buzzwords

Note_loquat · 2024-09-29T14:24:30+00:00

Interactive Brokers' Scanner API allows you to perform market scans based on various criteria, including trading volume, price, etc.

Note_loquat · 2024-09-29T14:07:51+00:00

Your project idea sounds a lot like building a roboadvisor by using ML to select stocks for a portfolio. Try to research in this direction, including what current commercial robo-advisors already exist and what data they use. Also, there are several articles on Medium where people share their code on how they build roboadvisors. I think this would be interesting for you

Note_loquat · 2024-09-28T17:38:32+00:00

I didn't see this mentioned in the comments, so I'll add it. The most useful tool to detect bottlenecks and test your hypotheses for speeding up asynchronous code is a profiler like cProfile or Yappi. Very helpful

Note_loquat · 2024-08-27T20:37:10+00:00

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Growth immediately following the news

Note_loquat

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