Have you guys used AI based tools where you can type your questions in natural language and get stocks? Like "Find me all large cap companies whose margins fall when oil prices go up". What has your experience been with such natural language screeners? or does the existing screeners such as one by yahoo finance and so on suffice? I have always felt like the manual screeners are inadequate to screen stocks based on more qualitative criteria's. Like say finding companies with significant revenue segment from AI, companies susceptible to copper prices or dependence on China and so on?

I’ve been thinking a lot about how most stock screeners out there seem built for simplistic value (using PE, PB ratios etc.), growth or momentum strategies, tons of flashy metrics, short-term filters, simple rules, and not much focus on real value. So I’m starting an open-source project to build a stock screener specifically for value investors. Something that actually reflects Buffett-style thinking, focusing on intrinsic value, sensible fundamentals, possibly moat assessment optimized for long-term returns instead of hype.

Before I get too deep into it, I really want to understand what frustrates other buffet style investors like me. What pain points do you hit when using existing screeners or analysis tools? What do you wish was easier? Perhaps they have too many arbitrary details that are not important, not enough transparency, or almost no way to quickly assess a company’s moat such that it's not influenced by market hype? What’s missing that keeps you from trusting or relying on most screeners out there? Or is there a good product that you know of?

I’m hoping I'd make something genuinely useful. Any thoughts or ideas are welcome... This is meant for buffet style value investors, hoping it ends up shaped by what matters to you.

Hi folks, Can you suggest some good stock screening app you are using and satisfied with?

I am interested in how you scan/search/filter for your favourite stocks based on criteria. It would be great if you can explain the flow.

Thanks 🙏

Hey everyone,

For me (and I'm guessing many of you), the biggest bottleneck is just time. The data is overwhelming. To find an edge, I've had to get more productive , and an AI-augmented workflow has been my solution.

I wanted to share a couple of the tools from my process, and I'm genuinely curious to hear what everyone else is doing. What tools or prompts are you all using?

Here are two of mine:

Tool 1: The Qualitative Screener (Custom 'Gems')

My quant screen gives me dozens of 'cheap' stocks. But many are "cheap for a reason." Instead of just culling by industry, I immediately apply a qualitative layer.

I built a custom 'Gems' (like a custom GPT) specifically to analyze qualitative drivers:

• Brand strength

• Competitive landscape

• Regulatory changes

• Technological shifts

It gives me a high-level summary so I can instantly decide if a stock is worth another minute of my time.

• You can build it yourself. Here are the exact instructions I use for the 'Gems':Qualitative factor analysis 

• Here’s an example of its output in a shared conversation: https://gemini.google.com/share/72a2488ee086

Tool 2: The 'Deep Research' Prompt Library

If a stock passes that first screen, I move to my 'Deep Research' toolbox. This helps me find the core debate around the stock before I commit to reading the 10-K.

How I use it: This isn't a 'Gems', it's a different workflow.

1. I open a new chat in Gemini and activate the Deep Research functionality.

2. I attach my custom prompt library (which is a Google Drive doc) directly to the chat.

3. To run a prompt, I just type the company name and an approximate prompt name, like “Coursera operation diagnostics”.

The Prompts:

• Part A: The 'Phase 1' Prompt: The first one I always run is [Company Name] phase 1. It generates an instant, balanced summary of the bull and bear arguments. Here's a real-world example of that command and its result: command 'Hudson Technologies Phase 1', results: Hudson Technologies Phase 1 Research 

• Part B: Drill-Down Prompts: After that, I use other prompts from the library to dig deeper:

  • Short-Seller / Panic Timeline: What are the most potent bear arguments?

  • Operation diagnostics / Customer Churn: How does the business actually work and why do customers leave?

  • Forensic Filing Analysis: Is the accounting clean? Is management aligned?

• Here's the full prompt library if you want to copy or adapt it: Company Deep Research tools

These two tools together are probably 30-50% of my initial research process and save me a massive amount of time.

They're just the first part of my system. I wrote up the full workflow (which includes how I use AI to analyze 10-Ks and NotebookLM for complex reports) in a post here if you're interested in the other tools:

https://thanh1.substack.com/p/reclaiming-my-time-an-ai-workflow

So, what's in your toolbox?

Let's share what we've built.

Wondering if anyone has gone this route yet. I feel like it's a requirement to dig into details and I think AI is the next step in making sure I have as much knowledge about what my investment strategy is doing.

I have just been using general ai tools but free limitations and no specialization in stocks makes me want to explore for something more suited.

Ideally it would use a brokerages api to pull portfolio data and be able to deliver alerts etc.

But even being free and being trained on more extensive trading data than a general AI would be useful.

I created NexusTrade, an AI-Powered algorithmic trading platform.

NexusTrade makes it easy for retail investors to develop algorithmic trading strategies and perform financial research. Some of its features include:

• Algorithmic Trading: Allows users to create, backtest, and deploy automated trading strategies without writing code.

• No-Code Platform: Designed for non-technical users, providing a user-friendly interface to configure and implement trading strategies.

• Genetic Optimization Engine: Incorporates advanced genetic algorithms for optimizing trading strategies.

• Paper Trading: Supports paper trading to test strategies in a simulated environment before live deployment.

• Strategy Library: Allow users to copy from a library of pre-existing strategies.

• Stock Research and Analysis: Offers tools for in-depth financial research and analysis directly within the platform.

• AI Stock Screener: Uses an LLM-powered stock screener to identify profitable stocks based on natural language queries.

• Customizable Watchlist: Allows users to track and monitor their favorite stocks and cryptocurrencies for quick and easy reference.

• Daily Stock News Digest: Provides an AI-generated daily stock news digest.

• Data-Driven Approach: Promotes a data-driven methodology for trading and investing decisions.

• Unique Investing Opportunities: Helps users find novel investing opportunities based on data.

I have a long (3 min) demo here and a 1 minute crash course on it. I would love to get feedback on the app.

EDIT: Thanks for all of the love! If you're interested in algorithmic trading, but don't want to use someone's platform, I have an open-source platform that you can download and run on your computer!

Feel free to explore my GitHub! There's lots of cool things there.

Last week, I wrote a technical article about a new concept: an intelligent AI-Powered screener. The feature is simple. Instead of using ChatGPT to interpret SQL queries, wrangling Excel spreadsheets, and using complicated stock screeners to find new investment opportunities, you’ll instead use a far more natural, intuitive approach: natural language.

This screener doesn’t just find stocks that hit a new all time high (poking fun at you, RobinHood). By combining Large Language Models, complex data queries, and fundamental stock data, I’ve created a seamless pipeline that can search for stocks based on virtually any fundamental indicator. This includes searching through over 130 industries including healthcare, biotechnology, 3D printing, and renewable energy. In addition, users can filter their search by market cap, price-to-earnings ratio, revenue, net income, EBITDA, free cash flow, and more. This solution offers an intuitive approach to finding new, novel stocks that meet your investment criteria. The best part is that literally anybody can use this feature.

This is a gamechanger in the realm of finance. Prior to something like this, finding new investment opportunities was extremely difficult, because everything you see on the internet is biased. Most articles are people trying to shill their own stocks, and have a financial incentive to make their investments seem sound.

But this tool makes financial research objective. It's intuitive, and requires no technical expertise to use. It's also completely free to try, so you can see how effective it is for yourself.

Curious to learn more? Read the official launch announcement on NexusTrade!

Last week, I wrote a technical article about a new concept: an intelligent AI-Powered screener. The feature is simple. Instead of using ChatGPT to interpret SQL queries, wrangling Excel spreadsheets, and using complicated stock screeners to find new investment opportunities, you’ll instead use a far more natural, intuitive approach: natural language.

This screener doesn’t just find stocks that hit a new all time high (poking fun at you, RobinHood). By combining Large Language Models, complex data queries, and fundamental stock data, I’ve created a seamless pipeline that can search for stocks based on virtually any fundamental indicator. This includes searching through over 130 industries including healthcare, biotechnology, 3D printing, and renewable energy. In addition, users can filter their search by market cap, price-to-earnings ratio, revenue, net income, EBITDA, free cash flow, and more. This solution offers an intuitive approach to finding new, novel stocks that meet your investment criteria. The best part is that literally anybody can use this feature.

This is a gamechanger in the realm of finance. Prior to something like this, finding new investment opportunities was extremely difficult, because everything you see on the internet is biased. Most articles are people trying to shill their own stocks, and have a financial incentive to make their investments seem sound.

But this tool makes financial research objective. It's intuitive, and requires no technical expertise to use. It's also completely free to try, so you can see how effective it is for yourself.

Curious to learn more? Read the official launch announcement on NexusTrade!

Financial research is a lot harder than it should be. When I go on Google and type in "the top 3 AI or semiconductor stocks with a market cap below $200 billion, sort by least expensive to most expensive", it will literally have no idea what to give you. It's going to send you ads, opinion pieces, and other fluff that makes true financial research tough.

At the same time, brokerages are absolutely not in the business of providing you this information. They want to to trade (especially day trade) because that's how they make money. So, if they show you "top gainers", "top losers", and "stocks that hit a 52 week high", then you are going to be more inclined to buy those stocks rather than stocks with solid fundamentals.

That's why I wrote an article on how to design an intelligent stock screener. This article is very technical and meant for software developers and Machine Learning engineers. By following along, a user can create this pipeline and design a screener that fits their specific investment thesis.

For example, I can say "find the top 3 biotechnology stocks that made a profit in 2023, and the algorithm will do the work of searching and returning these stocks for you. This is amazing because the results are objective, not subjective, and they are not shilled to you by a guy who's holding $25,000 of the stock they're writing about.

To save you a click, the steps to do this are:

• Generating industry data using an LLM

• Populating this data and fundamental data to a MongoDB Database

• Mastering the MongoDB Aggregation Pipeline

• Combine function-calling and few-shot prompting to translate the plain English query into a aggregation pipeline object.

Very curious to get some feedback from you guys! What do you think of this approach? Is it useful for finding novel stocks that meet your criteria?

Hey, I’m looking for the best stock screeners and could use some recommendations. I've had some paid subscriptions and free screeners in the past. But that was a long time ago and I’m sure things have changed.

I’m looking for:

• Advanced filtering options

• Fundamental and technical indicators

• Customizable screens

• Accurate data

So I ask:

1. What screeners are you using now?

2. What features do you like most?

3. Any screeners with unique tools or data?

P.S. Since the discussion brought up a lot of useful tools I decided to put together a quick summary of what's been mentioned. Hopefully, others will find it helpful too.

Unique or niche picks:

• TakeProfit.com – Uses trusted FactSet data, praised for small-cap accuracy.

• UnderstandStock.com – Built for value investors. No technicals but strong fundamental filters.

• Palmy Investing – 11k global stocks, 200+ metrics, free for 10 custom screeners.

• TipRanks – Good for analyst ratings and insider trades.

• Ophileo.com – Custom screener + downloadable financials. Free.

• Yahoo Finance & StockTwits – Mentioned for trend spotting and sentiment tracking.

• Smart Stock Screener on Poe – Natural language stock screening via AI.

Popular and solid options:

• Finviz – Free with many filters, great for beginners. Paid unlocks more features like intraday charts.

• TradingView – Excellent for technical analysis and charting, but now more limited for free users.

• StockAnalysis.com – Loved for its clean layout and educational popups on financials. Some features recently paywalled.

• Gurufocus – Deep data (30+ years of financials), highly customizable. Expensive but praised by advanced users.

• StockRover – Great fundamental screener, customizable views.

• Screener.co – Value investing focus, global stocks, strong fundamental filters. Paid after 30-day trial.

Hi guys, I need your help. I just released a beta of my startup. It is an AI-driven stock market screener, that can provide analysis of texts and financials related to the companies traded on stock market. It is intended to be useful for individual stock market participants. I want to collect some feedback about the website and product overall. I've made everything free for registered users so you can fully explore everything there without paying (I also removed the block on 10-minute emails in case you don't want to use your own). I will be very glad to receive any feedback! Here is the website: https://neurofinance-ai.com

The folks over at the r/ArtificialInteligence subreddit really liked this, so I thought to share it here too!

Last week, I wrote a technical article about a new concept: an intelligent AI-Powered screener. The feature is simple. Instead of using ChatGPT to interpret SQL queries, wrangling Excel spreadsheets, and using complicated stock screeners to find new investment opportunities, you’ll instead use a far more natural, intuitive approach: natural language.

This screener doesn’t just find stocks that hit a new all time high (poking fun at you, RobinHood). By combining Large Language Models, complex data queries, and fundamental stock data, I’ve created a seamless pipeline that can search for stocks based on virtually any fundamental indicator. This includes searching through over 130 industries including healthcare, biotechnology, 3D printing, and renewable energy. In addition, users can filter their search by market cap, price-to-earnings ratio, revenue, net income, EBITDA, free cash flow, and more. This solution offers an intuitive approach to finding new, novel stocks that meet your investment criteria. The best part is that literally anybody can use this feature.

Read the official launch announcement!

How does it work?

Like I said, I wrote an entire technical article about how it works. I don't really want to copy/paste the article text here because it's long and extremely detailed. To save you a click, I'll summarize the process here:

1. Using Yahoo Finance, I fetch the company statements

2. I feed the statements into an LLM and ask it to add tags from a list of 130+ tags to the company. This sounds simple but it requires very careful prompt engineering and rigorous testing to prevent hallucinations

3. I save the tags into a MongoDB database

4. I hydrate 10+ years of fundamental data about every US stock into a different MongoDB collection

5. I used an LLM as a parser to translate plain English into a MongoDB aggregation pipeline

6. I execute the pipeline against the database

7. I take the response and send another request to an LLM to summarize it in plain English

This is a simplified overview, because I also have ways to detect prompt injection attacks. I also plan to make the pipeline more sophisticated by introducing techniques like Tree of Thought Prompting. I thought this sub would find this interesting because it's a real, legitimate use-case of LLMs. It shows how AI can be used in industries like finance and bring legitimate value to users.

What this can do?

This feature is awesome because it allows users to search a rich database of stocks to find novel investing opportunities. For example:

• Users can search for stocks in a certain income and revenue range

• Users find stocks in certain niche industries like biotechnology, 3D printing, and alternative energy

• Users can find stocks that are overvalued/undervalued based on PE ratio, PS ratio, free cash flow, and other fundamental metrics

• Literally all of the above combined

What this cannot do?

In other posts, I've gotten a bunch of hate comments by people who didn't read post. To summarize what this feature isn't

• It doesn't pick stocks for you. It finds stocks by querying a database in natural language

• It doesn't make investment decisions for you

• It doesn't "beat the market" (it's a stock screener... it beating the market doesn't make sense)

• It doesn't search by technical indicators like RSI and SMA. I can work on this, but this would be a shit-ton of data to ingest

Happy to answer any questions about this! I'm very proud of the work I've done so far and can't wait to see how far I go with it!

Read more about this feature here!

So I am new to screener.in and used Gemini AI to create queries to scan the stocks.

So here is the query:

Debt to equity < 0.5 AND Free cash flow 5years > 0 AND Free cash flow 3years > 0 AND

(Price to Earning < (Industry PE * 0.8) OR Price to book value < 1) AND Market Capitalization >= 5000 AND Return on equity > 15 AND Current ratio > 1.5 AND Return on capital employed > 15 AND EPS growth 5Years > 15 AND Change in promoter holding >=0 AND GPM latest quarter > 50% AND Current price <= 1.1*Intrinsic Value

And here is the list of stocks I got.

What do you guys think?

Next stage is to do further analysis on Moneycontrol.

The folks over at the r/ArtificialInteligence subreddit really liked this, so I thought to share it here too!

Last week, I wrote a technical article about a new concept: an intelligent AI-Powered screener. The feature is simple. Instead of using ChatGPT to interpret SQL queries, wrangling Excel spreadsheets, and using complicated stock screeners to find new investment opportunities, you’ll instead use a far more natural, intuitive approach: natural language.

This screener doesn’t just find stocks that hit a new all time high (poking fun at you, RobinHood). By combining Large Language Models, complex data queries, and fundamental stock data, I’ve created a seamless pipeline that can search for stocks based on virtually any fundamental indicator. This includes searching through over 130 industries including healthcare, biotechnology, 3D printing, and renewable energy. In addition, users can filter their search by market cap, price-to-earnings ratio, revenue, net income, EBITDA, free cash flow, and more. This solution offers an intuitive approach to finding new, novel stocks that meet your investment criteria. The best part is that literally anybody can use this feature.

Read the official launch announcement!

How does it work?

Like I said, I wrote an entire technical article about how it works. I don't really want to copy/paste the article text here because it's long and extremely detailed. To save you a click, I'll summarize the process here:

1. Using Yahoo Finance, I fetch the company statements

2. I feed the statements into an LLM and ask it to add tags from a list of 130+ tags to the company. This sounds simple but it requires very careful prompt engineering and rigorous testing to prevent hallucinations

3. I save the tags into a MongoDB database

4. I hydrate 10+ years of fundamental data about every US stock into a different MongoDB collection

5. I used an LLM as a parser to translate plain English into a MongoDB aggregation pipeline

6. I execute the pipeline against the database

7. I take the response and send another request to an LLM to summarize it in plain English

This is a simplified overview, because I also have ways to detect prompt injection attacks. I also plan to make the pipeline more sophisticated by introducing techniques like Tree of Thought Prompting. I thought this sub would find this interesting because it's a real, legitimate use-case of LLMs. It shows how AI can be used in industries like finance and bring legitimate value to users.

What this can do?

This feature is awesome because it allows users to search a rich database of stocks to find novel investing opportunities. For example:

• Users can search for stocks in a certain income and revenue range

• Users find stocks in certain niche industries like biotechnology, 3D printing, and alternative energy

• Users can find stocks that are overvalued/undervalued based on PE ratio, PS ratio, free cash flow, and other fundamental metrics

• Literally all of the above combined

What this cannot do?

In other posts, I've gotten a bunch of hate comments by people who didn't read post. To summarize what this feature isn't

• It doesn't pick stocks for you. It finds stocks by querying a database in natural language

• It doesn't make investment decisions for you

• It doesn't "beat the market" (it's a stock screener... it beating the market doesn't make sense)

• It doesn't search by technical indicators like RSI and SMA. I can work on this, but this would be a shit-ton of data to ingest

I very much hope to get some feedback on this! I'm very proud of the work I've done so far and can't wait to see how far I go with it!

Read more about this feature here!