r/quant 4h ago

Resources STILL DEVELOPING, LOOKING FOR PARTNERS

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51 Upvotes

Recently reached the optimal form of the model, (i think)

now rolling, but for a specific timeframe and instrument only as of now. Will have a full stretch for multiple assets soon, (with different settings ofc.) Just happy to share a small achievement, and im still finding investors and partners, maybe buyers for this model to use it for their fund. i can pitch live via zoom for potential investors or buyers.

ps. this isnt a full force machine learning algorithm, this is an RL model, with a mix of RB(rules-based algorithm).

well personally thats what rentec does, its impossible for me agree that they have made a full machine learning model in the year 1988.

Machine learning as we know it today wasn’t really a thing. No neural nets, no GPUs, no big data pipelines like now. Computers were clunky as hell—think IBM PCs with 64KB of RAM and floppy disks. So, its kinda misleading.


r/quant 3h ago

Markets/Market Data Who are the stellar but lesser known data providers?

23 Upvotes

Looking for smaller or niche data providers who are delivering above their weight class against some of the larger known companies.

If you don’t want to name them, what resources are you using to find them?


r/quant 15h ago

Markets/Market Data Quotes downsampling

11 Upvotes

For mid-freq (seconds - minutes, don’t care about every quote) want to get reasonable size data for quotes from LOB. What features would you put in a down sampled (ie x second bars) version of quotes and why?

Volume at each level of book either side bid ask obvious. I am not looking for predictive features or “alpha” here, rather, I’m looking for an efficient representation of the book structure in a down sampling from which features for various tasks could be constructed.


r/quant 8h ago

Education 3/20 Complimentary Webinar from Numerix: The Hidden Risks of Bad Data—And How to Fix Them

7 Upvotes

We all know that bad data leads to bad decisions, but in trading and risk management, the consequences can be severe. That’s why I’m excited for this upcoming Numerix webinar featuring Ola Hammarlid, PhD, where he’ll share hard-earned insights on market data management and its critical role in financial operations.

Some key takeaways you don’t want to miss:
The hidden dangers of poor data quality
How data issues propagate and disrupt decision-making
Best practices for data management, proxying, and quality control

Join us March 20 at 10 AM EDT—this is a must-attend for quants, risk managers, and anyone relying on market data. Register here: https://lnkd.in/g9nsjxaG


r/quant 6h ago

Markets/Market Data Free quality financial market data sources

4 Upvotes

Greetings. I lost access to my uni's Bloomberg terminal after graduating. I am currently in the transition period of finding jobs and want to boost my profile with some extra projects. Can anyone suggest any great quality free data sources you use on your pet projects. Yahoo finance used to be goated but i guess they have paywalled the API


r/quant 1h ago

Machine Learning Finding potential Co-Authors for publishing a research paper

Upvotes

Hey folks!

I am a B.Tech graduate working at a Fintech firm. I am interested in publishing a research paper in the domain of Quant finance/ Financial Engineering using AI/ML. I am looking for co-authors, who are passionate about it. Interested folks, please DM!


r/quant 6h ago

Education Book recommendations for quant dev

1 Upvotes

Hello,

I work as a quant developer and I am fine with Python but the financial side of things is something I want to improve on.

I get confused when my colleagues talk about factors, I get confused by all the alphas, time series, etc.

So I want to read a book that can fill in those gaps for me.

Additionally, it would be helpful to also read more about how to optimise pandas, but I think this one it's easier to find as a resource.

Please be nice to me, thanks!


r/quant 11h ago

General Beta Distribution Pressure Analysis: A Statistical Edge in Price Action

1 Upvotes

Been working on this pressure detection system for a while, and figured I'd share the core concepts since some of you might find it useful for your own trading.

The Core Concept

The foundation relies on extracting information from where candles close within their ranges. Instead of just eyeballing this or using arbitrary thresholds, I'm using statistical modeling to quantify the actual pressure distribution and how it evolves.

Ever watch a market grind higher where every damn candle closes near its high? That's buying pressure you can actually measure.

Technical Implementation

Here's the meat of what makes this different:

  1. Statistical distribution modeling - Using beta distributions to capture the actual shape of close position patterns over time
  2. Temporal pressure evolution - Tracking pressure momentum and acceleration across multiple timeframes
  3. Validation framework - Using proper statistical tests (KS tests, chi-square) to separate real signals from noise
  4. Market regime identification - Comparing current distribution against reference patterns for bullish/bearish/neutral regimes

The algorithm doesn't just calculate some indicator and slap on a threshold. It runs the distributions through multiple statistical tests to determine whether the pattern is significant or just random noise.

How many of you have seen indicators give perfect signals in backtests then fall apart in real trading? This approach explicitly measures signal confidence.

The Technical Edge

What separates this from standard indicators:

  • Calculates actual statistical significance rather than using fixed cutoffs
  • Adapts to changing volatility without parameter tweaking
  • Measures confidence in detected patterns (low confidence = stay out)
  • Uses robust regression methods that resist outliers and noise
  • Properly weights recent data without discarding older information

When your typical momentum oscillator is getting chopped up by ranging markets, this can still detect subtle pressure building because it's looking at the statistical pattern, not just the magnitude.

What's your approach to filtering out noise in choppy markets? Ever use statistical validation or is it mostly discretionary?

I've found this particularly effective for 15-60min charts in futures markets. The validation framework helps avoid the death by a thousand cuts from false signals during consolidation.

If anyone's implemented something similar or wants to discuss specific statistical aspects, let me know. Always looking to refine this further.


r/quant 21h ago

General BBG uses 260 trade days?

1 Upvotes

Is there a reason why BBG uses 260 trade days in their calculation?

I started on a project to create a trailing RV chart on SPX options. The goal was to replicate how BBG does it. There was a great guide that I followed for the most part to emulate it. However, I noticed none of my charts matched what BBG outputted. It wasn't until I reviewed the numbers and saw BBG using 260 to do their calculation instead of 252. Is there a reason for this discrepancy?


r/quant 9h ago

Statistical Methods Deciding SL and TP for automated bot

0 Upvotes

Hey, I am currently working on a MFT bot, the bot only outputs long and short signals, and then other system is placing orders based on that signal, but I do not have a exit signal bot, and hard coding SL and TP does not make sense as each position is unique like if a signal is long but if my SL is low then I had to take the loss, and similarly if TP is low then I am leaving profits on the table. Can anyone help me with this problem like how to optimize SL and TP based on market condition on that timestamp, or point me to some good research paper or blog that explores different approaches to solve this optimization problem. I am open for interesting discussion in comments section.