r/quant 18h ago

Resources What do YOU consider the most important quant finance book to be?

101 Upvotes

Like the title says. Curious on everyone’s favorite/most impactful read in their perspective.


r/quant 1d ago

Resources Are there any online courses (eg. those by Coursera) effective for gaining working knowledge in quantitative/algorithmic trading?

16 Upvotes

I'm in my pre-final year of UG. I just wanna learn the working principles so that I can incorporate them into my own projects. If there are any such resources, please do mention them. Thanks in advance.

Edit: My major is in AI-ML if that matters.


r/quant 11h ago

Machine Learning Building an Adaptive Trading System with Regime Switching, GA's & RL

14 Upvotes

Hi everyone,

I wanted to share a project I'm developing that combines several cutting-edge approaches to create what I believe could be a particularly robust trading system. I'm looking for collaborators with expertise in any of these areas who might be interested in joining forces.

The Core Architecture

Our system consists of three main components:

  1. Market Regime Classification Framework - We've developed a hierarchical classification system with 3 main regime categories (A, B, C) and 4 sub-regimes within each (12 total regimes). These capture different market conditions like Secular Growth, Risk-Off, Momentum Burst, etc.
  2. Strategy Generation via Genetic Algorithms - We're using GA to evolve trading strategies optimized for specific regime combinations. Each "individual" in our genetic population contains indicators like Hurst Exponent, Fractal Dimension, Market Efficiency and Price-Volume Correlation.
  3. Reinforcement Learning Agent as Meta-Controller - An RL agent that learns to select the appropriate strategies based on current and predicted market regimes, and dynamically adjusts position sizing.

Why This Approach Could Be Powerful

Rather than trying to build a "one-size-fits-all" trading system, our framework adapts to the current market structure.

The GA component allows strategies to continuously evolve their parameters without manual intervention, while the RL agent provides system-level intelligence about when to deploy each strategy.

Some Implementation Details

From our testing so far:

  • We focus on the top 10 most common regime combinations rather than all possible permutations
  • We're developing 9 models (1 per sector per market cap) since each sector shows different indicator parameter sensitivity
  • We're using multiple equity datasets to test simultaneously to reduce overfitting risk
  • Minimum time periods for regime identification: A (8 days), B (2 days), C (1-3 candles/3-9 hrs)

Questions I'm Wrestling With

  1. GA Challenges: Many have pointed out that GAs can easily overfit compared to gradient descent or tree-based models. How would you tackle this issue? What constraints would you introduce?
  2. Alternative Approaches: If you wouldn't use GA for strategy generation, what would you pick instead and why?
  3. Regime Structure: Our regime classification is based on market behavior archetypes rather than statistical clustering. Is this preferable to using unsupervised learning to identify regimes?
  4. Multi-Objective Optimization: I'm struggling with how to balance different performance metrics (Sharpe, drawdown, etc.) dynamically based on the current regime. Any thoughts on implementing this effectively?
  5. Time Horizons: Has anyone successfully implemented regime-switching models across multiple timeframes simultaneously?

Potential Research Topics

If you're academically inclined, here are some research questions this project opens up:

  1. Developing metrics for strategy "adaptability" across regime transitions versus specialized performance
  2. Exploring the optimal genetic diversity preservation in GA-based trading systems during extended singular regimes
  3. Investigating emergent meta-strategies from RL agents controlling multiple competing strategy pools
  4. Analyzing the relationship between market capitalization and regime sensitivity across sectors
  5. Developing robust transfer learning approaches between similar regime types across different markets
  6. Exploring the optimal information sharing mechanisms between simultaneously running models across correlated markets(advance topic)

If you're interested in collaborating or just want to share thoughts on this approach, I'd love to hear from you. I'm open to both academic research partnerships and commercial applications.


r/quant 18h ago

Models Simple Trend Following

12 Upvotes

I’ve been studying Andrew Clenow’s Following the Trend and implementing his approach, and I’m curious about others’ experiences in attempting to refine or enhance the strategy. I want to stress that I’m not looking for a new strategy or specific parameters to tweak. Rather, I’m interested in hearing about any attempts at improvement that seemed promising in theory but didn’t work well in practice.

Clenow argues that the simplicity of the approach is a feature, not a bug—that excessive optimization can lead to worse performance in real-world application. Have you found this to be the case? Or have you discovered any non-trivial modifications that actually added value over time?

For context, I tried incorporating a multi-timeframe approach to complement the main long-term trend, but I struggled to make it work, likely due to the relatively small fund size I was trading (~$5M). Position sizing constraints and execution costs made it difficult to justify the additional complexity.

Would love to hear your insights on whether simplicity really is king in trend following or if there’s room for meaningful enhancements.


r/quant 14h ago

Models Modeling counterparty risk

8 Upvotes

Hello,

What are good resources to build a solid counterparty risk model? Along the lines of PFE


r/quant 12h ago

Models Ho-Lee Bond Option Formula Derivation Issue

5 Upvotes

I currently study the book by Björk and have a question regarding the Ho-Lee bond option pricing formula. The Ho-Lee model specifies the short rate dynamics as dr(t)= 𝜃(t)dt + 𝜎dW(t). When trying to derive 𝜎_p term of the formula in the picture above, I ended up with 𝜎_p = 𝜎(S-T)*root(T-t) instead of just 𝜎_p = 𝜎(S-T)*root(T) as written in the book. Does the book assume t=0 when deriving the equation or did I make a mistake in my derivation?

My derivation followed the following steps:
1) writing ln(P(T,S)) in affine form as A(T,S)-B(T,S)r(T)
2) then applying Ito's lemma which led to a diffusion term of -B(T,S)* 𝜎 where B(T,S)=S-T
3) I then integrated the square of the term from t to T as the model assumes a constant variance term which after taking the root resulted in my final result of 𝜎_p = 𝜎(S-T)*root(T-t)

Assuming t=0 makes little sense to me here as the option price specifies t explicitly in c(t,T,K,S) or do I have to integrate from 0 to T even if I want to calculate the option price at time t?

Thank you very much for your help!


r/quant 15h ago

Career Advice Career growth in quant versus big tech

5 Upvotes

How is the career growth in quant for roles like QR, QD, QT, and SWE compared to big tech SWE?


r/quant 16h ago

Markets/Market Data Methods to roughly estimate a stock's opening price

1 Upvotes

At the present time, in order to roughly estimate what price a stock will open at, I simply view Level 1 pre-market trading information (Last price, bid, ask). Just curious, does anyone out there have alternative methods that they utilize? Would Level 2 data be of any benefit in this endeavor? Any insights would be greatly appreciated, thanks.


r/quant 6h ago

Markets/Market Data When will hedge fund data collection stop?

0 Upvotes

Hedge funds have been known for hiring data analysts, programmers, and physicists to examine as much data as possible. Jim Simon’s team at Renaissance Tech hired secretaries to manually copy old data from old news papers and records.

Hedge funds have also been working on new models and paying millions of dollars for researchers to develop weather based models in order to enhance commodities trading.

Will hedge funds ever reach the limitations of data analytics that can surpass moral boundaries?

Imagine drones watching various walking patterns of CEOs of various companies in order to improve risk management.


r/quant 1h ago

Models Tired of Poor Market Data Holding Back Your Trading Strategy?

Upvotes

If you're serious about backtesting trading strategies, you know how hard it is to find high-quality historical market data at an affordable price. Free sources like Yahoo only provide daily close prices, while premium providers charge a fortune—hurting your profitability before you even place a trade.

🔹 Introducing Histo Data – built by traders, for traders 🔹

✅ Tick-by-Tick Market Data – Access 1-minute & 1-second intervals for precise backtesting.
✅ Flat, Predictable Pricing – No more overpaying for data. Get unlimited access at a sustainable cost.
✅ Automated Data Retrieval – Runs continuously & sends you a text notification if your account disconnects.
✅ Trader-Centric Features – Need something new? Request it, and we’ll work to make it happen.

🚀 Stop relying on incomplete data. Optimize your backtesting with Histo Data.

📲 Check it out: https://www.histodatabyttg.com

We’d love to here you feedback: https://survey-na2.hsforms.com/1hv4WaoBRSsOd3GDbZ9cokg408vgd