I realise this isn’t the most serious topic, but I rarely see anything like this and wanted to see if others have experienced something similar at work. I’m at a large prop firm, and a new hire somehow just churned out a “holy grail” 10+ alpha from nowhere. It’s honestly bizarre—I’ve never come across a signal like this. From day one in production, the results have been stellar. Now he’s already talking about starting his own fund (it may have gone to his head). Anyone have stories of researchers who suddenly struck gold like this?

Just curious. Any input would be appreciated.

Edit: It is clear I have a lot to learn. Don't know much. I'm a stats grad student, haven't really touched finance modeling. Thinking of getting into some of this stuff during PhD, but not main focus. Prof said become a top tier statistician and you'll learn finance stuff on the job. Anyone have any good beginner books? I'm taking stochastic models class this semester and we're covering stuff like Black-Scholes and other fundamentals.

https://www.bloomberg.com/news/articles/2025-01-31/point72-lures-marshall-wace-s-liu-with-50-million-pay-package?

I am quite intrigued by how the economics of such hires work. Based on his LinkedIn he looks like a discretionary equities L/S hire with 7 YOE. Pardon my ignorance: In my limited knowledge of Discretionary space SR of such PMs is not super high. Is it branding/client/capacity that he brings to the table? Keen to hear thoughts of experts.

As a lone algotrader I'm well aware that I can't win vs the large shops. I'm beaten on talent, resources, tech, etc.. so I don't want to try. My goal is to play in a different part of the sandbox.

I've got a mildly profitable strategy, while trying to refine it I'm considering where the rest of you are playing so I can stay clear of it.

If you can say -- which of the following zones are you finding alpha? Do they look more like A B C or none of the above?

https://preview.redd.it/qqldc8krncge1.png?width=1162&format=png&auto=webp&s=a4b9e85b7ca02862b5ab6cb511b9b4799b9d06cc

Currently I'm extracting value between A --> B. I was considering getting into C but pretty sure that's the losing battle.

Thx.

Wondering if anyone has recommendations for literature (books, links, PDFs, etc) on event risk and vol? I’m not a quant, just looking for some basic info on how weights are used to manipulate vol curves for events, measuring expected moves for future events, coming up with a “base” vol estimate, etc. this is primarily for FX, but fine with anything else too.

Thanks

I’m looking around myself and I am seeing a big, unfilled age gap between the people who only recently started working, and the people who have done this well into their old age. Where is the in-between?

Can anyone share some statistics? something like the number of years spent in this industry (before retiring/exiting)

Built this for my friend in finance who needed better tools for fund analysis.

It automatically handles data extraction, runs factor decomposition, generates risk-adjusted metrics, and creates style analysis with proper t-stats.

Even does correlation studies and rolling analytics.

He's been using it daily and helping me fine-tune the analytics.

Made it free for anyone to use: pascal

Curious what other metrics would be useful - always looking to improve the analysis capabilities.

Hi all, pretty new to the industry - I’ve always known what I was getting into but holy shit this job is crazy. Sometimes i’m sitting there with my d1ck in my hands and other times I’m putting out fking fires trying to figure out what’s going on. All of which is mostly out of my control, making me feel like I’m on standby the whole time, waiting to get fkd. I don’t want to sound like a little b1tch but how do you guys deal with pressure / stress / making mistakes. On my desk making a tiny mistake leads to proper $ being lost - and it’s hard to not beat yourself up over it. I guess this applies to quants, traders and devs - I’m assuming you all have this feeling in some shape or form. I hate it but I love it Ps: tc or gtfo

Hi, I am trying to implement a paper mentioned in the title. I am able to implement the first part but struglling to implement the ML-VAR part. They have used models like RF, GRU etc. But whenever am using them I get a constant value for predictors. I am not sure if inputting say 12 lags in a RF makes sense (as they can't make sense of sequence). I am willing to share my code if someone's interested.

My understanding

1. Take 12 lags of 5 variables and feed these 60 values to random forest and train.

2. For predicition I use my predicted values to forecast further into th future.

Please help I am stuck at this part for over a week! Thank you!

https://preview.redd.it/7kwontese0ge1.png?width=739&format=png&auto=webp&s=a8399c5aa0839ec75afeafb140a40be19c293e21

There's plenty of debate betwen the relative benefits and drawbacks of Event-driven vs. Vectorized backtesting. I've seen a couple passing mentions of a hybrid method in which one can use Vectorized initially to narrow down specific strategies using hyperparameter tuning, and then subsequently do fine-tuning and maximally accurate testing using Event-driven before production. Is this 2-step hybrid approach to backtesting viable? Any best practices to share in working across these two methods?

I am looking for a daily historical price data for a long-term U.S Treasury Bond (more particularly, "Bloomberg U.S Long Treasury Bond Index", or anything similar)

I am using a price data of VUSTX, which starts only from 1986, but I am looking for data since 1970's or earlier.

As far as I know, the only way to get it is from an expensive terminal. If there is a cheaper way to get it, please advise me. I am willing to pay if it is not too expensive.

Or if someone happens to have this data in hand, it would be appreciated if you could share with me.

I am just wondering, i saw a guy on youtube saying , chatgpt is prohibited at work, and the companies hire such people in the first place that don’t need GPT assistance while writing code… how true is this?

Guys, here is a summary of what I understand as the fundamentals of portfolio construction. I started as a “fundamental” investor many years ago and fell in love with math/quant based investing in 2023.

I have been studying by myself and I would like you to tell me what I am missing in the grand scheme of portfolio construction. This is what I learned in this time and I would like to know what i’m missing.

Understanding Factor Epistemology Factors are systematic risk drivers affecting asset returns, fundamentally derived from linear regressions. These factors are pervasive and need consideration when building a portfolio. The theoretical basis of factor investing comes from linear regression theory, with Stephen Ross (Arbitrage Pricing Theory) and Robert Barro as key figures.

There are three primary types of factor models: 1. Fundamental models, using company characteristics like value and growth 2. Statistical models, deriving factors through statistical analysis of asset returns 3. Time series models, identifying factors from return time series

Step-by-Step Guide 1. Identifying and Selecting Factors: • Market factors: market risk (beta), volatility, and country risks • Sector factors: performance of specific industries • Style factors: momentum, value, growth, and liquidity • Technical factors: momentum and mean reversion • Endogenous factors: short interest and hedge fund holdings 2. Data Collection and Preparation: • Define a universe of liquid stocks for trading • Gather data on stock prices and fundamental characteristics • Pre-process the data to ensure integrity, scaling, and centering the loadings • Create a loadings matrix (B) where rows represent stocks and columns represent factors 3. Executing Linear Regression: • Run a cross-sectional regression with stock returns as the dependent variable and factors as independent variables • Estimate factor returns and idiosyncratic returns • Construct factor-mimicking portfolios (FMP) to replicate each factor’s returns 4. Constructing the Hedging Matrix: • Estimate the covariance matrix of factors and idiosyncratic volatilities • Calculate individual stock exposures to different factors • Create a matrix to neutralize each factor by combining long and short positions 5. Hedging Types: • Internal Hedging: hedge using assets already in the portfolio • External Hedging: hedge risk with FMP portfolios 6. Implementing a Market-Neutral Strategy: • Take positions based on your investment thesis • Adjust positions to minimize factor exposure, creating a market-neutral position using the hedging matrix and FMP portfolios • Continuously monitor the portfolio for factor neutrality, using stress tests and stop-loss techniques • Optimize position sizing to maximize risk-adjusted returns while managing transaction costs • Separate alpha-based decisions from risk management 7. Monitoring and Optimization: • Decompose performance into factor and idiosyncratic components • Attribute returns to understand the source of returns and stock-picking skill • Continuously review and optimize the portfolio to adapt to market changes and improve return quality

Hi there, am quite new to the industry (<6m) and have a few questions with regards to hedging from a theoretical/industry point of view. Keen to talk about a few different players but ultimately want to talk about D1 hft/mft.

First let’s start with options market makers. These groups delta hedge because they’re mainly trading vol. However, is the real reason they do this because hedging eliminates a significant portion of variance without eating into a proportional amount of returns? E.g. if one is buying/selling options for less/more than they’re theoretically worth at any point in time, they will be profitable in the long run - and all delta hedging does is remove pnl swings and increase sharpe/lower risk of ruin?

Second, let’s talk ETF MM. I don’t have experience with this but am really interested in how this works in practise so would appreciate insight. So let’s start with an obvious one, ETFs with underlying futures. The trade is obvious here, buy/sell below/above NAV and hedge with the future (assuming the future is trading at fairval). Now, theoretically if I didn’t hedge - I’d still be profitable… right? And now the less obvious one where I feel like the question actually applies more is - ETFs with no underlying futures. You calc a Nav, trade around it - wtf do you hedge with, do u even hedge at all? Im assuming u have a list of correlated products and hedge with the most correlated? Or maybe you hedge with the highest constituents.. but wouldn’t crossing multiple spreads just completely eat into your pnl? I understand you can create/redeem the ETF but you only do this once a day right, so how does this actually work. Do you trade normally and hedge over the day, fill in a create/redeeming request before the close then unwind the hedge instantly as you’ve now locked in a price that settles on t+2? Can someone pls explain how this works in practise Any cases where you just run the risk and don’t hedge?

Lastly, what I’m most interested in is stat arb vs momentum. Is the literal difference here hedged vs unhedged? What am I missing. E.g I have some model for some asset with a bunch of signals, when do I hedge or not hedge. Does the answer lie in where my signals are derived from?

Let’s say we’re building a linear model to predict the 1-day future return. Our design matrix X consist of p features.

I’m looking for a systematic way to detect look-ahead bias in individual features. I had an idea but would love to hear your thoughts: So my idea is to shift the feature j forward in time and evaluate its impact on performance metrics like Sharpe or return. I guess there must be other ways to do that maybe by playing with the design matrix and changing the rows

For mid or low frequency strategies, hardly can signals work "all-weather", so it is naturally for me to think about filtering the market regime to backtest signals.

So I designed indicators to describe market regimes to filter the entrance of each signal and see their performances under each regime, I didn't intend to but finally I found I could make hundreds of market regime indicators:

• Imagine for the momentum indicator ind1 = ts_mean(marketRet, d: int) / ts_std(marketRet, d: int), setting d = 5, 10, 20, 40, 60 will give me 5 regime indicators, ind2 = ts_mean(marketRet, d: int ) will give me another 5 indicators
• I can build that for momentum, volatility, relative strength, sentiment, ...

This can easily boost the number of backtest scenarios for one signal, so very high risk for overfitting. But I don't get a good idea how to reduce that risk, maybe just fixed a small number of indicators under each market regime category? Even though I still get many.

For context I am a relatvley new quant (2 YOE) working in a firm that wants to start market making a spot product that has an underlying futures contract which can be used to hedge positions for risk managment purposes. As such I have been taking inspiration from the avellaneda-stoikov model and more resent adaptations proposed by Gueant et al.

However, it is evident that these models require a fitted probability distributuion of trade intensity with depth in order to calculate the optimum half spread for each side of the book. It seems to me that trying to fit this probability distribution is increadibly unstable and fails to account for intraday dynamics like changes in the spread and volatility of the underlying market that is being quoted into. Is there some way of normalising the historic trade and market data so that the probability distribution can be scaled based on the dynamics of the market being quoted into?

Also, I understand that in a competative liquidity pool the half spread will tend to be close to the short term market impact multiplied by 1/ (1-rho) [where rho is the autocorrelation of trades at the first lag] - as this accounts for adverse selection from trend following stratergies.

However, in the spot market we are considering quoting into it seems that the typical half spread is much larger than (> twice) this. Can anyone point me in the direction of why this may be the case?

Attention new and aspiring quants! We get a lot of threads about the simple education stuff (which college? which masters?), early career advice (is this a good first job? who should I apply to?), the hiring process, interviews (what are they like? How should I prepare?), online assignments, and timelines for these things, To try to centralize this info a bit better and cut down on this repetitive content we have these weekly megathreads, posted each Monday.

Previous megathreads can be found here.

Please use this thread for all questions about the above topics. Individual posts outside this thread will likely be removed by mods.

I am planning to learn maths(stats,calculas,linear algebra)required in Quantitative finance,if in case i am no longer interested in that field can i apply those skills and knowledge learnt in quant finance in any other industries? I know topics like derivatives pricing and stuff cant be used anywhere else but what are the stuff i would learn in quant finance be used in other industries as welll??

What’s your first impression of a model’s Sharpe Ratio improving with an increase in leverage?

For the sake of the discussion, let’s say an example model backtests a 1.06 Sharpe Ratio. But with 3x leverage, the same model backtests a 1.66 Sharpe Ratio.

What are your initial impressions? Are the wins being multiplied by leverage in this risk-heavy model merely being reflected in this new Sharpe? Would the inverse occur if this model’s Sharpe was less than 1.00?

I'm sure you've all been seeing the news about DeepSeek and their low cost LLM model.

They're developed and backed by a Chinese quant firm. This kinda makes sense it is adjacent to quant to some extent.

Do you think any of the US based quant firms might develop their own LLM, either for internal or external use, maybe D.E Shaw Research?

Hi, I’m currently the Sr. Investment analyst at a private wealth management company. I just obtained the CFA last year and I’m looking to switch over to quant because it seems to be way more interesting and my current job has no potential for growth (at least that’s what the owner of this company has told me). My question is - what skills do I need to sharpen to make this transition to quant? Would I need to go back to school to take specific math and computer science classes?

Any insight as to how I would make this change would be greatly appreciated.

Thank you!

I was speaking earlier today to one of the managers at DRW Trading about their LLM effort and realized that I don't really have a good understanding of how the industry of proprietary trading functions.

What is a good book on HFT firms? / Proprietary trading firms?

I'm not looking for information on the algorithms etc... but on how the companies are funded and organized, how they view risk and the markets, how they recruit and retain talent, how they manage vendors, etc....

I checked the book recommendation list and didn't see anything responsive.

How can we automate fundamental analysis? Specifically, if a company releases financial reports or other publications, how can we design a model to understand whether the information is positive or negative?

Sorry if what am I asking is wrong but I see everywhere that you can use technical analysis to make trades and predict stock prices, but doesn’t the Brownian motion say that stock prices are independent from the previous stock price ? And it follows a random pattern ? So how can people use technical analysis if the stock prices cannot be predicted? You could say momentum or any other general theory could be used, but I’m talking about analyzing charts. Sorry if the question sounds dumb

Is there any value on the research banks publish?

They don’t seem to provide any edge, however all major banks still have these teams and they seem to interact with (lesser known and fundamentally driven) buy side firms quite often.

I get that, previously, “research” was packaged with prime brokerage services, but that is not the case anymore. Now it needs to be a separate service, so I am just wondering who pays for this and why. Is there any value ?

I've recently been doing some ad hoc work on a strategy, which shows reasonable performance on a back test without transaction costs. However, after round trip spreads are considered, it consistently loses money. The reason for this is that the strategy operates in a residual space with incredibly low volatility. I was wondering whether there any common first steps in terms of increasing the volatility of a strategy in order to help combat this before shelving the idea all together.

Any help would be greatly appreciated

Hi everyone,

I am working on a machine learning task involving a multi-class classification problem with tabular, imbalanced data (no time series or categorical variables).

The goal is to predict class probabilities for a test set (150,000 rows x 9 classes) using models trained on the provided training data. To achieve lower log loss scores, I am exploring a multi-layered approach with stacking ensembles.

The first layer generates meta-features from diverse models (e.g., Random Forest, Extra Trees, KNN, etc.), while the second layer combines these predictions using techniques like LightGBM, SVM, or neural networks.

I am also experimenting with feature engineering (e.g., clustering, distance metrics, and embedding-based methods like UMAP and t-SNE), and advanced optimization techniques like Bayesian search for hyperparameters. Given the data imbalance, I am considering sampling techniques or class-weight adjustments.

Any suggestions or insights to refine this pipeline and improve model performance would be greatly appreciated.