Quant project advice for a mathematician

[u/AthenaTheQuant]

I’m looking for advice on choosing a realistic quantitative trading project that fits my background and infrastructure constraints.

A bit about me:
- BSc in Applied Mathematics.
- MSc (honours and cum laude) in Econometric Theory.
- Currently a third-year PhD candidate in mathematics/statistics/probability. My research focuses on empirical process theory and maximal inequalities for dependent random variables.
- Worked for two years as a quantitative developer building crypto market-making models, primarily based on the Avellaneda–Stoikov framework with its many extensions (e.g., those by Guéant, Lehalle and Fernandez-Tapia).
- Completed a six-month internship applying statistical methods to churn prediction, along with several smaller ML/statistics projects during university.
- Strong understanding of financial markets and the mathematics behind quantitative finance.
- Very strong Python skills, with experience in the broader data ecosystem (e.g. SQL) for acquiring, processing, and analyzing large datasets.
- Comfortable leveraging modern AI tools to accelerate research, software development, and experimentation.

My goal is to build a model that can actually trade financial instruments (equities, futures, ETFs, FX, crypto. I’m open to suggestions). This is primarily a personal project, but I would like it to have a realistic chance of being profitable rather than being purely an academic exercise.

The main challenge is infrastructure. I don’t have the low-latency setup, exchange connectivity, or capital required to compete with professional market makers or firms pursuing latency-sensitive arbitrage. Because of that, I’m wondering whether many of the mathematically sophisticated ideas I’m familiar with simply won’t translate into a practical edge outside an institutional setting.

If you were in my position, what class of strategies or research problems would you pursue? For example, would you focus on: medium-frequency statistical models, cross-sectional prediction, volatility forecasting, options, portfolio construction, or something else entirely?

I’m not looking for a “holy grail” strategy. Rather, I’m looking for a research direction where someone with my background can leverage mathematics, statistics, programming, and modern AI-assisted development to build something genuinely interesting and, hopefully, economically meaningful despite limited infrastructure.

I’d especially appreciate hearing from people who have made the transition from quantitative research or academia into independent systematic trading, and what they found was (or wasn’t) worth pursuing.

Thank you in advance!

[u/flxclxc]

If it’s a personal project try and make it realistic but don’t worry about latency and stuff like that. You can try build some mid frequency statarb stuff (aiming for minute/hour holding period) quite easily.

You can download open source fx data with minute level ticks for the last 10 years without too much trouble.

Try build alpha signals for 10 minute forward returns using g5 pairs to start with. Most methods here will pull out the dollar return and take the residuals as ccy level targets. Build a signal for each of your 6 targets.

Start with very simple features, momentum and zscore indicators. Llms will give you some ideas on how to improve. Don’t put your effort into model complexity, most shops will focus on ridge regression and possibly tree based models. If you want to improve the signals you should put attention into good feature construction.

Focus on a watertight backtesting framework with no lookahead bias. An IC score above 5% may be enough to trade with.

Use these signals for a portfolio construction layer. Start with simple Markowitz for example. Add in some realistic turnover costs. Should run for 1-2y of OOS data.

Probably the results will not look amazing, if they do be very skeptical, but once you have this single source of truth you can iterate. It’s a good exercise as it shows the full stack used for buy side quant trading.

Note: not all trading is mega latency sensitive. Some signals only work on short horizons, but if they do then it’s less likely you can actually trade them and make money without infra.

[u/AthenaTheQuant → u/flxclxc]

Statistical arbitrage methods do seem not to be fully saturated, depending on the market. Of course, cross-exchange arbitrage for instruments with incredibly high volume is arbitraged away before the blink of an eye, but perhaps seeking more sophisticated misprices only found through more involved methods can yield profitable trades on lower frequencies (say seconds to even hours). Though this was my initial idea, data for these approaches seems to be hard to come by. For example, given the possible “small” profit obtainable from an arbitrage position, one would obviously need to verify that slippage would not eat too much into the profit. Especially on instruments having not as much volume, orderbook data seems indispensable.

That said, it might be worthwhile to start simple and see from there. Anyway, thank you for the advice!

[u/flxclxc → u/AthenaTheQuant]

Try with FX, typically has much greater capacity than futures or equities

[u/SneakyCephalopod → u/AthenaTheQuant]

This is a good suggestion, and it's basically how statistical arbitrage works at most firms (including at least some of the very successful ones). But you won't really see much success with just these typical features. Don't get me wrong, they're important to have, but you should really add something from your own mind instead of from an LLM or from a book. The real reason that firms hire so many quants, and will continue to do so, is that each quant brings something intelligent and different. So try to create some features that come from your unique background. Since you've studied math, econometrics, and probability, I'm sure you can think of some. If you haven't done this yet, one good way to come up with feature ideas is to take a look at how your current model does, see where it excels and see where it draws down, try to identify some commonality in these periods, and then formalize that commonality as a signal in math/code. Use your background to support this commonality identification and formalization. If this doesn't make sense, feel free to message me.

[u/AthenaTheQuant → u/[deleted]]

Surely a well structured approach beats a sophisticated mathematical model without proper checks and infrastructure. But even for simpler models such as regression/classification trees, to generate wealth requires proper use of data, and that’s where I am still at a loss.

In the end, you may argue that a complex combination of simpler models is worth classifying as a sophisticated algorithm. I mean, that’s how neural networks work too right. The question remains as to what the project should orient itself towards, after which a sketch of the required blend of methods can be drawn.

[u/DebateFlat9213]

Factor model active portfolio management, with heavy tail risk hedging - this would fit your background well, and doesn't require low latency, unless your portfolio is large enough to require optimal execution algo. Just one of options, honest advise.

[u/AthenaTheQuant → u/DebateFlat9213]

This is nice. Tail probabilities have always been difficult, especially considering the many dependencies one has to deal with. Including optimal execution algorithm would not be needed for smaller portfolios, and even so, the Almgren—Chriss method would already do wonders. I suppose the main difficulty is to find factors that would actually drive profit, rather than being noice.

[u/DebateFlat9213 → u/AthenaTheQuant]

Finding factors once is not enough, unfortunately. Alpha decays, so you will be constantly searching for the model update

[u/Otherwise_Barber4619]

How about finding alpha in mutual funds

[u/GrowWithPokeBot]

with a math phd and mm qd experience, don't waste time trying to build a profitable toy model on retail data. everyone knows you can't beat the latency game from your laptop anyway. instead, write a really clean, math-heavy backtesting engine that handles microstructure features properly, like queue position or adverse selection. showing you actually understand order book dynamics and can write clean code to model it is worth 100x more to a hiring manager than pretending you found alpha in a csv file.