/r/quant
A subreddit for the quantitative finance: discussions, resources and research.
Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. Those working in the field are quantitative analysts (quants). Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management.
(from Wikipedia)
Please check out our Frequently Asked Questions, book recommendations and the rest of our wiki.
/r/quant
I come from a theoretical physics undergrad and I’ve been working as a risk quant in model validation at a Big 4 consulting firm. I was quite lucky to get this role out of undergrad as they usually only hire msc.
I want to try to transition to being a quant researcher at a MM or Investment bank in London. I’m thinking that the easiest way to make this transition might be to go back and do a Masters.
In particular I’m looking at either doing an MFE in Imperial/Oxford or an applied maths masters in Cambridge/Oxford/Imperial. Which would be best for going into quant research? At the moment I’m thinking that perhaps the contacts/career service in an MFE would give me a much better chance at landing a role.
Hey everyone!
I am a software engineer from Europe and have received an offer from an established trading firm for a European office. However, I also got headhunted for a position at a newly founded crypto trading firm. Apparently, the founders are really good with a lot of experience, and they started in crypto due to non-competes. So, they're looking to expand into other asset classes soon. Currently, this firm has 20ish employees, and they are looking to grow.
Now I don't know what I should do. The pay is higher at this newly founded company, but there is also some risk involved as they are not established yet apart from the good experience of the founders. Another disadvantage is that they don't have an office in Europe, and I would greatly prefer the other offer from a location perspective. However, the headhunter told me that this company would never hire experienced candidates, only new grads, so I could only join now and not later in my career.
Overall, I tend towards the offer at the more established firm, but I'm afraid that I might reject an opportunity that I won't get later on. I would greatly appreciate your thoughts on this.
Hello,
is there a typo here? How can the value of the put be 10 when the underlying is at 100 and strike at 90?
I guess he forgot to change the strike prices from the call chart, order should be 110,100,90 right?
Hello everyone, wondering if I could tap into the experience of any body who has experience consuming and working with market data from the CME exchange.
I have a direct feed to the CME exchange and I am trying to get options data for ES Futures contracts. I am trying to replicate some sort of options chain and basically want to determine all of the volumes of options and the prices at which each individual trade occurred for ES. One thing I am noticing is that there are outright trades ( someone buying/selling a singular option | ESH5 C6500) and multilegs (trades are placed together ie iron condor has 4 legs | UD:1V: VT 2662994) one thing I am noticing is that these "strategies" are a products themselves. So by getting the individual legs, it says what the quantity ratio is, but not what the trade price of the legs are. My question is how can I find out what the trade price of the legs are? the strategies show what the total price for the "strategy" traded at, but not the legs. Upon doing some research, all I am finding is that i would have to look at what the best bid and offer for the contract at the point in time where the strategy traded at then back door into the price for the legs. Wondering if this is the only way?
where the use case of ziglang appears in HFT Systems, and does it beat C/C++ in the compilation times ?
Im planning on taking postgrad in quant finance, former finance undergrad. Planning to break into the industry either being a sell side trader or quant trader. Due to the quantitative nature of the field, is there any particular books you recommend for beginners to read upon or some CS skills that I should brush up on? If you were to start over (being a student again), how would you approach learning/breaking into the field? Can someone not from a math major/CS major have a chance in breaking into the field (im tplanning to take quant finance as a major, but its under faculty of business and economics)
I'm pleased to announce the release of Riskfolio-XL, a Riskfolio-Lib add-in for Microsoft Excel based on PyXLL package. Riskfolio-XL allows non-programming users to build investment portfolios based on mathematically complex models with low effort through Riskfolio-XL spreadsheet functions. Its trial version allows to test all features using 7 assets and 3 risk factors. To get a full version you can see the instructions in PyPI page or Riskfolio-Lib docs.
PyPI: https://lnkd.in/ehASHgwM Riskfolio-Lib docs: https://lnkd.in/eV5q9Ykt
If we were to look at a stock price that follows a Brownian motion. Formula would tell us that variance = t. Why is it that the variance is in the value of time with unit in second/hours/day etc. Instead of the unit of $^2 (since value of SD is $ and variance is $^2 in this case)
I understand that the variance scales with time. But to me this doesn’t give an intuitive explanation of why variance is in terms of time.
To give an example as a counterargument (even though I know I’m wrong here). If we have a case where it is common to have really small discrete changes let’s say B1 = 0.000001 (where B0=0) over from t= 0 to t=1. It wouldn’t make sense to have a variance of 1 to me since the values deviating from the mean squared would be much smaller than 1 (since t=1 in this case).
I’m trying to get this right since it’s an extremely important concepts for stochastics. I’m sorry if this comes off as a really stupid question. Tried GPT but couldn’t really get a good answer.
Hey all, just as the title says, I'm a physics post doc (first post doc, 2 years in) trying to transition into a quantitative finance role, preferably research. I'm in theory and phenomenology of a somewhat niche high energy physics subject. I have a good amount of c++ and python experience, as well as 9 published papers with an h index of 8 according to inspire hep, and a first author paper from my Ph.D with almost 60 citations. Most of my work has been on hydrodynamics, kinetic theory, phase transitions, and some quantum field theory.
I can't even break into the screening interview phase for almost all applications submitted, and I've applied to a broad range of firms. I didn't graduate from any ivy league school, but other than that I'm not sure what I'm missing.
Any advice from some people who have gone through the same?
P.S. If anyone is willing to give my resume a look would I would he extremely grateful. Let me know and I can send you a PM or just PM me.
I completed my bachelor's this year and started working as a quant researcher in HFT firm. However, my plan is to do masters (and maybe even phd) in computer science. Hence, I want to do research project with my professors during my bachelor's and possibly publish a research paper as well. Would a HFT firm be okay with it if I make sure that the topic for project is not related to finance (it would be rather related to optimization like in operation research or related to graph theory)? Or would they usually be against any research publications?
Hi all - I see Tarpika as a listed MM on the Brazilian stock exchange but I have no idea who they are. I get that a lot of the firms use different names (Rigel / Tuscana) but I can’t figure who they are?
What SEC data is interesting for quantitative analysis? I'm curious what datasets to add to my python package.
Current datasets:
I am not sure what they would be. The ability to be more flexible? Follow intuition built on experience?
Will maybe join a physical Commodity trading firm as an intern an possibly full time afterwards. I will be in the research department. I have experience with data science and the employer wants me for that. Now I am also in the process for quant trader/researcher at other companies. Questions:
Thanks.
So I was watching this MIT lecture Stochastic Processes I and first example of stochastic process was:
F(t) = t with probability of 1 (which is just straight line)
So my understanding was that stochastic process has to involve some randomness. For example Hulls book says: "Any variable whose value changes over time in an uncertain way is said to follow a stochastic process" (start of chapter 14). This one looks like deterministic process? Thanks.
What SEC data is interesting for quantitative analysis? I'm curious what datasets to add to my python package. GitHub
Current datasets:
Hello,
Was wondering if anyone that worked for/knew of people that worked for CTC could share some insights on your experiences + the salary progression within the company. The position would be for a new grad quantitative trader.
Thanks!
I’m working on a personal side project: a wave-based survival game where players battle waves of demonic entities while balancing limited resources to survive. One of the core mechanics I want to implement is a dynamic economy that the player must learn to leverage for gaining resources, making contracts, and surviving longer.
In the game, the player will be able to summon and trade with different demons, each acting as a separate entity within the economy. My goal is to emulate a simplified version of real-life financial systems. For example, players could sign a contract with a demon to buy ammo at the current price but only receive it in 3 waves, simulating a futures contract which they will have the option to sell to other demons for more immediate rewards if the contract is speculated to be valuable. Similarly, players can trade on the spot for immediate exchanges.
I don't have a lot of experience or knowledge in finance so apologies if the ideas come off as naive although I figured this would be a good project to learn some new applied math and financial concepts.
I’m currently brainstorming ways to model these interactions and the pricing system. So far, I’ve considered:
Treating each demon as a node in a market with different personalities and characteristics, with each node setting prices based on factors like resource scarcity, player reputation, and in-game events (e.g. wave difficulty).
Using something like linear regression for price determination, influenced by supply/demand and player-demon interactions. Each demon would likely have their own person regression models to determine their own prices.
Incorporating state machines to remember previous interactions or events that could influence future decisions.
I’m looking for educational resources, models, or system frameworks to help flesh out this dynamic market simulation. Are there any good topics, articles, books, or even game dev resources that dive into simulated financial systems or market dynamics in games? Any advice or material on how to balance this system to feel dynamic yet fair would be a huge help! Thanks.
Project summary: I trained a Deep Learning model based on image processing using snapshots of historical candlestick charts. Once the model was trained, I ran a live production for which the system takes a snapshot of the most current candlestick price chart and feeds it to the model. The output will belong to one of the "Long", "short" or "Pass" categories. The live trading showed that candlestick alone can not result in any meaningful edge. I however found out that adding more visual features to the plot such as moving averages, Bollinger Bands (TM), trend lines, and several indicators resulted in improved results. Ultimately I found out that ensembling the signals over all the stocks of a sector provided me with an edge in finding reversal points.
Motivation: The idea of using image processing originated from an argument with a friend who was a strong believer in "Price-Action" methods. Dedicated to proving him wrong, given that computers are much better than humans in pattern recognition, I decided to train a deep network that learns from naked candle-stick plots without any numbers or digits. That experiment failed and the model could not predict real-time plots better than a tossed coin. My curiosity made me work on the problem and I noticed that adding simple elements to the plots such as moving averaging, Bollinger Bands (TM), and trendlines improved the results.
Labeling data: For labeling snapshots as "Long", "Short", or "Pass." As seen in this picture, If during the next 30 bars, a 1:3 risk to reward buying opportunity is possible, it is labeled as "Long." (See this one for "Short"). A typical mined snapshot looked like this.
Training: Using the above labeling approach, I used hundreds of thousands of snapshots from different assets to train two networks (5-layer Conv2D with 500 to 200 nodes in each hidden layer ), one for detecting "Long" and one for detecting "Short". Here is the confusion matrix for testing the Long network with the test accuracy reaching 80%.
Live production: I then started a live production by applying these models on the thousand most traded US stocks in two timeframes (60M and 5M) to predict the direction. The frequency of testing was every 5 minutes.
Results: The signal accuracy in live trading was 60% when a specific stock was studied. In most cases, the desired 1:3 risk to reward was not achieved. The wonder, however, started when I started looking at the ensemble. I noticed that when 50% of all the stocks of a particular sector or all the 1000 are "Long" or "Short," this coincides with turning points in the overall markets or the sectors.
Note: I would like to publish this research, preferably in a scientific journal. Those with helpful advice, please do not hesitate to share them with me.
I came across some resources where PCA was used to break down the returns of a portfolio and attribute it to different factors. However I am not able to wrap my head around how the individual principal components are mapped to different factors. What methodology is used to attribute factors to the PCs. Can anyone suggest some resource where I can read more about this?
I am a freshman who recently joined a quant club on campus. I did expect it from most of the exec board members being finance/econ majors and what we had to do for recruitment, but the club is very finance based and not much quantitative. I'm a statistics/math major who has little to no finance knowledge, and I lowkey did not understand anything they were talking about today. Based on what I've seen on this reddit, strong basis in math/programming is a lot more important than finance, and I was also planning to max out on math classes and take some econ and finance classes on the side. I'm not sure if this club would help me breaking into the quant field and would like to hear from you guys.
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.
Is there any repository for market data, wether minute level,or hourly spanning 10 years or longer,?
been tryin a lot of methods to fetch data lately but no luck to get a minute level with a bigger span of history data.
After testing all "state-of-the-art" machine learning models for over 3 years, I found 0 model has good out-of-sample performance for real trading. I wonder, for those surviving in the quant position for long term, do you believe market is really predictable, or the models are working just due to luck?
Hi, i'm a student of quantitative finance and i need to change laptop. I have the idea to buy a Macbook air M3 8Gb of ram and 256 SSD, but i want to be sure it is suitable for the field. So my question is : do i need something more powerful? 16 gb of ram and 512 ssd air m3? Or even go on a pro version?
Th usage would be writing code in R, Python, MatLab and using IB with the trader station.
Thank you for the answers
I am a quant in rates trading and am interested in learning more about foreign exchange markets to get a broader macro sense of things. Does anyone have any recommendations on books for this purpose? Preferably something that can be listened to as an audiobook, i.e. not so technical/dense that one would have to consume a paper version to understand the concepts.