Nlp in quant finance reddit.

Nlp in quant finance reddit See the dedicated Book Recommendations wiki page for some threads with book recommendations. CDOs are completely different disciplines. From traditional pricing quant roles to quant trading/research, you can pick whatever inspires you the most. I was thinking about how NLP is used to aid in investment practices. I am trying to build a portfolio project using NLP by aggregating news article I describe this as a Goldilocks problem because I am very interested in the data analytics / stats side of finance but have concerns with quant finance being too traditional math heavy. Hello, I’ve not completely decided on it, but have given thought to going into quantitative finance. Sorry not everyone is a quant with over 10 years experience in your specific industry. Crypto I grew up in the US and went to one of {Ivy, MIT, Stanford} for my undergrad, studying a combination of computer science, math, and finance/economics. 32 votes, 14 comments. You gonna work on the verge of tech but applied to Finance, alternative data science go brrr with over 250k salary". So it's a valid choice, and also you have peers and the associations to support you in career preparation for a trading firm. reddit comments Some Stats for quant or just for general usefulness: Again, I don't really have much knowledge of quant roles but I know for sure STATS 207 (time series analysis) is a necessity, since a lot of finance is modeled as a time series. Multi-Task Learning in Financial NLP: Improvements in Financial NLP's Multitask Learning can be achieved by considering skill diversity, task relatedness, and aggregation size. I'm a Software Engineer at a large tech company based out of Seattle, WA. In this work, we present BloombergGPT, a 50 Apr 19, 2025 · When I was deciding on quant finance programs, I knew I wanted something rigorous — but also something that would actually prepare me for the realities of working in the industry. My long term goal is to break into the quant field, specifically quant research, but I'm also open to gaining some experience in pure finance or management consulting along the way if I'm unable to immediately enter the I have background in quant finance, stats, CS and AI. This country and the market is a joke. I'd also recommend a course in stochastic processes like STATS 217/218/219, I found 217/218 fascinating and a Quantitative seemed like a interesting and exciting area to jump into, and I think a quant position would be a lot of fun. Mar 1, 2025 · This survey presents an in-depth review of the transformative role of Natural Language Processing (NLP) in finance, highlighting its impact on ten major financial applications: (1) financial sentiment analysis, (2) financial narrative processing, (3) financial forecasting, (4) portfolio management, (5) question answering, virtual assistant and chatbot, (6) risk management, (7) regulatory My goal is to go into quantitative finance (researcher/trader) or work at a startup. So ask yourself, do you want that life specifically or a good life, because you can get a good life in many ways. NLP is engineering first, science last. CS student here and looking to get into Quant Finance and eventually get a role within a HF/AM. I’m in undergrad third year, I’m planning on doing a project on predicting liquidity/market impact. Don’t even try healthcare or government. I have opportunities to do ML in big tech or quant dev at some hedge funds. 18 votes, 15 comments. Just take a look and CFA I Quantitative methods to get a clue on the subject. I’ve also done research in the NLP and computer vision space. If you want to be more involved in the actual trading/strategy side, then you’d need to look for roles like quant researcher at citadel securities or algo developer at hrt. Crypto Based on the dataset, a career path in data science, particularly in a senior or managerial role within the finance or tech industry, and located in a major financial hub like London, would likely offer some of the highest salaries. I am also currently starting my 2nd year for an MBA at Booth and concentrating in quantitative finance. This means I will likely do a math double major/minor along with perhaps pursuing graduate school in CS or Math. the behavior of reddit Yeah you’re asking a good question actually I’d say its very firm dependent tbh. A place for redditors to discuss quantitative trading, statistical methods, econometrics, programming, implementation, automated strategies, and bounce ideas off each other for constructive criticism. The cost difference between UW compared to UIUC and UMich is about 20k/year after scholarships and other stuff is included (have not gotten finaid So, it depends what you mean my Quantitative finance. Large audio, text, document, video, click stream, etc. Pick whatever you find most interesting (or nothing at all, I don’t know how PhDs work in the US). Nov 27, 2023 · Where can AI and ML make the biggest impact in quant finance? Chandni Bhan, Ph. It had nothing to do with its current applications, although faster networks for representations is a re-occurring concept in NLP, it was originally I think the point is that if you know how to learn a quantitative field (even a semi-quantitative field like pure math), and spend a bit of time on algorithms, then you will synthesize algorithms just fine. I could do the same courses except for a couple and I plan to become very proficient in NLP, RL and DL. Dec 8, 2021 · I keep hearing stories about how bad the market is and that a lot of buy-side firms have cut back on hiring. These powerful models, capable of processing vast amounts of unstructured text, are increasingly being used by professional traders to gain insights into market sentiment, develop trading strategies, and automate complex financial tasks. The MCAT (Medical College Admission Test) is offered by the AAMC and is a required exam for admission to medical schools in the USA and Canada. I plan on going to graduate school for statistics (MS/PhD) programs, but don’t know if it’s worth it rather than doing a two year Masters in Financial Engineering program. They're part of a broad toolkit. There is no finance area is remotely as interesting as AI is doing in the industry right now. I also know quite a few people with PhDs who work in finance and are clueless - you put them in a P&L generating role and they'll bankrupt you. Try non-quant hedge funds and market yourself as someone who can start a ML/AI team there. Find out why you want to work in quant finance and why people should hire you. Entering NLP to do linguistics would be like entering robotics to learn about biology. Look at the JD. Please read the sidebar and observe sub rules when posting. ***** NOTE: Automod nukes content from new accounts and those below a certain karma number. It’s more DS driven (trees, boost) and I haven’t seen good use cases for deep learning NN in my field outside of NLP. Members Online What are your favorite Quant papers, ranked by easiest to read to hardest? Posted by u/Illustrious-Pay-7516 - 208 votes and 58 comments DL have been highly successful in domains that have inherent and permanent structure - NLP and computer vision. Natural languages have syntax, spelling, and grammer. Threads about quant finance careers by professionals. The effectiveness of neural networks in finance hinges on the nuanced design of the network architecture, the choice of training methodology, the objective functions employed, and the regularization techniques used. I’m a PhD student in computational cognition. 9M subscribers in the finance community. I was wondering what is the most common type of machine learning models used. (2023-05-25, shares: 50. 71K subscribers in the quant community. Quant trader AMA: AMA by a quant trader. Quant -> tech is starting to become a popular transition. In many cases, those people are not put in money Business, Economics, and Finance. I just toured the office, and I'm pretty excited to start this summer. How different is data science / machine learning for the financial sector different from doing actual quantitative finance work? How is the adoption of machine learning or deep learning in the finance sector? A subreddit for the quantitative finance: discussions, resources and research. The quant dev roles are primarily C++/SWE roles so I didn't think that those align with my end goal of doing QR. Most skills are transferable across modalities anyway I was pretty interested in what you were saying untill you decided to become a dick about it. . This is the free web version of the O'Reilly book, which discusses the Natural Language ToolKit (NLTK) package for Python and how to apply it to applications in NLP. Some quant firms even test leetcode questions for quant analyst roles, so I would recommend you to grind leetcode and maths questions. The highly technical subject of quantitative finance, sometimes known as "quant finance," uses mathematical and computer techniques to study financial markets and develop trading strategies. Does that apply to all areas of quantitative finance? Is there any bright spots in the hiring landscape? Also, is the seeming down market cyclical (high interest rates and slowing economy) or structural (supply of quants >> demand of There are some aspects of quant finance I didn’t use much day-to-day, like more advanced numerical PDE/finite difference methods and volatility smiles, so I often read papers on those specifically to keep my knowledge base well rounded in case I shifted to a more “classical” role within the field. That being said, I am just curios, is there anything ML/DL related in Quant Research? From some people I've heard stuff like "Oh they do CV, and they do NLP a ton. I currently don’t do any NLP — 99% of posts were long anyway so I just flagged them all that way. If I were to declare an additional major to cultivate more “quantitative” skills that are necessary for quant trading, which would be a better option? Jul 22, 2024 · Reddit: Scrape relevant subreddits like r/wallstreetbets or r/investing for retail investor sentiment. Take Statistical Learning might be better. In general it's a good point to start a career historically, myself I started at a bank. r/datascience • Minimum 7 years exp the field and expertise in NLP for 70k-80k CAD contract job. And now that Twitter has a rate limit… Hi guys, I got in a top 10 uni (qs rankings) for a Msc in data science and in applied mathematics (same uni). Crypto Business, Economics, and Finance. Members Online Citadel finances a new Texas stock exchange set to launch in 2025 Quantitative. For eg i spoke to a citadel quant sometime ago - they made QR folks go through some rotation where they have to experience all parts of the value chain like data sourcing, cleaning, database, parsing and analysing data, coming up with strategies, managing risk, etc The #1 social media platform for MCAT advice. 84K subscribers in the quant community. Title: sr mle Tenure length: 1 yr Location: sf, working from canada Remote: fully remote Salary: $195,000 Company/Industry: ai/nlp startup Education: msc stats Prior Experience: ~4 years mle Relocation/Signing Bonus: 0 Stock and/or recurring bonuses: 15% bonus = $29,205, stock options between $0 - $90k (at price from last raise) Total comp I'm a 5th-year math PhD student, and I just signed an offer for a junior quant researcher role at a (solid, but not elite-elite) hedge fund. Quant is mainly used for: return attribution, curve calibration, credit beta calculation, portfolio optimization, scenario testing, regression/factor model to predict yield and FX prediction. 58 votes, 22 comments. Crypto My favorite book for quant interviews is Xinfeng Zhou's A Practical Guide to Quantitative Finance Interviews. Thank you! Yes, a mix of pushshift (historical) and the Reddit API (recent) because pushshift can be behind by hours. There are also many startups that recruit ex-quants though don't tend to offer the same magnitude of compensation packages as FAANG. Once you develop some research expertise and have some finance chops, you might be able to pivot into a quant finance role. Level 1: Fundamentals of Quantitative Finance Introduction to Quantitative Finance: Overview of quantitative finance and its applications Basic concepts in finance, including time value of money, risk, and return Probability and Statistics: Probability theory, random variables, and probability distributions Australian Personal Finance: budgeting, saving, getting out of debt, investing, and saving for retirement. My steer is that corporate finance isn’t a really that maths / stats heavy. Book Recommendations. Thanks for your feedback. The ticker detection is a set of heuris Dec 5, 2023 · Machine learning (ML) has become increasingly important in the world of quantitative finance, as financial market players seek to leverage the latest technology to gain a competitive edge. Members Online Wasted $500 on quant Courses — Are There Any Actually Worthwhile Alternatives? If needed I might try to squeeze in Quantitative methods in Finance and Advanced Derivatives modeling offered on Edx by MIT. Join the largest* procurement-specific forum in the world for everything related to the strategic acquisition of goods and services. g. Members Online General statistical / pattern discovery methods used by quants Specifically, quantitative finance, for all intents and purposes, is machine learning. It highly depends on your team and managers. If you purely want to be a software engineer at a finance firm, then all banks, hedge funds, and trading firms have that. The mandatory information written in the public announcement is very common, so if we could collect enough proper data, we could get any meaningful NLP model by training. The inception of quantitative finance, which amalgamates mathematical finance, numerical methods, and computer simulations, has transformed the industry. Best of / Resources A subreddit for the quantitative finance: discussions, resources and research. And trading/quant research are the main sponsor for every Econometrics student association in the country. You can just Google "quant maths questions" or "quant brain teasers" you will get plenty of questions to practice on. It’s a pretty big field and could cover most financial modelling roles (from Front-office Trading desk, to simpler corporate finance to actuaries). I am recently looking at applying machine learning to some quant finance things. in IB at risk management vs. I taught undergraduate level Linear Algebra and Multivariable Calculus as a TA, and took graduate level coursework in Statistics, ML (RL, CV, Game Theory, NLP and more). NLP vs audio vs vision vs robotics doesn’t really impact compensation. At the Princeton Quant Conference today the theme of AI -especially language AI- was described as a revolution in Alpha procurement. Hope you enjoy it! I was in NLP before transformers took off and the invention of transformers is just the realization that attention is useful and you don't need an RNN if you use it and encode positions as well. By using advanced algorithms and models to analyze vast amounts of data, market players can identify patterns and correlations that may not be immediately apparent to the human eye. Members Online Judge orders Jane Street to reveal strategies by next week Posted by u/MarkSignAlgo - 3 votes and no comments Junior quant researcher at a buy-side finance firm pays ~400K+/year for new grads (includes guaranteed year-end bonus). The role of a quantitative developer is to often implement the result of research in the most efficient manner possible. For example, trying to pick good company to invest in. Its going to come down to how much you are interested in the pure science with no relation to finance such as ms in CS, ms in data science, or MS in math / physics / stats. I wrote this hope someone in this sub, if you studied Physics like I do, you have better choices. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Members Online Dec 16, 2019 · Natural Language Processing with Python - Certain quantitative finance applications such as sentiment analysis make heavy use of Natural Language Processing (NLP) algorithms. What really makes this program stand out for me is the career services. An image classifier of dogs may have variance in the data presented (different angles, different breeds, different lighting etc), but the underlying concept of a dog is unchanging I personally know quite a few including some that started a PhD and left mid-way and are leading PM in quant finance. Members Online UChicago: GPT better than humans at predicting earnings Is there a greater demand for quants that have an extensive ML background? For example, take the “traditional” quant everyone here talks about, whose got the pure/applied math background or physics background and has the in depth knowledge of finance + stochastic calculus + probability, vs the quant who is an NLP researcher. Get the Reddit app Scan this QR code to download the app now a Masters of Quantitative Finance and would like to build out my quant programming portfolio to A subreddit for the quantitative finance: discussions, resources and research. You’ll be lucky to make over 100K. I would imagine a basic weighted count of keywords found on the Web. Like others have stated, courses on just vanilla statistical techniques would be better but if you have to strictly choose between NLP or deep learning, I would say NLP. Some managers without a quantitative background will likely grow impatient and actually blame you for providing no actual result, which is likely to be the case as this is a highly experimental endeavor. how is ml used in the quant industry? is it like glorified statistical modeling? is the ml mostly supervised, unsupervised, SVM, etc. A subreddit for the quantitative finance: discussions, resources and research. Members Online Where's the money earned by top prop trading firms are coming from? Microeconomics and Corporate Finance are useful, but not critical books to approach quantitative finance. Covers general finance stuff as well as the necessities for building a strategy followed by the necessary mathematics. Some NLP and various signal processing techniques. Hi guys, I don’t know if this is the right place to ask, but my goal is essentially to enter a niche in finance that matches my skill sets. It’s better than the stuff I see on youtube (obviously far better structured) and has a good practical element. More of a "boss has a PhD so he only hires people with PhDs to justify his PhD". I would assume something like RNN is better suited for finance than something like CNN. I've seen many ex-quants move to Facebook, Google, Amazon, and similar places. You gain alpha by having an information advantage. / volume, [1, horizon]), where the raw data is on a 1/2 second timeframe GenAI is just a transformer for a prediction of the average next token. I simply disagree that the actual course content of a CS degree would be more relevant to quant than a directly statistics-based subject which could net you those exact same skills. ,) and no MS degree have equal chances as the other? 1. Background: I graduated from a small (historic is a more appropriate description than prestigious) liberal arts college with a major in economics and a minor in Please see the separate Frequently Asked Questions page for questions about quant finance in general, what kind of jobs there are in quant finance, and what you should study to work in quant finance. The task itself is typically to use data to train models for predictive purposes, and although doing so might rely on a combination of other ML techniques (regression, clustering, PCA, etc), the overall goal is to create what is essentially a machine learning Wᴇʟᴄᴏᴍᴇ ᴛᴏ ʀ/SGExᴀᴍs – the largest community on reddit discussing education and student life in Singapore! SGExams is also more than a subreddit - we're a registered nonprofit that organises initiatives supporting students' academics, career guidance, mental health and holistic development, such as webinars and mentorship programmes. LSTMs are decent for NLP, and therefore the sentiment analysis step of this could use an LSTM. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Now I look back what I did as quant, gosh it’s BORING as hell. In particular, I focused on statistical learning and numerical optimization. I also think that you can join CCAs like NUS Quant finance society to build up your portfolio. With NVIDIA NeMo™, financial institutions can build, customize, and deploy generative AI models anywhere. Web scraping social media comments and news articles to be put into an NLP for A subreddit for the quantitative finance: discussions, resources and research. You do not want to start as a quant developer / programmer, the pay is ass compared to what you are getting now. I am curious to hear this subs thoughts on this and whether the average quant views this as a hype fest or if there is an expectation for a revolution in the field. Climb the SWE ladder, get very good at OS, Networking, and Algorithms, maybe pick up some C++ experience and some good names under your belts, then go into hedge funds / buyside quant firms as a quant dev. I want to move away from being a SWE and do ML and ultimately hope to do quant research one day. Upon decision to go further read Paul Wilmott's Quantitative Finance textbook carefully. Narang. Not surprising you work in finance. I’m currently pursuing an undergraduate degree of quantitative finance in HK, and want to become a quantitative trader in the future. Jan 10, 2025 · Undergrad at Georgia State with sub 3. That said, for NLP transformers seem to be taking over, so they might not use LSTMs any more but the example still holds. There is bound to be some skill that is scarce at your firm. I exited my quant job to AI couple years ago, working on GenAI solutions. Looking to obtain an MFE if I don't get into buy-side quant finance right out of undergrad, and I'm really interested in Columbia MFE and possibly CMU Computational Finance. 89K subscribers in the quant community. In one of the Arxiv papers I link they describe the process of turning the persistence diagram into a set of functions, grouping this set of functions into a sequence and then applying a norm to it in order to go from a persistence diagram to a single value you can track and use to compare. I haven't tested it on autogen yet - I'm also thinking about how to combine the factor mining of quantitative finance and the industry analysis of subjective investment in this framework. I don’t think anybody goes into NLP to uncover scientific truths about how language works. 31 votes, 22 comments. Given my background, would it be worthwhile doing one of these programs to try to break into quant? The Natural Language Processing (NLP) for Financial Markets team works on the development and application of NLP models for Financial Markets Including but not limited to Equity Market, Bond Market, and Cryptocurrency Market. 83K subscribers in the quant community. That’s what linguistics is for, or corpus/computational linguistics if you want to leverage statistics and computers. I'm a Junior CS major at a school typically considered top 20/30, but not a target for finance (none of the firms I'm interested in recruit here, but being on the west coast might have a little to do with it). The most modern reincarnation of quantitative finance involves identifying information arbitrage opportunities with alternative data. No hot topic is left without redline on r/procurement. Try 4+ years when you build the proper foundation and actually explore what development is. Have a good one. Q&A for finance professionals and academics. But quantitative finance is the League A, the majors, the NFL, NBA, whatever you want to call, many wants to get there and succeed but the competition is fierce and relentless. Alternative data is any data that is not price data or fundamental data. Nov 14, 2022 · That being said, yes alpha quant, for most cases, is not a sustainable job as you said. Machine Learning in Quant Finance . In general, with a minimum knowledge of stochastic calculus you can start with Financial Markets -> Volatility Theory -> Lévy processes and Jump Diffusion Models -> Interest Rate Modeling Theory -> Bayesian Statistics and Monte-Carlo Methods -> Numerical Finance -> Machine Learning and Financial Applications. Wᴇʟᴄᴏᴍᴇ ᴛᴏ ʀ/SGExᴀᴍs – the largest community on reddit discussing education and student life in Singapore! SGExams is also more than a subreddit - we're a registered nonprofit that organises initiatives supporting students' academics, career guidance, mental health and holistic development, such as webinars and mentorship I'd like to transition from being a Software Engineer to either a Quantitative Trader or Quantitative Developer. entering the industry from a more theoretical background), and what you had to learn or how you managed to snag a position in finance. This webinar delves into the nuances of building LLMs, with a focus on how they can be used in quantitative finance. Before going into quant, I interned as a software engineer at {unicorns, FAANG}. Hi, I am thinking about an AI service for writing any public announcement or posting in the stock market. It's several dozen problems that are extremely representative of the types/styles of questions asked in these interviews - a deep understanding of the math/logic behind each problem is good enough to make you succeed. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. Does anyone know of any good papers or resources for doing predictive modeling with NLP data? I've recently come into an S3 bucket at work that has raw Github data, news data, reddit data, and Google Trends data, and I want to use it to do something like forecast log returns on cryptoassets. 0) Our current Hi-Tech focus spans Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Robotic Process Automation (RPA), Data Science & Advanced Analytics, FinTech-Crypto, Computational Quantitative Finance & Trading, Blockchain and Cloud Computing, Cybersecurity & Cryptography, Penetration Testing & Ethical Hacking, Model Risk Management I am quite new to NLP, and as a learning project, I wanted to predict the stock market price changes with NLP. I’d wager that QR/QT interviews have/will become harder over time as more and more prep resources become public specifically to avoid ending up like tech companies who sometimes hire not-fantastic engineers because they can regurgitate Leetcodes they’ve memorised (not solved). So full grasp of the math is essential, yes. true. Hi, Could anyone share insights on how ‘important’ Masters degree in Financial Engineering is to pursue a career in quant finance? Do finance folks look for a Masters in Financial Engineering while hiring? or do the person with equivalent skills (doing self study, online courses or CFA etc. Of course software engineering skills are essential for quantitative finance. This will be unlimely to beat players on optimized markets. Nobody can be good at everything. Dec 26, 2024 · Can you break into quant trading or equity research in your late 20s? Aspired to do this out of undergrad and got lost along the way (covid among In quantitative finance, NLP is used to extract insights and information from textual data sources such as news articles, financial reports, social media posts, and regulatory filings. Although it is often thought of as a difficult and competitive career, success is absolutely attainable with effort and determination. Ah cool, it’s got lots of interesting stuff. Reflections from a senior quant: a Reddit thread by a senior quant about careers in quant finance, in response to wrong information spread by students/users with no quant experience. I study the workings of human intelligence, specifically in reasoning, classification, and problem solvi Oct 29, 2024 · Compiled by: Chainika Thakar In recent years, large language models (LLMs) like GPT-4 have revolutionised various industries, including finance. Figure out an underserved skill within the firm and master it. r/quant: A subreddit for the quantitative finance: discussions, resources and research. nlp, reinforcement learning). 0 GPA means quant trading is pretty much impossible to recruit for. By joining this webinar, you’ll learn: I am interested in (mathematical) finance and/or machine learning for finance. GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. May 8, 2020 · Unfortunately, I failed my interviews for quant finance this past year, so I'll be doing another SWE Internship. A relatively standard NLP workflow Bag of keywords, Polarity score, Category tag were used Predict the sign of next day's return rate You can use these code to build your experiment data-source. Which sounds kind of fun to me. I'm very interested in the world of finance, I have a background in physics and CS (obviously) and I understand CS algorithms very well. and how is it similar and different from other ml subfields like computer vision or NLP? PhD may not be necessary formally, but the quant job description has gone into overdrive with all the Big Data/AI/machine learning platforms out there, which you'll have to be familiar with both as developer and end user (may also require familiarity with NLP. I have knowledge/coursework in Stochastic Processes (and Ito Calculus) as well as a (more rudimentary) knowledge in Time Series Analysis (including ARMA, SARIMA, etc and OpenQuant is the #1 Quant Job Board featuring Quantitative Research, Quantitative Trading, Quantitative Developer, Data Scientist, and Machine Learning Engineer jobs. All… View community ranking In the Top 50% of largest communities on Reddit. Also I didn't mean nobody uses ml for anything in finance. I have been told that I need some serious practical based projects to work on - keen to speak anyone who may put me in the right direction or individual who may have a project for me to work on! It really depends on what you want to do as a quant. The MSCF program at Carnegie Mellon has delivered on both fronts. This interdisciplinary approach has led to the creation of intricate models for options pricing, risk management, and algorithmic trading, thereby enhancing the precision and efficiency of In fact, as I mentioned in the hf link, I trained this model precisely to work properly in a set of such agent frameworks. My GPA isn't too great A subreddit for the quantitative finance: discussions, resources and research. Meaning they hired Econometrics students the most. Also first-generation college student if that helps. I'll add a roadmap as soon as I can. In this book, which is well worth reading to get a good conceptual overview of the different components of a quant trading system, the author tells about "one of the most successful" quant funds hiring only the best academic researchers and outperforming competitors every year. You don’t need to. But I've also been lurking on some online quant and general finance forums, and it's honestly making me a little bit anxious. The field of quantitative finance is broad. The answer is in Chollet's quote. A subreddit for students, staff and all things related to the University of Edinburgh. The quant course is well taught. ) While you're doing this, soak up every bit of finance knowledge you can get your hands on: news, books, online courses, etc. Outside of academia, he works as a Principal Quant at Man Group leading execution research in futures and other derivatives. This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. As a traditional mutual fund shop, our team mainly focuses on macro and fundamental analysis. In my undergrad r I majored in CS/CE and minored in Math and Analytics. If your NLP model is better at quantifying sentiment and/or faster than everyone else's, then at some point in time (may be microseconds) you have information others don't, and that's where ML becomes useful. That being said, are there others out there with similar stories (e. Advanced NLP Techniques: Explore transformer models like BERT or GPT-3 for more nuanced The quant might use this sentiment score as one of many features in a model. D, Global Chief Risk Officer, Wise, shares her experience with AI, machine learning, NLP, and large language models in models and risk management. are people using like rnns, cnns, transformers etc. However if you work at an IB in a country where you don't have much trading like Philippines, India or Poland you aren't exposed to the same jobs as FO quants in NYC / London / Singapore and you won't have much opportunities to move to hedge fund etc as well as being lower paid and usually working on I don’t think finance does as much vision, video, audio, etc NN due to data differences but we do NLP. Business, Economics, and Finance. Anything would be appreciated. EDIT: Apologies, clearly this is a contentious comment. Like anything trading if you have an area that has a pricing deficienty, an LLM could average a solution to address the deficiency but overtime all this oportunities desapear as efiency increases. Stack Exchange Network. Working as a "quant" in HFT vs. I would like to know the following. datasets are all examples of alternative datasets that could power information Mar 30, 2023 · The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. Also it's a bit of a pedantic point that "Applied Math" as a field doesn't actually engage with direct applications of math A subreddit for the quantitative finance: discussions, resources and research. Most of the top quant hedge funds do not need any more ML managers anymore and I see it's a less likely path for you to quant finance. There seem to be a few ways to measure liquidity, my target will likely be something along the lines of Kyle’s Lambda: movsum((abs(ret)) . All the data will be stored in MongoDB by default in my code, you can extend the storage part by yourself "There is no secret sauce!" - Inside the Black Box: The Simple Truth About Quantitative Trading, by Rishi K. Sep 23, 2022 · Stefan’s research is focused on machine learning in finance, including deep learning, reinforcement learning, network and NLP approaches, as well as early use cases of quantum computing. Welcome to r/Finance! No Personal Finance, Homework, Personal blogs, or Career-related posts. Your motivation also is very weak - you might as well try breaking into tech instead of quant finance (plus tech -> quant dev is a reasonable path) You are not going to be competitive in 1 year. Members Online Citadel finances a new Texas stock exchange set to launch in 2025 Quant jobs which are more using modern statistical/data science methods (NLP, statistical learning concepts), or jobs which are considered quant roles but not on the stochastic calculus side? I’ve thought about being a quant but I’ve been told if I have never taken stochastic calculus I pretty much don’t have a shot at it. zqawv jlurk xhyta withfzo gzeezf qmbmj hnim qxn mmqj rgpefpj cch pioa legox ylrnpuh qbl