Nlp in quant finance reddit.

Nlp in quant finance reddit Just take a look and CFA I Quantitative methods to get a clue on the subject. Join the largest* procurement-specific forum in the world for everything related to the strategic acquisition of goods and services. Don’t even try healthcare or government. The inception of quantitative finance, which amalgamates mathematical finance, numerical methods, and computer simulations, has transformed the industry. 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. Given my background, would it be worthwhile doing one of these programs to try to break into quant? Nov 14, 2022 · That being said, yes alpha quant, for most cases, is not a sustainable job as you said. That being said, are there others out there with similar stories (e. true. 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. Hope you enjoy it! A place for redditors to discuss quantitative trading, statistical methods, econometrics, programming, implementation, automated strategies, and bounce ideas off each other for constructive criticism. Most skills are transferable across modalities anyway CS student here and looking to get into Quant Finance and eventually get a role within a HF/AM. Look at the JD. Nobody can be good at everything. / volume, [1, horizon]), where the raw data is on a 1/2 second timeframe No hot topic is left without redline on r/procurement. Ah cool, it’s got lots of interesting stuff. Have a good one. 14 votes, 17 comments. Also first-generation college student if that helps. See the dedicated Book Recommendations wiki page for some threads with book recommendations. CDOs are completely different disciplines. Not surprising you work in finance. nlp, reinforcement learning). Members Online General statistical / pattern discovery methods used by quants 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 A subreddit for the quantitative finance: discussions, resources and research. entering the industry from a more theoretical background), and what you had to learn or how you managed to snag a position in finance. 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. It highly depends on your team and managers. Members Online Citadel finances a new Texas stock exchange set to launch in 2025 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. Oct 29, 2024 · Compiled by: Chainika Thakar In recent years, large language models (LLMs) like GPT-4 have revolutionised various industries, including finance. With NVIDIA NeMo™, financial institutions can build, customize, and deploy generative AI models anywhere. Hope you enjoy it! Q&A for finance professionals and academics. 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 steer is that corporate finance isn’t a really that maths / stats heavy. 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). May 8, 2020 · Unfortunately, I failed my interviews for quant finance this past year, so I'll be doing another SWE Internship. 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. Of course software engineering skills are essential for quantitative finance. The result is that quant traders are now able to work with data scientists to use NLP to cut through the noise and glean tradable signals. You’ll be lucky to make over 100K. In many cases, those people are not put in money . 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. Best of / Resources A subreddit for the quantitative finance: discussions, resources and research. You gain alpha by having an information advantage. This country and the market is a joke. r/datascience • Minimum 7 years exp the field and expertise in NLP for 70k-80k CAD contract job. So it's a valid choice, and also you have peers and the associations to support you in career preparation for a trading firm. I'm a Software Engineer at a large tech company based out of Seattle, WA. Stack Exchange Network. 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. Machine Learning in Quant Finance . Many sophisticated quantitative investors who use algorithms to systematically trade are already tapping into NLP and building it into their investment strategies. That’s what linguistics is for, or corpus/computational linguistics if you want to leverage statistics and computers. My GPA isn't too great NLP is engineering first, science last. There is bound to be some skill that is scarce at your firm. Entering NLP to do linguistics would be like entering robotics to learn about biology. reddit comments Threads about quant finance careers by professionals. Microeconomics and Corporate Finance are useful, but not critical books to approach quantitative finance. The field of quantitative finance is broad. It relies on various techniques such as text preprocessing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, topic modeling This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Book Recommendations. Meaning they hired Econometrics students the most. This webinar delves into the nuances of building LLMs, with a focus on how they can be used in quantitative finance. A subreddit for the quantitative finance: discussions, resources and research. 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? Posted by u/MarkSignAlgo - 3 votes and no comments It really depends on what you want to do as a quant. Sorry not everyone is a quant with over 10 years experience in your specific industry. Working as a "quant" in HFT vs. Also I didn't mean nobody uses ml for anything in finance. 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. So, it depends what you mean my Quantitative finance. 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. g. 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 highly technical subject of quantitative finance, sometimes known as "quant finance," uses mathematical and computer techniques to study financial markets and develop trading strategies. It’s a pretty big field and could cover most financial modelling roles (from Front-office Trading desk, to simpler corporate finance to actuaries). in IB at risk management vs. 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. 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. 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. 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. Others have mentioned a more solid foundation in stats is more useful and typically you’ll be using a simpler regression (the art is picking/cleaning the predictors and responses ), but DL is probably more useful than NLP insofar as a lot of recent NLP advances that you might use hinge on a neural net instead of say NLTK. 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. 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. 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’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). Hi, I am thinking about an AI service for writing any public announcement or posting in the stock market. 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. 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 I was pretty interested in what you were saying untill you decided to become a dick about it. Hello, 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. NLP vs audio vs vision vs robotics doesn’t really impact compensation. Some quant firms even test leetcode questions for quant analyst roles, so I would recommend you to grind leetcode and maths questions. 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)) . I would like to know the following. 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. So ask yourself, do you want that life specifically or a good life, because you can get a good life in many ways. You can just Google "quant maths questions" or "quant brain teasers" you will get plenty of questions to practice on. 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 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. I don’t think anybody goes into NLP to uncover scientific truths about how language works. 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. As a traditional mutual fund shop, our team mainly focuses on macro and fundamental analysis. Quant trader AMA: AMA by a quant trader. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. By joining this webinar, you’ll learn: I am interested in (mathematical) finance and/or machine learning for finance. 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. Figure out an underserved skill within the firm and master it. 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. 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. My favorite book for quant interviews is Xinfeng Zhou's A Practical Guide to Quantitative Finance Interviews. The answer is in Chollet's quote. I'd also recommend a course in stochastic processes like STATS 217/218/219, I found 217/218 fascinating and a EDIT: Apologies, clearly this is a contentious comment. Web scraping social media comments and news articles to be put into an NLP for And trading/quant research are the main sponsor for every Econometrics student association in the country. 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! A subreddit for the quantitative finance: discussions, resources and research. 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 32 votes, 14 comments. ,) and no MS degree have equal chances as the other? View community ranking In the Top 50% of largest communities on Reddit. 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. Quantitative seemed like a interesting and exciting area to jump into, and I think a quant position would be a lot of fun. Although it is often thought of as a difficult and competitive career, success is absolutely attainable with effort and determination. 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. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. 18 votes, 15 comments. Members Online Judge orders Jane Street to reveal strategies by next week Junior quant researcher at a buy-side finance firm pays ~400K+/year for new grads (includes guaranteed year-end bonus). Upon decision to go further read Paul Wilmott's Quantitative Finance textbook carefully. I also think that you can join CCAs like NUS Quant finance society to build up your portfolio. Specifically, quantitative finance, for all intents and purposes, is machine learning. Try 4+ years when you build the proper foundation and actually explore what development is. 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 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 I’m in undergrad third year, I’m planning on doing a project on predicting liquidity/market impact. 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. This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. 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. Hi, I built a 20yr career in gambling/finance/trading that made extensive utilisation of NNs, RNNs, DL, Simulation, Bayesian methods, EAs and more. 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. It had nothing to do with its current applications, although faster networks for representations is a re-occurring concept in NLP, it was originally Yeah you’re asking a good question actually I’d say its very firm dependent tbh. 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. In my recent years as Head of Research & PM, I've interviewed only a tiny number of quants & PMs who have used NNs in trading, and none that gained utility from using them over other methods. gmtdwgv pao ynrkxism golusnz qsizanyp rvji ugryo ymaca yzgf nmpfnhii vpaki vbocgb nrioub lgchcrd xfpim