Lang Qin

My paper Risk Price Pinference with Xu Cheng and Eric Renault "Identifying the Volatility Risk Price Through the Leverage Effect" was officially accepted by the Journal of Econometrics!!

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Don't miss out on an exciting case study at the 6th Annual Machine Learning in Quantitative Finance conference! Igor Halperin from Fidelity Investments will explore the applications of reinforcement learning techniques to optimize quantitative trading. Key topics include: 1. Understanding how reinforcement learning empowers algorithmic trading 2. strategies to adapt and evolve in real time. 3. Identifying best practices for applying reinforcement learning techniques to portfolio optimization. 4. Delving into recent developments in reinforcement learning and their impact on quantitative finance. Join us from September 23-25, 2024, in New York, NY, to examine the best use cases of machine learning techniques in quantitative finance strategies and discover actionable solutions to overcome implementation challenges. #MachineLearning #QuantitativeFinance #ReinforcementLearning #AlgorithmicTrading #FinanceConference #NewYork Register now to secure your spot! Event Website: https://lnkd.in/etABFXzP

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Bloomberg makes proprietary alternative data available to quants Bloomberg has made its proprietary Bloomberg Second Measure (BSM) transaction data analytics feeds available via Bloomberg Data License. Data professionals and quantitative researchers can now connect this alternative dataset with Bloomberg’s more traditional Data License content. #data #datasets #markets #trading #finance #GlobalTrading https://lnkd.in/eMvGdWzi

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I am not coming God, first arrange a Bloomberg terminal for me there Kickstart your Quant Interview Prep with Quant Insider. Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte, Quantopia Library, and Quant Insider Project Handbook with Bonus Resources. ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) Quantopia Library is the goldmine for building your domain knowledge and technical skills. (https://lnkd.in/geThBB4d) Quant Insider Project Handbook has 10 industry-oriented projects based on competitions conducted by Top HFTs and Hedge Funds. (https://lnkd.in/gWBEn78U) Quant Insider Career Catalyst is your guide to all interview prep tips, preparation roadmap and job application strategies (https://lnkd.in/gVhA4tNG) Checkout our Course on Machine Learning for Finance - https://lnkd.in/eyXnPRwz Use Coupon code - "EARLYBIRD20" for 20% off on the MLFin course

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UPDATE on the CFT-Max (J1939) Data fields now include: Peak flows and total flow volumes since last reset as well as the current flow and flow averaged / 500 ms. Knowing more Flow Data increases the Value of the Data!

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0DTE options, known for 'lottery-like payoffs,' now make up about 50% of S&P 500 options trading volume. Garrett DeSimone, head of quantitative research at OptionMetrics warns of the risks: 'You can go completely broke, but there are occasional Lotto-sized payouts.' Despite their rapid growth, concerns linger about potential market destabilization if they expand to individual stocks. Read the full article below. #optionstrading #tradingstrategies #optionmetrics

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Discover how ExtractAlpha’s TrueBeats signal can predict earnings surprises with precision. Learn more about our award-winning team’s research and trading signals: https://lnkd.in/gtDXWkiM #quanttrading #quantitativeresearch #quants #alternativedata

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Hedge fund Millennium hires quant to head AI research in FX A number of hedge funds have been hiring AI researchers as of late, and Millennium has added itself to the list. Its most recent hire is a PhD nuclear physicist who's spent time in both fintech and FAANG. Aaron Key joined Millennium this week as its head of AI for fixed income technology. Key spent the last three years a senior machine learning scientist but spent the bulk of his career at Bloomberg. He joined the fintech as a graduate in an R&D development role before transitioning to a quant researcher and become head of enterprise quant research and development. He sits within the technology team, rather than on the investment side. There's much debate in finance about how AI is going to be used. Virtu Financial CEO Doug Cifu recently told CNBC that AI is an enhancer, allowing trading firms to "leverage [employees'] genius, and their excellence, and their abilities." He also said, however, that the "millions and millions of lines of code" that make up trading infrastructure "can be kind of rote." Using AI for him is also about being "a little more efficient with what we do." He also insinuated that these cost-cuts might benefit the pay packages of finance employees, following the previous comments with "we've got rocket scientists... and it's hard to find a good rocket scientist." #peoplemoves #technology #hedgefund

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Questions to ask Quant employers/hiring managers in your Quant interviews ➡️ Role Specific/Technical considerations - - How are trading and research teams organized at the firm? - What programming languages and technologies are primarily used by the quant team? - How robust are the testing and deployment pipelines for quantitative models? - What level of access do quants have to real-time market data? - How does the firm handle model risk and backtesting procedures? - What is the firm's approach to ethical and responsible AI in trading? ➡️ Firm specific/culture - - How does the firm promote collaboration and knowledge sharing across teams? - What opportunities are there for professional development and career advancement within the firm? - What metrics are used to evaluate employee performance, and how does compensation tie into these metrics? - What exchanges and financial products does the firm trade on? - How much turnover exists at the firm and how long is the non-compete clause? ➡️ Performance evaluation - - What tools and resources are available to quants for analyzing the performance of their strategies? - How often are performance reviews conducted, and what feedback mechanisms are in place? - Is there an open culture for discussing failures and learning from mistakes? - How are successful strategies rewarded and incentivized? - Does the firm encourage research and experimentation with new quantitative techniques? ➡️ Brownie questions - - What are some of the biggest challenges and opportunities facing the quant team currently? - Can you describe a typical day in the life of a quant at this firm? - What are some of the most successful projects or strategies developed by the quant team in recent years? - Do you have any specific advice for someone considering a career in quantitative finance at your firm? We are conducting a Master Class on "Cracking a career as a Quant, a Quant Developer or a Strat" with Andrey Chirikhin who has more than 20+ years of experience working across different roles in Quantitative Finance. He will be talking about the following - Cracking the first quant role across Buy side, sell side, Fintech, Financial consulting. - How to build a long-term career and grow as a Quant Professional - The inside of the Quant Industry across different roles. - Required skill sets (hard and soft skills) - Daily task of a Quant and how to excel them to faster career trajectory Date - 23rd June 2024 (Sunday) Time - 9:00 PM IST Register now to take advantage of the Early bird offer, use coupon code EarlyBirdQI TO GET 20% OFF Registration Link - https://lnkd.in/gEvN7Aw5

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Found out about kdb+/q from KX from a quant podcast I would love to try the godlike speed of this database. Read the book "Q for mortals" by Jeffry A. Borror and Q tips by Nick Psaris, CFA, I can't wait to try out the language. Visit the website and try to download the package, input my information and boom!!!! BIG FAT ERROR!!! Do any quant devs know how to fix this? Do you think kdb+/q is a good tool for quants or I am just wasting my time?

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Visual presentation is an effective to explain data.

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Examining the importance of mean reversion detection in quantitative finance in my latest piece. 📊 In this article, we cover: ✅ The importance of identifying mean-reverting behavior for profitable trading strategies ✅ Key statistical tests: Augmented Dickey-Fuller (ADF), Phillips-Perron, KPSS, Variance Ratio, and Hurst Exponent ✅ Practical considerations and challenges in implementing mean reversion strategies Check out the article and share your thoughts on the statistical methods that have worked best for you in identifying mean reversion! . . . #QuantitativeFinance #TradingStrategies #StatisticalAnalysis #MeanReversion #DataScience #Finance #QuantitativeTrading

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Interesting strategy assuming the market is endogenous. Using deep #RL to dynamically adjust portfolio allocation to max. returns while managing risk. An #Autoencoder to extract 3 latent features from the initial set of 12. Continuously learning from new data based on the input distribution. Would be interesting to see the performance when the strategy is exposed to exogenous factors. Missing a public repo for this but still a great read! https://bit.ly/4bwV1zt

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Interview Questions I have been asked as a Q-Quant in Derivatives Pricing roles at the Top sell side firms If you're wondering what a Q-Quant is, let's understand that first! Q-Quants are the sell side Quants who mainly work on pricing of derivatives and new products. Q-quants are concered with relative valuation and making sure that their pricing shemes are consistent with exchange traded products that are observed in the market. Here's a list of interview questions including technical, analytical, market awareness, behavioral and situational questions : 1. Walk me through the pricing of a European vanilla call option within the Black-Scholes framework. Now, explain the concept of the option Greeks: delta, gamma, vega, theta. How would you use these Greeks to manage the risk of a portfolio? 2. The price of a stock follows a geometric Brownian motion. How would you derive the partial differential equation satisfied by the price of a derivative of this stock? Can you offer a brief intuitive explanation of Ito's Lemma? 3. You're presented with a real-world volatility surface for an FX pair. Discuss the challenges involved in calibrating a model to this surface. What factors would you consider to choose a suitable model, and what trade-offs might you need to make? 4. Describe Monte Carlo simulation and its application in option pricing. How would you ensure the efficiency and accuracy of your simulations, especially when dealing with complex derivatives? 5. Discuss a recent trend in the derivatives markets that you find interesting and the potential quantitative challenges it presents. 6. If you could create a new, innovative exotic derivative, what would it be and what problem would you be trying to solve with it? Consider the potential market need for such a product. 7. Identify a potential risk in the current market environment – it could be related to regulation, products, or volatility. How would you analyze and quantify this risk from a derivatives quant perspective? 8. Describe a project where you encountered a significant challenge in your quantitative work. How did you identify the problem? What approach did you take to resolve it, and was it successful? We are conducting a Master Class on "Cracking a career as a Quant, a Quant Developer or a Strat" with Andrey Chirikhin who has more than 20+ years of experience working across different roles in Quantitative Finance. He will be talking about the following - Cracking the first quant role across the Buy side, sell side, Fintech, and Financial consulting. - How to build a long-term career and grow as a Quant Professional - The inside of the Quant Industry across different roles. - Required skill sets (hard and soft skills) - Date - 23rd June 2024 (Sunday) Time - 9:00 PM IST Register now to take advantage of the Early bird offer, use coupon code EarlyBirdQI TO GET 20% OFF Registration Link - https://lnkd.in/gEvN7Aw5

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https://lnkd.in/g7RpUWg7 Understanding Market Microstructure: A Deep Dive into Order Books and Price Movements - A THREAD 🧵 In this thread, we have done break down of key concepts, mathematical models, and the intricacies of order books (OB) Follow Us on X, for more interesting Thread

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