Yohan Fereres (PhD)

#Mathematics #particularinterest 👉 A paper titled ”Distance Correlation Market Graph: The Case of S&P500 Stocks” may raise your interest. 🔗 https://buff.ly/4e2pM0e #distance_correlation; #complex_networks; #market_graph; #stock_market_network; #non_linearity; #investment_science; #graph_theory #MDPIOpenAccess #ComSciMath_Mdpi

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That's awesome! Mistral's new features for building custom LLMs sound incredibly developer-friendly. The ability to fine-tune and create custom agents makes it a powerful tool for personalized AI solutions. Thanks for sharing the video link—can't wait to check it out! 🚀🔍 #CustomLLMs #AIInnovation #DeveloperTools #MachineLearning #MistralLLM

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Breaking down different Types of PCA with their Implementation in Quant Finance. ↔ Principal Component Analysis (PCA) A statistical technique for simplifying high-dimensional data. Transforms data into a new coordinate system. The greatest variance lies on the first principal component, the second greatest on the second, and so on. Application in Quant Finance: Reduces the dimensionality of financial data. Identifies key factors affecting asset returns. Enhances portfolio management and risk management. Decomposes the term structure of interest rates. Identifies common factors in stock returns for factor models. ↔ Kernel PCA Extension of PCA using kernel methods. Analyzes non-linear relationships by mapping data to a higher-dimensional space. Application in Quant Finance: Recognizes complex patterns in financial time series. Identifies non-linear factors influencing stock prices. Detects non-linear dependencies between financial instruments. Aids in constructing robust trading strategies and risk management. ↔ Incremental PCA Variation of PCA for large datasets. Processes data in mini-batches, updating principal components incrementally. Application in Quant Finance: Suitable for real-time financial applications with streaming data. Used in high-frequency trading for on-the-fly risk model updates. Maintains accurate and up-to-date models without reprocessing entire datasets. ↔ Sparse PCA Produces sparse principal components involving a subset of original variables. Imposes sparsity constraints during PCA process. Application in Quant Finance: Useful for feature selection in market movements. Improves interpretability of financial models. Enhances portfolio optimization by focusing on significant factors. ↔ Robust PCA Handles data with outliers. Separates data into a low-rank matrix and a sparse matrix. Application in Quant Finance: Valuable for financial time series with fat tails and volatility clustering. Cleans financial data by isolating extreme events or anomalies. Leads to more robust risk models and trading strategies. Independent Component Analysis (ICA) Separates multivariate signal into additive, independent components. Focuses on statistical independence, unlike PCA which focuses on variance. Application in Quant Finance: Separates mixed sources in financial data. Isolates noise from true market signals. Extracts independent factors from correlated financial time series. Improves accuracy of trading algorithms and hedging strategies. Kickstart your Quant Interview Prep with Quant Insider. Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte (1500+ Interview questions), 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)

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𝑪𝙤𝒎𝙥𝒖𝙩𝒂𝙩𝒊𝙤𝒏𝙖𝒍 𝑭𝙞𝒏𝙖𝒏𝙘𝒆 𝑩𝙤𝒐𝙠 𝙎𝒆𝙧𝒊𝙚𝒔 - 𝑩𝙤𝒐𝙠 2 George Levy's "𝘾𝙤𝙢𝙥𝙪𝙩𝙖𝙩𝙞𝙤𝙣𝙖𝙡 𝙁𝙞𝙣𝙖𝙣𝙘𝙚: 𝙉𝙪𝙢𝙚𝙧𝙞𝙘𝙖𝙡 𝙈𝙚𝙩𝙝𝙤𝙙𝙨 𝙛𝙤𝙧 𝙋𝙧𝙞𝙘𝙞𝙣𝙜 𝙁𝙞𝙣𝙖𝙣𝙘𝙞𝙖𝙡 𝙄𝙣𝙨𝙩𝙧𝙪𝙢𝙚𝙣𝙩𝙨" offers a practical guide for quantitative analysts and developers. This book dives into the world of derivative pricing, equipping you with: - 𝘾𝒐𝙙𝒊𝙣𝒈 𝑺𝙤𝒍𝙪𝒕𝙞𝒐𝙣𝒔: Implement financial algorithms in C/C++ to price complex instruments ️ - 𝙉𝙪𝙢𝙚𝙧𝙞𝙘𝙖𝙡 𝙏𝙚𝙘𝙝𝙣𝙞𝙦𝙪𝙚𝙨: Master essential methods like Monte Carlo simulations for accurate valuation - 𝙒𝙞𝙣𝙙𝙤𝙬𝙨 𝙄𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣: Build custom applications leveraging familiar Windows development tools This book empowers you to: - Develop bespoke software solutions for financial modelling, - Incorporate advanced techniques like GARCH volatility modelling, - Price stocks, bonds, and options with greater accuracy Boost your quantitative finance skillset with Levy's "Computational Finance" #quantfinance #derivatives #financialmodeling #computationalfinance #Cplusplus

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𝑪𝙤𝒎𝙥𝒖𝙩𝒂𝙩𝒊𝙤𝒏𝙖𝒍 𝑭𝙞𝒏𝙖𝒏𝙘𝒆 𝑩𝙤𝒐𝙠 𝙎𝒆𝙧𝒊𝙚𝒔 - 𝑩𝙤𝒐𝙠 2 George Levy's "𝘾𝙤𝙢𝙥𝙪𝙩𝙖𝙩𝙞𝙤𝙣𝙖𝙡 𝙁𝙞𝙣𝙖𝙣𝙘𝙚: 𝙉𝙪𝙢𝙚𝙧𝙞𝙘𝙖𝙡 𝙈𝙚𝙩𝙝𝙤𝙙𝙨 𝙛𝙤𝙧 𝙋𝙧𝙞𝙘𝙞𝙣𝙜 𝙁𝙞𝙣𝙖𝙣𝙘𝙞𝙖𝙡 𝙄𝙣𝙨𝙩𝙧𝙪𝙢𝙚𝙣𝙩𝙨" offers a practical guide for quantitative analysts and developers. This book dives into the world of derivative pricing, equipping you with: - 𝘾𝒐𝙙𝒊𝙣𝒈 𝑺𝙤𝒍𝙪𝒕𝙞𝒐𝙣𝒔: Implement financial algorithms in C/C++ to price complex instruments ️ - 𝙉𝙪𝙢𝙚𝙧𝙞𝙘𝙖𝙡 𝙏𝙚𝙘𝙝𝙣𝙞𝙦𝙪𝙚𝙨: Master essential methods like Monte Carlo simulations for accurate valuation - 𝙒𝙞𝙣𝙙𝙤𝙬𝙨 𝙄𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣: Build custom applications leveraging familiar Windows development tools This book empowers you to: - Develop bespoke software solutions for financial modelling, - Incorporate advanced techniques like GARCH volatility modelling, - Price stocks, bonds, and options with greater accuracy Boost your quantitative finance skillset with Levy's "Computational Finance" #quantfinance #derivatives #financialmodeling #computationalfinance #Cplusplus

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In context of debate if large context #LLM models will eventually negate the need for #RAG, an article from Databricks has some good insights - • Longer contexts boost RAG accuracy... to a point • Performance often peaks at 32k-64k tokens, then declines • Top models (GPT-4, Claude 3.5) maintain consistency at scale • Others show unique failure modes (e.g., copyright concerns, summarization instead of Q&A) • Optimal context size varies by model and task • Lack of long-context training data may explain some issues Key takeaway: Long context and RAG are synergistic, but LLMs need refinement to fully leverage extended contexts. https://lnkd.in/eehdKtCm

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Growth with Custom Neural Machine Translation (NMT) & LSPs Precise Translation, Localization #L10N, and Internationalisation #I18N for your software, knowledge bases, training and documentation is essential for international success. Why NMT? Both advanced NMT models and LLMs (GPTs) leverage the state-of-the-art Transformer Architecture. NMTs specialise in translation tasks, resulting in superior accuracy for industry specific e.g. finance, legal, and technical domains. They have been arround for longer then LLMs. NMTs are the engine behind most of the AI translation tools and workflows, such as: ♻️ Hybrid Translation 1. Machine Translation (MT): NMT engine performs the initial translation. 2. Quality Prediction: Tools like ModelFront can analyse the MT output and predict segments with high quality that might not require human editing. 3. Human Post-Editing: Translators focus on segments identified as needing improvement or specific domain expertise. 4. Terminology Management: Some TMS and CAT tools ensure advanced jargon consistency. 5. Machine Learning #ML: Improve 1. MT, ⚠️ consider the ownership of your Translation Memories and Privacy ☑️ The Challenge: Your Custom NMT ✔️ Your options: ✅️ Partner with Language Service Provider (LSP) with Established NMT capabilities: Google Translate | Amazon Translate Microsoft Translator | IBM Watson Translator Lionbridge | XTM Cloud | DeepL WorldServer, Language Weaver RWS | SDL Crowdin | Smartling | Translated | LanguageWire ✅️ Develop - Experiment - Learn In-House (open source) #Python: #OpenNMT ⚡️Sockeye https://lnkd.in/eumhW6DW Fairseq by AI at Meta ⚠️ C++ core T5 by Google Research AI ✔️ Benefits: Superior Accuracy: User-centric, can be critical Integration: Language Services with your workflows and strategy Domain-Specific Terminology Management and Transcreation 👏 Your Expertise Matters! We'd love to hear from practitioners who have tackled these challenges firsthand. Share your experience with custom NMT in the comments! 👏 Have I missed an interesting NMT open source, affordable developers ressource or a #LSP? #Fintech #Regtech #NeuralMachineTranslation #LLM #NLP #AIProductManagement #AITranslation #ProductGrowth

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https://lnkd.in/gxKxFeEA Hedging with Implied vs. Actual Volatility A detailed THREAD with an intuitive and mathematical explanation Follow Us on X, for more interesting Thread https://lnkd.in/ggsyM4sr

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Algorithmic Trading Market: Key Strategies & Market Dynamics Request a short version of this report @ https://lnkd.in/d-BPR5Mm A new research report Algorithmic Trading Market has been added into the vast repository of research reports by The Brainy Insights The persuasive Algorithmic Trading Market report has been worked upon with the best-practice models, comprehensive market analysis and research methodologies so that clients attain perfect market segmentation and insights. Market research studies conducted in this report are very attentive for the businesses which help them with the better decision making and develop better strategies about production, marketing, sales and promotion. To craft this market report in an outstanding manner, most up-to-date and advanced tools and techniques have been utilized so that client achieves maximum benefits. A most relevant, unique, and creditable global market research report has been brought for customers and clients depending upon their specific business needs in the form of Algorithmic Trading Market. Market Scope The report then delivers an in-depth analysis of the market by value, by production capacity, by companies, by applications, by segments, by region, etc. The competitive landscape view in industry, mergers & acquisitions, research, new technologies & upcoming companies is mentioned in the report. A review of market segments, as well as sub-segments, are also highlighted in this report to offer manufacturer suggestions on the growth potential of each of the segments. Current developments in the global Algorithmic Trading Market are also highlighted in the report. Discover more about report analysis with figures and data tables, along with the table of contents. Ask an Analyst@ https://lnkd.in/dT39AyMY Market Summary: On the basis of the type, the market has been bifurcated into by Deployment Mode: Cloud On-Premises by Components: Services Managed Services Professional Services Solutions Software Platforms by Trading Types: Stock Markets Exchange Traded Funds Foreign Exchange Cryptocurrencies Bonds Others Top Leading Companies: 63 moons technologies limited Argo Software AlgoTraders InfoReach, Inc. MetaQuotes Ltd. Kuberre Systems Refinitiv Tata Consultancy Services

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The true potential of #QuantLib: from 100 seconds to Live Risk. Pls PM for Jupyter link.

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Check Out This *Easy-To-Understand No-Code* Automated Trading Strategy for NinjaTrader ®: Conditional Position Sizing (Uses Else If, Else, and Nested If Blocks). No NinjaScript ® programming knowledge required. https://lnkd.in/edDUD8rk #NinjaTrader #NinjaScript

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A detailed note on 'Vasicek Model'!

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#AI Quant Research with a TradingViewTradingView Download for Multi-Second #Market Data Quantitative finance, or "quant" for short, has revolutionized the trading landscape. By leveraging advanced statistical analysis and computer programming, quants unlock hidden patterns and generate data-driven trading strategies. If you're a quant researcher or aspire to be one, having the right tools is crucial. Get your TradingView download for a powerful platform that empowers you to dissect markets with multi-second data and a wealth of analytical features. https://lnkd.in/efPqhWQ4

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Linear Algebra Notes from UC BERKELEY (World Best MFE Program) You cannot study Quant Finance without Linear Algebra Linear Algebra: Quant Finance's Powerhouse Linear algebra isn't just useful - it's fundamental to quant finance. Here's how: Data Efficiency: Vectors and matrices organize financial data (prices, volumes, etc.) for swift analysis. Imagine crunching massive datasets simultaneously - that's linear algebra in action. Modeling Magic: It allows building financial models by representing relationships as equations. This unlocks strategies, risk allocation, and even derivative pricing. Risk Ninja: Eigenvectors and eigenvalues pinpoint major risk factors in portfolios, enabling targeted mitigation. Machine Learning BFF: Linear algebra is the backbone of many machine learning algorithms, making it crucial for tasks like anomaly detection in financial data. Options Pricing Powerhouse: It facilitates calculations behind complex financial instruments like options. 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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[Free Python Workshop] Algorithmic Trading: From Trade Strategy to Responsible Execution This is what we're teaching live: Learn how to develop trading strategies, backtest them, and execute them all in Python. Inside the LIVE EVENT, we will demystify: 1. Which algorithmic trading strategies to avoid (and which strategies hedge funds actually use) 2. The secret to turning 1 foundational trading strategy into 100 testable trading strategies (Never run out of trading strategies again) 3. Why traders fail with "profitable strategies" (the secret to trading responsibly) 4. The Python Quant Stack: Which Python libraries we use to get 90% of our results. 5. How we automate trade execution (trade automatically just like hedge funds) BONUS Live Python Demo: How to trade a volatility targeting strategy using Python This is a Python Coding Workshop, so Python experience is a plus. What's your next step? Register here (500 seats): https://lnkd.in/eMKQPTWs

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