URL:github.com/aravindhrafa/Opti… Theme: Gamma scalping option buying. Tried mimicing Citadel level securities trading at POD level. #daylearning #quantlearning

பாக்டெஸ்டிங் உண்மைகள் & தவறான நம்பிக்கைகள் In this video, we discuss what every trader should really know about Backtesting 👇 1️⃣ எப்போது அது நம்பகமாக இருக்கும், எப்போது தவறும்? 2️⃣ Data, Logic, Sample Based Test எவ்வளவு தரமாக இருக்க வேண்டும்? 3️⃣ Back Test Result எப்போதும் reality ஆகாது; அதன் probability எப்படி புரிந்துகொள்வது? 4️⃣ நம்முடைய expectation எந்த அடிப்படையில் இருக்க வேண்டும்? This video is shared purely for educational and analytical learning, not as investment or trading advice. Interested in learning in-depth? Chennai Workshop (தமிழில்) – Nov 9, 2025 (Sunday) 🎟 Register: pages.razorpay.com/itjegan1 🔗 Details: capitalzone.in/it-jegan-trad… #Backtesting #TradingEducation #QuantLearning #CapitalZone #ITJegan

In this clip, explore the real side of Backtesting: Facts vs Myths and what every trader should know before trusting results: 1️⃣ How reliable is a back test — and when does it fail? 2️⃣ How to ensure the quality and accuracy of your test cases. 3️⃣ Understanding the probability and limitations behind results. 4️⃣ How to set realistic expectations based on data and logic. This video is shared purely for educational and analytical learning, not as investment or trading advice. Interested in learning in-depth? Chennai Workshop (தமிழில்) – Nov 9, 2025 (Sunday) 🎟 Register: pages.razorpay.com/itjegan1 🔗 Details: capitalzone.in/it-jegan-trad… #Backtesting #TradingEducation #QuantLearning #CapitalZone #ITJegan

Day 2 of Learning Quantitative Finance Risk lene se darte ho? Samjho Risk vs Return, Volatility aur Sharpe Ratio sab kuch 90 seconds mein #Day6 #QuantFinance #BrownianMotion #QuantLearning #CFA #FinanceConcepts #StochasticProcess #BlackScholes #QuantitativeFinance #AmirIrshad

📊 Why Scaling Matters in Trading Models Before your model learns anything, scaling ensures that no feature dominates just because it has a larger numeric range. It’s a critical step in preprocessing financial data, especially in regression or distance-based models. Here are the key steps from a recent notebook on preparing gold price data for prediction models: ✅ Import the data - Using custom input parameters ✅ Check & drop NaN values - Clean dataset = reliable model ✅ Scale features - Standardise to avoid bias in regression ✅ Split into X & Y variables - Independent & dependent sets 💡 Why scaling matters: Without it, features with larger numeric ranges can overshadow others, reducing the model’s ability to learn balanced patterns. Details of the Python code are in the FREE preview of the course in Section 3, Unit 5: quantra.quantinsti.com/start… Are you someone who wants to apply AI in trading? Curious how GenAI, LLMs, and machine learning are changing the trading landscape? Then this conference is for YOU. 🎯 QuantInsti’s Algorithmic Trading Conference 2025 📅 Date: 23 September 2025 🕒 Time: 6:00 PM IST | 8:30 PM SGT | 8:30 AM EDT 💻 Free | Online | Global What’s happening? Workshop by Tucker Balch (Emory University) Explore real-world use of AI, LLMs & price data in trading strategies. See how AI models are predicting inter-stock relationships, with live Q&A! Topics include: How AI is transforming trading desks Emerging skills for quants GenAI's role in quant education What the future of finance looks like with AI 👥 Who should attend? - Aspiring Quants - Traders & Finance Professionals - Coders & ML Engineers - Students & Career Switchers 🎟️ Ready to see how AI is shaping the future of trading? 🔗 Register now - spots are limited! 👉 quantinsti.com/algorithmic-t… #AlgoTrading #AIinTrading #QuantConference #QuantFinance #GenAI #LLM #FinanceCareers #QuantLearning #QuantInsti #EPAT #MachineLearning #AlgorithmicTrading #DataScience #Python #FeatureEngineering #Quant #TradingModels #DataPreprocessing #Finance

📘Learn the Language of Data with the SQL QuickStart Guide Whether you're stepping into data analytics or refreshing your database skills, SQL is the backbone of working with data. This practical, hands-on guide walks through the essentials of manipulating, programming, and managing data with clarity and real-world examples—perfect for beginners and professionals alike. Check out the book here: 👉amazon.com/-/es/SQL-QuickSta…? 💡 Question for you: What’s one SQL command you can’t live without? Are you someone who wants to apply AI in trading? Curious how GenAI, LLMs, and machine learning are changing the trading landscape? Then this conference is for YOU. 🎯 QuantInsti’s Algorithmic Trading Conference 2025 📅 Date: 23 September, 2025 🕒 Time: 6:00 PM IST | 8:30 PM SGT | 8:30 AM EDT 💻 Free | Online | Global What’s happening? Workshop by Tucker Balch (Emory University) Explore real-world use of AI, LLMs & price data in trading strategies. See how AI models are predicting inter-stock relationships, with live Q&A! Topics include: How AI is transforming trading desks Emerging skills for quants GenAI's role in quant education What the future of finance looks like with AI 👥 Who should attend? - Aspiring Quants - Traders & Finance Professionals - Coders & ML Engineers - Students & Career Switchers 🎟️ Ready to see how AI is shaping the future of trading? 🔗 Register now - spots are limited! 👉 quantinsti.com/algorithmic-t… #AlgoTrading #AIinTrading #QuantConference #QuantFinance #GenAI #LLM #FinanceCareers #QuantLearning #QuantInsti #EPAT #SQL #DataAnalytics #Database #Learning #Programming #TechSkills #DataScience #Upskill #CareerDevelopment #Books

Building machine learning models is just one part of the journey... Understanding them is the real challenge. Shapash is a Python library that brings transparency to machine learning by making model predictions understandable for everyone technical or not. 🛠️ Key Features of Shapash: - Generates a WebApp for navigating global and local explainability - Makes predictions easily interpretable with clear visualizations and labels - Supports regression, binary, and multiclass classification - Compatible with CatBoost, XGBoost, LightGBM, Scikit-learn, Linear Models, SVMs - Helps summarize and export local explanations - Offers metrics to evaluate the quality of explanations - Allows filtering subsets (correct/wrong predictions, feature values) for deeper analysis - Can be deployed via API or in batch mode for production use 🔍 Whether you're working on risk models, customer churn, or trading strategies if your ML model makes decisions, Shapash can help you explain why. GitHub: github.com/MAIF/shapash?tab=… Worth checking out if interpretability matters in your work. Are you someone who wants to apply AI in trading? Curious how GenAI, LLMs, and machine learning are changing the trading landscape? Then this conference is for YOU. 🎯 QuantInsti’s Algorithmic Trading Conference 2025 📅 Date: 23 September, 2025 🕒 Time: 6:00 PM IST | 8:30 PM SGT | 8:30 AM EDT 💻 Free | Online | Global What’s happening? Workshop by Tucker Balch (Emory University) Explore real-world use of AI, LLMs & price data in trading strategies. See how AI models are predicting inter-stock relationships, with live Q&A! Topics include: - How AI is transforming trading desks - Emerging skills for quants - GenAI's role in quant education - What the future of finance looks like with AI 👥 Who should attend? - Aspiring Quants - Traders & Finance Professionals - Coders & ML Engineers - Students & Career Switchers 🎟️ Ready to see how AI is shaping the future of trading? 🔗 Register now - spots are limited! 👉 quantinsti.com/algorithmic-t… #AlgoTrading #AIinTrading #QuantConference #QuantFinance #GenAI #LLM #FinanceCareers #QuantLearning #QuantInsti #EPAT #MachineLearning #ExplainableAI #MLInterpretability #XAI #Shapash #QuantFinance #AITransparency #PythonML #QuantInsti #MLTools

🔍 Alpha. Beta. Momentum. Value. Risk. What if you could harness the science behind returns, with code? 📈 Factor Investing is no longer a secret of institutional funds or hedge funds. It’s the quant edge that retail investors, fintech innovators, and trading strategists are now using to build robust portfolios and generate alpha consistently. 💡If you ever wanted to learn factor investing, then you’ll love this curated deep dive: watch, learn & unlock the quant edge: 🎯 1. Factor Investing: Concepts & Strategies | Course Overview 📌 Learn about the course from this video on how to master the building blocks of factor models & how pros use them. ▶️ youtu.be/QDqV5NVb3ys?si=gJC-… 📊 2. Factor Investing Made Easy: Understanding Alpha & Beta 📌 Demystifying Alpha, Beta & their real role in portfolio performance. ▶️ youtu.be/4CDWRciCzVQ?si=wtdj… ⚡ 3. Factor Investing | Momentum Trading | Python for Trading 📌 Build & backtest a momentum strategy in Python — step by step. ▶️ youtu.be/_Dj71p3WrdA?si=JOOD… 🔬 4. Introduction to Quantitative Factor Investing (Live Session) 📌 Explore how quants create and optimise multi-factor strategies. ▶️ youtube.com/live/7_BXyM7xapM… 🧠 5. Factor Investing with Algorithmic Trading (Live Webinar) 📌 Combine factors with algorithmic execution for scalable edge. ▶️ youtube.com/live/98Ozhi6nHeI… 🎥 These sessions above answer your top questions on Factor Investing, including: What are Alpha and Beta factors, and why do they matter? How can retail investors benefit from factor investing vs. value investing? What are the key building blocks of quantitative factor models? How can you use Python to create and test momentum strategies? What makes factor investing ideal for algorithmic trading? How does factor timing work, and why is it so powerful? How do risk premia and diversification come into play? Where can you begin your journey in factor-based quant trading? 👥 Who is this ideal for: Retail investors curious about quant strategies Traders and finance pros exploring momentum and factor timing Aspiring quants eager to learn Python for finance Anyone looking to bridge the gap between theory and real-world investing 💬 Drop a 🔍 if you're diving in. Tag someone who needs to see this! 🎯 Free Resource: Want to go from market basics to building data-driven strategies? 🚀 Explore this Free Learning Track for Beginners and start your quant journey today: quantra.quantinsti.com/learn… #FactorInvesting #PythonTrading #QuantResearch #AlphaBeta #MomentumStrategy #RetailInvesting #ValueVsFactor #Quantra #QuantLearning #FinanceEducation #AlgoTrading

🚫 Common Misunderstandings About Algorithmic Trading Myth 1: Algorithmic trading is rocket science, and all algorithmic strategies are insanely complicated. Not true! Some of the best strategies are actually built on simple ideas, like averages or how much prices move up and down. Yes, learning to code, understand stats, and build systems can help. But most successful traders start small, build on what they know, and slowly add new skills over time. Myth 2: Retail traders can never compete against the technical advantages of big HFT firms. This isn’t the full picture. HFT firms are mostly competing with each other, not with retail traders. They help make markets smoother for everyone. Because of them, you often get better prices when you place an order as a retail trader. Myth 3: Big players exploit every possible market opportunity, leaving little scope for retail investors. Large firms usually do not seek to exploit market inefficiencies or short-term opportunities if they are not scalable enough. This creates an opening for retail-level traders to design strategies to exploit them. Also, keep in mind that a big institution, whether an HFT firm or a hedge fund, has to comply with investment mandates and regulatory concerns, which prevents them from profiting from certain anomalies they spot. Fortunately, retail investors can capitalise on them Beginner’s Guide to Automated Trading To help you understand algorithmic trading from the ground up, we've created a free eBook for anyone new to the space. What you’ll find inside: The history and basics of algo trading Key terms and systems explained in plain language Pros and cons of automating your trading Core components of a good trading system Common strategies with real examples Skills required for learning or working in quant trading Resources for going deeper (courses, tools, books) This guide is useful for students, engineers, working professionals, or active traders exploring automation. 🔗 Download here: quantinsti.com/algo-trading-… Algo-Trader Aptitude Test: Are You Ready? Check your compatibility now! : Take this quick 10-minute test to see where you stand: quantinsti.com/admissions#be… Want guidance on starting your quant career? Book a free career counselling call with our team: Book now: calendly.com/counsellor-1/sp… #AlgorithmicTrading #AlgoTradingForBeginners #QuantFinance #LearnToTrade #TradingStrategy #PythonForTrading #QuantInsti #QuantLearning #RetailTrading #FinancialMarkets #QuantCareer #TradingEducation #AlgoEbook #TradingMyths #QuantRoles

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Your Next Steps & Beyond Once comfortable with these, you can explore more advanced topics like machine learning in finance, advanced numerical methods, or specific asset classes. The journey is continuous! What book helped you most when you started? #QuantLearning #CareerGrowth

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New Video Premiering on 22nd June at 6 pm: Learn How to Convert a Research Paper into Python Code (with ChatGPT) -> Reading a quant finance paper is tough. -> Implementing it in Python is even harder, unless you have the right process. In tomorrow’s video, Mohak Pachisia will break down a complex academic paper on SPY & GLD returns and show you how to: ✅ Use ChatGPT to simplify the paper ✅ Extract, process, and analyse financial data ✅ Calculate night vs day returns in Python ✅ Visualise patterns using Seaborn ✅ Build a modular codebase for future research This walkthrough is ideal for: Aspiring quants Finance students Analysts learning to code Developers exploring financial markets 👤 About the Speaker Mohak Pachisia, Senior Quant at QuantInsti, has trained analysts and traders across India and abroad. He’s worked with Upstox, and Evalueserve, and is known for his clear, structured approach to teaching quant skills using Python. 📅 Premieres Sunday, June 22 | 6 PM IST 🔗 youtube.com/watch?v=_KajD3Sb… #QuantLearning #PythonFinance #QuantDev #Backtesting #TradingStrategy #QuantInsti #QuantEducation

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New Video Premiering on 22nd June at 6 pm: Turning Quant Research into Python Code, with ChatGPT How do you go from reading a dense quant finance research paper to actually testing the idea in Python? In our upcoming video, Mohak Pachisia (Senior Quant, QuantInsti) breaks down a 48-page paper on night vs day returns and shows how to: Use ChatGPT to simplify academic research Calculate overnight vs intraday returns for SPY and GLD Build and visualise your strategy in Python If you're new to quant trading or finance research, this walkthrough is a great way to learn how to put theory into practice. 📅 Premieres June 22 at 6:00 PM IST 🎥 Set Reminder: youtube.com/watch?v=_KajD3Sb… 👤 About the Speaker Mohak Pachisia is a Senior Quant at QuantInsti, specializing in research and product development in quantitative and algorithmic trading. He has designed and delivered learning programmes for aspiring traders, analysts, and finance professionals across India and abroad.He’s worked with Upstox, and Evalueserve, and is known for his clear, structured approach to teaching quant skills using Python. #QuantFinance #QuantResearch #PythonTrading #ChatGPTFinance #Backtesting #QuantInsti #OvernightReturns #TradingStrategy #QuantLearning

📈 Still Think Linear Regression is Just a Classroom Concept? Think Again. In the world of algorithmic trading, linear regression is more than just stats on a whiteboard. It’s how quants: 🔹 Spot relative value in pairs trading 🔹 Estimate hedge ratios 🔹 Forecast returns using factor models 🔹 Quantify market exposure via beta 🔹 Catch mispricings in ETFs or index futures At its core, linear regression draws a line, but in finance, that line can point to profit, risk, or arbitrage. Here’s what makes it still a go-to method even with all the ML buzz: ✅ Interpretable: You know what each β means. ✅ Quick to train: Ideal for real-time strategies. ✅ Powerful base: It’s the backbone of more complex models. ✅ Used in risk models, factor investing & arbitrage. And yes, linear regression comes with assumptions: Linearity. Independence. Homoscedasticity. Normality. No multicollinearity. Break them, and your model might still run, but it can mislead you when you're drawing inferences or managing risk based on the output. For pure prediction, some violations are acceptable, as long as you understand the trade-offs. 📉 Finance isn’t always linear. Markets evolve. Relationships shift. That’s why advanced quants enhance regression with Lasso, Ridge, rolling windows, and robust testing. 🚀 Curious to see regression in action in the real world of trading? Find out the whole concept covered in this blog: blog.quantinsti.com/linear-r… 🎙️ Want to build your own trading system? Join our free webinar: Build Your Quant Portfolio: First Steps into Algorithmic Trading 🗓️ 24th June 2025 | 🕕 6 PM IST | 8:30 AM EST | 8:30 PM SGT 🔗 Register here quantinsti.com/algorithmic-t…? This is your beginner-friendly entry point into the world of systematic trading — no jargon, just real insights from expert mentors. #AlgorithmicTrading #QuantTrading #LinearRegression #StatisticalArbitrage #PairsTrading #TradingStrategies #FinancialModeling #TradingWebinar #RiskManagement #QuantitativeFinance #DataDrivenTrading #PythonForFinance #EPAT #QuantLearning #BacktestYourStrategy

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🔁 How New Information Changes What You Believe You think there’s a 60% chance a stock will go up today. Then you hear a major investor just took a big position. Now you think the chance is closer to 80%. That’s not guesswork, it’s conditional probability. 📉 Just like weather: “It usually rains 30% of the time.” But if you see dark clouds outside, you might revise that to 70%. Your estimate now depends on a condition, cloud cover. This happens all the time in trading. 📊 Maybe your model gives a stock a 75% chance of rising. Then a strong earnings report comes out. You update the number because the model learns and adapts. That’s the power of conditional thinking. It’s what separates rigid models from responsive ones in algorithmic trading. 🎯 Ready to explore how these ideas turn into real trading systems? 🎙️ Join our free webinar Build Your Quant Portfolio: First Steps into Algorithmic Trading 🗓️ 24th June 2025 | 🕕 6 PM IST | AM EST | PM SGT 🔗 Register now quantinsti.com/algorithmic-t…? Led by industry experts, this session introduces key concepts that drive modern quant strategies, with real-world clarity and zero jargon. #AlgorithmicTrading #ConditionalProbability #QuantTrading #TradingWebinar #DataDrivenDecisions #AdaptiveModels #FinancialEducation #TradingInsights #QuantPortfolio #QuantitativeFinance #ProbabilityInTrading #TradingStrategy #QuantLearning

📉 Stop-Loss and Take-Profit in Python: A Risk Management Walkthrough Managing risk is an essential part of building any trading strategy, and setting systematic exit rules is where it often begins. Below are the steps of an example in which we have explored how to implement fixed stop-loss and take-profit levels using Python, with a clear flow from logic to visualisation. 🔍 Steps for mitigating risk with Python: Importing and preparing trade data Initialising stop-loss and take-profit parameters Structuring entry and exit rules Logging trades with time and PnL Plotting entry, exit, and cumulative performance This type of logic can support consistent decision-making and offer insights into how a strategy performs across different market conditions. Whether you’re experimenting with basic strategies or refining your current system, applying these principles in code is a great step toward thoughtful strategy development. 🎓 For more exploration into using dynamic stop-loss and take-profit triggers, as well as volatility-based techniques and option pricing models, this example in Python is discussed in detail in our course: Volatility Trading Strategies for Beginners (Also includes GARCH, Greeks, and volatility estimators) Link to course: quantra.quantinsti.com/cours… 🎙️ Join our free webinar: Build Your Quant Portfolio: First Steps into Algorithmic Trading 🗓️ 24th June 2025 | 🕕 6 PM IST | AM EST | PM SGT 🔗 Register now: quantinsti.com/algorithmic-t… This session is your chance to explore the fundamentals of quantitative trading, without the hype. Learn how real traders build and manage quant strategies, and get clarity on what it takes to enter this fast-evolving domain. #tradingwithpython #riskmanagement #stoploss #takeprofit #quantitativefinance #backtesting #algotrading #datadriventrading #volatilitytrading #optionsanalytics #pythontrading #quantlearning #strategydevelopment #quantinsti #quantra #freewebinar #quantportfolio

🔁 Python-Based Backtesting – Part 2: Keep Sharpening Your Quant Edge 🔁 If you're serious about building robust trading systems, here are 4 essential books to strengthen your Python backtesting game: 📘 Hands-On Financial Trading with Python 👉 Ideal for getting hands-on with backtesting logic, performance analysis, and trade execution. 🔗 amazon.com/.../Hands-Financi… 📗 Python for Algorithmic Trading Cookbook 👉 Packed with 50 practical recipes. Great for tackling real-life trading problems with reusable code. 🔗 amazon.com/.../Python-Algori… 📕 Python for Algorithmic Trading: From Idea to Cloud Development 👉 Yves Hilpisch walks you through taking a strategy from concept to cloud-based deployment. Must-read for serious quants. 🔗 amazon.com/.../Python-Algori… 📙 Python for Finance: Mastering Data-Driven Finance 👉 A comprehensive reference for working with financial data and analytics using Python. Deep dive for finance nerds. 🔗 amazon.com/.../Python-Financ… 🎯 Ready to take your backtesting skills to the next level with Python? Build professional-grade, data-driven strategies with confidence. 🔗 quantinsti.com/epat 🚀 In the EPAT® Programme, you’ll master: ✅ Python-based backtesting frameworks ✅ Real-world strategy design and evaluation ✅ Advanced risk and capital management techniques 💼 Created by industry practitioners. Trusted by quants and traders worldwide. If you're serious about mastering backtesting with Python, EPAT® is your launchpad. 💻 Whether you're building your first trading model or fine-tuning risk management with TA-Lib and Backtrader — this is your roadmap! 💬 Got a favorite Python trading resource? Share it below – let's grow together! #PythonForFinance #AlgorithmicTrading #Backtesting #QuantitativeFinance #TradingStrategies #QuantLearning #PythonQuant #FinanceBooks #AlgoTrading