Taras Mazur’s Post

𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝗔𝗹𝗽𝗵𝗮, 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀... Do you know the difference? 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: At the heart of every trading algorithm lies the "feature" – a transformation of raw data. Think of features as the building blocks; they convert market noise into digestible, meaningful patterns. It's like converting crude oil into gasoline. 🔹 𝗔𝗹𝗽𝗵𝗮𝘀: Once we've defined our features, the next step is crafting the 'Alpha'. An alpha isn't just a sophisticated equation; it's where we generate actual value! By analyzing the relationship between features and a specific target (like price variation percentage), an alpha reveals potential market edges. In essence, it's the engine that, when revved up, gives power to a trading strategy. 🔹 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: And then we have the grand design – the trading strategy. It's not enough to just have an engine (Alpha); we need a complete vehicle. A trading strategy integrates multiple alphas, applies risk management techniques, and tests a financial hypothesis in the real-world market maze. The aim? Consistent, profitable outcomes. ⚠️ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 Trading and learn how to create a "𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘆", take a look on my website --> Quantreo. com! #QuantitativeTrading #TradingStrategies #Algorithms #RiskManagement #FinancialMarkets #DataScience #AlphaSignals #FeatureEngineering #TradingTips #QuantResearch

𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝗔𝗹𝗽𝗵𝗮, 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀... Do you know the difference? 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: At the heart of every trading algorithm lies the "feature" – a transformation of raw data. Think of features as the building blocks; they convert market noise into digestible, meaningful patterns. It's like converting crude oil into gasoline. 🔹 𝗔𝗹𝗽𝗵𝗮𝘀: Once we've defined our features, the next step is crafting the 'Alpha'. An alpha isn't just a sophisticated equation; it's where we generate actual value! By analyzing the relationship between features and a specific target (like price variation percentage), an alpha reveals potential market edges. In essence, it's the engine that, when revved up, gives power to a trading strategy. 🔹 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: And then we have the grand design – the trading strategy. It's not enough to just have an engine (Alpha); we need a complete vehicle. A trading strategy integrates multiple alphas, applies risk management techniques, and tests a financial hypothesis in the real-world market maze. The aim? Consistent, profitable outcomes. ⚠️ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 Trading and learn how to create a "𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘆", take a look on my website --> Quantreo. com! #QuantitativeTrading #TradingStrategies #Algorithms #RiskManagement #FinancialMarkets #DataScience #AlphaSignals #FeatureEngineering #TradingTips #QuantResearch

𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝗔𝗹𝗽𝗵𝗮, 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀... Do you know the difference? 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: At the heart of every trading algorithm lies the "feature" – a transformation of raw data. Think of features as the building blocks; they convert market noise into digestible, meaningful patterns. It's like converting crude oil into gasoline. 🔹 𝗔𝗹𝗽𝗵𝗮𝘀: Once we've defined our features, the next step is crafting the 'Alpha'. An alpha isn't just a sophisticated equation; it's where we generate actual value! By analyzing the relationship between features and a specific target (like price variation percentage), an alpha reveals potential market edges. In essence, it's the engine that, when revved up, gives power to a trading strategy. 🔹 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: And then we have the grand design – the trading strategy. It's not enough to just have an engine (Alpha); we need a complete vehicle. A trading strategy integrates multiple alphas, applies risk management techniques, and tests a financial hypothesis in the real-world market maze. The aim? Consistent, profitable outcomes. ⚠️ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 Trading and learn how to create a "𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘆", take a look on my website --> Quantreo. com! #QuantitativeTrading #TradingStrategies #Algorithms #RiskManagement #FinancialMarkets #DataScience #AlphaSignals #FeatureEngineering #TradingTips #QuantResearch

𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝗔𝗹𝗽𝗵𝗮, 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀... Do you know the difference? 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: At the heart of every trading algorithm lies the "feature" – a transformation of raw data. Think of features as the building blocks; they convert market noise into digestible, meaningful patterns. It's like converting crude oil into gasoline. 🔹 𝗔𝗹𝗽𝗵𝗮𝘀: Once we've defined our features, the next step is crafting the 'Alpha'. An alpha isn't just a sophisticated equation; it's where we generate actual value! By analyzing the relationship between features and a specific target (like price variation percentage), an alpha reveals potential market edges. In essence, it's the engine that, when revved up, gives power to a trading strategy. 🔹 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: And then we have the grand design – the trading strategy. It's not enough to just have an engine (Alpha); we need a complete vehicle. A trading strategy integrates multiple alphas, applies risk management techniques, and tests a financial hypothesis in the real-world market maze. The aim? Consistent, profitable outcomes. ⚠️ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 Trading and learn how to create a "𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘆", take a look on my website --> Quantreo. com! #QuantitativeTrading #algorithms #algorithmictrading #TradingStrategies #Algorithms #RiskManagement

Finance pros know all too well just how complex financial landscapes are within an enterprise. Getting a comprehensive answer to a single financial question often requires accessing multiple systems, then organizing that data into meaningful insights, contextualizing it for the business, then figuring out how to communicate (visualize) the insights. Each step requiring additional resources or expertise to get them done. Conversational Finance introduces a new paradigm for faster data-driven decisions: ✨ Access multiple financial systems in real-time using natural language. ✨ Get actionable insights instantly, with data visualized as a chart, graph or table. ✨ Spend more time on the important part - strategic recommendations. ✨ Make decisions. https://bit.ly/3QaEJTe #GenAIforFinance #finance

📈 Week Eleven at Columbia University’s Fintech Program 🔄 Week 11 propelled our exploration of Machine Learning forward, delving into Time Series Analysis. Time series, as we learned, comprises a sequential array of discrete time values spanning a defined period, holding immense significance in financial analysis for tasks ranging from discovery to forecasting. Standardization is paramount to harnessing the power of time series data. Standardizing time values ensures consistency and accuracy in analysis. Converting timestamps to a universal format like UTC aligns disparate datasets from various markets, enabling robust comparisons and trend analysis. Venturing deeper into predictive analytics, we embraced time series forecasting. Utilizing Prophet, an open-source library tailored for time series forecasting developed by Meta, we foster comprehensive outlooks of the data. Our journey commenced with the instantiation of a Prophet object, followed by model fitting using historical data, culminating in the generation of predictions for future time periods. Prophet not only includes trend lines but also upper and lower bounds, representing the best and worst-case scenarios, respectively, with each encompassing 80 percent of the data. These bounds provide a comprehensive view of potential trajectories, empowering stakeholders to navigate uncertainty with confidence. By dissecting time series data, we uncover valuable insights that inform business decisions. Whether identifying sales patterns for retail businesses or predicting market trends for investors, time series analysis empowers us to navigate the ever-changing landscape of finance with confidence. How does forecasting factor into your domain?💡📊 #fintech #machinelearning #timeseriesanalysis #forecasting #prophet #financialinsights

🚀 Excited to announce our latest project: a cutting-edge stock prediction web application! 📈 Our team has been hard at work developing advanced machine learning models to help investors make informed decisions in the stock market. Check out our innovative platform and stay ahead of the curve! #StockPrediction #MachineLearning #Investing #Finance #Tech #Innovation 📊✨

Up for Grabs! 49% Investment Alpha Annually with Sector Rotation Is it worth paying attention when investors and financial news discuss sector trends and performance? Is tactical asset allocation a profitable investment strategy? Absolutely! With a tactical sector rotation strategy, you have the potential to significantly outperform the market and generate 49% annual returns! https://lnkd.in/e2zN4jvZ What next? 1) investors subscribe to read how to implement this 2) data scientists reach out to learn how to publish genAI content from jupyter notebooks to web and social media 3) data vendors reach out if you have data you think can predict sector returns so we can add it to the model

Understanding the role of stochastic processes is critical for making informed decisions in the unpredictable world of finance. These mathematical models represent systems evolving over time with a degree of randomness and are at the heart of many financial models. They allow us to capture the inherent uncertainty and volatility that characterize financial markets. For instance, the Black-Scholes-Merton model, a cornerstone of modern financial theory, uses a stochastic process to model the price evolution of financial instruments. This model has revolutionized the way we price options and manage financial risk. Moreover, stochastic calculus, an offshoot of stochastic processes, is integral to the pricing of derivative securities. It provides the mathematical framework for modeling and predicting the future behavior of random variables, such as stock prices or interest rates. In the era of Big Data, the importance of stochastic processes has only grown. They are key to developing sophisticated machine learning algorithms for predictive analytics, algorithmic trading, portfolio optimization, and risk management. As a data scientist with over a decade of experience in the financial industry, I've come to appreciate stochastic processes' profound role. Understanding them is not just an academic exercise - it's a practical necessity for anyone looking to make informed decisions in today's financial landscape. So, let's keep stochastic processes an indispensable tool in our toolkit! 🧮📊 #finance #stochasticprocesses #datascience #riskmanagement

The future of financial analysis: How GPT-4 is disrupting the industry, according to new research 📈 Even without any narrative or industry-specific information, the 𝗟𝗟𝗠 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝘀 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 in its ability to predict earnings changes. 🔍 The LLM exhibits a relative 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝘃𝗲𝗿 𝗵𝘂𝗺𝗮𝗻 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 in situations when the analysts tend to struggle. 💡 LLM prediction does not stem from its training memory. Instead, 𝘁𝗵𝗲 𝗟𝗟𝗠 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝘂𝘀𝗲𝗳𝘂𝗹 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆’𝘀 𝗳𝘂𝘁𝘂𝗿𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. 📊 Trading strategies based on 𝗚𝗣𝗧’𝘀 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝘆𝗶𝗲𝗹𝗱 𝗮 𝗵𝗶𝗴𝗵𝗲𝗿 𝗦𝗵𝗮𝗿𝗽𝗲 𝗿𝗮𝘁𝗶𝗼 𝗮𝗻𝗱 𝗮𝗹𝗽𝗵𝗮𝘀 than strategies based on other models. 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/ewSxci34