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Check out the article that I have written for Crafting a Robust Portfolio: A Data Engineer's Guide to Building Success.

Decision Trees: A Simple Yet Powerful Tool for Stock Market Predictions 🌳📊 Decision trees are one of the most intuitive machine learning models used for both classification and regression tasks. The tree-like structure splits the data into smaller, more manageable chunks by asking a series of questions based on the features of the data, eventually leading to a prediction. How It Works: A decision tree starts at a root node and branches out based on the conditions defined by the features. Each branch represents a possible outcome of a decision, and the process continues until a final prediction (or decision) is reached at the leaf node. Example in the Stock Market: Let’s say we want to predict whether a stock is a good buy or not. The decision tree might ask the following questions: 1. Is the Price Momentum positive? – If yes, continue to the next question. 2. Is the Trading Volume above the average? – Higher volume indicates more interest in the stock. 3. Has the Company recently reported strong Earnings? – A positive earnings surprise could be a signal to buy. 4. Is the Sector Performance outperforming the broader market? – This tells us if the stock’s sector is leading the market. 5. Is the Volatility low? – Lower volatility might indicate less risk. The tree will split the data at each step, eventually leading to a decision on whether to buy the stock or not. Practical Use Case: Decision trees are especially useful for investors who want to incorporate multiple factors before making an investment decision. For instance, a decision tree can help traders quickly identify potential stock breakouts based on price momentum, trading volume, and earnings. It provides a clear, visual path of decisions, making it easy to understand why the model recommends a certain action (buy/sell). In the financial world, decision trees are valued for their simplicity and transparency, making them a favorite tool for risk assessment, trading strategies, and portfolio management. #StockMarket #DecisionTree #MachineLearning #InvestmentAnalysis #DataScienceInFinance #TradingStrategies #FinancialModeling #AIInFinance

"Intelligent naivety" is often the driving force for innovation Why can't it be done better ❓ 👉 No good reason, most of the time So when it comes to financial markets, and macro in particular, the world has been stuck in the realms of often contradictory stories and narratives. There is no other way to do it, right ❓ The arrival of lots of high frequency data on all things macro - including daily real GDP estimates for 20 countries, inflation expectations and more - and cheap processing power created an opportunity This isn't primarily about predicting asset prices, but about a consistent and repeatable framework to understand what is really driving asset prices 👉 How do you connect price action and information ❓ You can take all the noisy, trending, correlated data on macro and process it to get under the surface and find the underlying relationships among all the noise. And once you have done that, you can understand the underlying connections between information and prices. 📈 This in turn reveals the macro based price of an asset. 📉 We show the macro warranted value for the S&P500 index in the chart. 💪 As you can see, macro momentum is still strong. While I read many persuasive narratives about why stocks should go down, until that red line turns lower, the data is telling a different story. And following the evidence has kept me, and many other investors, out of trouble on many occasions. 🚨 A data driven framework for macro should be part of any investing mosaic or process. Period. Technology and data is a huge bandwidth expander, even in the realms of macro. You can get updates on 10,000+ securities across asset classes every 15 mins Adapting to new information and re-calibrating daily is key. Things shift. There are no fixed long term relationships. Everything is transparent as far as methodology - feel free to request a simple White Paper in the comments section Now battle tested for several years by investors managing $7Trillion+ Macro doesn't have to be the land of stories only When that red line turns lower it would be good to see it and be able to know why #macro #dataanalytics #machinelearning #stocks #hedgefunds #assetmanagement #fx #rates

Good visualization by Michael Ho on Valuation Multiples. You can see the time delay from the SaaS Capital Index of the public market multiples and private multiples for each round: 1️⃣ The public starts rising in early 2020 and peaks at 16.6x in Q4 2020 2️⃣ In Q1 2021, everything starts floating upwards 3️⃣ By Q2 2021, Series C peaks at a 32x multiple 4️⃣ Two quarters later in Q4 2021, Series B peaks at a 37x multiple 5️⃣ Followed by Series A peaking in Q1 2022 at a 32x multiple 6️⃣ Followed by the next quarter where Seed peaks in Q2 2022 at a 58x multiple 7️⃣ With the public markets bottoming out at a 6.3x multiple in Q4 2022 Top to bottom of the market index was 16.6x down to 6.3x over a two years period and you can watch the waterfall from peak C all the way down to Seed with about a quarter delay between each As expected, within each quarter, you can see the multiples start high at Seed and then trend down towards the public multiples through Series E+ -- ♻️ Repost to help a founder in your network Subscribe to ‘Siliconnector’ Telegram channel for insights and news from Tech world and Silicon Valley: https://t.me/siliconnector

Main Street Data API: Unlock 1,500+ metrics for US stocks The Main Street Data API, our latest addition to the Marketplace, is an invaluable resource. The team behind this innovative product has meticulously compiled over 1,500 metrics related to 354 US companies, offering unparalleled insights into businesses beyond standard financial statements. These KPIs provide a direct measure of business quality, giving investors a significant advantage by offering rare, hard-to-source data. The data includes detailed individual operating metrics, such as Tesla’s Supercharger Stations and Palantir’s Customer Count, as well as segmented metrics like Nvidia’s Operating Income, empowering users with granular and sector-specific insights. https://lnkd.in/dHZasj6X #stockmarket #investment #financialapi #EODHD #USstock

Ever wondered how fast the world of trading really is? 🤔 Let's dive into a mesmerizing aspect of the financial markets that's not just about numbers, but about speed – and not just any speed, but the kind measured in microseconds!  W𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗗𝗲𝗮𝗹 𝘄𝗶𝘁𝗵 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗶𝗻 𝗧𝗿𝗮𝗱𝗶𝗻𝗴?  Latency in high-frequency trading is a game of microseconds, where the execution time of trade orders can make or break a deal. This execution time varies with different trading strategies and exchanges -, making it a complex element to pinpoint. A study by Aquilina et al., highlights the intense competition and precision of this environment: ▪️Trade races are incredibly common, occurring roughly every minute. ▪️The gap between winning and losing in these races is astonishingly narrow, at just 5-10 microseconds. 🤏⚖️ ▪️Dominance in this high-speed world is held by a few, with the top six trading firms involved in over 80% of these races.    It illustrates the critical role of speed in modern finance, where a 5-microsecond delay could mean the difference between profit and loss. 💸➡️💰  In a world where milliseconds can dictate financial outcomes, the efficiency of machine learning algorithms becomes paramount, navigating through these microsecond battles to seize opportunities or avoid losses. 🧠💻🚀 #HighFrequencyTrading #FinancialMarkets #Xelerasilva #TradingTechnology #MarketAnalysis #MachineLearning #AlgorithmicTrading #FinanceInnovation #DigitalTransformation #Fintech #InvestmentStrategies #DataAnalytics #TechTrends 

📊 Excited to share a glimpse into my recent Financial Valuation Analysis project on Charles Schwab. By diving deep into the company’s historical stock performance and analyzing the financial statements from the last fiscal year, we’ve developed a comprehensive valuation framework. 🔍 Our approach integrated technical analysis and fundamental analysis to assess Schwab’s market position and financial health. We also explored stock volatility trends to provide a nuanced view of potential risks and returns. 🚀 Utilizing Monte Carlo Simulation, we projected future stock prices, offering valuable insights for strategic decision-making and investment opportunities. 💼 This analysis not only enhances our understanding of Schwab's financial landscape but also underscores the power of blending traditional financial metrics with advanced simulation techniques for robust valuation. #Finance #Valuation #CharlesSchwab #MonteCarloSimulation #InvestmentAnalysis #FinancialModeling #Prophet

Buy & Sell Signals (NEE) Technical Data: Stock Traders Daily has produced this trading report using a proprietary method.  This methodology [...] Look at the Charts

Buy & Sell Signals (TSLL) Technical Data: Stock Traders Daily has produced this trading report using a proprietary method.  This methodology [...] Look at the Charts

How Data Science is Revolutionizing the Stock Market 📈 Did you know that 70% of trading volume on the stock market is now driven by algorithms? Traditional stock analysis methods just aren't cutting it anymore 💼 🚀 Data Science can be your secret weapon for understanding market trends and making informed investment decisions. It helps with tasks like: Predictive Analytics 📊: Anticipate market movements and make data-driven trading decisions with advanced predictive models. Sentiment Analysis 🧠: Gauge market sentiment by analyzing news articles, social media, and financial reports to predict stock performance. Risk Management 🔒: Identify and mitigate potential risks by analyzing vast amounts of market data in real-time. Imagine the possibilities! Data Science can help you optimize your portfolio 📈, enhance trading strategies 📉, and stay ahead of the competition 🏆. #stockmarket #datascience #trading #innovation #investment