Python Applications in Business

There are two ways to solve a problem in Finance and FP&A Analogy: "We’ve always done it this way, let’s just tweak the template." First Principles: "What is undeniably true about this data, and how can we build it from scratch?" Method #1 keeps you stuck in manual reporting loops. Method #2 opens the door to Python, AI Agents, and full automation. Use this guide to start: https://lnkd.in/eB9Rvwbw I’m re-reading Thinking Like a Rocket Scientist by Ozan Varol, and I thought how to apply First Principles Thinking to AI in Finance: Step 1: Identify & Challenge Assumptions We carry invisible baggage that limits us. You need to identify these myths: ❌ "AI is too complex—only data scientists can use it." ❌ "Automation means buying expensive software." ❌ "Python is for coders, not finance people." Step 2: Break Down to First Principles Ask: What is undeniably true here? ✅ Data is Data. Whether it's a P&L or a CSV, it's just structured information. ✅ Python is Logic. It’s not "code"; it’s a language for expressing logic. ✅ LLMs are Engines. They can read your logic and write the code for you. Once you accept these truths, the barrier to entry disappears. Step 3: Rebuild from the Ground Up Here is your new roadmap to build an AI-powered Finance function: 1. Define the Workflow Don't automate "everything." Pick one high-value task: Variance Analysis, Cash Flow Projections, or Management Reporting. 2. Translate to Logic Map it out: Input (Excel) → Process (Calculate Variance) → Output (Email Summary). 3. Use LLMs as your Co-Developer You don't need to know the syntax. Just prompt: "Write a Python script to load this CSV, calculate the variance between Col A and Col B, and summarize the top 3 drivers." Detailed Guide here: https://lnkd.in/eF6ZxY4t 4. Build in Google Colab You don't need to download any software. Use cloud-based notebooks to test your ideas instantly. https://lnkd.in/eVjchqAS 5. Build Analytics Superpowers Move beyond dashboards. Use AI to generate "what-if" scenarios and predictive insights that Excel simply can't handle. A few final tips for the journey: Test small: Automate one report first. Question everything: "Why are we doing this manually?" Scale responsibly: Always validate the output. I think the future of Finance belongs to those who think like Rocket Scientists, not those who just copy last year's template. Which of those "Assumptions" in Step 1 is holding your team back the most? Let me know in the comments. 

So many business analysis questions come down to this: Which values appear in one table but not another? In accounting, that might mean invoices with no matching payment, payments with no matching invoice, purchase orders missing from the general ledger, or transactions that only appear in one system. Excel can handle this with lookup formulas. Power Query can handle it with anti-joins. Python in Excel gives you another option. In this post, I show how to use pandas to: - find invoices that do not have a matching payment - reconcile both tables in one direction and then both directions - use an outer merge to identify records found only on the left, only on the right, or in both - keep the analysis directly inside the workbook Python in Excel adds another useful tool to the toolkit, especially when the reconciliation is only the beginning of the analysis. I also included a downloadable exercise workbook so you can follow along. Read the post here: https://lnkd.in/gvBF8825

𝐀𝐈 𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐟𝐢𝐧𝐚𝐧𝐜𝐞 𝐟𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐚𝐧 𝐞𝐯𝐞𝐫. But most people only see the applications. Very few understand the technology behind them. One resource that stands out is "Artificial Intelligence in Finance – A Python-Based Guide." It connects AI concepts with practical financial use cases instead of focusing on theory alone. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐜𝐨𝐯𝐞𝐫𝐬: → 📈 Algorithmic Trading • Build data-driven trading strategies • Automate market execution • Analyze price patterns → 🛡️ Fraud Detection • Identify suspicious transactions • Detect anomalies in real time • Strengthen financial security → ⚖️ Risk Management • Evaluate financial risk • Improve decision-making • Build more resilient systems → 📊 Predictive Analytics • Forecast trends from historical data • Generate actionable insights • Support business planning → 💼 Portfolio Optimization • Balance risk and return • Optimize investment strategies • Improve asset allocation 𝐓𝐡𝐞 𝐛𝐨𝐨𝐤 𝐚𝐥𝐬𝐨 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐬 𝐭𝐡𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 𝐛𝐞𝐡𝐢𝐧𝐝 𝐦𝐨𝐝𝐞𝐫𝐧 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐀𝐈, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠: • Pandas • NumPy • Scikit-learn • TensorFlow The real opportunity is not just learning AI. It is learning how to apply AI to solve real business problems. Finance is one of the strongest examples of that. From fraud detection to intelligent trading systems, AI Engineers and Data Scientists are building solutions that analyze massive datasets, automate decisions, and generate insights at scale. As AI adoption accelerates across industries, the combination of Python + AI + Finance is becoming an increasingly valuable skill set. Follow Sneha Konnur for more insights

For the first time in over 3 years, I'm putting words to page and publishing Python tutorials again. During this time, I've been quietly obsessing over algo/quant trading. Not for the lambos and champagne showers (though I wouldn't say no), but because it's one of the hardest programming challenges I've ever tackled. Markets are essentially chaos engines designed to separate you from your money while making you question your intelligence, sanity, and life choices. In other words: pure fun. This new project has helped me rediscover that childlike wonder I had when I first stumbled into computer vision at 20 years old. In the coming weeks, I'll be sharing a series of Python tutorials on trading fundamentals (no prior market/trading experience required): - How to download market data with Python - Plotting stock charts with matplotlib and mplfinance - Computing Simple Moving Averages (SMAs) for trading - Market stage detection - Implementing TradingView's Stochastic RSI indicator in Python - Fundamentals of position sizing - Understanding R-multiples and risk/reward analysis with Python No, these tutorials won't help you make money in the market — but they are the "Hello world" equivalent to help you get up and running. And these aren't just technical guides — they're me running to the internet like an excited kid going "LOOK WHAT I MADE!" and hoping you'll respond with "WHAAAAT?! This is nuts!" That pure, unfiltered joy of creation and sharing never gets old, and I'm feeling it stronger than ever with this new chapter. Maybe you're a programmer curious about finance, or an investor looking to automate analysis. Or maybe you're just someone who understands what it means to find yourself again through your craft. Either way, welcome. This isn't just about Python or trading — it's about rediscovering our purpose through the work that makes us come alive. If this resonates with you, I'd be honored to have you along for this journey: https://lnkd.in/gp8YSkHi #Python #FinTech #Trading #Markets

Machine learning steps for quant finance. The most important part of creating a predictive investment strategy is setting up the data properly. In this thread, I'll share 3 months of research on developing predictive ML models in finance. 1. Investment Universe: The first step is collecting data on the universe of assets that you wish to analyze. With Python, we can freely access many assets and quickly get OHLCV data: - yfinance - openbb Next, clean the data. - Remove tickers with limited history - Remove tickers with limited volume (less liquid) 2. Add Alpha Factors: The next step is to add factors that may help predict price movements. Factors are just columns. With Python, we can add factors from these TA-Lib functions: - RSI - ATR - MACD - BBANDS 3. Price Trend Factors: Next, we can capture the price trend by adding lagged returns. Common lags for daily prices are: - 1: price last day - 5: price last week - 10: price 2 weeks ago - 21: price 4 weeks ago 4. Seasonal Factors: We can capture seasonality from the timestamp. In pandas, we can use methods like: - year - month Or, from pytimetk, we can get 30+ features with: - augment_timeseries_signature 5. Target forward returns: Next, make lookahead prices. This is what we predict. We can add multiple "lookahead" periods to see if the machine learning model does the best: - 1: Predict price 1 day ahead - 5: Predict price 1 week ahead - 10: Predict price 2 week - 21: 1 month Now you have a process for Machine Learning with Financial Data. Want to go further? We’re tackling the top 5 mistakes that beginner algorithmic traders make in this free 5-day course. 👉Join 150 students learning for free: https://lnkd.in/e_P9_-6M

Last week, I had fun playing with the old yet very popular Monte Carlo simulation. I explored building a Python app for portfolio optimization using this technique. Monte Carlo simulations are fantastic for modeling uncertainty and exploring a range of possible investment outcomes, rather than relying on a single prediction. I explored how these simulations can enhance portfolio optimization by accounting for risk and variability. By simulating thousands of potential future scenarios, we can better understand the trade-off between risk and return, and make more informed investment decisions. One of the fascinating aspects was implementing the mathematical foundations, like Geometric Brownian Motion, to model asset prices. This approach helps in assessing the probability of different outcomes in financial markets, which are inherently unpredictable due to numerous random variables. If you're interested in how Monte Carlo simulations can be applied in finance, especially for portfolio optimization, feel free to check out my latest article. I delve into the math behind it and share how I built the Python app: https://lnkd.in/egU6Tf8S You can access the app here: https://lnkd.in/euu5mpQC #Finance #MonteCarloSimulation #PortfolioOptimization #Python #Math

📊 Project Highlight: Financial Forecasting with Machine Learning Regression Models During one of my data science projects, I explored how machine learning can improve financial forecasting by predicting target sales based on various economic indicators such as GDP growth, inflation rate, unemployment rate, and market trends. This project focused on understanding the relationships between macroeconomic variables and business performance using advanced regression-based approaches. 🔍 Project Overview I began by performing extensive data preprocessing using Python libraries such as pandas, NumPy, and scikit-learn. Missing values were imputed with KNNImputer, numerical features were scaled using StandardScaler, and categorical data was encoded with LabelEncoder. To understand the dataset, I performed Exploratory Data Analysis (EDA): - Generated distribution plots for major financial indicators. - Built a correlation heatmap to visualize interdependencies among features. - Conducted descriptive statistics to gain insights into market patterns. 🤖 Machine Learning Models I implemented and compared two robust regression models: - Random Forest Regressor - optimized for accuracy and interpretability, allowing me to identify which features most strongly influenced target sales. - Gradient Boosting Regressor – fine-tuned for better predictive performance using an adjusted learning rate and multiple estimators. - Both models were evaluated using metrics such as Mean Squared Error (MSE), R² Score, Explained Variance, and Max Error. - I also created visualizations comparing predicted vs actual sales to assess the models’ accuracy and interpretability. 📈 Key Insights & Outcomes * The machine learning models demonstrated that macroeconomic factors significantly influence sales trends, especially in volatile market conditions. * Through feature importance and regression outcomes, I was able to pinpoint economic indicators that consistently contributed to accurate financial forecasting. 🧠 Skills & Tools Gained - Data Cleaning and Preprocessing (KNNImputer, Label Encoding, Scaling) - Exploratory Data Analysis (pandas, seaborn, matplotlib) - Machine Learning Regression (Random Forest, Gradient Boosting) - Model Evaluation and Visualization - Financial Data Analytics and Forecasting 💬 This project strengthened my ability to combine financial domain knowledge with machine learning techniques — helping transform data into actionable insights that support better business decisions. I’m currently seeking opportunities in data science, business analytics, and financial analytics where I can continue applying these techniques to solve real-world challenges. #MachineLearning #FinancialForecasting #DataScience #Python #RegressionModels #RandomForest #GradientBoosting #EDA #FinanceData #PredictiveAnalytics #BusinessIntelligence #JobSearch #DataAnalytics