Can Stock Prices Be Predicted? Quant Analysis with Python, Pandas, NumPy & SciPy

## 💰 Stock Price Analysis with Python: Normality Test, Directional Bias, and Random Walk | Pandas & SciPy Dive deep into **quantitative finance** with Python! This comprehensive tutorial uses **pandas**, **NumPy**, **matplotlib**, and **SciPy** to perform advanced statistical analysis on stock price data. This video is a primer for using Python for quantitative stock analysis, tackling three core questions covered in a college-level investment analysis course: 1. Can the stock price change be described as a **Normally Distributed** phenomenon? 2. Is there a **Directional Bias** in the daily price change (positive or negative)? 3. Can the price movement be described as a **Random Walk**? While the goal is to get you comfortable using **Python** and **pandas** to analyze stocks, these concepts form the foundation of **Algorithmic Trading** and **Financial Modeling**. ✅ **Please SUBSCRIBE:** https://www.youtube.com/subscription_center?add_user=mjmacarty ➡️ **Download the full Jupyter Notebook here:** https://github.com/mjmacarty/alphavantage.git --- ### Video Time Stamps (Quantitative Stock Analysis): 0:00 - Introduction to the 3 Analytical Questions 1:10 - Data Acquisition from APIs (Yahoo Finance/pandas-datareader) 2:20 - Calculating **Instantaneous Rate of Return** (Log Difference) 6:45 - **Interpreting Skewness and Excess Kurtosis** 9:30 - Visual Comparison: Returns Histogram vs. **Theoretical Normal Distribution** 12:00 - **Statistical Test 1: Kurtosis Test for Normality (SciPy)** 16:40 - **Statistical Test 2: T-Test for Directional Bias (Mean = 0)** 19:40 - **Statistical Test 3: Random Walk Hypothesis (Linear Regression)** 24:45 - Visualizing the Actual vs. Predicted Price Movement --- ### Financial Modeling & Resources: * **Related Course:** Watch my other Stock Analysis videos: https://alphabench.com/data/pandas-rolling-tutorial.html * **Full Tutorial Link:** https://alphabench.com/data/pandas-quantitative-analysis-tutorial.html ***Disclaimer: This video is for educational purposes only. The information provided should not be construed as investment advice. *** \#Python \#QuantitativeFinance \#StockAnalysis \#FinancialModeling \#DataScience \#AlgorithmicTrading \#pandas \#NumPy \#SciPy \#RandomWalk