🧠 10 Academy: AI Mastery - Week 1 Challenge

Project Structure

Week1_challenges/
├──githubWorkflow/
    ├──unittests.yml
├── .vscode
    ├──settings.json
├──Data/
    ├──raw data analysis rating.csv      #Folder for datasets
    ├──yfinance_data
├── notebooks/     # Jupyter notebooks
    ├── __init__.py
    ├──EDA for raw_analysis
    ├──quantitaive.ipynb
    ├──financial_analysis.ipynb
    ├──README.md
├──Script    #python scripts
    ├── __init__.py
    ├──data_loader.py
    ├──financial_analysis.py
├── src/                    # Main Python package
│   ├── __init__.py       # test
│   ├── evaluate.py     # Functions for accuracy,
├── tests/                  # Unit tests
│   └── run_tests.py
├── README.md               # Project overview
└── requirements.txt        # Dependencies

Getting Started

Prerequisites

• Python 3.8 or higher
• Libraries: Install dependencies from requirements.txt using:

  pip install -r requirements.txt

📈 Predicting Price Moves with News Sentiment

Analyze how financial news influences stock price movements using sentiment analysis, technical indicators, and statistical correlations.

📅 Duration: 28 May – 03 June 2025

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🚀 Challenge Overview

This project explores how news sentiment affects stock performance using the Financial News and Stock Price Integration Dataset (FNSPID). It combines skills from data engineering, financial analytics, and machine learning to extract actionable insights.

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🎯 Objectives

• To Perform sentiment analysis on financial headlines using NLP.
• To Analyze correlation between sentiment and stock price movements.
• To understand market trend for future prediction and recommendation

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📊 Key Tasks

✅ Task 1: Git, GitHub & EDA

• Git/GitHub with branches and commits
• Perform EDA on headline text and publication patterns
• Perform descriptive statistics and technical analysis

✅ Task 2: Technical Analysis

• Use TA-Lib & PyNance for financial indicators
• Visualize trends and metrics

✅ Task 3: Sentiment vs Stock Movement

• Analyze and align datasets by date
• Quantify sentiment with tools like TextBlob
• Compute stock returns and correlation scores

📰 Financial News Analysis

Analyze financial news headlines

✨ Features

🗞️ Financial News Analysis

• Load and preview financial news headlines
• Compute headline length stats (mean, median, min, max)
• Analyze article publication trends by date and day of the week
• Perform sentiment analysis using TextBlob
• Visualizations include:
• Sentiment distribution
• Article frequency by time
• Headline length histograms

📊 Stock Market Analysis

• Load historical stock price data using yfinance

• Preprocessing and cleaning of missing values

• Compute daily stats: mean, std, min, max, quartiles

• Generating of advanced visualizations with Matplotlib and Plotly

Apply technical indicators using TA-Lib:

• RSI, MACD, Bollinger Bands, etc.

• Integrate sentiment scores for enriched insights

🧰 Dependencies

Install the required packages:

• bash Copy Edit pip install pandas numpy matplotlib seaborn textblob plotly yfinance Optional (for technical indicators) bash Copy Edit
• Requires system-specific setup Refer to the TA-Lib installation guide for your OS pip install TA-Lib
• read the requirement.txt text

📂 Dataset Descriptions

• News Headlines Dataset Location: ../Data/raw/raw_analyst_ratings.csv Columns:

• Stock Market Data Fetched via: yfinance Columns: Open, High, Low, Close, Adj Close, Volume

▶️ Usage bash Copy Edit

1. Clone the repository git clone https://github.com/nanecha/
2. Install dependencies pip install -r requirements.txt
3. Launch Jupyter Lab or Notebook

🧠 Learning Outcomes

• Reproducible Python data science workflows
• Time series & NLP analysis
• Technical indicators: MA, RSI, MACD
• Correlation studies between sentiment and returns

🔗 Resources

• TA-Lib
• PyNance
• TextBlob
• Git Docs

📬 Contact

nanechakebede@gmail.com