https://github.com/amirreza81/financial_data_analysis_practice

First practice of Data Science and analyzing OHLCV data
https://github.com/amirreza81/financial_data_analysis_practice

anomaly-detection autocorrelation cointegration data-science ecm eda error-correction error-correction-models log-return ohlcv ohlcv-data resampling volatility

Last synced: 3 months ago
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First practice of Data Science and analyzing OHLCV data

• Host: GitHub
• URL: https://github.com/amirreza81/financial_data_analysis_practice
• Owner: Amirreza81
• License: mit
• Created: 2025-01-08T18:58:44.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-05T18:08:17.000Z (about 1 year ago)
• Last Synced: 2025-06-14T04:39:55.954Z (11 months ago)
• Topics: anomaly-detection, autocorrelation, cointegration, data-science, ecm, eda, error-correction, error-correction-models, log-return, ohlcv, ohlcv-data, resampling, volatility
• Language: Jupyter Notebook
• Homepage:
• Size: 6.73 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🚀 Financial Data Analysis Practice – Cryptocurrency Analysis  

🔍 **First Hands-On Practice in Data Science**  

This project explores financial data to analyze relationships between popular cryptocurrencies. It covers data collection, processing, and advanced statistical techniques to uncover insights into market behavior.  

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## 📌 Key Topics  

### 📊 **1. Data Collection, Processing & Resampling**  

- 🔹 **Data Collection:** Fetching OHLCV Candlestick Data  

  - Modular Python functions for API interactions  

  - Processing OHLCV data into a representative price series  

  - Extracting implied USDT-TMN price series  

- 🔹 **Resampling:**  

  - Selection of time scales  

  - Methodological approach  

- 🔹 **Handling Market Anomalies:**  

  - Missing data management  

  - Outlier detection and correction  

  - Data integrity assurance  

### 📈 **2. Exploratory Data Analysis (EDA)**  

- 📌 **Log Returns, Volatility & Normality Assessment:**  

  - Volatility estimation & clustering (EWMA)  

  - Statistical summaries  

  - Graphical & quantitative normality tests  

  - Importance of normality in financial models  

- 📌 **Autocorrelation & Stationarity Analysis:**  

  - ACF & PACF plots  

  - Stationarity testing  

  - Non-stationarity & autocorrelation interplay  

- 📌 **Inter-Market Analysis:**  

  - Synchronous & lagged correlations  

  - Strategic application  

### 🔗 **3. Cointegration Analysis**  

- ✅ Cointegration testing methodology  

- ✅ Dynamic analysis of cointegration parameters  

### 📉 **4. Error Correction Model (ECM)**  

- 🔄 ECM development  

- 📊 Analysis of reversion dynamics  

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📌 **Why This Matters?**  

Understanding market trends and price relationships is crucial for developing trading strategies and risk management in the crypto space. This project provides a structured approach to analyzing cryptocurrency data using statistical and econometric methods.  

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⚠️ **Note:** This project is my first experience in data science, and I acknowledge that it may have various shortcomings. I warmly welcome any collaboration, feedback, and suggestions to improve it. Your insights would be greatly appreciated! Also, if you need datasets, you can contact [me](amirrezaazari1381@gmail.com)