eseto2/Automated-FX-Market-Forecasting-System-MVP-

This is an automated MVP that ingests daily secondary-market FX exchange rate data for emerging African economies using scheduled data pipelines. # Automated-FX-Market-Forecasting-System-MVP- This is an automated MVP that ingests daily secondary-market FX exchange rate data for emerging African economies using scheduled data pipelines. # FX Rate Prediction & Analysis MVP ## Project Overview This project is a Minimum Viable Product (MVP) designed to analyze, track, and predict secondary market FX rates for three key African currency pairs against the US Dollar: - **USD/NGN** (Nigerian Naira) - **USD/KES** (Kenyan Shilling) - **USD/UGX** (Ugandan Shilling) The system leverages historical data to uncover patterns and uses Machine Learning algorithms to forecast future rates. ## Features 1. **Retrospective Analysis (The Past):** - Analyzes historical secondary market FX rates for the last 10 years. - Visualizes trends and patterns (Seasonality, Volatility) using Matplotlib and Seaborn. 2. **Real-Time Tracking (The Present):** - Fetches daily exchange rates via the Alpha Vantage API. - Tracks Open, High, Low, and Close positions. 3. **Predictive Modeling (The Future):** - Predicts future FX rate movements (Up/Down) for the next trading period. - Utilizes Machine Learning models including **Logistic Regression**, **Support Vector Machines (SVM)**, and **XGBoost**. ## Project Structure - `data/`: Contains historical CSV datasets for the currency pairs. - `notebooks/`: - `1_Data_Extraction.ipynb`: Scripts to fetch data from APIs. - `2_FX_Model_Training.ipynb`: Exploratory Data Analysis (EDA) and Model Training pipeline. ## Getting Started ### Prerequisites - Python 3.8+ - Alpha Vantage API Key (Free tier available) ### Installation 1. Clone the repository: ```bash git clone github.com 2. Install the required packages: Bash pip install -r requirements.txt Usage Open notebooks/1_Data_Extraction.ipynb and insert your API Key to fetch the latest data. Run notebooks/2_FX_Model_Training.ipynb to execute the analysis and view the prediction demos for USDNGN, USDKES, …