Financial-Forecasting-Platform

Project Overview

This repository contains an integrated platform designed to explore the application of Markov Chains for predicting directional movements in financial markets. It's more than a single model; it's a complete workflow that covers the entire data science lifecycle, from raw data acquisition to advanced performance analytics.

The core hypothesis is to test whether historical price action sequences can provide a statistical edge in forecasting the next period's direction (UP or DOWN). The system employs a two-layer approach:

Primary Forecast: A configurable Markov Chain model generates the initial directional forecast based on historical transition probabilities.

Confidence Validation: A Random Forest classifier, trained on the historical performance of the primary model, acts as a "validator" to score the confidence of each new forecast, providing a deeper layer of analysis.

Note: This project is presented as a completed, "frozen" piece of work. The data is not updated, and the goal for users is to run the pipeline to understand the methodology and reproduce the results.

Key Features

Automated Data Pipelines: From API fetching to cleaning and enrichment, the data workflow is streamlined and automated.

Multi-Asset Support: Easily configurable to handle Forex, Commodities, and Crypto assets.

Two-Layer Forecasting System: Combines a primary Markov model with a secondary Machine Learning validator for enhanced signal analysis.

Advanced Performance Analytics: Generates detailed reports including directional accuracy, day-of-week performance, and intraday streak analysis.

Dynamic & Flexible: Most tools are configuration-driven, allowing for easy experimentation and analysis of different assets and timeframes.