Spaces:
Sleeping
Sleeping
| # Technical Specification: Optimizing Stock Trading Strategy | |
| ## Architectural Overview | |
| **Optimizing Stock Trading Strategy With Reinforcement Learning** is a predictive modeling study designed to demonstrate the application of Q-Learning in optimizing trading decisions. The project serves as a digital exploration into machine learning heuristics for financial markets, established during a Data Science internship program at Technocolabs Software. | |
| ### Analytics Pipeline | |
| ```mermaid | |
| graph TD | |
| Start["Stock Data (CSV)"] --> Load["Data Ingestion (Pandas)"] | |
| Load --> Feature["Feature Engineering (Moving Averages)"] | |
| Feature --> Agent["Q-Learning Agent"] | |
| Agent --> State["State Definition (MA Crossover + Trend)"] | |
| State --> Action["Action Selection (Buy/Sell/Hold)"] | |
| Action --> Portfolio["Portfolio Update"] | |
| Portfolio --> Visualize["Streamlit Visualization"] | |
| ``` | |
| --- | |
| ## Technical Implementations | |
| ### 1. Modeling Architecture | |
| - **Core**: Built on **NumPy** and **Pandas**, utilizing custom Q-Learning logic for decision making. | |
| - **Estimation Logic**: Establishing a relationship between market states (Moving Averages) and optimal actions to maximize portfolio value. | |
| ### 2. Evaluation & Validation | |
| - **Metrics**: Evaluates performance based on net worth accumulation over a 5-year period compared to a buy-and-hold strategy. | |
| - **Reproducibility**: Utilizes historical stock data to promote consistent testing environments. | |
| - **Heuristics**: Scalable decision logic encapsulated in a python script to process real-time simulation. | |
| ### 3. Developmental Infrastructure | |
| - **Notebook Runtime**: The primary research was conducted in **Jupyter Notebook**, exploring state representation and reward functions. | |
| - **Source Production**: The analytical kernel is deployed via a **Streamlit App**, bridging the gap between statistical modeling and end-user interactive application. | |
| --- | |
| ## Technical Prerequisites | |
| - **Runtime**: Python 3.7+ environment (Local or Cloud-based). | |
| - **Dependencies**: `pandas`, `numpy`, `streamlit`, and `plotly` libraries. | |
| --- | |
| *Technical Specification | Data Science | Version 1.0* | |