docs/SPECIFICATION.md

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

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"]

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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.

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Technical Prerequisites

• Runtime: Python 3.7+ environment (Local or Cloud-based).
• Dependencies: pandas, numpy, streamlit, and plotly libraries.

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Technical Specification | Data Science | Version 1.0