Algorithmic options trading bot using machine learning

Financial markets have witnessed a rapid transformation with the integration of automation and data-driven decision-making systems. Traditional trading approaches, which rely heavily on human intuition and manual execution, are often limited by emotional biases, delayed reaction times, and inconsistent strategies. In contrast, algorithmic trading systems enable systematic, rule-based, and high-speed execution of trades, significantly improving efficiency and consistency. However, most existing algorithmic trading systems either rely on static rule-based strategies or require complex infrastructure for real-time deployment, making them less accessible for academic exploration and small-scale implementation. This project presents the design and development of a multi-instrument algorithmic trading bot framework powered by machine learning, capable of generating intelligent trading signals across different financial instruments such as equity indices (NIFTY, BANKNIFTY) and commodities (Crude Oil, Natural Gas). The system focuses on building a scalable and modular architecture where a common machine learning decision engine is shared across multiple trading bots, each configured for a specific instrument. The proposed framework operates through a structured pipeline consisting of market data acquisition, feature engineering using technical indicators, predictive modeling, strategy mapping, and back testing. Historical market data is collected using financial data APIs and processed to extract meaningful features such as moving averages and momentum indicators like the Relative Strength Index (RSI). These features are used to train supervised machine learning models, specifically Logistic Regression, to predict short-term market direction. The predicted direction is then mapped to options trading strategies, generating actionable signals such as “Buy At-The-Money Call” or “Buy At-The-Money Put”. To evaluate the effectiveness of the proposed system, a paper trading and back testing module is implemented. This module simulates trading decisions over historical data without involving real capital, allowing performance assessment in a risk-free environment. The system calculates key performance metrics such as cumulative profit/loss, trade frequency, and directional accuracy. Experimental results indicate that even modest prediction accuracy can lead to profitable outcomes when combined with structured risk management and disciplined execution logic. A key contribution of this project is the development of a multi-bot architecture, where the same predictive model can be extended to multiple instruments with minimal modification. This demonstrates the scalability of the system and its potential application in real-world trading environments. Additionally, a lightweight dashboard interface is proposed using modern web technologies to visualize trading signals, bot performance, and system status, making the framework user-friendly and accessible. The project emphasizes modularity, simplicity, and reproducibility. It avoids dependency on complex trading infrastructure while still demonstrating the core principles of algorithmic trading and machine learning integration. The system is designed as a prototype for research and educational purposes, with future scope including real-time data integration, advanced machine learning models such as LSTM networks, broker API connectivity, and deployment as a fully automated trading platform.