Authors : Lawrence Chidiebere Nwodo; John Otozi Ugah; Stella Ebere Edeh; Maduabuchi Ignatius Edeh

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/379ejxbn

Scribd : https://tinyurl.com/3nxjswd8

DOI : https://doi.org/10.38124/ijisrt/26aug236

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The project developed and optimized an AI‑driven autonomous trading bot tailored to the unique dynamics of cryptocurrency markets. The system would continuously in`gest live market feeds such as priceticks, order book snapshots, and volume data alongside historical records and optional sentiment indicators from news and social media. A deep reinforcement learning core would process this multi‑source input to generate real-time trade signals, dynamically balancing profit maximization with risk controls such as adaptive stop loss and take profit thresholds. Orders would be executed automatically via exchange APIs, with execution latency minimized to capture fleeting market opportunities. An object‑oriented design would structure the system around key entities like theTrader, TradingBot Controller, MarketData Module, RiskManager, OrderExecutor, PortfolioManager, and NotificationService modeled using UML class, sequence, and flow diagrams to ensure clarity, modularity, and ease of future extension. Implementation would employ a modern technology stack: a React.js and Tailwind CSS front end for trader configuration and performance dashboards; a Python Flask back-end hosting TensorFlow and Scikit‑Learn models; PostgreSQL for time‑series and relational data; Docker containers for reproducible development; Git for version control; and AWS for scalable deployment of microservices and data storage. Comprehensive testing unit, integration, and load would validate system robustness, speed, and security. Upon deployment, the bot is expected to deliver measurable improvements over traditional rule based strategies: higher risk‑adjusted returns through precision trade entries, a reduction in draw downs via intelligent risk management, and sub second execution to exploit high frequency opportunities. The intuitive dashboard and automated notifications would empower both retail and institutional traders with transparent, actionable insights.

Keywords : Automated Trading and Trading Bots, Artificial Intelligence and Mechine Learning in Trading, Data Source and Market Signals.

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