Background
A financial client engaged Coaldev to develop a fully automated trading platform for the U.S. stock market. The goal was to build a system that could independently select, buy, and sell stocks based on data-driven fundamentals and technical indicators.
The solution required building an intelligent trading engine capable of running both fundamental and technical analyses on major indices, including the S&P 500, to identify strong stocks and determine optimal entry and exit points.
Challenges
Developing an autonomous trading engine required orchestrating complex datasets, diverse market indicators, and precise execution logic within one reliable system. The platform had to merge long-term fundamentals with technical strategies while maintaining accuracy during real-time market fluctuations. Below are the primary challenges Coaldev solved while building this AI-driven trading environment.
Managing diverse financial datasets with variable market availability.
Combining fundamental and technical trading strategies effectively.
Automating buy/sell signals while ensuring accuracy and reliability.
Conducting backtesting over multiple years for statistical validation.
Solution
Coaldev developed a trading pipeline that utilized fundamental filters to isolate top-performing companies, and then applied technical indicators, such as Dow Theory, Fibonacci retracements, Moving Averages, and Trend Lines, to guide trading decisions.
Coaldev developed an intelligent, fully automated trading system that integrated AI-driven decision-making with real-time market data.
Key solution components included:
1. Hybrid Trading Strategy
Combined fundamental filters to identify high-performing companies and technical indicators such as Dow Theory, Fibonacci retracements, Moving Averages, and Trend Lines for trade execution.
2. Backtesting Framework
Implemented long-term simulation capabilities to evaluate performance across historical market conditions spanning several years.
3. AI Model Integration
Leveraged predictive algorithms to optimize risk-reward ratios and adapt dynamically to market volatility.
4. API Orchestration
Connected with Polygon.io and brokerage APIs for live data ingestion, trade execution, and account management.
5. System Monitoring
Integrated automated alerts and dashboards to track system performance, trade logs, and market events in real time.
The platform’s modular architecture enabled rapid adaptation to multiple asset classes, including cryptocurrency and foreign exchange (forex) markets.
Results
The system demonstrated outstanding performance in backtesting, achieving a 100% return over 3.5 years and successfully doubling the simulated investment value within a four-year timeframe.
Key results
Automated buy/sell decision-making based on multi-layered indicators.
Fully integrated data pipeline for market analysis and model training.
High-accuracy performance in predictive and reactive trading scenarios.
Modular system design enables rapid adaptation for cryptocurrency and forex.
Technology Stack
- Frontend: JavaScript
- Data Crunching: Python
- API: GoLang
- Market Data API: Polygon.io
