Finance isn’t a subject commonly taught thoroughly in schools, yet it plays a crucial role in everyone’s lives. Furthermore, professional trading tools are often inaccessible or too complex for beginners. We created traider to bridge this gap, providing a risk-free environment that combines high-performance C++ analytics with AI-driven insights to help users master the markets.
traider is a next-generation educational platform that leverages a hybrid C++/Python architecture to deliver professional-grade trading simulations:
- 🚀 High-Performance C++ Core – Backtesting and technical analysis executed with bare-metal speed.
- 📊 Real-Time Simulation – Trade using historical & current data with millisecond-latency processing.
- 🤖 AI-Powered Feedback – Instantaneous trade analysis using Llama 70B on NVIDIA Cloud Compute.
- 📈 Advanced Technical Indicators – Real-time calculation of SMA, EMA, RSI, VWAP, and Bollinger Bands using our custom C++ engine.
- 📰 Market Sentiment Analysis – AI synthesis of financial news to inform trading decisions.
- 🏆 Gamified Learning – Compete on leaderboards and track portfolio performance metrics like Sharpe Ratio and Max Drawdown.
traider differentiates itself by running its heavy-lifting simulation logic in C++, ensuring accuracy and scalability, while using Python and AI for high-level reasoning and user interaction.
The heart of traider is a high-performance C++ extension (traider_cpp) exposed to Python via Pybind11. This layer handles all compute-intensive tasks:
- Backtesting Engine: Simulates trading strategies over historical data with O(n) efficiency.
- Technical Indicators: Optimized implementations of SMA, EMA, RSI, VWAP, and Bollinger Bands.
- Data Processing: Fast normalization and manipulation of OHLCV (Open, High, Low, Close, Volume) market data.
- Portfolio Analytics: Real-time calculation of risk metrics like Sharpe Ratio, variance, and returns.
Our Python backend acts as the orchestration layer, integrating the C++ engine with modern AI capabilities:
- FastAPI – High-performance API server bridging the frontend and the C++ core.
- NVIDIA Cloud Compute – Hosting Llama 3.3 70B for deep semantic analysis of market news.
- Google Search API – Real-time financial news scraping.
- Yahoo Finance API – Source for raw historical market data.
- Next.js – React framework for a responsive, interactive dashboard.
- Tailwind CSS & ShadCN – Modern, clean UI components.
- Recharts – Visualizing the high-frequency data streams from our backend.
- Convex & Clerk – Real-time database and secure authentication.
- C++/Python Integration: Developing a seamless interface between the C++ simulation engine and the Python backend using Pybind11.
- Memory Management: Ensuring zero-copy data transfer where possible to maintain high performance.
- Cross-Platform Compilation: configuring the build system (
setup.py/CMake) to work reliably across different environments. - AI Hallucination Control: Fine-tuning prompts for the Llama 70B model to ensure financial advice remained grounded in data.
- Real-Time Data Sync: coordinating the C++ calculation pipeline with live frontend updates.
- Built a hybrid execution environment where C++ handles the math and Python handles the logic.
- Achieved significant performance gains in backtesting speed compared to pure Python implementations.
- Successfully integrated NVIDIA's Llama 70B for context-aware financial commentary.
- Designed a professional-grade dashboard that abstracts away the complexity of the underlying C++ engine.
- Created a robust educational tool that offers both quantitative rigor and qualitative insights.
- Systems Programming: The importance of memory safety and type strictness when building financial engines.
- Foreign Function Interfaces (FFI): How to effectively bridge high-level and low-level languages.
- Financial Engineering: Deepened our knowledge of technical analysis algorithms and portfolio theory.
- Scalable Architecture: Designing a system that leverages the best tools for each specific job (C++ for speed, AI for reasoning).
