Hi, I’m Tianru (Tiro) Peng, an Associate Software Engineer candidate with a strong passion for software development and problem-solving. With an MSc in Applied Computer Science and a background in building complex commercial code, I have experience in Python, SQL, and JavaScript. I enjoy designing scalable, secure, and efficient cloud software systems and am continuously expanding my skills in C, .NET, and React.
I thrive in fast-paced environments where I can apply my analytical skills and work collaboratively to deliver quality code. My work is driven by a commitment to learning and contributing to impactful projects, especially in cloud-based radiology software systems. I look forward to growing my expertise and making a positive impact in software engineering.
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DeepSeek AI Option Strategy Generator, a sophisticated, fully autonomous system for quantitative options trading. It leverages the DeepSeek-V3 large language model to analyze market conditions and generate, evaluate, and deploy trading strategies without human intervention.
Core Capabilities 💡
The platform operates through a three-stage, automated workflow:
Autonomous Strategy Generation: It uses DeepSeek-V3 AI to create a variety of options strategies, from simple bull/bear spreads to complex multi-leg positions like iron condors. A genetic algorithm continuously evolves and refines these strategies, adapting them in real-time to changes in market volatility and sentiment.
Advanced Strategy Evaluation: Before any strategy is deployed, it undergoes rigorous testing. This includes comprehensive backtesting with realistic costs, evaluation against multiple metrics (e.g., Sharpe ratio, max drawdown), and Monte Carlo simulations to test for robustness.
Intelligent Deployment System: The system’s AI selects the top-performing, risk-adjusted strategies for live trading. It features one-click deployment, real-time performance monitoring, and automatically rotates strategies based on changing market conditions to maintain optimal performance.
Technical Architecture & Performance 🛠️
The system is built on a modern, scalable technical stack designed for high performance and reliability.
AI & Data Stack: The core AI logic uses the DeepSeek API, TensorFlow, and Scikit-learn. Data processing is handled efficiently with Pandas, NumPy, and Asyncio, with Redis for caching.
Scalability: It’s designed as a microservices architecture using Docker and Kubernetes, ensuring it can scale on demand. RabbitMQ manages communication between services.
Performance Metrics: The platform claims impressive results, including generating 3-5 strategies per symbol daily, a 68% win rate, average annualized returns of 248%, and a maximum drawdown kept below 15%.
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