AI Trading Battles Reveal Model Performance

The Elo rating system, originally designed for chess, is increasingly used to benchmark AI models against each other rather than against static metrics. This system provides a dynamic ranking that adjusts as models "compete" in various tasks, offering a clearer view of their relative performance over time. DeepSeek's models, like the open-source DeepSeek Coder V2, often utilize a Mixture-of-Experts (MoE) architecture. This design activates only a fraction of the model's total parameters for any given task, which significantly enhances efficiency and reduces computational cost. Google's Gemini 2.5 Flash is built for speed and versatility, featuring a massive 1 million token context window and a "thinking budget" that allows developers to balance response time with reasoning depth. Unlike more specialized models, it has native multimodal capabilities, allowing it to process text, images, video, and audio inputs. The trend of lower-cost AI challenging premium models is disrupting pricing structures that traditionally involved high monthly subscriptions or performance-based fees. For instance, DeepSeek Coder V2 Lite is offered for free, making powerful AI tools more accessible for tasks like algorithmic trading development. The technical indicators used by Jaswanth's Algo are standard tools for swing traders aiming to capture short-to-medium term market moves. The Relative Strength Index (RSI) acts as a momentum oscillator, measuring the speed and change of price movements to identify potentially overbought (a reading over 70) or oversold (under 30) conditions. Moving Average Convergence Divergence (MACD) is used to gauge momentum by showing the relationship between two moving averages of a security's price. A "buy" signal can be generated when the MACD line crosses above its signal line, especially when confirmed by increasing trade volume, which suggests strength in the trend.