Document Type
Event
Start Date
23-4-2023 5:00 PM
Description
AI for quantitative trading involves using machine learning and other AI techniques to analyze financial data and make informed trading decisions. By automating the trading process and leveraging the power of AI, traders can potentially improve their performance and generate better returns. The goal is to identify and capitalize on available trading opportunities by using computer algorithms and programs based on simple or complex mathematical models. Stock market indicators are statistical measures that provide insights into the behavior of the stock market as a whole or of individual stocks. These indicators are used by investors, traders, and analysts to evaluate market trends and conditions, predict future price movements, and make informed investment decisions. After establishing an understanding of these indicators and performance metrics, one can understand the process of developing a trading simulator, strategy optimizer, and financial machine-learning pipeline. Multiple trading strategies are implemented systematically and adapted to real-time market conditions using machine learning. Stock movement is predicted by supervised models to demonstrate how machine learning can be applied to quantitative trading. In addition, multiple optimization techniques are used in trading to maximize returns and minimize risk. Several machine learning techniques and methodologies like feature selection are used to find accurate prediction.
GC-340 AI for Quantitative Trading
AI for quantitative trading involves using machine learning and other AI techniques to analyze financial data and make informed trading decisions. By automating the trading process and leveraging the power of AI, traders can potentially improve their performance and generate better returns. The goal is to identify and capitalize on available trading opportunities by using computer algorithms and programs based on simple or complex mathematical models. Stock market indicators are statistical measures that provide insights into the behavior of the stock market as a whole or of individual stocks. These indicators are used by investors, traders, and analysts to evaluate market trends and conditions, predict future price movements, and make informed investment decisions. After establishing an understanding of these indicators and performance metrics, one can understand the process of developing a trading simulator, strategy optimizer, and financial machine-learning pipeline. Multiple trading strategies are implemented systematically and adapted to real-time market conditions using machine learning. Stock movement is predicted by supervised models to demonstrate how machine learning can be applied to quantitative trading. In addition, multiple optimization techniques are used in trading to maximize returns and minimize risk. Several machine learning techniques and methodologies like feature selection are used to find accurate prediction.