• Study material – You get to learn all basics and advance material to learn share market trading • Videos – Our courses provide you ample video content to watch and study from the material provided by Market Experts’ • Experts’ Guidance – Get guidance from experts in share market...
01 Machine Learning for Trading: From Idea to Execution 02 Market & Fundamental Data: Sources and Techniques 03 Alternative Data for Finance: Categories and Use Cases 04 Financial Feature Engineering: How to research Alpha Factors 05 Portfolio Optimization and Performance Evaluation ...
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will
Pleasejoinour community and connect with fellow traders interested in leveraging ML for trading strategies, share your experience, and learn from each other! What's new in the 2ndEdition? First and foremost, thisbookdemonstrates how you can extract signals from a diverse set of data sources and...
Explore the intersection of AI and finance. Learn how machine learning algorithms can revolutionize stock market prediction, giving you a competitive edge in trading.
Pleasejoinour community and connect with fellow traders interested in leveraging ML for trading strategies, share your experience, and learn from each other! What's new in the 2ndEdition? First and foremost, thisbookdemonstrates how you can extract signals from a diverse set of data sources and...
The aim of this research paper is to create and analyse the machine learning methods for trading. The methods here help solve the most important problems, such as the sensitivity of a strategy performance to little parameter changes. For instance, a sudden shift in the market trend is the cha...
In this paper, we explore the potential of deep reinforcement learning in quantitative trading. A LSTM-based agent is proposed to learn the temporal pattern in data and automatically trades according to the current market condition and the historical data. The input to the agent is the raw ...
In the previous Reinforcement module I discover that because of the price fluctuation in daily data is not as high as day-trading, it will last a long period of bear of bull market, so the DQN agent will tend to choose buy at the start of bull market,and hold for a very long time....
In our experiment, we use trading data of Shanghai 50 ETF and Shanghai and Shenzhen 300 ETF (510300) to train the RL model. They are the only two ETFs that have exchange-traded option contracts in the A-share market. Figure 1 shows a close price trend of 50 ETF, from 26 January ...