You're expanding your technical analysis toolkit with machine learning. How do you make them work together?
Combining ML with TA allows you to uncover deeper insights and patterns in market data, offering a competitive edge. Here's how you can effectively integrate them:
How have you integrated ML with your trading strategies? Share your experiences.
You're expanding your technical analysis toolkit with machine learning. How do you make them work together?
Combining ML with TA allows you to uncover deeper insights and patterns in market data, offering a competitive edge. Here's how you can effectively integrate them:
How have you integrated ML with your trading strategies? Share your experiences.
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Machine Learning (ML) has revolutionized various industries, and the financial sector is no exception. By leveraging the power of ML algorithms, traders can gain a competitive edge and enhance their trading strategies. Here's how ML can be integrated into trading: Sentiment Analysis: By analyzing news articles, social media posts, and other textual data, ML can gauge market sentiment and its potential impact on asset prices. Event-Driven Trading: ML algorithms can identify and react to significant events, such as earnings announcements, economic reports, or geopolitical news, to capitalize on market volatility.
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I'm using python combined with tensorflow to make sure that what I do is actually doable for ML. Backtesting is one of the most important things to make sure your idea can be turned into an algorithm. Most of the times ideas die out because the idea seemed more attractive then it actually is.