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Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (July 31, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 822 pages
ISBN-10 ‏ : ‎ 1839217715
ISBN-13 ‏ : ‎ 978-1839217715
Item Weight ‏ : ‎ 3.1 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 1.69 inches

In today’s fast-paced financial markets, having a competitive edge can make all the difference in the world of algorithmic trading. Machine learning has revolutionized the way traders analyze data and make decisions, allowing them to extract valuable signals from market and alternative data to inform their trading strategies.

Python, with its powerful libraries such as scikit-learn and TensorFlow, has become a popular choice for building predictive models in algorithmic trading. By leveraging machine learning techniques, traders can uncover patterns in vast amounts of data that humans alone would struggle to identify.

In this post, we will explore how machine learning can be used to develop systematic trading strategies that are driven by predictive models. These models can analyze historical market data, as well as alternative data sources such as social media sentiment and news articles, to generate signals that inform trading decisions.

Some common machine learning algorithms used in algorithmic trading include linear regression, decision trees, random forests, and neural networks. These algorithms can be trained on historical data to identify patterns and relationships that can be used to predict future price movements.

By combining machine learning with algorithmic trading, traders can create more sophisticated and data-driven strategies that can adapt to changing market conditions in real-time. This can lead to more consistent profits and a competitive edge in the crowded world of trading.

Overall, machine learning has the potential to revolutionize algorithmic trading by allowing traders to extract valuable signals from market and alternative data. By leveraging the power of Python and machine learning algorithms, traders can develop systematic trading strategies that are driven by data-driven insights and predictive models.
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