Course Summary | Python for Machine Learning in Finance | Free Quantra Course
Summary of the FREE course on Python for Machine Learning in Finance:
https://quantra.quantinsti.com/course...
Congratulations! You have completed the course on Python for Machine Learning in Finance. Let's summarise some of the key learnings from the course.
Machine learning is all about how algorithms improvise themselves with the use of data. It has many applications in finance like algorithmic trading, fraud detection and insurance underwriting. And today, Python is one of the most preferred languages for the applications of machine learning.
One such application was covered in the course where you predicted whether to buy J.P. Morgan’s stock or not. You can easily perform all the machine learning tasks using the Python libraries like pandas and scikit-learn.
These tasks involve reading or importing data, cleaning and preparing the data, feature engineering, training the ML model on train data, and evaluating the performance on the test data.
Before starting with these tasks, you need to define the problem statement and choose a suitable ML algorithm like regression or classification accordingly. Remember, a well-defined problem statement is a problem half-solved.
The target variable would be such that it would be an answer to this problem statement. The machine learning algorithm will be responsible for predicting the target variable. It will use the features to do the same. Each feature should have some predictive power and be uncorrelated to each other. Most of the ML models also require the features to be stationary.
You will then have to split the data into the train and test set. On the train data, the ML algorithm will learn the relationship between the target and the features. The relationship
or the model is validated on the test data, and then used for future prediction.
You can use the metrics like precision, recall, and f1-score to evaluate the performance of the model. The final model is then used to create the trading strategy.
You should backtest the strategy thoroughly on the historical data and analyse the strategy performance. Few metrics that you can use to analyse the performance are returns, volatility, drawdown and Sharpe ratio. This summarises the whole structure of an ML process that we have learnt here through a classification problem.
Refine the sample strategy discussed in this course, and then you can paper trade and live-trade the existing trading strategy. You can also use ML to solve different problems with due research and proper implementation of the desired algorithm, and create your own strategies.
If you want to dive deeper into different algorithms, explore the other courses available on the Quantra portal. You can download all the codes from the downloadable unit at the end of this course. We hope that you enjoyed learning throughout this course.
If you have any queries, please post them on the Quantra community page and feel free to answer others’ questions as well. Thank you.
Quantra is an online education portal that specializes in Algorithmic and Quantitative trading. Quantra offers various bite-sized, self-paced and interactive courses that are perfect for busy professionals, seeking implementable knowledge in this domain.
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