Python for Machine Learning in Finance

Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.

No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.

Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community

Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.

There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.

We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.

Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.

We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.

Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.

No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.

Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.

Machine learning has various applications in our day to day life. The task can be as simple as recognizing human handwriting or as complex as self-driving cars.

For example, one of the common tasks of machine learning is face recognition. Facebook uses face and image detection to tag the person’s face with their name automatically.

Another typical example is product recommendation. When you search for an item on Google, you start getting similar product recommendations on various websites.

Other popular applications that everyone uses in their daily lives are speech recognition or speech to text recognition.

Due to the ability to solve complex mathematical problems, machine learning is widely used in the financial industry. It helps in predicting the market trend by going through a large set of datasets. The application ranges from portfolio management to fraud detection in financial institutions.

Broadly, there are three types of machine learning algorithms: Supervised, unsupervised and reinforcement learning.

In supervised learning, the expected outcome is clearly defined. Each training data consists of input objects and the desired output or target value. The main task is to produce a function that will map input values to the output value. This way, the machine learns to predict an output value with any new input value.

The supervised learning problems can be further divided into classification and regression problems.

• When the output is in classes such as buy or sell, it is a classification problem.
• Whereas, if the output is continuous, such as predicting the price of a stock, it is called a regression problem.

Unsupervised learning algorithms consist of input datasets without labelled responses. Hence the algorithm is forced to learn the correct way to produce an output in an unsupervised manner. One of the tasks of unsupervised learning is clustering. The goal of clustering is to find
similarities in the training data and cluster similar data points in one group.

In reinforcement learning, a model or agent makes a sequence of decisions in a complex environment to achieve a particular goal. The agent decides on the action and compares the outcome against a predefined reward system. The goal is to take action in such a manner to maximise the rewards.

The use of machine learning algorithms in finance depends on the task at hand. For example, if you want to predict the market trend, whether the price of a security will go up or down the next day, you can use classification algorithms.

Examples of classification algorithms are

• Logistic Regression,
• KNN classification,
• Support Vector Machine, and
• Artificial Neural Networks.

However, if the task is to predict the security price the next day, you can use regression algorithms such as:

• Linear regression,
• Regression tree, and
• Random Forest.

The task of machine learning is not only limited to security price prediction. More complex algorithms like deep neural networks are also used in finance.

Yes, Python is widely used in finance and trading. Python is a free, open-source platform that has a rich library for almost every task imaginable. And is suitable for medium to low-frequency trading that is trading on a time scale of minutes and above.

Python contains powerful libraries that help in achieving most of the financial tasks.
- Pandas is an open-source library used for data analysis and manipulation.
- Numpy and SciPy are two powerful tools for scientific and technical computing. It contains modules for statistics, linear algebra, and calculus.

Plotting and representation are crucial parts of financial modelling. Matplotlib library helps to generate plots, histograms, bar charts, error charts, scatter plots, etc.

The direction of the trend can be predicted using a classification model. For example, the decision tree models can be used to predict the direction of price movement for the next day.

Similarly, other models like hierarchical clustering and Support Vector Machines can be used to solve problems where a classification output is required.

The price of an asset can be predicted using a regression model. For example, the linear regression models can be used to predict the open price of an asset for the next trading day.

Similarly, the close, the high or the low price of the asset for the next day can be predicted using regression models.

The machine learning models are not specific to the particular asset taught in Quantra courses. The machine learning models can be applied to different assets of your choice.

But it must be noted that the price patterns of various assets are different, and they may give good returns for different types of machine learning models. There is no single model that can capture the trend of all possible assets.

All the code illustration on Quantra is done using the Jupyter notebooks. You can run most of the machine learning models right on the Quantra platform. You also exercise the code snippets in online interactive exercises.

Some of the heavier machine models, like neural networks and reinforcement learning models, can not run on Quantra because of memory limitations.

But don’t worry! All the code is available in a downloadable format and can be run on your local machine using Anaconda’s Jupyter notebook. You can set up your local environment by following the steps outlined in this article.

The quantitative principles learnt from Quantra are not specific to any broker and can be applied across different broker platforms. To make things simpler, we provide Blueshift or IBridgePy integration as they cover some of the most popular brokers for traders across the globe.

If you prefer to use your own broker, you can use the learnings from the Quantra courses and connect to your own broker’s API to perform the trades.