Financial Time Series Analysis for Trading

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.

What is a capstone project?

A capstone project is a multi-faceted hands-on project that lets you apply what you\'ve learned in an advanced course. This project is relevant to how the concepts are applied in real-world scenarios and helps to cement your understanding of the concepts.

Who will review my work?

Since the capstone project involves trading on real-world assets, the best measure for performance is the profitability and the performance metrics like the Sharpe ratio, drawdown, etc. There is no formal or peer review and you can refer to the model solution at the end of this section.

Will I get a solution for the above project?

We have provided a model solution to help you understand how to get things working. The input features or the hyperparameters can all be changed, added, or removed as per your need.

What to do if I\'m stuck?

If you get stuck, we encourage you to view the model solution and compare it with your solution to see what went wrong. If you still need help, feel free to reach out to us via the community forum and we will be happy to help you!

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.

As the name suggests, time series analysis works on time-based data (data collected at a certain frequency). Time series analysis identifies the serial correlation or autocorrelation and derives the hidden insights to make future predictions.

Algorithms like autoregression and moving average method assume that the statistical properties of the series, like mean and variance, remains constant across time. This property is called Stationarity. Most of the statistical tests rely on inferences drawn from a stationary series. However, in real practice, most of the data are non-stationary in nature. In such cases, algorithms like ARIMA are used. The time series models first convert the non-stationary data into stationary data before performing further analysis.

There are two types of time series data, regular time series and irregular time series.

Regular time series data are read at regular time intervals, like recording the temperature every hour, or average daily temperature. The data points are evenly spaced across the time span it is representing.

Irregular time series data are recorded at irregular time intervals, like withdrawal at an ATM. These data points are unevenly spaced across the time span it is representing.

These data are recorded against a timestamp and stored in chronological order. A new data point is always appended to the existing data.

The amount of data needed to forecast the future values would depend on the approach that you will use for modelling. Theoretically, the only limit is that the number of data points should be more than the number of parameters. However, in practice, you would require much more data points than that. Insufficient data might be misleading in actual practice. Forecasting results on insufficient data can ignore the underlying variability in the data and overlook the number of parameters to be estimated.

At the same time, you also need to be careful that the model might not perform very well on very old data. The difference between the actual values and values predicted by the model trained on large datasets might be very different. Thus a rolling window would be more appropriate than an expanding window if the recent lags (say, values upto lag t) have more predicting power than the older lag values (say, values from lag t+1).

Time series analysis is the most reliable method when it comes to analysing data recorded against time. It is useful in estimating trends and patterns in the data. Time series analysis is also very useful to measure the temporal effect.

While other methods like supervised learning techniques might fail if the data does not have enough variables, time series models forecast future values even with fewer variables/data.

The returns of an asset are volatile in nature. Time series models like ARCH and GARCH are very useful in predicting the volatility of a series.

Time series analysis assumes that the past behaviour of the data will continue indefinitely and using historical data to make predictions would lead to valid conclusions. It overlooks the external factors affecting the data.

Determining the appropriate model for the data is not very well defined.

Time series analysis is widely used to forecast the patterns in the financial markets. Time series analysis is also used to analyse how an asset or security changes over a period of time.

AR, MA, ARMA and ARIMA are some of the most popular time series models. However, each technique has its own shortcomings. As you move further, the complexity of the model increases. ARIMA is one of the most used algorithms in time series analysis/forecasting, however, it fails when the data has a seasonal component.

ARIMA stands for Autoregressive Integrated Moving Average model. Algorithms in time series analysis like autoregressive (AR) and moving averages (MA) assume the data to be stationary. However, in real life, almost all the data are non-stationary in nature. Hence the predictions made using these algorithms might not be accurate. What an ARIMA model does is, it converts the non-stationary series into a stationary series and then applies the AR and MA model. The parameter required to convert the non-stationary series into a stationary series is the I in ARIMA.