Options Trading Strategies In Python: Advanced

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.

Yes, you can deploy the backtested options trading strategies in the live market using IBridgePy API. A live trading strategy template will be provided in the course. The template uses IBridgePy to connect with Interactive Brokers. A subscription may be required for options data. You can tweak and optimise the strategy code, paper trade and finally deploy the strategies in the live market!

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.

Dispersion Trading is a mean-reverting strategy. To understand dispersion trading is to understand realized and implied volatility. As mentioned earlier, implied volatility is the market’s prediction of how much volatile an underlying asset will be in the future. Realized volatility is simply the actual volatility that takes place in the future. Now, whenever there is a difference between implied and realized volatility, dispersion trading can be executed by a trader. One can short index options and long individual stock options in this case. However, whenever the implied correlation in prices of stocks and index increases, the trader starts making losses.

Machine Learning in options trading can be used to find anomalies in the option prices. Take for example, in the case of a call and put option, for the same underlying asset and the same strike price, the put-call parity should hold true. However, in case it is not visible in option prices, supervised and unsupervised machine learning techniques like “Decision Tree Classifier” could be used for understanding the anomaly early for taking action to generate profits. Other uses of ML in options trading is for computing and predicting trading signals.

Exotic Options are Over the Counter(OTC) options, generally traded by Institutional Clients. These are generally different from vanilla options where the payoffs are simple and standard for all. Exotic Options involve structuring the option terms for tailoring the need of a particular transaction. Binary and Barrier Options are examples of exotic options where the options are structured and are used to hedge market risk by these market participants.

Risk is used synonymously for loss that can arise due to volatility in the financial markets. Risk management techniques in options trading include using strategies to mitigate the risk of losses in a trade. Using the option greeks, trading strategies are created for safeguarding the portfolio, so that in times of extreme losses, hedging strategy gains can help nullify the losses made earlier.