Learning Track: Quantitative Trading in Futures and Options Markets

This covers the types of options based on different parameters like exercisability, tradability etc. It also explains the important concepts of moneyness and put-call parity.

Different types of volatility like historical, implied and realised volatility are covered in this section. It also includes the Python code and calculation for historical volatility.

This section explains different options trading strategies like bull call, bear spread, protective put, Iron Condor strategy, and covered call strategy along with the Python code. It also acquaints one with the concept of hedging in options.

Learn to install the Python environment in your local machine.

This section summarises the course and provides downloadable strategy codes.

Futures contract is an obligation to buy or sell a specific asset at a predetermined price, at a predetermined date. In this section, you will learn what makes a futures contract unique. It talks about how speculators trade in the futures market, only to make economic gains. You will see what are the different assets that can be traded in the futures market. You will also learn about the properties of futures contracts.

Futures contracts are standardised and hence, enables you to trade in different assets without having an expertise in the traded material. This section will help you understand how the details like quality, quantity, place and time of delivery are regulated by a futures contract. Later in the section, you will also learn that the futures contract is an agreement between two parties. This agreement is centrally cleared by a clearing house.

To deal in the futures market, you need to know a set of standard properties for all futures markets. Knowing these properties will help you effectively treat all the futures markets in the same way. In this section, you will learn about the specific properties like the root symbol, month, ticker, expiry date, first notice date, margin and execution terminology.

Calculation of profit and loss on futures position works differently from other asset classes. Before creating a trading strategy, it is essential to understand the logic behind futures profit and loss calculation. In this section, you will learn about the concepts such as point value, mark to market and learn to calculate futures PnL with examples. Further in the section, you will learn about the effect of currency on futures PnL. You will also compare currency exposure on futures and stocks.

Futures contracts are each based on different underlying assets. These can be based on physical commodities, like the agricultural and non agricultural commodities. The futures can also be based on the interest rates and currency exchange rates. Apart from these, the futures contract can even be based on the equity index value! In this section, you will learn about the various sectors on which the futures contracts are based. You will also learn about the deliverable and non-deliverable contracts, and the first notice date concept in the futures market.

All the futures contracts have a futures chain. In this section, you will learn how to read the futures chain. The futures chain is like a snapshot of the futures trading activity in a particular asset. You will also learn about the importance of a definite expiry date in the futures contract. The definite expiry date gives a lot of opportunities in terms of trading but is also problematic when you are trying to perform long term analysis on the data. You will understand how the same commodity can be traded at different prices for contracts expiring on different dates.

To overcome the issue of the limited life span, different ways have been suggested to build a continuous price line of the same asset, in spite of different expiry dates. You will explore both the good and bad methods of building futures continuations. Finally, you will understand why the proportional adjustment method is good for analysis.

A very common problem with new traders is that they analyse one thing, and then trade the other. Like the index is analysed, and the futures are traded. This section highlights the importance of performing the correct analysis of tradable assets. With an example of an index and its continuous futures contract, you learn the importance of trading what you analyse.

This section talks about the history of the trend following strategy. Further, it discusses the principle behind creating a robust trend following strategy. It explains the logic behind the successful and profitable trend following models with the help of a rolling dice game. Further in the process, you will also learn about the calculation of the expected value.

In creating a robust trading model, both entry and exit rules are important. In this section, you will learn about the entry rules to buy and sell in the trend following model. It discusses the importance of a trend filter and trend breakout to buy and sell. You will also learn to code and visualise the entry points in Python.

Measuring risk is very critical for any trading approach. In this section, you will learn about financial risk and how these risks are measured. Further, you will see that to manage risk, it is very important that each position you hold has an equal impact on your portfolio. This section explains why we should allocate more to the slow moving markets and less to the fast moving markets. It explains the concept of actual risk exposure and the notional value. Further, you will also learn how futures markets are different from the cash markets. You will also apply the learnings and calculate the position size for two assets in order to allocate equal risk to both.

The exit rules for the trend following strategy are discussed in this section. A pullback indicator is calculated which exits the position if the market moves against our position by a threshold value. The overall strategy position for trend following is calculated by combining the entry and exit signals.

There are trading strategies that work well on both individual markets and portfolios of markets. This section discusses the trend following model rules for entry and exit on the individual markets. Further, you will apply these rules on the Palladium futures price and analyse the backtest results.

The trend following strategy works best when the portfolio is diversified. This section highlights the importance of diversification for obtaining good results using trend following strategies. For illustration purposes, a sample strategy is implemented on multiple assets and analysed.

Analysing your strategy is very important to safeguard your capital. Return is not the only metric that can help you in understanding the performance of your strategy. In this section, you will learn the importance of analysing your strategy through thorough backtesting. It talks about various metrics, like Sharpe ratio and maximum drawdown, and rolling analysis. Further, it talks about how the number of winning trades are much smaller than the number of failing trades. And yet, the less winning trades results in net profit. It also explains how optimising the rules of a strategy will lead to overfitting.

It is often seen that the trend following strategy stops out too early and too often. The counter trend strategy attempts to overcome this problem by entering into a position when the trend following strategy exits. You will learn how counter trend models try to capture the continuation of a trend, after the trend following strategy stops out.

In this section, you will learn about the importance of the entry points in the counter trend models. In the trend model, the exit is followed by an immediate entry signal, which leads to frustration in the trend followers. The section explains how the counter trend model overcomes this issue by reversing the logic of the trend model. It also explains the concept of pullback. Further, you will apply the learnings to generate your own entry signals on the S&P 500 Total Return index.

Experimenting with the exit rules is very important in order to create a proper exit rule. This section will walk you through a simple exit rule. It will explain how the model ends up with a reasonable profit even for a simple exit rule. You will further apply the learnings to generate your own exit signals.

The returns of the counter trend strategy are analysed in this section. You will learn how this strategy not only outperforms the benchmark but also performes well in equity bear markets.

The concept of term structure trading is quite different from anything you may have seen with stocks, currencies or other common asset classes. In this section, you will learn about contango and backwardation structure and visualise them. Next, you will learn about annualised implied yield and its calculation in Python. Further, you will plot the implied yield to quantify the term structure which helps to make trading decisions.

In this section, you will use the implied yield and open interest to decide if a contract that is further from the one nearing expiry can deliver more gains. Part of the gains will come from saving on the transaction cost of rolling over from the front contract to the one farther out. You will also look at the calendar spread strategy which considers the term structure as the sole indicator.

The concept of diversification using multiple assets is portfolio diversification. But we don’t know for sure which strategy to run for the assets in the portfolio. The concept of style diversification is introduced in this section where you learn that diversification of the strategy also results in a better return on investment.

Learn to install the Python environment in your local machine.

This section gives an overview of live trading your strategies using IBridgePy. It includes details about the code structure, how to place orders, and the link to our free course on IBridgePy.

This section includes a template for live trading which can be used on IBridgePy. The live trading strategy template is based on the strategies discussed in the course, modified to run in a live trading environment. A data subscription may be required based on the asset being traded in the strategy.

In this section, you will undertake a capstone project on real-world data. This project will require you to apply and practice the concepts learnt throughout this course.

In this section, you will go through the different concepts you learnt throughout the course. You will also be able to download all the strategy notebooks as a zip file. You can use these notebooks and modify its contents to create your own unique strategy.

This section moves on to further explain other options pricing models like Derman-Kani Model and Heston Model.

This section includes a primer on Options Greeks with a special focus on the intuitive explanation of sensitivity of Delta.

This section focuses on how the delta changes, or the Gamma factor in option pricing.

The section involves the study of how volatility affects option pricing by discussing the greek Vega.

This section focuses on the time to expiry and interest rates that influence option pricing. It also introduces some of the advanced Options Greeks concepts.

This section explains various options trading strategies like arbitrage strategy, calendar spread strategy, earnings strategy, box trading, and how to use them to trade in live markets. It also includes a case study on a strategy during the earnings announcement of the company.

Learn to install the Python environment in your local machine.

This section covers strategies based on implied volatility with concepts of Forward Volatility, Volatility Smile and Volatility Skew.

In this section, a live trading strategy template will be provided to you. You can tweak the strategy template to deploy your strategies in the live market!

This section summarises the course and provides downloadable strategy codes.

In this section, you’ll be taken through the entire process of automating an options trading strategy. You will learn about the order of the process. You will also be familiarised with each of the activities that have to be carried out for setting up an options trading system.

Data fetching is one of the most important parts of building a system, and this section covers every aspect of options data. In addition to the sources for fetching options data, it discusses how to organise the data, how to store the data, what tools you can use to store the data, and how to reduce the processing time.

Validating the data, performing quality checks and cleaning the data is yet another important step in the systematic options trading process that must not be overlooked. In this section, you will be introduced to some techniques that you can use to validate the quality of your options dataset.

Have you looked at an asset’s option chain and wondered which will be the right option to buy or sell? If yes, then you should definitely go through this section. By using criteria based on liquidity and options interest, you can filter out those options which will not maximise your returns. You can use these, as well as create your own screening criteria to shortlist the options which fit your requirements.

This section explains one of the most popular options trading strategies, which is the butterfly strategy. It also explains how to select a strategy based on your view of the market and reviews the best-suited strategy for an expected market scenario. In this section, you will learn how to construct a short butterfly strategy and how to implement this strategy using python.

In this section, you will first learn how to calculate the payoff for each of the four legs of the butterfly strategy. You will also learn how to arrive at the total payoff from the strategy. This section also includes the computation of net premium, maximum loss and maximum profit. After completing this section you will be able to compute the payoff from the strategy under different scenarios at expiry.

In this section, you will learn about the need for calculating the probability of profit for an option. You will also understand the intuition behind the probability of profit metric. It can be used as both, a screening tool as well as a performance measure.

There are multiple ways to calculate the probability of profit. One way is to use the lognormal distribution. In this section, you will understand the commonalities between the log of prices and lognormal distribution. You will also learn to plot the lognormal distribution of real-world data.

In this section, you will learn here how to compute the probability of profit using the lognormal distribution. Further, you will implement the lognormal distribution on the short and long butterfly strategy and find its probability of profit.

Lognormal distribution uses certain assumptions which may not reflect what is happening in the real world. We can use past data to create a distribution graph which is closer to real-world data. Further, you will learn here how to compute the probability of profit for a short or long butterfly strategy based on an empirical distribution obtained with the underlying asset prices.

In this section, you will understand the limitations of using the probability of profit as a standalone metric and how to overcome this by applying the concept of expected profit for trading options.

Volatility refers to the uncertainty or risk associated with the value of a security. In this section, you will learn about volatility and its various types in the context of options trading.

Certain strategies profit from fluctuations in the underlying security. And, for these strategies, forecasting the degree of movement based on market participants' expectations becomes important. You will learn about implied volatility and how to calculate it in this section.

In this section, you will learn what implied volatility percentile is and how it can be a useful metric when deploying your options trading strategy. You will also learn how to calculate and plot the implied volatility percentile levels to determine the ideal time to deploy your strategy.

When it comes to systematic options trading, having well-defined entry rules is crucial. In general, a single indicator may not be enough to make a trading decision because false signals may occur. We can use any other technical indicator in addition to the implied volatility percentile to improve the signal's quality. In this section, you will learn about the ADX indicator, and how it can be a useful metric when it comes to deploying your trading strategy.

In this section, you will understand the need for sound entry and exit conditions for any options trading strategy and finally, backtest the short butterfly trading strategy.

In this section, you will understand the need for risk management and position sizing and apply the concept of risk management using stop-loss and take-profit.

Analysing certain metrics will help you understand whether your strategy is working. Trade level analytics represents how a strategy has been performing over a given period. In this section, you will be learning how to calculate and interpret a few widely used analytics such as number of winning trades, number of losing trades, average profit or loss per trade, etc.

In this section, you will learn about the costs associated with setting up an options trading strategy. You will understand in detail the costs associated with setting up a short butterfly strategy.

Returns and risk are both factors that determine the performance of a strategy. As you proceed through this section, you will learn how to quantify the performance of your strategy based on the return and risk using measures such as Sharpe Ratio and Tail Risk.

The iron condor strategy, which is one of the most popular options trading strategies, will be covered in this section. It also discusses the appropriate market conditions for deploying the strategy. You'll also learn how to set up the strategy and put it into action with Python.

In this section, you learn about options spread trading. You will learn about the bull call, and bear put spread, the most well-suited market conditions to deploy these strategies, and how to set up and implement them in Python.

Every system needs to be built in a certain manner. If you build the system, you may be doing some activities that should be avoided and omitting others that should be exercised. This section reveals the four things that every trader should avoid and the four things they must do while building a system.

In this section, you will learn the options strategy to capitalise on theta and interest rates.

This section will help you to develop a ratio spread strategy and backtest it. You will also compute its performance metrics.

In this section, you would go through the different processes and API methods to build your own trading strategy for the live markets, and take it live as well.

To make sure that you can use your learning from the course in the live markets, a live trading template has been created which can be used to paper trade and analyse its performance. This template can be used as a starting point to create your very own unique trading strategy.

In this section, you will learn to install the Python environment on your local machine. You will also learn about some common problems while installing python and how to troubleshoot them.

In this section, we will briefly summarise everything that you have learned in this course. You will also find the downloadable unit which contains all the code as well as the data files used in the course, in a zip format for you to download and tweak on your local system.

This section aims to develop an understanding of market breadth and its importance, and the concept of price movements and traded volume. It explains the TRIN sentiment indicator, along with the Python code to implement the TRIN trading strategy.

This section covers the topics of put options, call options, volume, open interest etc. It explains the Put-Call indicator and devises a trading strategy based on it, along with the Python code for it.

This section covers the importance of volatility while trading with sentiment indicators. It presents the concept of Volatility Index and further explains the Python code to implement the VIX trading strategy.

This section covers the various risks which can influence your trade and demonstrates ways to mitigate them.

Learn to install the Python environment in your local machine.

In this section, a live trading strategy template will be provided to you. You can tweak the strategy template to deploy your strategies in the live market!

This section concludes the course and provides downloadable strategy codes and an e-book with the course contents.

This section explains how to use the concept of implied correlation and build a dispersion trading strategy, and code it in Python on Bank Nifty. Bank Nifty represents the 12 most liquid and large capitalised stocks from the banking sector which trade on the National Stock Exchange (NSE). It provides investors and market intermediaries a benchmark that captures the capital market performance of the Indian banking sector.