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Exploring the Essential Features of “QuantInsti – Systematic Options Trading”
Systematic Options Trading
Modern trading demands a systematic approach and the need to steer yourself away from trading from the gut. This course helps you create, backtest, implement, live trade and analyse the performance of options strategy. Learn to shortlist options, find the probability of profit, expected profit, and the payoff for any strategy and explore options trading strategies like a butterfly, iron condor, and spread strategies. You can also implement the course learnings in a capstone project.
LIVE TRADING
- Â List the steps required to trade options systematically
- Â Perform data quality checks
- Â Create an options screener to identify the liquid options
- Â Calculate the probability of profit using the lognormal and empirical distribution
- Â Explain the limitations of PoP.
- Â Identify the best strategy based on expected profit
- Â Analyse the sensitivity of expected profit to the historical data
- Â Apply technical indicators to determine the exit rule for options trading strategy
- Â Backtest options trading strategies such as butterfly, iron condor and spread strategies
- Â Add stop-loss and take profit to your strategy
- Â Evaluate the backtest performance using metrics such as sharpe ratio and maximum drawdown
-  Do’s and don’ts while trading options
- Â Paper trade and live trade the strategy
SKILLS COVERED
Python
Pandas
Numpy
Mibian
Matplotlib
Scipy.stats
Options Strategies
Butterfly
Iron Condor
Bull Call Spread
Bear Put Spread
Straddle
Concepts
Backtesting
Screener
Trade Level Analytics
Data Quality Checks
Risk Management
LEARNING TRACK 3
This course is a part of the Learning Track: Quantitative Trading in Futures and Options Markets
PREREQUISITES
You should be aware of the basics of options such as call and put options and have knowledge of the payoff from call and put options. It is also assumed that a learner knows how to place an order to buy and sell options and concepts such as strike price, expiry date, and underlying asset. You can check out our course on Options Trading Strategies: Basics if you are not familiar with options. Knowledge of Python including pandas dataframe, and matplotlib for visualisation, for loops, would be required.
SYLLABUS
Introduction
This course will serve as a step-by-step guide to assist you in trading options systematically and avoiding common mistakes made by traders while trading options. The interactive methods used in this course will assist you in not only understanding the concepts but also answering all questions about systematic options trading. This section explains the course structure as well as the various teaching tools used in the course, such as videos, quizzes, coding exercises, and the capstone project.
Introduction 5m 11s
Course Structure 10m
Quantra Features and Guidance 4m 9s
Datasets, Exchanges & Brokers covered in the course 2m
Backtesting and Automation Overview 3m 27s
Systematic Trading Process
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.
Systematic Trading Process 5m 48s
Data Structure of an Underlying Asset 2m
Options Data Structure 2m
Define the Purpose of Screener Tool 2m
Evaluation of Strategy Performance 2m
Identify the Correct Order of Process 2m
Options Data
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.
Data Vendors 10m
Data Structure 5m 31s
Option Price Data 2m
Mandatory Fields 2m
Data Derived from Options Data 2m
Need for Underlying Data 2m
Need for Dividend Data 2m
Data Storage 6m 5s
Greeks 2m
Storage of Derived Data 2m
Nonrecurrent Data 2m
Storing Structured Data 2m
CSV and Pickle 2m
Pickle File Extension 2m
Recurrent Calculations 10m
Recurrent Calculations for Options Data 2m
Advantages of Recurrent Calculations 2m
Sourcing US Options Data 2m
Storing US Options Data 5m
Data Pre-Processing
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.
Data Pre-Processing 4m 5s
Data Pre-Processing Steps 2m
Data Quality Checks 2m
Discrepancies in Data 2m
How to Use Jupyter Notebook? 2m 5s
Working With Pickle File 5m
Data Quality Checks and Data Cleaning 10m
How to Use Interactive Exercises? 5m
Check for NaN Values 5m
Drop Missing Values 5m
Identify Duplicate Values 5m
Drop Duplicate Values 5m
Conflicting Expiry Data 5m
Additional Reading 10m
Creation of an Options Screener
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.
Creation of an Options Screener 7m 45s
Notional Exposure of Option 2m
Preference of Expiry Date 2m
Selection of Option on Basis of Open Interest 2m
Selection of Option on Basis of Bid Ask Prices 2m
Choice of Option Based on Open Interest Level 2m
Preference of High Open Interest 2m
Inference of Option Chain 2m
Notional Exposure Requirement 2m
Options Liquidity Screener in Python 10m
Selection of Expiry with Highest Open Interest 5m
Test on Options Data and Screener 14m
Butterfly Strategy for Options Trading
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.
Strategy Selection 4m 27s
Straddle Creation 2m
Compute Maximum Loss 2m
Select the Best Strategy 2m
Compute Maximum Profit 2m
Butterfly Set-Up 5m 51s
4 Legs of the Butterfly Strategy 2m
Long Options 2m
OTM Options of Short Butterfly 2m
Break-Even Point 2m
Maximum Loss 2m
Maximum Profit 2m
Setup the Butterfly Strategy 10m
ATM Strike Price 5m
Call Options Premium 5m
Butterfly Strategy Payoff
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.
Payoff Calculation 2m
Long Call and Short Call Payoff 2m
Long Put and Short Put Payoff 2m
Butterfly Strategy Payoff 2m
The Long Call Payoff 2m
Payoff Diagram of the Butterfly Strategy 5m
The Short Put Payoff 2m
Compute the Net Premium 5m
Probability of Profit
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.
Need of Probability of Profit 2m 27s
Decision to Trade Using Probability of Profit 2m
Probability of Profit of Executed Trades 2m
Intuition of Probability of Profit 3m 7s
Breakeven of Put Option 2m
Probability of Profit of Put Option 2m
Lognormal Distribution
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.
Use of Lognormal Distribution to Calculate Probability 5m 21s
Need of Lognormal Distribution 2m
Identification of Lognormal Distribution 2m
Properties of Lognormal Distribution 2m
Volatility of Lognormal Distribution 2m
The Lognormal Distribution 10m
Mean of Lognormal Distribution 2m
Compute the Daily Standard Deviation 5m
Expected Volatility 2m
Probability of the Futures Prices 5m
Additional Reading 10m
Probability of Profit Using Lognormal Distribution
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.
Probability of Profit Using Lognormal Distribution 2m 21s
Essential Component of Lognormal Distribution 2m
Probability of Profit Using Breakeven Points 2m
Value of Probability of Profit 2m
Probability of Profit Using CDF 2m
Probability of Profit Using the Lognormal Distribution 10m
Cumulative Density Function Value 2m
Probability in Range of Prices 2m
Compute the Lognormal Distributed Probability of Profit 5m
Additional Reading 10m
Probability of Profit Using Empirical Distribution
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.
Probability of Profit Using Empirical Distribution 7m 39s
Aspects Not Incorporated in Lognormal Distribution 2m
Essential Components for Empirical Distribution 2m
Calculation of Probability of Price in a Range 2m
Probability of Profit Using Empirical Distribution 2m
Percentage Change in Price and Probability of Profit 2m
Probability of Profit Using the Empirical Distribution 10m
Forecast Prices From Historical Data 5m
Histogram From Forecasted Prices 5m
Fit a Distribution on the Forecasted Prices 5m
Additional Reading 10m
Expected Profit
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.
Expected Profit 4m 13s
Trade or No Trade 2m
Profitability in Long Run 2m
Biased Coin 2m
Expected Profit of Short Butterfly 2m
Expected Profit Notebook 10m
Calculate Expected Profit 5m
Test on Payoff and Probability of Profit 14m
Types of Volatility
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.
Volatility 3m 39s
What is Volatility? 2m
Implied Volatility 2m
Historical vs Realised Volatility 2m
Implied Volatility
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.
Implied Volatility 2m 8s
Implied Volatility Inference 2m
Efficient Way to Calculate Implied Volatility 2m
Calculate Implied Volatility 2m
Implied Volatility 10m
Calculate Implied Volatility 5m
Implied Volatility Percentile
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.
Implied Volatility Percentile 2m 42s
Disadvantage of Implied Volatility 2m
Need for Implied Volatility Percentile 2m
Short Butterfly Strategy 2m
IVP Value Inference 2m
Calculate IVP 2m
Low IVP Range 2m
Implied Volatility Percentile 10m
Calculate Implied Volatility Percentile 5m
Technical Indicator
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.
ADX 2m
Average Directional Index 10m
Calculate ADX 5m
Butterfly Strategy Backtest
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.
Backtesting Short Butterfly 6m
Entry and Exit Logic 2m
Short Butterfly 2m
Entry Conditions 2m
High IV Percentile 2m
IV Percentile 2m
ADX Value 2m
Exit Condition 2m
Extremely High IVP Value 2m
Backtesting Short Butterfly 5m
Entry Conditions for Short Butterfly 5m
Exit Condition for Short Butterfly 5m
Total PnL 2m
Risk Management
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.
Risk Management 5m 16s
Capital Allocation 2m
Black Swan Events 2m
Hedging Instruments 2m
Short Straddle 2m
Hedging Short Straddle 2m
Hedging Long Straddle 2m
SL Percentage 2m
Exit Price 2m
Backtesting Short Butterfly with SL and TP 5m
Valid Exit Conditions 2m
Start Date for Backtesting 2m
Additional Reading for Kelly Criterion 2m
Position Sizing 2m 20s
2% Rule 2m
Trading Capital 2m
10% Rule 2m
Position Sizing Techniques 2m
Trade Level Analytics
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.
Trade Level Analytics I 5m
Trade Level Analytics II 4m 21s
Define Win Trades 2m
Calculate Win/Loss Rate 2m
Calculate Average PnL Per Trade 2m
Identify the Correct Strategy-I 2m
Identify the Correct Strategy-II 2m
Limitations of Win Trade 2m
Calculate Average Trade Duration 2m
Interpret the Profit Factor 2m
Calculate the Profit Factor 2m
Trade Level Analytics 10m
Average PnL Per Trade 5m
Limitations of Profit Factor 2m
Average Trade Duration 5m
Win Percentage 5m
Analyse the Strategy Performance 2m
Cost of Setting Up Options Strategy
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.
Cost of Setting Up Butterfly Spread 6m 27s
Cost of Buying and Selling Options 5m
Notional Value 5m
Margin Required To Sell Options 5m
Cost of Buying Options 5m
Cost of Selling Options 5m
Margin Benefit 5m
Strategy Analysis
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.
Sharpe Ratio and Tail Risk 5m 49s
Sharpe Ratio of a Strategy 2m
Sharpe Ratio Calculation 2m
Strategy Comparison 2m
Drawback of Sharpe Ratio 2m
Maximum Drawdown Calculation 2m
Maximum Drawdown Comparison 2m
Maximum Drawdown of a Strategy 2m
Strategy Analysis 5m
CAGR 5m
Sharpe Ratio 5m
Maximum Drawdown 5m
Iron Condor
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.
Iron Condor 3m 59s
Butterfly vs Iron Condor 2m
Setup Iron Condor 2m
Maximum profit 2m
Maximum Loss 2m
Backtesting Iron Condor 5m
Spread Trading
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.
Spread Trading 4m 52s
Volatility vs Direction 2m
Call Option 2m
Bull Call Spread 2m
Bearish View 2m
Setup Bull Call Spread 2m
Bull Call Spread Payoff 2m
Setup Bear Put Spread 2m
Bear Put Spread Payoff 2m
Entry and Exit Conditions 10m
Backtesting Spreads 10m
Long Entry 5m
Do’s and Don’ts
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.
The 4 Do’s
Advantage of a Credible Source 2m
Transaction Costs and Slippages 2m
Identify the Need for Capital Buffers 2m
Calculate the Capital Buffer 2m
Identify the Need for Risk Management 2m
Calculate the Stop-Loss 2m
Ways to Diversify a Portfolio 2m
Identify the Do’s of Options Trading2m
The 4 Don’ts 4m 32s
Identify the Don’ts of Options Trading 2m
System Performance 2m
Identify the Correct Course of Action 2m
Options Strategy Selector
In this section, you will learn the options strategy to capitalise on theta and interest rates.
Options Strategy Selector 10m
Profitable Trade 2m
Calendar Spread 2m
Option Strategy – I 2m
Option Strategy – II 2m
Borrowing from the Market 2m
Payoff at Expiry 2m
Double Benefit 2m
Test on Indicators and Option Strategies 14m
Capstone Project
This section will help you to develop a ratio spread strategy and backtest it. You will also compute its performance metrics.
Getting Started 10m
Problem Statement 10m
Code Template and Data Files 2m
Capstone Project Model Solution 10m
Capstone Solution Downloadable 2m
Live Trading on IBridgePy
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.
Section Overview 2m 2s
Live Trading Overview 2m 41s
Vectorised vs Event Driven 2m
Process in Live Trading 2m
Real-Time Data Source 2m
Code Structure 2m 15s
API Methods 10m
Schedule Strategy Logic 2m
Fetch Historical Data 2m
Place Orders 2m
IBridgePy Course Link 10m
Additional Reading 10m
Frequently Asked Questions 10m
Paper and Live Trading
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.
Template Documentation 10m
Template Code File 2m
Run Codes Locally on Your Machine
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.
Python Installation Overview 1m 59s
Flow Diagram 10m
Install Anaconda on Windows 10m
Install Anaconda on Mac 10m
Know your Current Environment 2m
Troubleshooting Anaconda Installation Problems 10m
Creating a Python Environment 10m
Changing Environments 2m
Quantra Environment 2m
Troubleshooting Tips for Setting Up Environment 10m
How to Run Files in Downloadable Section? 10m
Troubleshooting for Running Files in Downloadable Section 10m
Summary
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.
Course Summary 3m 34s
Additional Reading – Trade Adjustments 2m
Sources and References 10m
Course Summary and Next Steps 10m
Python Codes and Data 2m
ABOUT AUTHOR
QuantInsti®
QuantInsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.
WHY QUANTRA®?
- Gain more in less time
- Get taught by practitioners
- Learn at your own pace
- Get data & strategy models to practice on your own
REVIEWS
IRIS XU China
After completion of the course, I feel the structure and codes are very helpful. I have to say the organization is totally beyond my expectation, it provides several trading strategies and how they work, also the implication of these strategies. For an option trader, it also provides a way for backtesting and performance evaluation.
ALESSANDRO GUIDO Professional Trader, United States
This course will help me to implement my actual options strategies with IB as well. Awesome Thanks!
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