By Viraj Bhagat
Another year has gone by, and with it, we all have become one more year wiser, gained more knowledge, applied it in our trading practises or started learning to build our own trading strategies.
Many took up learning Algo Trading - some to keep up with the times, some to build their career, some to follow their passion and some to grow in their career and grab their promotions, among many others.
In 2020, we published 140+ blogs that are freely available to our readers. Ranging from the basics to advanced, tutorials to strategies, reports, entire projects, and a lot more.
These articles are an extensive and in-depth exploration of various topics, concepts and more - in the domain of algo trading. Written after hundreds of hours of research, our articles are written by experts having years of experience in their respective domains. These article are curated by trading practitioners and industry stalwarts.
Looking back at 2020, today, we bring to you the blogs - The Top 20 of 2020!
These are categorised as:
Your single stop for all things Quant - this is a very comprehensive and robust compiled list of FREE resources that one would require or needs in the domain of Algorithmic Trading and Quantitative Trading.
Free resources for the following purposes are covered:
- Algorithmic Trading
- Python for Trading
- Machine Learning
- Options Trading
- Data Science
- Automated Trading
- Additional Resources
If you are aspiring to become a trader, it would be great to pick up a book on Algorithmic Trading and absorb all that various books have to offer. In this article, you will find the core areas on which aspiring quants need to focus, as well as the good reads for the same.
This article covers books within the following broad categories:
- Market Microstructure
- Statistics & Econometrics
- Technical Analysis
- Options Trading
- Advanced Statistics
- Python - Algorithmic Trading Foundation
To gain an insight into algorithmic trading as a retail trader, this comprehensive guide is sure to serve your purpose well. You will be able to find basic concepts as well as advanced concepts with regard to algorithmic trading. It explains:
- What is algorithmic trading?
- Why should retail traders do algorithmic trading?
- How can retail traders start algorithmic trading?
- Requirements for setting up an algorithmic trading desk
- Brokers for algorithmic trading
- Courses to learn algorithmic trading
The heart or the core of High-Frequency Trading is a combination of High-Speed Computer Systems and Real-Time Data Feed (which tracks trades and order book quickly). It always helps to get the basics right and with this article, you get to know all about HFT.
This article talks about:
- Introduction: What, Why and How?
- How does High-Frequency Trading work?
- High-Frequency Trading Orders
- History of HFT
- Unique & Interesting Facts about HFT
- Features of High-Frequency Data
- High-Frequency Trading Strategies
- Jobs and Careers in High-Frequency Trading
- Requirements for setting up a High-Frequency Trading Desk
- Regulatory requirements in High-Frequency Trading
- How is High-Frequency Trading different from Long-term Investments?
A key market participant in an exchange’s trading structure is the Market Maker. Market Makers are those who buy at the best bid in the current market scenario and also, sell at the best offer. This way, they indulge in both sides of financial markets.
To explain in detail about Market Making, this article covers:
- Who are Market Makers and what is Market Making Strategy?
- How do the Market Makers Earn and how Much do they Make?
- Can Market Makers Lose Money?
- How automated trading Enables Market-Making?
- Why is Market-Making Important?
- Difference between a Broker and a Market Maker
Usually, when quants work, they keep an eye on the performance of the market. They predict or forecast it on the basis of market data - by using Maths! This blog explains different mathematical concepts such as Statistics, Probability, Algebra, Linear Regression which play an important role in Algorithmic Trading. It covers:
- Who is a Trader?
- Who is a Quant/Quantitative Analyst?
- Why does Algorithmic Trading require Maths?
- When and How Mathematics made it to Trading: A historical tour
- Mathematical Concepts
A step-by-step explanation of the what, why and how of implied volatility. In addition to the theory, this blog explains how to calculate implied volatility mathematically and also create an IV calculator using python. It covers:
- Understanding Implied Volatility
- Math behind IV
- Calculating IV using python
- Factors affecting the IV of an option
- Uses of IV
- Interpreting IV
- Trading Strategies using IV
Predicting the future stock prices in the stock market is crucial for investors, Time Series and its related concepts hold an exceptional quality of organizing the data for accurate prediction.
This elaborate 20 min.-read ensure you are thorough with it. This blog talks about:
- What is Time Series and Time Series Analysis?
- Types of Time Series
- What are the Components of Time Series Analysis?
- Structures for the Components or Decomposing
- What is Time Series Forecasting?
- How to Import, Calculate & Plot and Validate Time Series in Python for Forecasting?
- Time Series Analysis: Working with Date-Time Data in Python
- Mean Reversion in Time Series Analysis
Did you know that unsystematic risk is also known as specific risk, diversifiable risk, idiosyncratic risk or residual risk? This is your go-to guide that explains all that you need to know about unsystematic risk, in a very comprehensive manner. Explore everything that you may want to know about unsystematic risk by covering:
- What is an unsystematic risk?
- Example of unsystematic risk
- Formula for unsystematic risk
- How to calculate unsystematic risk?
- Systematic vs unsystematic risk
- Types of unsystematic risks
- How to protect against business risk and financial risk?
If we had to describe the usefulness of Principal Component Analysis, we would say that it helps us reduce the amount of data we have to analyse. With this blog, we try to understand the principal component analysis and its application in trading. We also understand Eigenvalues and Eigenvectors along with covariance, which is used in Principal Component Analysis.
This blog covers:
- What is Principal Component Analysis?
- Eigen Vectors and Covariance Matrix
- When to use Principal Component Analysis?
- Principal Component Analysis in trading
Fischer Black and Myron Scholes had built the foundation for the Black Scholes Model, which was later worked on by Robert Merton to give us the equation which is popular all over the world now. Black Scholes Model computes the options price given the Exercise Price, Underlying Stock Price and its Volatility as well as Days to Expiry. With this article, we look at:
- Assumptions of the Black Scholes Model
- Black Scholes formula
- Black Scholes in Python
- Variants to overcome BSM
The theory of random walk suggests that the stock price today has no relation or influence on the stock price tomorrow, and the direction the stock price goes is entirely random and unpredictable. In this blog, we will see what is a simple random walk and create a simulation for the closing price of a stock. We read through:
- What is a Random Walk?
- Random Walk Theory in Markets
- Criticisms on Random Walk
- Geometric Brownian Motion in action
- Random Walk Simulation Of Stock Prices Using Geometric Brownian Motion
VADER sentiment helps us to decode and quantify the emotions contained in media such as text, audio or video. With this blog, we study what VADER Sentiment Analysis is and how to use it in our Algorithmic Trading Models using Python as well as how to implement VADER in our trading strategy. It covers the following topics:
- What is VADER?
- Demo using sentences explaining 5 Heuristics
- How to use VADER in Trading?
- Generating Trade calls using VADER and Simple Moving Averages
XGBoost! The supposed miracle worker which is the weapon of choice for machine learning enthusiasts and competition winners alike. XGBoost is a gradient boosting model which reduces computation time and consumes fewer resources. In this article, besides learning about XGBoost, we also learn how to use Python code to predict long-short on US stocks.
This article covers:
- What is XGBoost?
- Why is XGBoost so good?
- XGBoost feature importance
- How to install XGBoost in anaconda?
- XGBoost in Python
Build your Machine Learning trading strategy without writing a single line of computer code!
Learn to construct a machine learning strategy using Blueshift’s cool new addition, the Visual Programming interface. Also, no special software installation is required in your system. Everything is done on the Blueshift platform!
This article covers:
- What is Blueshift’s Visual Programming?
- Machine Learning Pipeline in Blueshift’s Visual Programming
- Building a Machine Learning Strategy
- Backtesting and Live Trading the Strategy
- Future Enhancements to the Strategy
Plotly was created to make data more meaningful by having interactive charts and plots which could be created online as well. The fact that we could visualise data online removed a lot of hurdles which are associated with the offline usage of a library. Plotly python library creates interactive diagrams like Scatter plot, line charts, OHLC and Contour charts etc.
This article covers:
- How to install Plotly in Python
- Online Vs Offline Usage
- Rendering as an HTML file or in the Notebook
- OHLC Chart
- Scatter Plot
- Line Chart using Plotly Express
- Contour Charts
- Scatter Plot in 3D
With the advent of several machine / deep learning models, there have been several theories emerging in applying these techniques for stock market prediction because of the difficulty and complexity it involves.
This project, by EPATian Balamurugan Ganesan tries to solve the problem using a classifier to predict whether the Bank Nifty index listed in NSE will go up or down, on the next day open using two Deep Learning models. Be sure to check it out!
Our most-demanded article provides a step-by-step technique to predict Gold price using Regression in Python. Learn right from defining the explanatory variables to creating a linear regression model and eventually predicting the Gold ETF prices - a fundamental yet strong machine learning technique.
This article covers:
- Import the libraries and read the Gold ETF data
- Define explanatory variables
- Define the dependent variable
- Split the data into train and test dataset
- Create a linear regression model
- Predict the Gold ETF prices
- Plotting cumulative returns
- How to use this model to predict daily moves?
Financial markets act as bellwethers and give us a reflection of the overall sentiment for the world economy. These sentiments are reflected not only in the price but also in other metrics such as open interest, rollover percent, FII/DII activity. This blog aims to analyse these important metrics for Nifty 50, the leading broad-based market index in India. We use Python to conduct this analysis.
- Importing Python libraries
- Nifty 50 futures
- Getting the data
- Analysing the price movement
- Analysing price vs volume
- Open interest analysis
- Rollover analysis
- FII/DII activity analysis
In this post, we get introduced and glance through the rationale of some popular portfolio construction methods and their implementation in Python.
When constructing a multi-asset portfolio, coming up with the strategy to allocate weights to the portfolio components is a very important step in the process. Coming up with weights for a portfolio given its components can be done in a number of ways and is a question that boggles even the most skilled managers. So what is the most optimal way to do this? This article introduces the most widely used methods and understands the intuition behind them. Read on to learn all about it.
According to the “Global Algorithmic Trading Market 2018-2022” report published by Research and Markets, the global algorithmic trading market size is projected to grow from $11.1 billion in 2019 to $18.8 billion by 2024, expanding at a CAGR of 11.1 per cent. Moreover, it is being used widely and is ever-expanding its reach in emerging markets.
This article is aimed to give you a thorough understanding of the following:
- What and Why of Algorithmic Trading?
- The Transformation from Manual to Algo Trading
- When did Algorithmic Trading start?
- Frequencies of Trading: HFT, MFT, LFT
- Algo Trading Strategies
- Algorithmic Trading Salaries
- What are the Rules and Regulations in India?
- How to Learn Algorithmic Trading
- The workflow of Algorithmic Trading
- How to build your own Algorithmic Trading Business?
The article on basic statistics goes through some basic terminologies as well as the types of probability distributions which are used for strategy analysis, and are employed in the domain of algorithmic trading.
It covers Historical Data Analysis, Probability distribution and Correlation. If you’re just getting started with Algorithmic Trading or want to brush up your knowledge, this blog is perfect for you.
The time-weighted average price is an execution strategy that is quite helpful for large trade orders. If you want to know the what, why and how of TWAP and its difference from VWAP, then this article will serve your purpose well as it will give you an insight into the time-weighted average price. It takes your through:
- What is TWAP?
- Example of TWAP
- How is TWAP calculated?
- Why choose TWAP?
- TWAP vs VWAP
- Pros & Cons of TWAP
Forward Propagation is practised to get an output and compare that output with the real value to acquire the error. Learn about forward propagation in neural networks with our elaborate article that explains:
- A brief history of Neural Networks
- What is forward propagation in Neural Networks?
- Components of forward propagation model
- Applications of forward propagation
Learn about Markov Model and review two of the best known Markov Models namely the Markov Chains, which serves as a basis for understanding the Markov Models and the Hidden Markov Model (HMM) that has been widely studied for multiple purposes in the field of forecasting and particularly in trading.
This blog answers the following questions:
- What is a Markov Model?
- What are Markov Models used for?
- How does a Markov Model work?
- What is the Hidden Markov Model?
- What is the difference between the Markov Model and the Hidden Markov Model?
Bonus - All Time Favourites
- Gini Index For Decision Trees
- Creating Heatmap Using Python Seaborn
- Stock Market Data And Analysis In Python
- How to install Ta-Lib in Python
- Popular Python Trading Platforms For Algorithmic Trading
- Basics Of Options Trading Explained
- Calculating The Covariance Matrix And Portfolio Variance
- Candlestick Trading Patterns - How To Read Candlestick Charts
- How To Install TensorFlow GPU (With Detailed Steps)
- Pairs Trading Basics: Correlation, Cointegration And Strategy
We hope you have a great time reading, learning and implementing all this knowledge in your trading practises. Feel free to share the links of these articles with your peers, friends, university, family and friends.
If you've referred our content in your publication, don’t forget to mention us and the links in the bibliography, or your articles or your research - we’d appreciate that. Do comment your thoughts about this blog in the comments section below.
Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.