Milind began his career in Gridstone Research, building earnings models and writing earnings notes for NYSE listed companies, covering Technology and REITs sectors. Milind has also worked at CRISIL and Deutsche Bank, where he was involved in modeling of Structured Finance deals covering Asset Backed

SEBI issued a discussion paper today with inputs from all stakeholders such as investors, infrastructure institutions and intermediary to understand how Algorithmic Trading has led to fairness, concerns and changes in market quality in recent years. It states that more than 80% of the orders placed

What is an Order book? With the growing popularity of Algorithmic and High Frequency Trading, study of order books has grown manifolds. “Order book” is essentially an electronic list of all Buy and Sell orders, arranged as per price time priority. This means that a person having higher price on

One of my favorite classes during EPAT was the one on statistical arbitrage, so the pair trading strategy seemed a nice idea for me. My strategy triggers new orders when the pair ratio of the prices of the stocks diverge from the mean. But in order to work, we first have to test for the pair to be

Python has emerged as one of the most popular language for programmers in financial trading, due to its ease of availability, user-friendliness and presence of sufficient scientific libraries like Pandas, NumPy, PyAlgoTrade, Pybacktest and more. Python serves as an excellent choice for automated

If you don’t know who you are, the stock market is an expensive place to find out – George Goodman In the previous post, I had a conversation with a few experts in the field of Algorithmic Trading to gain some insights into this seemingly “black-box”. That conversation not only helped me

Maxime Fages Maxime’s career spanned across the strategic aspects of value and risk, with a particular focus on trading behaviors and market microstructure over the past few years. He embraced a quantitative angle in M&A, fund management or currently corporate strategy and has always been an

As sources and volumes of news have grown, so has the techniques to gather, extract, aggregate and categorise them. Important news can result in large positive or negative returns. However, owing to many news sources, we need to ask a fundamental question: Is news analytics profitable in every

In this post we see how to compute historical volatility in python, and the different measures of risk-adjusted return based on it. We have also provided the python codes for these measures which might be of help to the readers. Introduction Volatility measures the dispersion of returns for a given

Python has emerged as one of the most popular language to code in Algorithmic Trading, owing to its ease of installation, free usage, easy structure, and availability of variety of modules. Globally, Algo Traders and researchers in Quant are extensively using Python for prototyping, backtesting,

Traders in the derivative market often exercise one of the following: Call option or Put Option. “Call option” is a financial contract between a buyer and seller, whereby the buyer has the right, but not the obligation, to buy an agreed quantity of a financial instrument from the seller of the

Feature Selection is the automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on. Feature selection methods aid you in your mission to create an accurate predictive model. They help you by choosing

Technical Indicator is essentially a mathematical representation based on data sets such as price (high, low, open, close, etc.) or volume of a security to forecast price trends. There are several kinds of technical indicators that are used to analyse and detect the direction of movement of the

The role of Algorithm in a person’s life is too substantial to be ignored. From a simple coffee-making machine to the music system in his car, from elevators to search engine like Google, all are governed by a set of logical instructions – Algorithms or Algos, which enable them to respond to a

In this post we discuss sentiment analysis in brief and then present a basic sentiment analysis model in R. Sentiment analysis is the analysis of the feelings (i.e. attitudes, emotions and opinions) which are expressed in the news reports/blog posts/twitter messages etc., using natural language

In our previous post on Machine learning we derived rules for a forex strategy using the SVM algorithm in R. In this post we take a step further, and demonstrate how to backtest our findings. To recap the last post, we used Parabolic SAR and MACD histogram as our indicators for machine learning.

In the last post we covered Machine learning (ML) concept in brief. In this post we explain some more ML terms, and then frame rules for a forex strategy using the SVM algorithm in R. To use ML in trading, we start with historical data (stock price/forex data) and add indicators to build a model in

While being a vibrant subfield of computer science, machine learning is used for drawing models and methods from statistics, algorithms, computational complexity, control theory and artificial intelligence. It focuses on efficient algorithms for inferring good predictive models from large data sets

To survive in the age of robots-it is necessary to learn a programming language that makes your trading algorithms smarter and not just faster. Having knowledge of a popular programming language is the building block to becoming a professional algorithmic trader. It is not just enough if a person

For those of you who have been following my blog posts for the last 6 months will know that I have taken part in the Executive Program in Algorithmic Trading offered by QuantInsti. It’s been a journey and this article serves as a report on my final project focusing on statistical arbitrage, coded

In one of our recent articles we’ve talked about most popular backtesting platforms for quantitative trading. Here we are sharing most widely used Python libraries for quantitative trading. Python is a free open-source and cross-platform language which has a rich library for almost every task

As a developer in the world of vast technologies available to us at the click of a button, many of us more often than not, care about the fun part of building a program from scratch and seeing it work, eventually. Hoping that requirements don’t change from a higher power that basically fills our

We have a large number of vendor-developed backtesting platforms available in market which can be very efficient in backtesting automated strategies; but to decide which once will suit your requirements, needs some research. Ideally custom development of a backtesting environment within a

In this post we will discuss about building a trading strategy using R. Before dwelling into the trading jargons using R let us spend some time understanding what R is. R is an open source. There are more than 4000 add on packages,18000 plus members of LinkedIn’s group and close to 80 R Meetup

After graduation I moved into a small, empty, apartment in the city. My grandmother, I’ll never forget, told me that moving into a new house is like meeting someone for the first time, you need to pick one room and make it yours, go slowly through the house, be polite and introduce yourself, so

Algorithmic automated trading or Algorithmic Trading has been at the centre-stage of the trading world for a few years now. The percentage of volumes attributed to this form of trading has been increasing in the past few years. As a result, it has become a highly competitive market that is heavily

About two weeks ago I decided to attempt to write a blog series on Pairs trading and statistical arbitrage. What I found is that everyone tends to reference the ADF test but I really don’t see a lot of posts that explain the test in full. As you read about building a pairs trading strategy there

Recently I had the privilege to attend the Python for Quants conference in London via live streaming. Each time I attend this series of lectures I try to capture one of the presentations in writing, this time I will be writing on a lecture given by Dr James Munro titled "Quant Strategies: from

It is important to know the difference between high frequency and low frequency trading before discussing the specific trading strategies. Opinions tend to differ on what constitutes high frequency but by and large there is a consensus that the duration of asset holding period is very low, ranging

In the previous post on this topic, we discussed the challenges and statistics involved in selecting a pair of stocks for statistical arbitrage. We understood how by using the co-integration tests we can say within a certain level of confidence interval that the spread between the two stocks is a

Algorithmic trading risks can be categorized into the following: Access Consistency Quality Algorithm Technology Scalability There are 2 places where Risk Management is handled – Within the application – We need to ensure that wrong parameters are not set by the trader. It should not allow a

Now for those of you who know me as a blogger might find this post a little unorthodox to my traditional style of writing, however in the spirit of evolution, inspired by a friend of mine Stuart Reid (TuringFinance.com), I will be following some of the tips suggested in the following blog post.

“The quantmod package for R is designed to assist the quantitative trader in the development, testing, and deployment of statistically based trading models.” It is a rapid prototyping environment where enthusiasts can explore various technical indicators with minimum effort. It offers charting

Back-testing of a trading strategy can be implemented in four stages. Getting the historical data Formulate the trading strategy and specify the rules Execute the strategy on the historical data Evaluate performance metrics In this post, we will back-test our trading strategy in R. The quantmod

Any trading strategy can be broken down into a set of events and the reaction to those events. The reactions can get infinitely complex and varying but essentially strategy writing is quite simply put exactly that. The kind of events and their frequency would depend on the markets and the

I recently enrolled in the QuantInsti Executive Program in Algorithmic Trading and one of the areas in quantitative finance that interests me greatly is the analysis of financial time series. During the course we will take on a massive project to build our own trading strategy with the help of a

Impact of Proliferation of Automated Trading Systems and Technology on Financial Markets With the advent of automated trading everything has become computerized. Risk management takes a whole new level in this technologically fast paced world. The trends in day-to-day trading have been changing.

In pair trading, usually a pair of stocks is traded in a market neutral strategy, i.e. it doesn’t matter whether market is trending upwards or downwards, the two open positions for each stock hedge against each other. To be able to pair trade, the key challenges are to: Choose a pair which will