Scikit-learn is one of the most versatile and efficient Machine Learning libraries available across the board. Built on top of other popular libraries such as NumPy, SciPy and Matplotlib, scikit learn contains a lot of powerful tools for machine learning and statistical modelling. No wonder scikit

A stop-loss order, or stops as is generally said, is an order placed with the broker to sell (or buy) if the stock of a company which you hold, reaches a pre-determined price in order to avoid large losses. In the trading world, the use of stops is seen as an essential part of risk control and money

In recent years, machine learning, more specifically machine learning in Python has become the buzz-word for many quant firms. In their quest to seek the elusive alpha, a number of funds and trading firms have adopted to machine learning. While the algorithms deployed by quant hedge funds are never

From showing related articles at the end of the article you have browsed through to creating a personalised recommendation based on your viewing habits, you would be surprised of the number of times you have been interacting with the K-means algorithm without even realising it. The above examples

You are probably wondering how a technical topic like Neural Network Tutorial is hosted on an algorithmic trading website. Neural network studies were started in an effort to map the human brain and understand how humans take decisions but algorithm tries to remove human emotions altogether from the

In this article, we will understand how natural language processing, sentiment analysis and social media play a role in the share markets with the help of Python. This would be explained with respect to the trading in China markets A-share stocks. This article is the final project submitted by the

Decision trees are often used while implementing machine learning algorithms. The hierarchical structure of a decision tree leads us to the final outcome by traversing through the nodes of the tree. Each node consists of an attribute or feature which is further split into more nodes as we move down

In a situation where you wish to determine the returns on investment, one may have all the expertise to do this but without certain information (missing pieces) it would not be possible to derive to a conclusive figure. In practical terms “assume you have the value of all returns of all assets in

In this blog, we’ll discuss what are Random Forests, how do they work, how they help in overcoming the limitations of decision trees. With the boom of Machine Learning and its techniques in the current environment, more and more of its algorithms find applications in various domains. The functions

Alan Turing, an English mathematician, computer scientist, logician, and cryptanalyst, surmised about machines that, “It would be like a pupil who had learnt much from his master but had added much more by his own work. When this happens I feel that one is obliged to regard the machine as showing

The application of the machine learning models is to learn from the existing data and use that knowledge to predict the future unseen events. The model needs to be thoroughly tested and cross-validated to profitably trade in live trading. After reading this, you will be able to: Cross validate

A game of cat and mouse. Technological development more often than not stays ahead of regulators. Each new technological advance or disruption carries risks for the stability of things and advantages for those who are at the forefront. Regulators try to set rules and good practices that limit

In this blog on random walk simulation, we will learn how to simulate stock prices. Future stock prices are very hard to predict and are dependent on the past trend and volatility. While simulating the stock prices one has to give reasonable weightage to these two parameters. The random walk model

In my previous article, we have developed a simple artificial neural network and predicted the stock price. However, in this article, we will use the power of RNN (Recurrent Neural Networks), LSTM (Short Term Memory networks) & GRU (Gated Recurrent Unit Network) and predict the stock price. We

Decision Trees, are a Machine Supervised Learning method used in Classification and Regression problems, also known as CART. Remember that a Classification problem tries to classify unknown elements into a class or category; the output always are categorical variables (i.e. yes/no, up/down,

In this blog, we will intuitively understand how a neural network functions and the math behind it with the help of an example. In this example, we will be using a 3-layer network (with 2 input units, 2 hidden layer units, and 2 output units). The network and parameters (or weights) can be

In this article, we are going to develop a machine learning technique called Deep learning (Artificial Neural network) by using tensor flow and predicting stock price in python. At the end of this article you will learn how to build artificial neural network by using tensor flow and how to code a

In this post, we will learn about the Stereoscopic Portfolio Optimization framework and how it can be used to improve a quantitative trading strategy. We’ll also review concepts such as Gaussian Mixture Models, K-Means Clustering, and Random Forests. Our objective is to determine whether we can

This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT™) at QuantInsti®. Do check our Projects page and have a look at what our students are building. About the Author Xing Tao is a Bachelor in Computer Science

In this post, we will create an intraday momentum strategy and use QDA as a means of optimizing our strategy. We’ll begin by reviewing Linear Discriminant Analysis or LDA and how it is associated with QDA, gain an understanding of QDA and when we might implement this technique instead of Linear

Technology moves at a startling speed and it has been the same case in the algorithmic and quantitative trading domain. Traders around the world are making use of Machine Learning, Artificial Intelligence, Blockchain, Neural Networks, Deep Learning and similar techniques to execute their trades. One

Machine Learning is one of the most popular approaches in Artificial Intelligence. Over the past decade, Machine Learning has become one of the integral parts of our life. It is implemented in a task as simple as recognizing human handwriting or as complex as self-driving cars. It is also expected

Is it possible to predict where the Gold price is headed? Yes, let’s use machine learning regression techniques to predict the price of one of the most important precious metal, the Gold. We will create a machine learning linear regression model that takes information from the past Gold ETF (GLD)

A covered call is used by an investor to make some small profit while holding the stock. Mostly the reason why a trader would want to create a covered call is because the trader is bullish on the underlying stock and wants to hold for long-term, but the stock doesn’t pay any dividend.The stock is

In this blog, we will step by step implement a machine learning classification algorithm on S&P500 using Support Vector Classifier (SVC). SVCs are supervised learning classification models. A set of training data is provided to the machine learning classification algorithm, each belonging to one

So, it’s been some time since you’ve been thinking of making more money out of your successful trading strategy. And why should you not? After all, you’ve worked hard for it and there is only a small % of people who are successful in this business. The idea is to add more funds to your trading

The strategy in this blog will cover no normal technical indicators, but some of my own creation. Also, we will see the difference between strategy performance on test and train data along with respect to the changes in the size of the train data and the prediction length. Unlike in my previous

You are doing well at work but have always felt that need to cater to the aspiration of doing something more, building something of your own? You are passionate about the chosen field of work. You have already explored different organizations and their work processes extensively. Entrepreneurship

We are back again with another post on Python. Our last post, “Basic Operations on Stock data using Python” was well received and we are glad to see the number of likes & shares for the post on various quant trading and Python forums. Keep them coming! Financial market data is a very

There are very few algo trading firms/strategies that are operating in the Mexican stock exchange. I believe this should provide great opportunities as there is little competition. Contrary to a more developed market, arbitrage opportunities aren’t readily realized which suggests there might be

This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT™) at QuantInsti™. Do check our Projects page and have a look at what our students are building. Introduction The Dispersion Trading is a strategy used to

At the end of my last blog, I had asked a few questions. Now, I will answer them all at the same time. I will also discuss a way to detect the regime/trend in the market without training the algorithm for trends. But before we go ahead, please use a fix to fetch the data from Google to run the code

Algorithmic trading has seen great traction in recent years and the numbers of students, engineering graduates, and finance professionals looking to explore this lucrative domain has been growing exponentially with each passing year. Are you among the ones looking to learn quant skills and also make

In today’s algorithmic trading having a trading edge is one of the most critical elements. It’s plain simple. If you don’t have an edge, don’t trade! Hence, as a quant, one is always on a look out for good trading ideas. One of the good resources for trading strategies that have been gaining

The webinar will explain Mixture Models and explore its application to predict an asset’s return distribution and identify outlier returns that are likely to mean revert. The webinar will cover Why bother? Motivating experimentation with Mixture Models How do Mixture Models work? (An intuitive

“Prediction is very difficult, especially about the future”. Many of you must have come across this famous quote by Neils Bohr, a Danish physicist. Prediction is the theme of this blog post. In this post, we will cover the popular ARIMA forecasting model to predict returns on a stock and

The advent of algorithmic trading in the late last century caused a massive tectonic shift in the way trading took place in exchanges worldwide. Be it trading in stocks, derivatives, Forex or commodities, trading firms worldwide adopted algorithmic trading in a big way. The last couple of decades

A decision tree is one of the widely used algorithms for building classification or regression models in data mining and machine learning. A decision tree is so named because the output resulting from it is the form of a tree structure. Visualizing a sample dataset and decision tree structure

These are some of the questions that popular forums get inundated with from aspiring novice algorithmic traders around the world. A good starting point for a wannabe trader would be to pick up a good book, immerse oneself, and absorb all that the book has to offer. This post details down the core

As 2016 nears its finish line, here we are with the list of recommended reading on our blog with the top-rated blog posts, as voted by you! Enjoy the last few days doing what you love most! Read on. System Architecture of Algorithmic Trading This one is straight out of a lecture in the curriculum of

This article is the final project submitted by the author as part of his coursework in Executive Programme in Algorithmic Trading (EPAT™) at QuantInsti. You can check out our Projects page and have a look at what our students are building after reading this article. About the AuthorEPAT student

In our previous post on sentiment analysis we briefly explained sentiment analysis within the context of trading, and also provided a model code in R. The R model was applied on an earnings call conference transcript of an NSE listed company, and the output of the model was compared with the

This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. Do check our Projects page and have a look at what our students are building. About the Author Harish Maranani did his Bachelors in Technology

In this post, we will explore a feature of Python pandas package. We usually find queries about converting tick-by-tick data into OHLC (Open, High, Low and Close) frequently. This can be accomplished with minimal effort using pandas package. The OHLC data is used for performing technical analysis of

A picture is worth a thousand words or said a wise woman a hundred years ago. True to every word of the idiom, the beauty of visualization lies in how clearly it might convey multiple messages. Visualization of data is one of the key functions of a data scientist and decoding the visual messages is

We talked about the defacto standard for message communication in our previous article on FIX protocol. “The Financial Information Exchange (FIX) Protocol is a message standard developed to facilitate the electronic exchange of information related to securities transactions. It is intended for use

With more than 70% of the trading volumes in the US markets being automated, the rise of the algorithms seem more inevitable than ever before. The mechanical jobs are shifting to computers and only those who can tame the machines can rule the trade markets. Equipping oneself with the skills of

Algorithmic Trading in Indian Markets using Python We have told you why Python is one of the preferred languages to do algo trading in this article. We have also told you how algorithmic trading in India. Since we are gearing up for our webinar on Trading in Indian Markets using Python (Not

Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends [1] The predictive modeling in trading is a modeling process wherein we predict the

In the previous article on IBPy Tutorial to implement Python in Interactive Brokers API, I talked about Interactive Brokers, its API and implementing Python codes using IBPy. In this article, I will be talking about implementing python in IB’s C++ API using a wrapper, written by Dr. Hui Liu. About