Quant Mashup - Quant Insti

Gold Price Prediction Using Machine Learning In Python [Quant Insti]

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. Machine Learning in Trading We will create a machine learning linear regression model that takes information

*- 2 months ago, 8 Jul 2020, 10:09am -*

VADER Sentiment Analysis in Algorithmic Trading [Quant Insti]

In Finance and Trading, a large amount of data is generated every day. This data comes in the form of News, Scheduled Economic releases, employment figures, etc. It is clear that the news has a great impact on the prices of stocks. Every trader takes great efforts in keeping track of the latest news

*- 3 months ago, 21 May 2020, 10:26am -*

LSTM Networks: Can They Predict Equity Index Prices? [Quant Insti]

In this article, we will study a deep learning framework based on recurrent neural networks to predict daily equity index price movements. Specifically, the focus will be on long short-term memory (LSTM) networks - which are a type of recurrent neural network. Different types of inputs and network

*- 4 months ago, 5 May 2020, 09:29am -*

Trend Analysis using Open Interest, Rollover and FII/DII Activity in Python [Quant Insti]

The first quarter of 2020 has been one of the most challenging times in the post World War II era. The crash in oil prices due to geopolitical reasons and the COVID-19 global pandemic were the dominant themes. Financial markets act as bellwethers and give us a reflection of the overall sentiment for

*- 4 months ago, 23 Apr 2020, 10:35am -*

Markov Model - An Introduction [Quant Insti]

In this post, we will 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

*- 5 months ago, 23 Mar 2020, 11:09am -*

Building and Regularizing Linear Regression Models in Scikit-learn [Quant Insti]

In the last blog, we examined the steps to train and optimize a classification model in scikit learn. In this blog, we bring our focus to linear regression models. We will discuss the concept of regularization, its examples(Ridge, Lasso and Elastic Net regularizations) and how they can be

*- 6 months ago, 9 Mar 2020, 10:51am -*

Essential Books on Algorithmic Trading [Quant Insti]

When you are completely immersed in wanting to learn something new, you start looking for everything that surrounds the learning process. And with the aspiration to learn Algorithmic Trading, there must be certain questions crowding your mind, like: How do I learn Algorithmic Trading? What are the

*- 6 months ago, 24 Feb 2020, 09:50am -*

All About Time Series: Analysis and Forecasting [Quant Insti]

Since predicting the future stock prices in the stock market is crucial for the investors, Time Series and its related concepts help in organizing the data for accurate prediction. In this article, we are focusing on Time Series, its analysis and forecasting. In this article, we aim to cover the

*- 6 months ago, 23 Feb 2020, 10:04am -*

Introduction to XGBoost in Python [Quant Insti]

Ah! XGBoost! The supposed miracle worker which is the weapon of choice for machine learning enthusiasts and competition winners alike. It is said that XGBoost was developed to increase computational speed and optimize model performance. As we were tinkering with the features and parameters of

*- 7 months ago, 13 Feb 2020, 09:59am -*

Predicting Bank Nifty Open Price Using Deep Learning [Quant Insti]

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. In this project, we’re trying to solve the problem using a classifier to predict

*- 8 months ago, 16 Jan 2020, 10:39pm -*

Principal Component Analysis in Trading [Quant Insti]

As trading becomes automated, we have seen that traders seek to use as much data as they can for their analyses. But we all know that adding more variables leads to more complications and that in turn might make it harder to come to solid conclusions. Think about it, we have more than 3000 companies

*- 8 months ago, 13 Jan 2020, 02:32am -*

Monte Carlo Simulation: Definition, Example, Code [Quant Insti]

Years ago, I had made it to the ﬁnal round in an interview for a Senior Delta One/Quantitative Futures position at an HFT ﬁrm (unnamed for privacy). Things were going well, I had answered two out of three of those ridiculous questions that are only applicable in Subsaharan Africa or Finance

*- 9 months ago, 10 Dec 2019, 10:55am -*

Automated Trading Systems: Architecture, Protocols, Types of Latency [Quant Insti]

The automated trading system or Algorithmic Trading has been at the centre-stage of the trading world for more than a decade now. A “trading system”, more commonly referred as a “trading strategy” is nothing but a set of rules, which is applied to the given input data to generate entry and

*- 9 months ago, 29 Nov 2019, 05:35am -*

Pivot Point Strategy [Quant Insti]

In this project, we analyze different intraday trading strategies with Pivot Points. After defining different ways of calculating the Pivot Point, we do a Backtest with the most classic strategies and a different variant to those normally taught in textbooks. To learn about Pivot Point and how to

*- 9 months ago, 23 Nov 2019, 01:35am -*

Parabolic SAR - An Introduction [Quant Insti]

In the market, it is crucial to spot the trend, but it is equally important to detect when the trend ends. Getting out of the trade is more difficult than entering the trade. In this blog, we will talk about one such technical indicator, the Parabolic SAR indicator, which helps in identifying when

*- 10 months ago, 18 Nov 2019, 08:38am -*

Introduction to Support Vector Machines [Quant Insti]

Support Vector Machines were widely used a decade back, but now they have fallen out of favour. The below data from google trends can establish this more clearly. (Source: Google Trends) Why did this happen? As more and more advanced models were developed, support vector machines fell out of favour.

*- 10 months ago, 5 Nov 2019, 09:38am -*

Pairs Trading Basics: Correlation, Cointegration And Strategy [Quant Insti]

Pairs trading is supposedly one of the most popular types of trading strategy. In this strategy, usually a pair of stocks are traded in a market-neutral strategy, i.e. it doesn’t matter whether the market is trending upwards or downwards, the two open positions for each stock hedge against each

*- 10 months ago, 23 Oct 2019, 11:45am -*

Scikit Learn Tutorial: Installation, Requirements And Building Classification Model [Quant Insti]

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

*- 11 months ago, 17 Oct 2019, 07:33pm -*

The Hidden Truths About Stop loss In Trading [Quant Insti]

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

*- 11 months ago, 7 Oct 2019, 06:55pm -*

Trading Using Machine Learning In Python [Quant Insti]

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

*- 11 months ago, 26 Sep 2019, 09:45am -*

K-Means Clustering Algorithm For Pair Selection In Python [Quant Insti]

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

*- 1 year ago, 11 Sep 2019, 11:42pm -*

Neural Network In Python: Introduction, Structure and Trading Strategies [Quant Insti]

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

*- 1 year ago, 5 Sep 2019, 12:47pm -*

Trading China A-Share Stocks Based On Social Media Data Analysis In Python [Quant Insti]

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

*- 1 year ago, 21 Jun 2019, 09:47am -*

Gini Index For Decision Trees [Quant Insti]

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

*- 1 year ago, 18 Apr 2019, 10:48am -*

Intro to Hidden Markov Chains [Quant Insti]

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

*- 1 year ago, 2 Apr 2019, 09:37am -*

Random Forest Algorithm In Trading Using Python [Quant Insti]

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

*- 1 year ago, 12 Mar 2019, 10:33am -*

Top 10 Machine Learning Algorithms For Beginners [Quant Insti]

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

*- 1 year ago, 14 Feb 2019, 12:36pm -*

Cross Validation in Machine Learning Trading Models [Quant Insti]

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

*- 1 year ago, 28 Jan 2019, 09:52pm -*

Algorithmic Trading Regulations - European Union [Quant Insti]

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

*- 1 year ago, 19 Dec 2018, 11:29am -*

Random Walk Simulation Of Stock Prices Using Geometric Brownian Motion [Quant Insti]

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

*- 1 year ago, 14 Dec 2018, 10:44am -*

RNN, LSTM, GRU For Trading [Quant Insti]

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

*- 1 year ago, 6 Dec 2018, 10:49am -*

Decision Tree For Trading Using Python [Quant Insti]

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,

*- 1 year ago, 22 Nov 2018, 06:44pm -*

Forward Propagation In Neural Networks [Quant Insti]

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

*- 1 year ago, 6 Nov 2018, 10:51am -*

Deep Learning - Artificial Neural Network Using Tensorflow In Python [Quant Insti]

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

*- 2 years ago, 10 Sep 2018, 11:22am -*

Optimal Portfolio Construction Using Machine Learning [Quant Insti]

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

*- 2 years ago, 11 Aug 2018, 10:46am -*

Kalman Filter Techniques And Statistical Arbitrage In China's Futures Market In Python [Quant Insti]

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

*- 2 years ago, 4 Jun 2018, 08:30am -*

Using Quadratic Discriminant Analysis To Optimize An Intraday Momentum Strategy [Quant Insti]

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

*- 2 years ago, 24 May 2018, 09:51am -*

The Unconventional Guide To The Best Websites For Quants [Quant Insti]

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

*- 2 years ago, 6 Feb 2018, 09:59am -*

Machine Learning K-Nearest Neighbors (KNN) Algorithm In Python [Quant Insti]

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

*- 2 years ago, 24 Jan 2018, 03:00pm -*

Gold Price Prediction Using Machine Learning In Python [Quant Insti]

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)

*- 2 years ago, 22 Jan 2018, 02:43pm -*

Covered Call Options Strategy using Machine Learning [Quant Insti]

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

*- 2 years ago, 17 Jan 2018, 01:10pm -*

Machine Learning Classification Strategy In Python [Quant Insti]

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

*- 2 years ago, 18 Dec 2017, 09:26am -*

How To Get Funding For Your Trading Strategy [Quant Insti]

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

*- 2 years ago, 10 Nov 2017, 01:03pm -*

Trading Using Decision Tree Classifier Part 1 [Quant Insti]

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

*- 2 years ago, 31 Oct 2017, 11:50am -*

Tips To Start Your Own Business In Algorithmic Trading [Quant Insti]

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

*- 2 years ago, 16 Oct 2017, 11:03am -*

Option Chain Extraction For NSE Stocks Using Python [Quant Insti]

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

*- 2 years ago, 21 Sep 2017, 12:31pm -*

Statistical Arbitrage Using Pair Trading In The Mexican Stock Market [Quant Insti]

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

*- 3 years ago, 29 Aug 2017, 11:03am -*

Dispersion Trading Using Options [Quant Insti]

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

*- 3 years ago, 29 Jun 2017, 05:30am -*

Machine Learning In Python for Trading [Quant Insti]

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

*- 3 years ago, 19 Jun 2017, 10:38am -*

An Example of Python Trading Strategy in Quantiacs Platform [Quant Insti]

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

*- 3 years ago, 23 May 2017, 11:18am -*