Pooja Jaiswal
Learn advanced trading strategies such as market microstructure, market making, turtle trading approach, and high-frequency trading strategies.
Source: https://blog.quantinsti.com/tag/more-trading-strategies/
Introduction to mean reversion strategies and learn building blocks of a simple mean reversion strategy using three steps.
Mean reversion theory states that security prices and economic indicators such as interest rates will tend to revert to the historical mean prices.
Topics covered:
Learn what Market Making is all about, the market making strategy, How they make profits, what are the benefits and the role of automated trading in making markets.
In today's ever-changing markets, market participants play an extremely imperative role. In Market Making, a key market participant in an exchange's trading structure is the Market Maker.
Market Maker plays a key role in increasing the liquidity in the market.
Mean reversion trading strategy is the most known and commonly used strategy. But, what makes it so interesting and why does it work?
Mean reversion is a simple concept of the stock prices eventually reverting to the mean after deviating from the mean for a certain period of time due to any significant changes in the economy, the organization etc. Let us find out more about mean reversion in time series with this blog.
In this blog, we will be learning a trading term known as the Bear Trap. We will discuss the meaning of this word, learn how to identify a trap, and learn how to protect oneself from losses when caught in such a trap.
Key Details:
There are several kinds of technical indicators that are used to analyse and detect the direction of movement of the price. For instance, momentum trading, mean reversion trading etc. Traders use indicators usually to predict future price levels while trading. Let us find out how to build technical indicators using Python with this blog
In this blog we will look into free and paid solutions, all of which have an easy to use Python Stock API wrapper around their services. For each type of solution, we will look at which asset type (stocks, ETF’s, FX, commodity futures, options, treasury and even crypto). These resources provide information for and how to retrieve it in various ways with - of course - an example in python code.
This article talks about the Butterfly Options Strategy, a combination of Bull Spread and Bear Spread, a Neutral Trading Strategy that has limited risk options. Traders and investors consider the movement in the markets as an opportunity to earn profits
Learn step-by-step on how to calculate VaR in Excel and Python using Historical Method and Variance-Covariance approach. Downloadable files for Value at Risk (VaR) calculation are available.
We'll cover the following about Value At Risk (VaR):
Through this blog, learn how to calculate the covariance matrix and analyse the portfolio variance.
Key points:
Learn all about Pairs trading! Covers Pairs trading basics and concepts like correlation, cointegration and Z-score. Create a pairs trading strategy.
Key points:
Candlestick trading - how can you use it for Momentum Trading? Find out how to create a momentum trading strategy in Excel using candlesticks, with this tutorial.
Candlestick charts basically show the high, low, open and close movements of a tradeable item, which can be a security, a derivative or currency.
Key points:
Learn about the most popular python libraries for algorithmic trading. Learn to create strategies and automate trading using Python platforms.
In this blog, along with popular Python Trading Platforms, we will also be looking at the popular Python Trading Libraries for various functions like:
Learn to work with historical market data to implement linear regression models on Python and R, with reusable codes.
In this post, we apply our knowledge of regression to actual financial data. We will model the relationships from the previous post using Python and R. I run it in both (with downloadable code where applicable) so that readers fluent in one language or application can hopefully get more intuition on its implementation in others.
Key Details
Implementation using Python
Implementation using R
In this blog, we use pre-packed Python Machine Learning libraries to use Logistic Regression classifier for predicting the stock price movement.
Logistic Regression is a type of supervised learning which group the dataset into classes by estimating the probabilities using a logistic/sigmoid function. We can use pre-packed Python Machine Learning libraries to use Logistic Regression classifier for predicting the stock price movement.
Decision trees are one of the widely used algorithms for building classification or regression models in data mining and machine learning.
In this blog you will learn, how you can predict stock movements (that’s either up or down) with the help of ‘Decision Trees’, one of the most commonly used ML algorithms.
In this post, we will cover the popular ARIMA forecasting model to predict returns on a stock and demonstrate a step-by-step process of ARIMA modeling using R programming.
China’s futures market - This project focuses to identify opportunities using Statistical Arbitrage, various Pair trading techniques, and Python. A project by EPATian Xing Tao.
This Project by Jirong Huang explains how you can use Time Series Momentum (TSMOM) and Continuous Forecasts (CF) to create a trend following trading strategy in Futures.
In this post, we will back-test our trading strategy in R. The quantmod package has made it really easy to pull historical data from Yahoo Finance.
Forex trading is the most popular kind of automated trading among retail traders, where currency pairs are traded based on the speculation of exchange rates.
Topics covered in this blog:
The following slide show is a summary of favourable and profitable outcomes of sentiment analysis, or news analytics, in trading.
This blog post and infographic describes the profitable avenues and pitfalls for quantitative news analytics or as its also called, sentiment analysis.
You will create a machine learning classification python algorithm for predicting the S&P500 price. For this task, you will use the Support Vector Classifier (SVC) algorithm. The classification algorithm builds a model based on the training data, classifies test data into the categories.
This elaborate article covers ten commonly encountered ways in which we make mistakes that lead to the failure of our trading strategy - through an interactive series of examples and quizzes.
Together this article covers ten commonly encountered ways in which we make mistakes through a series of examples/quizzes. The examples are based on various aspects of algo trading like look-ahead bias, overfitting, miscalculation of performance parameters, etc.
This post covers the basics of XGBoost machine learning model, along with a sample of XGBoost stock forecasting model using the “xgboost” package in R programming.
