This article is going to be a bit special. I am going to test the latest release from Yahoo Finance API for Python, which provide the possibility to get live data with less than a second lag for free.
Blueshift By QuantInsti: A Powerful New Tool for Algorithmic Trading. How to backtest, optimize, and automate your trading strategy using Blueshift integrated with Alpaca’s API.
In this post I will walk you gently to build your algorithmic trading code in R. R has several powerful quantitative finance libraries because of its long development history including Quantmod, TTR, PerformanceAnalytics.
Pragmatic Deep Learning Model for Forex Forecasting. Using LSTM and TensorFlow on the GBPUSD Time Series for multi-step prediction
Building an algorithmic bot, in a commercial platform, to trade based on a model’s prediction. In this tutorial, you'll see How to Use a TensorFlow Deep Learning Model for Forex Trading
Creating and Back-Testing a Pairs Trading Strategy in Python. Creating a Systematic Equity Pairs Trading Strategy in Python.
Let’s see how to calculate Pivot Points for day trading in Python. In this article, I explain how they work.
Sentiment Analysis with Vader and Algorithmic Trading.
In this article, you will learn to get the stock market data such as price, volume and fundamental data using Python packages. (In less than 3 lines of code)
Can Unsupervised learning create support and resistance lines? In this article, I will use the K-means clustering algorithm to find these different support and resistance channels, and trade with these insights.
You think of a program making money for you while you sip Mai Tais and smoke the finest ganja on the beach in Jamaica. Learn how to build trading bots with TD Ameritrade’s API and know that great reward comes with great risk.
This gives a good idea of how our model performs it not only tells us how many classifications are done are correctly and how many of them are classified incorrectly but it tells more.
With the evolution of technology rapidly evolving, so do strategies in the stock market. In this post, I’ll go over how I created a SMA(Short Moving Average) strategy.
Estimating Currency Volatility Using GARCH: How GARCH is used to model asset price volatility. Asset prices have a high degree of stochastic trends inherent in the time series.
Bayesian Pairs Trading using Corporate Supply Chain Data. Enables investors to construct hedges and build statistical arbitrage strategies for a given corporate supply chain.
Designing a simple algorithmic trading strategy using technical indicators. Have you ever been intrigued with how hedge funds build trading strategies using complex technical indicators?
Placing orders to the brokerage by using an API. Full reference code available at this repo.
Using Evolutionary Computing for Parametrical Optimization of Trading Strategies. The first part of this series can be found here: Algorithmic Trading- a novelty still inaccessible to the masses.
Forecasting S&P 500 Stock Index Using Classification Models. In this article, I will go over multiple classification algorithms in an attempt to find suitable models for market forecasting.
A simple algorithm for finding the best moving average for every stock or ETF.An algorithm to find the best moving average for stock trading.