Welcome to your FREE Algorithmic Trading resource where you will learn how to develop profitable algorithmic trading strategies and gain a career in quantitative trading.
In this article the concept of automated execution will be discussed. Broadly speaking, this is the process of allowing a trading strategy, via an electronic trading platform, to generate trade execution signals without any subsequent human intervention. Most of the systems discussed on QuantStart to date have been designed to be implemented as automated execution strategies. The article will describe software packages and programming languages that provide both backtesting and automated execution capabilities. Read more...
In this article we are going to consider our first intraday trading strategy. It will be using a classic trading idea, that of "trading pairs". In this instance we are going to be making use of two Exchange Traded Funds (ETFs), SPY and IWM, which are traded on the New York Stock Exchange (NYSE) and attempt to represent the US stock market indices, the S&P500 and the Russell 2000, respectively. Read more...
A while back we discussed how to set up an Interactive Brokers demo account. Interactive Brokers is one of the main brokerages used by retail algorithmic traders due to its relatively low minimal account balance requirements (10,000 USD) and (relatively) straightforward API. In this article we will make use of a demo account to automate trades against the Interactive Brokers API, via Python and the IBPy plugin. Read more...
In a previous article on QuantStart we investigated how to download free futures data from Quandl. In this article we are going to discuss the characteristics of futures contracts that present a data challenge from a backtesting point of view. In particular, the notion of the "continuous contract" and "roll returns". We will outline the main difficulties of futures and provide an implementation in Python with pandas that can partially alleviate the problems. Read more...
Recently on QuantStart we've discussed machine learning, forecasting, backtesting design and backtesting implementation. We are now going to combine all of these previous tools to backtest a financial forecasting algorithm for the S&P500 US stock market index by trading on the SPY ETF. Read more...
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