Category: Experience

Successful Backtesting of Algorithmic Trading Strategies - Part I

This article continues the series on quantitative trading, which started with the Beginner's Guide and Strategy Identification. Both of these longer, more involved articles have been very popular so I'll continue in this vein and provide detail on the topic of strategy backtesting. Algorithmic backtesting requires knowledge of many areas, including psychology, mathematics, statistics, software

Value at Risk (VaR) for Algorithmic Trading Risk Management

Value at Risk (VaR) for Algorithmic Trading Risk Management Estimating the risk of loss to an algorithmic trading strategy, or portfolio of strategies, is of extreme importance for long-term capital growth. Many techniques for risk management have been developed for use in institutional settings. One technique in particular, known as Value at Risk or VaR,

Should You Build Your Own Backtester?

About This Post The post is suitable for those who are beginning quantitative trading as well as those who have had some experience with the area. The post discusses the common pitfalls of backtesting, as well as some uncommon ones! It also looks at the different sorts of backtesting mechanisms as well as the software

Money Management via the Kelly Criterion

Risk and money management are absolutely critical topics in quantitative trading. We have yet to explore these concepts in any reasonable amount of detail beyond stating the different sources of risk that might affect strategy performance. In this article we will be considering a quantitative means of managing account equity in order to maximise long-term

Sharpe Ratio for Algorithmic Trading Performance Measurement

When carrying out an algorithmic trading strategy it is tempting to consider the annualised return as the most useful performance metric. However, there are many flaws with using this measure in isolation. The calculation of returns for certain strategies is not completely straightforward. This is especially true for strategies that aren't directional such as market-neutral

Continuous Futures Contracts for Backtesting Purposes

Brief Overview of Futures Contracts Futures are a form of contract drawn up between two parties for the purchase or sale of a quantity of an underlying asset at a specified date in the future. This date is known as the delivery or expiration. When this date is reached the buyer must deliver the physical

Research Backtesting Environments in Python with pandas

Backtesting is the research process of applying a trading strategy idea to historical data in order to ascertain past performance. In particular, a backtester makes no guarantee about the future performance of the strategy. They are however an essential component of the strategy pipeline research process, allowing strategies to be filtered out before being placed

How to make your own trading bot

Foreword I’m certainly not a great programmer, but writing this project taught me a lot (and kept me occupied). Most of my code were done on FMZ.COM, and if I were to refactor the python code I would use a more object orientated model. Nonetheless, I was pleasantly surprised with the results I got and

Top 5 Essential Beginner Books for Algorithmic Trading

Algorithmic trading is usually perceived as a complex area for beginners to get to grips with. It covers a wide range of disciplines, with certain aspects requiring a significant degree of mathematical and statistical maturity. Consequently it can be extremely off-putting for the uninitiated. In reality, the overall concepts are straightforward to grasp, while the

Can Algorithmic Traders Still Succeed at the Retail Level?

It is common, as a beginning algorithmic trader practising at retail level, to question whether it is still possible to compete with the large institutional quant funds. In this article I would like to argue that due to the nature of the institutional regulatory environment, the organisational structure and a need to maintain investor relations, that funds