It's been a while since we've considered the event-driven backtester, which we began discussing in this article. In Part VI I described how to code a stand-in ExecutionHandler model that worked for a

Original: FMZ Quant www.fmz.com The “real stuff” of quantitative trading gathering place where you can truly benefit from. NO.1 Warren Buffett's mentor, Benjamin Graham, once mentioned in the book

In the last article on the Event-Driven Backtester series we considered a basic ExecutionHandler hierarchy. In this article we are going to discuss how to assess the performance of a strategy post-bac

This article continues the discussion of event-driven backtesters in Python. In the previous article we considered a portfolio class hierarchy that handled current positions, generated trading orders

In the previous article on event-driven backtesting we considered how to construct a Strategy class hierarchy. Strategies, as defined here, are used to generate signals, which are used by a portfolio

The discussion of the event-driven backtesting implementation has previously considered the event-loop, the event class hierarchy and the data handling component. In this article a Strategy class hier

In the previous two articles of the series we discussed what an event-driven backtesting system is and the class hierarchy for the Event object. In this article we are going to consider how market dat

In the last article we described the concept of an event-driven backtester. The remainder of this series of articles will concentrate on each of the separate class hierarchies that make up the overall

We've spent the last couple of months on QuantStart backtesting various trading strategies utilising Python and pandas. The vectorised nature of pandas ensures that certain operations on large dataset

In the first article on successful backtesting we discussed statistical and behavioural biases that affect our backtest performance. We also discussed software packages for backtesting, including Exce

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

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

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 backtest

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 bey

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 isola

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

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 perfo

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 wou

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 mathematica

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 t