This series of strategy development articles serves to illustrate the development process used for my algorithmic (or automated) trading strategies. There are two main development workflows, depending on your choice of software. The first uses Metatrader 4’s strategy tester module, where we build a strategy from the ground up by incrementally adding entries, exits and finally filters. […]

A trading idea that is congruent with your market’s characteristics is likely to perform better. Market behaviour can be quantified in countless ways, but I typically focus on historical volatility and ability to trend well. This article will illustrate some methods to quantify these characteristics, giving you a head start in finding a trading solution. […]

After emerging from the rigours of optimization and robustness testing, it is time to verify our strategy’s performance using tick data. Tick data records every price movement over the historical period, helping maximize our backtest precision. The table below shows an example of AUDUSD tick data obtained from Tickstory, with numerous prices per second. We […]

Give yourself a pat on the back if you manage to make it to live trading. To get here, you probably had to develop a better understanding of your markets, backtest countless trading ideas, and finally create a diverse portfolio of strategies. It’s finally time to put real money on the line. If this is […]

Filters impose additional constraints upon your entry signals. They typically benefit the strategy by improving its win rate, or by preventing entry during periods of elevated market risk. Adding filters will invariably reduce the number of trades in your backtest, possibly making its results less reliable. The performance improvement must thus be significant enough to […]