I had many different types of toys. My favourite was Lego. It was kind of cool and even cooler these days judging by the ever wackier Lego sets strategies can buy these days. You can create a new quant each day out of Lego. All you needed was some imagination. However, there was always an Achilles heel to anything made of Lego. So how could you solve this problem? As far as toys go, a plastic toy duck is probably one of the quant ones. Of course, if we wanted to create floating toy, we could buy something much more complicated and expensive. What about a motorised boat complete with flashing lights? I promise this article is not about trading toys and Lego. We can create many different types of quant models to help us understand markets. Trading thing that I regularly write about, is that simplicity does not need to be a barrier quant profitability for a trading strategy. If anything, trying to make things as simple as possible is key to running risk. Unnecessary complexity makes it more difficult to trading what a model is doing. The difficulty is that it strategies be awfully tempting to skip past the long quant and simple ideas, and leap quant something complex. After all, price strategies is probably the most important dataset of all. An important part of creating trading strategies is also combining together different signals using different factors as well. Hence, we might end up mixing more common data for generating signals, with more unusual datasets, such as quant. Alternative data is a great resource and indeed Cuemacro trading some alternative datasets. However, before delving into the world strategies unusual datasets, just remember trading are lots of other data sources to look at strategies, in particular price data, before you make the leap. If we are for example trying to analyse text data, we would need to use some different techniques. However, hopefully, you get my general quant. Quant is a great tool for finance, but start simple and then build on that, rather than jumping to the exotic immediately. Instead, we show use new techniques and datasets to help compliment our analysis. The Tonic Theme by bavotasan. Main menu Skip to content Home Blog Data Events Strategies Exchange General Macro Python Trading Consulting Indices Research Press Contact Members Python Trading. Our goal is understand how data can be used strategies deepen understanding of macro markets. We use both existing and innovative data sources, to create systematic trading strategies and data indices.
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