In the first three posts of this mini-series on pairs trading with Zorro and R, we: Implemented a Kalman filter in R Implemented a simple pairs trading algorithm in Zorro Connected Zorro and R and exchanged data between the two platforms In this fourth and final post, we’re going to put it all together and …
In the last two posts, we implemented a Kalman filter in R for calculating a dynamic hedge ratio, and presented a Zorro script for backtesting and trading price-based spreads using a static hedge ratio. The goal is to get the best of both worlds and use our dynamic hedge ratio within the Zorro script. Rather …
In our previous post, we looked into implementing a Kalman filter in R for calculating the hedge ratio in a pairs trading strategy. You know, light reading… We saw that while R makes it easy to implement a relatively advanced algorithm like the Kalman filter, there are drawbacks to using it as a backtesting tool. …
This Kalman Filter Example post is the first in a series where we deploy the Kalman Filter in pairs trading. Be sure to follow our progress in Part 2: Pairs Trading in Zorro, and Part 3: Putting It All Together. Anyone who’s tried pairs trading will tell you that real financial series don’t exhibit truly …
Chart patterns have long been a favourite of the technical analysis community. Triangles, flags, pennants, cups, heads and shoulders…. Name a shape, someone somewhere is using it to predict market behaviour. But, we need to find out if there is a grain of truth or reliability in these patterns. Can attempts to objectively measure these …
When tinkering with trading ideas, have you ever wondered whether a certain variable might be correlated with the success of the trade? For instance, maybe you wonder if your strategy tends to do better when volatility is high? In this case, you can get very binary feedback by, say, running backtests with and without a …
As traders, we like to get waaaay ahead of ourselves in the race to understand and exploit the financial markets. One symptom of our eagerness is often wrongly assuming that more complexity = more profit. This assumption can lead us down long and unnecessary rabbit holes and away from the more mundane fundamentals that account …
In part 1 of this series, we talked about how a market-savvy systematic trader would approach a period of drawdown in a trading strategy. Specifically, they’d: do the best job possible of designing and building their trading strategy to be robust to a range of future market conditions chill out and let the strategy do …
Imagine you’ve tinkered for days or even weeks, perfecting a strategy idea that’s showing a whole lot of promise. You’ve meticulously tweaked a mouth-watering Sharpe Ratio out of your backtests….it even survived costs. YES! Systems go, let’s trade it. Imagine this new strategy enters a drawdown.…maybe a lengthy one….maybe from day one! How would you …
Some price series are mean reverting some of the time, but it is also possible to create portfolios which are specifically constructed to have mean-reverting properties. Series that can be combined to create stationary portfolios are called cointegrating, and there are a bunch of statistical tests for this property. We’ll return to these shortly. While …
