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Here are a few examples to give you a better idea of what bt is all about.
SMA Strategy¶
In PAVE version 2.1 alpha, it is possible to compute nHour averages using the -NhourAverage command line option, or from PAVE's main GUI select the Graphics menu item and then select the menu Create N-Hour Average Tile Plot. Prior to PAVE version 2.1 it was possible to do this with PAVE, but the syntax was currently difficult.
Let’s start off with a Simple Moving Average (SMA) strategy. We will start with a simple version of the strategy, namely:
Select the securities that are currently above their 50 day moving average
Weigh each selected security equally
Rebalance the portfolio to reflect the target weights
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This should be pretty simple to build. The only thing missing above is the calculation of the simple moving average. When should this take place?
Given the flexibility of bt, there is no strict rule. The average calculation could be performed in an Algo, but that would be pretty inefficient. A better way would be to calculate the moving average at the beginning - before starting the backtest. After all, all the data is known in advance.
Now that we know what we have to do, let’s get started. First we will download some data and calculate the simple moving average.
It’s always a good idea to plot your data to make sure it looks ok. So let’s see how the data + sma plot looks like.
Looks legit.
Now that we have our data, we will need to create our security selection logic. Let’s create a basic Algo that will select the securities that are above their moving average.
Before we do that, let’s think about how we will code it. We could pass the SMA data and then extract the row (from the sma DataFrame) on the current date, compare the values to the current prices, and then keep a list of those securities where the price is above the SMA. This is the most straightforward approach. However, this is not very re-usable because the logic within the Algo will be quite specific to the task at hand and if we wish to change the logic, we will have to write a new algo.
For example, what if we wanted to select securities that were below their sma? Or what if we only wanted securities that were 5% above their sma?
What we could do instead is pre-calculate the selection logic DataFrame (a fast, vectorized operation) and write a generic Algo that takes in this boolean DataFrame and returns the securities where the value is True on a given date. This will be must faster and much more reusable. Let’s see how the implementation looks like.
So there we have it. Our selection Algo.
Note
By the way, this Algo already exists - I just wanted to show you how you would code it from scratch.Hereisthecode.
All we have to do now is pass in a signal matrix. In our case, it’s quite easy:
Simple, concise and more importantly, fast! Let’s move on and test the strategy.
So just to recap, we created the strategy, created the backtest by joining Strategy+Data, and ran the backtest. Let’s see the results.
Nothing stellar but at least you learnt something along the way (I hope).
Oh, and one more thing. If you were to write your own “library” of backtests, you might want to write yourself a helper function that would allow you to test different parameters and securities. That function might look something like this:
This function allows us to easily generate backtests. We could easily compare a few different SMA periods. Also, let’s see if we can beat a long-only allocation to the SPY.
And there you have it. Beating the market ain’t that easy!
SMA Crossover Strategy¶
Let’s build on the last section to test a moving average crossover strategy. The easiest way to achieve this is to build an Algo similar to SelectWhere, but for the purpose of setting target weights. Let’s call this algo WeighTarget. This algo will take a DataFrame of target weights that we will pre-calculate.
Basically, when the 50 day moving average will be above the 200-day moving average, we will be long (+1 target weight). Conversely, when the 50 is below the 200, we will be short (-1 target weight).
Here’s the WeighTarget implementation (this Algo also already exists in the algos module):
So let’s start with a simple 50-200 day sma crossover for a single security.
Ok so we downloaded our data, calculated the simple moving averages, and then we setup our target weight (tw) DataFrame. Let’s take a look at our target weights to see if they make any sense.
As mentioned earlier, it’s always a good idea to plot your strategy data. It is usually easier to spot logic/programming errors this way, especially when dealing with lots of data.
Now let’s move on with the Strategy & Backtest.
Ok great so there we have our basic moving average crossover strategy.
Exploring the Tree Structure¶
So far, we have explored strategies that allocate capital to securities. But what if we wanted to test a strategy that allocated capital to sub-strategies?
The most straightforward way would be to test the different sub-strategies, extract their equity curves and create “synthetic securities” that would basically just represent the returns achieved from allocating capital to the different sub-strategies.
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Let’s see how this looks:
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As we can see above, the process is a bit more involved, but it works. It is not very elegant though, and obtaining security-level allocation information is problematic.
Luckily, bt has built-in functionality for dealing with strategies of strategies. It uses the same general principal as demonstrated above but does it seamlessly. Basically, when a strategy is a child of another strategy, it will create a “paper trade” version of itself internally. As we run our strategy, it will run its internal “paper version” and use the returns from that strategy to populate the price property.
This means that the parent strategy can use the price information (which reflects the returns of the strategy had it been employed) to determine the appropriate allocation. Again, this is basically the same process as above, just packed into 1 step.
Perhaps some code will help:
So there you have it. Simpler, and more complete.