When working in a Jupyter Notebook, roboquant comes with several types of charts that help to understand how the strategy is behaving. This pages shows some examples of those charts. The charts in a Notebook are also interactive, allowing you to zoom in areas of interest of filter only those values you want to see.
|You can try out these charts yourself, since they all come from the charts notebook.|
Plot a metric during a walk-forward test to provide insight into the performance of the strategy during different timeframes. Here we plot the account equity (= cash + positions) and that provides insights how a strategy performs during different timeframes. Clearly we can see that the strategy wasn’t profitable during every timeframe.
Like most charts in roboquant, you can zoom into areas of interest. You can do so with the slider at the bottom of the chart or use the zoom option from the toolbox at the top right.
An even more extensive back-test is to run the experiment over randomly sampled timeframes of a fixed duration. In this example we selected the duration to be 250 trading days. We then plot the results of all samples and this provide a good view of the maximum profit or loss of the strategy during for a certain duration.
Trades provide an overview of the trades for all the assets made during a certain time period. It can plot different aspects of a trade like the fee or volume. But one of the most useful ones is the realized profit and loss.
You can see when certain trades were executed in relation to the price of that asset at the same time. Especially for strategies based only on price actions this helps to validate if trades occurred when expected.
Another interesting visualization is the correlation matrix that shows how different assets are correlated to each other over a certain period of time.
See the asset allocation within the portfolio. If assets are denoted in different currencies, this will convert to the base currency of the account.
Plot a metric for each day of the year to see how it performed over time. This presents a nice overview of good and bad performing days during the back test.
The slider at the top of the chart allows to filter the range of values that is of interest.
Box plot aggregates a metric over a time interval so the different values for certain quantiles can be easily spotted. When we plot the change in account value as in the chart below, it provides quick insights into max draw downs and volatility of our strategy.