Other Kinds of Graphical Output?

QuantConnect's built-in charting capabilities are primarily designed for time series data. However, you can create different types of charts using the SeriesType enumeration. The supported chart types include Line, Scatter, Candle, Bar, Flag, StackedArea, Pie, Treemap, Heatmap, and Scatter3d.

For a column graph, you might want to use the SeriesType.Bar chart. However, please note that the Plot method only accepts one numerical value per time step, so it might not be ideal for data that does not have any time dependence.

Here is an example of how you can use the Plot method in Python:

self.Plot("ChartName", "SeriesName", value)

If you want to create more complex charts that are not time-dependent, you might want to consider saving your plot data in the Object Store and then loading it into the Research Environment. In the Research Environment, you can create other types of charts with third-party charting packages.

For more information, you can refer to the QuantConnect Documentation on charting.

Please note that if you run your algorithm in QuantConnect Cloud, there is a limit of 4,000 points per chart due to the intensive data generation. If you exceed this limit, the chart update will be skipped.

Thank you! I was wondering if you could give me an example of saving data (from a dict) in the ObjectStore in QuantConnect and loading it in the research environment! There's no pressure if you're too busy. :)

Sure, I can provide an example of saving data from a dictionary into the Object Store in QuantConnect and then loading it in the Research Environment.

First, let's look at how to save a dictionary into the Object Store. Here's an example in Python:

# Assume we have a dictionary
my_dict = {"key1": "value1", "key2": "value2"}

# Convert the dictionary to a JSON string
json_string = json.dumps(my_dict)

# Save the JSON string in the Object Store
self.ObjectStore.Save("my_dict", json_string)

Now, let's load this data in the Research Environment:

# Load the JSON string from the Object Store
json_string = qb.ObjectStore.Read("my_dict")

# Convert the JSON string back to a dictionary
my_dict = json.loads(json_string)

# Now you can use the dictionary
print(my_dict)

Please note that you need to have the necessary permissions to save data in the Object Store. You can check your permissions and edit your storage plan if needed. For more information, you can refer to the QuantConnect Documentation on storage permissions.