Datasets

Alternative Data

Introduction

This page explains how to request, manipulate, and visualize historical alternative data. This tutorial uses the VIX Daily Price dataset from the CBOE as the example dataset.

Prerequisites

Working knowledge of C#.

Working knowedge of Python and pandas. If you are not familiar with pandas, see the pandas documentation.

Create Subscriptions

Follow these steps to subscribe to an alternative dataset from the Dataset Market:

  1. Load the required assembly files and data types.
  2. #load "../Initialize.csx"
    #load "../QuantConnect.csx"
    
    using QuantConnect;
    using QuantConnect.Data;
    using QuantConnect.Algorithm;
    using QuantConnect.Research;
    using QuantConnect.DataSource;
    
  3. Load the dynamic link library (DLL) of the dataset.
  4. To load the DLL of any dataset, type:

    #r "../QuantConnect.DataSource.<nameOfAlternativeDatasetClass>.dll"

    For example, to load the DLL of the CBOE dataset, type:

    #r "../QuantConnect.DataSource.CBOE.dll"
  5. Create a QuantBook.
  6. var qb = new QuantBook();
    qb = QuantBook()
  7. Call the AddData method with the dataset class, a ticker, and a resolution and then save a reference to the alternative data Symbol.
  8. var vix = qb.AddData<CBOE>("VIX", Resolution.Daily).Symbol;
    var v3m = qb.AddData<CBOE>("VIX3M", Resolution.Daily).Symbol;
    vix = qb.AddData(CBOE, "VIX", Resolution.Daily).Symbol
    v3m = qb.AddData(CBOE, "VIX3M", Resolution.Daily).Symbol

    To view the arguments that the AddData method accepts for each dataset, see the dataset listing.

    If you don't pass a resolution argument, the default resolution of the dataset is used by default. To view the supported resolutions and the default resolution of each dataset, see the dataset listing.

Get Historical Data

You need a subscription before you can request historical data for a dataset. On the time dimension, you can request an amount of historical data based on a trailing number of bars, a trailing period of time, or a defined period of time. On the dataset dimension, you can request historical data for a single dataset subscription, a subset of the dataset subscriptions you created in your notebook, or all of the dataset subscriptions in your notebook.

Trailing Number of Bars

To get historical data for a number of trailing bars, call the History method with the Symbol object(s) and an integer.

// Slice objects
var singleHistorySlice = qb.History(vix, 10);
var subsetHistorySlice = qb.History(new[] {vix, v3m}, 10);
var allHistorySlice = qb.History(10);

// CBOE objects
var singleHistoryDataObjects = qb.History<CBOE>(vix, 10);
var subsetHistoryDataObjects = qb.History<CBOE>(new[] {vix, v3m}, 10);
var allHistoryDataObjects = qb.History<CBOE>(qb.Securities.Keys, 10);
# DataFrame
single_history_df = qb.History(vix, 10)
subset_history_df = qb.History([vix, v3m], 10)
all_history_df = qb.History(qb.Securities.Keys, 10)

# Slice objects
all_history_slice = qb.History(10)

# CBOE objects
single_history_data_objects = qb.History[CBOE](vix, 10)
subset_history_data_objects = qb.History[CBOE]([vix, v3m], 10)
all_history_data_objects = qb.History[CBOE](qb.Securities.Keys, 10)

The preceding calls return the most recent bars, excluding periods of time when the exchange was closed.

Trailing Period of Time

To get historical data for a trailing period of time, call the History method with the Symbol object(s) and a TimeSpantimedelta.

// Slice objects
var singleHistorySlice = qb.History(vix, TimeSpan.FromDays(3));
var subsetHistorySlice = qb.History(new[] {vix, v3m}, TimeSpan.FromDays(3));
var allHistorySlice = qb.History(10);

// CBOE objects
var singleHistoryDataObjects = qb.History<CBOE>(vix, TimeSpan.FromDays(3));
var subsetHistoryDataObjects = qb.History<CBOE>(new[] {vix, v3m}, TimeSpan.FromDays(3));
var allHistoryDataObjects = qb.History<CBOE>(TimeSpan.FromDays(3));
# DataFrame
single_history_df = qb.History(vix, timedelta(days=3))
subset_history_df = qb.History([vix, v3m], timedelta(days=3))
all_history_df = qb.History(qb.Securities.Keys, timedelta(days=3))

# Slice objects
all_history_slice = qb.History(timedelta(days=3))

# CBOE objects
single_history_data_objects = qb.History[CBOE](vix, timedelta(days=3))
subset_history_data_objects = qb.History[CBOE]([vix, v3m], timedelta(days=3))
all_history_data_objects = qb.History[CBOE](qb.Securities.Keys, timedelta(days=3))

The preceding calls return the most recent bars or ticks, excluding periods of time when the exchange was closed.

Defined Period of Time

To get historical data for a specific period of time, call the History method with the Symbol object(s), a start DateTimedatetime, and an end DateTimedatetime.

var startTime = new DateTime(2021, 1, 1);
var endTime = new DateTime(2021, 3, 1);

// Slice objects
var singleHistorySlice = qb.History(vix, startTime, endTime);
var subsetHistorySlice = qb.History(new[] {vix, v3m}, startTime, endTime);
var allHistorySlice = qb.History(startTime, endTime);

// CBOE objects
var singleHistoryDataObjects = qb.History<CBOE>(vix, startTime, endTime);
var subsetHistoryDataObjects = qb.History<CBOE>(new[] {vix, v3m}, startTime, endTime);
var allHistoryDataObjects = qb.History<CBOE>(qb.Securities.Keys, startTime, endTime);
start_time = datetime(2021, 1, 1)
end_time = datetime(2021, 3, 1)

# DataFrame
single_history_df = qb.History(vix, start_time, end_time)
subset_history_df = qb.History([vix, v3m], start_time, end_time)
all_history_df = qb.History(qb.Securities.Keys, start_time, end_time)

# Slice objects
all_history_slice = qb.History(start_time, end_time)

# CBOE objects
single_history_data_objects = qb.History[CBOE](vix, start_time, end_time)
subset_history_data_objects = qb.History[CBOE]([vix, v3m], start_time, end_time)
all_history_data_objects = qb.History[CBOE](qb.Securities.Keys, start_time, end_time)

The preceding calls return the bars or ticks that have a timestamp within the defined period of time.

If you do not pass a resolution to the History method, the History method uses the resolution that the AddData method used when you created the subscription.

Wrangle Data

You need some historical data to perform wrangling operations. The process to manipulate the historical data depends on its data type. To display pandas objects, run a cell in a notebook with the pandas object as the last line. To display other data formats, call the print method.

You need some historical data to perform wrangling operations. Use LINQ to wrangle the data and then call the Console.WriteLine method in a Jupyter Notebook to display the data. The process to manipulate the historical data depends on its data type.

DataFrame Objects

If the History method returns a DataFrame, the first level of the DataFrame index is the dataset Symbol and the second level is the EndTime of the data sample. The columns of the DataFrame are the data properties.

DataFrame of two tickers

To select the historical data of a single dataset, index the loc property of the DataFrame with the dataset Symbol.

all_history_df.loc[vix]  # or all_history_df.loc['VIX']
DataFrame of one dataset

To select a column of the DataFrame, index it with the column name.

all_history_df.loc[vix]['close']
Series of close values

If you request historical data for multiple tickers, you can transform the DataFrame so that it's a time series of close values for all of the tickers. To transform the DataFrame, select the column you want to display for each ticker and then call the unstack method.

all_history_df['close'].unstack(level=0)

The DataFrame is transformed so that the column indices are the Symbol of each ticker and each row contains the close value.

Slice Objects

If the History method returns Slice objects, iterate through the Slice objects to get each one. The Slice objects may not have data for all of your dataset subscriptions. To avoid issues, check if the Slice contains data for your ticker before you index it with the dataset Symbol.

foreach (var slice in allHistorySlice)
{
    if (slice.ContainsKey(vix))
    {
        var data = slice[vix];
    }
}
for slice in all_history_slice:
    if slice.ContainsKey(vix):
        data = slice[vix]

Plot Data

Jupyter Notebooks don't currently support libraries to plot historical data, but we are working on adding the functionality. Until we add the functionality, use Python to plot historical alternative data.

You need some historical alternative data to produce plots. You can use many of the supported plotting libraries to visualize data in various formats. For example, you can plot candlestick and line charts.

Candlestick Chart

You can only create candlestick charts for alternative datasets that have open, high, low, and close properties.

Follow these steps to plot candlestick charts:

  1. Get some historical data.
  2. history = qb.History(vix, datetime(2021, 1, 1), datetime(2021, 2, 1)).loc[vix]
  3. Import the plotly library.
  4. import plotly.graph_objects as go
  5. Create a Candlestick.
  6. candlestick = go.Candlestick(x=history.index,
                                 open=history['open'],
                                 high=history['high'],
                                 low=history['low'],
                                 close=history['close'])
  7. Create a Layout.
  8. layout = go.Layout(title=go.layout.Title(text='VIX from CBOE OHLC'),
                       xaxis_title='Date',
                       yaxis_title='Price',
                       xaxis_rangeslider_visible=False)
  9. Create a Figure.
  10. fig = go.Figure(data=[candlestick], layout=layout)
  11. Show the Figure.
  12. fig.show()

    Candlestick charts display the open, high, low, and close prices of the alternative data.

Line Chart

Follow these steps to plot line charts using built-in methods:

  1. Get some historical data.
  2. history = qb.History([vix, v3m], datetime(2021, 1, 1), datetime(2021, 2, 1))
  3. Select the data to plot.
  4. values = history['close'].unstack(0)
  5. Call the plot method on the pandas object.
  6. values.plot(title = 'Close', figsize=(15, 10))
  7. Show the plot.
  8. plt.show()

    Line charts display the value of the property you selected in a time series.

You can also see our Videos. You can also get in touch with us via Discord.

Did you find this page helpful?

Contribute to the documentation: