Datasets

CFD

Introduction

This page explains how to request, manipulate, and visualize historical CFD data.

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 a CFD security:

  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;
  3. Create a QuantBook.
  4. var qb = new QuantBook();
    qb = QuantBook()
  5. Call the AddCfd method with a ticker and then save a reference to the CFD Symbol.
  6. var spx = qb.AddCfd("SPX500USD").Symbol;
    var usb = qb.AddCfd("USB10YUSD").Symbol;
    spx = qb.AddCfd("SPX500USD").Symbol
    usb = qb.AddCfd("USB10YUSD").Symbol

To view all of the available contracts, see Supported Assets.

Get Historical Data

You need a subscription before you can request historical data for a security. 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 security dimension, you can request historical data for a single CFD contract, a subset of the contracts you created subscriptions for in your notebook, or all of the contracts 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(spx, 10);
var subsetHistorySlice = qb.History(new[] {spx, usb}, 10);
var allHistorySlice = qb.History(10);

// QuoteBar objects
var singleHistoryQuoteBars = qb.History<QuoteBar>(spx, 10);
var subsetHistoryQuoteBars = qb.History<QuoteBar>(new[] {spx, usb}, 10);
var allHistoryQuoteBars = qb.History<QuoteBar>(qb.Securities.Keys, 10);
# DataFrame
single_history_df = qb.History(spx, 10)
subset_history_df = qb.History([spx, usb], 10)
all_history_df = qb.History(qb.Securities.Keys, 10)

# Slice objects
all_history_slice = qb.History(10)

# QuoteBar objects
single_history_quote_bars = qb.History[QuoteBar](spx, 10)
subset_history_quote_bars = qb.History[QuoteBar]([spx, usb], 10)
all_history_quote_bars = qb.History[QuoteBar](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(spx, TimeSpan.FromDays(3));
var subsetHistorySlice = qb.History(new[] {spx, usb}, TimeSpan.FromDays(3));
var allHistorySlice = qb.History(10);

// QuoteBar objects
var singleHistoryQuoteBars = qb.History<QuoteBar>(spx, TimeSpan.FromDays(3), Resolution.Minute);
var subsetHistoryQuoteBars = qb.History<QuoteBar>(new[] {spx, usb}, TimeSpan.FromDays(3), Resolution.Minute);
var allHistoryQuoteBars = qb.History<QuoteBar>(qb.Securities.Keys, TimeSpan.FromDays(3), Resolution.Minute);

// Tick objects
var singleHistoryTicks = qb.History<Tick>(spx, TimeSpan.FromDays(3), Resolution.Tick);
var subsetHistoryTicks = qb.History<Tick>(new[] {spx, usb}, TimeSpan.FromDays(3), Resolution.Tick);
var allHistoryTicks = qb.History<Tick>(qb.Securities.Keys, TimeSpan.FromDays(3), Resolution.Tick);
# DataFrame
single_history_df = qb.History(spx, timedelta(days=3))
subset_history_df = qb.History([spx, usb], timedelta(days=3))
all_history_df = qb.History(qb.Securities.Keys, timedelta(days=3))

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

# QuoteBar objects
single_history_quote_bars = qb.History[QuoteBar](spx, timedelta(days=3), Resolution.Minute)
subset_history_quote_bars = qb.History[QuoteBar]([spx, usb], timedelta(days=3), Resolution.Minute)
all_history_quote_bars = qb.History[QuoteBar](qb.Securities.Keys, timedelta(days=3), Resolution.Minute)

# Tick objects
single_history_ticks = qb.History[Tick](spx, timedelta(days=3), Resolution.Tick)
subset_history_ticks = qb.History[Tick]([spx, usb], timedelta(days=3), Resolution.Tick)
all_history_ticks = qb.History[Tick](qb.Securities.Keys, timedelta(days=3), Resolution.Tick)

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, 2, 1);

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

// QuoteBar objects
var singleHistoryQuoteBars = qb.History<QuoteBar>(spx, startTime, endTime, Resolution.Minute);
var subsetHistoryQuoteBars = qb.History<QuoteBar>(new[] {spx, usb}, startTime, endTime, Resolution.Minute);
var allHistoryQuoteBars = qb.History<QuoteBar>(qb.Securities.Keys, startTime, endTime, Resolution.Minute);

// Tick objects
var singleHistoryTicks = qb.History<Tick>(spx, startTime, endTime, Resolution.Tick);
var subsetHistoryTicks = qb.History<Tick>(new[] {spx, usb}, startTime, endTime, Resolution.Tick);
var allHistoryTicks = qb.History<Tick>(qb.Securities.Keys, startTime, endTime, Resolution.Tick);
start_time = datetime(2021, 1, 1)
end_time = datetime(2021, 2, 1)

# DataFrame
single_history_df = qb.History(spx, start_time, end_time)
subset_history_df = qb.History([spx, usb], 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)

# QuoteBar objects
single_history_quote_bars = qb.History[QuoteBar](spx, start_time, end_time, Resolution.Minute)
subset_history_quote_bars = qb.History[QuoteBar]([spx, usb], start_time, end_time, Resolution.Minute)
all_history_quote_bars = qb.History[QuoteBar](qb.Securities.Keys, start_time, end_time, Resolution.Minute)

# Tick objects
single_history_ticks = qb.History[Tick](spx, start_time, end_time, Resolution.Tick)
subset_history_ticks = qb.History[Tick]([spx, usb], start_time, end_time, Resolution.Tick)
all_history_ticks = qb.History[Tick](qb.Securities.Keys, start_time, end_time, Resolution.Tick)

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

Resolutions

The following table shows the available resolutions and data formats for CFD subscriptions:

ResolutionTradeBarQuoteBarTrade TickQuote Tick
Tickgreen check
Second
green check
Minute
green check
Hour
green check
Daily
green check

Markets

The only market available for CFD contracts is Market.Oanda.

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 CFD Symbol and the second level is the EndTime of the data sample. The columns of the DataFrame are the data properties.

DataFrame of two CFD contracts

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

all_history_df.loc[spx]  # or all_history_df.loc['SPX500USD']
DataFrame of one CFD

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

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

If you request historical data for multiple CFD contracts, you can transform the DataFrame so that it's a time series of close values for all of the CFD contracts. To transform the DataFrame, select the column you want to display for each CFD contract 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 CFD contract 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 CFD subscriptions. To avoid issues, check if the Slice contains data for your CFD contract before you index it with the CFD Symbol.

foreach (var slice in allHistorySlice)
{
    if (slice.QuoteBars.ContainsKey(spx))
    {
        var quoteBar = slice.QuoteBars[spx];
    }
}
for slice in all_history_slice:
    if slice.QuoteBars.ContainsKey(spx):
        quote_bar = slice.QuoteBars[spx]

You can also iterate through each QuoteBar in the Slice.

foreach (var slice in allHistorySlice)
{
    foreach (var kvp in slice.QuoteBars)
    {
        var symbol = kvp.Key;
        var quoteBar = kvp.Value;
    }
}
for slice in all_history_slice:
    for kvp in slice.QuoteBars:
        symbol = kvp.Key
        quote_bar = kvp.Value

QuoteBar Objects

If the History method returns QuoteBar objects, iterate through the QuoteBar objects to get each one.

foreach (var quoteBar in singleHistoryQuoteBars)
{
    Console.WriteLine(quoteBar);
}
for quote_bar in single_history_quote_bars:
    print(quote_bar)

If the History method returns QuoteBars, iterate through the QuoteBars to get the QuoteBar of each CFD contract. The QuoteBars may not have data for all of your CFD subscriptions. To avoid issues, check if the QuoteBars object contains data for your security before you index it with the CFD Symbol.

foreach (var quoteBars in allHistoryQuoteBars)
{
    if (quoteBars.ContainsKey(spx))
    {
        var quoteBar = quoteBars[spx];
    }
}
for quote_bars in all_history_quote_bars:
    if quote_bars.ContainsKey(spx):
        quote_bar = quote_bars[spx]

You can also iterate through each of the QuoteBars.

foreach (var quoteBars in allHistoryQuoteBars)
{
    foreach (var kvp in quoteBars)
    {
        var symbol = kvp.Key;
        var quoteBar = kvp.Value;
    }
}
for quote_bars in all_history_quote_bars:
    for kvp in quote_bars:
        symbol = kvp.Key
        quote_bar = kvp.Value

Tick Objects

If the History method returns Tick objects, iterate through the Tick objects to get each one.

foreach (var tick in singleHistoryTicks)
{
    Console.WriteLine(tick);
}
for tick in single_history_ticks:
    print(tick)

If the History method returns Ticks, iterate through the Ticks to get the Tick of each CFD contract. The Ticks may not have data for all of your CFD subscriptions. To avoid issues, check if the Ticks object contains data for your security before you index it with the CFD Symbol.

foreach (var ticks in allHistoryTicks)
{
    if (ticks.ContainsKey(spx))
    {
        var tick = ticks[spx];
    }
}
for ticks in all_history_ticks:
    if ticks.ContainsKey(spx):
        ticks = ticks[spx]

You can also iterate through each of the Ticks.

foreach (var ticks in allHistoryTicks)
{
    foreach (var kvp in ticks)
    {
        var symbol = kvp.Key;
        var tick = kvp.Value;
    }
}
for ticks in all_history_ticks:
    for kvp in ticks:
        symbol = kvp.Key
        tick = kvp.Value

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 CFD data.

You need some historical CFD 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

Follow these steps to plot candlestick charts:

  1. Get some historical data.
  2. history = qb.History(spx, datetime(2021, 11, 26), datetime(2021, 12, 8), Resolution.Daily).loc[spx]
  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='SPX CFD 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 security.

Line Chart

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

  1. Get some historical data.
  2. history = qb.History([spx, usb], datetime(2021, 11, 26), datetime(2021, 12, 8), Resolution.Daily)
  3. Select the data to plot.
  4. pct_change = history['close'].unstack(0).pct_change().dropna()
  5. Call the plot method on the pandas object.
  6. pct_change.plot(title="Close Price %Change", figsize=(15, 10))
  7. Show the plot.
  8. plt.show()

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

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