---
title: "Handling Data"
description: "LEAN passes the data you request to the OnData method so you can make trading decisions. The Slice object groups the data together with a timestamp."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/cfd/handling-data
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/cfd/handling-data.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/03%20Securities/99%20Asset%20Classes/10%20CFD/02%20Handling%20Data
modified: 2026-11-01
index: https://www.quantconnect.com/llms.txt
---

# Handling Data

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Securities > [Asset Classes](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes.md) > [CFD](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/cfd.md) > Handling Data

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

LEAN passes the data you request to the `on_data` method so you can make trading decisions. The `Slice` object that the `on_data` method receives groups all the data together at a single moment in time. To access the `Slice` outside of the `on_data` method, use the `current_slice` property of your algorithm.

All the data formats use `DataDictionary` objects to group data by `Symbol` and provide easy access to information. The plural of the type denotes the collection of objects. For instance, the `QuoteBars` `DataDictionary` is made up of `QuoteBar` objects. To access individual data points in the dictionary, you can index the dictionary with the CFD ticker or `symbol`, but we recommend you use the `symbol`.

To view the resolutions that are available for CFD data, see [Resolutions](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/cfd/requesting-data.md#04-Resolutions).

## Quotes

`QuoteBar` objects are bars that consolidate NBBO quotes from the exchanges. They contain the open, high, low, and close prices of the bid and ask. The `open`, `high`, `low`, and `close` properties of the `QuoteBar` object are the mean of the respective bid and ask prices. If the bid or ask portion of the `QuoteBar` has no data, the `open`, `high`, `low`, and `close` properties of the `QuoteBar` copy the values of either the `bid` or `ask` instead of taking their mean.

![Quotebar decomposition](https://cdn.quantconnect.com/docs/i/dataformat-quotebar.png)

To get the `QuoteBar` objects in the `Slice`, index the `QuoteBars` property of the `Slice` with the CFD `symbol`. If the CFD doesn't actively get quotes or you are in the same time step as when you added the CFD subscription, the `Slice` may not contain data for your `symbol`. To avoid issues, check if the `Slice` contains data for your CFD before you index the `Slice` with the CFD `symbol`.

```python
def on_data(self, slice: Slice) -> None:
    # Obtain the mapped QuoteBar of the symbol if any
    quote_bar = slice.quote_bars.get(self._symbol)   # None if not found
```

You can also iterate through the `QuoteBars` dictionary. The keys of the dictionary are the `Symbol` objects and the values are the `QuoteBar` objects.

```python
def on_data(self, slice: Slice) -> None:
    # Iterate all received Symbol-QuoteBar key-value pairs
    for symbol, quote_bar in slice.quote_bars.items():
        ask_price = quote_bar.ask.close
```

`QuoteBar` objects let LEAN incorporate spread costs into your [simulated trade fills](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/trade-fills/key-concepts.md) to make backtest results more realistic.

`QuoteBar` objects have the following properties:

*Members of `QuantConnect.Data.Market.QuoteBar` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.QuoteBar&language=python*

CFD contracts have only quote data, so use a [`QuoteBarConsolidator`](https://www.quantconnect.com/docs/v2/writing-algorithms/consolidating-data/consolidator-types/time-period-consolidators.md#03-Consolidate-Quote-Bars) to consolidate their data. If you pass a `TradeBarConsolidator` for one of these securities to the `add_consolidator` method, LEAN throws an exception.

## Ticks

`Tick` objects represent a single trade or quote at a moment in time. A trade tick is a record of a transaction for the CFD. A quote tick is an offer to buy or sell the CFD at a specific price.

Trade ticks have a non-zero value for the `quantity` and `price` properties, but they have a zero value for the `bid_price`, `bid_size`, `ask_price`, and `ask_size` properties. Quote ticks have non-zero values for `bid_price` and `bid_size` properties or have non-zero values for `ask_price` and `ask_size` properties. To check if a tick is a trade or a quote, use the `ticktype` property.

In backtests, LEAN groups ticks into one millisecond buckets. In live trading, LEAN groups ticks into ~70-millisecond buckets. To get the `Tick` objects in the `Slice`, index the `Ticks` property of the `Slice` with a `symbol`. If the CFD doesn't actively trade or you are in the same time step as when you added the CFD subscription, the `Slice` may not contain data for your `symbol`. To avoid issues, check if the `Slice` contains data for your CFD before you index the `Slice` with the CFD `symbol`.

```python
def on_data(self, slice: Slice) -> None:
    ticks = slice.ticks.get(self._symbol, [])   # Empty if not found
    for tick in ticks:
        price = tick.price
```

You can also iterate through the `Ticks` dictionary. The keys of the dictionary are the `Symbol` objects and the values are the `list[Tick]` objects.

```python
def on_data(self, slice: Slice) -> None:
    for symbol, ticks in slice.ticks.items():
        for tick in ticks:
            price = tick.price
```

Tick data is raw and unfiltered, so it can contain bad ticks that skew your trade results. For example, some ticks come from dark pools, which aren't tradable. We recommend you only use tick data if you understand the risks and are able to perform your own online tick filtering.

`Tick` objects have the following properties:

*Members of `QuantConnect.Data.Market.Tick` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.Tick&language=python*

## Examples

The following examples demonstrate common practices for handling CFD data.

### Example 1: Buy And Hold

The following algorithm is a buy-and-hold strategy on the DE30EUR CFD contract. We only place orders when quote data is available to avoid stale fills. Also, we set the algorithm time zone to Berlin and the account currency to Euros for a more convenient comparison.

```python
class CfdExampleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        # Set the timezone as Berlin to conveniently compare the data.
        self.set_time_zone(TimeZones.BERLIN)
        # Set the account currency to EUR to trade the DAX CFD.
        self.set_account_currency("EUR", 10000)
        # Request the CFD data to trade.
        self._dax = self.add_cfd("DE30EUR").symbol

    def on_data(self, slice: Slice) -> None:
        # Trade based on updated data; CFDs only have quote data.
        bar = slice.quote_bars.get(self._dax)
        if bar:
            # Buy and hold DAX CFD.
            if not self.portfolio[self._dax].is_long:
                self.set_holdings(self._dax, 0.5)
```

### Example 2: Global Indices

The following algorithm buys and holds various Index CFDs that trade in different market hours and currencies during their opening hours.

```python
class CfdExampleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        # Seed the price of each asset with its last known price to
        # avoid trading errors.
        self.settings.seed_initial_prices = True
        # Let's select CFD contracts that trade in different market
        # hours so the algorithm is always invested.
        for ticker in ['DE30EUR', 'SG30SGD', 'US30USD']:
            # Add the CFD.
            symbol = self.add_cfd(ticker).symbol
            # Set scheduled events to hold each CFD contract during
            # their regular trading hours.
            # Buy after market open.
            self.schedule.on(
                self.date_rules.every_day(symbol),
                self.time_rules.after_market_open(symbol, 1),
                lambda symbol=symbol: self.set_holdings(symbol, 0.3)
            )
            # Liquidate before market close.
            self.schedule.on(
                self.date_rules.every_day(symbol),
                self.time_rules.before_market_close(symbol, 1),
                lambda symbol=symbol: self.liquidate(symbol)
            )
```