---
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/index/handling-data
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/index/handling-data.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/03%20Securities/99%20Asset%20Classes/08%20Index/02%20Handling%20Data
modified: 2026-10-30
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) > [Index](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/index.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 `TradeBars` `DataDictionary` is made up of `TradeBar` objects. To access individual data points in the dictionary, you can index the dictionary with the Index ticker or `symbol`, but we recommend you use the `symbol`.

To view the resolutions that are available for Index data, see [Resolutions](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/index/requesting-data.md#03-Resolutions).

## Bars

You can't trade Indices, but `TradeBar` objects are bars that represent the open, high, low, and close of an Index price over a period of time.

![Trade bar breakdown](https://cdn.quantconnect.com/i/tu/index-trade-bar.png)

To get the `TradeBar` objects in the `Slice`, index the `Slice` or index the `bars` property of the `Slice` with the Index `Symbol`. The `Slice` may not contain data for your `Symbol` at every time step. To avoid issues, check if the `Slice` contains data for your Index before you index the `Slice` with the Index `Symbol`.

```python
def on_data(self, slice: Slice) -> None:
    if self._symbol in slice.bars:
        trade_bar = slice.bars[self._symbol]
        value = trade_bar.value
```

`TradeBar` objects have the following properties:

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

## Examples

The following examples demonstrate some common practices for handling Index data.

### Example 1: Trading ES Futures Based on the SPX Index RSI

The following algorithm adds daily data for the SPX Index and uses the [Relative Strength Index (RSI)](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/relative-strength-index.md) indicator to generate trading signals for the E-Mini S&P 500 Future. When the RSI crosses below 30, it buys the front-month ES contract. When the RSI crosses above 70, it liquidates the portfolio.

```python
class IndexDemoAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        # Add daily SPX Index data.
        self._spx = self.add_index("SPX", Resolution.DAILY)
        # Create an RSI indicator for the SPX index.
        self._spx.rsi = self.rsi(self._spx.symbol, 15)
        # Add the ES front-month Futures contract.
        self._es = self.add_future(Futures.Indices.SP_500_E_MINI, data_mapping_mode=DataMappingMode.OPEN_INTEREST)
        self._es.set_filter(lambda future_filter_universe: future_filter_universe.standards_only().front_month())

    def on_data(self, slice: Slice) -> None:
        # Check if the indicator is ready and we have the front-month contract data.
        if not self._spx.rsi.is_ready or self._es.mapped not in slice.bars:
            return

        # If the SPX RSI is <30, buy the ES.
        if not self.portfolio.invested and self._spx.rsi.current.value < 30:
            self.set_holdings(self._es.mapped, 0.5)

        # If the SPX RSI is >70, liquidate the portfolio.
        elif self.portfolio.invested and self._spx.rsi.current.value > 70:
            self.liquidate()
```