---
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/us-equity/handling-data
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/us-equity/handling-data.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/03%20Securities/99%20Asset%20Classes/01%20US%20Equity/02%20Handling%20Data
modified: 2026-10-29
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) > [US Equity](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/us-equity.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 security ticker or `symbol`, but we recommend you use the `symbol`.

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

## Trades

`TradeBar` objects are price bars that consolidate individual trades from the exchanges. They contain the open, high, low, close, and volume of trading activity over a period of time.

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

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

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

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

```python
def on_data(self, slice: Slice) -> None:
    # Iterate all received Symbol-TradeBar key-value pairs
    for symbol, trade_bar in slice.bars.items():
        close_price = trade_bar.close
```

`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*

We adjust the daily open and close price of bars to reflect the official [auction prices](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/us-equity/data-preparation.md#05-Market-Auction-Prices).

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

## 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 security. A quote tick is an offer to buy or sell the security 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 security doesn't actively trade or you are in the same time step as when you added the security subscription, the `Slice` may not contain data for your `symbol`. To avoid issues, check if the `Slice` contains data for your security before you index the `Slice` with the security `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*

If [we detect a tick that may be suspicious](https://www.quantconnect.com/docs/v2/cloud-platform/datasets/quantconnect/us-equities.md#05-Suspicious-Ticks), we set its `suspicious` flag to true.

## Other Data Formats

For more information about data formats available for US Equities, see [Corporate Actions](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/us-equity/corporate-actions.md).

## Examples

The following examples demonstrate some common practices for handling US Equity data.

### Example 1: Various Data Resolutions and Formats

The following algorithm handles three data resolutions (Daily, Minute, and Tick) and three data formats (TradeBar, QuoteBar, and Tick). The Equity subscriptions provide the ticks and daily TradeBar objects. To create the 5-minute QuoteBar objects, it [consolidates](https://www.quantconnect.com/docs/v2/writing-algorithms/consolidating-data/consolidator-types/time-period-consolidators.md#05-Consolidate-Quote-Ticks) the AAPL ticks.

The algorithm enters a long position in AAPL when the following conditions are met:

- The 16-day [Fractal Adaptive Moving Average](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/fractal-adaptive-moving-average.md) (FRAMA) of QQQ is increasing, which indicates the trend is up.
- The [Relative Strength Index](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/relative-strength-index.md) (RSI) of AAPL is above 70, which indicates AAPL has been rising from large buying pressure.

The algorithm enters a short position in AAPL when the following conditions are met:

- The 16-day FRAMA of QQQ is rising, which indicates the trend is down.
- The RSI of AAPL is above, which indicates AAPL has been declining from large selling pressure.

The algorithm [liquidates the portfolio](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/liquidating-positions.md) when the FRAMA changes direction.

```python
class USEquityExampleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 4)
        self.set_end_date(2024, 9, 10)
        # Add QQQ and AAPL data.
        self._qqq = self.add_equity("QQQ", Resolution.DAILY)
        self._aapl = self.add_equity("AAPL", Resolution.TICK)
        # Create the indicators.
        self._qqq.frama = self.frama(self._qqq.symbol, 16)
        self._aapl.rsi = RelativeStrengthIndex(10)
        # Create a 5-minute consolidator to aggregate AAPL tick data into quote bars.
        consolidator = TickQuoteBarConsolidator(timedelta(minutes=5))
        # Subscribe the consolidator for automatic updates.
        self.subscription_manager.add_consolidator(self._aapl.symbol, consolidator)
        # Attach an event handler handler to update the RSI indicator with the consolidated bars.
        consolidator.data_consolidated += lambda _, bar: self._aapl.rsi.update(bar.end_time, bar.close)
        # Warm up the indicators.
        self.warm_up_indicator(self._qqq.symbol, self._qqq.frama)
        for quote_bar in self.history[QuoteBar](self._aapl.symbol, 10, Resolution.MINUTE):
            self._aapl.rsi.update(quote_bar.end_time, quote_bar.close)

    def on_data(self, slice: Slice) -> None:
        # Ensure AAPL ticks are in the current slice.
        if self._aapl.symbol not in slice.ticks:
            return
        # Get the indicator values.
        frama = self._qqq.frama.current.value
        prev_frama = self._qqq.frama.previous.value
        rsi = self._aapl.rsi.current.value

        # Check if we have an AAPL position.
        if self._aapl.holdings.invested:
            # If there is divergence between our position and the Frama direction, exit the position.
            if (self._aapl.holdings.is_long and frama < prev_frama or
                self._aapl.holdings.is_short and frama > prev_frama):
                self.liquidate(self._aapl.symbol)
        # Look for a long entry.
        elif frama > prev_frama and rsi >= 70:
            self.set_holdings(self._aapl.symbol, 0.5)
        # Look for a short entry.
        elif frama < prev_frama and rsi <= 30:
            self.set_holdings(self._aapl.symbol, -0.5)
```