---
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/crypto/handling-data
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/crypto/handling-data.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/03%20Securities/99%20Asset%20Classes/03%20Crypto/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) > [Crypto](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/crypto.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 Crypto data, see [Resolutions](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/crypto/requesting-data.md#04-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*

## 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*

## Examples

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

### Example 1: Dollar Cost Average BTC

Dollar cost averaging (DCA) is where you consistently invest a fixed dollar amount into an asset on a regular basis (for example, monthly), regardless of the asset's price. It can reduce the volatility in your PnL due to slowly increasing the position size over time. The following algorithm demonstrates a DCA investment into BTC. It buys $3,000 USD worth of BTC every midnight for 30 consecutive days.

```python
class CryptoExampleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        # Set a day count variable for counting the days of the DCA trade.
        self._day_count = 0
        # Set the brokerage and account type to match your brokerage environment for accurate fee and margin behavior.
        self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.CASH)
        # For daily DCA purchases, subscribe to daily asset data.
        self._symbol = self.add_crypto("BTCUSD", Resolution.DAILY, Market.BITFINEX).symbol

    def on_data(self, slice: Slice) -> None:
        # If you haven't invested for 30 consecutive days yet, continue buying.
        if self._symbol in slice.bars and self._day_count < 30:
            # Increase the day count.
            self._day_count += 1
            # Calculate the order size for $3,000 USD using the current price.
            quantity = 3_000 / slice.bars[self._symbol].close
            # Round down to observe the lot size.
            lot_size = self.securities[self._symbol].symbol_properties.lot_size
            quantity = int(quantity / lot_size) * lot_size
            if quantity:
                self.market_order(self._symbol, quantity)
```

### Example 2: Inter-Market Spread

There is always a small discrepancy in the price of the same Crypto pair trading on different exchanges. Although you can't currently live trade on algorithm with multiple brokerages, you can study the cointegration pattern and implement 2 live nodes to capture the arbitrage opportunity. The following algorithm demonstrates how to obtain the spread between the BTCUSD pair on Kraken and on Coinbase:

```python
class CryptoExampleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        # Subscribe to BTC/USD on 2 different exchanges.
        self.kraken_btc = self.add_crypto("BTCUSD", market=Market.KRAKEN).symbol
        self.coinbase_btc = self.add_crypto("BTCUSD", market=Market.COINBASE).symbol

    def on_data(self, slice: Slice) -> None:
        # Only calculate the spread if the prices on both exchanges are in the current Slice.
        if self.kraken_btc in slice.bars and self.coinbase_btc in slice.bars:
            # Calculate the spread between the 2 exchanges, making sure the comparison is always in the same direction.
            spread = slice.bars[self.kraken_btc].close - slice.bars[self.coinbase_btc].close
```