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data is a BarData object passed to handle_data, scheduled callbacks, and before_trading_start. It gives you current values and trailing history from the market data source and optional custom data sources. Ziplime data access is async and returns Polars data frames. 
current = await data.current(assets=[asset], fields=["close"])
history = await data.history(assets=[asset], fields=["close"], bar_count=20)

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Current values

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await data.current(assets, fields, data_source=None)

Read the latest available values at the current simulation timestamp. 
df = await data.current(
    assets=[context.asset],
    fields=["open", "high", "low", "close", "volume"],
)

close = df["close"][0]
volume = df["volume"][0]
For multiple assets: 
df = await data.current(
    assets=context.assets,
    fields=["close", "volume"],
)

for row in df.iter_rows(named=True):
    print(row.get("symbol"), row["close"], row["volume"])
The exact identifier columns depend on the loaded data source, but OHLCV fields are read by name.

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Historical windows

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await data.history(assets, bar_count, frequency=timedelta(days=1), fields=None, data_source=None)

Read a trailing window ending before the current bar. 
import datetime

hist = await data.history(
    assets=[context.asset],
    fields=["close"],
    bar_count=50,
    frequency=datetime.timedelta(days=1),
)

closes = hist["close"].to_numpy()
sma_50 = closes.mean()
The current implementation calls the data source with include_end_date=False, so history is best used for completed previous bars. If your signal needs the current bar too, read it separately with data.current(...). 
hist = await data.history(
    assets=[context.asset],
    fields=["close"],
    bar_count=19,
)
current = await data.current(
    assets=[context.asset],
    fields=["close"],
)

window = list(hist["close"]) + [current["close"][0]]

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Fields

Common market-data fields:
FieldMeaning
"open"Bar open price
"high"Bar high price
"low"Bar low price
"close"Bar close price
"volume"Bar volume
"price"Last known price, when supported by the data source
"last_traded"Last traded datetime, when supported by the data source
Custom data bundles can expose additional fields. Pass the bundle name through data_source if you need a non-default source: 
fundamentals = await data.current(
    assets=[context.asset],
    fields=["market_cap", "pe_ratio"],
    data_source="limex_us_fundamental_data",
)

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Working with Polars

Ziplime returns polars.DataFrame, so normal Polars operations apply. 
import polars as pl

hist = await data.history(
    assets=context.assets,
    fields=["close", "volume"],
    bar_count=20,
)

summary = hist.group_by("symbol").agg(
    pl.col("close").mean().alias("mean_close"),
    pl.col("volume").sum().alias("total_volume"),
)
For one asset and one field, the most common extraction is: 
df = await data.current(assets=[asset], fields=["close"])
close = df["close"][0]

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Data clock helpers

data.current_dt returns the current simulation datetime. 
dt = data.current_dt
data.current_session returns the current trading session label. 
session = data.current_session

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Tradability checks

data.can_trade(assets) exists for Zipline compatibility and is intended to check asset liveness, exchange hours, restrictions, and available price data. For portable strategy code in the current Ziplime runtime, prefer checking the data you actually need before ordering: 
current = await data.current(assets=[asset], fields=["close", "volume"])

if current.height == 0:
    return

close = current["close"][0]
volume = current["volume"][0]

if close is None or volume <= 0:
    return