> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ziplime.limex.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Market Data

> Reading current values and trailing history through the data object

`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.

```python theme={null}
current = await data.current(assets=[asset], fields=["close"])
history = await data.history(assets=[asset], fields=["close"], bar_count=20)
```

## Current values

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

Read the latest available values at the current simulation timestamp.

```python theme={null}
df = await data.current(
    assets=[context.asset],
    fields=["open", "high", "low", "close", "volume"],
)

close = df["close"][0]
volume = df["volume"][0]
```

For multiple assets:

```python theme={null}
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.

## Historical windows

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

Read a trailing window ending before the current bar.

```python theme={null}
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(...)`.

```python theme={null}
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]]
```

## Fields

Common market-data fields:

| Field           | Meaning                                                 |
| --------------- | ------------------------------------------------------- |
| `"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:

```python theme={null}
fundamentals = await data.current(
    assets=[context.asset],
    fields=["market_cap", "pe_ratio"],
    data_source="limex_us_fundamental_data",
)
```

## Working with Polars

Ziplime returns `polars.DataFrame`, so normal Polars operations apply.

```python theme={null}
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:

```python theme={null}
df = await data.current(assets=[asset], fields=["close"])
close = df["close"][0]
```

## Data clock helpers

`data.current_dt` returns the current simulation datetime.

```python theme={null}
dt = data.current_dt
```

`data.current_session` returns the current trading session label.

```python theme={null}
session = data.current_session
```

## 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:

```python theme={null}
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
```