# Script Helpers
get_chart_data, export_csv, and other helpers available to @script files.
## get_chart_data
```python
get_chart_data()
```
Get chart OHLCV data as a pandas DataFrame.
Returns the current chart's data with columns: `time`, [`open`](https://quantchartsllc.com/docs/python/py-data.md#open), [`high`](https://quantchartsllc.com/docs/python/py-data.md#high), [`low`](https://quantchartsllc.com/docs/python/py-data.md#low), [`close`](https://quantchartsllc.com/docs/python/py-data.md#close), [`volume`](https://quantchartsllc.com/docs/python/py-data.md#volume). Only available in [`@script`](https://quantchartsllc.com/docs/python/py-decorators.md#script) classes.
### Returns
pandas DataFrame
### Example
```python
from quant_charts.script_helpers import get_chart_data
df = get_chart_data()
print(df.head())
```
### Output
```text
>>> df.head()
time open high low close volume
0 2024-01-01 100.578931 102.408799 100.635641 100.993428 18702.0
1 2024-01-02 100.436809 101.137545 100.156115 100.716900 10384.0
2 2024-01-03 102.385924 102.354991 100.929226 102.012277 10404.0
3 2024-01-04 105.363522 105.860614 104.004534 105.058336 50943.0
4 2024-01-05 104.579579 104.751315 103.212360 104.590030 57926.0
```
## get_symbol
```python
get_symbol()
```
Get the current chart's symbol/ticker name.
### Returns
str
### Example
```python
symbol = get_symbol()
```
### Output
```text
>>> get_symbol()
'MNQM5'
```
## get_timeframe
```python
get_timeframe()
```
Get the current chart timeframe.
### Returns
str
### Example
```python
tf = get_timeframe()
```
### Output
```text
>>> get_timeframe()
'5m'
```
## export_csv
```python
export_csv(df, filename, subfolder?)
```
Export a DataFrame to a CSV file.
### Parameters
- `df` (`DataFrame`, default `required`): pandas DataFrame to export
- `filename` (`str`, default `required`): Output filename
- `subfolder` (`str`, default `None`): Subfolder within data directory
### Returns
str (full file path)
### Example
```python
path = export_csv(filtered, "high_volume.csv")
```
## export_parquet
```python
export_parquet(df, filename, subfolder?)
```
Export a DataFrame to Parquet (fast, compressed).
### Parameters
- `df` (`DataFrame`, default `required`): pandas DataFrame to export
- `filename` (`str`, default `required`): Output filename
- `subfolder` (`str`, default `None`): Subfolder within data directory
### Returns
str (full file path)
### Example
```python
path = export_parquet(df, "processed_data")
```
### Notes
- Parquet files are typically 5-10x smaller than CSV.
## export_json
```python
export_json(data, filename, subfolder?)
```
Export Python data to a JSON file.
### Parameters
- `data` (`Any`, default `required`): JSON-serializable data
- `filename` (`str`, default `required`): Output filename
- `subfolder` (`str`, default `None`): Subfolder within data directory
### Returns
str (full file path)
### Example
```python
export_json({"sharpe": 1.42}, "results")
```
## get_indicators
```python
get_indicators()
```
Get all indicators currently running on the chart.
### Returns
list[dict] with keys: name, id, parameters
### Example
```python
for ind in get_indicators():
print(f"{ind['name']}: {ind['parameters']}")
```
### Output
```text
>>> get_indicators()
[{'name': 'SMA', 'id': 'sma_1', 'parameters': {'period': 20}},
{'name': 'RSI', 'id': 'rsi_1', 'parameters': {'period': 14}}]
```
## get_indicator_values
```python
get_indicator_values(indicator_name)
```
Get output values from a running indicator.
### Parameters
- `indicator_name` (`str`, default `required`): Name of the indicator
### Returns
pandas DataFrame or `None`
### Example
```python
sma_data = get_indicator_values("SMA")
if sma_data is not None:
print(sma_data.tail())
```
### Output
```text
>>> get_indicator_values("SMA").tail()
SMA(20)
14519 82.341
14520 82.297
14521 82.156
14522 82.089
14523 81.934
```