527130e618
- Added `toolbox` to the common methods. - `toolbox.save_drawings_under` can save drawings under a specific `topbar` widget. eg `chart.toolbox.save_drawings_under(chart.topbar[’symbol’]`) - `toolbox.load_drawings` will load and display drawings stored under the tag/string given. - `toolbox.export_drawings` will export all currently saved drawings to the given file path. - `toolbox.import_drawings` will import the drawings stored at the given file path. Fixes/Enhancements: - `update` methods are no longer case sensitive. - HorizontalLines no longer throw cyclic structure errors in the web console. - `API` methods can now be normal methods or coroutines.
748 lines
22 KiB
Markdown
748 lines
22 KiB
Markdown
# Docs
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[](https://github.com/louisnw01/lightweight-charts-python "Go to GitHub repo")
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[](https://pypi.org/project/lightweight-charts/)
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[](https://python.org "Go to Python homepage")
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[](https://github.com/louisnw01/lightweight-charts-python/blob/main/LICENSE)
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[](https://github.com/louisnw01/lightweight-charts-python)
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[](https://github.com/louisnw01/lightweight-charts-python)
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___
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## Common Methods
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The methods below can be used within all chart objects.
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___
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### `set`
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`data: pd.DataFrame` `render_drawings: bool`
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Sets the initial data for the chart.
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The data must be given as a DataFrame, with the columns:
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`time | open | high | low | close | volume`
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The `time` column can also be named `date` or be the index, and the `volume` column can be omitted if volume is not enabled.
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Column names are not case sensitive.
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If `render_drawings` is `True`, any drawings made using the `toolbox` will be redrawn with the new data. This is designed to be used when switching to a different timeframe of the same symbol.
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```{important}
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the `time` column must have rows all of the same timezone and locale. This is particularly noticeable for data which crosses over daylight saving hours on data with intervals of less than 1 day. Errors are likely to be raised if they are not converted beforehand.
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```
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An empty `DataFrame` object can also be given to this method, which will erase all candle and volume data displayed on the chart.
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___
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### `update`
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`series: pd.Series`
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Updates the chart data from a given bar.
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The bar should contain values with labels of the same name as the columns required for using `chart.set()`.
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___
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### `update_from_tick`
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`series: pd.Series` | `cumulative_volume: bool`
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Updates the chart from a tick.
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The series should use the labels:
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`time | price | volume`
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As before, the `time` can also be named `date`, and the `volume` can be omitted if volume is not enabled.
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```{information}
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The provided ticks do not need to be rounded to an interval (1 min, 5 min etc.), as the library handles this automatically.```````
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```
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If `cumulative_volume` is used, the volume data given will be added onto the latest bar of volume data.
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___
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### `create_line`
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`color: str` | `width: int` | `price_line: bool` | `price_label: bool` | `-> Line`
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Creates and returns a [Line](#line) object.
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___
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### `lines`
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`-> List[Line]`
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Returns a list of all lines for the chart or subchart.
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___
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### `trend_line`
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`start_time: str/datetime` | `start_value: float/int` | `end_time: str/datetime` | `end_value: float/int` | `color: str` | `width: int` | `-> Line`
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Creates a trend line, drawn from the first point (`start_time`, `start_value`) to the last point (`end_time`, `end_value`).
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___
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### `ray_line`
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`start_time: str/datetime` | `value: float/int` | `color: str` | `width: int` | `-> Line`
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Creates a ray line, drawn from the first point (`start_time`, `value`) and onwards.
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___
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### `marker`
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`time: datetime` | `position: 'above'/'below'/'inside'` | `shape: 'arrow_up'/'arrow_down'/'circle'/'square'` | `color: str` | `text: str` | `-> str`
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Adds a marker to the chart, and returns its id.
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If the `time` parameter is not given, the marker will be placed at the latest bar.
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When using multiple markers, they should be placed in chronological order or display bugs may be present.
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___
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### `remove_marker`
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`marker_id: str`
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Removes the marker with the given id.
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Usage:
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```python
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marker = chart.marker(text='hello_world')
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chart.remove_marker(marker)
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```
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___
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### `horizontal_line`
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`price: float/int` | `color: str` | `width: int` | `style: 'solid'/'dotted'/'dashed'/'large_dashed'/'sparse_dotted'` | `text: str` | `axis_label_visible: bool` | `interactive: bool` | `-> HorizontalLine`
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Places a horizontal line at the given price, and returns a HorizontalLine object.
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If `interactive` is set to `True`, this horizontal line can be edited on the chart. Upon its movement a callback will also be emitted to an `on_horizontal_line_move` method, containing its ID and price. The toolbox should be enabled during its usage. It is designed to be used to update an order (limit, stop, etc.) directly on the chart.
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___
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### `remove_horizontal_line`
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`price: float/int`
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Removes a horizontal line at the given price.
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___
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### `clear_markers`
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Clears the markers displayed on the data.
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___
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### `clear_horizontal_lines`
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Clears the horizontal lines displayed on the data.
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___
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### `precision`
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`precision: int`
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Sets the precision of the chart based on the given number of decimal places.
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___
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### `price_scale`
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`mode: 'normal'/'logarithmic'/'percentage'/'index100'` | `align_labels: bool` | `border_visible: bool` | `border_color: str` | `text_color: str` | `entire_text_only: bool` | `ticks_visible: bool` | `scale_margin_top: float` | `scale_margin_bottom: float`
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Price scale options for the chart.
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___
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### `time_scale`
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`right_offset: int` | `min_bar_spacing: float` | `visible: bool` | `time_visible: bool` | `seconds_visible: bool` | `border_visible: bool` | `border_color: str`
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Timescale options for the chart.
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___
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### `layout`
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`background_color: str` | `text_color: str` | `font_size: int` | `font_family: str`
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Global layout options for the chart.
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___
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### `grid`
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`vert_enabled: bool` | `horz_enabled: bool` | `color: str` | `style: 'solid'/'dotted'/'dashed'/'large_dashed'/'sparse_dotted'`
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Grid options for the chart.
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___
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### `candle_style`
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`up_color: str` | `down_color: str` | `wick_enabled: bool` | `border_enabled: bool` | `border_up_color: str` | `border_down_color: str` | `wick_up_color: str` | `wick_down_color: str`
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Candle styling for each of the candle's parts (border, wick).
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```{admonition} Color Formats
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:class: note
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Throughout the library, colors should be given as either:
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* rgb: `rgb(100, 100, 100)`
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* rgba: `rgba(100, 100, 100, 0.7)`
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* hex: `#32a852`
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```
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___
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### `volume_config`
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`scale_margin_top: float` | `scale_margin_bottom: float` | `up_color: str` | `down_color: str`
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Volume config options.
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```{important}
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The float values given to scale the margins must be greater than 0 and less than 1.
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```
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___
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### `crosshair`
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`mode` | `vert_visible: bool` | `vert_width: int` | `vert_color: str` | `vert_style: str` | `vert_label_background_color: str` | `horz_visible: bool` | `horz_width: int` | `horz_color: str` | `horz_style: str` | `horz_label_background_color: str`
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Crosshair formatting for its vertical and horizontal axes.
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`vert_style` and `horz_style` should be given as one of: `'solid'/'dotted'/'dashed'/'large_dashed'/'sparse_dotted'`
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___
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### `watermark`
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`text: str` | `font_size: int` | `color: str`
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Overlays a watermark on top of the chart.
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___
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### `legend`
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`visible: bool` | `ohlc: bool` | `percent: bool` | `lines: bool` | `color: str` | `font_size: int` | `font_family: str`
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Configures the legend of the chart.
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___
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### `spinner`
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`visible: bool`
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Shows a loading spinner on the chart, which can be used to visualise the loading of large datasets, API calls, etc.
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___
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### `price_line`
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`label_visible: bool` | `line_visible: bool` | `title: str`
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Configures the visibility of the last value price line and its label.
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___
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### `fit`
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Attempts to fit all data displayed on the chart within the viewport (`fitContent()`).
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___
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### `hide_data`
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Hides the candles on the chart.
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___
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### `show_data`
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Shows the hidden candles on the chart.
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___
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### `polygon`
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Used to access Polygon.io's API (see [here](https://lightweight-charts-python.readthedocs.io/en/latest/polygon.html)).
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___
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### `create_subchart`
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`volume_enabled: bool` | `position: 'left'/'right'/'top'/'bottom'`, `width: float` | `height: float` | `sync: bool/str` | `-> SubChart`
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Creates and returns a [SubChart](#subchart) object, placing it adjacent to the declaring `Chart` or `SubChart`.
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`position`: specifies how the `SubChart` will float within the `Chart` window.
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`height` | `width`: Specifies the size of the `SubChart`, where `1` is the width/height of the window (100%)
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`sync`: If given as `True`, the `SubChart`'s timescale and crosshair will follow that of the declaring `Chart` or `SubChart`. If a `str` is passed, the `SubChart` will follow the panel with the given id. Chart ids can be accessed from the`chart.id` and `subchart.id` attributes.
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```{important}
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`width` and `height` should be given as a number between 0 and 1.
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```
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___
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## Chart
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`volume_enabled: bool` | `width: int` | `height: int` | `x: int` | `y: int` | `on_top: bool` | `maximize: bool` | `debug: bool` |
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`api: object` | `topbar: bool` | `searchbox: bool` | `toolbox: bool`
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The main object used for the normal functionality of lightweight-charts-python, built on the pywebview library.
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```{important}
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The `Chart` object should be defined within an `if __name__ == '__main__'` block.
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```
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___
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### `show`
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`block: bool`
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Shows the chart window, blocking until the chart has loaded. If `block` is enabled, the method will block code execution until the window is closed.
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___
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### `hide`
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Hides the chart window, which can be later shown by calling `chart.show()`.
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___
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### `exit`
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Exits and destroys the chart window.
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___
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### `show_async`
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`block: bool`
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Show the chart asynchronously. This should be utilised when using [Callbacks](#callbacks).
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### `screenshot`
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`-> bytes`
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Takes a screenshot of the chart, and returns a bytes object containing the image. For example:
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```python
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if __name__ == '__main__':
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chart = Chart()
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df = pd.read_csv('ohlcv.csv')
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chart.set(df)
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chart.show()
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img = chart.screenshot()
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with open('screenshot.png', 'wb') as f:
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f.write(img)
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```
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```{important}
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This method should be called after the chart window has loaded.
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```
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___
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## Line
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The `Line` object represents a `LineSeries` object in Lightweight Charts and can be used to create indicators. As well as the methods described below, the `Line` object also has access to:
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[`title`](#title), [`marker`](#marker), [`horizontal_line`](#horizontal-line) [`hide_data`](#hide-data), [`show_data`](#show-data) and[`price_line`](#price-line).
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```{important}
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The `Line` object should only be accessed from the [`create_line`](#create-line) method of `Chart`.
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```
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___
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### `set`
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`data: pd.DataFrame` `name: str`
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Sets the data for the line.
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When not using the `name` parameter, the columns should be named: `time | value` (Not case sensitive).
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Otherwise, the method will use the column named after the string given in `name`. This name will also be used within the legend of the chart. For example:
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```python
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line = chart.create_line()
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# DataFrame with columns: date | SMA 50
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df = pd.read_csv('sma50.csv')
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line.set(df, name='SMA 50')
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```
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___
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### `update`
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`series: pd.Series`
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Updates the data for the line.
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This should be given as a Series object, with labels akin to the `line.set()` function.
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___
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### `delete`
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Irreversibly deletes the line.
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___
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## HorizontalLine
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The `HorizontalLine` object represents a `PriceLine` in Lightweight Charts.
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```{important}
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The `HorizontalLine` object should only be accessed from the [`horizontal_line`](#horizontal-line) Common Method.
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```
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___
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### `update`
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`price: float/int`
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Updates the price of the horizontal line.
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___
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### `delete`
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Irreversibly deletes the horizontal line.
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___
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## SubChart
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The `SubChart` object allows for the use of multiple chart panels within the same `Chart` window. All of the [Common Methods](#common-methods) can be used within a `SubChart`. Its instance should be accessed using the [create_subchart](#create-subchart) method.
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`SubCharts` are arranged horizontally from left to right. When the available space is no longer sufficient, the subsequent `SubChart` will be positioned on a new row, starting from the left side.
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___
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### Grid of 4 Example:
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```python
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import pandas as pd
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from lightweight_charts import Chart
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if __name__ == '__main__':
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chart = Chart(inner_width=0.5, inner_height=0.5)
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chart2 = chart.create_subchart(position='right', width=0.5, height=0.5)
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chart3 = chart2.create_subchart(position='left', width=0.5, height=0.5)
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chart4 = chart3.create_subchart(position='right', width=0.5, height=0.5)
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chart.watermark('1')
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chart2.watermark('2')
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chart3.watermark('3')
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chart4.watermark('4')
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df = pd.read_csv('ohlcv.csv')
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chart.set(df)
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chart2.set(df)
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chart3.set(df)
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chart4.set(df)
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chart.show(block=True)
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```
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___
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### Synced Line Chart Example:
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```python
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import pandas as pd
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from lightweight_charts import Chart
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if __name__ == '__main__':
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chart = Chart(inner_width=1, inner_height=0.8)
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chart.time_scale(visible=False)
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chart2 = chart.create_subchart(width=1, height=0.2, sync=True, volume_enabled=False)
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df = pd.read_csv('ohlcv.csv')
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df2 = pd.read_csv('rsi.csv')
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chart.set(df)
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line = chart2.create_line()
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line.set(df2)
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chart.show(block=True)
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```
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___
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## Callbacks
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The `Chart` object allows for asyncronous callbacks to be passed back to python when using the `show_async` method, allowing for more sophisticated chart layouts including searching, timeframe selectors, and text boxes.
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[`QtChart`](#qtchart) and [`WxChart`](#wxchart) can also use callbacks, however they use their respective event loops to emit callbacks rather than asyncio.
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A variety of the parameters below should be passed to the Chart upon decaration.
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* `api`: The class object that the callbacks will be emitted to (see [How to use Callbacks](#how-to-use-callbacks)).
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* `topbar`: Adds a [TopBar](#topbar) to the `Chart` or `SubChart` and allows use of the `create_switcher` method.
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* `searchbox`: Adds a search box onto the `Chart` or `SubChart` that is activated by typing.
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___
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### How to use Callbacks
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Callbacks are emitted to the class given as the `api` parameter shown above.
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Take a look at this minimal example:
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```python
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class API:
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def __init__(self):
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self.chart = None
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async def on_search(self, string):
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print(f'Search Text: "{string}" | Chart/SubChart ID: "{self.chart.id}"')
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```
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Upon searching in a pane, the expected output would be akin to:
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```
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Search Text: "AAPL" | Chart/SubChart ID: "window.blyjagcr"
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```
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The ID shown above will change depending upon which pane was used to search, due to the instance of `self.chart` dynamically updating to the latest pane which triggered the callback.
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`self.chart` will update upon each callback, allowing for access to the specific [Common Methods](#common-methods) for the pane in question.
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```{important}
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* Search callbacks will always be emitted to a method named `on_search`
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* `API` class methods can be either coroutines or normal methods.
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```
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___
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### `TopBar`
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The `TopBar` class represents the top bar shown on the chart when using callbacks:
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This class is accessed from the `topbar` attribute of the chart object (`chart.topbar.<method>`), after setting the topbar parameter to `True` upon declaration of the chart.
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Switchers and text boxes can be created within the top bar, and their instances can be accessed through the `topbar` dictionary. For example:
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```python
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chart = Chart(api=api, topbar=True)
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chart.topbar.textbox('symbol', 'AAPL') # Declares a textbox displaying 'AAPL'.
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print(chart.topbar['symbol'].value) # Prints the value within ('AAPL')
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chart.topbar['symbol'].set('MSFT') # Sets the 'symbol' textbox to 'MSFT'
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print(chart.topbar['symbol'].value) # Prints the value again ('MSFT')
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```
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___
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|
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### `switcher`
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`name: str` | `method: function` | `*options: str` | `default: str`
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* `name`: the name of the switcher which can be used to access it from the `topbar` dictionary.
|
|
* `method`: The function from the `api` class given to the constructor that will receive the callback.
|
|
* `options`: The strings to be displayed within the switcher. This may be a variety of timeframes, security types, or whatever needs to be updated directly from the chart.
|
|
* `default`: The initial switcher option set.
|
|
___
|
|
|
|
### `textbox`
|
|
`name: str` | `initial_text: str`
|
|
|
|
* `name`: the name of the text box which can be used to access it from the `topbar` dictionary.
|
|
* `initial_text`: The text to show within the text box.
|
|
___
|
|
|
|
### Callbacks Example:
|
|
|
|
```python
|
|
import asyncio
|
|
import pandas as pd
|
|
from my_favorite_broker import get_bar_data
|
|
|
|
from lightweight_charts import Chart
|
|
|
|
|
|
class API:
|
|
def __init__(self):
|
|
self.chart = None
|
|
|
|
async def on_search(self, searched_string): # Called when the user searches.
|
|
timeframe = self.chart.topbar['timeframe'].value
|
|
new_data = await get_bar_data(searched_string, timeframe)
|
|
if not new_data:
|
|
return
|
|
self.chart.set(new_data) # sets data for the Chart or SubChart in question.
|
|
self.chart.topbar['symbol'].set(searched_string)
|
|
|
|
async def on_timeframe(self): # Called when the user changes the timeframe.
|
|
timeframe = self.chart.topbar['timeframe'].value
|
|
symbol = self.chart.topbar['symbol'].value
|
|
new_data = await get_bar_data(symbol, timeframe)
|
|
if not new_data:
|
|
return
|
|
self.chart.set(new_data)
|
|
|
|
|
|
async def main():
|
|
api = API()
|
|
|
|
chart = Chart(api=api, topbar=True, searchbox=True)
|
|
|
|
chart.topbar.textbox('symbol', 'TSLA')
|
|
chart.topbar.switcher('timeframe', api.on_timeframe, '1min', '5min', '30min', 'H', 'D', 'W', default='5min')
|
|
|
|
df = pd.read_csv('ohlcv.csv')
|
|
chart.set(df)
|
|
|
|
await chart.show_async(block=True)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
asyncio.run(main())
|
|
```
|
|
___
|
|
|
|
## Toolbox
|
|
The Toolbox allows for trendlines, ray lines and horizontal lines to be drawn and edited directly on the chart.
|
|
|
|
It can be used within any Chart object, and is enabled by setting the `toolbox` parameter to `True` upon Chart declaration.
|
|
|
|
The following hotkeys can also be used when the Toolbox is enabled:
|
|
* Alt+T: Trendline
|
|
* Alt+H: Horizontal Line
|
|
* Alt+R: Ray Line
|
|
* Meta+Z or Ctrl+Z: Undo
|
|
___
|
|
|
|
### `save_drawings_under`
|
|
`widget: Widget`
|
|
|
|
Saves drawings under a specific `topbar` text widget. For example:
|
|
|
|
```python
|
|
chart.toolbox.save_drawings_under(chart.topbar['symbol'])
|
|
```
|
|
___
|
|
|
|
### `load_drawings`
|
|
`tag: str`
|
|
|
|
Loads and displays drawings stored under the tag given.
|
|
___
|
|
### `import_drawings`
|
|
`file_path: str`
|
|
|
|
Imports the drawings stored at the JSON file given in `file_path`.
|
|
|
|
___
|
|
### `export_drawings`
|
|
`file_path: str`
|
|
|
|
Exports all currently saved drawings to the JSON file given in `file_path`.
|
|
___
|
|
|
|
## QtChart
|
|
`widget: QWidget` | `volume_enabled: bool`
|
|
|
|
The `QtChart` object allows the use of charts within a `QMainWindow` object, and has similar functionality to the `Chart` and `ChartAsync` objects for manipulating data, configuring and styling.
|
|
|
|
Callbacks can be recieved through the Qt event loop, using an [API](#how-to-use-callbacks) class that uses **syncronous** methods instead of **asyncronous** methods.
|
|
___
|
|
|
|
### `get_webview`
|
|
|
|
`-> QWebEngineView`
|
|
|
|
Returns the `QWebEngineView` object.
|
|
|
|
___
|
|
### Example:
|
|
|
|
```python
|
|
import pandas as pd
|
|
from PyQt5.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget
|
|
|
|
from lightweight_charts.widgets import QtChart
|
|
|
|
app = QApplication([])
|
|
window = QMainWindow()
|
|
layout = QVBoxLayout()
|
|
widget = QWidget()
|
|
widget.setLayout(layout)
|
|
|
|
window.resize(800, 500)
|
|
layout.setContentsMargins(0, 0, 0, 0)
|
|
|
|
chart = QtChart(widget)
|
|
|
|
df = pd.read_csv('ohlcv.csv')
|
|
chart.set(df)
|
|
|
|
layout.addWidget(chart.get_webview())
|
|
|
|
window.setCentralWidget(widget)
|
|
window.show()
|
|
|
|
app.exec_()
|
|
```
|
|
___
|
|
|
|
## WxChart
|
|
`parent: wx.Panel` | `volume_enabled: bool`
|
|
|
|
The WxChart object allows the use of charts within a `wx.Frame` object, and has similar functionality to the `Chart` and `ChartAsync` objects for manipulating data, configuring and styling.
|
|
|
|
Callbacks can be recieved through the Wx event loop, using an [API](#how-to-use-callbacks) class that uses **syncronous** methods instead of **asyncronous** methods.
|
|
___
|
|
|
|
### `get_webview`
|
|
`-> wx.html2.WebView`
|
|
|
|
Returns a `wx.html2.WebView` object which can be used to for positioning and styling within wxPython.
|
|
___
|
|
|
|
### Example:
|
|
|
|
```python
|
|
import wx
|
|
import pandas as pd
|
|
|
|
from lightweight_charts.widgets import WxChart
|
|
|
|
|
|
class MyFrame(wx.Frame):
|
|
def __init__(self):
|
|
super().__init__(None)
|
|
self.SetSize(1000, 500)
|
|
|
|
panel = wx.Panel(self)
|
|
sizer = wx.BoxSizer(wx.VERTICAL)
|
|
panel.SetSizer(sizer)
|
|
|
|
chart = WxChart(panel)
|
|
|
|
df = pd.read_csv('ohlcv.csv')
|
|
chart.set(df)
|
|
|
|
sizer.Add(chart.get_webview(), 1, wx.EXPAND | wx.ALL)
|
|
sizer.Layout()
|
|
self.Show()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
app = wx.App()
|
|
frame = MyFrame()
|
|
app.MainLoop()
|
|
|
|
```
|
|
___
|
|
|
|
## StreamlitChart
|
|
`parent: wx.Panel` | `volume_enabled: bool`
|
|
|
|
The `StreamlitChart` object allows the use of charts within a Streamlit app, and has similar functionality to the `Chart` object for manipulating data, configuring and styling.
|
|
|
|
This object only supports the displaying of **static** data, and should not be used with the `update_from_tick` or `update` methods. Every call to the chart object must occur **before** calling `load`.
|
|
___
|
|
|
|
### `load`
|
|
|
|
Loads the chart into the Streamlit app. This should be called after setting, styling, and configuring the chart, as no further calls to the `StreamlitChart` will be acknowledged.
|
|
___
|
|
|
|
### Example:
|
|
```python
|
|
import pandas as pd
|
|
from lightweight_charts.widgets import StreamlitChart
|
|
|
|
chart = StreamlitChart(width=900, height=600)
|
|
|
|
df = pd.read_csv('ohlcv.csv')
|
|
chart.set(df)
|
|
|
|
chart.load()
|
|
```
|
|
___
|
|
|
|
## JupyterChart
|
|
|
|
The `JupyterChart` object allows the use of charts within a notebook, and has similar functionality to the `Chart` object for manipulating data, configuring and styling.
|
|
|
|
This object only supports the displaying of **static** data, and should not be used with the `update_from_tick` or `update` methods. Every call to the chart object must occur **before** calling `load`.
|
|
___
|
|
|
|
### `load`
|
|
|
|
Renders the chart. This should be called after setting, styling, and configuring the chart, as no further calls to the `JupyterChart` will be acknowledged.
|
|
___
|
|
|
|
### Example:
|
|
```python
|
|
import pandas as pd
|
|
from lightweight_charts import JupyterChart
|
|
|
|
chart = JupyterChart()
|
|
|
|
df = pd.read_csv('ohlcv.csv')
|
|
chart.set(df)
|
|
|
|
chart.load()
|
|
```
|