lightweight-charts-python/docs/source/common_methods.md

Common Methods

The methods below can be used within all chart objects.

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set

data: pd.DataFrame render_drawings: bool

Sets the initial data for the chart. The data must be given as a DataFrame, with the columns:

time | open | high | low | close | volume

The time column can also be named date or be the index, and the volume column can be omitted if volume is not enabled. Column names are not case sensitive.

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.

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.

An empty DataFrame object or None can also be given to this method, which will erase all candle and volume data displayed on the chart.

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update

series: pd.Series

Updates the chart data from a pd.Series object. The bar should contain values with labels akin to set.

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update_from_tick

series: pd.Series | cumulative_volume: bool

Updates the chart from a tick. The series should use the labels:

time | price | volume

As before, the time can also be named date, and the volume can be omitted if volume is not enabled. The time column can also be the name of the Series object.

The provided ticks do not need to be rounded to an interval (1 min, 5 min etc.), as the library handles this automatically.

If cumulative_volume is used, the volume data given will be added onto the latest bar of volume data.

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create_line (Line)

color: str | width: int | price_line: bool | price_label: bool | -> Line

Creates and returns a Line object, representing 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: title, marker, horizontal_line hide_data, show_data andprice_line.

Its instance should only be accessed from this method.

set

data: pd.DataFrame name: str

Sets the data for the line.

When not using the name parameter, the columns should be named: time | value (Not case sensitive).

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:

line = chart.create_line()

# DataFrame with columns: date | SMA 50
df = pd.read_csv('sma50.csv')

line.set(df, name='SMA 50')

update

series: pd.Series

Updates the data for the line.

This should be given as a Series object, with labels akin to the line.set() function.

delete

Irreversibly deletes the line.

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lines

-> List[Line]

Returns a list of all lines for the chart or subchart.

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trend_line

start_time: str/datetime | start_value: float/int | end_time: str/datetime | end_value: float/int | color: str | width: int | -> Line

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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ray_line

start_time: str/datetime | value: float/int | color: str | width: int | -> Line

Creates a ray line, drawn from the first point (start_time, value) and onwards.

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marker

time: datetime | position: 'above'/'below'/'inside' | shape: 'arrow_up'/'arrow_down'/'circle'/'square' | color: str | text: str | -> str

Adds a marker to the chart, and returns its id.

If the time parameter is not given, the marker will be placed at the latest bar.

When using multiple markers, they should be placed in chronological order or display bugs may be present.

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remove_marker

marker_id: str

Removes the marker with the given id.

Usage:

marker = chart.marker(text='hello_world')
chart.remove_marker(marker)

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horizontal_line (HorizontalLine)

price: float/int | color: str | width: int | style: 'solid'/'dotted'/'dashed'/'large_dashed'/'sparse_dotted' | text: str | axis_label_visible: bool | interactive: bool | -> HorizontalLine

Places a horizontal line at the given price, and returns a HorizontalLine object, representing a PriceLine in Lightweight Charts.

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.

update

price: float/int

Updates the price of the horizontal line.

label

text: str

Updates the label of the horizontal line.

delete

Irreversibly deletes the horizontal line.

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remove_horizontal_line

price: float/int

Removes a horizontal line at the given price.

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clear_markers

Clears the markers displayed on the data.

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clear_horizontal_lines

Clears the horizontal lines displayed on the data.

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precision

precision: int

Sets the precision of the chart based on the given number of decimal places.

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price_scale

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

Price scale options for the chart.

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time_scale

right_offset: int | min_bar_spacing: float | visible: bool | time_visible: bool | seconds_visible: bool | border_visible: bool | border_color: str

Timescale options for the chart.

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layout

background_color: str | text_color: str | font_size: int | font_family: str

Global layout options for the chart.

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grid

vert_enabled: bool | horz_enabled: bool | color: str | style: 'solid'/'dotted'/'dashed'/'large_dashed'/'sparse_dotted'

Grid options for the chart.

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candle_style

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

Candle styling for each of the candle's parts (border, wick).

:class: note

Throughout the library, colors should be given as either: 
* rgb: `rgb(100, 100, 100)`
* rgba: `rgba(100, 100, 100, 0.7)`
* hex: `#32a852`

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volume_config

scale_margin_top: float | scale_margin_bottom: float | up_color: str | down_color: str

Volume config options.

The float values given to scale the margins must be greater than 0 and less than 1.

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crosshair

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

Crosshair formatting for its vertical and horizontal axes.

vert_style and horz_style should be given as one of: 'solid'/'dotted'/'dashed'/'large_dashed'/'sparse_dotted'

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watermark

text: str | font_size: int | color: str

Overlays a watermark on top of the chart.

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legend

visible: bool | ohlc: bool | percent: bool | lines: bool | color: str | font_size: int | font_family: str

Configures the legend of the chart.

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spinner

visible: bool

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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price_line

label_visible: bool | line_visible: bool | title: str

Configures the visibility of the last value price line and its label.

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fit

Attempts to fit all data displayed on the chart within the viewport (fitContent()).

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hide_data

Hides the candles on the chart.

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show_data

Shows the hidden candles on the chart.

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add_hotkey

modifier: 'ctrl'/'shift'/'alt'/'meta' | key: str/int/tuple | method: object

Adds a global hotkey to the chart window, which will execute the method or function given.

When using a number in key, it should be given as an integer. If multiple key commands are needed for the same function, you can pass a tuple to key. For example:

def place_buy_order(key):
    print(f'Buy {key} shares.')
    
    
def place_sell_order(key):
    print(f'Sell all shares, because I pressed {key}.')


chart.add_hotkey('shift', (1, 2, 3), place_buy_order)
chart.add_hotkey('shift', 'X', place_sell_order)

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create_table

width: int/float | height: int/float | headings: tuple[str] | widths: tuple[float] | alignments: tuple[str] | position: 'left'/'right'/'top'/'bottom' | draggable: bool | method: object

Creates and returns a Table object.

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create_subchart (SubChart)

volume_enabled: bool | position: 'left'/'right'/'top'/'bottom', width: float | height: float | sync: bool/str | -> SubChart

Creates and returns a SubChart object, placing it adjacent to the previous Chart or SubChart. This allows for the use of multiple chart panels within the same Chart window. Its instance should only be accessed by using this method.

position: specifies how the Subchart will float.

height | width: Specifies the size of the Subchart, where 1 is the width/height of the window (100%)

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 thechart.id and subchart.id attributes.

`width` and `height` should be given as a number between 0 and 1.

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.

Grid of 4 Example:

import pandas as pd
from lightweight_charts import Chart

if __name__ == '__main__':
    chart = Chart(inner_width=0.5, inner_height=0.5)
    chart2 = chart.create_subchart(position='right', width=0.5, height=0.5)
    chart3 = chart.create_subchart(position='left', width=0.5, height=0.5)
    chart4 = chart.create_subchart(position='right', width=0.5, height=0.5)

    chart.watermark('1')
    chart2.watermark('2')
    chart3.watermark('3')
    chart4.watermark('4')

    df = pd.read_csv('ohlcv.csv')
    chart.set(df)
    chart2.set(df)
    chart3.set(df)
    chart4.set(df)

    chart.show(block=True)

Synced Line Chart Example:

import pandas as pd
from lightweight_charts import Chart

if __name__ == '__main__':
    chart = Chart(inner_width=1, inner_height=0.8)
    chart.time_scale(visible=False)

    chart2 = chart.create_subchart(width=1, height=0.2, sync=True, volume_enabled=False)
    
    df = pd.read_csv('ohlcv.csv')
    df2 = pd.read_csv('rsi.csv')

    chart.set(df)
    line = chart2.create_line()
    line.set(df2)

    chart.show(block=True)