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- Added the `screen` parameter to `Chart`, allowing for monitor selection. This should be an index from 0 (0 = primary monitor, 1= second monitor, etc.) - `vertical_span` method, allowing for vertical lines/spans to be drawn across the chart. - `set_visible_range` method, which will set the visible range of the chart based on two given dates. - `resize` method, which resizes the chart to the given size. - `sync` will now sync both charts, regardless of which one is scrolled/zoomed.
2.1 KiB
2.1 KiB
Getting Started
Installation
To install the library, use pip:
pip install lightweight-charts
Pywebview's installation can differ depending on OS. Please refer to their documentation.
When using Docker or WSL, you may need to update your language tags; see this issue.
A simple static chart
import pandas as pd
from lightweight_charts import Chart
Download this
ohlcv.csv
file for this tutorial.
In this example, we are reading a csv file using pandas:
date open high low close volume
0 2010-06-29 1.2667 1.6667 1.1693 1.5927 277519500.0
1 2010-06-30 1.6713 2.0280 1.5533 1.5887 253039500.0
2 2010-07-01 1.6627 1.7280 1.3513 1.4640 121461000.0
3 2010-07-02 1.4700 1.5500 1.2473 1.2800 75871500.0
4..
..which can be used as data for the Chart object:
if __name__ == '__main__':
chart = Chart()
df = pd.read_csv('ohlcv.csv')
chart.set(df)
chart.show(block=True)
The block parameter is set to True in this case, as we do not want the program to exit.
Due to the library's use of multiprocessing, instantiations of `Chart` should be encapsulated within an `if __name__ == '__main__'` block.
Adding a line
Now lets add a moving average to the chart using the following function:
def calculate_sma(df, period: int = 50):
return pd.DataFrame({
'time': df['date'],
f'SMA {period}': df['close'].rolling(window=period).mean()
}).dropna()
calculate_sma derives the data column from f'SMA {period}', which we will use as the name of our line:
if __name__ == '__main__':
chart = Chart()
line = chart.create_line(name='SMA 50')
df = pd.read_csv('ohlcv.csv')
sma_df = calculate_sma(df, period=50)
chart.set(df)
line.set(sma_df)
chart.show(block=True)