a7c1dc8a30
- 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.
88 lines
2.1 KiB
Markdown
88 lines
2.1 KiB
Markdown
# Getting Started
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## Installation
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To install the library, use pip:
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```text
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pip install lightweight-charts
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```
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Pywebview's installation can differ depending on OS. Please refer to their [documentation](https://pywebview.flowrl.com/guide/installation.html#installation).
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When using Docker or WSL, you may need to update your language tags; see [this](https://github.com/louisnw01/lightweight-charts-python/issues/63#issuecomment-1670473651) issue.
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___
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## A simple static chart
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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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```
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Download this
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[`ohlcv.csv`](../../../examples/1_setting_data/ohlcv.csv)
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file for this tutorial.
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In this example, we are reading a csv file using pandas:
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```text
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date open high low close volume
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0 2010-06-29 1.2667 1.6667 1.1693 1.5927 277519500.0
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1 2010-06-30 1.6713 2.0280 1.5533 1.5887 253039500.0
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2 2010-07-01 1.6627 1.7280 1.3513 1.4640 121461000.0
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3 2010-07-02 1.4700 1.5500 1.2473 1.2800 75871500.0
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4..
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```
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..which can be used as data for the `Chart` object:
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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(block=True)
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```
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The `block` parameter is set to `True` in this case, as we do not want the program to exit.
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```{warning}
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Due to the library's use of multiprocessing, instantiations of `Chart` should be encapsulated within an `if __name__ == '__main__'` block.
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```
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## Adding a line
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Now lets add a moving average to the chart using the following function:
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```python
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def calculate_sma(df, period: int = 50):
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return pd.DataFrame({
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'time': df['date'],
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f'SMA {period}': df['close'].rolling(window=period).mean()
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}).dropna()
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```
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`calculate_sma` derives the data column from `f'SMA {period}'`, which we will use as the name of our line:
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```python
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if __name__ == '__main__':
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chart = Chart()
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line = chart.create_line(name='SMA 50')
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df = pd.read_csv('ohlcv.csv')
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sma_df = calculate_sma(df, period=50)
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chart.set(df)
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line.set(sma_df)
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chart.show(block=True)
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```
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