lightweight-charts 1.0.15

Python framework for TradingView's Lightweight Charts JavaScript library.

lightweight-charts-python

lightweight-charts-python aims to provide a simple and pythonic way to access and implement TradingView's Lightweight Charts.

Installation

pip install lightweight-charts

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Features

1. Simple and easy to use.
2. Blocking or non-blocking GUI.
3. Streamlined for live data, with methods for updating directly from tick data.
4. Multi-Pane Charts using the SubChart.
5. The Toolbox, allowing for trendlines, rays and horizontal lines to be drawn directly onto charts.
6. Callbacks allowing for timeframe (1min, 5min, 30min etc.) selectors, searching, hotkeys, and more.
7. Tables for watchlists, order entry, and trade management.
8. Direct integration of market data through Polygon.io's market data API.

Supports: Jupyter Notebooks, PyQt, wxPython, Streamlit, and asyncio.

PartTimeLarry: Interactive Brokers API and TradingView Charts in Python

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1. Display data from a csv:

import pandas as pd
from lightweight_charts import Chart


if __name__ == '__main__':
    
    chart = Chart()
    
    # Columns: | time | open | high | low | close | volume (if volume is enabled) |
    df = pd.read_csv('ohlcv.csv')
    chart.set(df)
    
    chart.show(block=True)

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2. Updating bars in real-time:

import pandas as pd
from time import sleep
from lightweight_charts import Chart

if __name__ == '__main__':

    chart = Chart()

    df1 = pd.read_csv('ohlcv.csv')
    df2 = pd.read_csv('next_ohlcv.csv')

    chart.set(df1)

    chart.show()

    last_close = df1.iloc[-1]
    
    for i, series in df2.iterrows():
        chart.update(series)

        if series['close'] > 20 and last_close < 20:
            chart.marker(text='The price crossed $20!')
            
        last_close = series['close']
        sleep(0.1)

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3. Updating bars from tick data in real-time:

import pandas as pd
from time import sleep
from lightweight_charts import Chart


if __name__ == '__main__':
    
    df1 = pd.read_csv('ohlc.csv')
    
    # Columns: | time | price | volume (if volume is enabled) |
    df2 = pd.read_csv('ticks.csv')
    
    chart = Chart(volume_enabled=False)
    
    chart.set(df1)
    
    chart.show()
    
    for i, tick in df2.iterrows():
        chart.update_from_tick(tick)
            
        sleep(0.3)

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4. Line Indicators:

import pandas as pd
from lightweight_charts import Chart


def calculate_sma(data: pd.DataFrame, period: int = 50):
   def avg(d: pd.DataFrame):
      return d['close'].mean()

   result = []
   for i in range(period - 1, len(data)):
      val = avg(data.iloc[i - period + 1:i])
      result.append({'time': data.iloc[i]['date'], f'SMA {period}': val})
   return pd.DataFrame(result)


if __name__ == '__main__':
   chart = Chart()
   chart.legend(visible=True)
   
   df = pd.read_csv('ohlcv.csv')
   chart.set(df)

   line = chart.create_line()
   sma_data = calculate_sma(df, period=50)
   line.set(sma_data, name='SMA 50')

   chart.show(block=True)

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5. Styling:

import pandas as pd
from lightweight_charts import Chart


if __name__ == '__main__':
    
    chart = Chart(debug=True)

    df = pd.read_csv('ohlcv.csv')

    chart.layout(background_color='#090008', text_color='#FFFFFF', font_size=16,
                 font_family='Helvetica')

    chart.candle_style(up_color='#00ff55', down_color='#ed4807',
                       border_up_color='#FFFFFF', border_down_color='#FFFFFF',
                       wick_up_color='#FFFFFF', wick_down_color='#FFFFFF')

    chart.volume_config(up_color='#00ff55', down_color='#ed4807')

    chart.watermark('1D', color='rgba(180, 180, 240, 0.7)')

    chart.crosshair(mode='normal', vert_color='#FFFFFF', vert_style='dotted',
                    horz_color='#FFFFFF', horz_style='dotted')

    chart.legend(visible=True, font_size=14)

    chart.set(df)

    chart.show(block=True)

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6. Callbacks:

import pandas as pd
from lightweight_charts import Chart


def get_bar_data(symbol, timeframe):
    if symbol not in ('AAPL', 'GOOGL', 'TSLA'):
        print(f'No data for "{symbol}"')
        return pd.DataFrame()
    return pd.read_csv(f'../examples/6_callbacks/bar_data/{symbol}_{timeframe}.csv')


class API:
    def __init__(self):
        self.chart = None  # Changes after each callback.

    def on_search(self, searched_string):  # Called when the user searches.
        new_data = get_bar_data(searched_string, self.chart.topbar['timeframe'].value)
        if new_data.empty:
            return
        self.chart.topbar['symbol'].set(searched_string)
        self.chart.set(new_data)

    def on_timeframe_selection(self):  # Called when the user changes the timeframe.
        new_data = get_bar_data(self.chart.topbar['symbol'].value, self.chart.topbar['timeframe'].value)
        if new_data.empty:
            return
        self.chart.set(new_data, True)

    def on_horizontal_line_move(self, line_id, price):
        print(f'Horizontal line moved to: {price}')


if __name__ == '__main__':
    api = API()

    chart = Chart(api=api, topbar=True, searchbox=True, toolbox=True)
    chart.legend(True)

    chart.topbar.textbox('symbol', 'TSLA')
    chart.topbar.switcher('timeframe', api.on_timeframe_selection, '1min', '5min', '30min', default='5min')

    df = get_bar_data('TSLA', '5min')
    chart.set(df)

    chart.horizontal_line(200, interactive=True)

    chart.show(block=True)

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Inquiries: [e-mail]

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This package is an independent creation and has not been endorsed, sponsored, or approved by TradingView. The author of this package does not have any official relationship with TradingView, and the package does not represent the views or opinions of TradingView.