LightningChart Python Trader is out now!
The most complete Python library for visualizing financial data.
What is LightningChart Python Trader?
LightingChart Python Trader is today’s most complete Python library for visualizing financial data. Version 1.0 delivers unmatched technical analysis features, flexible dashboard layouts, extensive customization options, downloadable examples, and programmatic access to chart data. LightningChart Python Trader significantly reduces development time while enabling advanced financial and trading use cases.
Features
In this section, you can read more about the latest features introduced in version 1.0. You may also refer to the documentation for implementation guidance.
Technical Indicators
LightningChart Python Trader features more than 100 technical indicators. Some of the technical indicator types included are:
- Envelopes: Bollinger Band, Donchian Channels, Fractal Chaos Bands…
- Moving Averages: EMA, SMA, TMA…
- Oscillators – Money Flow: A/D, Chaikin Money Flow, Ease of Movement…
- Oscillators – Price: Aroon Oscillator, Awesome Oscillator, Balance of Power…
- Statistics: Correlation coefficient, Kurtosis, Median Price…
- Trend Indicators: ASI, ADX, Aroon
- Volatility: Average True Range, Chaikin Volatility, Z-Value…
Dashboards
LightningChart Python Trader introduces the dashboard feature, allowing users to add multiple charts in fully customizable layouts. It is possible to create custom workspaces with different chart types, timeframes, and indicators in the same view.
Technical Analysis Chart (TAChart) Constructor
When creating a new chart, it is possible to define values for theme, load_from_store, axis position, and HTML text rendering. There are also helper methods available, including set_dat, add_data_array, and load_csv, now supporting unsorted datasets.
trader = TAChart(
license_key,
html_text_rendering=True,
load_from_storage=False,
theme='turquoiseHexagon',
axis_on_right=True
)
Data Retrieval Methods
A new set of helper methods (named like get_*) lets users easily access things like chart data, buttons and inputs, symbol tables, time settings, and volume info—all through code. This means you can now fully control and analyze charts automatically, or build your custom user interfaces using this data.
Menu Options Control
LightningChart Python introduces 41 new menu_options()to show/hide UI elements. This is helpful to build custom data visualization interfaces and simpler experiences.
menu = trader.menu_options()
menu.show_chart_title_input(False)
menu.show_currency_input(False)
menu.show_watermark_text_input(False)
Mountain Gradients
LightningChart Python 1.0 introduces better gradient options to different chart types including mountain charts.
Explore the LightningChart Python Trader
Getting started with LightningChart Python Trader is easy. We’ve compiled a comprehensive documentation and 26+ examples that can be easily downloaded as ZIP files (requires a license key to run) to help you get started visualizing financial data in Python.
See more news
React Charting Performance
Here's a scenario that plays out across development teams every few months. Someone builds a React dashboard, picks Recharts or Victory because they're popular, the devs love them, and the first demo looks great. Then the dataset gets bigger. Or the data starts...
Javascript Charting Library comparison 2026
Every year someone publishes a listicle of JavaScript charting libraries. They screenshot a demo, note the GitHub stars, paste in the npm install command, and call it a day. Then you follow their recommendation, get six months into your project, and discover that your...
Visualizing 10 Million Data Points in the Browser (2026): The Technical Deep-Dive
Ten Million Data Points in the Browser: How WebGL Makes Mass Datasets Interactive Ten million data points. That number used to mean a database problem, not a front-end problem. Now research teams want to explore it interactively in a browser. Trading desks want...
Elliott Wave Theory in Trading
Financial markets often appear chaotic, but many traders believe that price movements follow recurring patterns driven by human psychology. One of the most influential approaches based on this idea is the Elliott Wave Theory. Developed nearly a century ago, it remains one of the most widely studied methods of technical analysis.



