In this version, LightningChart Python Trader introduces four new features: drawing tool pointer events, chart-level pointer events, value label controls, and data point management.

Drawing Tool Pointer Events

Drawing tools now support comprehensive pointer event handling, allowing for custom interactions and dynamic chart updates. All drawing tools can now respond to user interactions via Python callbacks. The available events are:

• on_pointer_down(): Triggered when clicking on a drawing tool
• on_pointer_up(): Triggered when releasing the mouse button
• on_pointer_enter(): Triggered when the cursor enters the tool area
• on_pointer_leave(): Triggered when the cursor exits the tool area
• on_drawing_tool_moved(): Triggered when the tool position changes

Chart-Level Pointer Events

The charts now support pointer event tracking, which allows building custom interactions, tooltips, or analysis tools. The available events are:

• on_pointer_down(): Triggered when clicking on a drawing tool
• on_pointer_up(): Triggered when releasing the mouse button
• on_pointer_move(): Triggered when the cursor moves

Value Label Controls

Fine-grained control over value labels allows customization of how the latest values are displayed for both price data and indicators. The new available controls are:

• show_value_labels(bool): Show/hide all value labels
• set_latest_value_label_type(str): Control the latest price label display

Data Point Management

The new data point management methods provide users with programmatic access to the chart’s data and automatic data-point limiting for real-time applications. All the data points containing full OHLC information, date-time stamps, and optional volume / open interest fields can now be retrieved using the get_data_point_array() function.

The chart also includes a built-in mechanism for automatically dropping old data points in streaming scenarios to maintain performance. This can be enabled or disabled via enable_data_point_limit(bool) and configured with set_data_point_limit(int) to control the maximum number of data points kept in memory.

LightningChart Python Trader 1.1 also prioritizes improving the developer experience and overall UX. Here are some of the enhacements:

New Visual Customization Methods

We have introduced new styling methods to provide greater control over how the chart looks.

• chart.set_splitter_color("#FF6600"): #Orange splitter lines
• chart.set_border_color('#00FF00’):#Green border
• chart.set_axis_color('#0000FF’): #Blue axes
• chart.show_chart_border(True)
• chart.set_zooming_sensitivity(1.5)

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.