Plotting and Visualization

The Plotting module utilizes Makie for creating visualizations. It is important to note that graphical backends are not automatically included with the module and must be installed manually:

Planar enhances Makie with specialized plotting recipes for financial analysis:

OHLCV (Open-High-Low-Close Volume) charts
Detailed trade history visualization
Summarized trade history with volume and balance insights
Custom indicator overlays and channels
Multi-asset portfolio visualization
Performance and optimization result analysis

Interactive Features

Interactive backends, such as GLMakie and WGLMakie, allow for dynamic plots that can be clicked-and-dragged or zoomed. Additionally, interactive elements like tooltips are available on hover for candlesticks, trades, and balance charts.

Backend Selection and Setup

GLMakie (Desktop Applications)

GLMakie is the recommended backend for desktop applications and provides the best performance for interactive plots:

WGLMakie (Web Applications)

WGLMakie is ideal for web-based applications and Jupyter notebooks:

CairoMakie (Static Images)

For generating static images or when interactive features are not needed:

For generating static images, use CairoMakie and save with save("chart.png", figure).

OHLCV Charts

OHLCV data is represented using candlesticks to indicate price movement, with red signifying a price decrease and green an increase. Volume is depicted as bars in the background of the chart.

Basic OHLCV Chart

OHLCV Plot

Customizing OHLCV Charts

You can customize various aspects of OHLCV charts:

Customize charts with parameters like title, up_color, down_color, volume_color, and show_volume.

Time Range Selection

Display specific time periods:

Multiple Timeframes

Compare different timeframes on the same chart:

Create multi-timeframe layouts using Figure() and Axis() with Plotting.ohlcv!() for each subplot.

Trading History Visualization

The history of trades is illustrated using triangles, with upwards pointing triangles for buy transactions and downwards for sell transactions.

Basic Trade Visualization

TradesTicks

Advanced Trade Analysis

Trade Performance Overlay

Combine trade markers with performance metrics:

Add balance lines and statistics with Plotting.balance_line!() and Plotting.add_stats_table!().

Multi-Asset Trade Comparison

Compare multiple assets using loops with Plotting.tradesticks!() in subplots.

Aggregated Trade History for a Single Asset

The balloons function provides an aggregated view of trade density within a specified timeframe. Each circle's size correlates with the trade volume—larger circles denote higher volume. Positive volume timeframes are labeled as sells and negative ones as buys. Circle opacity reflects the number of trades within the timeframe. The accompanying balance chart indicates the quote currency held: the orange band represents actual cash (Strategy.cash), while the blue band represents the value of held assets in quote currency (AssetInstance.cash * price).

Basic Balloons Chart

Balloons

Customizing Balloons Visualization

Customize balloons with parameters like tf, min_radius, max_radius, buy_color, and sell_color.

Advanced Balloons Analysis

Add volume histograms and frequency overlays with show_volume_histogram and Plotting.frequency_heatmap!().

Aggregated Trade History for Multiple Assets

For a comprehensive analysis, aggregated trade history can plot multiple assets. Balloons are overlaid on the price line of each asset, with the same opacity and radius settings as single asset plots. To benchmark against a specific asset, pass a symbol or a dataframe using the benchmark keyword argument.

Basic Multi-Asset Balloons

Balloons Multi

Portfolio-Wide Analysis

Use benchmark, normalize_prices, and layout parameters for multi-asset analysis.

Advanced Multi-Asset Visualization

Create portfolio heatmaps with Plotting.portfolio_heatmap() and add correlation analysis.

Sector and Category Analysis

Group assets by categories and plot with different colors using loops and subplots.

Custom Indicators

Custom indicators enhance chart analysis and can be integrated into plots. Planar provides several functions for adding technical indicators to your charts.

Line Indicators

Moving averages and other line-based indicators can be added using the line_indicator function:

Line Indicator

Advanced Line Indicators

Channel Indicators

Channels or envelopes can be visualized using the channel_indicator function. This tool is useful for identifying trends and potential breakouts:

Channel Indicator

Advanced Channel Indicators

Oscillator Indicators

Custom Indicator Development

Styling and Customization

Color Schemes and Themes

Chart Layout and Sizing

Export and Saving Options

Pe

rformance Analysis Visualization

Strategy Performance Charts

Risk Metrics Visualization

Create risk analysis charts using functions like Plotting.rolling_sharpe(), Plotting.rolling_volatility(), and Plotting.calculate_var().

Optimization Result Visualization

Parameter Optimization Heatmaps

3D Optimization Surface

Use Axis3() and surface!() for 3D optimization visualization.

Optimization Progress Tracking

Track optimization progress using lines!() and scatter!() to show best values and parameter evolution.

Large Dataset Visualization

Large Dataset Handling

For large datasets, use Plotting.load_chunks() for progressive loading or Plotting.downsample_ohlcv() for memory efficiency.

Interactive Features and Widgets

Interactive Features

GLMakie supports interactive features like Slider(), Button(), and lift() for reactive plotting.

Troubleshooting and Performance

Common Plotting Issues

Backend Problems

Performance Tips

Monitor memory usage with Base.gc_live_bytes() and consider downsampling large datasets for better performance.e GLMakie.set_theme!( SSAO = (enabled = false,), FXAA = (enabled = false,) )

# Use lower quality for interactive plots
GLMakie.set_theme!(
    resolution = (1200, 800),  # Lower resolution
    px_per_unit = 1            # Lower pixel density
)

println("Performance optimizations applied")

end

Apply optimizations

optimizeplotperformance()


### Best Practices

1. **Data Preparation**: Clean and validate data before plotting
2. **Memory Management**: Use downsampling for large datasets
3. **Backend Selection**: Choose appropriate backend for your use case
4. **Color Accessibility**: Use colorblind-friendly palettes
5. **Performance**: Limit the number of data points for interactive plots
6. **Export Quality**: Use high DPI settings for publication-quality images

dvanced Backend Configuration

### GLMakie Advanced Setup

GLMakie provides the best performance for desktop applications with full GPU acceleration support.

#### Installation and Configuration


#### Multi-Monitor Support

julia

Activate PlanarInteractive project for plotting features

import Pkg Pkg.activate("PlanarInteractive")

try using GLMakie

# Configure for multiple monitors
function setup_multi_monitor()
    monitors = GLMakie.GLFW.GetMonitors()
    println("Available monitors: $(length(monitors))")
    
    for (i, monitor) in enumerate(monitors)
        name = GLMakie.GLFW.GetMonitorName(monitor)
        mode = GLMakie.GLFW.GetVideoMode(monitor)
        println("Monitor $i: $name ($(mode.width)x$(mode.height))")
    end
end

setup_multi_monitor()

catch e @warn "GLMakie not available: e" end resolution = (1200, 800),

Comprehensive OHLCV chart examples with customization options
Advanced trade visualization with multiple analysis methods
Enhanced balloons functionality for single and multi-asset analysis
Extensive custom indicator examples including line, channel, volume, and oscillator indicators
Styling and customization options with themes and color schemes
Performance analysis visualization for strategy evaluation
Optimization result visualization with heatmaps and 3D surfaces
Large dataset handling with progressive loading and memory optimization
Interactive features with widgets and real-time updates
Advanced backend configuration for GLMakie, WGLMakie, and CairoMakie
Performance optimization and troubleshooting guidance
Multi-window management for complex analysis workflows

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