Conditional Plotting in Pine Script: A Comprehensive Guide
What is conditional plotting and why should you care?
Conditional plotting in Pine Script is basically teaching your charts to be smart about what they show you. Instead of cluttering your screen with the same static lines every single bar, you can make your indicators appear only when certain market conditions happen.
Here's the thing - regular plotting shows you everything, all the time. Conditional plotting shows you what matters, when it matters. Want to highlight only the really volatile days? Easy. Want your moving average to change colors when the trend shifts? Done. Want to mark specific price patterns but ignore the noise? You got it.
The beauty of conditional plotting lies in its simplicity. You're essentially asking Pine Script: "Should I show this value right now?" If the answer is yes, you plot it. If not, you plot nothing (or something else entirely).
This technique pairs beautifully with other Pine Script features too. For instance, if you're working with Bollinger Bands in Pine Script, you might only want to highlight when price touches the outer bands. Or if you're building buy/sell signals, you could show arrows only when your criteria are perfectly aligned.
The classic show-or-hide technique
The most straightforward approach to conditional plotting is the binary choice: show something or don't show it. In Pine Script, when you want to hide a plot, you use na (not available), which tells the chart "there's nothing to display here."
This technique is perfect for highlighting specific moments without cluttering your chart. Think of it as a spotlight that only turns on when something interesting happens.
Marking every third bar - a simple example
Let's start with something basic. Here's how you can plot the high price only every three bars:
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Pineify
//======================================================================//
// ____ _ _ __ //
// | _ \(_)_ __ ___(_)/ _|_ _ //
// | |_) | | '_ \ / _ \ | |_| | | | //
// | __/| | | | | __/ | _| |_| | //
// |_| |_|_| |_|\___|_|_| \__, | //
// |___/ //
//======================================================================//
//@version=6
indicator("Every third bar", "", true)
bool shouldPlot = bar_index % 3 == 0
plot(shouldPlot ? high : na, color = color.fuchsia, linewidth = 6, style = plot.style_linebr)
What's happening here? We're using the modulo operator (%) to check if the current bar number is divisible by 3. When it is, we show the high price. When it's not, we plot na and nothing appears. This creates a dotted pattern that highlights every third bar without overwhelming your chart.
The plot.style_linebr style ensures that Pine Script doesn't try to connect the dots with lines, which would look messy.
Date-based conditional plotting
Sometimes you want to start your analysis from a specific point in time. This is incredibly useful for backtesting strategies after major market events or policy changes:
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Pineify
//======================================================================//
// ____ _ _ __ //
// | _ \(_)_ __ ___(_)/ _|_ _ //
// | |_) | | '_ \ / _ \ | |_| | | | //
// | __/| | | | | __/ | _| |_| | //
// |_| |_|_| |_|\___|_|_| \__, | //
// |___/ //
//======================================================================//
//@version=6
indicator("Plot after date", "", true)
startDate = input.time(timestamp("2025-03-01"))
plot(time > startDate ? close : na)
This approach is beautifully simple - if the current bar's timestamp is after our chosen start date, we show the closing price. Otherwise, we plot nothing. This creates a clean chart that only displays data from your specified start point forward.
This technique is particularly powerful when you're backtesting Pine Script strategies and want to exclude certain historical periods that might not be relevant to current market conditions.
Making things visual with Pineify
Now, I have to mention something cool here. If you're not super comfortable writing all this code from scratch, there's this tool called Pineify that lets you build these conditional plots visually. You basically drag and drop conditions instead of typing them out.
I've seen people set up crossover strategies where moving averages trigger different plot styles, and they do it all through a visual interface. No more scratching your head over syntax errors or forgetting semicolons. Plus, when you change timeframes, everything adapts automatically.
The best part? Once you get a lifetime license, you're set for all future updates. I know some traders who've cut their development time by more than half using it, especially when they're dealing with multiple conditions across different assets.
Website: Pineify
Check out all the features here.
The color-changing approach
The second major technique for conditional plotting involves changing colors instead of hiding values entirely. This approach is fantastic when you want to maintain a continuous line or indicator while visually distinguishing between different market states.
Color-based conditional plotting is particularly effective for trend analysis. Instead of having multiple separate indicators, you can pack different types of information into a single, color-coded display.
If you're new to visual customization in Pine Script, you might want to explore drawing lines with Pine Script first - it covers fundamental visual elements that complement conditional plotting beautifully.
Dynamic moving average colors
Here's a practical example that many traders find invaluable - a moving average that changes color based on its direction:
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Pineify
//======================================================================//
// ____ _ _ __ //
// | _ \(_)_ __ ___(_)/ _|_ _ //
// | |_) | | '_ \ / _ \ | |_| | | | //
// | __/| | | | | __/ | _| |_| | //
// |_| |_|_| |_|\___|_|_| \__, | //
// |___/ //
//======================================================================//
//@version=6
indicator("Colored moving average", "", true)
int length = input.int(20, "Length", minval = 2)
color bullColor = input.color(color.green, "Bullish")
color bearColor = input.color(color.maroon, "Bearish")
float ma = ta.sma(close, length)
bool isRising = ta.rising(ma, 1)
color maColor = isRising ? bullColor : bearColor
plot(ma, "MA", maColor, 2)
This approach transforms a simple moving average into a powerful visual trend indicator. At a glance, you can see whether the trend is strengthening (green) or weakening (maroon). The ta.rising() function checks if the moving average value is higher than it was one bar ago, making the color change responsive to trend direction.
This technique becomes even more powerful when you combine it with other conditional elements. For example, you could add plotshape() markers that only appear when the moving average changes color, giving you both continuous trend information and discrete signal points.
Advanced conditional plotting techniques
Beyond basic show/hide and color changes, conditional plotting opens up sophisticated possibilities for chart analysis:
Volume-based conditional plotting
You can make plots appear only during high-volume periods, helping you focus on significant market moves while filtering out low-volume noise.
Multi-timeframe conditional logic
Combine conditional plotting with multi-timeframe analysis to show indicators that only activate when conditions align across different timeframes.
Pattern-based conditions
Create plots that only appear when specific candlestick patterns or technical formations occur, turning your chart into a smart pattern recognition system.
Key principles to remember
Here are the fundamental concepts that will make your conditional plotting more effective:
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The
natechnique: Usenawhen you want to conditionally hide plots entirely. This keeps your charts clean and focused on relevant information. For more advanced value display techniques, check out displaying values with Pine Script. -
Color-based conditions: Change colors instead of hiding values when you want to maintain visual continuity while highlighting different market states.
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Flexible condition logic: You can base conditions on anything - price action, volume, time periods, mathematical relationships, or combinations of multiple factors.
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Performance considerations: Complex conditional logic can slow down your scripts, so keep conditions as simple and efficient as possible.
Taking your conditional plotting to the next level
Conditional plotting transforms static indicators into intelligent, responsive tools that adapt to market conditions. This isn't just about making prettier charts - it's about creating indicators that provide better, more relevant information for your trading decisions.
Start with the simple techniques we've covered: basic show/hide logic and color changes. These fundamentals will serve you well in most situations. As you become more comfortable, experiment with combining multiple conditions, incorporating volume analysis, and building more sophisticated logic.
Practical next steps
- Practice with basic examples: Start by modifying existing indicators to include simple conditional elements
- Combine techniques: Mix color changes with show/hide logic for maximum visual impact
- Test extensively: Always backtest your conditional indicators to ensure they provide meaningful signals
- Keep it simple: Complex conditions can create false signals and slow performance
The real power comes when you start thinking about what market conditions actually matter for your specific trading style. Are you looking for breakouts? Trend reversals? Volume spikes? Once you identify what matters, conditional plotting becomes the tool that highlights those exact moments on your charts.
Whether you're building simple alerts or complex automated trading strategies, conditional plotting will become an essential part of your Pine Script toolkit. It's the difference between charts that show you everything and charts that show you what matters.