Candlestick Patterns Study for MotiveWave

Overview

The Candlestick Patterns Study is a comprehensive MotiveWave study that automatically detects and highlights bullish, bearish, and neutral candlestick patterns on your charts. This study recognizes over 30 different patterns including single-bar, double-bar, and triple-bar formations with visual markers and tooltips.

Key Features:

• 33+ candlestick patterns with detailed tooltips
• Intelligent trend detection using 50/200 moving averages
• Context-aware pattern filtering (patterns only appear in correct market trends)
• Customizable complexity controls (1-bar, 2-bar, 3-bar patterns)
• Dedicated confirmation pattern toggle
• Trading signal support for automated strategies

Features

Pattern Detection

The study detects 33+ candlestick patterns across three categories. For detailed descriptions of each pattern, see PATTERNS.md.

Single-Bar Patterns

Bullish:

• Hammer
• Inverted Hammer
• Dragonfly Doji
• Bullish Marubozu

Bearish:

• Shooting Star
• Hanging Man
• Gravestone Doji
• Bearish Marubozu

Neutral:

• Doji
• Long-Legged Doji
• Spinning Top

Double-Bar Patterns

Bullish:

• Bullish Engulfing
• Bullish Harami
• Piercing Line
• Tweezer Bottom
• Bullish Kicker

Bearish:

• Bearish Engulfing
• Bearish Harami
• Dark Cloud Cover
• Tweezer Top
• Bearish Kicker

Triple-Bar Patterns

Bullish:

• Morning Star
• Morning Doji Star
• Bullish Abandoned Baby
• Three White Soldiers
• Three Inside Up
• Three Outside Up

Bearish:

• Evening Star
• Evening Doji Star
• Bearish Abandoned Baby
• Three Black Crows
• Three Inside Down
• Three Outside Down

Visual Elements

• Colored Markers: Each pattern is marked with a colored marker
  • Green triangles (bottom, very small) for bullish patterns
  • Red triangles (top, very small) for bearish patterns
  • Orange circles (center, medium) for neutral patterns
• Tooltips: Hover over any marker to see the pattern name and its meaning from The Chart Guys
• Customizable Markers: Adjust colors, sizes, and styles for each pattern type
• Smart Detection: Prevents duplicate markings of the same pattern in continuous trends
• Trend-Aware Display: Patterns only appear when market conditions make them meaningful
• Signal Support: Emits BULLISH_PATTERN and BEARISH_PATTERN signals for automated strategies

Configuration Options

Pattern Types

• Detect Bullish Patterns: Enable/disable bullish pattern detection
• Detect Bearish Patterns: Enable/disable bearish pattern detection
• Detect Neutral Patterns: Enable/disable neutral pattern detection

Pattern Complexity

• Detect 1-Bar Patterns: Toggle single-bar patterns (Doji, Hammer, Marubozu, etc.)
• Detect 2-Bar Patterns: Toggle double-bar patterns (Engulfing, Harami, Piercing Line, etc.)
• Detect 3-Bar Patterns: Toggle triple-bar patterns (Morning/Evening Star, Three White Soldiers, etc.)
• Detect Confirmation Patterns: Toggle confirmation patterns (Three Inside Up/Down, Three Outside Up/Down)

Trend Detection

• Require Correct Trend Context: Enable/disable trend-based pattern filtering
• Fast MA Period: Fast moving average period for trend detection (default: 50)
• Slow MA Period: Slow moving average period for trend detection (default: 200)
• Trend Threshold %: Minimum price deviation from 200MA to confirm trend (default: 0.5%)

When trend filtering is enabled, reversal patterns only appear in the correct market context:

• Bullish reversals (Hammer, Morning Star, etc.) only show in downtrends
• Bearish reversals (Shooting Star, Evening Star, etc.) only show in uptrends
• Neutral patterns (Doji, Spinning Top) appear in all market conditions

This significantly improves signal quality by preventing patterns from appearing where they lack predictive value.

Display Settings

• Bullish Marker: Customize color, size, and style for bullish patterns
• Bearish Marker: Customize color, size, and style for bearish patterns
• Neutral Marker: Customize color, size, and style for neutral patterns

Installation

Download from GitHub Releases (Recommended)

1. Download the JAR file:

   • Go to Releases
   • Download CandlestickPatterns.jar from the latest release

2. Import into MotiveWave:

   • Open MotiveWave
   • Click Study menu in the top menu bar
   • Select All Studies
   • Click Import button in the dialog
   • Navigate to and select the downloaded JAR file
   • Click OK

3. Add to Chart:

   • In the All Studies dialog, search for "Candlestick Patterns"
   • Select it and click Add to add it to your chart

Building from Source

If you want to modify the study or build it yourself:

1. Prerequisites:

   • Java 21 or higher
   • MotiveWave installed on your system

2. Build the JAR:

   chmod +x build.sh
   ./build.sh
   

   The JAR will be created in dist/CandlestickPatterns.jar

3. Import to MotiveWave (same as above):

   • Study → All Studies → Import → Select the JAR from dist/ folder

Development & Testing

For rapid development iteration:

chmod +x deploy.sh
./deploy.sh

This script:

• Compiles the study
• Deploys directly to ~/MotiveWave Extensions/dev/
• MotiveWave automatically reloads the study (if running)
• Ideal for testing changes without manual import/restart

Usage

1. Add Study to Chart:

   • Study menu → All Studies
   • Search for "Candlestick Patterns"
   • Click Add

2. Configure Settings:

   • Right-click the study on chart → Edit Study
   • Enable/disable pattern types (bullish, bearish, neutral)
   • Toggle pattern complexity (1-bar, 2-bar, 3-bar patterns)
   • Customize marker colors and sizes

3. Analyze: Patterns are automatically detected and displayed with tooltips

Parameters

| Parameter | Description | Default | |-----------|-------------|---------|| | Pattern Types | | | | Detect Bullish Patterns | Show bullish pattern markers | true | | Detect Bearish Patterns | Show bearish pattern markers | true | | Detect Neutral Patterns | Show neutral pattern markers | true | | Pattern Complexity | | | | Detect 1-Bar Patterns | Show single-bar patterns | true | | Detect 2-Bar Patterns | Show double-bar patterns | true | | Detect 3-Bar Patterns | Show triple-bar patterns | true | | Detect Confirmation Patterns | Show Three Inside/Outside patterns | true | | Trend Detection | | | | Require Correct Trend Context | Filter patterns by market trend | true | | Fast MA Period | Fast moving average period | 50 | | Slow MA Period | Slow moving average period | 200 | | Trend Threshold % | Price deviation threshold | 0.5% | | Display | | | | Bullish Marker | Marker style for bullish patterns | Green arrow (very small, bottom) | | Bearish Marker | Marker style for bearish patterns | Red arrow (very small, top) | | Neutral Marker | Marker style for neutral patterns | Orange circle (medium, center) |

File Structure

Candlestick-Patterns/
├── src/
│   └── CandlestickPatterns.java  # Main study implementation
├── build.sh                       # Build script
├── deploy.sh                      # Development deployment script
├── MANIFEST.MF                    # JAR manifest file
├── CHANGELOG.md                   # Version history
├── PATTERNS.md                    # Detailed pattern reference guide
└── README.md                      # This file

Technical Details

• Namespace: com.motivewave
• Study ID: CANDLESTICK_PATTERNS
• Menu Location: General
• Overlay: Yes (displays on price chart)
• Signal Support: Yes (emits BULLISH_PATTERN and BEARISH_PATTERN signals)
• Bar Updates Required: Yes (for real-time pattern detection)

Signals

The study emits two types of signals that can be used for alerts and automated trading:

BULLISH_PATTERN

• Triggered: When a bullish candlestick pattern is detected
• Data: Pattern name and closing price
• Use Cases: Long entry signals, bullish reversal alerts, trend confirmation

BEARISH_PATTERN

• Triggered: When a bearish candlestick pattern is detected
• Data: Pattern name and closing price
• Use Cases: Short entry signals, bearish reversal alerts, exit signals

To use signals:

1. Right-click on the study → Signals
2. Enable "BULLISH_PATTERN" and/or "BEARISH_PATTERN"
3. Configure alerts or connect to trading strategies

Pattern Recognition Logic

The study uses sophisticated algorithms to identify patterns based on:

• Body-to-range ratios
• Shadow lengths
• Multi-candle relationships
• Open/close positioning
• High/low comparisons

Each pattern is checked according to t