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README.md Update Article-18760-Dijktras-Algorithm/README.md 2026-06-21 13:24:23 +00:00

README.md

MQL5

Article-490717-Dijkstra-Algorithim-Applied-In-Trading

This repository is an article-derived reference project based on the original MQL5 article. It does not claim to reproduce the full original source code unless files are explicitly attached.

Overview

This project documents an experimental MQL5 trading system that applies Dijkstra's shortest-path algorithm to financial market structure. Instead of treating price action as a series of isolated candles, the system models the market as a graph where swing highs and swing lows become nodes and price distances between those nodes become weighted connections.

The Expert Advisor automatically:

  • Detects swing highs and swing lows
  • Builds a graph of market structure
  • Assigns edge weights using price distance
  • Applies Dijkstra's shortest-path algorithm
  • Identifies the most probable next swing target
  • Generates buy or sell signals from the resulting path
  • Calculates stop loss and take profit levels using node distances
  • Visualizes swings and shortest-path relationships directly on the chart
  • Filters invalidated swing points and removes obsolete market structure

The project demonstrates how graph theory concepts can be adapted to price-action trading and market structure analysis within MetaTrader 5.

Original Article

Article ID: 490717

Author: Hlomohang John Borotho

Publication Date: 2025.07.09

Category: Experts

URL: https://www.mql5.com/en/articles/18760

Repository Purpose

This repository should be treated as a reference or reconstruction project derived from the article content.

Its purpose is to:

  • Study practical applications of graph theory in trading
  • Explore Dijkstra's shortest-path algorithm within financial markets
  • Demonstrate market structure modeling using swing highs and lows
  • Provide a foundation for advanced pathfinding-based Expert Advisors
  • Serve as a framework for future liquidity and structure-based trading systems

Key Concepts

Graph Representation of Market Structure

The market is modeled as a graph consisting of:

  • Swing highs
  • Swing lows
  • Connections between swings
  • Weighted paths representing movement costs

Swing Points as Nodes

Every valid swing high or swing low becomes a graph node.

Each node contains:

  • Bar index
  • Timestamp
  • Price level
  • Swing type (high or low)
  • Distance from source node
  • Previous node reference
  • Visited state
  • Usage state

Edge Weights

The weight between two nodes is calculated using the absolute price difference:

Weight = |Price(Node A) - Price(Node B)|

This represents the cost required for price to travel between two structural points.

Source Node

The algorithm begins from the selected starting node, which can be:

  • The earliest detected swing
  • The most recent valid swing
  • A user-defined structure point

Visited Nodes

Once a node has been processed by Dijkstra's algorithm, it is marked as visited and excluded from future shortest-path calculations.

Distance Table

Each node maintains a cumulative distance value representing the lowest known cost from the source node.

Algorithm / Architecture Summary

1. Swing Detection

The EA scans historical candles and identifies:

  • Swing highs
  • Swing lows

using configurable left and right bar lookback periods.

2. Node Construction

Each detected swing is converted into a graph node.

Node attributes include:

  • Price
  • Time
  • Index
  • Distance
  • Previous node
  • Visited status

3. Swing Validation

Detected swings are validated by checking whether future price action has already passed through them.

Invalid swings are discarded.

Valid swings remain available for graph analysis.

4. Graph Creation

A graph is formed where:

  • Nodes = Swing highs and lows
  • Edges = Price relationships between nodes
  • Weights = Price distance between nodes

5. Dijkstra Processing

The algorithm:

  1. Starts from the source node
  2. Finds the nearest unvisited node
  3. Updates neighboring node distances
  4. Records shortest paths
  5. Marks processed nodes as visited

This continues until all reachable nodes have been processed.

6. Path Reconstruction

Using the previous node references, the EA reconstructs the shortest path through market structure.

7. Signal Generation

Trading signals are generated using the relationship between connected nodes.

Buy Signal

Next Node Price > Current Node Price

Sell Signal

Next Node Price < Current Node Price

8. Risk Management

Stop Loss and Take Profit are calculated from node distances.

Distance = |Current Node - Previous Node|

A configurable point buffer can be added.

9. Trade Execution

Orders are executed using the MQL5 CTrade class.

Supported functionality:

  • Buy orders
  • Sell orders
  • Stop loss placement
  • Take profit placement
  • Position management

10. Visualization

The EA draws:

  • Swing highs
  • Swing lows
  • Dijkstra path connections
  • Valid structure levels

directly on the chart.

11. Cleanup

Old swing objects are automatically removed to prevent chart clutter.

Dijkstra Interpretation in Trading

Dijkstra Term Trading Interpretation
Graph Market structure built from swing highs and lows
Node Swing high or swing low
Edge Price relationship between swings
Weight Price distance between swings
Source Node Starting swing point
Visited Set Processed or invalidated swings
Distance Table Lowest-cost route through market structure
Shortest Path Most probable route price may follow

Mentioned or Attached Files

Primary Components

  • Dijkstra Swing EA.mq5
  • README.md

Possible Future Components

  • DijkstraVisualizer
  • SwingDetector
  • GraphBuilder
  • LiquidityEngine
  • RiskManager

Statistics

Trading Components

  • Swing Detection Module: 1
  • Graph Construction Module: 1
  • Dijkstra Engine: 1
  • Signal Generator: 1
  • Visualization Module: 1
  • Trade Execution Module: 1

Market Structure Features

  • Swing High Detection
  • Swing Low Detection
  • Node Validation
  • Pathfinding
  • Structure Visualization
  • Dynamic SL/TP

Algorithm Complexity

Dijkstra Complexity:

O(V²)

where:

V = Number of swing nodes

Tags

MQL5, MetaTrader5, Graph-Theory, Dijkstra, Swing-Trading, Price-Action, Market-Structure, Expert-Advisor, Algorithmic-Trading, Pathfinding, Quantitative-Finance

Difficulty

Moderate

Requires familiarity with:

  • MQL5 Expert Advisor development
  • Market structure concepts
  • Swing high and swing low analysis
  • Graph theory fundamentals
  • Dijkstra's shortest-path algorithm
  • Risk management
  • Trade execution using CTrade

Limitations

  • Price distance alone may not fully represent market behavior.
  • Dijkstra's algorithm assumes static edge weights.
  • Liquidity, volume, and volatility are not incorporated unless explicitly added.
  • Swing quality depends on the chosen lookback parameters.
  • The shortest path does not guarantee future price movement.
  • Performance may decrease when processing large numbers of nodes.

Future Improvements

Potential enhancements include:

  • ATR-adjusted edge weights
  • Liquidity-based weighting
  • Volume profile integration
  • Multi-timeframe graph construction
  • A* search implementation
  • Dynamic graph updates
  • Reinforcement learning assisted node selection
  • Order block and fair value gap nodes
  • Market regime detection
  • Probabilistic path scoring

Reference

Original MQL5 article: https://www.mql5.com/en/articles/18760

Related Concepts:

  • Dijkstra's Shortest Path Algorithm
  • Graph Theory
  • Market Structure Trading
  • Swing High and Swing Low Analysis
  • Pathfinding Algorithms
  • Algorithmic Trading

MQL5 Algo Forge / johnhlomohang