Price Prediction Using Nearest Neighbor Algorithm
Info
The Price Prediction Using Nearest Neighbor Algorithm is a Indicator for MetaTrader 5 that the k-nearest neighbor algorithm (k-nn) is a powerful technique for forecasting future prices based on historical patterns. This indicator employs a 1-NN approach, identifying the single nearest neighbor pattern and predicting future prices accordingly.
Usage
This tool is typically used for enhancing chart analysis and decision making.
Platform
This Indicator works exclusively on MetaTrader 5 (both build 600+ and newer versions).
Setup
Place the downloaded file in MQL5/Indicators folder via File ? Open Data Folder in MetaTrader 5.
How to Install and Use Price Prediction Using Nearest Neighbor Algorithm
1. Installation: Place your file in the MQL/Indicators folder via "Open Data Folder" and restart your terminal.
2. Loading: Find the indicator in the Navigator, drag it onto your chart, and configure the input parameters in the popup window.
3. Customization: Press Ctrl+I to open the indicator list, select your tool, and click "Properties" to change colors, levels, or visual styles.
4. Updating: Replace the old file in the Indicators folder with the new version and restart the platform to apply changes.
Frequently Asked Questions
Q: Why is my indicator not showing? A: Verify the file is in the MQL/Indicators folder, or try right-clicking the "Indicators" tree in the Navigator and clicking "Refresh."
Q: Do custom indicators slow down the platform? A: Too many complex indicators can impact performance; remove unused ones via the "Indicator List" (Ctrl+I).
Q: Can I use MT4 indicators on MT5? A: No, MQL4 and MQL5 are distinct languages; ensure the indicator is compiled specifically for your platform version.
What this tool does
The k-Nearest Neighbor algorithm (k-NN) is a powerful technique for forecasting future prices based on historical patterns.
Typical Use Case
This Indicator excels in automated trading and technical analysis on MetaTrader 5.
Compatible Platform & Setup
This Indicator works on MetaTrader 5. Place the file in the MQL5/Indicators folder and restart the terminal.
Description & Settings
Related: Price Channel Indicator - another powerful indicator for MetaTrader 5 traders.
The k-Nearest Neighbor algorithm (k-NN) is a powerful technique for forecasting future prices based on historical patterns. This indicator employs a 1-NN approach, identifying the single nearest neighbor pattern and predicting future prices accordingly.Also recommended: Kinematic Price Physics: Velocity and Acceleration Indicators - similar indicator with strong performance on MetaTrader 5.
The algorithm calculates the Pearson correlation coefficient between the current pattern and historical patterns, using this as a distance metric. The input parameters are:
- Npast: The number of past bars in a pattern.
- Nfut: The number of future bars in a pattern, which must be less than Npast.
The indicator visually represents the past and future prices of the nearest neighbor pattern with blue and red curves, respectively. The nearest neighbor is scaled using linear regression to align with the current pattern. Additionally, the indicator provides valuable information, including the starting date of the nearest neighbor and its correlation coefficient with the present pattern. For instance, it might display:
Nearest_Neighbor (EURUSD,H1): Nearest neighbor dated 2026.08.26 23:00:00 with a correlation of 0.9432442047577905 to the current pattern.
This indicator offers a unique perspective on price prediction, leveraging historical patterns to inform future price movements.
You may also like: Institutional Kalman Filter: Dynamic True Price Estimation - excellent alternative for indicator users on MetaTrader 5.
⚠ Limitations & Risk Warning
- This tool is provided for educational and testing purposes only.
- Past performance does not guarantee future results.
- Trading involves substantial risk of loss. Use on a demo account first.
- Results may vary depending on market conditions, broker, and settings.
- We recommend thorough backtesting and forward testing before using with real funds.