Skip to content

This project designed to analyze historical OHLC (Open-High-Low-Close) data of financial markets and predict potential breakout patterns. It utilizes the Maximum Subarray algorithm with Depth-First Search (DFS) to identify periods of significant price movement.

License

Notifications You must be signed in to change notification settings

cdw1p/MaximumSubarrayDFS

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

MaximumSubarrayDFS

This project class is designed for predicting potential breakout patterns in historical financial market data. It utilizes the Maximum Subarray algorithm with Depth-First Search (DFS) to identify periods of significant price movement.

Install

npm install ccxt
npm install maximumsubarraydfs

Example

import ccxt from 'ccxt'
import MaximumSubarrayDFS from 'maximumsubarraydfs'

/**
 * Fetch historical data
 */
const exchange = new ccxt.binance()
const symbol = 'BTC/USDT'
const timeframe = '1h'
const limit = 1000
const historicalData = await exchange.fetchOHLCV(symbol, timeframe, undefined, limit)

/**
 * Find maximum subarray
 */
const algoInit = new MaximumSubarrayDFS(historicalData)
const prediction = algoInit.findMaxSubarray()
console.log({ prediction })

Results

{
  prediction: {
    price: 68625.96,
    timestamp: 1711562400000,
    direction: 'bullish'
  }
}

License

MIT

About

This project designed to analyze historical OHLC (Open-High-Low-Close) data of financial markets and predict potential breakout patterns. It utilizes the Maximum Subarray algorithm with Depth-First Search (DFS) to identify periods of significant price movement.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published