Skip to content

Latest commit

 

History

1 Commit

Folders and files

NameName
Last commit message
Last commit date
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

📊 StockQueryX — Stock Market Range Query Analyzer (Mo's Algorithm)

StockQueryX ek high-performance range-query analytics engine hai jo stock price data par complex queries ko ultra-fast process karta hai using Mo’s Algorithm.


🚀 Features Supported

StockQueryX total 6 advanced stock-analysis range queries support karta hai:

✅ 1. Distinct Prices (L–R)

  • Range me kitne unique price values the.

✅ 2. Max-Frequency Price (Mode)

  • Jo price sabse zyada baar aaya ho L–R me.

✅ 3. Volatility Count

  • Up → Down → Up → Down type direction change count.

✅ 4. U-D-U-D Pattern Count

  • Zig-zag micro-patterns detect karta hai.

✅ 5. Trend Detection

  • Uptrend
  • Downtrend
  • Flat trend

✅ 6. Peak Count

  • Kitne local maxima (peaks) aaye is range me.

⚙️ Mo’s Algorithm — Core Engine

Mo’s Algorithm contiguous range queries ko optimize karta hai by minimizing operations.

📌 Time Complexity:

O((N + Q) * √N)

Boht speed optimization hota hai especially jab N aur Q bohot bade ho.

📌 Sorting Queries:

O(Q · log Q)

📌 Add / Remove Operations:

O(1) each

📏 Maximum Dataset Capacity

⚡ Smooth Performance:

N = 100,000 prices  
Q = 100,000 queries

⚡ Acceptable Performance:

N = 200,000  
Q = 200,000

⚠ Stress-tested Upper Limit:

N = 300,000+  
Q = 300,000+

Real bottleneck = Browser + device RAM (JS single-threaded).
Algorithmically, Mo’s 500k+ dataset bhi handle kar sakta hai.


🧠 Why Mo's Algorithm for Stocks?

Stock market analytics heavily depends on range-based insights:

  • Intraday volatility
  • Local peaks / dips
  • Trend shifts
  • Micro-patterns
  • Clustering behavior
  • Price diversity

Ye sab continuous segments par hote hain → Mo’s Algorithm = perfect match.


🧪 Performance Example

For:

N = 100,000  
Q = 20,000

Naive:

~2.5 billion operations → browser freeze

Mo’s Algorithm:

~6–7 million operations → fast & smooth

🖥️ Real-World Use Cases

  • Trading platforms
  • Back-testing engines
  • Financial research dashboards
  • Market pattern detectors
  • Volatility monitors
  • Historical price analytics
  • Quantitative ML feature extraction

About

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages