Research Article

QuantSys: A Stock breakout value prediction system using an Algorithmic approach

by  Kashyap Mavani, Rahul M. Samant, Pranjal Mulay, Vaishnavi Jadhav
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Issue 16
Published: June 2025
Authors: Kashyap Mavani, Rahul M. Samant, Pranjal Mulay, Vaishnavi Jadhav
10.5120/ijca2025925163
PDF

Kashyap Mavani, Rahul M. Samant, Pranjal Mulay, Vaishnavi Jadhav . QuantSys: A Stock breakout value prediction system using an Algorithmic approach. International Journal of Computer Applications. 187, 16 (June 2025), 12-18. DOI=10.5120/ijca2025925163

                        @article{ 10.5120/ijca2025925163,
                        author  = { Kashyap Mavani,Rahul M. Samant,Pranjal Mulay,Vaishnavi Jadhav },
                        title   = { QuantSys: A Stock breakout value prediction system using an Algorithmic approach },
                        journal = { International Journal of Computer Applications },
                        year    = { 2025 },
                        volume  = { 187 },
                        number  = { 16 },
                        pages   = { 12-18 },
                        doi     = { 10.5120/ijca2025925163 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2025
                        %A Kashyap Mavani
                        %A Rahul M. Samant
                        %A Pranjal Mulay
                        %A Vaishnavi Jadhav
                        %T QuantSys: A Stock breakout value prediction system using an Algorithmic approach%T 
                        %J International Journal of Computer Applications
                        %V 187
                        %N 16
                        %P 12-18
                        %R 10.5120/ijca2025925163
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

In today’s fast-moving financial markets, traders—whether beginners or seasoned professionals—need tools that can simplify decision-making and help them stay ahead of market trends. This paper presents QuantSys, a smart and user-friendly stock market prediction system designed to support better and faster trading decisions using well-established technical indicators. Instead of relying on complex and hard-to-understand models, QuantSys focuses on trusted, time-tested tools like Simple Moving Averages (SMA) and Exponential Moving Averages (EMA) to analyze stock price behavior. These indicators are widely used in the trading community for spotting trends, identifying momentum, and predicting possible breakout or reversal points. QuantSys is built using Python and leverages the yfinance module to collect real-time stock data directly from the financial markets. This data is analyzed automatically, and the system continuously monitors stock movement across various timeframes, such as 1-minute, 5-minute, daily, and weekly intervals. The use of different timeframes allows the system to cater to various types of traders—from intraday scalpers to long-term investors—by giving context-aware signals suited to different strategies. One of the standout features of QuantSys is its seamless integration with a Telegram bot, which acts as a real-time communication channel between the system and the user. As soon as a potential trading opportunity is identified—such as a price crossover the moving average—the system sends a detailed and instant alert to the user’s Telegram account. This ensures that the trader can react quickly without having to sit in front of charts all day. The goal of QuantSys is to reduce the effort required in technical analysis, improve response time, and make reliable predictions accessible to all types of users. By automating the collection, analysis, and alert delivery processes, the system saves time and reduces the chance of missed opportunities. Overall, QuantSys offers a practical and efficient solution for anyone looking to trade smarter, faster, and with more confidence in today’s volatile market conditions.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Stock Prediction Exponential Moving Average (EMA) Simple Moving Average (SMA) Breakout Detection yfinance (yahoo finance) Trade Alerts Trend Analysis Real-Time Data Intraday Trading Moving Average Crossover Automated Trading System.

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