An online stock market simulator built using Django.

Korea stock broker bridge server with Flask

An online stock market simulator built using Django.

The Blood Bank Management Portal is a desktop-based application developed using Core Java. It is designed to bridge the gap between blood donors and recipients. This system automates the maintenance of blood stock, donor records, and patient requests, which is traditionally managed manually.

Links the Create Stock network with Applied Energistics

📦 PharmaGo | Fullstack Medical Ecosystem PharmaGo is a high-load platform bridging the gap between customers, pharmacies, and depots. 🚀 Tech: .NET 8, PostgreSQL, React, React Native & Telegram Bot. 🔹 Features: Intelligent generics search, real-time stock mapping, SignalR notifications, and dedicated inventory management for pharmacists.

Created an Account Management System which manages a stock portfolio account and a bank account using C++ and STL *Created the capability to dynamically manage portfolios by allowing users to view price, buy shares, sell shares *Developed features to simulate bank account transactions like deposit/withdraw money and view history *Plotted variation in the value of portfolio over a period of time using MATLAB *Developed a Graphic User Interface using QT to make the whole system more User-friendly *Used Design Patterns like Bridge and Adapter to enhance the experience to ensure reusability of software

Bridging the Gap between Machine and Human in Stock Prediction for IEEE ACCESS

Vector AI is a unified decision support platform bridging the gap between strategy and execution. It uses Zero-Shot AI forecasting to predict demand from stock snapshots, automating EOQ-based procurement and expiry alerts. Admins set high-level strategy, while staff receive actionable digitized tasks to optimize inventory.

Changes the color of all Philips Hue lights connected to a bridge when the stock price of GME changes.

NeuroCapital is a lightweight yet powerful sentiment-driven stock analysis engine that bridges Natural Language Processing with financial insights. It harnesses real-time and historical market sentiment to identify underlying emotional signals and correlate them with stock price movements.

| Connector Solutions - Bridging Metatrader 5 and Stock Exchanges | Software Company | Fintech Solutions |Trading Software | Empowering Traders with Seamless Metatrader 5 Integration | Specializing in developing advanced software solutions that enable traders to connect their Metatrader 5 platform with various stock exchanges.

A comprehensive retail management solution designed to bridge the gap between inventory control and sales processing. This system automates stock updates, streamlines the checkout process, and provides actionable sales analytics for small-to-medium enterprises.

Food alchemist is a non-profitable website providing a bridge between the charities which need food for homeless and the restaurants which intend to donate their remaining food, and it offers restaurants with a data-driven platform to manage their stock, waste less and effortlessly donate to charities. Therefore, our website focus on two targeted audience in Melbourne – Charities and Restaurants.

Recent business research interests concentrated on areas of future predictions of stock prices movements which make it challenging and demanding. Researchers, business communities, and interested users who assume that future occurrence depends on present and past data, are keen to identify the stock price prediction of movements in stock markets. . Predicting market prices are seen as problematical, and as explained in the efficient market hypotheses (EMH) that was put forward by Fama (1990), the EMH is considered as bridging the gap between financial information and the financial market; it also affirms that the fluctuations in prices are only a result of newly available information; and that all available information reflected in market prices. We applied k-nearest neighbour algorithm in order to predict stock prices for a sample of five major companies listed on the NASDAQ stock market to assist investors, management, decision makers, and users in making correct and informed investments decisions. According to the results, the k-NN algorithm is mildly robust with a good accuracy; consequently, the results were rational and also reasonable. In addition, depending on the actual stock prices data; the prediction results were close and fairly parallel to actual stock prices. We implemented the k-NN algorithm from scratch on python 2.7 to conduct the experiments for the project. k-NN is an instance-based, competitive learning, and lazy learning algorithm. Instance based algorithms, sometimes called memory-based learning, are those algorithms that, instead of performing explicit generalization, use the instances seen in the training as a comparison standard. For k-NN, the entire training dataset is the model. When a prediction is required for an unseen data instance, the k-NN algorithm will search through the training dataset for the k-most similar instances. k-NN is a competitive learning model because a majority vote is performed among the selected k records to determine the class label and then assigned it to the query record. k-NN is considered a lazy learning that does not build a model or function previously, but yields the closest k records of the training data set that have the highest similarity to the test (i.e., query record). The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. The similarity measure is dependent on the type of data. For real-valued data, the Euclidean distance can be used. Other types of data such as categorical or binary data, Hamming distance can be used. In the case of regression problems, the average of the predicted attribute may be returned. In the case of classification, the most prevalent class may be returned.

a simple buy/hold/sell stock game using a brownian bridge.

Turn your home into a disco party via real-time stock ticker price alerts with Philips Hue Bridge

Findian is a conversational fintech platform that allows users to interact with the stock market using natural language via a Telegram Bot. It bridges professional-grade data with a simple, chat-based interface.

IndiaQuant is an MCP (Model Context Protocol) server for Claude Desktop that bridges AI with the Indian stock market (NSE/BSE). Powered by Python, it natively gives Claude 10 financial tools including live stock prices, options chain analysis, Black-Scholes Greeks calculation, news sentiment AI scoring, and virtual portfolio management.

Stock Shield is an advanced Decision Support System (DSS) designed to protect retail investors from extreme market volatility. By combining Machine Learning (Random Forest) with Temporal Intelligence, it bridges the gap between raw market data and safe investment strategies.

SignalCraft is a comprehensive algorithmic trading platform that empowers traders to discover opportunities, build custom strategies, and automate execution — all in one place. Built with modern technologies (FastAPI, Next.js, PostgreSQL, Redis) and designed for the Indian stock market, SignalCraft bridges the gap between idea and execution.

PharmaPredict is an industry-ready capstone for small pharmacies. It bridges the gap between manual records and predictive intelligence using the MERN stack and Python. By analyzing inventory data, it proactively identifies expiry risks and stock-outs, helping healthcare providers reduce waste and ensure medicine is always available.

Med-Map is a web-based AI-powered application that helps users locate nearby pharmacies with specific medications in stock, while also providing smart health advice via an AI assistant. Designed with scalability in mind, it aims to bridge the gap between patients and accessible, reliable healthcare in Nigeria and beyond.

The biggest bottleneck in AI right now is the manual copy-paste loop. ​I built a system to bridge that gap. You forward a doc on WhatsApp → Gemini Vision files it. You ask for a file → It finds the link in 3 seconds. It also monitors my stock/gold alerts while I'm away. ​Architecture is local and private

Recent business research interests concentrated on areas of future predictions of stock prices movements which make it challenging and demanding. Researchers, business communities, and interested users who assume that future occurrence depends on present and past data, are keen to identify the stock price prediction of movements in stock markets (Kim, 2003). However, financial data is considered as complex data to forecast and or predict. Predicting market prices are seen as problematical, and as explained in the efficient market hypotheses (EMH) that was put forward by Fama (1990), the EMH is considered as bridging the gap between financial information and the financial market; it also affirms that the fluctuations in prices are only a result of newly available information; and that all available information reflected in market prices. In this project, we attempt to do such an analysis but with an emphasis of using a machine learning algorithm. We applied k-nearest neighbour algorithm in order to predict stock prices for a sample of five major companies listed on the NASDAQ stock market to assist investors, management, decision makers, and users in making correct and informed investments decisions. According to the results, the k-NN algorithm is mildly robust with a good accuracy; consequently, the results were rational and also reasonable. In addition, depending on the actual stock prices data; the prediction results were close and fairly parallel to actual stock prices.

Indaprojekt

stock-bridge

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CryptoStocks is an application which will allow people to buy stocks using cryptocurrencies, essentially bridging the gap between a Stock Exchange and a Crypto Exchange, henceforth, integrating the two together