TDengine is an open source, high-performance, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, Industrial IoT and DevOps.
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Updated
May 23, 2024 - C
TDengine is an open source, high-performance, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, Industrial IoT and DevOps.
Credit Card Analysis Report using Power BI and PostgreSQL, provides comprehensive insights into the credit card transactions and customer behavior. This report analyzes transaction trends, customer spending patterns, and demographic insights to optimize marketing strategies and improve customer satisfaction and overall business performance.
An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
An open source time-series database for fast ingest and SQL queries
A simple tool-kit written in python for sourcing and displaying macroeconomic and financial data.
Py-Alpaca-Api is a python package aimed at simplifying communication with Alpaca Markets REST API
Easily parse SDR data with this web application!
Python tools to analyze stock market behaviour
Automated Asset Value Analysis Service
Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau
km-feb24-jayapura-10 created by GitHub Classroom
FinTwit-Bot is a Discord bot designed to track and analyze financial markets by pulling data from platforms like Twitter, Reddit, and Binance. It features customizable tools for sentiment analysis, market trends, and portfolio tracking to help traders stay informed and make data-driven decisions.
Predictor for stock and ETF prices
Portfolio optimisation library.
This strategy works for every market condition irrespective of the movement
A functional trading journal with statistical analysis and balance sheets.
This project demonstrates two quantitative investing strategies: a momentum strategy and a value strategy. Each strategy involves selecting 50 stocks from the S&P 500 based on specific criteria and calculating recommended trades for an equal-weight portfolio of these stocks, as well as more advanced strategies.
This project demonstrates a quantitative value investing strategy that selects the 50 stocks with the best value metrics from the S&P 500. The strategy involves calculating the recommended trades for an equal-weight portfolio of these 50 stocks, as well as a more advanced robust value strategy.
Predict future stock prices with this Streamlit web app. Choose a company, set the forecast period, and visualize historical data and forecasted trends. Powered by machine learning with the Prophet library. Try it now!
📉 Providing enhanced visibility into short positions on the Australian Stock Exchange
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