Displaying 1 to 11 from 11 results

Timescaledb - An open-source time-series database optimized for fast ingest and complex queries

  •    PLpgSQL

TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license.

Marketstore - DataFrame Server for Financial Timeseries Data

  •    Go

MarketStore is a database server optimized for financial timeseries data. You can think of it as an extensible DataFrame service that is accessible from anywhere in your system, at higher scalability. It is designed from the ground up to address scalability issues around handling large amounts of financial market data used in algorithmic trading backtesting, charting, and analyzing price history with data spanning many years, including tick-level for the all US equities or the exploding crypto currencies space. If you are struggling with managing lots of HDF5 files, this is perfect solution to your problem.

tidyquant - Bringing financial analysis to the tidyverse

  •    R

tidyquant integrates the best resources for collecting and analyzing financial data, zoo, xts, quantmod, TTR, and PerformanceAnalytics, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each. You can now perform complete financial analyses in the tidyverse. Our short introduction to tidyquant on YouTube.




pwned - Simple C++ code for simple tasks

  •    C++

This library provides simple C++ interfaces to common programming tasks. Dependencies: boost-filesystem boost-system boost-iostreams boost curl jsoncpp leveldb pthread gtest htmlcxx hcxselect Special thanks to httpbin for providing a way to test Formicator.

financial-asset-comparison-tool - R Shiny app to compare the historical performance of crypto-assets and equities

  •    R

Welcome! The Financial Asset Comparison Tool is an R Shiny App that facilitates the comparison of a myriad of assets--both traditional and crypto--across time. The idea for this tool came to me when I was trading crypto-currencies actively, and spending a decent amount of time in investor telegram chats and forums. A common argument I would see was over what asset one should have invested in a short while ago, but it was clear that most such discussions were fueled by emotion--primarily "FOMO"--as opposed to testable metrics. This isn't just a popular type of discussion in the crypto investing space; in fact, it may be even more common in traditional finance. I wanted to create a tool that would be able to settle all such asset performance comparison questions, regardless of whether the question was about traditional assets such as equities, crypto-assets like Bitcoin and Ethereum, or some combination of both. The tools made available via this app allow for analysis of varying degrees of complexity, as can be seen in the visualization below. This scaling of metric complexity is also intuitively integrated into the UI design of the app itself, as illustrated by the screenshot below.


gobacktest - event-driven backtesting framework written in golang

  •    Go

Heads up: This is a framework in development, with only basic functionality. An event-driven backtesting framework to test stock trading strategies based on fundamental analysis. Preferably this package will be the core of a backend service exposed via a REST API.

cryptocurrency-analysis - Analysis and visualisation of the cryptocurrency market

  •    R

Having followed the cryptocurrency market for a while now, I decided to do some exploring in the data available from coinmarketcap. Especially in light of the increasing number of successful coins and decreasing Bitcoin dominance in terms of market capitalisation, I assume many investors are eager to understand the dynamics in this market. To replicate, first head over to coinmarketcap-scraper; this lets you download data from coinmarketcap into a local database. The script analysis.R can then be run on this database - copy the database file to your R working directory.

pipeline-live - Pipeline Extension for Live Trading

  •    Python

pipeline-live is an extension for zipline pipeline independently usable for live trading, outside of zipline. While zipline is a great backtesting library, the default Pipeline API requires complicated setup for data bundle, which is often challenging to average users. Quantopian's proprietary data sources such as Morningstar is also not available to many. This library is to address this issue by using online API data sources and simplify the interface for live trading usage. The interface complies the original zipline/pipeline for the most part. For more details about the Pipeline API, please see Quantopian's tutorial and zipline document. If you are looking to use this library for your Quantopian algorithm, check out the migration document.