Replicating orderbooks from Websocket stream with Python and Asyncio

Introduction We are currently working on time-series database solution for collecting high-frequency crypto-exchanges data – namely for tick data and one-minute orderbook snapshots. We’ve developed REST API collector bots which are continuously fetching data from numerous REST API endpoints and saving them to database. This solution would work in the perfect world but that’s not where we do live. During …

Open-source database solution for daily data in R

This post will guide you through building up the database solution for market data from scratch. Database will daily update itself with data directly from CME. I will use R6 Parser class as interface between the database and CME settlement files. As a database I utilize PostgreSQL(PSQL) as it’s pretty fast, free, features rich, has clear documentation and is based …

Introduction to volatility models with Matlab (ARCH, GARCH, GJR-GARCH)

In this article you get familiar with basic concepts behind GARCH models family and practical use of it. General properties, terms and notation of conditional variance models Advantage of conditional variance models is that they better describe following time series properties: Returns of an asset have positive excess kurtosis[1] which means their PDF peak is sharper than the normal PDF …

Introduction to volatility models with Matlab (Implied volatility)

Implied volatility (IV) is the volatility of an asset derived from changes in value of corresponding option in such way that if we input IV into option pricing model, it will return theoretical value equal to the current option value. Contrary to historical volatility, IV is the volatility forecast for price of the underlying asset from current time to option …