Algo Trading News Headlines 6/1/2018

Getting Started in Algorithmic Trading Includes Overcoming Common Problems


A trading algorithm takes time to develop. Most people get stuck quickly and even give up. When you get stuck, or your algo doesn’t show progress, you need to stop and re-evaluate. An approach that works well within most algo shops is to break the strategy down into small components.

Photo by  Yung Chang  on  Unsplash

Photo by Yung Chang on Unsplash

Emporio Trading Explains the Benefits of Algorithmic Trading


According to a recent study from FinTech research company Technavio, algorithm trading is expected to continue growing at a global annual rate of 10.3 percent through the year 2020. Emporio Trading explains why traditional investment institutions are building tech-savvy algorithm platforms to help improve market intelligence and long-term trade decisions.

Algorithmic Trading: Using Quantopian’s Zipline Python Library In R And Backtest Optimizations By Grid Search And Parallel Processing


We are ready to demo our new experimental package for Algorithmic Trading, flyingfox, which uses reticulate to bring Quantopian’s open source algorithmic trading Python library, Zipline, to R. This article includes a long-form code tutorial on how to perform backtest optimizations of trading algorithms via grid search and parallel processing.

Global Automated Trading Market: Ability to Offer End to End Solutions to Drive Market Growth


The global automated trading market is expected to witness high growth during the forecast period. The report provides key statistics on the market status of the leading market players and offers key trends and opportunities in the market. Automated trading system helps to let traders develop specific rules for trade entries and exits that were once programmed and now can be automatically executed via a computer. This program automatically helps to generate orders which are constructed on predefined set of rules which is based on technical analysis or input from other electronic sources.

NSE Algo Scam: CBI Steps in Where SEBI Was Pussyfooting


In a sensational twist to the investigation into the misuse of co-location facilities of the National Stock Exchange (NSE) by select brokers (also known as the algo scam), the Central Bureau of Investigation (CBI) seems to have sidelined the powerful market regulator and has filed a first information report (FIR) on 30 May 2018. The FIR lists unnamed officials of the NSE, the Securities and Exchange Board of India (SEBI) and specifically names Ajay Narottam Shah, an academic who has worked exceedingly closely with the Finance Ministry, the NSE top brass and SEBI, for many years now.