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#TraderTerminology: ►Long/Short◄ A model which takes long and short positions often aiming for an equal share of capital on each side.

#TraderTerminology: ►Long-Only◄ A model which only takes long trades - Risky if the market suddenly takes a downturn. Hence it is suggested that this kind of model is hedged or turned into a long/short model.

Goldman is seeking to hire engineers to help traders automate. Those wanting to move over really should have some experience like building algos on CloudQuant!

#altData news for 8/21: The use of AltData from Quantitative Finance to actionable intelligence is growing. ● Are these lists of “In Demand” jobs their own alternative data? ● #AI and AltData continue to overlap. ● The number of Datasets continues to…

#TraderTerminology: ►Long◄ Buying shares on the assumption that the price will rise putting you in profit.

ML/AI News covering Towards Data Science's Weekly Selection, US and China Jockey for AI Lead, Bloomberg Uses Predictive Intelligence in Corporate Credit, Ideas on pricing alternative data and Neueda & Levyx bring Python into big data era.

#TraderTerminology: ►Large, Medium and Small Cap◄ Based on Market Capitalization : Small cap = $250 million-$2 billion, Mid cap = $2-$10 billion, Large cap = $10 to -$100 billion. Used to represent Large Established vs Small companies.

#TraderTerminology: ►HFT or High Frequency Traders◄ are traders running algorithms which can see other peoples trades entering the market and race ahead of the trade buying and selling, in order to make a penny or two in the process.

#TraderTerminology: ►Emerging Markets◄ Countries which are developed but still have a great deal of positive potential. Whilst a number of their top companies are US listed the easiest way to trade these countries is via an ETF like EEM.

CloudQuant Thoughts: Technology skews towards the wealthy white male and so it is obvious that AI data will skew too. It is imperative that all people involved in creating AI/ML give regular thought to whether or not their systems may be displaying a bias. #DataScience

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