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$TSLA Skyrocketed Ahead of Stock Split
mid-June to August: 2 spikes in the acceleration of change of volatility spread implied the 2 rallies of the stock price one week ahead of time, while the downward spike predicted the post-split drop
#AltData

https://t.co/ozEYvCBjgN

8005 datasets now available on CloudQuant.
Way to go team.
#AltData

$TSLA Skyrocketed Ahead of Stock Split
mid-June to August: 2 spikes in the acceleration of change of volatility spread implied the 2 rallies of the stock price one week ahead of time, while the downward spike predicted the post-split drop
#AltData

https://t.co/t0XKX2KGPI

Alternative Data News : SpiderRock dataset's indications of $TSLA price split ahead of event : CloudQuant releases results of latest disruptive data set analysis : Gender reveals responsible for 0.4% of Cali burn : IEX Innovation In US Displayed Markets https://t.co/LGBUTgYy3g

Data engineering - Data Orchestration
- Ingesting -> Analyzing -> Tickerizing -> Researching
Making the data useful is essential!

This is the core value proposition form CloudQuant.
#TradingShowUS @TheTradingShow

Making data available for Point-in-Time analysis, without forward looking corruption, is essential to the Quantitative Analysts. One must avoid corrupting your analysis by peaking into the future!

What are the risks of externally managed data sources and signals for money managers?
- "Be prepared to manage the data like a money manager. A systematic process that you don't understand is a discretionary operation" - Steve Cannon formerly of AQR
#TradingShowUS

The trend is the quantamental space is away from spreadsheets into Jypyter notebooks for money managers.

#tradingshowUS #Jupyter #Python

What are the risks of externally managed data sources and signals for money managers?
- "Be prepared to manage the data like a money manager. A systematic process that you don't understand is a discretionary operation" - Steve Cannon formerly of AQR
#TradingShowUS

Raw data is great and every researcher loves raw data. But the engineering effort to get the data into a useful format is time consuming. Therefore tickerized data is very useful says Eli Berstein of Wells Fargo. -- From @TheTradingShow #TradingShowUS
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