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On February 11, 2016, the Financial Industry Regulatory Authority (“FINRA”) filed a proposed rule with the Securities and Exchange Commission (“SEC”) that would require individuals who “design, develop or significantly modify algorithmic trading strategies” (or “ATS”) as well as individuals responsible for the “day-to-day supervision or direction of the development process,” to pass a qualification exam and register with FINRA as securities traders.
Systematic trading is an ever-evolving competition. One key arena in that contest: the inputs used in quantitative trading models. Over the years, the list of data sets that quants use has expanded from those based on historical prices, to statistical cross-asset correlations, to exposures to unique risk factors and beyond. To get an edge, many quant researchers and portfolio managers are continually looking for unconventional, independent factors that make sense.
It’s inside baseball for finance’s brainiest brainiacs: the Erdos Number.
Global economic indicators are important data points for developing, refining and executing algorithmic strategies. Getting these indicators as soon as they are publicly available is paramount to allowing market participants to make timely, informed investment decisions.
On January 15th 2015, the Swiss National Bank announced that it unexpectedly scrapped its three-year policy of capping the Swiss franc against the euro.
EcoNext is Bloomberg’s most efficient delivery mechanism for key global economic data – the delivery path is fully optimized from the source.