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intelligration_vestly

intelligration_vestly by Intelligration

Dataset Name: intelligration_vestly


Group: trading_signal
Vendor: Intelligration
Data Starts at: 2016-03-01 00:00:00
Data Currently Ends at: 2020-12-30 00:00:00, contact sales for full data set.
Symbol Set: US Equities (2,700+)
Asset Class: Equity
Data Update Time(s): 9:05 AM EST
Data Update Frequency: day


CloudQuant long-short backtest results show the following:


Sharpe Ratio Total Return Alpha Return Beta Return
0.411 23.49 16.02 -8.19


Long/Short scores for 2,700 NYSE and Nasdaq tickers (no ETFs) based simulated trading decisions by users on the Vestly stock trading app.

Data Contained in this Dataset

Column Type Description
_seq uint Internal sequence number used to keep data rows in order
timestamp string Timestamp of the Data - America/New York Time.
muts uint64 Microseconds Unix Timestamp. An integer representation of a timestamp with microsecond precision that can be compared directly to other timestamps. (underlying field is nc_publish_date_actual)
userBoughtAtSymbol string NASDAQ/NYSE symbol at time of trade
symbol string Trading Symbol or Ticker
rawVoteLong int64 Raw vote count, long positions
rawVoteShort int64 Raw vote count, short positions
decayedVoteLong double Adjusted vote count, long positions; Exponential decay
decayedVoteShort double Adjusted vote count, short positions; Exponential decay
trendScore double Vote trend score [for selected stocks]
quartile double Quartile of selected stocks (1-4) based on trendScore ranking


Important Dataset Notes

This dataset is not available for direct online purchase. Please contact sales directly at sales@CloudQuant.com. The data is available through our normal sales department who can provide you with current pricing and a quote for accessing this valuable dataset. This may be due to a number of reasons such as dataset intended use, size of the company (or investment fund) using the dataset, or for simple legal requirements that CloudQuant needs to ensure are in place prior to licensing the dataset to you.