MarketScience identifies structural breaks in financial time-series to indicate favorable risk-adjusted return. MarketScience provides indicators of significant price movement with a six-week future horizon. These feature findings can be collected to produce investment algorithms.
|
Date |
Date of closing price |
|
Symbol |
Ticker symbol |
|
Close |
Closing Price |
|
Pplus |
Computed, machine learned probability that the value of the measured asset will go up in the next time step (next day probability). The probability is computed exactly (up to double precision numerical error). Values between 0 and 1. p_plus + p_minus = 1 |
|
Pminus |
Computed, machine learned probability that the value of the measured asset will go down in the next time step (next day probability). The probability is computed exactly (up to double precision numerical error). Values between 0 and 1. p_plus + p_minus = 1 |
|
Emotion |
Computed, machine learned Behavioral Energy measured from the equilibrium energy. Measured in millivolts |
Benzinga’s data samples are intended to provide a data sample large enough for testing data quality and application for the financial markets. These files demonstrate a sample of the formats and content that can be delivered.
