READY SIGNAL CONTROL DATA

S&P 500

Why Use This Data Source In Your Models?

The S&P 500 represents the stock market's performance by reporting the risks and returns of the biggest companies. It is used as the benchmark of the overall US stock market, to which all other investments are compared.

S&P 500

Instantly Download this Data Using Our Automated Feature Engineering Tool.

Automated Data Profiling

Ready Signal automatically profiles each data set and offers up suggested industry standard data science treatments to utilize with these data in your models.

Suggested Treatment:

The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Order Norm transformation, provides the best normality, the Arcsin variable will also perform well.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.

Auto Correlation Analysis:

Data shows auto correlation indicating a need for differencing

The ACF indicates 1 order differencing is appropriate.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.11

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 3.01 p-value = 0.01 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.93 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 0.66 indicates the data are moderately skewed.

Hartigan's dip test score of 0.02 with a p-value of 0.00 inidcates the data is multimodal

Statistics (Pearson P/ df, lower => more normal)

No transform

32.94

Box-cox

4.33

Log_b(x-a)

4.20

sqrt(x+a)

6.00

exp(x)

arcsinh(x)

4.20

Yeo-Johnson

4.33

OrderNorm

1.09

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

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400+ Data Sources

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Data Science Treatments

Instantly apply industry-standard data science treatments and transformations, including (but not limited to) Differencing, Lead/Lag, Box Cox. Easily manipulate data across different time and geographic grains.

Auto Discovery

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Data Ingestion

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