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Alpha#49

Type: alpha_49 • Category: indicators

Description​

Alpha#49 - conditional acceleration with threshold.

Parameters​

NameTypeDescriptionRequiredDefault
dataExpstringprice datano
closestringselect the column with closing pricesno

Help​

Alpha#49

Description​

Alpha#49 is a worker indicator from the Alpha 101 library, used in investing strategies based on the 101 Formulaic Alphas. This indicator specifically implements a conditional acceleration with a threshold.

What does this worker do?​

The Alpha#49 worker calculates a conditional acceleration with a threshold based on the provided price data. It uses the closing prices to determine the acceleration and applies a threshold to filter the results.

How to interpret the results​

The results of the Alpha#49 worker can be interpreted as a measure of the acceleration of the price movement, conditioned on a threshold. A higher value indicates a stronger acceleration, while a lower value indicates a weaker acceleration.

Parameters​

The following parameters are required to configure the Alpha#49 worker:

  • dataExp: price data
    • Type: DataFrame
    • Description: The input price data.
  • close: select the column with closing prices
    • Type: String
    • Description: The column name of the closing prices in the dataExp DataFrame.

Usage​

To use the Alpha#49 worker, follow these steps:

  1. Provide the price data in a DataFrame format.
  2. Specify the column name of the closing prices.

Example usage:

import pandas as pd

# Load price data
data = pd.read_csv('price_data.csv')

# Create an Alpha#49 worker
alpha49 = Alpha49(dataExp=data, close='Close')

# Run the worker
result = alpha49.run()

Visualizing the Indicator​

The following GIFs demonstrate how to use the Alpha#49 worker:

Full GIF Short GIF

Publication​

The Alpha 101 library, including the Alpha#49 indicator, is based on the research paper "101 Formulaic Alphas" by Zura Kakushadze. You can find the publication on arXiv.

References​

  • Kakushadze, Z. (2016). 101 Formulaic Alphas. arXiv preprint arXiv:1601.00991.