tessl/pypi-ta-lib

Python wrapper for TA-LIB providing 175+ technical analysis indicators for financial market data

—

Pending

Quality

Pending

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Overview

Eval results

Files

Statistical Functions

Name: tessl/pypi-ta-lib
Author: tessl

Statistical analysis functions for correlation, regression, and statistical measures of price data. These functions provide mathematical insights into price relationships, trends, and variability patterns.

Capabilities

Beta Coefficient

Measures the sensitivity of a security's returns relative to market returns, indicating systematic risk.

def BETA(real0, real1, timeperiod=5):
    """
    Beta
    
    Calculates beta coefficient measuring correlation and relative volatility between two price series.
    
    Parameters:
    - real0: array-like, dependent variable (typically individual security)
    - real1: array-like, independent variable (typically market index)
    - timeperiod: int, number of periods for calculation (default: 5)
    
    Returns:
    numpy.ndarray: Beta coefficient values
    """

Pearson Correlation Coefficient

Measures the linear correlation between two price series, ranging from -1 to +1.

def CORREL(real0, real1, timeperiod=30):
    """
    Pearson's Correlation Coefficient (r)
    
    Measures linear relationship strength between two data series.
    
    Parameters:
    - real0: array-like, first data series
    - real1: array-like, second data series  
    - timeperiod: int, number of periods for calculation (default: 30)
    
    Returns:
    numpy.ndarray: Correlation coefficient values (-1 to +1)
    """

Linear Regression

Calculates linear regression line values, providing trend line based on least squares method.

def LINEARREG(real, timeperiod=14):
    """
    Linear Regression
    
    Calculates linear regression line values using least squares method.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods for regression (default: 14)
    
    Returns:
    numpy.ndarray: Linear regression line values
    """

Linear Regression Angle

Calculates the angle of the linear regression line, indicating trend steepness.

def LINEARREG_ANGLE(real, timeperiod=14):
    """
    Linear Regression Angle
    
    Calculates the angle of the linear regression line in degrees.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods for regression (default: 14)
    
    Returns:
    numpy.ndarray: Regression line angle values (degrees)
    """

Linear Regression Intercept

Calculates the y-intercept of the linear regression line.

def LINEARREG_INTERCEPT(real, timeperiod=14):
    """
    Linear Regression Intercept
    
    Calculates the y-intercept of the linear regression line.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods for regression (default: 14)
    
    Returns:
    numpy.ndarray: Regression line intercept values
    """

Linear Regression Slope

Calculates the slope of the linear regression line, indicating trend direction and strength.

def LINEARREG_SLOPE(real, timeperiod=14):
    """
    Linear Regression Slope
    
    Calculates the slope of the linear regression line.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods for regression (default: 14)
    
    Returns:
    numpy.ndarray: Regression line slope values
    """

Standard Deviation

Measures the dispersion of price data around the mean, indicating volatility.

def STDDEV(real, timeperiod=5, nbdev=1):
    """
    Standard Deviation
    
    Calculates standard deviation of price data over specified period.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods (default: 5)
    - nbdev: float, number of deviations (default: 1)
    
    Returns:
    numpy.ndarray: Standard deviation values
    """

Time Series Forecast

Projects future price values based on linear regression analysis.

def TSF(real, timeperiod=14):
    """
    Time Series Forecast
    
    Forecasts next period value using linear regression.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods for forecast calculation (default: 14)
    
    Returns:
    numpy.ndarray: Forecasted values
    """

Variance

Calculates the variance of price data, measuring the average of squared deviations from the mean.

def VAR(real, timeperiod=5, nbdev=1):
    """
    Variance
    
    Calculates variance of price data over specified period.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods (default: 5)
    - nbdev: float, number of deviations (default: 1)
    
    Returns:
    numpy.ndarray: Variance values
    """

Average Deviation

Calculates the average deviation from the mean, providing a measure of dispersion that is less sensitive to outliers than standard deviation.

def AVGDEV(real, timeperiod=14):
    """
    Average Deviation
    
    Calculates average deviation from the mean over specified period.
    Provides a robust measure of dispersion less sensitive to outliers.
    
    Parameters:
    - real: array-like, input price data
    - timeperiod: int, number of periods (default: 14)
    
    Returns:
    numpy.ndarray: Average deviation values
    """

Usage Examples

import talib
import numpy as np

# Sample data - individual stock and market index
stock_prices = np.array([100, 102, 101, 103, 105, 104, 106, 108, 107, 109])
market_prices = np.array([1000, 1020, 1010, 1025, 1040, 1035, 1050, 1065, 1055, 1070])

# Calculate statistical measures
beta = talib.BETA(stock_prices, market_prices, timeperiod=5)
correlation = talib.CORREL(stock_prices, market_prices, timeperiod=5)
linear_reg = talib.LINEARREG(stock_prices, timeperiod=5)
reg_slope = talib.LINEARREG_SLOPE(stock_prices, timeperiod=5)
reg_angle = talib.LINEARREG_ANGLE(stock_prices, timeperiod=5)
std_dev = talib.STDDEV(stock_prices, timeperiod=5)
variance = talib.VAR(stock_prices, timeperiod=5)
forecast = talib.TSF(stock_prices, timeperiod=5)

print("Statistical Analysis Results:")
print(f"Beta: {beta[-1]:.3f}")
print(f"Correlation: {correlation[-1]:.3f}")
print(f"Current Price: {stock_prices[-1]}")
print(f"Linear Regression: {linear_reg[-1]:.2f}")
print(f"Trend Slope: {reg_slope[-1]:.4f}")
print(f"Trend Angle: {reg_angle[-1]:.2f} degrees")
print(f"Standard Deviation: {std_dev[-1]:.3f}")
print(f"Variance: {variance[-1]:.3f}")
print(f"Next Period Forecast: {forecast[-1]:.2f}")

# Interpretation:
# - Beta > 1: Stock is more volatile than market
# - Correlation near 1: Strong positive relationship with market
# - Positive slope/angle: Upward trend
# - High std dev/variance: High volatility