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Beta Coefficient

get_beta(eval_history, market_quotes, lookback_periods, beta_type=BetaType.STANDARD)

Parameters

name	type	notes
eval_history	Iterable[Quote]	Historical [evaluation stock] Quotes data should be at any consistent frequency (day, hour, minute, etc). • See here for usage with pandas.DataFrame
market_history	Iterable[Quote]	Historical [market] Quotes data should be at any consistent frequency (day, hour, minute, etc). This market quotes will be used to establish the baseline.
lookback_periods	int	Number of periods (N) in the lookback period. Must be greater than 0 to calculate; however we suggest a larger period for statistically appropriate sample size and especially when using Beta +/-.
beta_type	BetaType, default BetaType.STANDARD	Type of Beta to calculate. See BetaType options below.

Historical quotes requirements

You must have at least N periods of quotes to cover the warmup periods. You must have at least the same matching date elements of market_history. Exception will be thrown if not matched. Historical price quotes should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.

BetaType options

from stock_indicators.indicators.common.enums import BetaType

type	description
STANDARD	Standard Beta only. Uses all historical quotes.
UP	Upside Beta only. Uses historical quotes from market up bars only.
DOWN	Downside Beta only. Uses historical quotes from market down bars only.
ALL	Returns all of the above. Use this option if you want ratio and convexity values returned. Note: 3× slower to calculate.

Returns

BetaResults[BetaResult]

• This method returns a time series of all available indicator values for the quotes provided.
• BetaResults is just a list of BetaResult.
• It always returns the same number of elements as there are in the historical quotes.
• It does not return a single incremental indicator value.
• The first N-1 periods will have None values since there’s not enough data to calculate.

BetaResult

name	type	notes
date	datetime	Date
beta	float, Optional	Beta coefficient based on N lookback periods
beta_up	float, Optional	Beta+ (Up Beta)
beta_down	float, Optional	Beta- (Down Beta)
ratio	float, Optional	Beta ratio is beta_up/beta_down
convexity	float, Optional	Beta convexity is (beta_up-beta_down)2
returns_eval	float, Optional	Returns of evaluated quotes (R)
returns_mrkt	float, Optional	Returns of market quotes (Rm)

Utilities

• .condense()
• .find(lookup_date)
• .remove_warmup_periods()
• .remove_warmup_periods(qty)

See Utilities and Helpers for more information.

Example

from stock_indicators import indicators
from stock_indicators import BetaType      # Short path, version >= 0.8.1

# This method is NOT a part of the library.
history_SPX = get_historical_quotes("SPX")
history_TSLA = get_historical_quotes("TSLA")

# calculate 20-period Beta coefficient
results = indicators.get_beta(history_SPX, history_TSLA, 20, BetaType.STANDARD)

About Beta Coefficient

Beta shows how strongly one stock responds to systemic volatility of the entire market. Upside Beta (Beta+) and Downside Beta (Beta-), popularized by Harry M. Markowitz, are also included. [Discuss] 💬

Sources

• C# core
• Python wrapper