Functions to evaluate stock statistics

stock_stats.py: function

This module contains all the statistics needed for stocks.

src.stock_stats.evaluate_annualized_return(data: Series, n_years: int) → float

Evaluate the annualized return.

The annualized return for n_years is ((1+ Rc) ^ (1/n_years)) - 1

Parameters:

• data (pd.Series) – The data series of stock price.

• n_years (int) – The number of years we want to calculate the return for.

Returns:

The annualized cumulative return. If data is not long enough, return np.nan.

Return type:

float

src.stock_stats.evaluate_annualized_volatility(data: Series, n_years: int = 1) → float

Evaluate the annualized volatility.

Parameters:

• data (pd.Series) – The data.

• n_years (int) – The number of years we want to calculate the volatility for.

Returns:

The annualized volatility.

Return type:

float

src.stock_stats.evaluate_cumulative_return(data: Series) → float | str

Evaluate the cumulative return of a series in percent.

Lets write P_initial the initial value of our series, and P_current the current value of our series.

The cumulative return is then (P_current - P_initial) / P_initial

Parameters:

data (pd.Series) – The data series of stock price.

Returns:

The cumulative return. If data is not long enough, return np.nan.

Return type:

float

src.stock_stats.evaluate_stats_information(data: Series, symbol: str) → Dict[str, float | str]

Gives several statistics about stock time-series.

This function evaluates several statistics about a stock time-series. The statistics are: - cumulative return - annualized cumulative return - annualized volatility

Parameters:

• data (pd.Series) – The data, sorted in ascending time.

• symbol (str) – The stock symbol.

Returns:

The result dict.

Return type:

Dict[str, float | str]

Examples

>>> data = pd.Series(
...     [1, 5, 7, 2, 3],
...     index=pd.Index(
...         [
...             pd.Timestamp(0, unit="d"),
...             pd.Timestamp(7, unit="d"),
...             pd.Timestamp(3, unit="d"),
...             pd.Timestamp(4, unit="d"),
...             pd.Timestamp(365, unit="d"),
...         ]
...     ),
... )
>>> evaluate_stats_information(data, "AAPL")
{'symbol': 'AAPL', 'cumulativeReturn': -40.0, 'annualizedCumulativeReturn': 200.0, 'annualizedVolatility': 2.41}