returns_stats
Calculate distributional and risk-adjusted statistics on simple returns.
Examples
from openbb import obb
obb.technical.returns_stats(data=[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}])
Parameters
- standard
data: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input price series.
index: str
Default: date
Index column name in data.
target: str
Default: close
Column to evaluate returns on.
frequency: str
Default: daily
Annualisation frequency for Sharpe/Sortino/Calmar/risk-free.
risk_free_rate: float
Default: 0.0
Annualised risk-free rate used for Sharpe and Sortino.
window: int
Rolling window. None returns a single summary row.
Returns
results: list[ReturnsStatsData]
Serializable results.
provider: str
Provider name.
warnings: Optional[list[Warning_]]
List of warnings.
chart: Optional[Chart]
Chart object.
extra: dict[str, Any]
Extra info.
Data
- standard
date: date | datetime | str
Window end date, or None for the summary row.
mean_return: float
Arithmetic mean of simple returns over the window.
std_return: float
Sample standard deviation of returns (ddof=1).
skew: float
Skewness of the return distribution.
kurtosis: float
Excess kurtosis of the return distribution.
sharpe: float
Annualised Sharpe ratio of excess returns.
sortino: float
Annualised Sortino ratio using downside deviation.
calmar: float
Annualised return divided by the absolute value of max drawdown.
max_drawdown: float
Worst peak-to-trough drawdown over the window (negative).
var_95: float
5th-percentile return - historical Value-at-Risk at 95%.
cvar_95: float
Mean of returns at or below var_95 - historical Conditional VaR.