The Risk module is meant to calculate important risk metrics such as Value at Risk (VaR), Conditional Value at Risk (cVaR), Maximum Drawdown, Correlations, GARCH, EWMA and more.

To install the FinanceToolkit it simply requires the following:

pip install financetoolkit -U

If you are looking for documentation regarding the toolkit, discovery, ratios, models, technicals, fixed income, performance and economics, please have a look below:

Toolkit Discovery Ratios Models Options Technicals Fixed Income Risk Performance Economics

init

Initializes the Risk Controller Class.

Args:

  • tickers (str | list[str]): The tickers to use for the Toolkit instance.
  • daily_historical (pd.DataFrame, optional): The daily historical data for the tickers. Defaults to pd.DataFrame().
  • weekly_historical (pd.DataFrame, optional): The weekly historical data for the tickers. Defaults to pd.DataFrame().
  • monthly_historical (pd.DataFrame, optional): The monthly historical data for the tickers. Defaults to pd.DataFrame().
  • quarterly_historical (pd.DataFrame, optional): The quarterly historical data for the tickers. Defaults to pd.DataFrame().
  • yearly_historical (pd.DataFrame, optional): The yearly historical data for the tickers. Defaults to pd.DataFrame().
  • quarterly (bool, optional): Whether to use quarterly data. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_value_at_risk(period='yearly')

Which returns:

Date AAPL TSLA
2012 0 0
2013 0.1754 4.96
2014 1.7515 0.9481
2015 -0.1958 0.1454
2016 0.4177 -0.3437
2017 2.6368 1.2225
2018 -0.2786 0.0718
2019 3.2243 0.4707
2020 1.729 8.3319
2021 1.3179 0.8797
2022 -0.8026 -1.0046
2023 1.8549 1.8238

collect_all_metrics

Calculates and collects all risk metrics.

Args:

  • rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the ratios. Defaults to False.
  • lag (int | str, optional): The lag to use for the growth calculation. Defaults to 1.
  • trailing (int): Defines whether to select a trailing period. E.g. when selecting 4 with quarterly data, the TTM is calculated.

Returns: pd.Series or pd.DataFrame: Risk metrics calculated based on the specified parameters.

Notes:

  • The method calculates various risk metrics for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of the ratio values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.collect_all_metrics()

get_value_at_risk

Calculate the Value at Risk (VaR) of an investment portfolio or asset’s returns.

Value at Risk (VaR) is a risk management metric that quantifies the maximum potential loss an investment portfolio or asset may experience over a specified time horizon and confidence level. It provides insights into the downside risk associated with an investment and helps investors make informed decisions about risk tolerance.

The VaR is calculated as the quantile of the return distribution, representing the loss threshold that is not expected to be exceeded with a given confidence level (e.g., 5% for alpha=0.05).

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “yearly”.
  • alpha (float, optional): The confidence level for VaR calculation (e.g., 0.05 for 95% confidence). Defaults to 0.05.
  • within_period (bool, optional): Whether to calculate VaR within the specified period or for the entire period. Thus whether to look at the VaR within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the VaR values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.
  • distribution (str): The distribution to use for the VaR calculations (historic, gaussian, cf or studentt). Defaults to “historic”.

Returns: pd.Series: VaR values with time as the index.

Notes:

  • The method retrieves historical return data based on the specified period and calculates VaR for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of VaR values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_value_at_risk()

Which returns:

  AMZN TSLA
2012 -0.0244 -0.0343
2013 -0.0204 -0.0537
2014 -0.0312 -0.0423
2015 -0.0208 -0.0422
2016 -0.0288 -0.0394
2017 -0.0154 -0.0345
2018 -0.0416 -0.0503
2019 -0.0232 -0.0492
2020 -0.0369 -0.0741
2021 -0.0252 -0.0499
2022 -0.0518 -0.0713
2023 -0.0271 -0.054

get_conditional_value_at_risk

Calculate the Conditional Value at Risk (CVaR) of an investment portfolio or asset’s returns.

Conditional Value at Risk (CVaR) is a risk management metric that quantifies the loss in the worst % of cases of an investment portfolio or asset may experience over a specified time horizon and confidence level. It provides insights into the downside risk associated with an investment and helps investors make informed decisions about risk tolerance.

The CVaR is calculated as the expected loss given that the loss threshold (VaR) with a given confidence level (e.g., 5% for alpha=0.05) is excceeded.

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “yearly”.
  • alpha (float, optional): The confidence level for CVaR calculation (e.g., 0.05 for 95% confidence). Defaults to 0.05.
  • within_period (bool, optional): Whether to calculate CVaR within the specified period or for the entire period. Thus whether to look at the CVaR within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the CVaR values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.
  • distribution (str): The distribution to use for the CVaR calculations (historic, gaussian, studentt, laplace or logistic). Defaults to “historic”.

Returns: pd.Series: CVaR values with time as the index.

Notes:

  • The method retrieves historical return data based on the specified period and calculates CVaR for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of CVaR values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_conditional_value_at_risk()

Which returns:

  AMZN TSLA
2012 -0.0302 -0.0622
2013 -0.0323 -0.0807
2014 -0.0552 -0.0607
2015 -0.0318 -0.053
2016 -0.0456 -0.0604
2017 -0.0236 -0.0483
2018 -0.0540 -0.0746
2019 -0.0327 -0.0758
2020 -0.0510 -0.1262
2021 -0.0327 -0.0683
2022 -0.0685 -0.0914
2023 -0.0397 -0.0747

get_entropic_value_at_risk

Calculate the Entropic Value at Risk (EVaR) of an investment portfolio or asset’s returns.

Entropic Value at Risk (EVaR) is a risk management metric that quantifies upper bound for the value at risk (VaR) and the conditional value at risk (CVaR) over a specified time horizon and confidence level. EVaR is obtained from the Chernoff inequality. It provides insights into the downside risk associated with an investment and helps investors make informed decisions about risk tolerance.

The EVaR is calculated as the upper bound of VaR and CVaR with a given confidence level (e.g., 5% for alpha=0.05).

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “yearly”.
  • alpha (float, optional): The confidence level for EVaR calculation (e.g., 0.05 for 95% confidence). Defaults to 0.05.
  • within_period (bool, optional): Whether to calculate EVaR within the specified period or for the entire period. Thus whether to look at the CVaR within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the CVaR values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.

Returns: pd.Series: EVaR values with time as the index.

Notes:

  • The method retrieves historical return data based on the specified period and calculates EVaR for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of EVaR values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_entropic_value_at_risk()

Which returns:

  AMZN TSLA SPY
2012 -0.0392 -0.0604 -0.0177
2013 -0.0377 -0.0928 -0.0152
2014 -0.0481 -0.0689 -0.0162
2015 -0.046 -0.0564 -0.0227
2016 -0.043 -0.0571 -0.0188
2017 -0.0289 -0.0501 -0.0091
2018 -0.0518 -0.085 -0.0252
2019 -0.0327 -0.071 -0.0173
2020 -0.054 -0.1211 -0.0497
2021 -0.0352 -0.0782 -0.0183
2022 -0.0758 -0.1012 -0.0362
2023 -0.0471 -0.0793 -0.0188

get_maximum_drawdown

Calculate the Maximum Drawdown (MDD) of an investment portfolio or asset’s returns.

Maximum Drawdown (MDD) is a risk management metric that quantifies the largest historical loss of n investment portfolio or asset experienced over a specified time horizon. It provides insights into the downside risk associated with an investment and helps investors make informed decisions about risk tolerance.

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “yearly”.
  • alpha (float, optional): The confidence level for CVaR calculation (e.g., 0.05 for 95% confidence). Defaults to 0.05.
  • within_period (bool, optional): Whether to calculate CVaR within the specified period or for the entire period. Thus whether to look at the CVaR within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the CVaR values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.

Returns: pd.Series: Maximum Drawdown values with time as the index.

Notes:

  • The method retrieves historical return data based on the specified period and calculates MMD for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of MMD values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_maximum_drawdown()

Which returns:

  AMZN TSLA
2012 -0.1570 -0.1601
2013 -0.1259 -0.3768
2014 -0.2948 -0.3085
2015 -0.1371 -0.2669
2016 -0.2432 -0.357
2017 -0.1085 -0.2227
2018 -0.3410 -0.3399
2019 -0.1561 -0.4847
2020 -0.2274 -0.6063
2021 -0.1457 -0.3625
2022 -0.5198 -0.7272
2023 -0.1964 -0.2823

get_ulcer_index

The Ulcer Index is a financial metric used to assess the risk and volatility of an investment portfolio or asset. Developed by Peter Martin in the 1980s, the Ulcer Index is particularly useful for evaluating the downside risk and drawdowns associated with investments.

The Ulcer Index differs from traditional volatility measures like standard deviation or variance because it focuses on the depth and duration of drawdowns rather than the dispersion of returns.

The formula is a follows:

Ulcer Index = SQRT(SUM[(Pn / Highest High)^2] / n)

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “yearly”.
  • rolling (int, optional): The rolling period to use for the calculation. Defaults to 14.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the UI values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.

Returns: pd.Series: UI values with time as the index.

Notes:

  • The method retrieves historical return data based on the specified period and calculates UI for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of VaR values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_ulcer_index()

Which returns:

  AMZN TSLA Benchmark
2012 0.0497 0.0454 0.0234
2013 0.035 0.0829 0.0142
2014 0.0659 0.0746 0.0174
2015 0.0273 0.0624 0.0238
2016 0.0519 0.0799 0.0151
2017 0.0241 0.0616 0.0067
2018 0.0619 0.0892 0.0356
2019 0.0373 0.0839 0.016
2020 0.0536 0.1205 0.0594
2021 0.0427 0.085 0.0136
2022 0.1081 0.1373 0.0492
2023 0.0475 0.0815 0.0186

get_garch

Calculates volatility forecasts based on the GARCH model.

GARCH (Generalized autoregressive conditional heteroskedasticity) is stochastic model for time series, which is for instance used to model volatility clusters, stock return and inflation. It is a generalisation of the ARCH models.

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “weekly”.
  • t (int, optional): Time steps to calculate GARCH for.
  • optimization_t (int, optional): Time steps to optimize GRACH for. It is only used if no weights are given.
  • within_period (bool, optional): Whether to calculate GARCH within the specified period or for the entire period. Thus whether to look at the GARCH within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the GARCH values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.

Returns: pd.DataFrame | pd.Series: GARCH values

Notes:

  • The method retrieves historical return data based on the specified period and calculates GARCH for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of GARCH values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_garch()

Which returns:

Date AMZN TSLA Benchmark
2012Q4 0 0 0
2013Q1 0.0147 0.214 0.0008
2013Q2 0.0223 0.214 0.0024
2013Q3 0.0262 0.214 0.0029
2013Q4 0.0282 0.214 0.0034
2014Q1 0.0293 0.214 0.0045
2014Q2 0.0298 0.214 0.0045
2014Q3 0.03 0.214 0.0047
2014Q4 0.0302 0.214 0.0047
2015Q1 0.0303 0.214 0.0048

get_garch_forecast

Calculates sigma_2 forecasts.

GARCH (Generalized autoregressive conditional heteroskedasticity) is stochastic model for time series, which is for instance used to model volatility clusters, stock return and inflation. It is a generalisation of the ARCH models.

The forecasting with GARCH is done with the following formula:

  • sigma_l ** 2 + (sigma_t ** 2
  • sigma_l ** 2) * (alpha + beta) ** (t
  • 1)

For more information about the method, see the following book:

  • Finance Compact Plus Band 1, by Yvonne Seler Zimmerman and Heinz Zimmerman; ISBN: 978 -3 -907291 -31 -1

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “quarterly”.
  • t (int, optional): Time steps to calculate GARCH and to forecast sigma_2 values for.
  • within_period (bool, optional): Whether to calculate GARCH within each specified period or all at once. Thus whether to look at the GARCH within each specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
  • growth (bool, optional): Whether to calculate the growth of the GARCH values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.

Returns: pd.DataFrame | pd.Series: sigma_2 forecast values

Notes:

  • The method retrieves historical return data based on the specified period and calculates the sigma_2 forecast for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of the forecasted simga_2 values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_garch_forecast()

Which returns:

  AMZN TSLA Benchmark
2024 0 0 0
2025 0 0 0
2026 0.4156 252.921 0.0058
2027 0.7897 480.55 0.011
2028 1.1263 685.417 0.0156
2029 1.4293 869.796 0.0198
2030 1.702 1035.74 0.0236
2031 1.9474 1185.09 0.027
2032 2.1683 1319.5 0.0301
2033 2.3671 1440.47 0.0329

get_skewness

Calculate the Skewness of an investment portfolio or asset’s returns.

Skewness is a statistical measure used in finance to assess the asymmetry in the distribution of returns for an investment portfolio or asset over a defined period. It offers valuable insights into the shape of the return distribution, indicating whether returns are skewed towards the positive or negative side of the mean. Skewness is a crucial tool for investors and analysts seeking to understand the potential risk and return characteristics of an investment, aiding in the assessment of the distribution’s tails and potential outliers. It provides a means to gauge the level of skew in returns, enabling more informed investment decisions and risk management strategies.

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “yearly”.
  • alpha (float, optional): The confidence level for CVaR calculation (e.g., 0.05 for 95% confidence). Defaults to 0.05.
  • within_period (bool, optional): Whether to calculate CVaR within the specified period or for the entire period. Thus whether to look at the CVaR within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the CVaR values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.

Returns: pd.Series: CVaR values with time as the index.

Notes:

  • The method retrieves historical return data based on the specified period and calculates Skew for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of VaR values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["MSFT", "AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_skewness()

Which returns:

  MSFT AAPL TSLA
2019 -0.194 -0.9216 -0.0646
2020 -0.0747 -0.0586 -0.1824
2021 -0.0194 -0.0716 0.6572
2022 0.1478 0.3164 -0.0263
2023 0.5252 0.0318 -0.0972

get_kurtosis

Calculate the Kurtosis of an investment portfolio or asset’s returns.

Kurtosis is a statistical measure used in finance to evaluate the shape of the probability distribution of returns for an investment portfolio or asset over a defined time period. It assesses the “tailedness” of the return distribution, indicating whether returns have fatter or thinner tails compared to a normal distribution. Kurtosis plays a critical role in risk assessment by revealing the potential presence of extreme outliers or the likelihood of heavy tails in the return data. This information aids investors and analysts in understanding the degree of risk associated with an investment and assists in making more informed decisions regarding risk tolerance. In essence, kurtosis serves as a valuable tool for comprehending the distribution characteristics of returns, offering insights into the potential for rare but significant events in the financial markets.

Args:

  • period (str, optional): The data frequency for returns (daily, weekly, quarterly, or yearly). Defaults to “yearly”.
  • within_period (bool, optional): Whether to calculate CVaR within the specified period or for the entire period. Thus whether to look at the CVaR within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to True.
  • fisher (bool, optional): Whether to use Fisher’s definition of kurtosis (kurtosis = 0.0 for a normal distribution).
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the CVaR values over time. efaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.

Returns: pd.Series: CVaR values with time as the index.

Notes:

  • The method retrieves historical return data based on the specified period and calculates VaR for each asset in the Toolkit instance.
  • If growth is set to True, the method calculates the growth of VaR values using the specified lag.

As an example: 

from financetoolkit import Toolkit

toolkit = Toolkit(["MSFT", "AAPL", "TSLA"]], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_kurtosis()

Which returns:

  MSFT AAPL TSLA
2019 4.0972 10.0741 9.128
2020 9.2914 6.6307 5.2189
2021 3.3152 3.3352 7.3197
2022 3.852 4.0085 3.3553
2023 4.2908 4.4568 4.07