Adding VaR and Adjusted VaR (#1269)

* Adding avar command to qa context * Adding documentation and VaR command * Linting * More linting * Somehow discordbot tests didn't like my linting * Fixing tests * Fixing requested suggestions. Fixing jmasleks comments, but I sadly couldn't sort the table by the VaR (if the manually entered percentage is 75 it's currently still displayed below the 99% VaR) * linting * Linting fix * Adding every qa command to hugo Also lint fixing * Fixing previous error Co-authored-by: jmaslek <jmaslek11@gmail.com>
2026-11-07 09:52:13 +00:00 · 2022-02-01 12:43:30 +01:00
parent 90b05d6641
commit 2e7b48fe85
5 changed files with 230 additions and 1 deletions
--- a/gamestonk_terminal/common/quantitative_analysis/qa_model.py
+++ b/gamestonk_terminal/common/quantitative_analysis/qa_model.py
@@ -4,7 +4,6 @@ __docformat__ = "numpy"
 import logging
 import warnings
 from typing import Any, Tuple
-
 import pandas as pd
 import statsmodels.api as sm
 from scipy import stats
@@ -31,6 +30,7 @@ def get_summary(df: pd.DataFrame) -> pd.DataFrame:
    """

    df_stats = df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9])
+    print(df_stats.to_string())
    df_stats.loc["var"] = df_stats.loc["std"] ** 2

    return df_stats
@@ -164,3 +164,119 @@ def get_unitroot(df: pd.DataFrame, fuller_reg: str, kpss_reg: str) -> pd.DataFra
    vals2 = [res2[0], res2[1], res2[2], "", ""]
    data["KPSS"] = vals2
    return data
+
+
+def calculate_adjusted_var(
+    kurtosis: float, skew: float, ndp: float, std: float, mean: float
+):
+    """Calculates VaR, which is adjusted for skew and kurtosis (Cornish-Fischer-Expansion)
+
+    Parameters
+    ----------
+    kurtosis: float
+        kurtosis of data
+    skew: float
+        skew of data
+    ndp: float
+        normal distribution percentage number (99% -> -2.326)
+    std: float
+        standard deviation of data
+    mean: float
+        mean of data
+
+    Returns
+    -------
+    float
+        Real adjusted VaR
+    """
+
+    # Derived from Cornish-Fisher-Expansion
+    # Formula for quantile from "Finance Compact Plus" by Zimmerman; Part 1, page 130-131
+    # More material/resources:
+    #       "Numerical Methods and Optimization in Finance" by Gilli, Maringer & Schumann;
+    #       https://www.value-at-risk.net/the-cornish-fisher-expansion/;
+    #       "Risk Management and Financial Institutions" by John C. Hull
+
+    skew_component = skew / 6 * (ndp**2 - 1) ** 2 - skew**2 / 36 * ndp * (
+        2 * ndp**2 - 5
+    )
+    kurtosis_component = (kurtosis - 3) / 24 * ndp * (ndp**2 - 3)
+    quantile = ndp + skew_component + kurtosis_component
+    log_return = mean + quantile * std
+    real_return = 2.7182818**log_return - 1
+    return real_return
+
+
+def get_var(data: pd.DataFrame, use_mean: bool, adjusted_var: bool, percentile: int):
+    """Gets value at risk for specified stock dataframe
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of a stock
+    use_mean: bool
+        If one should use the stocks mean for calculation
+    adjusted_var: bool
+        If one should return VaR adjusted for skew and kurtosis
+    percentile: int
+        VaR percentile
+
+    Returns
+    -------
+    list
+        list of VaR
+    list
+        list of historical VaR
+    """
+    data = data[["adjclose"]].copy()
+    data.loc[:, "return"] = data.adjclose.pct_change()
+
+    # Distribution percentages
+    percentile_90 = -1.282
+    percentile_95 = -1.645
+    percentile_99 = -2.326
+    percentile_custom = stats.norm.ppf(1 - percentile)
+
+    # Mean
+    if use_mean:
+        mean = data["return"].mean()
+    else:
+        mean = 0
+
+    # Standard Deviation
+    std = data["return"].std(axis=0)
+
+    if adjusted_var:
+
+        # Kurtosis
+        # Measures height and sharpness of the central peak relative to that of a standard bell curve
+        k = data["return"].kurtosis(axis=0)
+
+        # Skewness
+        # Measure of the asymmetry of the probability distribution of a random variable about its mean
+        s = data["return"].skew(axis=0)
+
+        # Adjusted VaR
+        var_90 = calculate_adjusted_var(k, s, percentile_90, std, mean)
+        var_95 = calculate_adjusted_var(k, s, percentile_95, std, mean)
+        var_99 = calculate_adjusted_var(k, s, percentile_99, std, mean)
+        var_custom = calculate_adjusted_var(k, s, percentile_custom, std, mean)
+
+    else:
+        # Regular Var
+        var_90 = 2.7182818 ** (mean + percentile_90 * std) - 1
+        var_95 = 2.7182818 ** (mean + percentile_95 * std) - 1
+        var_99 = 2.7182818 ** (mean + percentile_99 * std) - 1
+        var_custom = 2.7182818 ** (mean + percentile_custom * std) - 1
+
+    data.sort_values("return", inplace=True, ascending=True)
+
+    # Historical VaR
+    hist_var_90 = data["return"].quantile(0.1)
+    hist_var_95 = data["return"].quantile(0.05)
+    hist_var_99 = data["return"].quantile(0.01)
+    hist_var_custom = data["return"].quantile(1 - percentile)
+
+    var_list = [var_90, var_95, var_99, var_custom]
+    hist_var_list = [hist_var_90, hist_var_95, hist_var_99, hist_var_custom]
+    return var_list, hist_var_list
--- a/gamestonk_terminal/common/quantitative_analysis/qa_view.py
+++ b/gamestonk_terminal/common/quantitative_analysis/qa_view.py
@@ -635,3 +635,47 @@ def display_line(
        os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
        "line",
    )
+
+
+def display_var(
+    data: pd.DataFrame, use_mean: bool, ticker: str, adjusted_var: bool, percentile: int
+):
+    """Displays VaR of dataframe
+
+    Parameters
+    ----------
+    data: pd.Dataframe
+        stock dataframe
+    use_mean: bool
+        if one should use the stocks mean return
+    ticker: str
+        ticker of the stock
+    adjusted_var: bool
+        if one should have VaR adjusted for skew and kurtosis (Cornish-Fisher-Expansion)
+    percentile: int
+        var percentile
+    """
+    var_list, hist_var_list = qa_model.get_var(data, use_mean, adjusted_var, percentile)
+
+    str_hist_label = "Historical VaR:"
+
+    if adjusted_var:
+        str_var_label = "Adjusted Var:"
+        str_title = "Adjusted "
+    else:
+        str_var_label = "VaR:"
+        str_title = ""
+
+    data_dictionary = {str_var_label: var_list, str_hist_label: hist_var_list}
+    data = pd.DataFrame(
+        data_dictionary, index=["90.0%", "95.0%", "99.0%", f"{percentile*100}%"]
+    )
+
+    print_rich_table(
+        data,
+        show_index=True,
+        headers=list(data.columns),
+        title=f"[bold]{ticker} {str_title}Value at Risk[/bold]",
+        floatfmt=".4f",
+    )
+    console.print("")
--- a/gamestonk_terminal/stocks/quantitative_analysis/qa_controller.py
+++ b/gamestonk_terminal/stocks/quantitative_analysis/qa_controller.py
@@ -52,6 +52,7 @@ class QaController(StockController):
        "goodness",
        "unitroot",
        "capm",
+        "var",
    ]

    stock_interval = [1, 5, 15, 30, 60]
@@ -125,6 +126,8 @@ class QaController(StockController):
    quantile    rolling median and quantile of prices
    skew        rolling skewness of distribution of prices
    kurtosis    rolling kurtosis of distribution of prices
+[info]Risk:[/info]
+    var         value at risk
 [info]Other:[/info]
    raw         print raw data
    decompose   decomposition in cyclic-trend, season, and residuals of prices
@@ -699,3 +702,53 @@ class QaController(StockController):
        ns_parser = parse_known_args_and_warn(parser, other_args)
        if ns_parser:
            capm_view(self.ticker)
+
+    def call_var(self, other_args: List[str]):
+        """Process var command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="var",
+            description="""
+                Provides value at risk (short: VaR) of the selected stock.
+            """,
+        )
+        parser.add_argument(
+            "-m",
+            "--mean",
+            action="store_true",
+            default=False,
+            dest="use_mean",
+            help="If one should use the mean of the stocks return",
+        )
+        parser.add_argument(
+            "-a",
+            "--adjusted",
+            action="store_true",
+            default=False,
+            dest="adjusted",
+            help="""
+                If the VaR should be adjusted for skew and kurtosis (Cornish-Fisher-Expansion)
+            """,
+        )
+        parser.add_argument(
+            "-p",
+            "--percentile",
+            action="store",
+            dest="percentile",
+            type=float,
+            default=99.9,
+            help="""
+                Percentile used for VaR calculations, for example input 99.9 equals a 99.9% VaR
+            """,
+        )
+
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if ns_parser:
+            qa_view.display_var(
+                self.stock,
+                ns_parser.use_mean,
+                self.ticker,
+                ns_parser.adjusted,
+                ns_parser.percentile / 100,
+            )
--- a/tests/gamestonk_terminal/stocks/quantitative_analysis/txt/test_qa_controller/test_print_help.txt
+++ b/tests/gamestonk_terminal/stocks/quantitative_analysis/txt/test_qa_controller/test_print_help.txt
@@ -22,6 +22,8 @@ Rolling Metrics:
    quantile    rolling median and quantile of prices
    skew        rolling skewness of distribution of prices
    kurtosis    rolling kurtosis of distribution of prices
+Risk:
+    var         value at risk
 Other:
    raw         print raw data
    decompose   decomposition in cyclic-trend, season, and residuals of prices
--- a/website/content/common/quantitative_analysis/var/_index.md
+++ b/website/content/common/quantitative_analysis/var/_index.md
@@ -0,0 +1,14 @@
+```
+usage: var [-m] [-a] [-p PERCENTILE] [-h]
+```
+
+Provides value at risk (short: VaR) of the selected stock.
+
+```
+optional arguments:
+  -m, --mean            If one should use the mean of the stocks return
+  -a, --adjusted        If the VaR should be adjusted for skew and kurtosis (Cornish-Fisher-Expansion)
+  -p PERCENTILE, --percent PERCENTILE 
+                        Percentile used for VaR calculations, for example input 99.9 equals a 99.9% VaR
+  -h, --help            show this help message (default: False)
+```