Adding GARCH (#4815)

* Add GARCH * Linting * Rearrange the econometrics menu * Add integration test * Add ret command + fixes * Fix linting * Fix linting * Fix pylint * Add logger to functions * bump pywry * fix rm command (#4890) Co-authored-by: Henrique Joaquim <[email protected]> * Fix `crypto/defi/anchor` (#4892) * handle defi anchor exceptions * reason * mypy --------- Co-authored-by: Henrique Joaquim <[email protected]> * fix windows pyinstaller subprocess fail if space in path * remove API_IEX_TOKEN * remove API_SENTIMENTINVESTOR_TOKEN * Convert to latex + requested changes * Fix linting * Remove unnecessary logger * Add example * SDK updated * fix syntax on elite level math that I def know * Fix docstring * Fix example * Fix * Linting --------- Co-authored-by: James Maslek <[email protected]> Co-authored-by: teh_coderer <[email protected]> Co-authored-by: montezdesousa <[email protected]> Co-authored-by: Henrique Joaquim <[email protected]> Co-authored-by: andrewkenreich <[email protected]>
OpenBB-finance · May 10, 2023 · bde21f0 · bde21f0
1 parent 33da518
commit bde21f0
Show file tree

Hide file tree

Showing 9 changed files with 353 additions and 19 deletions.
diff --git a/openbb_terminal/core/sdk/models/econometrics_sdk_model.py b/openbb_terminal/core/sdk/models/econometrics_sdk_model.py
@@ -22,6 +22,8 @@ class EconometricsRoot(Category):
         `dwat_chart`: Show Durbin-Watson autocorrelation tests\n
         `fdols`: First differencing is an alternative to using fixed effects when there might be correlation.\n
         `fe`: When effects are correlated with the regressors the RE and BE estimators are not consistent.\n
+        `garch`: Calculates annualized volatility forecasts based on GARCH.\n
+        `garch_chart`: Plots the annualized volatility forecasts based on GARCH\n
         `get_regression_data`: This function creates a DataFrame with the required regression data as\n
         `granger`: Calculate granger tests\n
         `granger_chart`: Show granger tests\n
@@ -56,6 +58,8 @@ def __init__(self):
         self.dwat_chart = lib.econometrics_regression_view.display_dwat
         self.fdols = lib.econometrics_regression_model.get_fdols
         self.fe = lib.econometrics_regression_model.get_fe
+        self.garch = lib.econometrics_model.get_garch
+        self.garch_chart = lib.econometrics_view.display_garch
         self.get_regression_data = lib.econometrics_regression_model.get_regression_data
         self.granger = lib.econometrics_model.get_granger_causality
         self.granger_chart = lib.econometrics_view.display_granger

diff --git a/openbb_terminal/core/sdk/trail_map.csv b/openbb_terminal/core/sdk/trail_map.csv
@@ -183,6 +183,7 @@ econometrics.panel,econometrics_regression_model.get_regressions_results,econome
 econometrics.pols,econometrics_regression_model.get_pols,
 econometrics.re,econometrics_regression_model.get_re,
 econometrics.root,econometrics_model.get_root,econometrics_view.display_root
+econometrics.garch,econometrics_model.get_garch,econometrics_view.display_garch
 economy.available_indices,economy_yfinance_model.get_available_indices,
 economy.balance,economy_oecd_model.get_balance,economy_oecd_view.plot_balance
 economy.bigmac,economy_nasdaq_model.get_big_mac_indices,economy_nasdaq_view.display_big_mac_index

diff --git a/openbb_terminal/econometrics/econometrics_controller.py b/openbb_terminal/econometrics/econometrics_controller.py
@@ -65,6 +65,7 @@ class EconometricsController(BaseController):
         "combine",
         "rename",
         "lag",
+        "ret",
         "ols",
         "norm",
         "root",
@@ -73,6 +74,7 @@ class EconometricsController(BaseController):
         "dwat",
         "bgod",
         "bpag",
+        "garch",
         "granger",
         "coint",
     ]
@@ -181,6 +183,7 @@ def __init__(self, queue: Optional[List[str]] = None):
                 "plot",
                 "norm",
                 "root",
+                "garch",
                 "granger",
                 "coint",
                 "corr",
@@ -217,6 +220,7 @@ def update_runtime_choices(self):
                 "ols",
                 "panel",
                 "delete",
+                "garch",
             ]:
                 self.choices[feature] = dataset_columns
             for feature in [
@@ -280,17 +284,19 @@ def print_help(self):
         mt.add_cmd("combine", self.files)
         mt.add_cmd("rename", self.files)
         mt.add_cmd("lag", self.files)
+        mt.add_cmd("ret", self.files)
         mt.add_cmd("export", self.files)
-        mt.add_info("_tests_")
+        mt.add_info("time_series_")
         mt.add_cmd("norm", self.files)
-        mt.add_cmd("root", self.files)
+        mt.add_cmd("ols", self.files)
         mt.add_cmd("granger", self.files)
+        mt.add_cmd("root", self.files)
         mt.add_cmd("coint", self.files)
-        mt.add_info("_regression_")
-        mt.add_cmd("ols", self.files)
+        mt.add_cmd("garch", self.files)
+        mt.add_info("_panel_")
         mt.add_cmd("panel", self.files)
         mt.add_cmd("compare", self.files)
-        mt.add_info("_regression_tests_")
+        mt.add_info("_residuals_")
         mt.add_cmd("dwat", self.files and self.regression["OLS"]["model"])
         mt.add_cmd("bgod", self.files and self.regression["OLS"]["model"])
         mt.add_cmd("bpag", self.files and self.regression["OLS"]["model"])
@@ -1224,6 +1230,50 @@ def call_lag(self, other_args: List[str]):
 
         self.update_runtime_choices()
 
+    @log_start_end(log=logger)
+    def call_ret(self, other_args: List[str]):
+        """Process ret command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="ret",
+            description="Calculate returns for the given column.",
+        )
+        parser.add_argument(
+            "-v",
+            "--values",
+            help="Dataset.column values to calculate returns.",
+            dest="values",
+            choices={
+                f"{dataset}.{column}": {column: None, dataset: None}
+                for dataset, dataframe in self.datasets.items()
+                for column in dataframe.columns
+            },
+            type=str,
+            required="-h" not in other_args,
+        )
+
+        if other_args and "-" not in other_args[0][0]:
+            other_args.insert(0, "-v")
+        ns_parser = self.parse_known_args_and_warn(
+            parser, other_args, export_allowed=NO_EXPORT
+        )
+
+        if not ns_parser:
+            return
+
+        try:
+            dataset, col = ns_parser.values.split(".")
+            data = self.datasets[dataset]
+        except ValueError:
+            console.print("[red]Please enter 'dataset'.'column'.[/red]\n")
+            return
+
+        data[col + "_returns"] = econometrics_model.get_returns(data[col])
+        self.datasets[dataset] = data
+
+        self.update_runtime_choices()
+
     @log_start_end(log=logger)
     def call_eval(self, other_args):
         """Process eval command"""
@@ -1890,6 +1940,124 @@ def call_bpag(self, other_args):
                     self.regression["OLS"]["model"], ns_parser.export
                 )
 
+    @log_start_end(log=logger)
+    def call_garch(self, other_args: List[str]):
+        """Process garch command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="garch",
+            description=r"""Calculates annualized volatility forecasts based on GARCH.
+            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.
+
+            $\text{GARCH}(p, q)  = (1 - \alpha - \beta) \sigma_l + \sum_{i=1}^q \alpha u_{t-i}^2 + \sum_{i=1}^p \beta
+            \sigma_{t-i}^2$ [1]
+
+            The GARCH-model assumes that the variance estimate consists of 3 components:
+            - $\sigma_l$ ; the long term component, which is unrelated to the current market conditions
+            - $u_t$ ; the innovation/discovery through current market price changes
+            - $\sigma_t$ ; the last estimate
+
+            GARCH can be understood as a model, which allows to optimize these 3 variance components to the time
+            series. This is done assigning weights to variance components: $(1 - \alpha - \beta)$ for $\sigma_l$ ,
+            $\alpha$ for $u_t$ and $\beta$ for $\sigma_t$ . [2]
+
+            The weights can be estimated by iterating over different values of $(1 - \alpha - \beta) \sigma_l$
+            which we will call $\omega$ , $\alpha$ and $\beta$ , while maximizing:
+            $\sum_{i} -ln(v_i) - (u_i ^ 2) / v_i$ . With the constraints:
+            - $\alpha > 0$
+            - $\beta > 0$
+            - $\alpha + \beta < 1$
+            Note that there is no restriction on $\omega$ .
+
+            Another method used for estimation is "variance targeting", where one first sets $\omega$
+            equal to the variance of the time series. This method nearly as effective as the previously mentioned and
+            is less computationally effective.
+
+            One can measure the fit of the time series to the GARCH method by using the Ljung-Box statistic. [3]
+
+            See the sources below for reference and for greater detail.
+
+            Sources:
+            [1] Generalized Autoregressive Conditional Heteroskedasticity, by Tim Bollerslev
+            [2] Finance Compact Plus Band 1, by Yvonne Seler Zimmerman and Heinz Zimmerman; ISBN: 978-3-907291-31-1
+            [3] Options, Futures & other Derivates, by John C. Hull; ISBN: 0-13-022444-8""",
+        )
+        parser.add_argument(
+            "-v",
+            "--value",
+            type=str,
+            choices=self.choices["garch"],
+            dest="column",
+            help="The column and name of the database you want to estimate volatility for",
+            required="-h" not in other_args,
+        )
+        parser.add_argument(
+            "-p",
+            help="The lag order of the symmetric innovation",
+            dest="p",
+            type=int,
+            default=1,
+        )
+        parser.add_argument(
+            "-o",
+            help="The lag order of the asymmetric innovation",
+            dest="o",
+            type=int,
+            default=0,
+        )
+        parser.add_argument(
+            "-q",
+            help="The lag order of lagged volatility or equivalent",
+            dest="q",
+            type=int,
+            default=1,
+        )
+        parser.add_argument(
+            "-m",
+            "--mean",
+            help="Choose mean model",
+            choices=["LS", "AR", "ARX", "HAR", "HARX", "constant", "zero"],
+            default="constant",
+            type=str,
+            dest="mean",
+        )
+        parser.add_argument(
+            "-l",
+            "--length",
+            help="The length of the estimate",
+            dest="horizon",
+            type=int,
+            default=100,
+        )
+        parser.add_argument(
+            "-d",
+            "--detailed",
+            help="Display the details about the parameter fit, for instance the confidence interval",
+            dest="detailed",
+            action="store_true",
+            default=False,
+        )
+        if other_args and "-" not in other_args[0][0]:
+            other_args.insert(0, "-v")
+        ns_parser = self.parse_known_args_and_warn(
+            parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES
+        )
+        if ns_parser:
+            dataset, column = ns_parser.column.split(".")
+            econometrics_view.display_garch(
+                self.datasets[dataset],
+                column,
+                ns_parser.p,
+                ns_parser.o,
+                ns_parser.q,
+                ns_parser.mean,
+                ns_parser.horizon,
+                ns_parser.detailed,
+            )
+
     @log_start_end(log=logger)
     def call_granger(self, other_args: List[str]):
         """Process granger command"""