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Code Issues Packages Projects Releases Wiki Activity

Econometrics Menu (#1403)

Browse Source 
* Add Statistics Menu

* Add Granger Causality test

* Apply Black formatting

* Add Cointegration Tests

* Adjust plotting for Cointegration test

* Add Significant parameter to Cointegration tests

* Move regression functions to seperate .py files

* Progress on Panel Data

* A lot of progress for Panel Data

* Make functions robust and improve documentation

* Re-enable Breusch-Godfrey

* Add modify functionality

* Improve modify functionality

* Add Breusch-Pagan heteroscedasticity test

* Capitalize a word

* Include documentatin for the Statistics Menu

* Update _index.md

* Update _index.md

* Update _index.md

* Fix export statements and add Example datasets

* Update example with Longley's dataset

* Update documentation with a legit source

* Compare the results from each regression models based on the wage_panel dataset

* Updated with all relevant types of regression tables

* Update with all relevant regression types for Panel data

* Update _index.md

* Add change column type, improve OLS, add time and entity effects for FE

* Update documentation and fix a small bug

* Add in Statistics menu, replacing Custom menu

* Remove custom menu

* Add in documentation

* Add in gst files

* Cointegration can be used on any amount of columns

* Add Tests

* Make tests time invariant

* Update Poetry and Requirements

* Change name of Statistics menu to Econometrics menu

* Rename scripts

* Add type in Documentation

* Change names from Statistics to Econometrics

* Add graph

* Update tests with rounding and naming

* Make minor adjustments to fix the tests

* Updating tests : allow extra args for capture

* Apply recorder formatting

* Adding some minor formatting

* Fix error with MyPy

* Attempt to fix MyPy annoyance

* super small style things

* Fix small bugs and add plot command to dwat

* Small description mistake

* Update documentation with missing argument

* Update styling

* Add error handling and add improve show functionality

* Fix merge issue

* Allow import from custom_imports

Co-authored-by: Jeroen Bouma <jeroenbouma@MacBook-Pro-van-Jeroen.local>
Co-authored-by: jmaslek <jmaslek11@gmail.com>
Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
This commit is contained in:
Jeroen Bouma
2022-03-04 21:44:59 +01:00
committed by GitHub
parent b8b3b49896
commit f2ca215132
121 changed files with 11627 additions and 245 deletions
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gamestonk_terminal/api.py
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from .stocks import stocks_api as stocks
from .alternative import alt_api as alt
from .cryptocurrency import crypto_api as crypto
from .custom import custom_api as custom
from .economy import economy_api as economy
from .etf import etf_api as etf
from .forex import forex_api as forex

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gamestonk_terminal/econometrics/__init__.py Normal file
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gamestonk_terminal/econometrics/econometrics_controller.py Normal file
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gamestonk_terminal/econometrics/econometrics_model.py Normal file
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"""Econometrics Model"""
__docformat__ = "numpy"
# pylint: disable=eval-used
import logging
from pathlib import Path
import warnings
from typing import Dict, Union, Any
import pandas as pd
from pandas import DataFrame
from scipy import stats
import statsmodels.api as sm
from statsmodels.tsa.stattools import adfuller, kpss, grangercausalitytests
from linearmodels.datasets import wage_panel
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.rich_config import console
logger = logging.getLogger(__name__)
@log_start_end(log=logger)
def load(
file: str,
file_types: list,
data_files: Dict[Any, Any],
data_examples: Dict[Any, Any],
) -> pd.DataFrame:
"""Load custom file into dataframe.
Parameters
----------
file: str
Path to file
file_types: list
Supported file types
data_files: dict
Contains all available data files within the Export folder
data_examples: dict
Contains all available examples from Statsmodels
Returns
-------
pd.DataFrame:
Dataframe with custom data
"""
if file in data_examples:
if file == "wage_panel":
return wage_panel.load()
return eval(f"sm.datasets.{file}.load_pandas().data")
if file in data_files:
file = data_files[file]
if not Path(file).exists():
console.print(f"[red]Can not find the file {file}[/red]\n")
return pd.DataFrame()
file_type = Path(file).suffix
if file_type == ".xlsx":
data = pd.read_excel(file)
elif file_type == ".csv":
data = pd.read_csv(file)
else:
return console.print(
f"The file type {file_type} is not supported. Please choose one of the following: "
f"{', '.join(file_types)}"
)
return data
@log_start_end(log=logger)
def get_options(
datasets: Dict[str, pd.DataFrame], dataset_name: str = None
) -> Dict[Union[str, Any], DataFrame]:
"""Obtain columns-dataset combinations from loaded in datasets that can be used in other commands
Parameters
----------
datasets: dict
The available datasets.
dataset_name: str
The dataset you wish to show the options for.
Returns
-------
option_tables: dict
A dictionary with a DataFrame for each option. With dataset_name set, only shows one options table.
"""
option_tables = {}
if dataset_name:
columns = datasets[dataset_name].columns
option_tables[dataset_name] = pd.DataFrame(
{
"dataset": [dataset_name] * len(columns),
"column": columns,
"option": [f"{column}-{dataset_name}" for column in columns],
}
)
else:
for dataset, data_values in datasets.items():
columns = data_values.columns
option_tables[dataset] = pd.DataFrame(
{
"dataset": [dataset] * len(columns),
"column": columns,
"option": [f"{column}-{dataset}" for column in columns],
}
)
return option_tables
@log_start_end(log=logger)
def clean(dataset: pd.DataFrame, fill: str, drop: str, limit: int) -> pd.DataFrame:
"""Clean up NaNs from the dataset
Parameters
----------
dataset : pd.DataFrame
The dataset you wish to clean
fill : str
The method of filling Nans
drop : str
The method of dropping NaNs
limit : int
The maximum limit you wish to apply that can be forward or backward filled
Returns
-------
pd.DataFrame:
Dataframe with cleaned up data
"""
if fill:
if fill == "rfill":
dataset = dataset.fillna(axis="index", value=0)
if fill == "cfill":
dataset = dataset.fillna(axis="columns", value=0)
elif fill == "rbfill":
dataset = dataset.fillna(axis="index", method="bfill", limit=limit)
elif fill == "cbfill":
dataset = dataset.fillna(axis="columns", method="bfill", limit=limit)
elif fill == "rffill":
dataset = dataset.fillna(axis="index", method="ffill", limit=limit)
elif fill == "cffill":
dataset = dataset.fillna(axis="columns", method="ffill", limit=limit)
if drop:
if drop == "rdrop":
dataset = dataset.dropna(how="any", axis="index")
elif drop == "cdrop":
dataset = dataset.dropna(how="any", axis="columns")
return dataset
@log_start_end(log=logger)
def get_normality(data: pd.Series) -> pd.DataFrame:
"""
Look at the distribution of returns and generate statistics on the relation to the normal curve.
This function calculates skew and kurtosis (the third and fourth moments) and performs both
a Jarque-Bera and Shapiro Wilk test to determine if data is normally distributed.
Parameters
----------
data : pd.Series
A series or column of a DataFrame to test normality for
Returns
-------
pd.DataFrame
Dataframe containing statistics of normality
"""
# Kurtosis: measures height and sharpness of the central peak relative to that of a standard bell curve
k, kpval = stats.kurtosistest(data)
# Skewness: measure of the asymmetry of the probability distribution of a random variable about its mean
s, spval = stats.skewtest(data)
# Jarque-Bera goodness of fit test on sample data: tests if the sample data has the skewness and kurtosis
# matching a normal distribution
jb, jbpval = stats.jarque_bera(data)
# The Shapiro-Wilk test: tests the null hypothesis that the data was drawn from a normal distribution.
sh, shpval = stats.shapiro(data)
# Kolmogorov-Smirnov: the one-sample test compares the underlying distribution F(x) of a sample
# against a given distribution G(x). Comparing to normal here.
ks, kspval = stats.kstest(data, "norm")
l_statistic = [k, s, jb, sh, ks]
l_pvalue = [kpval, spval, jbpval, shpval, kspval]
return pd.DataFrame(
[l_statistic, l_pvalue],
columns=[
"Kurtosis",
"Skewness",
"Jarque-Bera",
"Shapiro-Wilk",
"Kolmogorov-Smirnov",
],
index=["Statistic", "p-value"],
)
@log_start_end(log=logger)
def get_root(df: pd.Series, fuller_reg: str, kpss_reg: str) -> pd.DataFrame:
"""Calculate test statistics for unit roots
Parameters
----------
df : pd.Series
Series or column of DataFrame of target variable
fuller_reg : str
Type of regression of ADF test
kpss_reg : str
Type of regression for KPSS test
Returns
-------
pd.DataFrame
Dataframe with results of ADF test and KPSS test
"""
# The Augmented Dickey-Fuller test: used to test for a unit root in a univariate process in the
# presence of serial correlation.
result = adfuller(df, regression=fuller_reg)
cols = ["Test Statistic", "P-Value", "NLags", "Nobs", "ICBest"]
vals = [result[0], result[1], result[2], result[3], result[5]]
data = pd.DataFrame(data=vals, index=cols, columns=["ADF"])
# Kwiatkowski-Phillips-Schmidt-Shin test: test for level or trend stationarity
# Note: this test seems to produce an Interpolation Error which says
# The test statistic is outside the range of p-values available in the
# look-up table. The actual p-value is greater than the p-value returned.
# Wrap this in catch_warnings to prevent this
with warnings.catch_warnings():
warnings.simplefilter("ignore")
res2 = kpss(df, regression=kpss_reg, nlags="auto")
vals2 = [res2[0], res2[1], res2[2], 0, 0]
data["KPSS"] = vals2
return data
@log_start_end(log=logger)
def get_granger_causality(time_series_y, time_series_x, lags):
"""Calculate granger tests
Parameters
----------
time_series_y : Series
The series you want to test Granger Causality for.
time_series_x : Series
The series that you want to test whether it Granger-causes time_series_y
lags : int
The amoiunt of lags for the Granger test. By default, this is set to 3.
"""
granger_set = pd.concat([time_series_y, time_series_x], axis=1)
granger = grangercausalitytests(granger_set, [lags], verbose=False)
return granger
def get_engle_granger_two_step_cointegration_test(y, x):
"""Estimates long-run and short-run cointegration relationship for series y and x and apply
the two-step Engle & Granger test for cointegration.
Uses a 2-step process to first estimate coefficients for the long-run relationship
y_t = c + gamma * x_t + z_t
and then the short-term relationship,
y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t,
with z the found residuals of the first equation.
Then tests cointegration by Dickey-Fuller phi=1 vs phi < 1 in
z_t = phi * z_(t-1) + eta_t
If this implies phi < 1, the z series is stationary is concluded to be
stationary, and thus the series y and x are concluded to be cointegrated.
Parameters
Parameters
----------
y : pd.Series
The first time series of the pair to analyse.
x : pd.Series
The second time series of the pair to analyse.
Returns
-------
c : float
The constant term in the long-run relationship y_t = c + gamma * x_t + z_t. This
describes the static shift of y with respect to gamma * x.
gamma : float
The gamma term in the long-run relationship y_t = c + gamma * x_t + z_t. This
describes the ratio between the const-shifted y and x.
alpha : float
The alpha term in the short-run relationship y_t - y_(t-1) = alpha * z_(t-1) + epsilon. This
gives an indication of the strength of the error correction toward the long-run mean.
z : pd.Series
Series of residuals z_t from the long-run relationship y_t = c + gamma * x_t + z_t, representing
the value of the error correction term.
dfstat : float
The Dickey Fuller test-statistic for phi = 1 vs phi < 1 in the second equation. A more
negative value implies the existence of stronger cointegration.
pvalue : float
The p-value corresponding to the Dickey Fuller test-statistic. A lower value implies
stronger rejection of no-cointegration, thus stronger evidence of cointegration.
"""
warnings.simplefilter(action="ignore", category=FutureWarning)
long_run_ols = sm.OLS(y, sm.add_constant(x))
warnings.simplefilter(action="default", category=FutureWarning)
long_run_ols_fit = long_run_ols.fit()
c, gamma = long_run_ols_fit.params
z = long_run_ols_fit.resid
short_run_ols = sm.OLS(y.diff().iloc[1:], (z.shift().iloc[1:]))
short_run_ols_fit = short_run_ols.fit()
alpha = short_run_ols_fit.params[0]
# NOTE: The p-value returned by the adfuller function assumes we do not estimate z first, but test
# stationarity of an unestimated series directly. This assumption should have limited effect for high N, however.
# Critical values taking this into account more accurately are provided in e.g. McKinnon (1990) and
# Engle & Yoo (1987).
adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None)
return c, gamma, alpha, z, adfstat, pvalue

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gamestonk_terminal/econometrics/econometrics_view.py Normal file
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"""Econometrics View"""
__docformat__ = "numpy"
import logging
import os
from itertools import combinations
from typing import Dict, Any, Optional, List
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.helper_funcs import (
export_data,
plot_autoscale,
)
from gamestonk_terminal.helper_funcs import (
print_rich_table,
)
from gamestonk_terminal.rich_config import console
from gamestonk_terminal.econometrics import econometrics_model
from gamestonk_terminal.config_terminal import theme
logger = logging.getLogger(__name__)
register_matplotlib_converters()
@log_start_end(log=logger)
def show_options(
datasets: Dict[str, pd.DataFrame],
dataset_name: str = None,
export: str = "",
):
"""Plot custom data
Parameters
----------
datasets: dict
The loaded in datasets
dataset_name: str
The name of the dataset you wish to show options for
export: str
Format to export image
"""
if not datasets:
console.print(
"Please load in a dataset by using the 'load' command before using this feature."
)
else:
option_tables = econometrics_model.get_options(datasets, dataset_name)
for dataset, data_values in option_tables.items():
print_rich_table(
data_values,
headers=list(data_values.columns),
show_index=False,
title=f"Options for {dataset}",
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{dataset}_options",
data_values.set_index("column"),
)
@log_start_end(log=logger)
def get_plot(
data: pd.DataFrame,
dataset: str,
column: str,
export: str = "",
external_axes: Optional[List[plt.axes]] = None,
):
"""Plot data from a dataset
Parameters
----------
data: pd.DataFrame
Dataframe of custom data
dataset: str
Dataset name
column: str
Column for y data
export: str
Format to export image
external_axes:Optional[List[plt.axes]]
External axes to plot on
"""
if isinstance(data.index, pd.MultiIndex):
console.print(
"The index appears to be a multi-index. "
"Therefore, it is not possible to plot the data."
)
else:
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes[0]
if isinstance(data, pd.Series):
ax.plot(data)
elif isinstance(data, pd.DataFrame):
ax.plot(data[column])
ax.set_title(f"{column} data from dataset {dataset}")
theme.style_primary_axis(ax)
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{column}_{dataset}_plot",
)
@log_start_end(log=logger)
def display_norm(
data: pd.Series,
dataset: str,
column: str,
plot: bool = False,
export: str = "",
external_axes: Optional[List[plt.axes]] = None,
):
"""Determine the normality of a timeseries.
Parameters
----------
data: pd.Series
Series of custom data
dataset: str
Dataset name
column: str
Column for y data
plot : bool
Whether you wish to plot a histogram
export: str
Format to export data.
external_axes: Optional[List[plt.axes]]
External axes to plot on
"""
if data.dtype not in [int, float]:
console.print(
f"The column type must be numeric. The {column}-{dataset} type is {data.dtype}. "
f"Consider using the command 'type' to change this."
)
else:
results = econometrics_model.get_normality(data)
print_rich_table(
results,
headers=list(results.columns),
show_index=True,
title=f"Normality Test [Column: {column} | Dataset: {dataset}]",
)
if plot:
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes[0]
ax.hist(data, bins=100)
ax.set_title(f"Histogram of {column} data from dataset {dataset}")
theme.style_primary_axis(ax)
if external_axes is None:
theme.visualize_output()
if export:
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{column}_{dataset}_norm",
results,
)
else:
console.print()
@log_start_end(log=logger)
def display_root(
df: pd.Series,
dataset_name: str,
column_name: str,
fuller_reg: str,
kpss_reg: str,
export: str = "",
):
"""Determine the normality of a timeseries.
Parameters
----------
df : pd.Series
Series of target variable
dataset_name: str
Name of the dataset
column_name: str
Name of the column
fuller_reg : str
Type of regression of ADF test
kpss_reg : str
Type of regression for KPSS test
export: str
Format to export data.
"""
if df.dtype not in [int, float]:
console.print(
f"The column type must be numeric. The {column_name}-{dataset_name} "
f"type is {df.dtype}. Consider using the command 'type' to change this."
)
else:
results = econometrics_model.get_root(df, fuller_reg, kpss_reg)
print_rich_table(
results,
headers=list(results.columns),
show_index=True,
title=f"Unitroot Test [Column: {column_name} | Dataset: {dataset_name}]",
)
if export:
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{column_name}_{dataset_name}_root",
results,
)
else:
console.print()
@log_start_end(log=logger)
def display_granger(
time_series_y: pd.Series,
time_series_x: pd.Series,
lags: int = 3,
confidence_level: float = 0.05,
export: str = "",
):
"""Show granger tests
Parameters
----------
time_series_y : Series
The series you want to test Granger Causality for.
time_series_x : Series
The series that you want to test whether it Granger-causes time_series_y
lags : int
The amount of lags for the Granger test. By default, this is set to 3.
confidence_level: float
The confidence level you wish to use. By default, this is set to 0.05.
export : str
Format to export data
"""
if time_series_y.dtype not in [int, float]:
console.print(
f"The time series {time_series_y.name} needs to be numeric but is type {time_series_y.dtype}. "
f"Consider using the command 'type' to change this."
)
elif time_series_x.dtype not in [int, float]:
console.print(
f"The time series {time_series_x.name} needs to be numeric but is type {time_series_x.dtype}. "
f"Consider using the command 'type' to change this."
)
else:
granger = econometrics_model.get_granger_causality(
time_series_y, time_series_x, lags
)
for test in granger[lags][0]:
# As ssr_chi2test and lrtest have one less value in the tuple, we fill
# this value with a '-' to allow the conversion to a DataFrame
if len(granger[lags][0][test]) != 4:
pars = granger[lags][0][test]
granger[lags][0][test] = (pars[0], pars[1], "-", pars[2])
granger_df = pd.DataFrame(
granger[lags][0], index=["F-test", "P-value", "Count", "Lags"]
).T
print_rich_table(
granger_df,
headers=list(granger_df.columns),
show_index=True,
title=f"Granger Causality Test [Y: {time_series_y.name} | X: {time_series_x.name} | Lags: {lags}]",
)
result_ftest = round(granger[lags][0]["params_ftest"][1], 3)
if result_ftest > confidence_level:
console.print(
f"As the p-value of the F-test is {result_ftest}, we can not reject the null hypothesis at "
f"the {confidence_level} confidence level."
)
else:
console.print(
f"As the p-value of the F-test is {result_ftest}, we can reject the null hypothesis at "
f"the {confidence_level} confidence level and find the Series '{time_series_x.name}' "
f"to Granger-cause the Series '{time_series_y.name}'"
)
if export:
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{time_series_y.name}_{time_series_x.name}_granger",
granger_df,
)
else:
console.print()
@log_start_end(log=logger)
def display_cointegration_test(
datasets: Dict[pd.Series, Any],
significant: bool = False,
plot: bool = False,
export: str = "",
external_axes: Optional[List[plt.axes]] = None,
):
"""Estimates long-run and short-run cointegration relationship for series y and x and apply
the two-step Engle & Granger test for cointegration.
Uses a 2-step process to first estimate coefficients for the long-run relationship
y_t = c + gamma * x_t + z_t
and then the short-term relationship,
y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t,
with z the found residuals of the first equation.
Then tests co-integration with the Dickey-Fuller phi=1 vs phi < 1 in
z_t = phi * z_(t-1) + eta_t
If this implies phi < 1, the z series is stationary is concluded to be
stationary, and thus the series y and x are concluded to be cointegrated.
Parameters
----------
datasets: Dict[pd.Series, Any]
All time series to perform co-integration tests on.
significant: float
Show only companies that have p-values lower than this percentage
plot: bool
Whether you wish to plot the z-values of all pairs.
export : str
Format to export data
external_axes:Optional[List[plt.axes]]
External axes to plot on
"""
pairs = list(combinations(datasets.keys(), 2))
result: Dict[str, list] = dict()
z_values: Dict[str, pd.Series] = dict()
for x, y in pairs:
if sum(datasets[y].isnull()) > 0:
console.print(
f"The Series {y} has nan-values. Please consider dropping or filling these "
f"values with 'clean'."
)
elif sum(datasets[x].isnull()) > 0:
console.print(
f"The Series {x} has nan-values. Please consider dropping or filling these "
f"values with 'clean'."
)
elif not datasets[y].index.equals(datasets[x].index):
console.print(f"The Series {y} and {x} do not have the same index.")
else:
(
c,
gamma,
alpha,
z,
adfstat,
pvalue,
) = econometrics_model.get_engle_granger_two_step_cointegration_test(
datasets[x], datasets[y]
)
result[f"{x}/{y}"] = [c, gamma, alpha, adfstat, pvalue]
z_values[f"{x}/{y}"] = z
if result and z_values:
df = pd.DataFrame.from_dict(
result,
orient="index",
columns=["Constant", "Gamma", "Alpha", "Dickey-Fuller", "P Value"],
)
if significant:
console.print(
f"Only showing pairs that are statistically significant ({significant} > p-value)."
)
df = df[significant > df["P Value"]]
console.print()
print_rich_table(
df,
headers=list(df.columns),
show_index=True,
index_name="Pairs",
title="Cointegration Tests",
)
if plot:
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes[0]
for pair, values in z_values.items():
ax.plot(values, label=pair)
ax.legend()
ax.set_title("Error correction terms")
theme.style_primary_axis(ax)
if external_axes is None:
theme.visualize_output()
if export:
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"results_cointegration",
df,
)
else:
console.print()

568
gamestonk_terminal/econometrics/regression_model.py Normal file
View File

@@ -0,0 +1,568 @@
"""Regression Model"""
__docformat__ = "numpy"
import os
import warnings
import logging
from typing import List, Tuple, Dict, Any
import pandas as pd
from linearmodels import PooledOLS
from linearmodels.panel import (
RandomEffects,
BetweenOLS,
PanelOLS,
FirstDifferenceOLS,
compare,
)
from pandas import DataFrame
from statsmodels.api import add_constant
import statsmodels.api as sm
from statsmodels.stats.api import het_breuschpagan
from statsmodels.stats.diagnostic import acorr_breusch_godfrey
from statsmodels.stats.stattools import durbin_watson
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.helper_funcs import export_data
from gamestonk_terminal.rich_config import console
logger = logging.getLogger(__name__)
def get_regressions_results(
regression_type: str,
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
entity_effects: bool = False,
time_effects: bool = False,
) -> Tuple[DataFrame, Any, List[Any], Any]:
"""Based on the regression type, this function decides what regression to run.
Parameters
----------
regression_type: str
The type of regression you wish to execute.
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
entity_effects: bool
Whether to apply Fixed Effects on entities.
time_effects: bool
Whether to apply Fixed Effects on time.
Returns
-------
The dataset used, the dependent variable, the independent variable and
the regression model.
"""
if regression_type == "OLS":
return get_ols(regression_variables, data, datasets, False)
if regression_type == "POLS":
return get_pols(regression_variables, data, datasets)
if regression_type == "RE":
return get_re(regression_variables, data, datasets)
if regression_type == "BOLS":
return get_bols(regression_variables, data, datasets)
if regression_type == "FE":
return get_fe(
regression_variables, data, datasets, entity_effects, time_effects
)
if regression_type == "FDOLS":
return get_fdols(regression_variables, data, datasets)
return console.print(f"{regression_type} is not an option.")
def get_regression_data(
regression_variables: List[tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
regression_type: str = "",
) -> Tuple[DataFrame, Any, List[Any]]:
"""This function creates a DataFrame with the required regression data as
well sets up the dependent and independent variables.
Parameters
----------
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
regression_type: str
The type of regression that is executed.
Returns
-------
The dataset used, the dependent variable, the independent variable and
the OLS model.
"""
regression = {}
independent_variables = []
dependent_variable = None
for variable in regression_variables:
column, dataset = datasets[variable].keys()
regression[f"{column}_{dataset}"] = data[dataset][column]
if variable == regression_variables[0]:
dependent_variable = f"{column}_{dataset}"
elif variable in regression_variables[1:]:
independent_variables.append(f"{column}_{dataset}")
regression_df = pd.DataFrame(regression)
nan_values = regression_df.isnull().sum().sum()
if nan_values > 0:
regression_df = regression_df.dropna(how="any", axis="index")
if regression_df.empty:
console.print(
f"The resulting DataFrame only consists of NaN values. This is usually due to an index "
f"mismatch. Therefore, no {regression_type} regression can be performed. Consider revisiting your "
"dataset(s) and adjust accordingly."
)
else:
console.print(
f"The resulting DataFrame has {nan_values} NaN values. This is usually due to "
f"an index mismatch. Rows that contain NaNs are dropped to be able to perform the {regression_type} "
f"regression. Consider revisiting your dataset(s) and adjust accordingly."
)
return regression_df, dependent_variable, independent_variables
@log_start_end(log=logger)
def get_ols(
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
show_regression: bool = True,
export: str = "",
) -> Tuple[DataFrame, Any, List[Any], Any]:
"""Performs an OLS regression on timeseries data. [Source: Statsmodels]
Parameters
----------
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
show_regression: bool
Whether to show the regression results table.
export: str
Format to export data
Returns
-------
The dataset used, the dependent variable, the independent variable and
the OLS model.
"""
regression_df, dependent_variable, independent_variables = get_regression_data(
regression_variables, data, datasets, "OLS"
)
if regression_df.empty:
model = None
else:
with warnings.catch_warnings(record=True) as warning_messages:
model = sm.OLS(
regression_df[dependent_variable], regression_df[independent_variables]
).fit()
if show_regression:
console.print(model.summary())
console.print("")
if len(warning_messages) > 0:
console.print("Warnings:")
for warning in warning_messages:
console.print(f"[red]{warning.message}[/red]".replace("\n", ""))
if export:
results_as_html = model.summary().tables[1].as_html()
df = pd.read_html(results_as_html, header=0, index_col=0)[0]
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{dependent_variable}_ols_regression",
df,
)
else:
console.print("")
return regression_df, dependent_variable, independent_variables, model
@log_start_end(log=logger)
def get_pols(
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
) -> Tuple[DataFrame, Any, List[Any], Any]:
"""PooledOLS is just plain OLS that understands that various panel data structures.
It is useful as a base model. [Source: LinearModels]
Parameters
----------
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
Returns
-------
The dataset used, the dependent variable, the independent variable and
the Pooled OLS model.
"""
regression_df, dependent_variable, independent_variables = get_regression_data(
regression_variables, data, datasets, "POLS"
)
if regression_df.empty:
model = None
else:
with warnings.catch_warnings(record=True) as warning_messages:
exogenous = add_constant(regression_df[independent_variables])
model = PooledOLS(regression_df[dependent_variable], exogenous).fit()
console.print(model)
if len(warning_messages) > 0:
console.print("Warnings:")
for warning in warning_messages:
console.print(f"[red]{warning.message}[/red]".replace("\n", ""))
return regression_df, dependent_variable, independent_variables, model
@log_start_end(log=logger)
def get_re(
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
) -> Tuple[DataFrame, Any, List[Any], Any]:
"""The random effects model is virtually identical to the pooled OLS model except that is accounts for the
structure of the model and so is more efficient. Random effects uses a quasi-demeaning strategy which
subtracts the time average of the within entity values to account for the common shock. [Source: LinearModels]
Parameters
----------
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
Returns
-------
The dataset used, the dependent variable, the independent variable and
the RandomEffects model.
"""
regression_df, dependent_variable, independent_variables = get_regression_data(
regression_variables, data, datasets, "RE"
)
if regression_df.empty:
model = None
else:
with warnings.catch_warnings(record=True) as warning_messages:
exogenous = add_constant(regression_df[independent_variables])
model = RandomEffects(regression_df[dependent_variable], exogenous).fit()
console.print(model)
if len(warning_messages) > 0:
console.print("Warnings:")
for warning in warning_messages:
console.print(f"[red]{warning.message}[/red]".replace("\n", ""))
return regression_df, dependent_variable, independent_variables, model
@log_start_end(log=logger)
def get_bols(
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
) -> Tuple[DataFrame, Any, List[Any], Any]:
"""The between estimator is an alternative, usually less efficient estimator, can can be used to
estimate model parameters. It is particular simple since it first computes the time averages of
y and x and then runs a simple regression using these averages. [Source: LinearModels]
Parameters
----------
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
Returns
-------
The dataset used, the dependent variable, the independent variable and
the Between OLS model.
"""
regression_df, dependent_variable, independent_variables = get_regression_data(
regression_variables, data, datasets, "BOLS"
)
if regression_df.empty:
model = None
else:
with warnings.catch_warnings(record=True) as warning_messages:
exogenous = add_constant(regression_df[independent_variables])
model = BetweenOLS(regression_df[dependent_variable], exogenous).fit()
console.print(model)
if len(warning_messages) > 0:
console.print("Warnings:")
for warning in warning_messages:
console.print(f"[red]{warning.message}[/red]".replace("\n", ""))
return regression_df, dependent_variable, independent_variables, model
@log_start_end(log=logger)
def get_fe(
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
entity_effects: bool = False,
time_effects: bool = False,
) -> Tuple[DataFrame, Any, List[Any], Any]:
"""When effects are correlated with the regressors the RE and BE estimators are not consistent.
The usual solution is to use Fixed Effects which are called entity_effects when applied to
entities and time_effects when applied to the time dimension. [Source: LinearModels]
Parameters
----------
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
entity_effects : bool
Whether to include entity effects
time_effects : bool
Whether to include time effects
Returns
-------
The dataset used, the dependent variable, the independent variable and
the OLS model.
"""
regression_df, dependent_variable, independent_variables = get_regression_data(
regression_variables, data, datasets, "FE"
)
if regression_df.empty:
model = None
else:
with warnings.catch_warnings(record=True) as warning_messages:
exogenous = add_constant(regression_df[independent_variables])
model = PanelOLS(
regression_df[dependent_variable],
exogenous,
entity_effects=entity_effects,
time_effects=time_effects,
).fit()
console.print(model)
if len(warning_messages) > 0:
console.print("Warnings:")
for warning in warning_messages:
console.print(f"[red]{warning.message}[/red]".replace("\n", ""))
return regression_df, dependent_variable, independent_variables, model
@log_start_end(log=logger)
def get_fdols(
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
) -> Tuple[DataFrame, Any, List[Any], Any]:
"""First differencing is an alternative to using fixed effects when there might be correlation.
When using first differences, time-invariant variables must be excluded. Additionally,
only one linear time-trending variable can be included since this will look like a constant.
This variable will soak up all time-trends in the data, and so interpretations of
these variable can be challenging. [Source: LinearModels]
Parameters
----------
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
Returns
-------
The dataset used, the dependent variable, the independent variable and
the OLS model.
"""
regression_df, dependent_variable, independent_variables = get_regression_data(
regression_variables, data, datasets, "FDOLS"
)
if regression_df.empty:
model = None
else:
with warnings.catch_warnings(record=True) as warning_messages:
model = FirstDifferenceOLS(
regression_df[dependent_variable], regression_df[independent_variables]
).fit()
console.print(model)
if len(warning_messages) > 0:
console.print("Warnings:")
for warning in warning_messages:
console.print(f"[red]{warning.message}[/red]".replace("\n", ""))
return regression_df, dependent_variable, independent_variables, model
@log_start_end(log=logger)
def get_comparison(regressions, export: str = ""):
"""Compare regression results between Panel Data regressions.
Parameters
----------
regressions : Dict
Dictionary with regression results.
export : str
Format to export data
Returns
-------
Returns a PanelModelComparison which shows an overview of the different regression results.
"""
comparison = {}
for regression_type, data in regressions.items():
if regression_type == "OLS":
continue
if data["model"]:
comparison[regression_type] = data["model"]
if not comparison:
# When the dictionary is empty, it means no Panel regression
# estimates are available and thus the function will have no output
return console.print(
"No Panel regression estimates available. Please use the "
"command 'panel' before using this command."
)
comparison_result = compare(comparison)
console.print(comparison_result)
if export:
results_as_html = comparison_result.summary.tables[0].as_html()
df = pd.read_html(results_as_html, header=0, index_col=0)[0]
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"regressions_compare",
df,
)
return comparison_result
@log_start_end(log=logger)
def get_dwat(residual: pd.DataFrame) -> pd.DataFrame:
"""Calculate test statistics for Durbing Watson autocorrelation
Parameters
----------
residual : OLS Model
Model containing residual values.
Returns
-------
Test statistic of the Durbin Watson test.
"""
# Durbin Watson test: The test statistic is approximately equal to 2*(1-r) where r is the
# sample autocorrelation of the residuals. Thus, for r == 0, indicating no serial correlation,
# the test statistic equals 2. This statistic will always be between 0 and 4. The closer
# to 0 the statistic, the more evidence for positive serial correlation. The closer to 4,
# the more evidence for negative serial correlation.
result = durbin_watson(residual)
return round(result, 2)
@log_start_end(log=logger)
def get_bgod(model: pd.DataFrame, lags: int) -> tuple:
"""Calculate test statistics for autocorrelation
Parameters
----------
model : OLS Model
Model containing residual values.
lags : int
The amount of lags.
Returns
-------
Test results from the Breusch-Godfrey Test
"""
lm_stat, p_value, f_stat, fp_value = acorr_breusch_godfrey(model, nlags=lags)
return lm_stat, p_value, f_stat, fp_value
@log_start_end(log=logger)
def get_bpag(model: pd.DataFrame) -> tuple:
"""Calculate test statistics for heteroscedasticity
Parameters
----------
model : OLS Model
Model containing residual values.
Returns
-------
Test results from the Breusch-Pagan Test
"""
lm_stat, p_value, f_stat, fp_value = het_breuschpagan(model.resid, model.model.exog)
return lm_stat, p_value, f_stat, fp_value

238
gamestonk_terminal/econometrics/regression_view.py Normal file
View File

@@ -0,0 +1,238 @@
"""Regression View"""
__docformat__ = "numpy"
from typing import Optional, List, Tuple, Dict, Any
import os
import logging
import pandas as pd
from matplotlib import pyplot as plt
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.helper_funcs import plot_autoscale, export_data
from gamestonk_terminal.rich_config import console
from gamestonk_terminal.econometrics import regression_model
from gamestonk_terminal.helper_funcs import (
print_rich_table,
)
from gamestonk_terminal.config_terminal import theme
logger = logging.getLogger(__name__)
@log_start_end(log=logger)
def display_panel(
regression_type: str,
regression_variables: List[Tuple],
data: Dict[str, pd.DataFrame],
datasets: Dict[pd.DataFrame, Any],
entity_effects: bool = False,
time_effects: bool = False,
export: str = "",
):
"""Based on the regression type, this function decides what regression to run.
Parameters
----------
regression_type: str
The type of regression you wish to execute.
regression_variables : list
The regressions variables entered where the first variable is
the dependent variable.
data : dict
A dictionary containing the datasets.
datasets: dict
A dictionary containing the column and dataset names of
each column/dataset combination.
entity_effects: bool
Whether to apply Fixed Effects on entities.
time_effects: bool
Whether to apply Fixed Effects on time.
export : str
Format to export data
Returns
-------
The dataset used, the dependent variable, the independent variable and
the regression model.
"""
(
regression_df,
dependent,
independent,
model,
) = regression_model.get_regressions_results(
regression_type,
regression_variables,
data,
datasets,
entity_effects,
time_effects,
)
if export:
results_as_html = model.summary.tables[1].as_html()
df = pd.read_html(results_as_html, header=0, index_col=0)[0]
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{dependent}_{regression_type}_regression",
df,
)
return regression_df, dependent, independent, model
@log_start_end(log=logger)
def display_dwat(
dependent_variable: pd.Series,
residual: pd.DataFrame,
plot: bool = False,
export: str = "",
external_axes: Optional[List[plt.axes]] = None,
):
"""Show Durbin-Watson autocorrelation tests
Parameters
----------
dependent_variable : pd.Series
The dependent variable.
residual : OLS Model
The residual of an OLS model.
plot : bool
Whether to plot the residuals
export : str
Format to export data
external_axes: Optional[List[plt.axes]]
External axes to plot on
"""
autocorrelation = regression_model.get_dwat(residual)
if 1.5 < autocorrelation < 2.5:
console.print(
f"The result {autocorrelation} is within the range 1.5 and 2.5 which therefore indicates "
f"autocorrelation not to be problematic."
)
else:
console.print(
f"The result {autocorrelation} is outside the range 1.5 and 2.5 and therefore autocorrelation "
f"can be problematic. Please consider lags of the dependent or independent variable."
)
if plot:
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
ax = external_axes[0]
ax.scatter(dependent_variable, residual)
ax.axhline(y=0, color="r", linestyle="-")
ax.set_ylabel("Residual")
ax.set_xlabel(dependent_variable.name.capitalize())
ax.set_title("Plot of Residuals")
theme.style_primary_axis(ax)
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
f"{dependent_variable.name}_dwat",
autocorrelation,
)
console.print()
@log_start_end(log=logger)
def display_bgod(model: pd.DataFrame, lags: int, export: str = ""):
"""Show Breusch-Godfrey autocorrelation test
Parameters
----------
model : OLS Model
Model containing residual values.
lags : int
The amount of lags included.
export : str
Format to export data
"""
(
lm_stat,
p_value,
f_stat,
fp_value,
) = regression_model.get_bgod(model, lags)
df = pd.DataFrame(
[lm_stat, p_value, f_stat, fp_value],
index=["LM-stat", "p-value", "f-stat", "fp-value"],
)
print_rich_table(
df,
headers=list(["Breusch-Godfrey"]),
show_index=True,
title=f"Breusch-Godfrey autocorrelation test [Lags: {lags}]",
)
if p_value > 0.05:
console.print(
f"The result {round(p_value, 2)} indicates the existence of autocorrelation. Consider re-estimating "
f"with clustered standard errors and applying the Random Effects or Fixed Effects model."
)
else:
console.print(
f"The result {round(p_value, 2)} indicates no existence of autocorrelation."
)
export_data(export, os.path.dirname(os.path.abspath(__file__)), "results_bgod", df)
console.print()
@log_start_end(log=logger)
def display_bpag(model: pd.DataFrame, export: str = ""):
"""Show Breusch-Pagan heteroscedasticity test
Parameters
----------
model : OLS Model
Model containing residual values.
export : str
Format to export data
"""
(
lm_stat,
p_value,
f_stat,
fp_value,
) = regression_model.get_bpag(model)
df = pd.DataFrame(
[lm_stat, p_value, f_stat, fp_value],
index=["lm-stat", "p-value", "f-stat", "fp-value"],
)
print_rich_table(
df,
headers=list(["Breusch-Pagan"]),
show_index=True,
title="Breusch-Pagan heteroscedasticity test",
)
if p_value > 0.05:
console.print(
f"The result {round(p_value, 2)} indicates the existence of heteroscedasticity. Consider taking the log "
f"or a rate for the dependent variable."
)
else:
console.print(
f"The result {round(p_value, 2)} indicates no existence of heteroscedasticity."
)
export_data(export, os.path.dirname(os.path.abspath(__file__)), "results_bpag", df)
console.print()

129
poetry.lock generated vendored
View File

@@ -158,6 +158,14 @@ python-versions = ">=3.7"
[package.extras]
tests = ["pytest", "pytest-asyncio", "mypy (>=0.800)"]
[[package]]
name = "astor"
version = "0.8.1"
description = "Read/rewrite/write Python ASTs"
category = "main"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
[[package]]
name = "astroid"
version = "2.9.3"
@@ -917,6 +925,29 @@ ufo = ["fs (>=2.2.0,<3)"]
unicode = ["unicodedata2 (>=14.0.0)"]
woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
[[package]]
name = "formulaic"
version = "0.2.4"
description = "An implementation of Wilkinson formulas."
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
astor = "*"
interface-meta = ">=1.2"
numpy = "*"
pandas = "*"
scipy = "*"
wrapt = "*"
[package.extras]
arrow = ["pyarrow"]
benchmarks = ["patsy", "rpy2", "uncertainties"]
calculus = ["sympy"]
docs = ["mkdocs-material", "pymdown-extensions", "pygments"]
test = ["flake8", "pytest", "pytest-cov"]
[[package]]
name = "fred"
version = "3.1"
@@ -1160,6 +1191,14 @@ category = "dev"
optional = false
python-versions = "*"
[[package]]
name = "interface-meta"
version = "1.2.4"
description = "InterfaceMeta"
category = "main"
optional = false
python-versions = ">=3.4"
[[package]]
name = "investpy"
version = "1.0.8"
@@ -1595,6 +1634,26 @@ category = "main"
optional = true
python-versions = "*"
[[package]]
name = "linearmodels"
version = "4.25"
description = "Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
Cython = ">=0.29.21"
formulaic = "*"
mypy_extensions = ">=0.4"
numpy = ">=1.16"
pandas = ">=0.24"
patsy = "*"
property_cached = ">=1.6.3"
pyhdfe = ">=0.1"
scipy = ">=1.2"
statsmodels = ">=0.11"
[[package]]
name = "lxml"
version = "4.8.0"
@@ -2336,6 +2395,14 @@ python-versions = ">=3.6.2"
[package.dependencies]
wcwidth = "*"
[[package]]
name = "property-cached"
version = "1.6.4"
description = "A decorator for caching properties in classes (forked from cached-property)."
category = "main"
optional = false
python-versions = ">= 3.5"
[[package]]
name = "protobuf"
version = "3.19.4"
@@ -2498,6 +2565,22 @@ category = "main"
optional = false
python-versions = ">=3.5"
[[package]]
name = "pyhdfe"
version = "0.1.0"
description = "High dimensional fixed effect absorption with Python 3"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
numpy = ">=1.12.0"
scipy = ">=1.0.0"
[package.extras]
docs = ["sphinx", "ipython", "astunparse", "sphinx-rtd-theme", "nbsphinx", "jupyter-client"]
tests = ["pytest", "pytest-xdist"]
[[package]]
name = "pyimgur"
version = "0.6.0"
@@ -3999,7 +4082,6 @@ prediction = ["tensorflow"]
[metadata]
lock-version = "1.1"
python-versions = "^3.8,<3.10"
content-hash = "5627f70eca6b911461177c361ec7374f5f3576c96dbc9052887ab0c7749b4e5a"
[metadata.files]
absl-py = [
@@ -4143,6 +4225,10 @@ asgiref = [
{file = "asgiref-3.5.0-py3-none-any.whl", hash = "sha256:88d59c13d634dcffe0510be048210188edd79aeccb6a6c9028cdad6f31d730a9"},
{file = "asgiref-3.5.0.tar.gz", hash = "sha256:2f8abc20f7248433085eda803936d98992f1343ddb022065779f37c5da0181d0"},
]
astor = [
{file = "astor-0.8.1-py2.py3-none-any.whl", hash = "sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5"},
{file = "astor-0.8.1.tar.gz", hash = "sha256:6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e"},
]
astroid = [
{file = "astroid-2.9.3-py3-none-any.whl", hash = "sha256:506daabe5edffb7e696ad82483ad0228245a9742ed7d2d8c9cdb31537decf9f6"},
{file = "astroid-2.9.3.tar.gz", hash = "sha256:1efdf4e867d4d8ba4a9f6cf9ce07cd182c4c41de77f23814feb27ca93ca9d877"},
@@ -4591,6 +4677,10 @@ fonttools = [
{file = "fonttools-4.29.1-py3-none-any.whl", hash = "sha256:1933415e0fbdf068815cb1baaa1f159e17830215f7e8624e5731122761627557"},
{file = "fonttools-4.29.1.zip", hash = "sha256:2b18a172120e32128a80efee04cff487d5d140fe7d817deb648b2eee023a40e4"},
]
formulaic = [
{file = "formulaic-0.2.4-py3-none-any.whl", hash = "sha256:775620d93f24f01b33a17aa2cf65a04112003c5112f12015368e4e4605a5013b"},
{file = "formulaic-0.2.4.tar.gz", hash = "sha256:15b71ea8972fb451f80684203cddd49620fc9ed5c2e35f31e0874e9c41910d1a"},
]
fred = [
{file = "fred-3.1.tar.gz", hash = "sha256:f31327d648917694b8d15d66ca4e82a082dbb88ca027bdcc9d56738cbc836a6a"},
]
@@ -4810,6 +4900,10 @@ iniconfig = [
{file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
{file = "iniconfig-1.1.1.tar.gz", hash = "sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32"},
]
interface-meta = [
{file = "interface_meta-1.2.4-py2.py3-none-any.whl", hash = "sha256:8d11375064d51e73764a02b8225af87b1ed63c20c1df52d3867611a5e70a5fc0"},
{file = "interface_meta-1.2.4.tar.gz", hash = "sha256:4c7725dd4b80f97b7eecfb26023e1a8a7cdbb6d6a7207a8e93f9d4bfef9ee566"},
]
investpy = [
{file = "investpy-1.0.8.tar.gz", hash = "sha256:6a7632a94b484ee07cd54afd416bdc759cd905ba60c62dbb8a8d157229b267c6"},
]
@@ -5008,6 +5102,31 @@ libclang = [
{file = "libclang-11.1.0-py2.py3-none-manylinux2014_aarch64.whl", hash = "sha256:39d07c8965a3c963fb2686496339b6dbacf42acbd32e4bc7361373a45e98cebe"},
{file = "libclang-11.1.0-py2.py3-none-win_amd64.whl", hash = "sha256:8cb0082a30b9e1e615d7f4211b0c428b607a9bd1e43ddc6c0cabdd9ea5b244bf"},
]
linearmodels = [
{file = "linearmodels-4.25-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e8d3b34c62357701b60d043356563c247096ad79b8b95ac9bbafb1820a455f48"},
{file = "linearmodels-4.25-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b1b2e7e4e27b12e9d5ec4e2de41e9aeaa685405bc37beaff0f080a2ad3e24f61"},
{file = "linearmodels-4.25-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6e107d3d97819cdc4bfe64c85870e0ed655f2e1bd04911e22127dee0e3fab8d2"},
{file = "linearmodels-4.25-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:728d82fac702f65cc128742145b3702428b6aed5283450365ec5110f42e883dd"},
{file = "linearmodels-4.25-cp310-cp310-win_amd64.whl", hash = "sha256:c0aec5de380c4daf0596bb3be647a8cc8d0081c06715465df7a87efced08014e"},
{file = "linearmodels-4.25-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:db4c313127f3307a7c174ee12d9306fbc8dbb8a585b55234ebfa435fa5dd9ee9"},
{file = "linearmodels-4.25-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:339f01a68e504db1cccb4be9d4167c38c92ea5700cd3eba03ad0352d869c15eb"},
{file = "linearmodels-4.25-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f55f68eb675f863f80860526143451f516d9b77c580c0afb6c86f8bf7292da86"},
{file = "linearmodels-4.25-cp37-cp37m-win32.whl", hash = "sha256:ab9cb6a6fcad05bc5e790341c321713232883a8757aab4c7ce038a0a1af55728"},
{file = "linearmodels-4.25-cp37-cp37m-win_amd64.whl", hash = "sha256:f9fcb1eaaaf268ffcffbf4c2333bc9634d7a9ecf2ced0b5222bab6a8491d9fc6"},
{file = "linearmodels-4.25-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:397201c401f1d21bbfa9b2f4c44694c2f8c557d69271c244f43ac2ec5c6f2376"},
{file = "linearmodels-4.25-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e989cc12320e4cc599f3ceb83e8fa16e27af2584c3f76020bc56c20f76866839"},
{file = "linearmodels-4.25-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c24f4aa2f44bf9e04606e15851b8874b2c8097f533c47bf11d6dab4954ecc6db"},
{file = "linearmodels-4.25-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6843e0a8971d4d0539f22967ebf7dc9b381e55f087b6ee7bb32ad7ee728c91a7"},
{file = "linearmodels-4.25-cp38-cp38-win32.whl", hash = "sha256:cced735e5c645902c3c2003826d75ff04dd5caa60096918e8f7f4fc0eb79ed31"},
{file = "linearmodels-4.25-cp38-cp38-win_amd64.whl", hash = "sha256:4d5d401d1c2c9b8c2e8fd2dd8b064e513bf4cb0d6a30b9198f33f67163cbc7d8"},
{file = "linearmodels-4.25-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3bd3ddc0753d29fcd26ff80d87957904f7d91c3f7d898c8839b13a226ee5fede"},
{file = "linearmodels-4.25-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:11b3f66bdf17434b04be916f16ddb319bf8dae54b89b8c268cb37a8ad7b0f4ee"},
{file = "linearmodels-4.25-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5a1b691e840189a2ed17796a806960166e30e86e6970c80e60fe10762456005"},
{file = "linearmodels-4.25-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4abd59e3dda544807676d94ba1ea2ceba87624ab30491ea561430f31379229bc"},
{file = "linearmodels-4.25-cp39-cp39-win32.whl", hash = "sha256:061f2970191fcee284f600d5cc84a556a411729494325af8c557047ab733ae46"},
{file = "linearmodels-4.25-cp39-cp39-win_amd64.whl", hash = "sha256:19f8cb6237b61badb687a4a020527552a70e077cf1b303959c33faeb8cdde285"},
{file = "linearmodels-4.25.tar.gz", hash = "sha256:a73e94195f486f74be6176809a515bb498b5371d8acb9e4ae6f0e59a7c28210a"},
]
lxml = [
{file = "lxml-4.8.0-cp27-cp27m-macosx_10_14_x86_64.whl", hash = "sha256:e1ab2fac607842ac36864e358c42feb0960ae62c34aa4caaf12ada0a1fb5d99b"},
{file = "lxml-4.8.0-cp27-cp27m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:28d1af847786f68bec57961f31221125c29d6f52d9187c01cd34dc14e2b29430"},
@@ -5541,6 +5660,10 @@ prompt-toolkit = [
{file = "prompt_toolkit-3.0.28-py3-none-any.whl", hash = "sha256:30129d870dcb0b3b6a53efdc9d0a83ea96162ffd28ffe077e94215b233dc670c"},
{file = "prompt_toolkit-3.0.28.tar.gz", hash = "sha256:9f1cd16b1e86c2968f2519d7fb31dd9d669916f515612c269d14e9ed52b51650"},
]
property-cached = [
{file = "property-cached-1.6.4.zip", hash = "sha256:3e9c4ef1ed3653909147510481d7df62a3cfb483461a6986a6f1dcd09b2ebb73"},
{file = "property_cached-1.6.4-py2.py3-none-any.whl", hash = "sha256:135fc059ec969c1646424a0db15e7fbe1b5f8c36c0006d0b3c91ba568c11e7d8"},
]
protobuf = [
{file = "protobuf-3.19.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f51d5a9f137f7a2cec2d326a74b6e3fc79d635d69ffe1b036d39fc7d75430d37"},
{file = "protobuf-3.19.4-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:09297b7972da685ce269ec52af761743714996b4381c085205914c41fcab59fb"},
@@ -5679,6 +5802,10 @@ pygments = [
{file = "Pygments-2.11.2-py3-none-any.whl", hash = "sha256:44238f1b60a76d78fc8ca0528ee429702aae011c265fe6a8dd8b63049ae41c65"},
{file = "Pygments-2.11.2.tar.gz", hash = "sha256:4e426f72023d88d03b2fa258de560726ce890ff3b630f88c21cbb8b2503b8c6a"},
]
pyhdfe = [
{file = "pyhdfe-0.1.0-py3-none-any.whl", hash = "sha256:21c6f7d9f464929ce5acfce54d46684c4449a5bbe14d2986237ecb30a723b9ee"},
{file = "pyhdfe-0.1.0.tar.gz", hash = "sha256:820a40722d70e94cec1dcd140d6c480f02578fee96660b722a2e722c2222e377"},
]
pyimgur = [
{file = "pyimgur-0.6.0-py2-none-any.whl", hash = "sha256:ad22deea062fe8e7412d0bdfc70cbf305a828c6390096d3523ed3d7dbeb2bdff"},
{file = "pyimgur-0.6.0.tar.gz", hash = "sha256:f776ca7f14663bd9ac14a1380db62c87ed32231d159d2d167ad1cb817dcb9144"},

1
pyproject.toml vendored
View File

@@ -87,6 +87,7 @@ PyNaCl = {version = "^1.5.0", optional = true}
ascii-magic = "^1.6"
squarify = "^0.4.3"
finnhub-python = "^2.4.10"
linearmodels = "^4.25"
[tool.poetry.dev-dependencies]

58
requirements-full.txt vendored
View File

@@ -7,33 +7,34 @@ ansiwrap==0.8.4; python_version >= "3.6"
anyio==3.5.0; python_full_version >= "3.6.2" and python_version >= "3.7"
appdirs==1.4.4; python_full_version >= "3.6.2" and python_version >= "3.8"
appnope==0.1.2; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin"
argon2-cffi-bindings==21.2.0; python_version >= "3.6"
argon2-cffi-bindings==21.2.0; python_version >= "3.7"
argon2-cffi==21.3.0; python_version >= "3.7"
ascii-magic==1.6; python_version >= "3.5"
astor==0.8.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
astroid==2.9.3; python_full_version >= "3.6.2"
asttokens==2.0.5; python_version >= "3.8"
astunparse==1.6.3
async-timeout==4.0.2; python_version >= "3.6"
atomicwrites==1.4.0; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.4.0"
attrs==21.4.0; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.5.0" and python_version >= "3.7" and python_version < "4.0"
attrs==21.4.0; python_full_version >= "3.7.0" and python_version >= "3.7" and python_version < "4.0"
babel==2.9.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
backcall==0.2.0; python_version >= "3.8"
backports.zoneinfo==0.2.1; python_version < "3.9" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
bandit==1.7.2; python_version >= "3.7"
beartype==0.7.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.6.0"
beautifulsoup4==4.10.0; python_full_version > "3.0.0" and python_version >= "3.5"
beautifulsoup4==4.10.0; python_full_version > "3.0.0" and python_version >= "3.7"
black==21.7b0; python_full_version >= "3.6.2"
bleach==4.1.0; python_version >= "3.7"
bs4==0.0.1
bt==0.2.9; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
cachetools==5.0.0; python_version >= "3.7" and python_version < "4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
certifi==2021.10.8; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
cffi==1.15.0; implementation_name == "pypy" and python_version >= "3.6"
certifi==2021.10.8; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
cffi==1.15.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.7.0"
cfgv==3.3.1; python_full_version >= "3.6.1"
charset-normalizer==2.0.12; python_full_version >= "3.6.0" and python_version >= "3.6"
charset-normalizer==2.0.12; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
click==8.0.4; python_version >= "3.8" and python_full_version >= "3.6.2"
codespell==2.1.0; python_version >= "3.5"
colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "2.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.6" and python_full_version >= "3.5.0") and (python_version >= "3.8" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.8" and python_full_version >= "3.5.0")
colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.8" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.8" and python_full_version >= "3.5.0")
commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0"
convertdate==2.4.0; python_version >= "3.7" and python_version < "4"
coverage==6.3.2; python_version >= "3.7"
@@ -54,7 +55,7 @@ distlib==0.3.4; python_full_version >= "3.6.1"
dnspython==2.2.0; python_version >= "3.6" and python_version < "4.0"
docutils==0.16; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
ecos==2.0.10; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6"
entrypoints==0.4; python_full_version >= "3.6.1" and python_version >= "3.7"
entrypoints==0.4; python_full_version >= "3.7.0" and python_version >= "3.7"
et-xmlfile==1.1.0; python_version >= "3.6"
exchange-calendars==3.6; python_version >= "3.7" and python_full_version >= "3.7.0"
executing==0.8.2; python_version >= "3.8"
@@ -68,6 +69,7 @@ finvizfinance==0.9.10; python_version >= "3.5"
flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
flatbuffers==2.0
fonttools==4.29.1; python_version >= "3.7"
formulaic==0.2.4; python_version >= "3.7"
fred==3.1
fredapi==0.4.3
frozendict==2.3.0; python_version >= "3.6"
@@ -85,27 +87,28 @@ h5py==3.6.0; python_version >= "3.7"
hijri-converter==2.2.3; python_version >= "3.6"
holidays==0.11.3.1; python_version >= "3.6"
identify==2.4.11; python_version >= "3.7" and python_full_version >= "3.6.1"
idna==3.3; python_full_version >= "3.6.2" and python_version >= "3.7"
idna==3.3; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
imagesize==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
importlib-metadata==4.11.1; python_version < "3.10" and python_version >= "3.7"
inflection==0.5.1; python_version >= "3.6"
iniconfig==1.1.1; python_version >= "3.7"
interface-meta==1.2.4; python_version >= "3.7"
investpy==1.0.8; python_version >= "3.7"
ipykernel==6.9.1; python_version >= "3.7"
ipympl==0.8.8
ipython-genutils==0.2.0; python_version >= "3.7"
ipython-genutils==0.2.0; python_full_version >= "3.7.0" and python_version >= "3.7"
ipython==8.0.1; python_version >= "3.8"
ipywidgets==7.6.5
iso8601==0.1.16
isort==5.10.1; python_full_version >= "3.6.2" and python_version < "4.0"
jedi==0.18.1; python_version >= "3.8"
jinja2==3.0.3; python_version >= "3.7"
joblib==1.1.0; python_version >= "3.7"
json5==0.9.6; python_version >= "3.6"
joblib==1.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
json5==0.9.6; python_version >= "3.7"
jsonschema==3.2.0
jupyter-client==7.1.2; python_full_version >= "3.7.0" and python_version >= "3.7"
jupyter-console==6.4.0; python_version >= "3.6"
jupyter-core==4.9.2; python_full_version >= "3.6.1" and python_version >= "3.7"
jupyter-core==4.9.2; python_full_version >= "3.7.0" and python_version >= "3.7"
jupyter-server==1.13.5; python_version >= "3.7"
jupyter==1.0.0
jupyterlab-pygments==0.1.2; python_version >= "3.7"
@@ -118,6 +121,7 @@ kiwisolver==1.3.2; python_version >= "3.7"
korean-lunar-calendar==0.2.1; python_version >= "3.7" and python_full_version >= "3.7.0"
lazy-object-proxy==1.7.1; python_version >= "3.6" and python_full_version >= "3.6.2"
libclang==11.1.0
linearmodels==4.25; python_version >= "3.7"
lxml==4.8.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
m2r2==0.2.8; python_version >= "3.7"
markdown-it-py==1.1.0; python_version >= "3.6" and python_version < "4.0"
@@ -173,20 +177,22 @@ prawcore==2.3.0; python_version >= "3.6" and python_version < "4.0"
pre-commit==2.17.0; python_full_version >= "3.6.1"
prometheus-client==0.13.1; python_version >= "3.7"
prompt-toolkit==3.0.28; python_full_version >= "3.6.2"
property-cached==1.6.4; python_version >= "3.7"
protobuf==3.19.4; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6"
psaw==0.0.12; python_version >= "3"
ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32"
pure-eval==0.2.2; python_version >= "3.8"
py==1.11.0; python_version >= "3.7" and python_full_version < "3.0.0" and implementation_name == "pypy" or python_full_version >= "3.5.0" and python_version >= "3.7" and implementation_name == "pypy"
py==1.11.0; python_full_version >= "3.7.0" and python_version >= "3.7" and implementation_name == "pypy"
pyally==1.1.2
pyasn1-modules==0.2.8; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
pyasn1==0.4.8; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") or python_full_version >= "3.6.0" and python_version >= "3.6" and python_version < "4" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
pycodestyle==2.7.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
pycoingecko==2.2.0
pycparser==2.21; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
pycparser==2.21; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.7.0"
pyex==0.5.0
pyflakes==2.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
pygments==2.11.2; python_version >= "3.5"
pyhdfe==0.1.0; python_version >= "3.7"
pylint==2.12.2; python_full_version >= "3.6.2"
pyluach==1.4.0; python_version >= "3.7" and python_full_version >= "3.7.0"
pymeeus==0.5.11; python_version >= "3.7" and python_version < "4"
@@ -204,16 +210,16 @@ pytest-recording==0.12.0; python_version >= "3.5"
pytest==6.2.5; python_version >= "3.6"
python-binance==1.0.15
python-coinmarketcap==0.2
python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
python-dotenv==0.19.2; python_version >= "3.5"
pytrends==4.8.0
pytz-deprecation-shim==0.1.0.post0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7")
pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7")
pyupgrade==2.31.0; python_full_version >= "3.6.1"
pywin32==303; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.6"
pywin32==303; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" and python_full_version >= "3.7.0"
pywinpty==1.1.6; os_name == "nt" and python_version >= "3.7"
pyyaml==6.0; python_version >= "3.7" and python_full_version >= "3.6.1"
pyzmq==22.3.0; python_full_version >= "3.6.1" and python_version >= "3.7"
pyzmq==22.3.0; python_full_version >= "3.7.0" and python_version >= "3.7"
qdldl==0.1.5.post0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6"
qtconsole==5.2.2; python_version >= "3.6"
qtpy==2.0.1; python_version >= "3.6"
@@ -227,7 +233,7 @@ rich==10.16.2; python_full_version >= "3.6.2" and python_full_version < "4.0.0"
robin-stocks==2.1.0; python_version >= "3"
rsa==4.8; python_version >= "3.6" and python_version < "4" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
scikit-learn==1.0.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
scipy==1.8.0; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.6.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0")
scipy==1.8.0; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.6.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
screeninfo==0.6.7
scs==3.2.0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6"
seaborn==0.11.2; python_version >= "3.6"
@@ -235,7 +241,7 @@ selenium==3.141.0
send2trash==1.8.0; python_version >= "3.7"
sentiment-investor==2.1.0; python_version >= "3.8" and python_version < "4.0"
setuptools-scm==6.4.2; python_version >= "3.7"
six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") and (python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.8")
six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") and (python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.8")
smmap==5.0.0; python_version >= "3.7"
sniffio==1.2.0; python_full_version >= "3.6.2" and python_version >= "3.7"
snowballstemmer==2.2.0; python_version >= "3.7"
@@ -268,12 +274,12 @@ testpath==0.5.0; python_version >= "3.7"
textwrap3==0.9.2; python_version >= "3.6"
tf-estimator-nightly==2.8.0.dev2021122109
thepassiveinvestor==1.0.10
threadpoolctl==3.1.0; python_version >= "3.7"
threadpoolctl==3.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
tokenize-rt==4.2.1; python_full_version >= "3.6.1"
toml==0.10.2; python_full_version >= "3.6.2" and python_version >= "3.6" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7")
tomli==1.2.3; python_version >= "3.8" and python_full_version >= "3.6.2"
toolz==0.11.2; python_version >= "3.7" and python_full_version >= "3.7.0"
tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7"
tornado==6.1; python_full_version >= "3.7.0" and python_version >= "3.7"
tqdm==4.62.3; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
tradingview-ta==3.2.10; python_version >= "3.6"
traitlets==5.1.1; python_full_version >= "3.7.0" and python_version >= "3.8"
@@ -289,20 +295,20 @@ tzlocal==4.1; python_version >= "3.6"
ujson==5.1.0; python_version >= "3.7"
unidecode==1.3.3; python_version >= "3.7"
update-checker==0.18.0; python_version >= "3.6" and python_version < "4.0"
urllib3==1.26.8; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7"
urllib3==1.26.8; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
user-agent==0.1.10
vadersentiment==3.3.2
valinvest==0.0.2; python_version >= "3.6"
vcrpy==4.1.1; python_version >= "3.5"
virtualenv==20.13.1; python_full_version >= "3.6.1"
voila==0.3.2; python_version >= "3.7"
wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.6"
wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8"
webencodings==0.5.1; python_version >= "3.7"
websocket-client==1.2.3; python_version >= "3.8" and python_version < "4.0"
websockets==9.1; python_full_version >= "3.6.1" and python_version >= "3.7"
werkzeug==2.0.3; python_version >= "3.6"
widgetsnbextension==3.5.2
wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5")
wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7")
yarl==1.7.2; python_version >= "3.6"
yfinance==0.1.70
zipp==3.7.0; python_version < "3.10" and python_version >= "3.7"

58
requirements.txt vendored
View File

@@ -6,26 +6,27 @@ ansiwrap==0.8.4; python_version >= "3.6"
anyio==3.5.0; python_full_version >= "3.6.2" and python_version >= "3.7"
appdirs==1.4.4; python_full_version >= "3.6.2" and python_version >= "3.8"
appnope==0.1.2; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin"
argon2-cffi-bindings==21.2.0; python_version >= "3.6"
argon2-cffi-bindings==21.2.0; python_version >= "3.7"
argon2-cffi==21.3.0; python_version >= "3.7"
ascii-magic==1.6; python_version >= "3.5"
astor==0.8.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
asttokens==2.0.5; python_version >= "3.8"
async-timeout==4.0.2; python_version >= "3.6"
attrs==21.4.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
attrs==21.4.0; python_full_version >= "3.7.0" and python_version >= "3.7"
babel==2.9.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
backcall==0.2.0; python_version >= "3.8"
backports.zoneinfo==0.2.1; python_version < "3.9" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
beartype==0.7.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.6.0"
beautifulsoup4==4.10.0; python_full_version > "3.0.0" and python_version >= "3.5"
beautifulsoup4==4.10.0; python_full_version > "3.0.0" and python_version >= "3.7"
black==21.7b0; python_full_version >= "3.6.2"
bleach==4.1.0; python_version >= "3.7"
bs4==0.0.1
bt==0.2.9; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
certifi==2021.10.8; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
cffi==1.15.0; implementation_name == "pypy" and python_version >= "3.6"
charset-normalizer==2.0.12; python_full_version >= "3.6.0" and python_version >= "3.6"
certifi==2021.10.8; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
cffi==1.15.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.7.0"
charset-normalizer==2.0.12; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
click==8.0.4; python_version >= "3.8" and python_full_version >= "3.6.2"
colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "2.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.6" and python_full_version >= "3.5.0") and (python_version >= "3.8" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.8" and python_full_version >= "3.5.0")
colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.8" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.8" and python_full_version >= "3.5.0")
commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0"
convertdate==2.4.0; python_version >= "3.7" and python_version < "4"
cryptography==36.0.1; python_version >= "3.6"
@@ -44,7 +45,7 @@ detecta==0.0.5; python_version >= "3.6"
dnspython==2.2.0; python_version >= "3.6" and python_version < "4.0"
docutils==0.16; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
ecos==2.0.10; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6"
entrypoints==0.4; python_full_version >= "3.6.1" and python_version >= "3.7"
entrypoints==0.4; python_full_version >= "3.7.0" and python_version >= "3.7"
et-xmlfile==1.1.0; python_version >= "3.6"
executing==0.8.2; python_version >= "3.8"
fear-greed-index==0.1.4; python_version >= "3.7"
@@ -54,6 +55,7 @@ finnhub-python==2.4.10
finviz==1.4.3
finvizfinance==0.9.10; python_version >= "3.5"
fonttools==4.29.1; python_version >= "3.7"
formulaic==0.2.4; python_version >= "3.7"
fred==3.1
fredapi==0.4.3
frozendict==2.3.0; python_version >= "3.6"
@@ -65,24 +67,25 @@ gitpython==3.1.27; python_version >= "3.7"
grpcio==1.44.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6"
hijri-converter==2.2.3; python_version >= "3.6"
holidays==0.11.3.1; python_version >= "3.6"
idna==3.3; python_full_version >= "3.6.2" and python_version >= "3.7"
idna==3.3; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
imagesize==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
inflection==0.5.1; python_version >= "3.6"
interface-meta==1.2.4; python_version >= "3.7"
investpy==1.0.8; python_version >= "3.7"
ipykernel==6.9.1; python_version >= "3.7"
ipympl==0.8.8
ipython-genutils==0.2.0; python_version >= "3.7"
ipython-genutils==0.2.0; python_full_version >= "3.7.0" and python_version >= "3.7"
ipython==8.0.1; python_version >= "3.8"
ipywidgets==7.6.5
iso8601==0.1.16
jedi==0.18.1; python_version >= "3.8"
jinja2==3.0.3; python_version >= "3.7"
joblib==1.1.0; python_version >= "3.7"
json5==0.9.6; python_version >= "3.6"
joblib==1.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
json5==0.9.6; python_version >= "3.7"
jsonschema==3.2.0
jupyter-client==7.1.2; python_full_version >= "3.7.0" and python_version >= "3.7"
jupyter-console==6.4.0; python_version >= "3.6"
jupyter-core==4.9.2; python_full_version >= "3.6.1" and python_version >= "3.7"
jupyter-core==4.9.2; python_full_version >= "3.7.0" and python_version >= "3.7"
jupyter-server==1.13.5; python_version >= "3.7"
jupyter==1.0.0
jupyterlab-pygments==0.1.2; python_version >= "3.7"
@@ -91,6 +94,7 @@ jupyterlab-widgets==1.0.2; python_version >= "3.6"
jupyterlab==3.2.9; python_version >= "3.6"
kiwisolver==1.3.2; python_version >= "3.7"
korean-lunar-calendar==0.2.1; python_version >= "3.6"
linearmodels==4.25; python_version >= "3.7"
lxml==4.8.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
m2r2==0.2.8; python_version >= "3.7"
markupsafe==2.1.0; python_version >= "3.7"
@@ -133,16 +137,18 @@ praw==7.5.0; python_version >= "3.6" and python_version < "4.0"
prawcore==2.3.0; python_version >= "3.6" and python_version < "4.0"
prometheus-client==0.13.1; python_version >= "3.7"
prompt-toolkit==3.0.28; python_full_version >= "3.6.2"
property-cached==1.6.4; python_version >= "3.7"
protobuf==3.19.4; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.5"
psaw==0.0.12; python_version >= "3"
ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32"
pure-eval==0.2.2; python_version >= "3.8"
py==1.11.0; python_version >= "3.6" and python_full_version < "3.0.0" and implementation_name == "pypy" or implementation_name == "pypy" and python_version >= "3.6" and python_full_version >= "3.5.0"
py==1.11.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.7.0"
pyally==1.1.2
pycoingecko==2.2.0
pycparser==2.21; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
pycparser==2.21; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.7.0"
pyex==0.5.0
pygments==2.11.2; python_version >= "3.5"
pyhdfe==0.1.0; python_version >= "3.7"
pymeeus==0.5.11; python_version >= "3.7" and python_version < "4"
pymongo==3.11.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
pyobjc-core==8.2; python_version >= "3.6" and sys_platform == "darwin"
@@ -154,15 +160,15 @@ pyprind==2.11.3; python_version >= "2.7" and python_full_version < "3.0.0" or py
pyrsistent==0.18.1; python_version >= "3.7"
python-binance==1.0.15
python-coinmarketcap==0.2
python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
python-dotenv==0.19.2; python_version >= "3.5"
pytrends==4.8.0
pytz-deprecation-shim==0.1.0.post0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7")
pywin32==303; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.6"
pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7")
pywin32==303; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" and python_full_version >= "3.7.0"
pywinpty==1.1.6; os_name == "nt" and python_version >= "3.7"
pyyaml==6.0; python_version >= "3.6"
pyzmq==22.3.0; python_full_version >= "3.6.1" and python_version >= "3.7"
pyzmq==22.3.0; python_full_version >= "3.7.0" and python_version >= "3.7"
qdldl==0.1.5.post0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6"
qtconsole==5.2.2; python_version >= "3.6"
qtpy==2.0.1; python_version >= "3.6"
@@ -175,7 +181,7 @@ requests==2.27.1; (python_version >= "2.7" and python_full_version < "3.0.0") or
rich==10.16.2; python_full_version >= "3.6.2" and python_full_version < "4.0.0"
robin-stocks==2.1.0; python_version >= "3"
scikit-learn==1.0.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
scipy==1.8.0; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.6.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0")
scipy==1.8.0; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.6.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
screeninfo==0.6.7
scs==3.2.0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6"
seaborn==0.11.2; python_version >= "3.6"
@@ -183,7 +189,7 @@ selenium==3.141.0
send2trash==1.8.0; python_version >= "3.7"
sentiment-investor==2.1.0; python_version >= "3.8" and python_version < "4.0"
setuptools-scm==6.4.2; python_version >= "3.7"
six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.8")
six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.8")
smmap==5.0.0; python_version >= "3.7"
sniffio==1.2.0; python_full_version >= "3.6.2" and python_version >= "3.7"
snowballstemmer==2.2.0; python_version >= "3.7"
@@ -208,9 +214,9 @@ terminado==0.13.1; python_version >= "3.7"
testpath==0.5.0; python_version >= "3.7"
textwrap3==0.9.2; python_version >= "3.6"
thepassiveinvestor==1.0.10
threadpoolctl==3.1.0; python_version >= "3.7"
threadpoolctl==3.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
tomli==1.2.3; python_version >= "3.8" and python_full_version >= "3.6.2"
tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7"
tornado==6.1; python_full_version >= "3.7.0" and python_version >= "3.7"
tqdm==4.62.3; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
tradingview-ta==3.2.10; python_version >= "3.6"
traitlets==5.1.1; python_full_version >= "3.7.0" and python_version >= "3.8"
@@ -220,17 +226,17 @@ tzlocal==4.1; python_version >= "3.6"
ujson==5.1.0; python_version >= "3.7"
unidecode==1.3.3; python_version >= "3.7"
update-checker==0.18.0; python_version >= "3.6" and python_version < "4.0"
urllib3==1.26.8; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7"
urllib3==1.26.8; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
user-agent==0.1.10
vadersentiment==3.3.2
valinvest==0.0.2; python_version >= "3.6"
voila==0.3.2; python_version >= "3.7"
wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.6"
wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8"
webencodings==0.5.1; python_version >= "3.7"
websocket-client==1.2.3; python_version >= "3.8" and python_version < "4.0"
websockets==9.1; python_full_version >= "3.6.1" and python_version >= "3.7"
widgetsnbextension==3.5.2
wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0"
wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7")
yarl==1.7.2; python_version >= "3.6"
yfinance==0.1.70
zope.interface==5.4.0; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5"

30
scripts/test_econometrics_panel.gst vendored Normal file
View File

@@ -0,0 +1,30 @@
econometrics
load wage_panel wp
options
options wp
show wp
index wp nr year
clean wp -f cfill
clean wp -d rdrop
type
type year-wp category
desc wp
modify -a dummy_married_educ-wp married-wp * educ-wp
modify -d married-wp
load wage_panel wp2
index wp2 nr year
modify -c wp married-wp2
modify -r wp married_wp2 married
remove wp2
panel lwage-wp black-wp hisp-wp exper-wp expersq-wp married-wp educ-wp union-wp year-wp
panel lwage-wp black-wp hisp-wp exper-wp married-wp educ-wp union-wp -t bols
panel lwage-wp black-wp hisp-wp exper-wp expersq-wp married-wp educ-wp union-wp year-wp -t re
panel lwage-wp expersq-wp union-wp married-wp year-wp -t fe
panel lwage-wp expersq-wp union-wp married-wp year-wp -t fe -ee
panel lwage-wp expersq-wp union-wp married-wp -t fe -te
panel lwage-wp expersq-wp union-wp married-wp -t fe -te -ee
panel lwage-wp exper-wp expersq-wp union-wp married-wp -t fdols
dwat -p
bgod
granger married-wp educ-wp
coint black-wp hisp-wp exper-wp expersq-wp married-wp educ-wp union-wp

26
scripts/test_econometrics_ts.gst vendored Normal file
View File

@@ -0,0 +1,26 @@
econometrics
load longley ll
load longley ll2
options
options ll
show ll
index ll year
plot pop-ll
clean ll -f cfill
clean ll -d rdrop
type
type year-ll category
desc ll
modify -a emp_ratio-ll totemp-ll div pop-ll
modify -d totemp-ll
index ll2 year
modify -c ll totemp-ll2
modify -r ll totemp_ll2 totemp
remove ll2
ols totemp-ll gnpdefl-ll gnp-ll unemp-ll armed-ll pop-ll year-ll
norm totemp-ll
root unemp-ll
dwat -p
bgod
granger gnp-ll pop-ll
coint totemp-ll gnpdefl-ll gnp-ll unemp-ll armed-ll pop-ll year-ll

14
terminal.py
View File

@@ -61,7 +61,7 @@ class TerminalController(BaseController):
"jupyter",
"funds",
"alternative",
"custom",
"econometrics",
]
PATH = "/"
@@ -122,7 +122,7 @@ class TerminalController(BaseController):
> alternative
> portfolio
> jupyter
> custom[/menu]
> econometrics[/menu]
""",
menu="Home",
)
@@ -199,13 +199,13 @@ class TerminalController(BaseController):
self.queue = self.load_class(AlternativeDataController, self.queue)
def call_custom(self, _):
"""Process custom command"""
from gamestonk_terminal.custom.custom_controller import (
CustomDataController,
def call_econometrics(self, _):
"""Process econometrics command"""
from gamestonk_terminal.econometrics.econometrics_controller import (
EconometricsController,
)
self.queue = CustomDataController(self.queue).menu()
self.queue = EconometricsController(self.queue).menu()
def call_portfolio(self, _):
"""Process portfolio command"""

32
tests/FIXTURES.md vendored
View File

@@ -351,4 +351,34 @@ def test_function(recorder):
recorder.record_mode = "rewrite"
recorder.capture(some_text)
```
```
**EXTRA ARGUMENTS - PD.TO_CSV**
If you capture a DataFrame it will be exported as a CSV using the function `pandas.DataFrame.to_csv`.
This function contains arguments that can be useful to the test builder.
Everything you pass as extra `kwargs` to the `capture` function will be passed to this `to_csv` function.
Example :
```python
import random
def test_function(recorder):
table_of_floats = pd.DataFrame(
np.random.uniform(low=1, high=10, size=(5,)),
columns=["numbers"],
)
recorder.capture(table_of_floats, float_format="%.2f")
# This will truncate the numbers
```
**EXTRA ARGUMENTS - JSON.DUMPS**
If you capture a TUPLE, LIST, DICT... it will be exported as a json using the function `json.dumps`.
This function contains arguments that can be useful to the test builder.
Everything you pass as extra `kwargs` to the `capture` function will be passed to this `dumps` function.

11
tests/conftest.py
View File

@@ -33,7 +33,7 @@ EXTENSIONS_MATCHING: Dict[str, List[Type]] = {
class Record:
@staticmethod
def extract_string(data: Any) -> str:
def extract_string(data: Any, **kwargs) -> str:
if isinstance(data, tuple(EXTENSIONS_MATCHING["txt"])):
string_value = data
elif isinstance(data, tuple(EXTENSIONS_MATCHING["csv"])):
@@ -41,9 +41,10 @@ class Record:
encoding="utf-8",
line_terminator="\n",
# date_format="%Y-%m-%d %H:%M:%S",
**kwargs,
)
elif isinstance(data, tuple(EXTENSIONS_MATCHING["json"])):
string_value = json.dumps(data)
string_value = json.dumps(data, **kwargs)
else:
raise AttributeError(f"Unsupported type : {type(data)}")
@@ -99,8 +100,9 @@ class Record:
captured: Any,
record_path: str,
strip: bool = False,
**kwargs,
) -> None:
self.__captured = self.extract_string(data=captured)
self.__captured = self.extract_string(data=captured, **kwargs)
self.__record_path = record_path
self.__strip = strip
@@ -201,7 +203,7 @@ class Recorder:
self.__record_list: List[Record] = list()
def capture(self, captured: Any, strip: bool = False):
def capture(self, captured: Any, strip: bool = False, **kwargs):
record_list = self.__record_list
record_path = self.__path_template.build_path_by_data(
data=captured,
@@ -211,6 +213,7 @@ class Recorder:
captured=captured,
record_path=record_path,
strip=strip,
**kwargs,
)
self.__record_list.append(record)

0
tests/gamestonk_terminal/econometrics/__init__.py Normal file
View File

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset0-rfill-None-5].csv vendored Normal file
View File

@@ -0,0 +1,17 @@
,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset1-cfill-None-5].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset10-rfill-None-10].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

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tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset11-None-rdrop-10].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

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tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset2-rbfill-None-5].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

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tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset3-cbfill-None-5].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

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tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset4-rffill-None-5].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset5-cffill-None-5].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset6-None-rdrop-5].csv vendored Normal file
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@@ -0,0 +1,17 @@
,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset7-None-cdrop-5].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset8-rfill-rdrop-5].csv vendored Normal file
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,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

17
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_clean[dataset9-rfill-cdrop-5].csv vendored Normal file
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@@ -0,0 +1,17 @@
,TOTEMP,GNPDEFL,GNP,UNEMP,ARMED,POP,YEAR
0,60323.0,83.0,234289.0,2356.0,1590.0,107608.0,1947.0
1,61122.0,88.5,259426.0,2325.0,1456.0,108632.0,1948.0
2,60171.0,88.2,258054.0,3682.0,1616.0,109773.0,1949.0
3,61187.0,89.5,284599.0,3351.0,1650.0,110929.0,1950.0
4,63221.0,96.2,328975.0,2099.0,3099.0,112075.0,1951.0
5,63639.0,98.1,346999.0,1932.0,3594.0,113270.0,1952.0
6,64989.0,99.0,365385.0,1870.0,3547.0,115094.0,1953.0
7,63761.0,100.0,363112.0,3578.0,3350.0,116219.0,1954.0
8,66019.0,101.2,397469.0,2904.0,3048.0,117388.0,1955.0
9,67857.0,104.6,419180.0,2822.0,2857.0,118734.0,1956.0
10,68169.0,108.4,442769.0,2936.0,2798.0,120445.0,1957.0
11,66513.0,110.8,444546.0,4681.0,2637.0,121950.0,1958.0
12,68655.0,112.6,482704.0,3813.0,2552.0,123366.0,1959.0
13,69564.0,114.2,502601.0,3931.0,2514.0,125368.0,1960.0
14,69331.0,115.7,518173.0,4806.0,2572.0,127852.0,1961.0
15,70551.0,116.9,554894.0,4007.0,2827.0,130081.0,1962.0
1 TOTEMP GNPDEFL GNP UNEMP ARMED POP YEAR
2 0 60323.0 83.0 234289.0 2356.0 1590.0 107608.0 1947.0
3 1 61122.0 88.5 259426.0 2325.0 1456.0 108632.0 1948.0
4 2 60171.0 88.2 258054.0 3682.0 1616.0 109773.0 1949.0
5 3 61187.0 89.5 284599.0 3351.0 1650.0 110929.0 1950.0
6 4 63221.0 96.2 328975.0 2099.0 3099.0 112075.0 1951.0
7 5 63639.0 98.1 346999.0 1932.0 3594.0 113270.0 1952.0
8 6 64989.0 99.0 365385.0 1870.0 3547.0 115094.0 1953.0
9 7 63761.0 100.0 363112.0 3578.0 3350.0 116219.0 1954.0
10 8 66019.0 101.2 397469.0 2904.0 3048.0 117388.0 1955.0
11 9 67857.0 104.6 419180.0 2822.0 2857.0 118734.0 1956.0
12 10 68169.0 108.4 442769.0 2936.0 2798.0 120445.0 1957.0
13 11 66513.0 110.8 444546.0 4681.0 2637.0 121950.0 1958.0
14 12 68655.0 112.6 482704.0 3813.0 2552.0 123366.0 1959.0
15 13 69564.0 114.2 502601.0 3931.0 2514.0 125368.0 1960.0
16 14 69331.0 115.7 518173.0 4806.0 2572.0 127852.0 1961.0
17 15 70551.0 116.9 554894.0 4007.0 2827.0 130081.0 1962.0

6
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_engle_granger_two_step_cointegration_test[y0-x0].csv vendored Normal file
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@@ -0,0 +1,6 @@
,0
0,-0.00313
1,0.15451
2,-1.28840
3,-13.12366
4,0.00000
1 0
2 0 -0.00313
3 1 0.15451
4 2 -1.28840
5 3 -13.12366
6 4 0.00000

108
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_engle_granger_two_step_cointegration_test[y0-x0]_1.csv vendored Normal file
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@@ -0,0 +1,108 @@
,0
0,-0.01283
1,0.00917
2,0.01449
3,0.00596
4,-0.01234
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11,-0.02100
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50,0.02974
51,-0.03797
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97,0.00044
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102,0.01949
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104,-0.00618
105,-0.00073
106,0.02118
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3
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_normality[data0].csv vendored Normal file
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@@ -0,0 +1,3 @@
,Kurtosis,Skewness,Jarque-Bera,Shapiro-Wilk,Kolmogorov-Smirnov
Statistic,1.47991,6.17020,52.21623,0.90912,0.97114
p-value,0.13890,0.00000,0.00000,0.00000,0.00000
1 Kurtosis Skewness Jarque-Bera Shapiro-Wilk Kolmogorov-Smirnov
2 Statistic 1.47991 6.17020 52.21623 0.90912 0.97114
3 p-value 0.13890 0.00000 0.00000 0.00000 0.00000

3
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_normality[data1].csv vendored Normal file
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@@ -0,0 +1,3 @@
,Kurtosis,Skewness,Jarque-Bera,Shapiro-Wilk,Kolmogorov-Smirnov
Statistic,3.80111,4.03782,60.66372,0.91086,0.78828
p-value,0.00014,0.00005,0.00000,0.00000,0.00000
1 Kurtosis Skewness Jarque-Bera Shapiro-Wilk Kolmogorov-Smirnov
2 Statistic 3.80111 4.03782 60.66372 0.91086 0.78828
3 p-value 0.00014 0.00005 0.00000 0.00000 0.00000

3
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_normality[data2].csv vendored Normal file
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@@ -0,0 +1,3 @@
,Kurtosis,Skewness,Jarque-Bera,Shapiro-Wilk,Kolmogorov-Smirnov
Statistic,3.55614,4.17188,68.87032,0.88711,1.00000
p-value,0.00038,0.00003,0.00000,0.00004,0.00000
1 Kurtosis Skewness Jarque-Bera Shapiro-Wilk Kolmogorov-Smirnov
2 Statistic 3.55614 4.17188 68.87032 0.88711 1.00000
3 p-value 0.00038 0.00003 0.00000 0.00004 0.00000

6
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_root[df0-c-c].csv vendored Normal file
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@@ -0,0 +1,6 @@
,ADF,KPSS
Test Statistic,-2.83778,0.66987
P-Value,0.05308,0.01628
NLags,8.00000,7.00000
Nobs,300.00000,0.00000
ICBest,2430.50034,0.00000
1 ADF KPSS
2 Test Statistic -2.83778 0.66987
3 P-Value 0.05308 0.01628
4 NLags 8.00000 7.00000
5 Nobs 300.00000 0.00000
6 ICBest 2430.50034 0.00000

6
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_root[df1-c-ct].csv vendored Normal file
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@@ -0,0 +1,6 @@
,ADF,KPSS
Test Statistic,-2.83778,0.11583
P-Value,0.05308,0.10000
NLags,8.00000,7.00000
Nobs,300.00000,0.00000
ICBest,2430.50034,0.00000
1 ADF KPSS
2 Test Statistic -2.83778 0.11583
3 P-Value 0.05308 0.10000
4 NLags 8.00000 7.00000
5 Nobs 300.00000 0.00000
6 ICBest 2430.50034 0.00000

6
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_root[df2-ct-c].csv vendored Normal file
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,ADF,KPSS
Test Statistic,-2.92437,0.66987
P-Value,0.15447,0.01628
NLags,8.00000,7.00000
Nobs,300.00000,0.00000
ICBest,2431.97673,0.00000
1 ADF KPSS
2 Test Statistic -2.92437 0.66987
3 P-Value 0.15447 0.01628
4 NLags 8.00000 7.00000
5 Nobs 300.00000 0.00000
6 ICBest 2431.97673 0.00000

6
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_root[df3-ctt-c].csv vendored Normal file
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,ADF,KPSS
Test Statistic,-3.00490,0.66987
P-Value,0.29285,0.01628
NLags,8.00000,7.00000
Nobs,300.00000,0.00000
ICBest,2432.53846,0.00000
1 ADF KPSS
2 Test Statistic -3.00490 0.66987
3 P-Value 0.29285 0.01628
4 NLags 8.00000 7.00000
5 Nobs 300.00000 0.00000
6 ICBest 2432.53846 0.00000

6
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_get_root[df4-nc-c].csv vendored Normal file
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,ADF,KPSS
Test Statistic,-0.75222,0.66987
P-Value,0.39061,0.01628
NLags,8.00000,7.00000
Nobs,300.00000,0.00000
ICBest,2436.91406,0.00000
1 ADF KPSS
2 Test Statistic -0.75222 0.66987
3 P-Value 0.39061 0.01628
4 NLags 8.00000 7.00000
5 Nobs 300.00000 0.00000
6 ICBest 2436.91406 0.00000

310
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_load[sunspots-file_types1-data_files1-data_examples1].csv vendored Normal file
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,YEAR,SUNACTIVITY
0,1700.0,5.0
1,1701.0,11.0
2,1702.0,16.0
3,1703.0,23.0
4,1704.0,36.0
5,1705.0,58.0
6,1706.0,29.0
7,1707.0,20.0
8,1708.0,10.0
9,1709.0,8.0
10,1710.0,3.0
11,1711.0,0.0
12,1712.0,0.0
13,1713.0,2.0
14,1714.0,11.0
15,1715.0,27.0
16,1716.0,47.0
17,1717.0,63.0
18,1718.0,60.0
19,1719.0,39.0
20,1720.0,28.0
21,1721.0,26.0
22,1722.0,22.0
23,1723.0,11.0
24,1724.0,21.0
25,1725.0,40.0
26,1726.0,78.0
27,1727.0,122.0
28,1728.0,103.0
29,1729.0,73.0
30,1730.0,47.0
31,1731.0,35.0
32,1732.0,11.0
33,1733.0,5.0
34,1734.0,16.0
35,1735.0,34.0
36,1736.0,70.0
37,1737.0,81.0
38,1738.0,111.0
39,1739.0,101.0
40,1740.0,73.0
41,1741.0,40.0
42,1742.0,20.0
43,1743.0,16.0
44,1744.0,5.0
45,1745.0,11.0
46,1746.0,22.0
47,1747.0,40.0
48,1748.0,60.0
49,1749.0,80.9
50,1750.0,83.4
51,1751.0,47.7
52,1752.0,47.8
53,1753.0,30.7
54,1754.0,12.2
55,1755.0,9.6
56,1756.0,10.2
57,1757.0,32.4
58,1758.0,47.6
59,1759.0,54.0
60,1760.0,62.9
61,1761.0,85.9
62,1762.0,61.2
63,1763.0,45.1
64,1764.0,36.4
65,1765.0,20.9
66,1766.0,11.4
67,1767.0,37.8
68,1768.0,69.8
69,1769.0,106.1
70,1770.0,100.8
71,1771.0,81.6
72,1772.0,66.5
73,1773.0,34.8
74,1774.0,30.6
75,1775.0,7.0
76,1776.0,19.8
77,1777.0,92.5
78,1778.0,154.4
79,1779.0,125.9
80,1780.0,84.8
81,1781.0,68.1
82,1782.0,38.5
83,1783.0,22.8
84,1784.0,10.2
85,1785.0,24.1
86,1786.0,82.9
87,1787.0,132.0
88,1788.0,130.9
89,1789.0,118.1
90,1790.0,89.9
91,1791.0,66.6
92,1792.0,60.0
93,1793.0,46.9
94,1794.0,41.0
95,1795.0,21.3
96,1796.0,16.0
97,1797.0,6.4
98,1798.0,4.1
99,1799.0,6.8
100,1800.0,14.5
101,1801.0,34.0
102,1802.0,45.0
103,1803.0,43.1
104,1804.0,47.5
105,1805.0,42.2
106,1806.0,28.1
107,1807.0,10.1
108,1808.0,8.1
109,1809.0,2.5
110,1810.0,0.0
111,1811.0,1.4
112,1812.0,5.0
113,1813.0,12.2
114,1814.0,13.9
115,1815.0,35.4
116,1816.0,45.8
117,1817.0,41.1
118,1818.0,30.1
119,1819.0,23.9
120,1820.0,15.6
121,1821.0,6.6
122,1822.0,4.0
123,1823.0,1.8
124,1824.0,8.5
125,1825.0,16.6
126,1826.0,36.3
127,1827.0,49.6
128,1828.0,64.2
129,1829.0,67.0
130,1830.0,70.9
131,1831.0,47.8
132,1832.0,27.5
133,1833.0,8.5
134,1834.0,13.2
135,1835.0,56.9
136,1836.0,121.5
137,1837.0,138.3
138,1838.0,103.2
139,1839.0,85.7
140,1840.0,64.6
141,1841.0,36.7
142,1842.0,24.2
143,1843.0,10.7
144,1844.0,15.0
145,1845.0,40.1
146,1846.0,61.5
147,1847.0,98.5
148,1848.0,124.7
149,1849.0,96.3
150,1850.0,66.6
151,1851.0,64.5
152,1852.0,54.1
153,1853.0,39.0
154,1854.0,20.6
155,1855.0,6.7
156,1856.0,4.3
157,1857.0,22.7
158,1858.0,54.8
159,1859.0,93.8
160,1860.0,95.8
161,1861.0,77.2
162,1862.0,59.1
163,1863.0,44.0
164,1864.0,47.0
165,1865.0,30.5
166,1866.0,16.3
167,1867.0,7.3
168,1868.0,37.6
169,1869.0,74.0
170,1870.0,139.0
171,1871.0,111.2
172,1872.0,101.6
173,1873.0,66.2
174,1874.0,44.7
175,1875.0,17.0
176,1876.0,11.3
177,1877.0,12.4
178,1878.0,3.4
179,1879.0,6.0
180,1880.0,32.3
181,1881.0,54.3
182,1882.0,59.7
183,1883.0,63.7
184,1884.0,63.5
185,1885.0,52.2
186,1886.0,25.4
187,1887.0,13.1
188,1888.0,6.8
189,1889.0,6.3
190,1890.0,7.1
191,1891.0,35.6
192,1892.0,73.0
193,1893.0,85.1
194,1894.0,78.0
195,1895.0,64.0
196,1896.0,41.8
197,1897.0,26.2
198,1898.0,26.7
199,1899.0,12.1
200,1900.0,9.5
201,1901.0,2.7
202,1902.0,5.0
203,1903.0,24.4
204,1904.0,42.0
205,1905.0,63.5
206,1906.0,53.8
207,1907.0,62.0
208,1908.0,48.5
209,1909.0,43.9
210,1910.0,18.6
211,1911.0,5.7
212,1912.0,3.6
213,1913.0,1.4
214,1914.0,9.6
215,1915.0,47.4
216,1916.0,57.1
217,1917.0,103.9
218,1918.0,80.6
219,1919.0,63.6
220,1920.0,37.6
221,1921.0,26.1
222,1922.0,14.2
223,1923.0,5.8
224,1924.0,16.7
225,1925.0,44.3
226,1926.0,63.9
227,1927.0,69.0
228,1928.0,77.8
229,1929.0,64.9
230,1930.0,35.7
231,1931.0,21.2
232,1932.0,11.1
233,1933.0,5.7
234,1934.0,8.7
235,1935.0,36.1
236,1936.0,79.7
237,1937.0,114.4
238,1938.0,109.6
239,1939.0,88.8
240,1940.0,67.8
241,1941.0,47.5
242,1942.0,30.6
243,1943.0,16.3
244,1944.0,9.6
245,1945.0,33.2
246,1946.0,92.6
247,1947.0,151.6
248,1948.0,136.3
249,1949.0,134.7
250,1950.0,83.9
251,1951.0,69.4
252,1952.0,31.5
253,1953.0,13.9
254,1954.0,4.4
255,1955.0,38.0
256,1956.0,141.7
257,1957.0,190.2
258,1958.0,184.8
259,1959.0,159.0
260,1960.0,112.3
261,1961.0,53.9
262,1962.0,37.6
263,1963.0,27.9
264,1964.0,10.2
265,1965.0,15.1
266,1966.0,47.0
267,1967.0,93.8
268,1968.0,105.9
269,1969.0,105.5
270,1970.0,104.5
271,1971.0,66.6
272,1972.0,68.9
273,1973.0,38.0
274,1974.0,34.5
275,1975.0,15.5
276,1976.0,12.6
277,1977.0,27.5
278,1978.0,92.5
279,1979.0,155.4
280,1980.0,154.6
281,1981.0,140.4
282,1982.0,115.9
283,1983.0,66.6
284,1984.0,45.9
285,1985.0,17.9
286,1986.0,13.4
287,1987.0,29.4
288,1988.0,100.2
289,1989.0,157.6
290,1990.0,142.6
291,1991.0,145.7
292,1992.0,94.3
293,1993.0,54.6
294,1994.0,29.9
295,1995.0,17.5
296,1996.0,8.6
297,1997.0,21.5
298,1998.0,64.3
299,1999.0,93.3
300,2000.0,119.6
301,2001.0,111.0
302,2002.0,104.0
303,2003.0,63.7
304,2004.0,40.4
305,2005.0,29.8
306,2006.0,15.2
307,2007.0,7.5
308,2008.0,2.9
1 YEAR SUNACTIVITY
2 0 1700.0 5.0
3 1 1701.0 11.0
4 2 1702.0 16.0
5 3 1703.0 23.0
6 4 1704.0 36.0
7 5 1705.0 58.0
8 6 1706.0 29.0
9 7 1707.0 20.0
10 8 1708.0 10.0
11 9 1709.0 8.0
12 10 1710.0 3.0
13 11 1711.0 0.0
14 12 1712.0 0.0
15 13 1713.0 2.0
16 14 1714.0 11.0
17 15 1715.0 27.0
18 16 1716.0 47.0
19 17 1717.0 63.0
20 18 1718.0 60.0
21 19 1719.0 39.0
22 20 1720.0 28.0
23 21 1721.0 26.0
24 22 1722.0 22.0
25 23 1723.0 11.0
26 24 1724.0 21.0
27 25 1725.0 40.0
28 26 1726.0 78.0
29 27 1727.0 122.0
30 28 1728.0 103.0
31 29 1729.0 73.0
32 30 1730.0 47.0
33 31 1731.0 35.0
34 32 1732.0 11.0
35 33 1733.0 5.0
36 34 1734.0 16.0
37 35 1735.0 34.0
38 36 1736.0 70.0
39 37 1737.0 81.0
40 38 1738.0 111.0
41 39 1739.0 101.0
42 40 1740.0 73.0
43 41 1741.0 40.0
44 42 1742.0 20.0
45 43 1743.0 16.0
46 44 1744.0 5.0
47 45 1745.0 11.0
48 46 1746.0 22.0
49 47 1747.0 40.0
50 48 1748.0 60.0
51 49 1749.0 80.9
52 50 1750.0 83.4
53 51 1751.0 47.7
54 52 1752.0 47.8
55 53 1753.0 30.7
56 54 1754.0 12.2
57 55 1755.0 9.6
58 56 1756.0 10.2
59 57 1757.0 32.4
60 58 1758.0 47.6
61 59 1759.0 54.0
62 60 1760.0 62.9
63 61 1761.0 85.9
64 62 1762.0 61.2
65 63 1763.0 45.1
66 64 1764.0 36.4
67 65 1765.0 20.9
68 66 1766.0 11.4
69 67 1767.0 37.8
70 68 1768.0 69.8
71 69 1769.0 106.1
72 70 1770.0 100.8
73 71 1771.0 81.6
74 72 1772.0 66.5
75 73 1773.0 34.8
76 74 1774.0 30.6
77 75 1775.0 7.0
78 76 1776.0 19.8
79 77 1777.0 92.5
80 78 1778.0 154.4
81 79 1779.0 125.9
82 80 1780.0 84.8
83 81 1781.0 68.1
84 82 1782.0 38.5
85 83 1783.0 22.8
86 84 1784.0 10.2
87 85 1785.0 24.1
88 86 1786.0 82.9
89 87 1787.0 132.0
90 88 1788.0 130.9
91 89 1789.0 118.1
92 90 1790.0 89.9
93 91 1791.0 66.6
94 92 1792.0 60.0
95 93 1793.0 46.9
96 94 1794.0 41.0
97 95 1795.0 21.3
98 96 1796.0 16.0
99 97 1797.0 6.4
100 98 1798.0 4.1
101 99 1799.0 6.8
102 100 1800.0 14.5
103 101 1801.0 34.0
104 102 1802.0 45.0
105 103 1803.0 43.1
106 104 1804.0 47.5
107 105 1805.0 42.2
108 106 1806.0 28.1
109 107 1807.0 10.1
110 108 1808.0 8.1
111 109 1809.0 2.5
112 110 1810.0 0.0
113 111 1811.0 1.4
114 112 1812.0 5.0
115 113 1813.0 12.2
116 114 1814.0 13.9
117 115 1815.0 35.4
118 116 1816.0 45.8
119 117 1817.0 41.1
120 118 1818.0 30.1
121 119 1819.0 23.9
122 120 1820.0 15.6
123 121 1821.0 6.6
124 122 1822.0 4.0
125 123 1823.0 1.8
126 124 1824.0 8.5
127 125 1825.0 16.6
128 126 1826.0 36.3
129 127 1827.0 49.6
130 128 1828.0 64.2
131 129 1829.0 67.0
132 130 1830.0 70.9
133 131 1831.0 47.8
134 132 1832.0 27.5
135 133 1833.0 8.5
136 134 1834.0 13.2
137 135 1835.0 56.9
138 136 1836.0 121.5
139 137 1837.0 138.3
140 138 1838.0 103.2
141 139 1839.0 85.7
142 140 1840.0 64.6
143 141 1841.0 36.7
144 142 1842.0 24.2
145 143 1843.0 10.7
146 144 1844.0 15.0
147 145 1845.0 40.1
148 146 1846.0 61.5
149 147 1847.0 98.5
150 148 1848.0 124.7
151 149 1849.0 96.3
152 150 1850.0 66.6
153 151 1851.0 64.5
154 152 1852.0 54.1
155 153 1853.0 39.0
156 154 1854.0 20.6
157 155 1855.0 6.7
158 156 1856.0 4.3
159 157 1857.0 22.7
160 158 1858.0 54.8
161 159 1859.0 93.8
162 160 1860.0 95.8
163 161 1861.0 77.2
164 162 1862.0 59.1
165 163 1863.0 44.0
166 164 1864.0 47.0
167 165 1865.0 30.5
168 166 1866.0 16.3
169 167 1867.0 7.3
170 168 1868.0 37.6
171 169 1869.0 74.0
172 170 1870.0 139.0
173 171 1871.0 111.2
174 172 1872.0 101.6
175 173 1873.0 66.2
176 174 1874.0 44.7
177 175 1875.0 17.0
178 176 1876.0 11.3
179 177 1877.0 12.4
180 178 1878.0 3.4
181 179 1879.0 6.0
182 180 1880.0 32.3
183 181 1881.0 54.3
184 182 1882.0 59.7
185 183 1883.0 63.7
186 184 1884.0 63.5
187 185 1885.0 52.2
188 186 1886.0 25.4
189 187 1887.0 13.1
190 188 1888.0 6.8
191 189 1889.0 6.3
192 190 1890.0 7.1
193 191 1891.0 35.6
194 192 1892.0 73.0
195 193 1893.0 85.1
196 194 1894.0 78.0
197 195 1895.0 64.0
198 196 1896.0 41.8
199 197 1897.0 26.2
200 198 1898.0 26.7
201 199 1899.0 12.1
202 200 1900.0 9.5
203 201 1901.0 2.7
204 202 1902.0 5.0
205 203 1903.0 24.4
206 204 1904.0 42.0
207 205 1905.0 63.5
208 206 1906.0 53.8
209 207 1907.0 62.0
210 208 1908.0 48.5
211 209 1909.0 43.9
212 210 1910.0 18.6
213 211 1911.0 5.7
214 212 1912.0 3.6
215 213 1913.0 1.4
216 214 1914.0 9.6
217 215 1915.0 47.4
218 216 1916.0 57.1
219 217 1917.0 103.9
220 218 1918.0 80.6
221 219 1919.0 63.6
222 220 1920.0 37.6
223 221 1921.0 26.1
224 222 1922.0 14.2
225 223 1923.0 5.8
226 224 1924.0 16.7
227 225 1925.0 44.3
228 226 1926.0 63.9
229 227 1927.0 69.0
230 228 1928.0 77.8
231 229 1929.0 64.9
232 230 1930.0 35.7
233 231 1931.0 21.2
234 232 1932.0 11.1
235 233 1933.0 5.7
236 234 1934.0 8.7
237 235 1935.0 36.1
238 236 1936.0 79.7
239 237 1937.0 114.4
240 238 1938.0 109.6
241 239 1939.0 88.8
242 240 1940.0 67.8
243 241 1941.0 47.5
244 242 1942.0 30.6
245 243 1943.0 16.3
246 244 1944.0 9.6
247 245 1945.0 33.2
248 246 1946.0 92.6
249 247 1947.0 151.6
250 248 1948.0 136.3
251 249 1949.0 134.7
252 250 1950.0 83.9
253 251 1951.0 69.4
254 252 1952.0 31.5
255 253 1953.0 13.9
256 254 1954.0 4.4
257 255 1955.0 38.0
258 256 1956.0 141.7
259 257 1957.0 190.2
260 258 1958.0 184.8
261 259 1959.0 159.0
262 260 1960.0 112.3
263 261 1961.0 53.9
264 262 1962.0 37.6
265 263 1963.0 27.9
266 264 1964.0 10.2
267 265 1965.0 15.1
268 266 1966.0 47.0
269 267 1967.0 93.8
270 268 1968.0 105.9
271 269 1969.0 105.5
272 270 1970.0 104.5
273 271 1971.0 66.6
274 272 1972.0 68.9
275 273 1973.0 38.0
276 274 1974.0 34.5
277 275 1975.0 15.5
278 276 1976.0 12.6
279 277 1977.0 27.5
280 278 1978.0 92.5
281 279 1979.0 155.4
282 280 1980.0 154.6
283 281 1981.0 140.4
284 282 1982.0 115.9
285 283 1983.0 66.6
286 284 1984.0 45.9
287 285 1985.0 17.9
288 286 1986.0 13.4
289 287 1987.0 29.4
290 288 1988.0 100.2
291 289 1989.0 157.6
292 290 1990.0 142.6
293 291 1991.0 145.7
294 292 1992.0 94.3
295 293 1993.0 54.6
296 294 1994.0 29.9
297 295 1995.0 17.5
298 296 1996.0 8.6
299 297 1997.0 21.5
300 298 1998.0 64.3
301 299 1999.0 93.3
302 300 2000.0 119.6
303 301 2001.0 111.0
304 302 2002.0 104.0
305 303 2003.0 63.7
306 304 2004.0 40.4
307 305 2005.0 29.8
308 306 2006.0 15.2
309 307 2007.0 7.5
310 308 2008.0 2.9

4361
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_load[wage_panel-file_types0-data_files0-data_examples0].csv vendored Normal file
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4
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_options[datasets0-heart].csv vendored Normal file
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@@ -0,0 +1,4 @@
,dataset,column,option
0,heart,survival,survival-heart
1,heart,censors,censors-heart
2,heart,age,age-heart
1 dataset column option
2 0 heart survival survival-heart
3 1 heart censors censors-heart
4 2 heart age age-heart

4
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_options[datasets1-None].csv vendored Normal file
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@@ -0,0 +1,4 @@
,dataset,column,option
0,heart,survival,survival-heart
1,heart,censors,censors-heart
2,heart,age,age-heart
1 dataset column option
2 0 heart survival survival-heart
3 1 heart censors censors-heart
4 2 heart age age-heart

15
tests/gamestonk_terminal/econometrics/csv/test_econometrics_model/test_options[datasets1-None]_1.csv vendored Normal file
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@@ -0,0 +1,15 @@
,dataset,column,option
0,macrodata,year,year-macrodata
1,macrodata,quarter,quarter-macrodata
2,macrodata,realgdp,realgdp-macrodata
3,macrodata,realcons,realcons-macrodata
4,macrodata,realinv,realinv-macrodata
5,macrodata,realgovt,realgovt-macrodata
6,macrodata,realdpi,realdpi-macrodata
7,macrodata,cpi,cpi-macrodata
8,macrodata,m1,m1-macrodata
9,macrodata,tbilrate,tbilrate-macrodata
10,macrodata,unemp,unemp-macrodata
11,macrodata,pop,pop-macrodata
12,macrodata,infl,infl-macrodata
13,macrodata,realint,realint-macrodata
1 dataset column option
2 0 macrodata year year-macrodata
3 1 macrodata quarter quarter-macrodata
4 2 macrodata realgdp realgdp-macrodata
5 3 macrodata realcons realcons-macrodata
6 4 macrodata realinv realinv-macrodata
7 5 macrodata realgovt realgovt-macrodata
8 6 macrodata realdpi realdpi-macrodata
9 7 macrodata cpi cpi-macrodata
10 8 macrodata m1 m1-macrodata
11 9 macrodata tbilrate tbilrate-macrodata
12 10 macrodata unemp unemp-macrodata
13 11 macrodata pop pop-macrodata
14 12 macrodata infl infl-macrodata
15 13 macrodata realint realint-macrodata

5
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_bgod[regression_variables0-data0-datasets0-True-3].csv vendored Normal file
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@@ -0,0 +1,5 @@
,0
0,12.30369
1,0.00641
2,3.85222
3,0.05032
1 0
2 0 12.30369
3 1 0.00641
4 2 3.85222
5 3 0.05032

5
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_bgod[regression_variables1-data1-datasets1-True-1].csv vendored Normal file
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@@ -0,0 +1,5 @@
,0
0,15.52753
1,0.00008
2,0.20175
3,0.66071
1 0
2 0 15.52753
3 1 0.00008
4 2 0.20175
5 3 0.66071

2
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_bols[regression_variables0-data0-datasets0].csv vendored Normal file
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@@ -0,0 +1,2 @@
,const,married_wage_panel,lwage_wage_panel,hisp_wage_panel,black_wage_panel
parameter,9.50097,-0.21638,1.53397,-0.93806,-0.19418
1 const married_wage_panel lwage_wage_panel hisp_wage_panel black_wage_panel
2 parameter 9.50097 -0.21638 1.53397 -0.93806 -0.19418

5
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_bpag[regression_variables0-data0-datasets0-True].csv vendored Normal file
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@@ -0,0 +1,5 @@
,0
0,8.34753
1,0.00386
2,7.63580
3,0.00572
1 0
2 0 8.34753
3 1 0.00386
4 2 7.63580
5 3 0.00572

5
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_bpag[regression_variables1-data1-datasets1-True].csv vendored Normal file
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@@ -0,0 +1,5 @@
,0
0,5.65587
1,
2,8.20157
3,0.01183
1 0
2 0 5.65587
3 1
4 2 8.20157
5 3 0.01183

5
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_comparison[regression_variables0-data0-datasets0].csv vendored Normal file
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@@ -0,0 +1,5 @@
,RE,FE
const,0.14530,0.16091
lwage_wage_panel,0.19126,0.18194
hisp_wage_panel,-0.00687,-0.00737
black_wage_panel,-0.17866,-0.18008
1 RE FE
2 const 0.14530 0.16091
3 lwage_wage_panel 0.19126 0.18194
4 hisp_wage_panel -0.00687 -0.00737
5 black_wage_panel -0.17866 -0.18008

2
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_fdols[regression_variables0-data0-datasets0].csv vendored Normal file
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@@ -0,0 +1,2 @@
,married_wage_panel
parameter,0.08226
1 married_wage_panel
2 parameter 0.08226

2
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_fe[regression_variables0-data0-datasets0].csv vendored Normal file
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@@ -0,0 +1,2 @@
,const,married_wage_panel,lwage_wage_panel,hisp_wage_panel,black_wage_panel
parameter,10.65398,-0.13342,0.82259,-0.97489,-0.28511
1 const married_wage_panel lwage_wage_panel hisp_wage_panel black_wage_panel
2 parameter 10.65398 -0.13342 0.82259 -0.97489 -0.28511

2
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_ols[regression_variables0-data0-datasets0-True].csv vendored Normal file
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@@ -0,0 +1,2 @@
,GNP_longley,ARMED_longley,POP_longley
0,-0.00605,0.68417,0.56098
1 GNP_longley ARMED_longley POP_longley
2 0 -0.00605 0.68417 0.56098

2
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_ols[regression_variables1-data1-datasets1-False].csv vendored Normal file
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@@ -0,0 +1,2 @@
,GNP_longley,ARMED_longley,POP_longley
0,-0.00605,0.68417,0.56098
1 GNP_longley ARMED_longley POP_longley
2 0 -0.00605 0.68417 0.56098

2
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_pols[regression_variables0-data0-datasets0].csv vendored Normal file
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@@ -0,0 +1,2 @@
,const,married_wage_panel,lwage_wage_panel,hisp_wage_panel,black_wage_panel
parameter,10.65398,-0.13342,0.82259,-0.97489,-0.28511
1 const married_wage_panel lwage_wage_panel hisp_wage_panel black_wage_panel
2 parameter 10.65398 -0.13342 0.82259 -0.97489 -0.28511

2
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_re[regression_variables0-data0-datasets0].csv vendored Normal file
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@@ -0,0 +1,2 @@
,const,lwage_wage_panel,hisp_wage_panel,black_wage_panel
parameter,0.14530,0.19126,-0.00687,-0.17866
1 const lwage_wage_panel hisp_wage_panel black_wage_panel
2 parameter 0.14530 0.19126 -0.00687 -0.17866

20
tests/gamestonk_terminal/econometrics/csv/test_regression_model/test_get_regression_data[regression_variables0-data0-datasets0].csv vendored Normal file
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@@ -0,0 +1,20 @@
,0
0," TOTEMP_longley GNP_longley ARMED_longley POP_longley
0 60323.0 234289.0 1590.0 107608.0
1 61122.0 259426.0 1456.0 108632.0
2 60171.0 258054.0 1616.0 109773.0
3 61187.0 284599.0 1650.0 110929.0
4 63221.0 328975.0 3099.0 112075.0
5 63639.0 346999.0 3594.0 113270.0
6 64989.0 365385.0 3547.0 115094.0
7 63761.0 363112.0 3350.0 116219.0
8 66019.0 397469.0 3048.0 117388.0
9 67857.0 419180.0 2857.0 118734.0
10 68169.0 442769.0 2798.0 120445.0
11 66513.0 444546.0 2637.0 121950.0
12 68655.0 482704.0 2552.0 123366.0
13 69564.0 502601.0 2514.0 125368.0
14 69331.0 518173.0 2572.0 127852.0
15 70551.0 554894.0 2827.0 130081.0"
1,TOTEMP_longley
2,['GNP_longley' 'ARMED_longley' 'POP_longley']
1 0
2 0 TOTEMP_longley GNP_longley ARMED_longley POP_longley 0 60323.0 234289.0 1590.0 107608.0 1 61122.0 259426.0 1456.0 108632.0 2 60171.0 258054.0 1616.0 109773.0 3 61187.0 284599.0 1650.0 110929.0 4 63221.0 328975.0 3099.0 112075.0 5 63639.0 346999.0 3594.0 113270.0 6 64989.0 365385.0 3547.0 115094.0 7 63761.0 363112.0 3350.0 116219.0 8 66019.0 397469.0 3048.0 117388.0 9 67857.0 419180.0 2857.0 118734.0 10 68169.0 442769.0 2798.0 120445.0 11 66513.0 444546.0 2637.0 121950.0 12 68655.0 482704.0 2552.0 123366.0 13 69564.0 502601.0 2514.0 125368.0 14 69331.0 518173.0 2572.0 127852.0 15 70551.0 554894.0 2827.0 130081.0
3 1 TOTEMP_longley
4 2 ['GNP_longley' 'ARMED_longley' 'POP_longley']

1
tests/gamestonk_terminal/econometrics/json/test_regression_model/test_get_dwat[regression_variables0-data0-datasets0-True].json vendored Normal file
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@@ -0,0 +1 @@
1.8

1
tests/gamestonk_terminal/econometrics/json/test_regression_model/test_get_dwat[regression_variables1-data1-datasets1-True].json vendored Normal file
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@@ -0,0 +1 @@
0.08

325
tests/gamestonk_terminal/econometrics/test_econometrics_model.py Normal file
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@@ -0,0 +1,325 @@
# IMPORTATION STANDARD
import pandas as pd
# IMPORTATION THIRDPARTY
import pytest
# IMPORTATION INTERNAL
from gamestonk_terminal.econometrics import econometrics_model
@pytest.mark.vcr()
@pytest.mark.parametrize(
"file, file_types, data_files, data_examples",
[
("wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"}),
("sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}),
],
)
def test_load(recorder, file, file_types, data_files, data_examples):
result = econometrics_model.load(
file=file,
file_types=file_types,
data_files=data_files,
data_examples=data_examples,
)
recorder.capture(result)
@pytest.mark.vcr()
@pytest.mark.parametrize(
"datasets, dataset_name",
[
(
{
"heart": econometrics_model.load(
"heart", ["csv", "xlsx"], {}, {"heart": "heart"}
)
},
"heart",
),
(
{
"heart": econometrics_model.load(
"heart", ["csv", "xlsx"], {}, {"heart": "heart"}
),
"macrodata": econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
),
},
None,
),
],
)
def test_options(recorder, datasets, dataset_name):
result = econometrics_model.get_options(
datasets=datasets, dataset_name=dataset_name
)
recorder.capture_list(result.values())
@pytest.mark.vcr()
@pytest.mark.parametrize(
"dataset, fill, drop, limit",
[
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"rfill",
None,
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"cfill",
None,
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"rbfill",
None,
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"cbfill",
None,
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"rffill",
None,
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"cffill",
None,
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
None,
"rdrop",
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
None,
"cdrop",
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"rfill",
"rdrop",
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"rfill",
"cdrop",
5,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
"rfill",
None,
10,
),
(
econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
),
None,
"rdrop",
10,
),
],
)
def test_clean(recorder, dataset, fill, drop, limit):
result = econometrics_model.clean(
dataset=dataset, fill=fill, drop=drop, limit=limit
)
recorder.capture(result)
@pytest.mark.vcr()
@pytest.mark.parametrize(
"data",
[
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"]
),
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["infl"]
),
(
econometrics_model.load(
"elnino", ["csv", "xlsx"], {}, {"elnino": "elnino"}
)["JAN"]
),
],
)
def test_get_normality(recorder, data):
result = econometrics_model.get_normality(data=data)
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"df, fuller_reg, kpss_reg",
[
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"c",
"c",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"c",
"ct",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"ct",
"c",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"ctt",
"c",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"nc",
"c",
),
],
)
def test_get_root(recorder, df, fuller_reg, kpss_reg):
result = econometrics_model.get_root(
df=df, fuller_reg=fuller_reg, kpss_reg=kpss_reg
)
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"time_series_y, time_series_x, lags",
[
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realgdp"],
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["pop"],
3,
),
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realgovt"],
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realinv"],
2,
),
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realdpi"],
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["cpi"],
1,
),
],
)
def test_get_granger_causality(recorder, time_series_y, time_series_x, lags):
result = econometrics_model.get_granger_causality(
time_series_y=time_series_y, time_series_x=time_series_x, lags=lags
)
# The first item is taken since the second item contains Statsmodels
# objects (which change their identifier on every iteration)
recorder.capture_list(result[lags][0])
@pytest.mark.vcr()
@pytest.mark.parametrize(
"y, x",
[
(
econometrics_model.load(
"interest_inflation",
["csv", "xlsx"],
{},
{"interest_inflation": "interest_inflation"},
)["Dp"],
econometrics_model.load(
"interest_inflation",
["csv", "xlsx"],
{},
{"interest_inflation": "interest_inflation"},
)["R"],
)
],
)
def test_get_engle_granger_two_step_cointegration_test(recorder, y, x):
(
c,
gamma,
alpha,
z,
adfstat,
pvalue,
) = econometrics_model.get_engle_granger_two_step_cointegration_test(y=y, x=x)
result = pd.DataFrame([c, gamma, alpha, adfstat, pvalue])
recorder.capture(result, float_format="%.5f")
recorder.capture(z, float_format="%.5f")

219
tests/gamestonk_terminal/econometrics/test_econometrics_view.py Normal file
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@@ -0,0 +1,219 @@
# IMPORTATION THIRDPARTY
import pytest
# IMPORTATION INTERNAL
from gamestonk_terminal.econometrics import econometrics_model, econometrics_view
@pytest.mark.vcr()
@pytest.mark.record_stdout
@pytest.mark.parametrize(
"datasets, dataset_name",
[
(
{
"heart": econometrics_model.load(
"heart", ["csv", "xlsx"], {}, {"heart": "heart"}
),
"macrodata": econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
),
},
None,
)
],
)
def test_show_options(datasets, dataset_name):
econometrics_view.show_options(datasets=datasets, dataset_name=dataset_name)
@pytest.mark.vcr()
@pytest.mark.record_stdout
@pytest.mark.parametrize(
"data, dataset, column, plot",
[
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"Sunset",
"Sunactivity",
False,
),
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["infl"],
"Macrodata",
"Inflation",
False,
),
(
econometrics_model.load(
"elnino", ["csv", "xlsx"], {}, {"elnino": "elnino"}
)["JAN"],
"El Nino",
"January",
False,
),
],
)
def test_display_norm(data, dataset, column, plot):
econometrics_view.display_norm(data=data, dataset=dataset, column=column, plot=plot)
@pytest.mark.vcr()
@pytest.mark.record_stdout
@pytest.mark.parametrize(
"df, dataset_name, column_name, fuller_reg, kpss_reg",
[
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"Sunspot",
"Sunactivity",
"c",
"c",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"Sunspot",
"Sunactivity",
"c",
"ct",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"Sunspot",
"Sunactivity",
"ct",
"c",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"Sunspot",
"Sunactivity",
"ctt",
"c",
),
(
econometrics_model.load(
"sunspots", ["csv", "xlsx"], {}, {"sunspots": "sunspots"}
)["SUNACTIVITY"],
"Sunspot",
"Sunactivity",
"nc",
"c",
),
],
)
def test_display_root(df, dataset_name, column_name, fuller_reg, kpss_reg):
econometrics_view.display_root(
df=df,
dataset_name=dataset_name,
column_name=column_name,
fuller_reg=fuller_reg,
kpss_reg=kpss_reg,
)
@pytest.mark.vcr()
@pytest.mark.record_stdout
@pytest.mark.parametrize(
"time_series_y, time_series_x, lags, confidence_level",
[
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realgdp"],
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["pop"],
3,
0.05,
),
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realgovt"],
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realinv"],
2,
0.10,
),
(
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["realdpi"],
econometrics_model.load(
"macrodata", ["csv", "xlsx"], {}, {"macrodata": "macrodata"}
)["cpi"],
1,
0.01,
),
],
)
def test_display_granger(time_series_y, time_series_x, lags, confidence_level):
econometrics_view.display_granger(
time_series_y=time_series_y,
time_series_x=time_series_x,
lags=lags,
confidence_level=confidence_level,
)
@pytest.mark.vcr()
@pytest.mark.record_stdout
@pytest.mark.parametrize(
"datasets, significant, plot",
[
(
{
"y": econometrics_model.load(
"interest_inflation",
["csv", "xlsx"],
{},
{"interest_inflation": "interest_inflation"},
)["Dp"],
"x": econometrics_model.load(
"interest_inflation",
["csv", "xlsx"],
{},
{"interest_inflation": "interest_inflation"},
)["R"],
},
True,
False,
),
(
{
"y": econometrics_model.load(
"interest_inflation",
["csv", "xlsx"],
{},
{"interest_inflation": "interest_inflation"},
)["Dp"],
"x": econometrics_model.load(
"interest_inflation",
["csv", "xlsx"],
{},
{"interest_inflation": "interest_inflation"},
)["R"],
},
False,
False,
),
],
)
def test_get_engle_granger_two_step_cointegration_test(datasets, significant, plot):
econometrics_view.display_cointegration_test(
datasets=datasets, significant=significant, plot=plot
)

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# IMPORTATION STANDARD
import pandas as pd
# IMPORTATION THIRDPARTY
import pytest
# IMPORTATION INTERNAL
from gamestonk_terminal.econometrics import regression_model, econometrics_model
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets",
[
(
["TOTEMP-longley", "GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
)
],
)
def test_get_regression_data(recorder, regression_variables, data, datasets):
(
regression_df,
dependent_variable,
independent_variables,
) = regression_model.get_regression_data(
regression_variables=regression_variables, data=data, datasets=datasets
)
recorder.capture(
pd.DataFrame([regression_df, dependent_variable, independent_variables])
)
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets, show_regression",
[
(
["TOTEMP-longley", "GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
True,
),
(
["TOTEMP-longley", "GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
False,
),
],
)
def test_get_ols(recorder, regression_variables, data, datasets, show_regression):
_, _, _, model = regression_model.get_ols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
show_regression=show_regression,
)
result = pd.DataFrame([model.params])
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets",
[
(
[
"educ-wage_panel",
"married-wage_panel",
"lwage-wage_panel",
"hisp-wage_panel",
"black-wage_panel",
],
{
"wage_panel": econometrics_model.load(
"wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"}
).set_index(["nr", "year"])
},
{
"educ-wage_panel": {"educ": None, "wage_panel": None},
"married-wage_panel": {"married": None, "wage_panel": None},
"lwage-wage_panel": {"lwage": None, "wage_panel": None},
"hisp-wage_panel": {"hisp": None, "wage_panel": None},
"black-wage_panel": {"black": None, "wage_panel": None},
},
)
],
)
def test_get_pols(recorder, regression_variables, data, datasets):
_, _, _, model = regression_model.get_pols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
)
result = pd.DataFrame([model.params])
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets",
[
(
[
"married-wage_panel",
"lwage-wage_panel",
"hisp-wage_panel",
"black-wage_panel",
],
{
"wage_panel": econometrics_model.load(
"wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"}
).set_index(["nr", "year"])
},
{
"educ-wage_panel": {"educ": None, "wage_panel": None},
"married-wage_panel": {"married": None, "wage_panel": None},
"lwage-wage_panel": {"lwage": None, "wage_panel": None},
"hisp-wage_panel": {"hisp": None, "wage_panel": None},
"black-wage_panel": {"black": None, "wage_panel": None},
},
)
],
)
def test_get_re(recorder, regression_variables, data, datasets):
_, _, _, model = regression_model.get_re(
regression_variables=regression_variables,
data=data,
datasets=datasets,
)
result = pd.DataFrame([model.params])
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets",
[
(
[
"educ-wage_panel",
"married-wage_panel",
"lwage-wage_panel",
"hisp-wage_panel",
"black-wage_panel",
],
{
"wage_panel": econometrics_model.load(
"wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"}
).set_index(["nr", "year"])
},
{
"educ-wage_panel": {"educ": None, "wage_panel": None},
"married-wage_panel": {"married": None, "wage_panel": None},
"lwage-wage_panel": {"lwage": None, "wage_panel": None},
"hisp-wage_panel": {"hisp": None, "wage_panel": None},
"black-wage_panel": {"black": None, "wage_panel": None},
},
)
],
)
def test_get_bols(recorder, regression_variables, data, datasets):
_, _, _, model = regression_model.get_bols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
)
result = pd.DataFrame([model.params])
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets",
[
(
[
"educ-wage_panel",
"married-wage_panel",
"lwage-wage_panel",
"hisp-wage_panel",
"black-wage_panel",
],
{
"wage_panel": econometrics_model.load(
"wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"}
).set_index(["nr", "year"])
},
{
"educ-wage_panel": {"educ": None, "wage_panel": None},
"married-wage_panel": {"married": None, "wage_panel": None},
"lwage-wage_panel": {"lwage": None, "wage_panel": None},
"hisp-wage_panel": {"hisp": None, "wage_panel": None},
"black-wage_panel": {"black": None, "wage_panel": None},
},
)
],
)
def test_get_fe(recorder, regression_variables, data, datasets):
_, _, _, model = regression_model.get_fe(
regression_variables=regression_variables,
data=data,
datasets=datasets,
)
result = pd.DataFrame([model.params])
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets",
[
(
["lwage-wage_panel", "married-wage_panel"],
{
"wage_panel": econometrics_model.load(
"wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"}
).set_index(["nr", "year"])
},
{
"married-wage_panel": {"married": None, "wage_panel": None},
"lwage-wage_panel": {"lwage": None, "wage_panel": None},
},
)
],
)
def test_get_fdols(recorder, regression_variables, data, datasets):
_, _, _, model = regression_model.get_fdols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
)
result = pd.DataFrame([model.params])
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets",
[
(
[
"married-wage_panel",
"lwage-wage_panel",
"hisp-wage_panel",
"black-wage_panel",
],
{
"wage_panel": econometrics_model.load(
"wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"}
).set_index(["nr", "year"])
},
{
"educ-wage_panel": {"educ": None, "wage_panel": None},
"married-wage_panel": {"married": None, "wage_panel": None},
"lwage-wage_panel": {"lwage": None, "wage_panel": None},
"hisp-wage_panel": {"hisp": None, "wage_panel": None},
"black-wage_panel": {"black": None, "wage_panel": None},
},
)
],
)
def test_get_comparison(recorder, regression_variables, data, datasets):
regressions = {"RE": {}, "FE": {}}
_, _, _, regressions["RE"]["model"] = regression_model.get_re(
regression_variables=regression_variables,
data=data,
datasets=datasets,
)
_, _, _, regressions["FE"]["model"] = regression_model.get_fe(
regression_variables=regression_variables,
data=data,
datasets=datasets,
)
comparison_result = regression_model.get_comparison(regressions)
recorder.capture(comparison_result.params, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets, show_regression",
[
(
["TOTEMP-longley", "GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
True,
),
(
["GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
True,
),
],
)
def test_get_dwat(recorder, regression_variables, data, datasets, show_regression):
_, _, _, model = regression_model.get_ols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
show_regression=show_regression,
)
result = regression_model.get_dwat(model.resid).round(5)
recorder.capture(result)
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets, show_regression, lags",
[
(
["ARMED-longley", "GNP-longley", "TOTEMP-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
True,
3,
),
(
["GNP-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
True,
1,
),
],
)
def test_get_bgod(
recorder, regression_variables, data, datasets, show_regression, lags
):
_, _, _, model = regression_model.get_ols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
show_regression=show_regression,
)
lm_stat, p_value, f_stat, fp_value = regression_model.get_bgod(model, lags)
result = pd.DataFrame([lm_stat, p_value, f_stat, fp_value])
recorder.capture(result, float_format="%.5f")
@pytest.mark.vcr()
@pytest.mark.parametrize(
"regression_variables, data, datasets, show_regression",
[
(
["GNP-longley", "TOTEMP-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
True,
),
(
["POP-longley", "GNP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
True,
),
],
)
def test_get_bpag(recorder, regression_variables, data, datasets, show_regression):
_, _, _, model = regression_model.get_ols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
show_regression=show_regression,
)
lm_stat, p_value, f_stat, fp_value = regression_model.get_bpag(model)
result = pd.DataFrame([lm_stat, p_value, f_stat, fp_value])
recorder.capture(result, float_format="%.5f")

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# IMPORTATION THIRDPARTY
import pytest
# IMPORTATION INTERNAL
from gamestonk_terminal.econometrics import (
regression_model,
econometrics_model,
regression_view,
)
@pytest.mark.vcr()
@pytest.mark.record_stdout
@pytest.mark.parametrize(
"regression_variables, data, datasets, show_regression, lags",
[
(
["TOTEMP-longley", "GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
False,
1,
),
(
["TOTEMP-longley", "GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
False,
2,
),
],
)
def test_display_bgod(regression_variables, data, datasets, show_regression, lags):
_, _, _, model = regression_model.get_ols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
show_regression=show_regression,
)
regression_view.display_bgod(model=model, lags=lags)
@pytest.mark.vcr()
@pytest.mark.record_stdout
@pytest.mark.parametrize(
"regression_variables, data, datasets, show_regression",
[
(
["TOTEMP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
False,
),
(
["GNP-longley", "ARMED-longley", "POP-longley"],
{
"longley": econometrics_model.load(
"longley", ["csv", "xlsx"], {}, {"longley": "longley"}
)
},
{
"TOTEMP-longley": {"TOTEMP": None, "longley": None},
"GNP-longley": {"GNP": None, "longley": None},
"ARMED-longley": {"ARMED": None, "longley": None},
"POP-longley": {"POP": None, "longley": None},
},
False,
),
],
)
def test_display_bpag(regression_variables, data, datasets, show_regression):
_, _, _, model = regression_model.get_ols(
regression_variables=regression_variables,
data=data,
datasets=datasets,
show_regression=show_regression,
)
regression_view.display_bpag(model=model)

1
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ssr_ftest

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ssr_chi2test

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tests/gamestonk_terminal/econometrics/txt/test_econometrics_model/test_get_granger_causality[time_series_y0-time_series_x0-3]_2.txt vendored Normal file
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lrtest

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params_ftest

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ssr_ftest

1
tests/gamestonk_terminal/econometrics/txt/test_econometrics_model/test_get_granger_causality[time_series_y1-time_series_x1-2]_1.txt vendored Normal file
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ssr_chi2test

1
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lrtest

1
tests/gamestonk_terminal/econometrics/txt/test_econometrics_model/test_get_granger_causality[time_series_y1-time_series_x1-2]_3.txt vendored Normal file
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params_ftest

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tests/gamestonk_terminal/econometrics/txt/test_econometrics_model/test_get_granger_causality[time_series_y2-time_series_x2-1].txt vendored Normal file
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ssr_ftest

1
tests/gamestonk_terminal/econometrics/txt/test_econometrics_model/test_get_granger_causality[time_series_y2-time_series_x2-1]_1.txt vendored Normal file
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ssr_chi2test

1
tests/gamestonk_terminal/econometrics/txt/test_econometrics_model/test_get_granger_causality[time_series_y2-time_series_x2-1]_2.txt vendored Normal file
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lrtest

1
tests/gamestonk_terminal/econometrics/txt/test_econometrics_model/test_get_granger_causality[time_series_y2-time_series_x2-1]_3.txt vendored Normal file
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params_ftest

7
tests/gamestonk_terminal/econometrics/txt/test_econometrics_view/test_display_granger[time_series_y0-time_series_x0-3-0.05].txt vendored Normal file
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F-test P-value Count Lags
ssr_ftest 2.669911 0.048815 193.0 3.0
ssr_chi2test 8.300242 0.040198 - 3
lrtest 8.132629 0.043349 - 3
params_ftest 2.669911 0.048815 193.0 3.0
As the p-value of the F-test is 0.049, we can reject the null hypothesis at the 0.05 confidence level and find the Series 'pop' to Granger-cause the Series 'realgdp'

7
tests/gamestonk_terminal/econometrics/txt/test_econometrics_view/test_display_granger[time_series_y1-time_series_x1-2-0.1].txt vendored Normal file
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F-test P-value Count Lags
ssr_ftest 3.265591 0.040261 196.0 2.0
ssr_chi2test 6.697793 0.035123 - 2
lrtest 6.588619 0.037094 - 2
params_ftest 3.265591 0.040261 196.0 2.0
As the p-value of the F-test is 0.04, we can reject the null hypothesis at the 0.1 confidence level and find the Series 'realinv' to Granger-cause the Series 'realgovt'

7
tests/gamestonk_terminal/econometrics/txt/test_econometrics_view/test_display_granger[time_series_y2-time_series_x2-1-0.01].txt vendored Normal file
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F-test P-value Count Lags
ssr_ftest 1.012638 0.315494 199.0 1.0
ssr_chi2test 1.027904 0.310651 - 1
lrtest 1.025298 0.311266 - 1
params_ftest 1.012638 0.315494 199.0 1.0
As the p-value of the F-test is 0.315, we can not reject the null hypothesis at the 0.01 confidence level.

4
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Kurtosis Skewness Jarque-Bera Shapiro-Wilk Kolmogorov-Smirnov
Statistic 1.479908 6.170200e+00 5.221623e+01 9.091216e-01 0.971137
p-value 0.138898 6.820348e-10 4.585554e-12 1.052443e-12 0.000000

4
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Kurtosis Skewness Jarque-Bera Shapiro-Wilk Kolmogorov-Smirnov
Statistic 3.801112 4.037817 6.066372e+01 9.108574e-01 7.882806e-01
p-value 0.000144 0.000054 6.716849e-14 1.060935e-09 6.730586e-135

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Kurtosis Skewness Jarque-Bera Shapiro-Wilk Kolmogorov-Smirnov
Statistic 3.556139 4.171875 6.887032e+01 0.887108 1.0
p-value 0.000376 0.000030 1.110223e-15 0.000040 0.0

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ADF KPSS
Test Statistic -2.837781 0.669866
P-Value 0.053076 0.016285
NLags 8.000000 7.000000
Nobs 300.000000 0.000000
ICBest 2430.500342 0.000000

7
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ADF KPSS
Test Statistic -2.837781 0.115831
P-Value 0.053076 0.100000
NLags 8.000000 7.000000
Nobs 300.000000 0.000000
ICBest 2430.500342 0.000000

7
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ADF KPSS
Test Statistic -2.924375 0.669866
P-Value 0.154465 0.016285
NLags 8.000000 7.000000
Nobs 300.000000 0.000000
ICBest 2431.976734 0.000000

7
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ADF KPSS
Test Statistic -3.004904 0.669866
P-Value 0.292850 0.016285
NLags 8.000000 7.000000
Nobs 300.000000 0.000000
ICBest 2432.538460 0.000000

7
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ADF KPSS
Test Statistic -0.752220 0.669866
P-Value 0.390609 0.016285
NLags 8.000000 7.000000
Nobs 300.000000 0.000000
ICBest 2436.914059 0.000000

5
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Only showing pairs that are statistically significant (True > p-value).
Constant Gamma Alpha Dickey-Fuller P Value
y/x -0.003126 0.154512 -1.2884 -13.123662 1.544757e-24

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Constant Gamma Alpha Dickey-Fuller P Value
y/x -0.003126 0.154512 -1.2884 -13.123662 1.544757e-24

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dataset column option
0 heart survival survival-heart
1 heart censors censors-heart
2 heart age age-heart
dataset column option
0 macrodata year year-macrodata
1 macrodata quarter quarter-macrodata
2 macrodata realgdp realgdp-macrodata
3 macrodata realcons realcons-macrodata
4 macrodata realinv realinv-macrodata
5 macrodata realgovt realgovt-macrodata
6 macrodata realdpi realdpi-macrodata
7 macrodata cpi cpi-macrodata
8 macrodata m1 m1-macrodata
9 macrodata tbilrate tbilrate-macrodata
10 macrodata unemp unemp-macrodata
11 macrodata pop pop-macrodata
12 macrodata infl infl-macrodata
13 macrodata realint realint-macrodata

8
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0
LM-stat 12.591323
p-value 0.000388
f-stat 0.688985
fp-value 0.424158
The result 0.0 indicates no existence of autocorrelation.

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0
LM-stat 13.283202
p-value 0.001305
f-stat 1.666265
fp-value 0.237376
The result 0.0 indicates no existence of autocorrelation.

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0
lm-stat 8.266655
p-value 0.004038
f-stat 7.482736
fp-value 0.006162
The result 0.0 indicates no existence of heteroscedasticity.

8
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0
lm-stat 11.318704
p-value 0.000767
f-stat 16.924998
fp-value 0.000184
The result 0.0 indicates no existence of heteroscedasticity.

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---
geekdocCollapseSection: true
---
{{< toc-tree >}}
# Loading in custom data
There is a directory in root, labeled `custom_imports`. Here there is a sample
csv file titled `test.csv` that is __randomly__ generated data for visualization purposes.
When loading in the data, we recommend working with time series. The prediction techniques menu
will plot against time data, so not using that may result in undesired outputs.
## How to load in a time variable
Note in the sample csv, the first row has the following form `Date,ColNorm,Col2,ColNorm3,ColB`.
The process for loading data into Gamestonk Terminal is that the code loops through all input columns and looks through
for one column that is either `Date`,`Time` or `Timestamp` (not case-sensitive).
If one of these are found, then the dataframe sets this column to be the index.
If onc of these columns are not found, then the index will just be integers from 0 to the length of your data.
Once again: __the prediction menu is designed for data with a datetime index__.

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```
usage: info [-h]
```
Show information of custom data.
```
optional arguments:
-h, --help show this help message (default: False)
```
Sample Output:
```python
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 732 entries, 2020-01-01 to 2022-01-01
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 colnorm 732 non-null float64
1 col2 732 non-null float64
2 colnorm3 732 non-null float64
3 colb 732 non-null float64
dtypes: float64(4)
memory usage: 28.6 KB
None
```
This tells us the loaded dataframe, (which there is one saved as a default template as test.csv)
has 4 columns that are all floats (numeric). The index is a date time range from the start of 2020 to the start of 2022.

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```
usage: load [-f {test.csv}] [-h]
```
Load custom data set into a dataframe
```
optional arguments:
-f {test.csv}, --file {test.csv}
File to load in. (default: test.csv)
-h, --help show this help message (default: False)
```

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```
usage: plot [-y {colnorm,col2,colnorm3,colb}] [-x {colnorm,col2,colnorm3,colb}] [-k {line,scatter,bar,barh,hist,box,kde,area,pie,hexbin}] [-h] [--export {png,jpg,pdf,svg}]
```
Plot 2 columns of loaded data.
```
optional arguments:
-y {colnorm,col2,colnorm3,colb}
Variable to plot along y (default: None)
-x {colnorm,col2,colnorm3,colb}, --vs {colnorm,col2,colnorm3,colb}
Variable along x axis (default: )
-k {line,scatter,bar,barh,hist,box,kde,area,pie,hexbin}, --kind {line,scatter,bar,barh,hist,box,kde,area,pie,hexbin}
Type of plot for data (default: scatter)
-h, --help show this help message (default: False)
--export {png,jpg,pdf,svg}
Export figure into png, jpg, pdf, svg (default: )
```
Note that when no "-x/--vs" is supplied, the x axis will be the dataframe index. The -x and -y defaults will be the
columns present in your data. In this scenario, the columns loaded are `colnorm,col2,colnorm3,colb` .
The different kinds of plots can be seen in the `kind` kwarg of the
[pandas plot function](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)

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```
usage: show [-s SORTCOL [SORTCOL ...]] [-a] [-h] [-l LIMIT]
```
Show loaded dataframe
```
optional arguments:
-s SORTCOL [SORTCOL ...], --sortcol SORTCOL [SORTCOL ...]
-a, --ascend
-h, --help show this help message (default: False)
-l LIMIT, --limit LIMIT
Number of entries to show in data. (default: 5)
```
Sample Output:
```
2022 Jan 18, 17:53 (✨) /custom/ $ show -l 3
colnorm col2 colnorm3 colb
date
2020-01-01 0.999953 1.012313 1.008667 0.001203
2020-01-02 0.983850 1.023421 1.004225 0.004517
2020-01-03 1.002692 1.019749 1.006244 0.007035
```
This shows the top 3 rows of my custom dataframe. This should primarily be used as a method of debugging that the load worked.

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---
geekdocCollapseSection: true
---
The Econometrics menu's purpose is to provide the user the ability to perform statistical research on custom datasets.
The menu allows the user to load in his/her own dataset(s), modify the data by (for example) adding columns or setting
indices, apply statistical tests (e.g. Breusch-Godfrey autocorrelation tests) as well as OLS regressions and Panel
regressions (e.g. Random Effects and Fixed Effects)
{{< toc-tree >}}

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```
usage: bgod [-l LAGS] [-h] [--export {csv,json,xlsx}]
```
Show Breusch-Pagan heteroscedasticity test results.
In statistics, heteroskedasticity (or heteroscedasticity) happens when the standard deviations of a predicted variable, monitored over different values of an independent variable or as related to prior time periods, are non-constant. With heteroskedasticity, the tell-tale sign upon visual inspection of the residual errors is that they will tend to fan out over time. [Source: Investopedia]
```
optional arguments:
-l LAGS, --lags LAGS The lags for the Breusch-Godfrey test (default: 3)
-h, --help show this help message (default: False)
--export {csv,json,xlsx}
Export raw data into csv, json, xlsx (default: )
```
Example:
```
2022 Feb 24, 06:00 (✨) /econometrics/ $ ols adj_close-msft adj_close-aapl adj_close-googl adj_close-tsla
OLS Regression Results
==============================================================================
Dep. Variable: adj_close_msft R-squared: 0.977
Model: OLS Adj. R-squared: 0.977
Method: Least Squares F-statistic: 1.068e+04
Date: Thu, 24 Feb 2022 Prob (F-statistic): 0.00
Time: 12:01:09 Log-Likelihood: -2830.6
No. Observations: 759 AIC: 5669.
Df Residuals: 755 BIC: 5688.
Df Model: 3
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 27.7984 2.166 12.832 0.000 23.546 32.051
adj_close_aapl 0.8662 0.034 25.503 0.000 0.800 0.933
adj_close_googl 0.0508 0.002 30.374 0.000 0.048 0.054
adj_close_tsla -0.0007 0.004 -0.181 0.856 -0.009 0.007
==============================================================================
Omnibus: 41.445 Durbin-Watson: 0.044
Prob(Omnibus): 0.000 Jarque-Bera (JB): 47.398
Skew: 0.612 Prob(JB): 5.10e-11
Kurtosis: 2.995 Cond. No. 1.16e+04
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.16e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
2022 Feb 24, 06:01 (✨) /econometrics/ $ bgod
Breusch-Godfrey autocorrelation test [Lags: 3]
┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ ┃ Breusch-Godfrey ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ LM-stat │ 723.75 │
├──────────┼─────────────────┤
│ p-value │ 0.00 │
├──────────┼─────────────────┤
│ f-stat │ 5147.20 │
├──────────┼─────────────────┤
│ fp-value │ 0.00 │
└──────────┴─────────────────┘
The result 0.0 indicates no existence of autocorrelation.
```

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