mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-27 12:40:35 +00:00
Add prediction to economy/fred (#1133)
This commit is contained in:
jmaslek
@@ -163,6 +163,9 @@ def display_exponential_smoothing(
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linestyle="--",
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color="k",
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)
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dateFmt = mdates.DateFormatter("%m/%d/%Y")
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ax.xaxis.set_major_formatter(dateFmt)
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ax.tick_params(axis="x", labelrotation=45)
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# BACKTESTING
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if s_end_date:
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@@ -25,6 +25,7 @@ def display_mc_forecast(
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n_future: int,
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n_sims: int,
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use_log=True,
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fig_title: str = "",
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export: str = "",
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time_res: str = "",
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):
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@@ -40,6 +41,8 @@ def display_mc_forecast(
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Number of simulations to run
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use_log : bool, optional
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Flag to use lognormal, by default True
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fig_title : str
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Figure title
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export: str
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Format to export data
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time_res : str
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@@ -63,13 +66,14 @@ def display_mc_forecast(
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ax[0].grid("on")
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sns.histplot(predicted_values[-1, :], ax=ax[1], kde=True)
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ax[1].set_xlabel("Price")
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ax[1].set_xlabel("Final Value")
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ax[1].axvline(x=data.values[-1], c="k", label="Last Value", lw=3, ls="-") # type: ignore
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ax[1].set_title(f"Distribution of final values after {n_future} steps.")
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ax[1].set_xlim(np.min(predicted_values[-1, :]), np.max(predicted_values[-1, :]))
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ax[1].grid("on")
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ax[1].legend()
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if fig_title:
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fig.suptitle(fig_title)
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fig.tight_layout(pad=2)
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if gtff.USE_ION:
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@@ -10,6 +10,7 @@ import numpy as np
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import pandas as pd
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from pandas.plotting import register_matplotlib_converters
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from colorama import Fore, Style
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from sklearn.metrics import (
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mean_absolute_error,
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@@ -413,8 +414,9 @@ def plot_data_predictions(
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):
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"""Plots data predictions for the different ML techniques"""
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plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
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plt.plot(
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# plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
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fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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ax.plot(
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data.index,
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data.values,
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"-ob",
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@@ -430,13 +432,13 @@ def plot_data_predictions(
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else:
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y_pred = preds[i].ravel()
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y_act = y_valid[i].ravel()
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plt.plot(
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ax.plot(
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y_dates_valid[i],
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y_pred,
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"r",
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lw=1,
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)
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plt.fill_between(
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ax.fill_between(
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y_dates_valid[i],
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y_pred,
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y_act,
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@@ -444,7 +446,7 @@ def plot_data_predictions(
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color="r",
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alpha=0.2,
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)
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plt.fill_between(
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ax.fill_between(
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y_dates_valid[i],
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y_pred,
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y_act,
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@@ -460,14 +462,14 @@ def plot_data_predictions(
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else:
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final_pred = preds[-1].reshape(-1, 1).ravel()
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final_valid = y_valid[-1].reshape(-1, 1).ravel()
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plt.plot(
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ax.plot(
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y_dates_valid[-1],
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final_pred,
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"r",
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lw=2,
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label="Predictions",
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)
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plt.fill_between(
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ax.fill_between(
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y_dates_valid[-1],
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final_pred,
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final_valid,
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@@ -476,7 +478,7 @@ def plot_data_predictions(
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)
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_, _, ymin, ymax = plt.axis()
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plt.vlines(
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ax.vlines(
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forecast_data.index[0],
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ymin,
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ymax,
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@@ -486,7 +488,7 @@ def plot_data_predictions(
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color="k",
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)
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if n_loops == 1:
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plt.plot(
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ax.plot(
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forecast_data.index,
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forecast_data.values,
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"-og",
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@@ -494,14 +496,14 @@ def plot_data_predictions(
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label="Forecast",
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)
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else:
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plt.plot(
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ax.plot(
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forecast_data.index,
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forecast_data.median(axis=1).values,
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"-og",
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ms=3,
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label="Forecast",
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)
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plt.fill_between(
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ax.fill_between(
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forecast_data.index,
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forecast_data.quantile(0.25, axis=1).values,
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forecast_data.quantile(0.75, axis=1).values,
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@@ -510,28 +512,32 @@ def plot_data_predictions(
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)
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# Subtracting 1 day only works nicely for daily data. For now if not daily, then start line on last point
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if not time_str or time_str == "1D":
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plt.axvspan(
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ax.axvspan(
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forecast_data.index[0] - timedelta(days=1),
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forecast_data.index[-1],
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facecolor="tab:orange",
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alpha=0.2,
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)
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plt.xlim(data.index[0], forecast_data.index[-1] + timedelta(days=1))
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ax.set_xlim(data.index[0], forecast_data.index[-1] + timedelta(days=1))
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else:
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plt.axvspan(
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ax.axvspan(
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forecast_data.index[0],
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forecast_data.index[-1],
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facecolor="tab:orange",
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alpha=0.2,
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)
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plt.xlim(data.index[0], forecast_data.index[-1])
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ax.set_xlim(data.index[0], forecast_data.index[-1])
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plt.legend(loc=0)
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plt.xlabel("Time")
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plt.ylabel("Value")
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plt.grid(b=True, which="major", color="#666666", linestyle="-")
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plt.title(title)
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ax.legend(loc=0)
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ax.set_xlabel("Time")
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ax.set_ylabel("Value")
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ax.grid(b=True, which="major", color="#666666", linestyle="-")
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dateFmt = mdates.DateFormatter("%m/%d/%Y")
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ax.xaxis.set_major_formatter(dateFmt)
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ax.tick_params(axis="x", labelrotation=45)
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fig.suptitle(title)
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fig.tight_layout(pad=2)
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if gtff.USE_ION:
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plt.ion()
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plt.show()
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@@ -7,6 +7,7 @@ from datetime import datetime, timedelta
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import os
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from typing import List, Union
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from rich.console import Console
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import pandas as pd
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from prompt_toolkit.completion import NestedCompleter
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@@ -31,6 +32,7 @@ from gamestonk_terminal.helper_funcs import (
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)
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from gamestonk_terminal.menu import session
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t_console = Console()
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# pylint: disable=R1710,R0904,C0415
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@@ -226,7 +228,7 @@ NASDAQ DataLink (formerly Quandl):
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> fred Federal Reserve Economic Data submenu
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"""
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print(help_text)
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t_console.print(help_text)
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def switch(self, an_input: str):
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"""Process and dispatch input
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@@ -238,7 +240,7 @@ NASDAQ DataLink (formerly Quandl):
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"""
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# Empty command
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if not an_input:
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print("")
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t_console.print("")
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return self.queue
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# Navigation slash is being used
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@@ -290,7 +292,6 @@ NASDAQ DataLink (formerly Quandl):
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def call_quit(self, _):
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"""Process quit menu command"""
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print("")
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self.queue.insert(0, "quit")
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def call_exit(self, _):
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@@ -1137,7 +1138,9 @@ NASDAQ DataLink (formerly Quandl):
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file = os.path.join(
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os.path.dirname(__file__), "NASDAQ_CountryCodes.csv"
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)
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print(pd.read_csv(file, index_col=0).to_string(index=False), "\n")
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t_console.print(
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pd.read_csv(file, index_col=0).to_string(index=False), "\n"
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)
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else:
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nasdaq_view.display_big_mac_index(
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country_codes=ns_parser.countries,
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@@ -1162,7 +1165,7 @@ def menu(queue: List[str] = None):
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if econ_controller.queue and len(econ_controller.queue) > 0:
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# If the command is quitting the menu we want to return in here
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if econ_controller.queue[0] in ("q", "..", "quit"):
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print("")
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t_console.print("")
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if len(econ_controller.queue) > 1:
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return econ_controller.queue[1:]
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return []
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@@ -1173,7 +1176,7 @@ def menu(queue: List[str] = None):
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# Print the current location because this was an instruction and we want user to know what was the action
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if an_input and an_input.split(" ")[0] in econ_controller.CHOICES_COMMANDS:
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print(f"{get_flair()} /economy/ $ {an_input}")
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t_console.print(f"{get_flair()} /economy/ $ {an_input}")
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# Get input command from user
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else:
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@@ -1201,7 +1204,7 @@ def menu(queue: List[str] = None):
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econ_controller.queue = econ_controller.switch(an_input)
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except SystemExit:
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print(
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t_console.print(
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f"\nThe command '{an_input}' doesn't exist on the /economy menu.",
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end="",
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)
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@@ -1219,13 +1222,13 @@ def menu(queue: List[str] = None):
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if candidate_input == an_input:
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an_input = ""
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econ_controller.queue = []
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print("\n")
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t_console.print("")
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continue
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an_input = candidate_input
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else:
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an_input = similar_cmd[0]
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print(f" Replacing by '{an_input}'.")
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t_console.print(f" Replacing by '{an_input}'.")
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econ_controller.queue.insert(0, an_input)
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else:
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print("\n")
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t_console.print("")
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@@ -5,7 +5,7 @@ import argparse
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import difflib
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from typing import List, Union, Dict
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from colorama import Style
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from rich.console import Console
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from prompt_toolkit.completion import NestedCompleter
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from gamestonk_terminal import feature_flags as gtff
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@@ -21,6 +21,10 @@ from gamestonk_terminal.helper_funcs import (
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)
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from gamestonk_terminal.menu import session
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t_console = Console()
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# pylint:disable=import-outside-toplevel
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class FredController:
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"""FRED Controller Class"""
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@@ -41,8 +45,9 @@ class FredController:
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]
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CHOICES_COMMANDS = ["search", "add", "rmv", "plot"]
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CHOICES_MENUS = ["pred"]
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CHOICES += CHOICES_COMMANDS
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CHOICES += CHOICES_COMMANDS + CHOICES_MENUS
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def __init__(self, queue: List[str] = None):
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"""Constructor"""
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@@ -52,7 +57,7 @@ class FredController:
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choices=self.CHOICES,
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)
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self.current_series: Dict = dict()
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self.current_long_id = 0
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self.long_id = 0
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self.completer: Union[None, NestedCompleter] = None
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@@ -70,17 +75,24 @@ class FredController:
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"""Print help"""
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id_string = ""
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for s_id, sub_dict in self.current_series.items():
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id_string += f" {s_id.upper()}{(self.current_long_id-len(s_id)) * ' '} : {sub_dict['title']}\n"
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id_string += (
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f" [blue]{s_id.upper()}{(self.long_id-len(s_id)) * ' '}[/blue] :"
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f" [italic]{sub_dict['title']}[/italic]\n"
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)
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if not id_string:
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id_string += " [bold][red]None[/red][/bold]\n"
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help_text = f"""
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search search FRED series notes
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add add series ID to list
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rmv remove series ID from list
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Current Series IDs:
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{id_string}{Style.DIM if not self.current_series else ""}
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plot plot selected series {Style.RESET_ALL}
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{id_string}{'[dim]'if not self.current_series else ""}
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plot plot selected series {'[/dim]'if not self.current_series else ""}
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{'[dim]'if len(self.current_series.keys())!=1 else ""}
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> pred prediction techniques (single SeriesID){'[/dim]'if len(self.current_series.keys())!=1 else ""}
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"""
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print(help_text)
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t_console.print(help_text)
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def switch(self, an_input: str):
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"""Process and dispatch input
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@@ -92,7 +104,7 @@ Current Series IDs:
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"""
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# Empty command
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if not an_input:
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print("")
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t_console.print("\n")
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return self.queue
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# Navigation slash is being used
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@@ -145,13 +157,9 @@ Current Series IDs:
|
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def call_quit(self, _):
|
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"""Process quit menu command"""
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print("")
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self.queue.insert(0, "quit")
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return ["quit"]
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def call_exit(self, _):
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print("")
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self.queue.insert(0, "quit")
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self.queue.insert(0, "quit")
|
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self.queue.insert(0, "quit")
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@@ -230,10 +238,12 @@ Current Series IDs:
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"title": information["seriess"][0]["title"],
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"units": information["seriess"][0]["units_short"],
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}
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self.current_long_id = max(self.current_long_id, len(s_id))
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self.long_id = max(self.long_id, len(s_id))
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else:
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t_console.print(f"[red]{s_id} not found[/red].")
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print(
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f"Current Series: {', '.join(self.current_series.keys()) .upper() or None}\n"
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t_console.print(
|
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f"Current Series:[blue] {', '.join(self.current_series.keys()) .upper() or None}[/blue]\n"
|
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)
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|
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@try_except
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@@ -255,7 +265,7 @@ Current Series IDs:
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parser.add_argument(
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"-i",
|
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"--id",
|
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type=str,
|
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type=lambda x: x.lower(),
|
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choices=self.current_series.keys(),
|
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required="-h" not in other_args
|
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and "-a" not in other_args
|
||||
@@ -277,12 +287,12 @@ Current Series IDs:
|
||||
if ns_parser:
|
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if ns_parser.all:
|
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self.current_series = {}
|
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self.current_long_id = 0
|
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print("")
|
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self.long_id = 0
|
||||
t_console.print("")
|
||||
|
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self.current_series.pop(ns_parser.series_id)
|
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print(
|
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f"Current Series Ids: {', '.join(self.current_series.keys()) or None}\n"
|
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t_console.print(
|
||||
f"Current Series Ids: [blue]{', '.join(self.current_series.keys()) or None}[/blue]\n"
|
||||
)
|
||||
|
||||
@try_except
|
||||
@@ -307,6 +317,14 @@ Current Series IDs:
|
||||
action="store_true",
|
||||
default=False,
|
||||
)
|
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parser.add_argument(
|
||||
"-l",
|
||||
"--lim",
|
||||
dest="limit",
|
||||
help="Number of rows to show for limit",
|
||||
type=check_positive,
|
||||
default=10,
|
||||
)
|
||||
|
||||
ns_parser = parse_known_args_and_warn(
|
||||
parser, other_args, export_allowed=EXPORT_BOTH_RAW_DATA_AND_FIGURES
|
||||
@@ -317,8 +335,22 @@ Current Series IDs:
|
||||
ns_parser.start_date,
|
||||
ns_parser.raw,
|
||||
ns_parser.export,
|
||||
ns_parser.LIMIT,
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_pred(self, _):
|
||||
"""Process pred command"""
|
||||
if not self.current_series:
|
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t_console.print("Please select 1 Series to use.\n")
|
||||
return
|
||||
if len(self.current_series.keys()) != 1:
|
||||
t_console.print("Only 1 Series can be input into prediction.\n")
|
||||
return
|
||||
from gamestonk_terminal.economy.fred.prediction import pred_controller
|
||||
|
||||
self.queue = pred_controller.menu(self.current_series, self.queue)
|
||||
|
||||
|
||||
def menu(queue: List[str] = None):
|
||||
"""Fred Menu"""
|
||||
@@ -330,7 +362,7 @@ def menu(queue: List[str] = None):
|
||||
if fred_controller.queue and len(fred_controller.queue) > 0:
|
||||
# If the command is quitting the menu we want to return in here
|
||||
if fred_controller.queue[0] in ("q", "..", "quit"):
|
||||
print("")
|
||||
t_console.print("")
|
||||
if len(fred_controller.queue) > 1:
|
||||
return fred_controller.queue[1:]
|
||||
return []
|
||||
@@ -341,7 +373,7 @@ def menu(queue: List[str] = None):
|
||||
|
||||
# Print the current location because this was an instruction and we want user to know what was the action
|
||||
if an_input and an_input.split(" ")[0] in fred_controller.CHOICES_COMMANDS:
|
||||
print(f"{get_flair()} /economy/fred/ $ {an_input}")
|
||||
t_console.print(f"{get_flair()} /economy/fred/ $ {an_input}")
|
||||
|
||||
# Get input command from user
|
||||
else:
|
||||
@@ -369,7 +401,7 @@ def menu(queue: List[str] = None):
|
||||
fred_controller.queue = fred_controller.switch(an_input)
|
||||
|
||||
except SystemExit:
|
||||
print(
|
||||
t_console.print(
|
||||
f"\nThe command '{an_input}' doesn't exist on the /economy/fred menu.",
|
||||
end="",
|
||||
)
|
||||
@@ -387,13 +419,13 @@ def menu(queue: List[str] = None):
|
||||
if candidate_input == an_input:
|
||||
an_input = ""
|
||||
fred_controller.queue = []
|
||||
print("\n")
|
||||
t_console.print("\n")
|
||||
continue
|
||||
an_input = candidate_input
|
||||
else:
|
||||
an_input = similar_cmd[0]
|
||||
|
||||
print(f" Replacing by '{an_input}'.")
|
||||
t_console.print(f" Replacing by '{an_input}'.")
|
||||
fred_controller.queue.insert(0, an_input)
|
||||
else:
|
||||
print("\n")
|
||||
t_console.print("\n")
|
||||
|
||||
@@ -8,15 +8,21 @@ import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pandas.plotting import register_matplotlib_converters
|
||||
from tabulate import tabulate
|
||||
from rich.console import Console
|
||||
|
||||
from gamestonk_terminal import feature_flags as gtff
|
||||
from gamestonk_terminal.config_plot import PLOT_DPI
|
||||
from gamestonk_terminal.economy.fred import fred_model
|
||||
from gamestonk_terminal.helper_funcs import export_data, plot_autoscale
|
||||
from gamestonk_terminal.helper_funcs import (
|
||||
export_data,
|
||||
plot_autoscale,
|
||||
rich_table_from_df,
|
||||
)
|
||||
|
||||
register_matplotlib_converters()
|
||||
|
||||
t_console = Console()
|
||||
|
||||
|
||||
def format_units(num: int) -> str:
|
||||
"""Helper to format number into string with K,M,B,T. Number will be in form of 10^n"""
|
||||
@@ -45,7 +51,7 @@ def notes(series_term: str, num: int):
|
||||
"""
|
||||
df_search = fred_model.get_series_notes(series_term)
|
||||
if df_search.empty:
|
||||
print("No matches found. \n")
|
||||
t_console.print("No matches found. \n")
|
||||
return
|
||||
df_search["notes"] = df_search["notes"].apply(
|
||||
lambda x: "\n".join(textwrap.wrap(x, width=100)) if isinstance(x, str) else x
|
||||
@@ -54,17 +60,19 @@ def notes(series_term: str, num: int):
|
||||
lambda x: "\n".join(textwrap.wrap(x, width=50)) if isinstance(x, str) else x
|
||||
)
|
||||
if gtff.USE_TABULATE_DF:
|
||||
print(
|
||||
tabulate(
|
||||
t_console.print(
|
||||
rich_table_from_df(
|
||||
df_search[["id", "title", "notes"]].head(num),
|
||||
tablefmt="fancy_grid",
|
||||
title=f"[bold]Search results for {series_term}[/bold]",
|
||||
show_index=False,
|
||||
headers=["Series ID", "Title", "Description"],
|
||||
showindex=False,
|
||||
)
|
||||
)
|
||||
else:
|
||||
print(df_search[["id", "title", "notes"]].head(num).to_string(index=False))
|
||||
print("")
|
||||
t_console.print(
|
||||
df_search[["id", "title", "notes"]].head(num).to_string(index=False)
|
||||
)
|
||||
t_console.print("")
|
||||
|
||||
|
||||
def display_fred_series(
|
||||
@@ -72,6 +80,7 @@ def display_fred_series(
|
||||
start_date: str,
|
||||
raw: bool = False,
|
||||
export: str = "",
|
||||
limit: int = 10,
|
||||
):
|
||||
"""Display (multiple) series from https://fred.stlouisfed.org. [Source: FRED]
|
||||
|
||||
@@ -85,6 +94,8 @@ def display_fred_series(
|
||||
Output only raw data
|
||||
export : str
|
||||
Export data to csv,json,xlsx or png,jpg,pdf,svg file
|
||||
limit: int
|
||||
Number of raw data rows to show
|
||||
"""
|
||||
series_ids = list(d_series.keys())
|
||||
data = pd.DataFrame()
|
||||
@@ -99,7 +110,7 @@ def display_fred_series(
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
|
||||
data = data.dropna()
|
||||
# Try to get everything onto the same 0-10 scale.
|
||||
# To do so, think in scientific notation. Divide the data by whatever the E would be
|
||||
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
|
||||
@@ -131,15 +142,23 @@ def display_fred_series(
|
||||
plt.gcf().autofmt_xdate()
|
||||
fig.tight_layout()
|
||||
plt.show()
|
||||
data.index = [x.strftime("%Y-%m-%d") for x in data.index]
|
||||
if raw:
|
||||
if gtff.USE_TABULATE_DF:
|
||||
print(tabulate(data.tail(20), headers=data.columns, tablefmt="fancy_grid"))
|
||||
t_console.print(
|
||||
rich_table_from_df(
|
||||
data.tail(limit),
|
||||
headers=list(data.columns),
|
||||
show_index=True,
|
||||
index_name="Date",
|
||||
)
|
||||
)
|
||||
else:
|
||||
print(data.tail(20).to_string())
|
||||
t_console.print(data.tail(limit).to_string())
|
||||
export_data(
|
||||
export,
|
||||
os.path.dirname(os.path.abspath(__file__)),
|
||||
"plot",
|
||||
data,
|
||||
)
|
||||
print("")
|
||||
t_console.print("")
|
||||
|
||||
947
gamestonk_terminal/economy/fred/prediction/pred_controller.py
Normal file
947
gamestonk_terminal/economy/fred/prediction/pred_controller.py
Normal file
@@ -0,0 +1,947 @@
|
||||
"""Fred Series Prediction Controller"""
|
||||
__docformat__ = "numpy"
|
||||
|
||||
import argparse
|
||||
import difflib
|
||||
from typing import List, Union, Dict
|
||||
import logging
|
||||
|
||||
from prompt_toolkit.completion import NestedCompleter
|
||||
from rich.console import Console
|
||||
|
||||
from gamestonk_terminal import feature_flags as gtff
|
||||
from gamestonk_terminal.helper_funcs import (
|
||||
get_flair,
|
||||
parse_known_args_and_warn,
|
||||
check_positive,
|
||||
valid_date,
|
||||
get_next_stock_market_days,
|
||||
EXPORT_ONLY_FIGURES_ALLOWED,
|
||||
try_except,
|
||||
system_clear,
|
||||
)
|
||||
from gamestonk_terminal.menu import session
|
||||
from gamestonk_terminal.common.prediction_techniques import (
|
||||
arima_model,
|
||||
arima_view,
|
||||
ets_model,
|
||||
ets_view,
|
||||
knn_view,
|
||||
mc_model,
|
||||
neural_networks_view,
|
||||
regression_view,
|
||||
pred_helper,
|
||||
mc_view,
|
||||
)
|
||||
from gamestonk_terminal.economy.fred import fred_model
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
t_console = Console()
|
||||
|
||||
|
||||
class PredictionTechniquesController:
|
||||
"""Prediction Techniques Controller class"""
|
||||
|
||||
# Command choices
|
||||
CHOICES = [
|
||||
"cls",
|
||||
"home",
|
||||
"h",
|
||||
"?",
|
||||
"help",
|
||||
"q",
|
||||
"quit",
|
||||
"..",
|
||||
"exit",
|
||||
"r",
|
||||
"reset",
|
||||
]
|
||||
|
||||
CHOICES_COMMANDS = [
|
||||
"load",
|
||||
"ets",
|
||||
"knn",
|
||||
"regression",
|
||||
"arima",
|
||||
"mlp",
|
||||
"rnn",
|
||||
"lstm",
|
||||
"conv1d",
|
||||
"mc",
|
||||
]
|
||||
CHOICES += CHOICES_COMMANDS
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
current_series: Dict,
|
||||
queue: List[str] = None,
|
||||
):
|
||||
"""Constructor"""
|
||||
self.pred_parser = argparse.ArgumentParser(add_help=False, prog="pred")
|
||||
self.pred_parser.add_argument(
|
||||
"cmd",
|
||||
choices=self.CHOICES,
|
||||
)
|
||||
self.start_date = "2020-01-01"
|
||||
self.current_series = current_series
|
||||
self.current_id = list(current_series.keys())[0].upper()
|
||||
self.data = fred_model.get_series_data(
|
||||
list(current_series.keys())[0], self.start_date
|
||||
).dropna()
|
||||
self.resolution = "" # For the views
|
||||
self.completer: Union[None, NestedCompleter] = None
|
||||
if session and gtff.USE_PROMPT_TOOLKIT:
|
||||
choices: dict = {c: {} for c in self.CHOICES}
|
||||
choices["ets"]["-t"] = {c: {} for c in ets_model.TRENDS}
|
||||
choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS}
|
||||
choices["arima"]["-i"] = {c: {} for c in arima_model.ICS}
|
||||
choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS}
|
||||
self.completer = NestedCompleter.from_nested_dict(choices)
|
||||
|
||||
if queue:
|
||||
self.queue = queue
|
||||
else:
|
||||
self.queue = list()
|
||||
|
||||
def print_help(self):
|
||||
"""Print help"""
|
||||
id_string = ""
|
||||
for s_id, sub_dict in self.current_series.items():
|
||||
id_string += f" [cyan]{s_id.upper()}[/cyan] : {sub_dict['title']}"
|
||||
help_string = f"""
|
||||
Prediction Techniques Menu:
|
||||
|
||||
load load new series
|
||||
|
||||
Selected Series (starting from [green]{self.start_date}[/green]):
|
||||
{id_string}
|
||||
|
||||
Models:
|
||||
ets exponential smoothing (e.g. Holt-Winters)
|
||||
knn k-Nearest Neighbors
|
||||
regression polynomial regression
|
||||
arima autoregressive integrated moving average
|
||||
mlp MultiLayer Perceptron
|
||||
rnn Recurrent Neural Network
|
||||
lstm Long-Short Term Memory
|
||||
conv1d 1D Convolutional Neural Network
|
||||
mc Monte-Carlo simulations
|
||||
"""
|
||||
t_console.print(help_string)
|
||||
|
||||
def switch(self, an_input: str):
|
||||
"""Process and dispatch input
|
||||
|
||||
Parameters
|
||||
-------
|
||||
an_input : str
|
||||
string with input arguments
|
||||
|
||||
Returns
|
||||
-------
|
||||
List[str]
|
||||
List of commands in the queue to execute
|
||||
"""
|
||||
|
||||
# Empty command
|
||||
if not an_input:
|
||||
t_console.print("")
|
||||
return self.queue
|
||||
|
||||
# Navigation slash is being used
|
||||
if "/" in an_input:
|
||||
actions = an_input.split("/")
|
||||
|
||||
# Absolute path is specified
|
||||
if not actions[0]:
|
||||
an_input = "home"
|
||||
# Relative path so execute first instruction
|
||||
else:
|
||||
an_input = actions[0]
|
||||
|
||||
# Add all instructions to the queue
|
||||
for cmd in actions[1:][::-1]:
|
||||
if cmd:
|
||||
self.queue.insert(0, cmd)
|
||||
|
||||
(known_args, other_args) = self.pred_parser.parse_known_args(an_input.split())
|
||||
|
||||
# Redirect commands to their correct functions
|
||||
if known_args.cmd:
|
||||
if known_args.cmd in ("..", "q"):
|
||||
known_args.cmd = "quit"
|
||||
elif known_args.cmd in ("?", "h"):
|
||||
known_args.cmd = "help"
|
||||
elif known_args.cmd == "r":
|
||||
known_args.cmd = "reset"
|
||||
|
||||
getattr(
|
||||
self,
|
||||
"call_" + known_args.cmd,
|
||||
lambda _: "Command not recognized!",
|
||||
)(other_args)
|
||||
|
||||
return self.queue
|
||||
|
||||
def call_cls(self, _):
|
||||
"""Process cls command"""
|
||||
system_clear()
|
||||
|
||||
def call_home(self, _):
|
||||
"""Process home command"""
|
||||
self.queue.insert(0, "quit")
|
||||
self.queue.insert(0, "quit")
|
||||
|
||||
def call_help(self, _):
|
||||
"""Process help command"""
|
||||
self.print_help()
|
||||
|
||||
def call_quit(self, _):
|
||||
"""Process quit menu command"""
|
||||
self.queue.insert(0, "quit")
|
||||
|
||||
def call_exit(self, _):
|
||||
"""Process exit terminal command"""
|
||||
self.queue.insert(0, "quit")
|
||||
self.queue.insert(0, "quit")
|
||||
self.queue.insert(0, "quit")
|
||||
self.queue.insert(0, "quit")
|
||||
|
||||
def call_reset(self, _):
|
||||
"""Process reset command"""
|
||||
self.queue.insert(0, "pred")
|
||||
if self.current_series:
|
||||
self.queue.insert(0, f"add {list(self.current_series.keys())[0]}")
|
||||
self.queue.insert(0, "fred")
|
||||
self.queue.insert(0, "economy")
|
||||
self.queue.insert(0, "reset")
|
||||
self.queue.insert(0, "quit")
|
||||
self.queue.insert(0, "quit")
|
||||
self.queue.insert(0, "quit")
|
||||
|
||||
@try_except
|
||||
def call_load(self, other_args: List[str]):
|
||||
"""Process add command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
prog="load",
|
||||
description="Load a FRED series to current selection",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--id",
|
||||
dest="series_id",
|
||||
required="-h" not in other_args,
|
||||
type=str,
|
||||
help="FRED Series from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
dest="start_date",
|
||||
type=valid_date,
|
||||
default=self.start_date,
|
||||
help="Starting date (YYYY-MM-DD) of data",
|
||||
)
|
||||
if other_args and "-" not in other_args[0][0]:
|
||||
other_args.insert(0, "-i")
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if ns_parser:
|
||||
self.current_series = {}
|
||||
exists, information = fred_model.check_series_id(ns_parser.series_id)
|
||||
if exists:
|
||||
self.current_series[ns_parser.series_id] = {
|
||||
"title": information["seriess"][0]["title"],
|
||||
"units": information["seriess"][0]["units_short"],
|
||||
}
|
||||
self.start_date = ns_parser.start_date
|
||||
self.current_id = ns_parser.series_id.upper()
|
||||
self.data = fred_model.get_series_data(
|
||||
ns_parser.series_id, ns_parser.start_date
|
||||
).dropna()
|
||||
else:
|
||||
t_console.print(f"[red]{ns_parser.series_id} not found[/red].")
|
||||
t_console.print(
|
||||
f"Current Series: {', '.join(self.current_series.keys()).upper() or None}\n"
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_ets(self, other_args: List[str]):
|
||||
"""Process ets command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="ets",
|
||||
description="""
|
||||
Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html
|
||||
|
||||
Trend='N', Seasonal='N': Simple Exponential Smoothing
|
||||
Trend='N', Seasonal='A': Exponential Smoothing
|
||||
Trend='N', Seasonal='M': Exponential Smoothing
|
||||
Trend='A', Seasonal='N': Holt’s linear method
|
||||
Trend='A', Seasonal='A': Additive Holt-Winters’ method
|
||||
Trend='A', Seasonal='M': Multiplicative Holt-Winters’ method
|
||||
Trend='Ad', Seasonal='N': Additive damped trend method
|
||||
Trend='Ad', Seasonal='A': Exponential Smoothing
|
||||
Trend='Ad', Seasonal='M': Holt-Winters’ damped method
|
||||
Trend component: N: None, A: Additive, Ad: Additive Damped
|
||||
Seasonality component: N: None, A: Additive, M: Multiplicative
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--days",
|
||||
action="store",
|
||||
dest="n_days",
|
||||
type=check_positive,
|
||||
default=5,
|
||||
help="prediction days.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--trend",
|
||||
action="store",
|
||||
dest="trend",
|
||||
choices=ets_model.TRENDS,
|
||||
default="N",
|
||||
help="Trend component: N: None, A: Additive, Ad: Additive Damped.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
"--seasonal",
|
||||
action="store",
|
||||
dest="seasonal",
|
||||
choices=ets_model.SEASONS,
|
||||
default="N",
|
||||
help="Seasonality component: N: None, A: Additive, M: Multiplicative.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-p",
|
||||
"--periods",
|
||||
action="store",
|
||||
dest="seasonal_periods",
|
||||
type=check_positive,
|
||||
default=5,
|
||||
help="Seasonal periods.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--end",
|
||||
action="store",
|
||||
type=valid_date,
|
||||
dest="s_end_date",
|
||||
default=None,
|
||||
help="The end date (format YYYY-MM-DD) to select - Backtesting",
|
||||
)
|
||||
ns_parser = parse_known_args_and_warn(
|
||||
parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
|
||||
)
|
||||
if ns_parser:
|
||||
if ns_parser.s_end_date:
|
||||
if ns_parser.s_end_date < self.data.index[0]:
|
||||
t_console.print(
|
||||
"Backtesting not allowed, since End Date is older than Start Date of historical data\n"
|
||||
)
|
||||
|
||||
if ns_parser.s_end_date < get_next_stock_market_days(
|
||||
last_stock_day=self.data.index[0],
|
||||
n_next_days=5 + ns_parser.n_days,
|
||||
)[-1]:
|
||||
t_console.print(
|
||||
"Backtesting not allowed, since End Date is too close to Start Date to train model\n"
|
||||
)
|
||||
|
||||
ets_view.display_exponential_smoothing(
|
||||
ticker=self.current_id,
|
||||
values=self.data,
|
||||
n_predict=ns_parser.n_days,
|
||||
trend=ns_parser.trend,
|
||||
seasonal=ns_parser.seasonal,
|
||||
seasonal_periods=ns_parser.seasonal_periods,
|
||||
s_end_date=ns_parser.s_end_date,
|
||||
export=ns_parser.export,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_knn(self, other_args: List[str]):
|
||||
"""Process knn command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="knn",
|
||||
description="""
|
||||
K nearest neighbors is a simple algorithm that stores all
|
||||
available cases and predict the numerical target based on a similarity measure
|
||||
(e.g. distance functions).
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--input",
|
||||
action="store",
|
||||
dest="n_inputs",
|
||||
type=check_positive,
|
||||
default=40,
|
||||
help="number of days to use as input for prediction.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--days",
|
||||
action="store",
|
||||
dest="n_days",
|
||||
type=check_positive,
|
||||
default=5,
|
||||
help="prediction days.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-j",
|
||||
"--jumps",
|
||||
action="store",
|
||||
dest="n_jumps",
|
||||
type=check_positive,
|
||||
default=1,
|
||||
help="number of jumps in training data.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-n",
|
||||
"--neighbors",
|
||||
action="store",
|
||||
dest="n_neighbors",
|
||||
type=check_positive,
|
||||
default=20,
|
||||
help="number of neighbors to use on the algorithm.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--end",
|
||||
action="store",
|
||||
type=valid_date,
|
||||
dest="s_end_date",
|
||||
default=None,
|
||||
help="The end date (format YYYY-MM-DD) to select for testing",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--test_size",
|
||||
default=0.2,
|
||||
dest="valid_split",
|
||||
type=float,
|
||||
help="Percentage of data to validate in sample",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no_shuffle",
|
||||
action="store_false",
|
||||
dest="no_shuffle",
|
||||
default=True,
|
||||
help="Specify if shuffling validation inputs.",
|
||||
)
|
||||
ns_parser = parse_known_args_and_warn(
|
||||
parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED
|
||||
)
|
||||
if ns_parser:
|
||||
if ns_parser.n_inputs > len(self.data):
|
||||
t_console.print(
|
||||
f"[red]Data only contains {len(self.data)} samples and the model is trying "
|
||||
f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with"
|
||||
f" an earlier start date[/red]\n"
|
||||
)
|
||||
return
|
||||
try:
|
||||
knn_view.display_k_nearest_neighbors(
|
||||
ticker=self.current_id,
|
||||
data=self.data,
|
||||
n_neighbors=ns_parser.n_neighbors,
|
||||
n_input_days=ns_parser.n_inputs,
|
||||
n_predict_days=ns_parser.n_days,
|
||||
test_size=ns_parser.valid_split,
|
||||
end_date=ns_parser.s_end_date,
|
||||
no_shuffle=ns_parser.no_shuffle,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
except ValueError:
|
||||
t_console.print("The loaded data does not have enough data")
|
||||
|
||||
@try_except
|
||||
def call_regression(self, other_args: List[str]):
|
||||
"""Process linear command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
prog="regression",
|
||||
description="""
|
||||
Regression attempts to model the relationship between
|
||||
two variables by fitting a linear/quadratic/cubic/other equation to
|
||||
observed data. One variable is considered to be an explanatory variable,
|
||||
and the other is considered to be a dependent variable.
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--input",
|
||||
action="store",
|
||||
dest="n_inputs",
|
||||
type=check_positive,
|
||||
default=40,
|
||||
help="number of days to use for prediction.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--days",
|
||||
action="store",
|
||||
dest="n_days",
|
||||
type=check_positive,
|
||||
default=5,
|
||||
help="prediction days.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-j",
|
||||
"--jumps",
|
||||
action="store",
|
||||
dest="n_jumps",
|
||||
type=check_positive,
|
||||
default=1,
|
||||
help="number of jumps in training data.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--end",
|
||||
action="store",
|
||||
type=valid_date,
|
||||
dest="s_end_date",
|
||||
default=None,
|
||||
help="The end date (format YYYY-MM-DD) to select - Backtesting",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-p",
|
||||
"--polynomial",
|
||||
action="store",
|
||||
dest="n_polynomial",
|
||||
type=check_positive,
|
||||
default=1,
|
||||
help="polynomial associated with regression.",
|
||||
)
|
||||
if (
|
||||
other_args
|
||||
and "-h" not in other_args
|
||||
and ("-p" not in other_args or "--polynomial" not in other_args)
|
||||
):
|
||||
other_args.insert(0, "-p")
|
||||
ns_parser = parse_known_args_and_warn(
|
||||
parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
|
||||
)
|
||||
if ns_parser:
|
||||
# BACKTESTING CHECK
|
||||
if ns_parser.s_end_date:
|
||||
if ns_parser.s_end_date < self.data.index[0]:
|
||||
t_console.print(
|
||||
"Backtesting not allowed, since End Date is older than Start Date of historical data\n"
|
||||
)
|
||||
return
|
||||
|
||||
if ns_parser.s_end_date < get_next_stock_market_days(
|
||||
last_stock_day=self.data.index[0],
|
||||
n_next_days=5 + ns_parser.n_days,
|
||||
)[-1]:
|
||||
t_console.print(
|
||||
"Backtesting not allowed, since End Date is too close to Start Date to train model\n"
|
||||
)
|
||||
return
|
||||
|
||||
try:
|
||||
if ns_parser.n_inputs > len(self.data):
|
||||
t_console.print(
|
||||
f"[red]Data only contains {len(self.data)} samples and the model is trying "
|
||||
f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with"
|
||||
f" an earlier start date[/red]\n"
|
||||
)
|
||||
return
|
||||
regression_view.display_regression(
|
||||
dataset=self.current_id,
|
||||
values=self.data,
|
||||
poly_order=ns_parser.n_polynomial,
|
||||
n_input=ns_parser.n_inputs,
|
||||
n_predict=ns_parser.n_days,
|
||||
n_jumps=ns_parser.n_jumps,
|
||||
s_end_date=ns_parser.s_end_date,
|
||||
export=ns_parser.export,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
except ValueError as e:
|
||||
t_console.print(e)
|
||||
|
||||
@try_except
|
||||
def call_arima(self, other_args: List[str]):
|
||||
"""Process arima command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="arima",
|
||||
description="""
|
||||
In statistics and econometrics, and in particular in time series analysis, an
|
||||
autoregressive integrated moving average (ARIMA) model is a generalization of an
|
||||
autoregressive moving average (ARMA) model. Both of these models are fitted to time
|
||||
series data either to better understand the data or to predict future points in the
|
||||
series (forecasting). ARIMA(p,d,q) where parameters p, d, and q are non-negative
|
||||
integers, p is the order (number of time lags) of the autoregressive model, d is the
|
||||
degree of differencing (the number of times the data have had past values subtracted),
|
||||
and q is the order of the moving-average model.
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--days",
|
||||
action="store",
|
||||
dest="n_days",
|
||||
type=check_positive,
|
||||
default=5,
|
||||
help="prediction days.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--ic",
|
||||
action="store",
|
||||
dest="s_ic",
|
||||
type=str,
|
||||
default="aic",
|
||||
choices=arima_model.ICS,
|
||||
help="information criteria.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
"--seasonal",
|
||||
action="store_true",
|
||||
default=False,
|
||||
dest="b_seasonal",
|
||||
help="Use weekly seasonal data.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-o",
|
||||
"--order",
|
||||
action="store",
|
||||
dest="s_order",
|
||||
default="",
|
||||
type=str,
|
||||
help="arima model order (p,d,q) in format: p,d,q.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-r",
|
||||
"--results",
|
||||
action="store_true",
|
||||
dest="b_results",
|
||||
default=False,
|
||||
help="results about ARIMA summary flag.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--end",
|
||||
action="store",
|
||||
type=valid_date,
|
||||
dest="s_end_date",
|
||||
default=None,
|
||||
help="The end date (format YYYY-MM-DD) to select - Backtesting",
|
||||
)
|
||||
ns_parser = parse_known_args_and_warn(
|
||||
parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
|
||||
)
|
||||
if ns_parser:
|
||||
# BACKTESTING CHECK
|
||||
if ns_parser.s_end_date:
|
||||
if ns_parser.s_end_date < self.data.index[0]:
|
||||
t_console.print(
|
||||
"Backtesting not allowed, since End Date is older than Start Date of historical data\n"
|
||||
)
|
||||
return
|
||||
|
||||
if ns_parser.s_end_date < get_next_stock_market_days(
|
||||
last_stock_day=self.data.index[0],
|
||||
n_next_days=5 + ns_parser.n_days,
|
||||
)[-1]:
|
||||
t_console.print(
|
||||
"Backtesting not allowed, since End Date is too close to Start Date to train model\n"
|
||||
)
|
||||
return
|
||||
|
||||
arima_view.display_arima(
|
||||
dataset=self.current_id,
|
||||
values=self.data,
|
||||
arima_order=ns_parser.s_order,
|
||||
n_predict=ns_parser.n_days,
|
||||
seasonal=ns_parser.b_seasonal,
|
||||
ic=ns_parser.s_ic,
|
||||
results=ns_parser.b_results,
|
||||
s_end_date=ns_parser.s_end_date,
|
||||
export=ns_parser.export,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_mlp(self, other_args: List[str]):
|
||||
"""Process mlp command"""
|
||||
try:
|
||||
ns_parser = pred_helper.parse_args(
|
||||
prog="mlp",
|
||||
description="""Multi-Layered-Perceptron. """,
|
||||
other_args=other_args,
|
||||
)
|
||||
if ns_parser:
|
||||
if ns_parser.n_inputs > len(self.data):
|
||||
t_console.print(
|
||||
f"[red]Data only contains {len(self.data)} samples and the model is trying "
|
||||
f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with"
|
||||
f" an earlier start date[/red]\n"
|
||||
)
|
||||
return
|
||||
neural_networks_view.display_mlp(
|
||||
dataset=self.current_id,
|
||||
data=self.data,
|
||||
n_input_days=ns_parser.n_inputs,
|
||||
n_predict_days=ns_parser.n_days,
|
||||
learning_rate=ns_parser.lr,
|
||||
epochs=ns_parser.n_epochs,
|
||||
batch_size=ns_parser.n_batch_size,
|
||||
test_size=ns_parser.valid_split,
|
||||
n_loops=ns_parser.n_loops,
|
||||
no_shuffle=ns_parser.no_shuffle,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
except Exception as e:
|
||||
t_console.print(e, "\n")
|
||||
|
||||
finally:
|
||||
pred_helper.restore_env()
|
||||
|
||||
def call_rnn(self, other_args: List[str]):
|
||||
"""Process rnn command"""
|
||||
try:
|
||||
ns_parser = pred_helper.parse_args(
|
||||
prog="rnn",
|
||||
description="""Recurrent Neural Network. """,
|
||||
other_args=other_args,
|
||||
)
|
||||
if ns_parser:
|
||||
if ns_parser.n_inputs > len(self.data):
|
||||
t_console.print(
|
||||
f"[red]Data only contains {len(self.data)} samples and the model is trying "
|
||||
f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with"
|
||||
f" an earlier start date[/red]\n"
|
||||
)
|
||||
return
|
||||
neural_networks_view.display_rnn(
|
||||
dataset=self.current_id,
|
||||
data=self.data,
|
||||
n_input_days=ns_parser.n_inputs,
|
||||
n_predict_days=ns_parser.n_days,
|
||||
learning_rate=ns_parser.lr,
|
||||
epochs=ns_parser.n_epochs,
|
||||
batch_size=ns_parser.n_batch_size,
|
||||
test_size=ns_parser.valid_split,
|
||||
n_loops=ns_parser.n_loops,
|
||||
no_shuffle=ns_parser.no_shuffle,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
t_console.print(e)
|
||||
|
||||
finally:
|
||||
pred_helper.restore_env()
|
||||
|
||||
def call_lstm(self, other_args: List[str]):
|
||||
"""Process lstm command"""
|
||||
try:
|
||||
ns_parser = pred_helper.parse_args(
|
||||
prog="lstm",
|
||||
description="""Long-Short Term Memory. """,
|
||||
other_args=other_args,
|
||||
)
|
||||
if ns_parser:
|
||||
if ns_parser.n_inputs > len(self.data):
|
||||
t_console.print(
|
||||
f"[red]Data only contains {len(self.data)} samples and the model is trying "
|
||||
f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with"
|
||||
f" an earlier start date[/red]\n"
|
||||
)
|
||||
return
|
||||
neural_networks_view.display_lstm(
|
||||
dataset=self.current_id,
|
||||
data=self.data,
|
||||
n_input_days=ns_parser.n_inputs,
|
||||
n_predict_days=ns_parser.n_days,
|
||||
learning_rate=ns_parser.lr,
|
||||
epochs=ns_parser.n_epochs,
|
||||
batch_size=ns_parser.n_batch_size,
|
||||
test_size=ns_parser.valid_split,
|
||||
n_loops=ns_parser.n_loops,
|
||||
no_shuffle=ns_parser.no_shuffle,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
t_console.print(e, "\n")
|
||||
|
||||
finally:
|
||||
pred_helper.restore_env()
|
||||
|
||||
def call_conv1d(self, other_args: List[str]):
|
||||
"""Process conv1d command"""
|
||||
try:
|
||||
ns_parser = pred_helper.parse_args(
|
||||
prog="conv1d",
|
||||
description="""1D CNN.""",
|
||||
other_args=other_args,
|
||||
)
|
||||
if ns_parser:
|
||||
if ns_parser.n_inputs > len(self.data):
|
||||
t_console.print(
|
||||
f"[red]Data only contains {len(self.data)} samples and the model is trying "
|
||||
f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with"
|
||||
f" an earlier start date[/red]\n"
|
||||
)
|
||||
return
|
||||
neural_networks_view.display_conv1d(
|
||||
dataset=self.current_id,
|
||||
data=self.data,
|
||||
n_input_days=ns_parser.n_inputs,
|
||||
n_predict_days=ns_parser.n_days,
|
||||
learning_rate=ns_parser.lr,
|
||||
epochs=ns_parser.n_epochs,
|
||||
batch_size=ns_parser.n_batch_size,
|
||||
test_size=ns_parser.valid_split,
|
||||
n_loops=ns_parser.n_loops,
|
||||
no_shuffle=ns_parser.no_shuffle,
|
||||
time_res=self.resolution,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
t_console.print(e, "\n")
|
||||
|
||||
finally:
|
||||
pred_helper.restore_env()
|
||||
|
||||
@try_except
|
||||
def call_mc(self, other_args: List[str]):
|
||||
"""Process mc command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
add_help=False,
|
||||
prog="mc",
|
||||
description="""
|
||||
Perform Monte Carlo forecasting
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--days",
|
||||
help="Days to predict",
|
||||
dest="n_days",
|
||||
type=check_positive,
|
||||
default=30,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-n",
|
||||
"--num",
|
||||
help="Number of simulations to perform",
|
||||
dest="n_sims",
|
||||
type=check_positive,
|
||||
default=100,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dist",
|
||||
choices=mc_model.DISTRIBUTIONS,
|
||||
default="lognormal",
|
||||
dest="dist",
|
||||
help="Whether to model returns or log returns",
|
||||
)
|
||||
|
||||
ns_parser = parse_known_args_and_warn(
|
||||
parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
|
||||
)
|
||||
|
||||
if ns_parser:
|
||||
mc_view.display_mc_forecast(
|
||||
data=self.data,
|
||||
n_future=ns_parser.n_days,
|
||||
n_sims=ns_parser.n_sims,
|
||||
use_log=ns_parser.dist == "lognormal",
|
||||
export=ns_parser.export,
|
||||
fig_title=f"Monte Carlo Forecast for {self.current_id}",
|
||||
)
|
||||
|
||||
|
||||
def menu(series: Dict, queue: List[str] = None):
|
||||
"""Prediction Techniques Menu"""
|
||||
|
||||
pred_controller = PredictionTechniquesController(series, queue)
|
||||
an_input = "HELP_ME"
|
||||
|
||||
while True:
|
||||
# There is a command in the queue
|
||||
if pred_controller.queue and len(pred_controller.queue) > 0:
|
||||
# If the command is quitting the menu we want to return in here
|
||||
if pred_controller.queue[0] in ("q", "..", "quit"):
|
||||
t_console.print("")
|
||||
if len(pred_controller.queue) > 1:
|
||||
return pred_controller.queue[1:]
|
||||
return []
|
||||
|
||||
# Consume 1 element from the queue
|
||||
an_input = pred_controller.queue[0]
|
||||
pred_controller.queue = pred_controller.queue[1:]
|
||||
|
||||
# Print the current location because this was an instruction and we want user to know what was the action
|
||||
if an_input and an_input.split(" ")[0] in pred_controller.CHOICES_COMMANDS:
|
||||
t_console.print(f"{get_flair()} /economy/fred/pred/ $ {an_input}")
|
||||
|
||||
# Get input command from user
|
||||
else:
|
||||
# Display help menu when entering on this menu from a level above
|
||||
if an_input == "HELP_ME":
|
||||
pred_controller.print_help()
|
||||
|
||||
# Get input from user using auto-completion
|
||||
if session and gtff.USE_PROMPT_TOOLKIT and pred_controller.completer:
|
||||
try:
|
||||
an_input = session.prompt(
|
||||
f"{get_flair()} /economy/fred/pred/ $ ",
|
||||
completer=pred_controller.completer,
|
||||
search_ignore_case=True,
|
||||
)
|
||||
except KeyboardInterrupt:
|
||||
# Exit in case of keyboard interrupt
|
||||
an_input = "exit"
|
||||
# Get input from user without auto-completion
|
||||
else:
|
||||
an_input = input(f"{get_flair()} /economy/fred/pred/ $ ")
|
||||
|
||||
try:
|
||||
# Process the input command
|
||||
pred_controller.queue = pred_controller.switch(an_input)
|
||||
|
||||
except SystemExit:
|
||||
t_console.print(
|
||||
f"\nThe command '{an_input}' doesn't exist on the /economy/fred/pred menu.\n",
|
||||
)
|
||||
similar_cmd = difflib.get_close_matches(
|
||||
an_input.split(" ")[0] if " " in an_input else an_input,
|
||||
pred_controller.CHOICES,
|
||||
n=1,
|
||||
cutoff=0.7,
|
||||
)
|
||||
if similar_cmd:
|
||||
if " " in an_input:
|
||||
candidate_input = (
|
||||
f"{similar_cmd[0]} {' '.join(an_input.split(' ')[1:])}"
|
||||
)
|
||||
if candidate_input == an_input:
|
||||
an_input = ""
|
||||
pred_controller.queue = []
|
||||
t_console.print("")
|
||||
continue
|
||||
an_input = candidate_input
|
||||
else:
|
||||
an_input = similar_cmd[0]
|
||||
|
||||
t_console.print(f" Replacing by '{an_input}'.")
|
||||
pred_controller.queue.insert(0, an_input)
|
||||
else:
|
||||
t_console.print("")
|
||||
13
scripts/test_economy_fred_pred.gst
vendored
Normal file
13
scripts/test_economy_fred_pred.gst
vendored
Normal file
@@ -0,0 +1,13 @@
|
||||
economy
|
||||
fred
|
||||
add dgs5,dgs10
|
||||
pred
|
||||
rmv dgs10
|
||||
reset
|
||||
add dgs2
|
||||
pred
|
||||
mc
|
||||
regression
|
||||
lstm --epochs 2
|
||||
knn
|
||||
exit
|
||||
0
tests/gamestonk_terminal/economy/fred/__init__.py
Normal file
0
tests/gamestonk_terminal/economy/fred/__init__.py
Normal file
@@ -0,0 +1,740 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: null
|
||||
headers:
|
||||
Connection:
|
||||
- close
|
||||
Host:
|
||||
- api.stlouisfed.org
|
||||
method: GET
|
||||
uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2020-01-01&series_id=DGS10
|
||||
response:
|
||||
body:
|
||||
string: "<?xml version=\"1.0\" encoding=\"utf-8\" ?>\n<observations realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" observation_start=\"2020-01-01\" observation_end=\"9999-12-31\"
|
||||
units=\"lin\" output_type=\"1\" file_type=\"xml\" order_by=\"observation_date\"
|
||||
sort_order=\"asc\" count=\"523\" offset=\"0\" limit=\"100000\">\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-01\"
|
||||
value=\".\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-02\" value=\"1.88\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-01-03\" value=\"1.80\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-06\"
|
||||
value=\"1.81\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-07\" value=\"1.83\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-01-08\" value=\"1.87\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-09\"
|
||||
value=\"1.85\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-10\" value=\"1.83\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-01-13\" value=\"1.85\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-14\"
|
||||
value=\"1.82\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-15\" value=\"1.79\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-01-16\" value=\"1.81\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-17\"
|
||||
value=\"1.84\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-20\" value=\".\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-01-21\" value=\"1.78\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-22\"
|
||||
value=\"1.77\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-23\" value=\"1.74\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-01-24\" value=\"1.70\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-27\"
|
||||
value=\"1.61\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-28\" value=\"1.65\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-01-29\" value=\"1.60\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-01-30\"
|
||||
value=\"1.57\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-01-31\" value=\"1.51\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-02-03\" value=\"1.54\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-02-04\"
|
||||
value=\"1.61\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-02-05\" value=\"1.66\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-02-06\" value=\"1.65\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-02-07\"
|
||||
value=\"1.59\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-02-10\" value=\"1.56\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-02-11\" value=\"1.59\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-02-12\"
|
||||
value=\"1.62\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-02-13\" value=\"1.61\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-02-14\" value=\"1.59\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-02-17\"
|
||||
value=\".\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-02-18\" value=\"1.55\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-02-19\" value=\"1.56\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-02-20\"
|
||||
value=\"1.52\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-02-21\" value=\"1.46\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-02-24\" value=\"1.38\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-02-25\"
|
||||
value=\"1.33\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-02-26\" value=\"1.33\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-02-27\" value=\"1.30\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-02-28\"
|
||||
value=\"1.13\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-02\" value=\"1.10\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-03-03\" value=\"1.02\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-03-04\"
|
||||
value=\"1.02\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-05\" value=\"0.92\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-03-06\" value=\"0.74\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-03-09\"
|
||||
value=\"0.54\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-10\" value=\"0.76\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-03-11\" value=\"0.82\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-03-12\"
|
||||
value=\"0.88\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-13\" value=\"0.94\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-03-16\" value=\"0.73\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-03-17\"
|
||||
value=\"1.02\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-18\" value=\"1.18\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-03-19\" value=\"1.12\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-03-20\"
|
||||
value=\"0.92\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-23\" value=\"0.76\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-03-24\" value=\"0.84\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-03-25\"
|
||||
value=\"0.88\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-26\" value=\"0.83\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-03-27\" value=\"0.72\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-03-30\"
|
||||
value=\"0.70\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-03-31\" value=\"0.70\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-01\" value=\"0.62\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-04-02\"
|
||||
value=\"0.63\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-04-03\" value=\"0.62\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-06\" value=\"0.67\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-04-07\"
|
||||
value=\"0.75\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-04-08\" value=\"0.77\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-09\" value=\"0.73\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-04-10\"
|
||||
value=\".\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-04-13\" value=\"0.76\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-14\" value=\"0.76\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-04-15\"
|
||||
value=\"0.63\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-04-16\" value=\"0.61\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-17\" value=\"0.65\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-04-20\"
|
||||
value=\"0.63\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-04-21\" value=\"0.58\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-22\" value=\"0.63\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-04-23\"
|
||||
value=\"0.61\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-04-24\" value=\"0.60\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-27\" value=\"0.67\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-04-28\"
|
||||
value=\"0.62\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-04-29\" value=\"0.63\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-04-30\" value=\"0.64\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-05-01\"
|
||||
value=\"0.64\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-05-04\" value=\"0.64\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-05-05\" value=\"0.66\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-05-06\"
|
||||
value=\"0.72\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-05-07\" value=\"0.63\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-05-08\" value=\"0.69\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-05-11\"
|
||||
value=\"0.73\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-05-12\" value=\"0.69\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-05-13\" value=\"0.64\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-05-14\"
|
||||
value=\"0.63\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-05-15\" value=\"0.64\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-05-18\" value=\"0.73\"/>\n <observation
|
||||
realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\" date=\"2020-05-19\"
|
||||
value=\"0.70\"/>\n <observation realtime_start=\"2022-01-04\" realtime_end=\"2022-01-04\"
|
||||
date=\"2020-05-20\" value=\"0.68\"/>\n <observation realtime_start=\"2022-01-04\"
|
||||
realtime_end=\"2022-01-04\" date=\"2020-05-21\" value=\"0.68\"/>\n <observation
|
||||
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|
||||
2020-07-23,0.59
|
||||
2020-07-24,0.59
|
||||
2020-07-27,0.62
|
||||
2020-07-28,0.59
|
||||
2020-07-29,0.58
|
||||
2020-07-30,0.55
|
||||
2020-07-31,0.55
|
||||
2020-08-03,0.56
|
||||
2020-08-04,0.52
|
||||
2020-08-05,0.55
|
||||
2020-08-06,0.55
|
||||
2020-08-07,0.57
|
||||
2020-08-10,0.59
|
||||
2020-08-11,0.64
|
||||
2020-08-12,0.69
|
||||
2020-08-13,0.71
|
||||
2020-08-14,0.71
|
||||
2020-08-17,0.69
|
||||
2020-08-18,0.67
|
||||
2020-08-19,0.68
|
||||
2020-08-20,0.65
|
||||
2020-08-21,0.64
|
||||
2020-08-24,0.65
|
||||
2020-08-25,0.69
|
||||
2020-08-26,0.69
|
||||
2020-08-27,0.74
|
||||
2020-08-28,0.74
|
||||
2020-08-31,0.72
|
||||
2020-09-01,0.68
|
||||
2020-09-02,0.66
|
||||
2020-09-03,0.63
|
||||
2020-09-04,0.72
|
||||
2020-09-07,
|
||||
2020-09-08,0.69
|
||||
2020-09-09,0.71
|
||||
2020-09-10,0.68
|
||||
2020-09-11,0.67
|
||||
2020-09-14,0.68
|
||||
2020-09-15,0.68
|
||||
2020-09-16,0.69
|
||||
2020-09-17,0.69
|
||||
2020-09-18,0.7
|
||||
2020-09-21,0.68
|
||||
2020-09-22,0.68
|
||||
2020-09-23,0.68
|
||||
2020-09-24,0.67
|
||||
2020-09-25,0.66
|
||||
2020-09-28,0.67
|
||||
2020-09-29,0.66
|
||||
2020-09-30,0.69
|
||||
2020-10-01,0.68
|
||||
2020-10-02,0.7
|
||||
2020-10-05,0.78
|
||||
2020-10-06,0.76
|
||||
2020-10-07,0.81
|
||||
2020-10-08,0.78
|
||||
2020-10-09,0.79
|
||||
2020-10-12,
|
||||
2020-10-13,0.74
|
||||
2020-10-14,0.73
|
||||
2020-10-15,0.74
|
||||
2020-10-16,0.76
|
||||
2020-10-19,0.78
|
||||
2020-10-20,0.81
|
||||
2020-10-21,0.83
|
||||
2020-10-22,0.87
|
||||
2020-10-23,0.85
|
||||
2020-10-26,0.81
|
||||
2020-10-27,0.79
|
||||
2020-10-28,0.79
|
||||
2020-10-29,0.85
|
||||
2020-10-30,0.88
|
||||
2020-11-02,0.87
|
||||
2020-11-03,0.9
|
||||
2020-11-04,0.78
|
||||
2020-11-05,0.79
|
||||
2020-11-06,0.83
|
||||
2020-11-09,0.96
|
||||
2020-11-10,0.98
|
||||
2020-11-11,
|
||||
2020-11-12,0.88
|
||||
2020-11-13,0.89
|
||||
2020-11-16,0.91
|
||||
2020-11-17,0.87
|
||||
2020-11-18,0.88
|
||||
2020-11-19,0.86
|
||||
2020-11-20,0.83
|
||||
2020-11-23,0.86
|
||||
2020-11-24,0.88
|
||||
2020-11-25,0.88
|
||||
2020-11-26,
|
||||
2020-11-27,0.84
|
||||
2020-11-30,0.84
|
||||
2020-12-01,0.92
|
||||
2020-12-02,0.95
|
||||
2020-12-03,0.92
|
||||
2020-12-04,0.97
|
||||
2020-12-07,0.94
|
||||
2020-12-08,0.92
|
||||
2020-12-09,0.95
|
||||
2020-12-10,0.92
|
||||
2020-12-11,0.9
|
||||
2020-12-14,0.9
|
||||
2020-12-15,0.92
|
||||
2020-12-16,0.92
|
||||
2020-12-17,0.94
|
||||
2020-12-18,0.95
|
||||
2020-12-21,0.95
|
||||
2020-12-22,0.93
|
||||
2020-12-23,0.96
|
||||
2020-12-24,0.94
|
||||
2020-12-25,
|
||||
2020-12-28,0.94
|
||||
2020-12-29,0.94
|
||||
2020-12-30,0.93
|
||||
2020-12-31,0.93
|
||||
2021-01-01,
|
||||
2021-01-04,0.93
|
||||
2021-01-05,0.96
|
||||
2021-01-06,1.04
|
||||
2021-01-07,1.08
|
||||
2021-01-08,1.13
|
||||
2021-01-11,1.15
|
||||
2021-01-12,1.15
|
||||
2021-01-13,1.1
|
||||
2021-01-14,1.15
|
||||
2021-01-15,1.11
|
||||
2021-01-18,
|
||||
2021-01-19,1.1
|
||||
2021-01-20,1.1
|
||||
2021-01-21,1.12
|
||||
2021-01-22,1.1
|
||||
2021-01-25,1.05
|
||||
2021-01-26,1.05
|
||||
2021-01-27,1.04
|
||||
2021-01-28,1.07
|
||||
2021-01-29,1.11
|
||||
2021-02-01,1.09
|
||||
2021-02-02,1.12
|
||||
2021-02-03,1.15
|
||||
2021-02-04,1.15
|
||||
2021-02-05,1.19
|
||||
2021-02-08,1.19
|
||||
2021-02-09,1.18
|
||||
2021-02-10,1.15
|
||||
2021-02-11,1.16
|
||||
2021-02-12,1.2
|
||||
2021-02-15,
|
||||
2021-02-16,1.3
|
||||
2021-02-17,1.29
|
||||
2021-02-18,1.29
|
||||
2021-02-19,1.34
|
||||
2021-02-22,1.37
|
||||
2021-02-23,1.37
|
||||
2021-02-24,1.38
|
||||
2021-02-25,1.54
|
||||
2021-02-26,1.44
|
||||
2021-03-01,1.45
|
||||
2021-03-02,1.42
|
||||
2021-03-03,1.47
|
||||
2021-03-04,1.54
|
||||
2021-03-05,1.56
|
||||
2021-03-08,1.59
|
||||
2021-03-09,1.55
|
||||
2021-03-10,1.53
|
||||
2021-03-11,1.54
|
||||
2021-03-12,1.64
|
||||
2021-03-15,1.62
|
||||
2021-03-16,1.62
|
||||
2021-03-17,1.63
|
||||
2021-03-18,1.71
|
||||
2021-03-19,1.74
|
||||
2021-03-22,1.69
|
||||
2021-03-23,1.63
|
||||
2021-03-24,1.62
|
||||
2021-03-25,1.63
|
||||
2021-03-26,1.67
|
||||
2021-03-29,1.73
|
||||
2021-03-30,1.73
|
||||
2021-03-31,1.74
|
||||
2021-04-01,1.69
|
||||
2021-04-02,1.72
|
||||
2021-04-05,1.73
|
||||
2021-04-06,1.67
|
||||
2021-04-07,1.68
|
||||
2021-04-08,1.64
|
||||
2021-04-09,1.67
|
||||
2021-04-12,1.69
|
||||
2021-04-13,1.64
|
||||
2021-04-14,1.64
|
||||
2021-04-15,1.56
|
||||
2021-04-16,1.59
|
||||
2021-04-19,1.61
|
||||
2021-04-20,1.58
|
||||
2021-04-21,1.57
|
||||
2021-04-22,1.57
|
||||
2021-04-23,1.58
|
||||
2021-04-26,1.58
|
||||
2021-04-27,1.63
|
||||
2021-04-28,1.63
|
||||
2021-04-29,1.65
|
||||
2021-04-30,1.65
|
||||
2021-05-03,1.63
|
||||
2021-05-04,1.61
|
||||
2021-05-05,1.59
|
||||
2021-05-06,1.58
|
||||
2021-05-07,1.6
|
||||
2021-05-10,1.63
|
||||
2021-05-11,1.64
|
||||
2021-05-12,1.69
|
||||
2021-05-13,1.66
|
||||
2021-05-14,1.63
|
||||
2021-05-17,1.64
|
||||
2021-05-18,1.64
|
||||
2021-05-19,1.68
|
||||
2021-05-20,1.63
|
||||
2021-05-21,1.63
|
||||
2021-05-24,1.61
|
||||
2021-05-25,1.56
|
||||
2021-05-26,1.58
|
||||
2021-05-27,1.61
|
||||
2021-05-28,1.58
|
||||
2021-05-31,
|
||||
2021-06-01,1.62
|
||||
2021-06-02,1.59
|
||||
2021-06-03,1.63
|
||||
2021-06-04,1.56
|
||||
2021-06-07,1.57
|
||||
2021-06-08,1.53
|
||||
2021-06-09,1.5
|
||||
2021-06-10,1.45
|
||||
2021-06-11,1.47
|
||||
2021-06-14,1.51
|
||||
2021-06-15,1.51
|
||||
2021-06-16,1.57
|
||||
2021-06-17,1.52
|
||||
2021-06-18,1.45
|
||||
2021-06-21,1.5
|
||||
2021-06-22,1.48
|
||||
2021-06-23,1.5
|
||||
2021-06-24,1.49
|
||||
2021-06-25,1.54
|
||||
2021-06-28,1.49
|
||||
2021-06-29,1.49
|
||||
2021-06-30,1.45
|
||||
2021-07-01,1.48
|
||||
2021-07-02,1.44
|
||||
2021-07-05,
|
||||
2021-07-06,1.37
|
||||
2021-07-07,1.33
|
||||
2021-07-08,1.3
|
||||
2021-07-09,1.37
|
||||
2021-07-12,1.38
|
||||
2021-07-13,1.42
|
||||
2021-07-14,1.37
|
||||
2021-07-15,1.31
|
||||
2021-07-16,1.31
|
||||
2021-07-19,1.19
|
||||
2021-07-20,1.23
|
||||
2021-07-21,1.3
|
||||
2021-07-22,1.27
|
||||
2021-07-23,1.3
|
||||
2021-07-26,1.29
|
||||
2021-07-27,1.25
|
||||
2021-07-28,1.26
|
||||
2021-07-29,1.28
|
||||
2021-07-30,1.24
|
||||
2021-08-02,1.2
|
||||
2021-08-03,1.19
|
||||
2021-08-04,1.19
|
||||
2021-08-05,1.23
|
||||
2021-08-06,1.31
|
||||
2021-08-09,1.33
|
||||
2021-08-10,1.36
|
||||
2021-08-11,1.35
|
||||
2021-08-12,1.36
|
||||
2021-08-13,1.29
|
||||
2021-08-16,1.26
|
||||
2021-08-17,1.26
|
||||
2021-08-18,1.27
|
||||
2021-08-19,1.24
|
||||
2021-08-20,1.26
|
||||
2021-08-23,1.25
|
||||
2021-08-24,1.29
|
||||
2021-08-25,1.35
|
||||
2021-08-26,1.34
|
||||
2021-08-27,1.31
|
||||
2021-08-30,1.29
|
||||
2021-08-31,1.3
|
||||
2021-09-01,1.31
|
||||
2021-09-02,1.29
|
||||
2021-09-03,1.33
|
||||
2021-09-06,
|
||||
2021-09-07,1.38
|
||||
2021-09-08,1.35
|
||||
2021-09-09,1.3
|
||||
2021-09-10,1.35
|
||||
2021-09-13,1.33
|
||||
2021-09-14,1.28
|
||||
2021-09-15,1.31
|
||||
2021-09-16,1.34
|
||||
2021-09-17,1.37
|
||||
2021-09-20,1.31
|
||||
2021-09-21,1.33
|
||||
2021-09-22,1.32
|
||||
2021-09-23,1.41
|
||||
2021-09-24,1.47
|
||||
2021-09-27,1.48
|
||||
2021-09-28,1.54
|
||||
2021-09-29,1.55
|
||||
2021-09-30,1.52
|
||||
2021-10-01,1.48
|
||||
2021-10-04,1.49
|
||||
2021-10-05,1.54
|
||||
2021-10-06,1.53
|
||||
2021-10-07,1.58
|
||||
2021-10-08,1.61
|
||||
2021-10-11,
|
||||
2021-10-12,1.59
|
||||
2021-10-13,1.56
|
||||
2021-10-14,1.52
|
||||
2021-10-15,1.59
|
||||
2021-10-18,1.59
|
||||
2021-10-19,1.65
|
||||
2021-10-20,1.65
|
||||
2021-10-21,1.68
|
||||
2021-10-22,1.66
|
||||
2021-10-25,1.64
|
||||
2021-10-26,1.63
|
||||
2021-10-27,1.54
|
||||
2021-10-28,1.57
|
||||
2021-10-29,1.55
|
||||
2021-11-01,1.58
|
||||
2021-11-02,1.56
|
||||
2021-11-03,1.6
|
||||
2021-11-04,1.53
|
||||
2021-11-05,1.45
|
||||
2021-11-08,1.51
|
||||
2021-11-09,1.46
|
||||
2021-11-10,1.56
|
||||
2021-11-11,
|
||||
2021-11-12,1.58
|
||||
2021-11-15,1.63
|
||||
2021-11-16,1.63
|
||||
2021-11-17,1.6
|
||||
2021-11-18,1.59
|
||||
2021-11-19,1.54
|
||||
2021-11-22,1.63
|
||||
2021-11-23,1.67
|
||||
2021-11-24,1.64
|
||||
2021-11-25,
|
||||
2021-11-26,1.48
|
||||
2021-11-29,1.52
|
||||
2021-11-30,1.43
|
||||
2021-12-01,1.43
|
||||
2021-12-02,1.44
|
||||
2021-12-03,1.35
|
||||
2021-12-06,1.43
|
||||
2021-12-07,1.48
|
||||
2021-12-08,1.52
|
||||
2021-12-09,1.49
|
||||
2021-12-10,1.48
|
||||
2021-12-13,1.42
|
||||
2021-12-14,1.44
|
||||
2021-12-15,1.47
|
||||
2021-12-16,1.44
|
||||
2021-12-17,1.41
|
||||
2021-12-20,1.43
|
||||
2021-12-21,1.48
|
||||
2021-12-22,1.46
|
||||
2021-12-23,1.5
|
||||
2021-12-24,
|
||||
2021-12-27,1.48
|
||||
2021-12-28,1.49
|
||||
2021-12-29,1.55
|
||||
2021-12-30,1.52
|
||||
2021-12-31,1.52
|
||||
|
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,524 @@
|
||||
,0
|
||||
2020-01-01,
|
||||
2020-01-02,1.88
|
||||
2020-01-03,1.8
|
||||
2020-01-06,1.81
|
||||
2020-01-07,1.83
|
||||
2020-01-08,1.87
|
||||
2020-01-09,1.85
|
||||
2020-01-10,1.83
|
||||
2020-01-13,1.85
|
||||
2020-01-14,1.82
|
||||
2020-01-15,1.79
|
||||
2020-01-16,1.81
|
||||
2020-01-17,1.84
|
||||
2020-01-20,
|
||||
2020-01-21,1.78
|
||||
2020-01-22,1.77
|
||||
2020-01-23,1.74
|
||||
2020-01-24,1.7
|
||||
2020-01-27,1.61
|
||||
2020-01-28,1.65
|
||||
2020-01-29,1.6
|
||||
2020-01-30,1.57
|
||||
2020-01-31,1.51
|
||||
2020-02-03,1.54
|
||||
2020-02-04,1.61
|
||||
2020-02-05,1.66
|
||||
2020-02-06,1.65
|
||||
2020-02-07,1.59
|
||||
2020-02-10,1.56
|
||||
2020-02-11,1.59
|
||||
2020-02-12,1.62
|
||||
2020-02-13,1.61
|
||||
2020-02-14,1.59
|
||||
2020-02-17,
|
||||
2020-02-18,1.55
|
||||
2020-02-19,1.56
|
||||
2020-02-20,1.52
|
||||
2020-02-21,1.46
|
||||
2020-02-24,1.38
|
||||
2020-02-25,1.33
|
||||
2020-02-26,1.33
|
||||
2020-02-27,1.3
|
||||
2020-02-28,1.13
|
||||
2020-03-02,1.1
|
||||
2020-03-03,1.02
|
||||
2020-03-04,1.02
|
||||
2020-03-05,0.92
|
||||
2020-03-06,0.74
|
||||
2020-03-09,0.54
|
||||
2020-03-10,0.76
|
||||
2020-03-11,0.82
|
||||
2020-03-12,0.88
|
||||
2020-03-13,0.94
|
||||
2020-03-16,0.73
|
||||
2020-03-17,1.02
|
||||
2020-03-18,1.18
|
||||
2020-03-19,1.12
|
||||
2020-03-20,0.92
|
||||
2020-03-23,0.76
|
||||
2020-03-24,0.84
|
||||
2020-03-25,0.88
|
||||
2020-03-26,0.83
|
||||
2020-03-27,0.72
|
||||
2020-03-30,0.7
|
||||
2020-03-31,0.7
|
||||
2020-04-01,0.62
|
||||
2020-04-02,0.63
|
||||
2020-04-03,0.62
|
||||
2020-04-06,0.67
|
||||
2020-04-07,0.75
|
||||
2020-04-08,0.77
|
||||
2020-04-09,0.73
|
||||
2020-04-10,
|
||||
2020-04-13,0.76
|
||||
2020-04-14,0.76
|
||||
2020-04-15,0.63
|
||||
2020-04-16,0.61
|
||||
2020-04-17,0.65
|
||||
2020-04-20,0.63
|
||||
2020-04-21,0.58
|
||||
2020-04-22,0.63
|
||||
2020-04-23,0.61
|
||||
2020-04-24,0.6
|
||||
2020-04-27,0.67
|
||||
2020-04-28,0.62
|
||||
2020-04-29,0.63
|
||||
2020-04-30,0.64
|
||||
2020-05-01,0.64
|
||||
2020-05-04,0.64
|
||||
2020-05-05,0.66
|
||||
2020-05-06,0.72
|
||||
2020-05-07,0.63
|
||||
2020-05-08,0.69
|
||||
2020-05-11,0.73
|
||||
2020-05-12,0.69
|
||||
2020-05-13,0.64
|
||||
2020-05-14,0.63
|
||||
2020-05-15,0.64
|
||||
2020-05-18,0.73
|
||||
2020-05-19,0.7
|
||||
2020-05-20,0.68
|
||||
2020-05-21,0.68
|
||||
2020-05-22,0.66
|
||||
2020-05-25,
|
||||
2020-05-26,0.69
|
||||
2020-05-27,0.68
|
||||
2020-05-28,0.7
|
||||
2020-05-29,0.65
|
||||
2020-06-01,0.66
|
||||
2020-06-02,0.68
|
||||
2020-06-03,0.77
|
||||
2020-06-04,0.82
|
||||
2020-06-05,0.91
|
||||
2020-06-08,0.88
|
||||
2020-06-09,0.84
|
||||
2020-06-10,0.75
|
||||
2020-06-11,0.66
|
||||
2020-06-12,0.71
|
||||
2020-06-15,0.71
|
||||
2020-06-16,0.75
|
||||
2020-06-17,0.74
|
||||
2020-06-18,0.71
|
||||
2020-06-19,0.7
|
||||
2020-06-22,0.71
|
||||
2020-06-23,0.72
|
||||
2020-06-24,0.69
|
||||
2020-06-25,0.68
|
||||
2020-06-26,0.64
|
||||
2020-06-29,0.64
|
||||
2020-06-30,0.66
|
||||
2020-07-01,0.69
|
||||
2020-07-02,0.68
|
||||
2020-07-03,
|
||||
2020-07-06,0.69
|
||||
2020-07-07,0.65
|
||||
2020-07-08,0.67
|
||||
2020-07-09,0.62
|
||||
2020-07-10,0.65
|
||||
2020-07-13,0.64
|
||||
2020-07-14,0.63
|
||||
2020-07-15,0.64
|
||||
2020-07-16,0.62
|
||||
2020-07-17,0.64
|
||||
2020-07-20,0.62
|
||||
2020-07-21,0.61
|
||||
2020-07-22,0.6
|
||||
2020-07-23,0.59
|
||||
2020-07-24,0.59
|
||||
2020-07-27,0.62
|
||||
2020-07-28,0.59
|
||||
2020-07-29,0.58
|
||||
2020-07-30,0.55
|
||||
2020-07-31,0.55
|
||||
2020-08-03,0.56
|
||||
2020-08-04,0.52
|
||||
2020-08-05,0.55
|
||||
2020-08-06,0.55
|
||||
2020-08-07,0.57
|
||||
2020-08-10,0.59
|
||||
2020-08-11,0.64
|
||||
2020-08-12,0.69
|
||||
2020-08-13,0.71
|
||||
2020-08-14,0.71
|
||||
2020-08-17,0.69
|
||||
2020-08-18,0.67
|
||||
2020-08-19,0.68
|
||||
2020-08-20,0.65
|
||||
2020-08-21,0.64
|
||||
2020-08-24,0.65
|
||||
2020-08-25,0.69
|
||||
2020-08-26,0.69
|
||||
2020-08-27,0.74
|
||||
2020-08-28,0.74
|
||||
2020-08-31,0.72
|
||||
2020-09-01,0.68
|
||||
2020-09-02,0.66
|
||||
2020-09-03,0.63
|
||||
2020-09-04,0.72
|
||||
2020-09-07,
|
||||
2020-09-08,0.69
|
||||
2020-09-09,0.71
|
||||
2020-09-10,0.68
|
||||
2020-09-11,0.67
|
||||
2020-09-14,0.68
|
||||
2020-09-15,0.68
|
||||
2020-09-16,0.69
|
||||
2020-09-17,0.69
|
||||
2020-09-18,0.7
|
||||
2020-09-21,0.68
|
||||
2020-09-22,0.68
|
||||
2020-09-23,0.68
|
||||
2020-09-24,0.67
|
||||
2020-09-25,0.66
|
||||
2020-09-28,0.67
|
||||
2020-09-29,0.66
|
||||
2020-09-30,0.69
|
||||
2020-10-01,0.68
|
||||
2020-10-02,0.7
|
||||
2020-10-05,0.78
|
||||
2020-10-06,0.76
|
||||
2020-10-07,0.81
|
||||
2020-10-08,0.78
|
||||
2020-10-09,0.79
|
||||
2020-10-12,
|
||||
2020-10-13,0.74
|
||||
2020-10-14,0.73
|
||||
2020-10-15,0.74
|
||||
2020-10-16,0.76
|
||||
2020-10-19,0.78
|
||||
2020-10-20,0.81
|
||||
2020-10-21,0.83
|
||||
2020-10-22,0.87
|
||||
2020-10-23,0.85
|
||||
2020-10-26,0.81
|
||||
2020-10-27,0.79
|
||||
2020-10-28,0.79
|
||||
2020-10-29,0.85
|
||||
2020-10-30,0.88
|
||||
2020-11-02,0.87
|
||||
2020-11-03,0.9
|
||||
2020-11-04,0.78
|
||||
2020-11-05,0.79
|
||||
2020-11-06,0.83
|
||||
2020-11-09,0.96
|
||||
2020-11-10,0.98
|
||||
2020-11-11,
|
||||
2020-11-12,0.88
|
||||
2020-11-13,0.89
|
||||
2020-11-16,0.91
|
||||
2020-11-17,0.87
|
||||
2020-11-18,0.88
|
||||
2020-11-19,0.86
|
||||
2020-11-20,0.83
|
||||
2020-11-23,0.86
|
||||
2020-11-24,0.88
|
||||
2020-11-25,0.88
|
||||
2020-11-26,
|
||||
2020-11-27,0.84
|
||||
2020-11-30,0.84
|
||||
2020-12-01,0.92
|
||||
2020-12-02,0.95
|
||||
2020-12-03,0.92
|
||||
2020-12-04,0.97
|
||||
2020-12-07,0.94
|
||||
2020-12-08,0.92
|
||||
2020-12-09,0.95
|
||||
2020-12-10,0.92
|
||||
2020-12-11,0.9
|
||||
2020-12-14,0.9
|
||||
2020-12-15,0.92
|
||||
2020-12-16,0.92
|
||||
2020-12-17,0.94
|
||||
2020-12-18,0.95
|
||||
2020-12-21,0.95
|
||||
2020-12-22,0.93
|
||||
2020-12-23,0.96
|
||||
2020-12-24,0.94
|
||||
2020-12-25,
|
||||
2020-12-28,0.94
|
||||
2020-12-29,0.94
|
||||
2020-12-30,0.93
|
||||
2020-12-31,0.93
|
||||
2021-01-01,
|
||||
2021-01-04,0.93
|
||||
2021-01-05,0.96
|
||||
2021-01-06,1.04
|
||||
2021-01-07,1.08
|
||||
2021-01-08,1.13
|
||||
2021-01-11,1.15
|
||||
2021-01-12,1.15
|
||||
2021-01-13,1.1
|
||||
2021-01-14,1.15
|
||||
2021-01-15,1.11
|
||||
2021-01-18,
|
||||
2021-01-19,1.1
|
||||
2021-01-20,1.1
|
||||
2021-01-21,1.12
|
||||
2021-01-22,1.1
|
||||
2021-01-25,1.05
|
||||
2021-01-26,1.05
|
||||
2021-01-27,1.04
|
||||
2021-01-28,1.07
|
||||
2021-01-29,1.11
|
||||
2021-02-01,1.09
|
||||
2021-02-02,1.12
|
||||
2021-02-03,1.15
|
||||
2021-02-04,1.15
|
||||
2021-02-05,1.19
|
||||
2021-02-08,1.19
|
||||
2021-02-09,1.18
|
||||
2021-02-10,1.15
|
||||
2021-02-11,1.16
|
||||
2021-02-12,1.2
|
||||
2021-02-15,
|
||||
2021-02-16,1.3
|
||||
2021-02-17,1.29
|
||||
2021-02-18,1.29
|
||||
2021-02-19,1.34
|
||||
2021-02-22,1.37
|
||||
2021-02-23,1.37
|
||||
2021-02-24,1.38
|
||||
2021-02-25,1.54
|
||||
2021-02-26,1.44
|
||||
2021-03-01,1.45
|
||||
2021-03-02,1.42
|
||||
2021-03-03,1.47
|
||||
2021-03-04,1.54
|
||||
2021-03-05,1.56
|
||||
2021-03-08,1.59
|
||||
2021-03-09,1.55
|
||||
2021-03-10,1.53
|
||||
2021-03-11,1.54
|
||||
2021-03-12,1.64
|
||||
2021-03-15,1.62
|
||||
2021-03-16,1.62
|
||||
2021-03-17,1.63
|
||||
2021-03-18,1.71
|
||||
2021-03-19,1.74
|
||||
2021-03-22,1.69
|
||||
2021-03-23,1.63
|
||||
2021-03-24,1.62
|
||||
2021-03-25,1.63
|
||||
2021-03-26,1.67
|
||||
2021-03-29,1.73
|
||||
2021-03-30,1.73
|
||||
2021-03-31,1.74
|
||||
2021-04-01,1.69
|
||||
2021-04-02,1.72
|
||||
2021-04-05,1.73
|
||||
2021-04-06,1.67
|
||||
2021-04-07,1.68
|
||||
2021-04-08,1.64
|
||||
2021-04-09,1.67
|
||||
2021-04-12,1.69
|
||||
2021-04-13,1.64
|
||||
2021-04-14,1.64
|
||||
2021-04-15,1.56
|
||||
2021-04-16,1.59
|
||||
2021-04-19,1.61
|
||||
2021-04-20,1.58
|
||||
2021-04-21,1.57
|
||||
2021-04-22,1.57
|
||||
2021-04-23,1.58
|
||||
2021-04-26,1.58
|
||||
2021-04-27,1.63
|
||||
2021-04-28,1.63
|
||||
2021-04-29,1.65
|
||||
2021-04-30,1.65
|
||||
2021-05-03,1.63
|
||||
2021-05-04,1.61
|
||||
2021-05-05,1.59
|
||||
2021-05-06,1.58
|
||||
2021-05-07,1.6
|
||||
2021-05-10,1.63
|
||||
2021-05-11,1.64
|
||||
2021-05-12,1.69
|
||||
2021-05-13,1.66
|
||||
2021-05-14,1.63
|
||||
2021-05-17,1.64
|
||||
2021-05-18,1.64
|
||||
2021-05-19,1.68
|
||||
2021-05-20,1.63
|
||||
2021-05-21,1.63
|
||||
2021-05-24,1.61
|
||||
2021-05-25,1.56
|
||||
2021-05-26,1.58
|
||||
2021-05-27,1.61
|
||||
2021-05-28,1.58
|
||||
2021-05-31,
|
||||
2021-06-01,1.62
|
||||
2021-06-02,1.59
|
||||
2021-06-03,1.63
|
||||
2021-06-04,1.56
|
||||
2021-06-07,1.57
|
||||
2021-06-08,1.53
|
||||
2021-06-09,1.5
|
||||
2021-06-10,1.45
|
||||
2021-06-11,1.47
|
||||
2021-06-14,1.51
|
||||
2021-06-15,1.51
|
||||
2021-06-16,1.57
|
||||
2021-06-17,1.52
|
||||
2021-06-18,1.45
|
||||
2021-06-21,1.5
|
||||
2021-06-22,1.48
|
||||
2021-06-23,1.5
|
||||
2021-06-24,1.49
|
||||
2021-06-25,1.54
|
||||
2021-06-28,1.49
|
||||
2021-06-29,1.49
|
||||
2021-06-30,1.45
|
||||
2021-07-01,1.48
|
||||
2021-07-02,1.44
|
||||
2021-07-05,
|
||||
2021-07-06,1.37
|
||||
2021-07-07,1.33
|
||||
2021-07-08,1.3
|
||||
2021-07-09,1.37
|
||||
2021-07-12,1.38
|
||||
2021-07-13,1.42
|
||||
2021-07-14,1.37
|
||||
2021-07-15,1.31
|
||||
2021-07-16,1.31
|
||||
2021-07-19,1.19
|
||||
2021-07-20,1.23
|
||||
2021-07-21,1.3
|
||||
2021-07-22,1.27
|
||||
2021-07-23,1.3
|
||||
2021-07-26,1.29
|
||||
2021-07-27,1.25
|
||||
2021-07-28,1.26
|
||||
2021-07-29,1.28
|
||||
2021-07-30,1.24
|
||||
2021-08-02,1.2
|
||||
2021-08-03,1.19
|
||||
2021-08-04,1.19
|
||||
2021-08-05,1.23
|
||||
2021-08-06,1.31
|
||||
2021-08-09,1.33
|
||||
2021-08-10,1.36
|
||||
2021-08-11,1.35
|
||||
2021-08-12,1.36
|
||||
2021-08-13,1.29
|
||||
2021-08-16,1.26
|
||||
2021-08-17,1.26
|
||||
2021-08-18,1.27
|
||||
2021-08-19,1.24
|
||||
2021-08-20,1.26
|
||||
2021-08-23,1.25
|
||||
2021-08-24,1.29
|
||||
2021-08-25,1.35
|
||||
2021-08-26,1.34
|
||||
2021-08-27,1.31
|
||||
2021-08-30,1.29
|
||||
2021-08-31,1.3
|
||||
2021-09-01,1.31
|
||||
2021-09-02,1.29
|
||||
2021-09-03,1.33
|
||||
2021-09-06,
|
||||
2021-09-07,1.38
|
||||
2021-09-08,1.35
|
||||
2021-09-09,1.3
|
||||
2021-09-10,1.35
|
||||
2021-09-13,1.33
|
||||
2021-09-14,1.28
|
||||
2021-09-15,1.31
|
||||
2021-09-16,1.34
|
||||
2021-09-17,1.37
|
||||
2021-09-20,1.31
|
||||
2021-09-21,1.33
|
||||
2021-09-22,1.32
|
||||
2021-09-23,1.41
|
||||
2021-09-24,1.47
|
||||
2021-09-27,1.48
|
||||
2021-09-28,1.54
|
||||
2021-09-29,1.55
|
||||
2021-09-30,1.52
|
||||
2021-10-01,1.48
|
||||
2021-10-04,1.49
|
||||
2021-10-05,1.54
|
||||
2021-10-06,1.53
|
||||
2021-10-07,1.58
|
||||
2021-10-08,1.61
|
||||
2021-10-11,
|
||||
2021-10-12,1.59
|
||||
2021-10-13,1.56
|
||||
2021-10-14,1.52
|
||||
2021-10-15,1.59
|
||||
2021-10-18,1.59
|
||||
2021-10-19,1.65
|
||||
2021-10-20,1.65
|
||||
2021-10-21,1.68
|
||||
2021-10-22,1.66
|
||||
2021-10-25,1.64
|
||||
2021-10-26,1.63
|
||||
2021-10-27,1.54
|
||||
2021-10-28,1.57
|
||||
2021-10-29,1.55
|
||||
2021-11-01,1.58
|
||||
2021-11-02,1.56
|
||||
2021-11-03,1.6
|
||||
2021-11-04,1.53
|
||||
2021-11-05,1.45
|
||||
2021-11-08,1.51
|
||||
2021-11-09,1.46
|
||||
2021-11-10,1.56
|
||||
2021-11-11,
|
||||
2021-11-12,1.58
|
||||
2021-11-15,1.63
|
||||
2021-11-16,1.63
|
||||
2021-11-17,1.6
|
||||
2021-11-18,1.59
|
||||
2021-11-19,1.54
|
||||
2021-11-22,1.63
|
||||
2021-11-23,1.67
|
||||
2021-11-24,1.64
|
||||
2021-11-25,
|
||||
2021-11-26,1.48
|
||||
2021-11-29,1.52
|
||||
2021-11-30,1.43
|
||||
2021-12-01,1.43
|
||||
2021-12-02,1.44
|
||||
2021-12-03,1.35
|
||||
2021-12-06,1.43
|
||||
2021-12-07,1.48
|
||||
2021-12-08,1.52
|
||||
2021-12-09,1.49
|
||||
2021-12-10,1.48
|
||||
2021-12-13,1.42
|
||||
2021-12-14,1.44
|
||||
2021-12-15,1.47
|
||||
2021-12-16,1.44
|
||||
2021-12-17,1.41
|
||||
2021-12-20,1.43
|
||||
2021-12-21,1.48
|
||||
2021-12-22,1.46
|
||||
2021-12-23,1.5
|
||||
2021-12-24,
|
||||
2021-12-27,1.48
|
||||
2021-12-28,1.49
|
||||
2021-12-29,1.55
|
||||
2021-12-30,1.52
|
||||
2021-12-31,1.52
|
||||
|
@@ -0,0 +1 @@
|
||||
[{"id": "DGS5", "realtime_start": "2022-01-04", "realtime_end": "2022-01-04", "title": "Market Yield on U.S. Treasury Securities at 5-Year Constant Maturity", "observation_start": "1962-01-02", "observation_end": "2021-12-31", "frequency": "Daily", "frequency_short": "D", "units": "Percent", "units_short": "%", "seasonal_adjustment": "Not Seasonally Adjusted", "seasonal_adjustment_short": "NSA", "last_updated": "2022-01-03 15:23:07-06", "popularity": 76, "notes": "For further information regarding treasury constant maturity data, please refer to the Board of Governors (http://www.federalreserve.gov/releases/h15/current/h15.pdf) and the Treasury (http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/yieldmethod.aspx)."}]
|
||||
72
tests/gamestonk_terminal/economy/fred/test_fred_model.py
Normal file
72
tests/gamestonk_terminal/economy/fred/test_fred_model.py
Normal file
@@ -0,0 +1,72 @@
|
||||
import pytest
|
||||
import requests
|
||||
from gamestonk_terminal.economy.fred import fred_model
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def vcr_config():
|
||||
return {
|
||||
"filter_headers": [("User-Agent", None)],
|
||||
"filter_query_parameters": [
|
||||
("api_key", "MOCK_API_KEY"),
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.vcr
|
||||
@pytest.mark.parametrize(
|
||||
"func, kwargs_dict",
|
||||
[("check_series_id", {"series_id": "DGS5"})],
|
||||
)
|
||||
def test_output(func, kwargs_dict, recorder):
|
||||
result_tuple = getattr(fred_model, func)(**kwargs_dict)
|
||||
assert result_tuple[0] is True
|
||||
assert "seriess" in result_tuple[1]
|
||||
recorder.capture(result_tuple[1]["seriess"])
|
||||
|
||||
|
||||
@pytest.mark.vcr
|
||||
@pytest.mark.parametrize(
|
||||
"func, kwargs_dict",
|
||||
[
|
||||
("get_series_notes", {"series_term": "finance"}),
|
||||
("get_series_data", {"series_id": "DGS10", "start": "2020-01-01"}),
|
||||
],
|
||||
)
|
||||
def test_check_output(func, kwargs_dict, recorder):
|
||||
result_df = getattr(fred_model, func)(**kwargs_dict)
|
||||
|
||||
assert not result_df.empty
|
||||
recorder.capture(result_df)
|
||||
|
||||
|
||||
@pytest.mark.vcr
|
||||
@pytest.mark.parametrize(
|
||||
"func, kwargs_dict",
|
||||
[
|
||||
("check_series_id", {"series_id": "DGS5"}),
|
||||
],
|
||||
)
|
||||
def test_invalid_response_status(func, kwargs_dict, mocker):
|
||||
mock_response = requests.Response()
|
||||
mock_response.status_code = 500
|
||||
mocker.patch(
|
||||
target="requests.get",
|
||||
new=mocker.Mock(return_value=mock_response),
|
||||
)
|
||||
|
||||
result_tuple = getattr(fred_model, func)(**kwargs_dict)
|
||||
assert result_tuple[0] is False
|
||||
assert bool(result_tuple[1]) is False
|
||||
|
||||
|
||||
@pytest.mark.vcr
|
||||
@pytest.mark.parametrize(
|
||||
"func, kwargs_dict",
|
||||
[("get_series_data", {"series_id": "DGS10", "start": "2020-01-01"})],
|
||||
)
|
||||
def test_load_data(func, kwargs_dict, recorder):
|
||||
result_df = getattr(fred_model, func)(**kwargs_dict)
|
||||
|
||||
assert not result_df.empty
|
||||
recorder.capture(result_df)
|
||||
@@ -1,43 +0,0 @@
|
||||
""" econ/fred_view.py tests """
|
||||
import unittest
|
||||
from unittest import mock
|
||||
import io
|
||||
import pytest
|
||||
import pandas as pd
|
||||
|
||||
from gamestonk_terminal.economy.fred.fred_view import display_fred_series
|
||||
from tests.helpers.helpers import check_print
|
||||
|
||||
fred_data_mock = """
|
||||
For 'gdp', series IDs found: GDP, GDPC1, M2V, GFDEGDQ188S, PAYEMS.
|
||||
|
||||
GDP
|
||||
---
|
||||
Date
|
||||
2019-01-01 21001.591
|
||||
2019-04-01 21289.268
|
||||
2019-07-01 21505.012
|
||||
2019-10-01 21694.458
|
||||
2020-01-01 21481.367
|
||||
2020-04-01 19477.444
|
||||
2020-07-01 21138.574
|
||||
2020-10-01 21477.597
|
||||
2021-01-01 22038.226
|
||||
2021-04-01 22722.581
|
||||
"""
|
||||
|
||||
|
||||
@pytest.mark.skip
|
||||
class TestFredFredView(unittest.TestCase):
|
||||
@check_print(assert_in="gdp")
|
||||
@mock.patch("gamestonk_terminal.economy.fred.fred_model.get_series_data")
|
||||
def test_display_fred(self, mock_get_series):
|
||||
fred_data = pd.read_csv(io.StringIO(fred_data_mock), header=0, index_col=0)
|
||||
|
||||
mock_get_series.return_value = fred_data
|
||||
display_fred_series(
|
||||
d_series={"gdp": {"title": "GDP", "units": "Bil Dollars"}},
|
||||
start_date="2019-01-01",
|
||||
raw=True,
|
||||
export="",
|
||||
)
|
||||
@@ -236,14 +236,14 @@ def test_call_func_expect_queue(expected_queue, func, queue):
|
||||
(
|
||||
"call_ca",
|
||||
[],
|
||||
"",
|
||||
"ca_controller.menu",
|
||||
[],
|
||||
dict(),
|
||||
),
|
||||
(
|
||||
"call_po",
|
||||
[],
|
||||
"",
|
||||
"po_controller.menu",
|
||||
[],
|
||||
dict(),
|
||||
),
|
||||
|
||||
7
website/content/economy/fred/_index.md
vendored
7
website/content/economy/fred/_index.md
vendored
@@ -0,0 +1,7 @@
|
||||
---
|
||||
geekdocCollapseSection: true
|
||||
---
|
||||
|
||||
{{< toc-tree >}}
|
||||
|
||||
Menu for accessing Federal Reserve Economic Data (FRED)
|
||||
21
website/data/menu/main.yml
vendored
21
website/data/menu/main.yml
vendored
@@ -922,6 +922,27 @@ main:
|
||||
ref: "/economy/fred/rmv"
|
||||
- name: plot
|
||||
ref: "/economy/fred/plot"
|
||||
- name: prediction techniques
|
||||
ref: "/common/prediction_techniques"
|
||||
sub:
|
||||
- name: ets
|
||||
ref: "/common/prediction_techniques/ets"
|
||||
- name: knn
|
||||
ref: "/common/prediction_techniques/knn"
|
||||
- name: regression
|
||||
ref: "/common/prediction_techniques/regression"
|
||||
- name: arima
|
||||
ref: "/common/prediction_techniques/arima"
|
||||
- name: mlp
|
||||
ref: "/common/prediction_techniques/mlp"
|
||||
- name: rnn
|
||||
ref: "/common/prediction_techniques/rnn"
|
||||
- name: lstm
|
||||
ref: "/common/prediction_techniques/lstm"
|
||||
- name: conv1d
|
||||
ref: "/common/prediction_techniques/conv1d"
|
||||
- name: mc
|
||||
ref: "/common/prediction_techniques/mc"
|
||||
- name: etf
|
||||
ref: "/etf"
|
||||
sub:
|
||||
|
||||
Reference in New Issue
Block a user