Files
OpenBB/gamestonk_terminal/common/behavioural_analysis/google_view.py
jmaslek 876d1aab3e Refactor BA Menu (#768)
* Finnhub and finbrain fns

* Twitter menu (forex/ba  adjusted)

* Google menu

* Reddit menu pt1

* Reddit commands

* Stocktwits commands

* Sentimentinvestor commands

* Update crypto-dd

* Review pt 1

* Hugo server

* Review comments - get rid of all prints/errors in model...
2021-10-01 12:01:00 -04:00

161 lines
4.6 KiB
Python

"""Google View"""
__docformat__ = "numpy"
import os
from datetime import datetime
from tabulate import tabulate
import matplotlib.pyplot as plt
from gamestonk_terminal.helper_funcs import plot_autoscale, export_data
from gamestonk_terminal.common.behavioural_analysis import google_model
from gamestonk_terminal import config_plot as cfp, feature_flags as gtff
def display_mentions(ticker: str, start: datetime, export: str = ""):
"""Plot weekly bars of stock's interest over time. other users watchlist. [Source: Google]
Parameters
----------
ticker : str
Ticker
start : datetime
Start date
export: str
Format to export data
"""
df_interest = google_model.get_mentions(ticker)
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
ax.set_title(f"Interest over time on {ticker}")
if start:
df_interest = df_interest[start:] # type: ignore
ax.bar(df_interest.index, df_interest[ticker], width=2)
ax.bar(
df_interest.index[-1],
df_interest[ticker].values[-1],
color="tab:orange",
width=2,
)
else:
ax.bar(df_interest.index, df_interest[ticker], width=1)
ax.bar(
df_interest.index[-1],
df_interest[ticker].values[-1],
color="tab:orange",
width=1,
)
ax.grid(b=True, which="major", color="#666666", linestyle="-")
ax.set_ylabel("Interest [%]")
ax.set_xlabel("Time")
if gtff.USE_ION:
plt.ion()
fig.tight_layout()
plt.show()
print("")
export_data(
export, os.path.dirname(os.path.abspath(__file__)), "mentions", df_interest
)
def display_regions(ticker: str, num: int = 5, export: str = ""):
"""Plot bars of regions based on stock's interest. [Source: Google]
Parameters
----------
ticker : str
Ticker
num: int
Number of regions to show
export: str
Format to export data
"""
df_interest_region = google_model.get_regions(ticker)
if not df_interest_region.empty:
df_interest_region = df_interest_region.sort_values(
[ticker], ascending=False
).head(num)
df = df_interest_region.copy()
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
ax.set_title(f"Top's regions interest on {ticker}")
ax.bar(df_interest_region.index, df_interest_region[ticker], width=0.8)
ax.grid(b=True, which="major", color="#666666", linestyle="-")
ax.set_ylabel("Interest [%]")
ax.set_xlabel("Region")
if gtff.USE_ION:
plt.ion()
fig.tight_layout()
plt.show()
else:
print("No region data found.")
print("")
export_data(export, os.path.dirname(os.path.abspath(__file__)), "regions", df)
def display_queries(ticker: str, num: int = 5, export: str = ""):
"""Print top related queries with this stock's query. [Source: Google]
Parameters
----------
ticker : str
Ticker
num: int
Number of regions to show
export: str
Format to export data
"""
df_related_queries = google_model.get_queries(ticker)
df = df_related_queries.copy()
df_related_queries = df_related_queries[ticker]["top"].head(num)
df_related_queries["value"] = df_related_queries["value"].apply(
lambda x: str(x) + "%"
)
print(f"Top {ticker}'s related queries")
if gtff.USE_TABULATE_DF:
print(
tabulate(
df_related_queries,
headers=df_related_queries.columns,
tablefmt="fancy_grid",
)
)
else:
print(df_related_queries.to_string(index=False))
print("")
export_data(export, os.path.dirname(os.path.abspath(__file__)), "queries", df)
def display_rise(ticker: str, num: int, export: str = ""):
"""Print top rising related queries with this stock's query. [Source: Google]
Parameters
----------
ticker : str
Ticker
num: int
Number of regions to show
export: str
Format to export data
"""
df_related_queries = google_model.get_rise(ticker)
df = df_related_queries.copy()
df_related_queries = df_related_queries.head(num)
print(f"Top rising {ticker}'s related queries")
if gtff.USE_TABULATE_DF:
print(
tabulate(
df_related_queries,
headers=df_related_queries.columns,
tablefmt="fancy_grid",
)
)
else:
print(df_related_queries.to_string(index=False))
print("")
export_data(export, os.path.dirname(os.path.abspath(__file__)), "rise", df)