def display_sentiment_correlation(similar: List[str],
raw: bool = False,
export: str = ""):
"""Plot correlation sentiments heatmap across similar companies. [Source: FinBrain]
Parameters
----------
similar : List[str]
Similar companies to compare income with
raw : bool, optional
Output raw values, by default False
export : str, optional
Format to export data
"""
df_sentiment = finbrain_model.get_sentiments(similar)
corrs = df_sentiment.corr()
if df_sentiment.empty:
console.print("No sentiments found.")
else:
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
mask = np.zeros((len(similar), len(similar)), dtype=bool)
mask[np.triu_indices(len(mask))] = True
sns.heatmap(
corrs,
cbar_kws={"ticks": [-1.0, -0.5, 0.0, 0.5, 1.0]},
cmap="RdYlGn",
linewidths=1,
annot=True,
vmin=-1,
vmax=1,
mask=mask,
ax=ax,
)
fig.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
if raw:
print_rich_table(
corrs,
headers=list(corrs.columns),
show_index=True,
title="Correlation Sentiments",
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"scorr",
corrs,
)
console.print("")
def display_sentiment_compare(
similar: List[str],
raw: bool = False,
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
):
"""Display sentiment for all ticker. [Source: FinBrain]
Parameters
----------
similar : List[str]
Similar companies to compare income with
raw : bool, optional
Output raw values, by default False
export : str, optional
Format to export data
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
df_sentiment = finbrain_model.get_sentiments(similar)
if df_sentiment.empty:
console.print("No sentiments found.")
else:
# This plot has 1 axis
if not external_axes:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
if len(external_axes) != 1:
console.print("[red]Expected list of one axis item./n[/red]")
return
(ax,) = external_axes
for idx, tick in enumerate(similar):
offset = 2 * idx
ax.axhline(y=offset, color="white", linestyle="--", lw=1)
ax.axhline(y=offset + 1, color="white", linestyle="--", lw=1)
senValues = np.array(pd.to_numeric(df_sentiment[tick].values))
senNone = np.array(0 * len(df_sentiment))
ax.fill_between(
df_sentiment.index,
pd.to_numeric(df_sentiment[tick].values) + offset,
offset,
where=(senValues < senNone),
color=theme.down_color,
interpolate=True,
)
ax.fill_between(
df_sentiment.index,
pd.to_numeric(df_sentiment[tick].values) + offset,
offset,
where=(senValues >= senNone),
color=theme.up_color,
interpolate=True,
)
ax.set_ylabel("Sentiment")
ax.axhline(y=-1, color="white", linestyle="--", lw=1)
ax.set_yticks(np.arange(len(similar)) * 2)
ax.set_yticklabels(similar)
ax.set_xlim(df_sentiment.index[0], df_sentiment.index[-1])
ax.set_title(f"FinBrain's Sentiment Analysis since {df_sentiment.index[0]}")
theme.style_primary_axis(ax)
if not external_axes:
theme.visualize_output()
if raw:
print_rich_table(
df_sentiment,
headers=list(df_sentiment.columns),
title="Ticker Sentiment",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"sentiment",
df_sentiment,
)
console.print("")
def display_sentiment_correlation(
similar: List[str],
raw: bool = False,
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
):
"""Plot correlation sentiments heatmap across similar companies. [Source: FinBrain]
Parameters
----------
similar : List[str]
Similar companies to compare income with
raw : bool, optional
Output raw values, by default False
export : str, optional
Format to export data
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
df_sentiment = finbrain_model.get_sentiments(similar)
corrs = df_sentiment.corr()
if df_sentiment.empty:
console.print("No sentiments found.")
else:
# This plot has 1 axis
if not external_axes:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
if len(external_axes) != 1:
console.print("[red]Expected list of one axis item./n[/red]")
return
(ax,) = external_axes
mask = np.zeros((len(similar), len(similar)), dtype=bool)
mask[np.triu_indices(len(mask))] = True
sns.heatmap(
corrs,
cbar_kws={"ticks": [-1.0, -0.5, 0.0, 0.5, 1.0]},
cmap="RdYlGn",
linewidths=1,
annot=True,
vmin=-1,
vmax=1,
mask=mask,
ax=ax,
)
similar_string = ",".join(similar)
ax.set_title(
f"Sentiment correlation heatmap across \n{similar_string}", fontsize=11
)
if not external_axes:
theme.visualize_output()
if raw:
print_rich_table(
corrs,
headers=list(corrs.columns),
show_index=True,
title="Correlation Sentiments",
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"scorr",
corrs,
)
console.print("")
def display_sentiment_compare(similar: List[str],
raw: bool = False,
export: str = ""):
"""Display sentiment for all ticker. [Source: FinBrain]
Parameters
----------
similar : List[str]
Similar companies to compare income with
raw : bool, optional
Output raw values, by default False
export : str, optional
Format to export data
"""
df_sentiment = finbrain_model.get_sentiments(similar)
if df_sentiment.empty:
console.print("No sentiments found.")
else:
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
for idx, tick in enumerate(similar):
offset = 2 * idx
ax.axhline(y=offset, color="k", linestyle="--", lw=2)
ax.axhline(y=offset + 1, color="k", linestyle="--", lw=1)
senValues = np.array(pd.to_numeric(df_sentiment[tick].values))
senNone = np.array(0 * len(df_sentiment))
ax.fill_between(
df_sentiment.index,
pd.to_numeric(df_sentiment[tick].values) + offset,
offset,
where=(senValues < senNone),
alpha=0.60,
color="red",
interpolate=True,
)
ax.fill_between(
df_sentiment.index,
pd.to_numeric(df_sentiment[tick].values) + offset,
offset,
where=(senValues >= senNone),
alpha=0.60,
color="green",
interpolate=True,
)
ax.set_xlabel("Time")
ax.set_ylabel("Sentiment")
ax.axhline(y=-1, color="k", linestyle="--", lw=1)
ax.grid(b=True, which="major", color="#666666", linestyle="-")
ax.minorticks_on()
ax.grid(b=True,
which="minor",
color="#999999",
linestyle="-",
alpha=0.2)
ax.set_yticks(np.arange(len(similar)) * 2)
ax.set_yticklabels(similar)
ax.set_title(
f"FinBrain's Sentiment Analysis since {df_sentiment.index[0]}")
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=1))
plt.gcf().autofmt_xdate()
fig.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
if raw:
print_rich_table(
df_sentiment,
headers=list(df_sentiment.columns),
title="Ticker Sentiment",
)
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"sentiment",
df_sentiment,
)
console.print("")
def test_get_sentiments(recorder):
result_df = finbrain_model.get_sentiments(tickers=["TSLA"])
recorder.capture(result_df)
def display_sentiment_correlation(ticker: str,
similar: List[str],
raw: bool = False,
export: str = ""):
"""Plot correlation sentiments heatmap across similar companies
Parameters
----------
ticker : str
Main ticker to compare income
similar : List[str]
Similar companies to compare income with
raw : bool, optional
Output raw values, by default False
export : str, optional
Format to export data
"""
all_tickers = [ticker, *similar]
df_sentiment = finbrain_model.get_sentiments(all_tickers)
corrs = df_sentiment.corr()
if df_sentiment.empty:
print("No sentiments found.")
else:
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
mask = np.zeros((len(all_tickers), len(all_tickers)), dtype=bool)
mask[np.triu_indices(len(mask))] = True
sns.heatmap(
corrs,
cbar_kws={"ticks": [-1.0, -0.5, 0.0, 0.5, 1.0]},
cmap="RdYlGn",
linewidths=1,
annot=True,
vmin=-1,
vmax=1,
mask=mask,
ax=ax,
)
fig.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
if raw:
if gtff.USE_TABULATE_DF:
print(
tabulate(
corrs,
headers=corrs.columns,
showindex=True,
tablefmt="fancy_grid",
))
else:
print(corrs.to_string())
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"scorr",
corrs,
)
print("")