def display_historical(
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
candle_type: str = "a",
normalize: bool = True,
export: str = "",
):
"""Display historical stock prices. [Source: Yahoo Finance]
Parameters
----------
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
candle_type : str, optional
OHLCA column to use, by default "a" for Adjusted Close
normalize : bool, optional
Boolean to normalize all stock prices using MinMax defaults True
export : str, optional
Format to export historical prices, by default ""
"""
df_similar = yahoo_finance_model.get_historical(similar_tickers, start,
candle_type)
df_similar = df_similar[similar_tickers]
if np.any(df_similar.isna()):
nan_tickers = df_similar.columns[
df_similar.isna().sum() >= 1].to_list()
console.print(
f"NaN values found in: {', '.join(nan_tickers)}. Replacing with zeros."
)
df_similar = df_similar.fillna(0)
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
# This puts everything on 0-1 scale for visualizing
if normalize:
mm_scale = MinMaxScaler()
df_similar = pd.DataFrame(
mm_scale.fit_transform(df_similar),
columns=df_similar.columns,
index=df_similar.index,
)
df_similar.plot(ax=ax)
ax.set_title("Historical price of similar companies")
ax.set_xlabel("Time")
ax.set_ylabel(
f"{['','Normalized'][normalize]} Share Price {['($)',''][normalize]}")
ax.grid(b=True, which="major", color="#666666", linestyle="-")
# ensures that the historical data starts from same datapoint
ax.set_xlim([df_similar.index[0], df_similar.index[-1]])
plt.gcf().autofmt_xdate()
fig.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
export_data(export, os.path.dirname(os.path.abspath(__file__)),
"historical", df_similar)
console.print("")
def display_historical(
ticker: str,
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
candle_type: str = "a",
normalize: bool = True,
export: str = "",
):
"""Display historical stock prices
Parameters
----------
ticker : str
Base ticker
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
candle_type : str, optional
OHLCA column to use, by default "a" for Adjusted Close
normalize : bool, optional
Boolean to normalize all stock prices using MinMax defaults True
export : str, optional
Format to export historical prices, by default ""
"""
ordered_tickers = [ticker, *similar_tickers]
df_similar = yahoo_finance_model.get_historical(ticker, similar_tickers,
start, candle_type)
# To plot with ticker first
df_similar = df_similar[ordered_tickers]
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
# This puts everything on 0-1 scale for visualizing
if normalize:
mm_scale = MinMaxScaler()
df_similar = pd.DataFrame(
mm_scale.fit_transform(df_similar),
columns=df_similar.columns,
index=df_similar.index,
)
df_similar.plot(ax=ax)
ax.set_title(f"Similar companies to {ticker}")
ax.plot(df_similar.index, df_similar[ticker].values)
ax.set_xlabel("Time")
ax.set_ylabel(
f"{['','Normalized'][normalize]} Share Price {['($)',''][normalize]}")
ax.grid(b=True, which="major", color="#666666", linestyle="-")
# ensures that the historical data starts from same datapoint
ax.set_xlim([df_similar.index[0], df_similar.index[-1]])
plt.gcf().autofmt_xdate()
fig.tight_layout()
plt.show()
export_data(export, os.path.dirname(os.path.abspath(__file__)),
"historical", df_similar)
print("")
def display_correlation(
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
candle_type: str = "a",
):
"""
Correlation heatmap based on historical price comparison
between similar companies. [Source: Yahoo Finance]
Parameters
----------
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
candle_type : str, optional
OHLCA column to use, by default "a" for Adjusted Close
"""
df_similar = yahoo_finance_model.get_historical(similar_tickers, start,
candle_type)
df_similar = df_similar[similar_tickers]
if np.any(df_similar.isna()):
nan_tickers = df_similar.columns[
df_similar.isna().sum() >= 1].to_list()
console.print(
f"NaN values found in: {', '.join(nan_tickers)}. Backfilling data"
)
df_similar = df_similar.fillna(method="bfill")
df_similar = df_similar.dropna(axis=1, how="all")
mask = np.zeros((df_similar.shape[1], df_similar.shape[1]), dtype=bool)
mask[np.triu_indices(len(mask))] = True
plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
sns.heatmap(
df_similar.corr(),
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,
)
plt.title(f"Correlation Heatmap of similar companies from {start}")
if gtff.USE_ION:
plt.ion()
plt.show()
console.print("")
def display_volume(
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
):
"""Display volume stock prices. [Source: Yahoo Finance]
Parameters
----------
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
export : str, optional
Format to export historical prices, by default ""
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
df_similar = yahoo_finance_model.get_historical(similar_tickers, start, "v")
df_similar = df_similar[similar_tickers]
# This plot has 1 axis
if not external_axes:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
if len(external_axes) != 1:
logger.error("Expected list of one axis item.")
console.print("[red]Expected list of one axis item./n[/red]")
return
(ax,) = external_axes
df_similar = df_similar.div(1_000_000)
companies_names = df_similar.columns.to_list()
ax.plot(df_similar, label=companies_names)
ax.set_title("Historical volume of similar companies")
ax.set_ylabel("Volume [M]")
# ensures that the historical data starts from same datapoint
ax.set_xlim([df_similar.index[0], df_similar.index[-1]])
ax.legend()
theme.style_primary_axis(ax)
if not external_axes:
theme.visualize_output()
export_data(
export, os.path.dirname(os.path.abspath(__file__)), "volume", df_similar
)
console.print("")
def test_get_historical(mocker, recorder):
# FORCE SINGLE THREADING
yf_download = yahoo_finance_model.yf.download
def mock_yf_download(*args, **kwargs):
kwargs["threads"] = False
return yf_download(*args, **kwargs)
mocker.patch("yfinance.download", side_effect=mock_yf_download)
result_df = yahoo_finance_model.get_historical(
similar_tickers=["TSLA", "GM"],
start=datetime.strptime("2020-12-21", "%Y-%m-%d"),
candle_type="o",
)
recorder.capture(result_df)
def display_correlation(
ticker: str,
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
candle_type: str = "a",
):
"""
Correlation heatmap based on historical price comparison
between similar companies. [Source: Yahoo Finance]
Parameters
----------
ticker : str
Base ticker
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
candle_type : str, optional
OHLCA column to use, by default "a" for Adjusted Close
"""
ordered_tickers = [ticker, *similar_tickers]
df_similar = yahoo_finance_model.get_historical(ticker, similar_tickers,
start, candle_type)
# To plot with ticker first
df_similar = df_similar[ordered_tickers]
mask = np.zeros((df_similar.shape[1], df_similar.shape[1]), dtype=bool)
mask[np.triu_indices(len(mask))] = True
sns.heatmap(
df_similar.corr(),
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,
)
plt.title("Correlation Heatmap")
plt.show()
print("")
def display_volume(
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
export: str = "",
):
"""Display volume stock prices. [Source: Yahoo Finance]
Parameters
----------
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
export : str, optional
Format to export historical prices, by default ""
"""
df_similar = yahoo_finance_model.get_historical(similar_tickers, start,
"v")
df_similar = df_similar[similar_tickers]
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
df_similar = df_similar.div(1_000_000)
df_similar.plot(ax=ax)
ax.set_title("Historical volume of similar companies")
# ax.plot(df_similar.index, df_similar[ticker].values/1_000_000)
ax.set_xlabel("Date")
ax.set_ylabel("Volume [M]")
ax.grid(b=True, which="major", color="#666666", linestyle="-")
# ensures that the historical data starts from same datapoint
ax.set_xlim([df_similar.index[0], df_similar.index[-1]])
plt.gcf().autofmt_xdate()
fig.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
export_data(export, os.path.dirname(os.path.abspath(__file__)), "volume",
df_similar)
console.print("")
def display_historical(
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
candle_type: str = "a",
normalize: bool = True,
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
):
"""Display historical stock prices. [Source: Yahoo Finance]
Parameters
----------
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
candle_type : str, optional
OHLCA column to use, by default "a" for Adjusted Close
normalize : bool, optional
Boolean to normalize all stock prices using MinMax defaults True
export : str, optional
Format to export historical prices, by default ""
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
df_similar = yahoo_finance_model.get_historical(similar_tickers, start, candle_type)
df_similar = df_similar[similar_tickers]
if np.any(df_similar.isna()):
nan_tickers = df_similar.columns[df_similar.isna().sum() >= 1].to_list()
console.print(
f"NaN values found in: {', '.join(nan_tickers)}. Replacing with zeros."
)
df_similar = df_similar.fillna(0)
# This puts everything on 0-1 scale for visualizing
if normalize:
mm_scale = MinMaxScaler()
df_similar = pd.DataFrame(
mm_scale.fit_transform(df_similar),
columns=df_similar.columns,
index=df_similar.index,
)
# 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
# breakpoint()
companies_names = df_similar.columns.to_list()
ax.plot(df_similar, label=companies_names)
ax.set_title("Historical price of similar companies")
ax.set_ylabel(f"{['','Normalized'][normalize]} Share Price {['($)',''][normalize]}")
# ensures that the historical data starts from same datapoint
ax.set_xlim([df_similar.index[0], df_similar.index[-1]])
ax.legend(loc="best")
theme.style_primary_axis(ax)
if not external_axes:
theme.visualize_output()
export_data(
export, os.path.dirname(os.path.abspath(__file__)), "historical", df_similar
)
console.print("")
def display_correlation(
similar_tickers: List[str],
start: str = (datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"),
candle_type: str = "a",
external_axes: Optional[List[plt.Axes]] = None,
):
"""
Correlation heatmap based on historical price comparison
between similar companies. [Source: Yahoo Finance]
Parameters
----------
similar_tickers : List[str]
List of similar tickers
start : str, optional
Start date of comparison, by default 1 year ago
candle_type : str, optional
OHLCA column to use, by default "a" for Adjusted Close
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
df_similar = yahoo_finance_model.get_historical(similar_tickers, start, candle_type)
df_similar = df_similar[similar_tickers]
if np.any(df_similar.isna()):
nan_tickers = df_similar.columns[df_similar.isna().sum() >= 1].to_list()
console.print(
f"NaN values found in: {', '.join(nan_tickers)}. Backfilling data"
)
df_similar = df_similar.fillna(method="bfill")
df_similar = df_similar.dropna(axis=1, how="all")
mask = np.zeros((df_similar.shape[1], df_similar.shape[1]), dtype=bool)
mask[np.triu_indices(len(mask))] = True
# 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
sns.heatmap(
df_similar.corr(),
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,
)
ax.set_title(f"Correlation Heatmap of similar companies from {start}")
if not external_axes:
theme.visualize_output()
console.print("")
