def historical(other_args: List[str], preset_loaded: str):
"""View historical price of stocks that meet preset
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker : str
Loaded preset filter
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="historical",
description=
"""Historical price comparison between similar companies [Source: Yahoo Finance]
""",
)
parser.add_argument(
"--start",
type=valid_date,
default=datetime.datetime.now() - datetime.timedelta(days=6 * 30),
dest="start",
help=
"The starting date (format YYYY-MM-DD) of the historical price to plot",
)
parser.add_argument(
"-t",
"--type",
action="store",
dest="type_candle",
type=check_one_of_ohlca,
default="a", # in case it's adjusted close
help=(
"type of candles: o-open, h-high, l-low, c-close, a-adjusted close."
),
)
parser.add_argument(
"-s",
"--signal",
action="store",
dest="signal",
type=str,
default=None,
help="Signal",
choices=list(finviz_view.d_signals.keys()),
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
preset_filter = configparser.RawConfigParser()
preset_filter.optionxform = str # type: ignore
preset_filter.read("gamestonk_terminal/screener/presets/" +
preset_loaded + ".ini")
d_general = preset_filter["General"]
d_filters = {
**preset_filter["Descriptive"],
**preset_filter["Fundamental"],
**preset_filter["Technical"],
}
d_filters = {k: v for k, v in d_filters.items() if v}
screen = ticker.Ticker()
if ns_parser.signal:
screen.set_filter(signal=finviz_view.d_signals[ns_parser.signal])
else:
if d_general["Signal"]:
screen.set_filter(filters_dict=d_filters,
signal=d_general["Signal"])
else:
screen.set_filter(filters_dict=d_filters)
l_min = list()
l_leg = list()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
l_stocks = screen.ScreenerView(verbose=0)
if len(l_stocks) > 10:
print(
"\nThe limit of stocks to compare with are 10. Hence, 10 random similar stocks will be displayed.",
"\nThe selected list will be:",
)
random.shuffle(l_stocks)
l_stocks = sorted(l_stocks[:10])
print(", ".join(l_stocks))
while l_stocks:
l_parsed_stocks = list()
for symbol in l_stocks:
try:
df_similar_stock = yf.download(
symbol,
start=datetime.datetime.strftime(
ns_parser.start, "%Y-%m-%d"),
progress=False,
threads=False,
)
if not df_similar_stock.empty:
plt.plot(
df_similar_stock.index,
df_similar_stock[d_candle_types[
ns_parser.type_candle]].values,
)
l_min.append(df_similar_stock.index[0])
l_leg.append(symbol)
l_parsed_stocks.append(symbol)
except Exception as e:
print("")
print(e)
print(
"Disregard previous error, which is due to API Rate limits from Yahoo Finance."
)
print(
f"Because we like '{symbol}', and we won't leave without getting data from it."
)
for parsed_stock in l_parsed_stocks:
l_stocks.remove(parsed_stock)
if ns_parser.signal:
plt.title(
f"Screener Historical Price using {finviz_view.d_signals[ns_parser.signal]} signal"
)
else:
plt.title(
f"Screener Historical Price using {preset_loaded} preset")
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.legend(l_leg)
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True,
which="minor",
color="#999999",
linestyle="-",
alpha=0.2)
# ensures that the historical data starts from same datapoint
plt.xlim([max(l_min), df_similar_stock.index[-1]])
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
return l_parsed_stocks
except SystemExit:
print("Similar companies need to be provided", "\n")
return []
except Exception as e:
print(e, "\n")
return []
def historical(
preset_loaded: str,
limit: int = 10,
start: datetime.datetime = datetime.datetime.now() -
datetime.timedelta(days=6 * 30),
type_candle: str = "a",
normalize: bool = True,
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
) -> List[str]:
"""View historical price of stocks that meet preset
Parameters
----------
preset_loaded: str
Preset loaded to filter for tickers
limit: int
Number of stocks to display
start: datetime
Start datetime to display historical data
type_candle: str
Type of candle to display
normalize : bool
Boolean to normalize all stock prices using MinMax
export : str
Export dataframe data to csv,json,xlsx file
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None"""
screen = ticker.Ticker()
if preset_loaded in finviz_model.d_signals:
screen.set_filter(signal=finviz_model.d_signals[preset_loaded])
else:
preset_filter = configparser.RawConfigParser()
preset_filter.optionxform = str # type: ignore
preset_filter.read(presets_path + preset_loaded + ".ini")
d_general = preset_filter["General"]
d_filters = {
**preset_filter["Descriptive"],
**preset_filter["Fundamental"],
**preset_filter["Technical"],
}
d_filters = {k: v for k, v in d_filters.items() if v}
if "Signal" in d_general and d_general["Signal"]:
screen.set_filter(filters_dict=d_filters,
signal=d_general["Signal"])
else:
screen.set_filter(filters_dict=d_filters)
l_stocks = screen.ScreenerView(verbose=0)
limit_random_stocks = False
if l_stocks:
if len(l_stocks) > limit:
random.shuffle(l_stocks)
l_stocks = sorted(l_stocks[:limit])
console.print(
"\nThe limit of stocks to compare with are 10. Hence, 10 random similar stocks will be displayed.",
f"\nThe selected list will be: {', '.join(l_stocks)}",
)
limit_random_stocks = True
df_screener = yf.download(
l_stocks, start=start, progress=False,
threads=False)[d_candle_types[type_candle]][l_stocks]
df_screener = df_screener[l_stocks]
if np.any(df_screener.isna()):
nan_tickers = df_screener.columns[
df_screener.isna().sum() >= 1].to_list()
console.print(
f"NaN values found in: {', '.join(nan_tickers)}. Replacing with zeros."
)
df_screener = df_screener.fillna(0)
# 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 empty list to be compatible with the other return statement
return []
(ax, ) = external_axes
# This puts everything on 0-1 scale for visualizing
if normalize:
mm_scale = MinMaxScaler()
df_screener = pd.DataFrame(
mm_scale.fit_transform(df_screener),
columns=df_screener.columns,
index=df_screener.index,
)
df_screener.plot(ax=ax)
if limit_random_stocks:
ax.set_title(
f"Screener Historical Price with {preset_loaded}\non 10 random stocks"
)
else:
ax.set_title(f"Screener Historical Price with {preset_loaded}")
ax.set_ylabel(
f"{['','Normalized'][normalize]} Share Price {['($)',''][normalize]}"
)
ax.legend()
# ensures that the historical data starts from same datapoint
ax.set_xlim([df_screener.index[0], df_screener.index[-1]])
theme.style_primary_axis(ax)
if not external_axes:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"historical",
df_screener,
)
return l_stocks
console.print("No screener stocks found with this preset", "\n")
return []
def historical(
preset_loaded: str,
limit: int = 10,
start: datetime.datetime = datetime.datetime.now()
- datetime.timedelta(days=6 * 30),
type_candle: str = "a",
normalize: bool = True,
export: str = "",
) -> List[str]:
"""View historical price of stocks that meet preset
Parameters
----------
preset_loaded: str
Preset loaded to filter for tickers
limit: int
Number of stocks to display
start: datetime
Start datetime to display historical data
type_candle: str
Type of candle to display
normalize : bool
Boolean to normalize all stock prices using MinMax
export : str
Export dataframe data to csv,json,xlsx file
"""
screen = ticker.Ticker()
if preset_loaded in finviz_model.d_signals:
screen.set_filter(signal=finviz_model.d_signals[preset_loaded])
else:
preset_filter = configparser.RawConfigParser()
preset_filter.optionxform = str # type: ignore
preset_filter.read(presets_path + preset_loaded + ".ini")
d_general = preset_filter["General"]
d_filters = {
**preset_filter["Descriptive"],
**preset_filter["Fundamental"],
**preset_filter["Technical"],
}
d_filters = {k: v for k, v in d_filters.items() if v}
if "Signal" in d_general and d_general["Signal"]:
screen.set_filter(filters_dict=d_filters, signal=d_general["Signal"])
else:
screen.set_filter(filters_dict=d_filters)
l_stocks = screen.ScreenerView(verbose=0)
limit_random_stocks = False
if l_stocks:
if len(l_stocks) > limit:
random.shuffle(l_stocks)
l_stocks = sorted(l_stocks[:limit])
console.print(
"\nThe limit of stocks to compare with are 10. Hence, 10 random similar stocks will be displayed.",
f"\nThe selected list will be: {', '.join(l_stocks)}",
)
limit_random_stocks = True
df_screener = yf.download(l_stocks, start=start, progress=False, threads=False)[
d_candle_types[type_candle]
][l_stocks]
df_screener = df_screener[l_stocks]
if np.any(df_screener.isna()):
nan_tickers = df_screener.columns[df_screener.isna().sum() >= 1].to_list()
console.print(
f"NaN values found in: {', '.join(nan_tickers)}. Replacing with zeros."
)
df_screener = df_screener.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_screener = pd.DataFrame(
mm_scale.fit_transform(df_screener),
columns=df_screener.columns,
index=df_screener.index,
)
df_screener.plot(ax=ax)
if limit_random_stocks:
ax.set_title(
f"Screener Historical Price using {preset_loaded} on 10 of those stocks"
)
else:
ax.set_title(f"Screener Historical Price using {preset_loaded}")
ax.set_xlabel("Time")
ax.set_ylabel(
f"{['','Normalized'][normalize]} Share Price {['($)',''][normalize]}"
)
ax.grid(b=True, which="major", color="#666666", linestyle="-")
ax.legend(l_stocks, bbox_to_anchor=(1.04, 1), loc="upper left")
# ensures that the historical data starts from same datapoint
ax.set_xlim([df_screener.index[0], df_screener.index[-1]])
plt.gcf().autofmt_xdate()
fig.tight_layout()
if gtff.USE_ION:
plt.ion()
plt.show()
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"historical",
df_screener,
)
return l_stocks
console.print("No screener stocks found with this preset", "\n")
return []
async def historical_command(ctx, signal="", start=""):
"""Displays historical price comparison between similar companies [Yahoo Finance]"""
try:
# Debug user input
if cfg.DEBUG:
logger.debug("!stocks.scr.historical %s %s", signal, start)
# Check for argument
if signal == "" or signal not in list(so.d_signals_desc.keys):
raise Exception("Invalid preset selected!")
register_matplotlib_converters()
screen = ticker.Ticker()
if signal in finviz_model.d_signals:
screen.set_filter(signal=finviz_model.d_signals[signal])
else:
preset_filter = configparser.RawConfigParser()
preset_filter.optionxform = str # type: ignore
preset_filter.read(so.presets_path + signal + ".ini")
d_general = preset_filter["General"]
d_filters = {
**preset_filter["Descriptive"],
**preset_filter["Fundamental"],
**preset_filter["Technical"],
}
d_filters = {k: v for k, v in d_filters.items() if v}
if d_general["Signal"]:
screen.set_filter(filters_dict=d_filters,
signal=d_general["Signal"])
else:
screen.set_filter(filters_dict=d_filters)
if start == "":
start = datetime.now() - timedelta(days=365)
else:
start = datetime.strptime(start, cfg.DATE_FORMAT)
# Output Data
l_min = []
l_leg = []
l_stocks = screen.ScreenerView(verbose=0)
if len(l_stocks) > 10:
description = (
"\nThe limit of stocks to compare with are 10. Hence, 10 random similar stocks will be displayed."
"\nThe selected list will be: ")
random.shuffle(l_stocks)
l_stocks = sorted(l_stocks[:10])
description = description + (", ".join(l_stocks))
logger.debug(description)
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
while l_stocks:
l_parsed_stocks = []
for symbol in l_stocks:
try:
df_similar_stock = yf.download(
symbol,
start=datetime.strftime(start, "%Y-%m-%d"),
progress=False,
threads=False,
)
if not df_similar_stock.empty:
plt.plot(
df_similar_stock.index,
df_similar_stock["Adj Close"].values,
)
l_min.append(df_similar_stock.index[0])
l_leg.append(symbol)
l_parsed_stocks.append(symbol)
except Exception as e:
error = (
f"{e}\nDisregard previous error, which is due to API Rate limits from Yahoo Finance. "
f"Because we like '{symbol}', and we won't leave without getting data from it."
)
embed = discord.Embed(
title=
"ERROR Stocks: [Yahoo Finance] Historical Screener",
colour=cfg.COLOR,
description=error,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed)
for parsed_stock in l_parsed_stocks:
l_stocks.remove(parsed_stock)
if signal:
plt.title(
f"Screener Historical Price using {finviz_model.d_signals[signal]} signal"
)
else:
plt.title(f"Screener Historical Price using {signal} preset")
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.legend(l_leg)
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True,
which="minor",
color="#999999",
linestyle="-",
alpha=0.2)
# ensures that the historical data starts from same datapoint
plt.xlim([max(l_min), df_similar_stock.index[-1]])
plt.savefig("scr_historical.png")
uploaded_image = gst_imgur.upload_image("scr_historical.png",
title="something")
image_link = uploaded_image.link
if cfg.DEBUG:
logger.debug("Image URL: %s", image_link)
title = "Stocks: [Yahoo Finance] Historical Screener"
embed = discord.Embed(title=title,
description=description,
colour=cfg.COLOR)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
embed.set_image(url=image_link)
os.remove("scr_historical.png")
await ctx.send(embed=embed)
except Exception as e:
embed = discord.Embed(
title="ERROR Stocks: [Yahoo Finance] Historical Screener",
colour=cfg.COLOR,
description=e,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed)
def historical_command(signal: str = "", start=""):
"""Displays historical price comparison between similar companies [Yahoo Finance]"""
# Debug user input
if imps.DEBUG:
logger.debug("scr-historical %s %s", signal, start)
# Check for argument
if signal not in so.d_signals_desc:
raise Exception("Invalid preset selected!")
register_matplotlib_converters()
screen = ticker.Ticker()
if signal in finviz_model.d_signals:
screen.set_filter(signal=finviz_model.d_signals[signal])
else:
preset_filter = configparser.RawConfigParser()
preset_filter.optionxform = str # type: ignore
preset_filter.read(so.presets_path + signal + ".ini")
d_general = preset_filter["General"]
d_filters = {
**preset_filter["Descriptive"],
**preset_filter["Fundamental"],
**preset_filter["Technical"],
}
d_filters = {k: v for k, v in d_filters.items() if v}
if d_general["Signal"]:
screen.set_filter(filters_dict=d_filters,
signal=d_general["Signal"])
else:
screen.set_filter(filters_dict=d_filters)
if start == "":
start = datetime.now() - timedelta(days=365)
else:
start = datetime.strptime(start, imps.DATE_FORMAT)
# Output Data
l_min = []
l_leg = []
l_stocks = screen.screener_view(verbose=0)
if len(l_stocks) > 10:
description = (
"\nThe limit of stocks to compare with are 10. Hence, 10 random similar stocks will be displayed."
"\nThe selected list will be: ")
random.shuffle(l_stocks)
l_stocks = sorted(l_stocks[:10])
description = description + (", ".join(l_stocks))
logger.debug(description)
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
while l_stocks:
l_parsed_stocks = []
for symbol in l_stocks:
try:
df_similar_stock = yf.download(
symbol,
start=datetime.strftime(start, "%Y-%m-%d"),
progress=False,
threads=False,
)
if not df_similar_stock.empty:
plt.plot(
df_similar_stock.index,
df_similar_stock["Adj Close"].values,
)
l_min.append(df_similar_stock.index[0])
l_leg.append(symbol)
l_parsed_stocks.append(symbol)
except Exception as e:
error = (
f"{e}\nDisregard previous error, which is due to API Rate limits from Yahoo Finance. "
f"Because we like '{symbol}', and we won't leave without getting data from it."
)
return {
"title":
"ERROR Stocks: [Yahoo Finance] Historical Screener",
"description": error,
}
for parsed_stock in l_parsed_stocks:
l_stocks.remove(parsed_stock)
if signal:
plt.title(
f"Screener Historical Price using {finviz_model.d_signals[signal]} signal"
)
else:
plt.title(f"Screener Historical Price using {signal} preset")
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.legend(l_leg)
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
# ensures that the historical data starts from same datapoint
plt.xlim([max(l_min), df_similar_stock.index[-1]])
imagefile = "scr_historical.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
dataBytesIO.seek(0)
plt.close("all")
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
return {
"title": "Stocks: [Yahoo Finance] Historical Screener",
"description": description,
"imagefile": imagefile,
}