def histcont_command(ticker=""):
"""Displays historical quarterly-contracts [quiverquant.com]"""
# Debug user input
if imps.DEBUG:
logger.debug("gov histcont %s", ticker)
if ticker == "":
raise Exception("A ticker is required")
# Retrieve Data
df_contracts = quiverquant_model.get_government_trading(
"quarter-contracts", ticker=ticker)
if df_contracts.empty:
logger.debug("No quarterly government contracts found")
raise Exception("No quarterly government contracts found")
# Output Data
amounts = df_contracts.sort_values(by=["Year", "Qtr"])["Amount"].values
qtr = df_contracts.sort_values(by=["Year", "Qtr"])["Qtr"].values
year = df_contracts.sort_values(by=["Year", "Qtr"])["Year"].values
quarter_ticks = [
f"{quarter[0]}" if quarter[1] == 1 else ""
for quarter in zip(year, qtr)
]
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.plot(np.arange(0, len(amounts)), amounts / 1000, "-*", lw=2, ms=15)
ax.set_xlim([-0.5, len(amounts) - 0.5])
ax.set_xticks(np.arange(0, len(amounts)))
ax.set_xticklabels(quarter_ticks)
ax.grid()
ax.set_title(
f"Historical Quarterly Government Contracts for {ticker.upper()}")
ax.set_xlabel("Date")
ax.set_ylabel("Amount ($1k)")
fig.tight_layout()
imagefile = "gov_histcont.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
dataBytesIO.seek(0)
plt.close("all")
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
return {
"title": "Stocks: Historical Quarterly Government Contract ",
"imagefile": imagefile,
}
def contracts_command(ticker: str = "",
past_transaction_days: Union[int, str] = 10,
raw: bool = False):
"""Displays contracts associated with tickers [quiverquant.com]"""
past_transaction_days = int(past_transaction_days)
# Debug user input
if imps.DEBUG:
logger.debug("gov contracts %s %s %s", ticker, past_transaction_days,
raw)
if ticker == "":
raise Exception("A ticker is required")
# Retrieve Data
df_contracts = quiverquant_model.get_government_trading(
"contracts", ticker)
if df_contracts.empty:
return {
"title": f"Stocks: [quiverquant.com] Contracts by {ticker}",
"description": f"{ticker} does not have any contracts",
}
# Output Data
df_contracts["Date"] = pd.to_datetime(df_contracts["Date"]).dt.date
df_contracts = df_contracts[df_contracts["Date"].isin(
df_contracts["Date"].unique()[:past_transaction_days])]
df_contracts.drop_duplicates(inplace=True)
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
df_contracts.groupby("Date").sum().div(1000).plot(kind="bar", rot=0, ax=ax)
ax.set_ylabel("Amount ($1k)")
ax.set_title(f"Sum of latest government contracts to {ticker}")
fig.tight_layout()
imagefile = "gov_contracts.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
plt.close("all")
dataBytesIO.seek(0)
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
return {
"title": f"Stocks: [quiverquant.com] Contracts by {ticker}",
"imagefile": imagefile,
}
def toplobbying_command(num: int = 10, raw: bool = False):
"""Displays top lobbying firms [quiverquant.com]"""
# Debug user input
if imps.DEBUG:
logger.debug("gov-toplobbying %s %s", num, raw)
# Retrieve Data
df_lobbying = quiverquant_model.get_government_trading(
"corporate-lobbying")
if df_lobbying.empty:
raise Exception("No corporate lobbying found")
df_lobbying["Amount"] = df_lobbying.Amount.astype(float).fillna(
0) / 100_000
lobbying_by_ticker = pd.DataFrame(
df_lobbying.groupby("Ticker")["Amount"].agg("sum")).sort_values(
by="Amount", ascending=False)
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
lobbying_by_ticker.head(num).plot(kind="bar", ax=ax)
ax.set_xlabel("Ticker")
ax.set_ylabel("Total Amount ($100k)")
ax.set_title(f"Corporate Lobbying Spent since {df_lobbying['Date'].min()}")
fig.tight_layout()
imagefile = "ta_toplobbying.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
dataBytesIO.seek(0)
plt.close("all")
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
return {
"title": "Stocks: [quiverquant.com] Top Lobbying Firms",
"imagefile": imagefile,
}
def ma_command(
ticker="",
interval: int = 15,
past_days: int = 0,
ma_mode="ema",
window="",
offset: int = 0,
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with selected Moving Average [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta ma %s %s %s %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
ma_mode,
window,
offset,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
overlap_ma = {
"ema": overlap_model.ema,
"hma": overlap_model.hma,
"sma": overlap_model.sma,
"wma": overlap_model.wma,
"zlma": overlap_model.zlma,
}
if ticker == "":
raise Exception("Stock ticker is required")
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
if window == "":
window = [20, 50]
else:
window_temp = list()
for wind in window.split(","):
try:
window_temp.append(int(wind))
except Exception as e:
raise Exception("Window needs to be a float") from e
window = window_temp
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
if df_ta.empty:
raise Exception("No Data Found")
for win in window:
ema_data = overlap_ma[ma_mode](values=df_ta["Adj Close"],
length=win,
offset=offset)
df_ta = df_ta.join(ema_data)
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
)
title = f"<b>{plot['plt_title']} Moving Average ({ma_mode.upper()})</b>"
fig = plot["fig"]
i2 = 6 if interval != 1440 else 11
for i in range(i2, df_ta.shape[1]):
fig.add_trace(
go.Scatter(
name=f"{df_ta.iloc[:, i].name}",
mode="lines",
x=df_ta.index,
y=df_ta.iloc[:, i].values,
opacity=1,
),
secondary_y=True,
row=1,
col=1,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.1,
title_font_size=14,
dragmode="pan",
)
imagefile = "ta_ma.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Moving Average {ma_mode.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def pt_command(ticker: str = "", raw: bool = False, start=""):
"""Displays price targets [Business Insider]"""
# Debug
if imps.DEBUG:
logger.debug("dd pt %s", ticker)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
if start == "":
start = datetime.now() - timedelta(days=365)
else:
start = datetime.strptime(start, imps.DATE_FORMAT)
if raw not in [True, False]:
raise Exception("raw argument has to be true or false")
df_analyst_data = business_insider_model.get_price_target_from_analysts(ticker)
stock = imps.load(ticker, start)
title = f"Stocks: [Business Insider] Price Targets {ticker}"
if df_analyst_data.empty or stock.empty:
raise Exception("Enter valid ticker")
# Output Data
if raw:
df_analyst_data.sort_index(ascending=False)
report = "```" + df_analyst_data.to_string() + "```"
output = {
"title": title,
"description": report,
}
else:
plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
if start:
df_analyst_data = df_analyst_data[start:]
plt.plot(stock.index, stock["Adj Close"].values, lw=3)
plt.plot(df_analyst_data.groupby(by=["Date"]).mean())
plt.scatter(df_analyst_data.index, df_analyst_data["Price Target"], c="r", s=40)
plt.legend(["Closing Price", "Average Price Target", "Price Target"])
plt.title(f"{ticker.upper()} (Time Series) and Price Target")
plt.xlim(stock.index[0], stock.index[-1])
plt.xlabel("Time")
plt.ylabel("Share Price")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
imagefile = "ta_pt.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
plt.close("all")
dataBytesIO.seek(0)
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
output = {
"title": title,
"imagefile": imagefile,
}
return output
def fib_command(
ticker="",
interval: int = 15,
past_days: int = 0,
start: str = "",
end: str = "",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with fibonacci retracement [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta fib %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
start,
end,
extended_hours,
heikin_candles,
news,
)
past_days = (past_days + 1) if (interval != 1440) or (start != "") else 365
# Retrieve Data
(
df_stock,
start,
end,
bar_start,
) = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception(f"No data found for {ticker.upper()}.")
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
(
df_fib,
min_date,
max_date,
min_pr,
max_pr,
) = custom_indicators_model.calculate_fib_levels(df_ta, 12, bar_start,
None)
levels = df_fib.Price
# Output Data
fibs = [
"<b>0</b>",
"<b>0.235</b>",
"<b>0.382</b>",
"<b>0.5</b>",
"<b>0.618</b>",
"<b>0.65</b>",
"<b>1</b>",
]
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
shared_xaxes=True,
vertical_spacing=0.07,
)
title = f"<b>{plot['plt_title']} Fibonacci-Retracement-Levels</b>"
lvl_text: str = "right" if min_date > max_date else "left"
fig = plot["fig"]
fig.add_trace(
go.Scatter(
x=[min_date, max_date],
y=[min_pr, max_pr],
opacity=1,
mode="lines",
line=imps.PLT_FIB_COLORWAY[8],
showlegend=False,
),
row=1,
col=1,
secondary_y=True,
)
for i in range(6):
fig.add_trace(
go.Scatter(
name=fibs[i],
x=[min_date, max_date],
y=[levels[i], levels[i]],
opacity=0.2,
mode="lines",
line_color=imps.PLT_FIB_COLORWAY[i],
showlegend=False,
),
row=1,
col=1,
secondary_y=True,
)
fig.add_trace(
go.Scatter(
name=fibs[i + 1],
x=[min_date, max_date],
y=[levels[i + 1], levels[i + 1]],
opacity=0.2,
mode="lines",
fill="tonexty",
line_color=imps.PLT_FIB_COLORWAY[i + 1],
showlegend=False,
),
row=1,
col=1,
secondary_y=True,
)
for i in range(7):
fig.add_trace(
go.Scatter(
name=fibs[i],
x=[min_date],
y=[levels[i]],
opacity=0.9,
mode="text",
text=fibs[i],
textposition=f"middle {lvl_text}"
if i != 5 else f"bottom {lvl_text}",
textfont=dict(imps.PLT_FIB_COLORWAY[7],
color=imps.PLT_FIB_COLORWAY[i]),
showlegend=False,
),
row=1,
col=1,
secondary_y=True,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
title=title,
title_x=0.02,
title_font_size=14,
dragmode="pan",
)
imagefile = "ta_fib.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Fibonacci-Retracement-Levels {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def macd_command(
ticker="",
interval: int = 15,
past_days: int = 0,
fast="12",
slow="26",
signal="9",
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with moving average convergence/divergence [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta macd %s %s %s %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
fast,
slow,
signal,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
if not fast.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
fast = int(fast)
if not slow.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
slow = int(slow)
if not signal.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
signal = int(signal)
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
if df_ta.empty:
raise Exception("No Data Found")
ta_data = momentum_model.macd(df_stock["Adj Close"], fast, slow, signal)
df_ta = df_ta.join(ta_data)
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.07,
row_width=[0.4, 0.6],
specs=[[{"secondary_y": True}], [{"secondary_y": False}]],
)
title = f"<b>{plot['plt_title']} MACD {fast} {slow} {signal}</b>"
fig = plot["fig"]
idx = 6 if interval != 1440 else 11
fig.add_trace(
go.Bar(
name="MACD Histogram",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 1)].values,
opacity=(plot["bar_opacity"] + 0.3),
marker_color="#d81aea",
),
row=2,
col=1,
secondary_y=False,
)
fig.add_trace(
go.Scatter(
name="MACD Line",
mode="lines",
x=df_ta.index,
y=df_ta.iloc[:, idx].values,
opacity=0.8,
line=dict(color="#00e6c3"),
),
row=2,
col=1,
secondary_y=False,
)
fig.add_trace(
go.Scatter(
name="Signal Line",
mode="lines",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 2)].values,
opacity=1,
line=dict(color="#9467bd"),
),
row=2,
col=1,
secondary_y=False,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.02,
title_font_size=14,
dragmode="pan",
)
imagefile = "ta_macd.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Moving-Average-Convergence-Divergence {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def vol_command(
ticker: str = None,
expiry: str = "",
min_sp: float = None,
max_sp: float = None,
):
"""Options VOL"""
# Debug
if imps.DEBUG:
logger.debug("opt-vol %s %s %s %s", ticker, expiry, min_sp, max_sp)
# Check for argument
if ticker is None:
raise Exception("Stock ticker is required")
dates = yfinance_model.option_expirations(ticker)
if not dates:
raise Exception("Stock ticker is invalid")
options = yfinance_model.get_option_chain(ticker, expiry)
current_price = yfinance_model.get_price(ticker)
if min_sp is None:
min_strike = 0.75 * current_price
else:
min_strike = min_sp
if max_sp is None:
max_strike = 1.25 * current_price
else:
max_strike = max_sp
calls = options.calls
puts = options.puts
call_v = calls.set_index("strike")["volume"] / 1000
put_v = puts.set_index("strike")["volume"] / 1000
call_v = call_v.fillna(0.0)
put_v = put_v.fillna(0.0)
df_opt = pd.merge(put_v, call_v, left_index=True, right_index=True)
dmax = df_opt.values.max()
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=call_v.index,
y=call_v.values,
name="Calls",
mode="lines+markers",
line=dict(color="#00ACFF", width=3),
)
)
fig.add_trace(
go.Scatter(
x=put_v.index,
y=put_v.values,
name="Puts",
mode="lines+markers",
line=dict(color="#e4003a", width=3),
)
)
fig.add_trace(
go.Scatter(
x=[current_price, current_price],
y=[0, dmax],
mode="lines",
line=dict(color="gold", width=2),
name="Current Price",
)
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_xaxes(
range=[min_strike, max_strike],
constrain="domain",
)
fig.update_layout(
margin=dict(l=0, r=0, t=60, b=20),
template=imps.PLT_SCAT_STYLE_TEMPLATE,
title=f"Volume for {ticker.upper()} expiring {expiry}",
title_x=0.5,
legend_title="",
xaxis_title="Strike",
yaxis_title="Volume (1k)",
yaxis=dict(
fixedrange=False,
nticks=20,
),
xaxis=dict(
rangeslider=dict(visible=False),
),
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
dragmode="pan",
)
imagefile = "opt_vol.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Volume for {ticker.upper()} expiring {expiry}",
"description": plt_link,
"imagefile": imagefile,
}
def topsells_command(
gov_type="",
past_transactions_months: int = 5,
num: int = 10,
raw: bool = False,
):
"""Displays most sold stocks by the congress/senate/house [quiverquant.com]"""
# Debug user input
if imps.DEBUG:
logger.debug(
"gov-topsells %s %s %s %s",
gov_type,
past_transactions_months,
num,
raw,
)
possible_args = ["congress", "senate", "house"]
if gov_type == "":
gov_type = "congress"
elif gov_type not in possible_args:
raise Exception(
"Enter a valid government argument, options are: congress, senate and house"
)
# Retrieve Data
df_gov = quiverquant_model.get_government_trading(gov_type)
# Output Data
if df_gov.empty:
raise Exception(f"No {gov_type} trading data found\n")
df_gov = df_gov.sort_values("TransactionDate", ascending=False)
start_date = datetime.now() - timedelta(days=past_transactions_months * 30)
df_gov["TransactionDate"] = pd.to_datetime(df_gov["TransactionDate"])
df_gov = df_gov[df_gov["TransactionDate"] > start_date].dropna()
df_gov["Range"] = df_gov["Range"].apply(
lambda x: "$5,000,001-$5,000,001" if x == ">$5,000,000" else x
)
df_gov["min"] = df_gov["Range"].apply(
lambda x: x.split("-")[0]
.strip("$")
.replace(",", "")
.strip()
.replace(">$", "")
.strip()
)
df_gov["max"] = df_gov["Range"].apply(
lambda x: x.split("-")[1]
.replace(",", "")
.strip()
.strip("$")
.replace(">$", "")
.strip()
if "-" in x
else x.strip("$").replace(",", "").replace(">$", "").strip()
)
df_gov["lower"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: float(x["min"])
if x["Transaction"] == "Purchase"
else -float(x["max"]),
axis=1,
)
df_gov["upper"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: float(x["max"])
if x["Transaction"] == "Purchase"
else -float(x["min"]),
axis=1,
)
description = None
df_gov = df_gov.sort_values("TransactionDate", ascending=True)
if raw:
df = pd.DataFrame(
df_gov.groupby("Ticker")["upper"]
.sum()
.div(1000)
.sort_values(ascending=True)
.abs()
.head(n=num)
)
description = "```" + df.to_string() + "```"
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
df_gov.groupby("Ticker")["upper"].sum().div(1000).sort_values().abs().head(
n=num
).plot(kind="bar", rot=0, ax=ax)
ax.set_ylabel("Amount ($1k)")
ax.set_title(
f"{num} most sold stocks over last {past_transactions_months} months"
f" (upper bound) for {gov_type}"
)
plt.gcf().axes[0].yaxis.get_major_formatter().set_scientific(False)
fig.tight_layout()
imagefile = "gov_topsells.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
dataBytesIO.seek(0)
plt.close("all")
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
return {
"title": f"Stocks: [quiverquant.com] Top sells for {gov_type.upper()}",
"imagefile": imagefile,
"description": description,
}
def vsurf_command(
ticker: str = "",
z: str = "IV",
):
"""Display vol surface
Parameters
----------
ticker: Stock Ticker
z : The variable for the Z axis
"""
# Debug
if imps.DEBUG:
logger.debug("opt vsurf %s %s", ticker, z)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
data = yfinance_model.get_iv_surface(ticker)
if data.empty:
raise Exception(f"No options data found for {ticker}.\n")
Y = data.dte
X = data.strike
if z == "IV":
Z = data.impliedVolatility
label = "Volatility"
elif z == "OI":
Z = data.openInterest
label = "Open Interest"
elif z == "LP":
Z = data.lastPrice
label = "Last Price"
points3D = np.vstack((X, Y, Z)).T
points2D = points3D[:, :2]
tri = Delaunay(points2D)
I, J, K = tri.simplices.T
lighting_effects = dict(
ambient=0.5, diffuse=0.5, roughness=0.5, specular=0.4, fresnel=0.4
)
fig = go.Figure(
data=[
go.Mesh3d(
z=Z,
x=X,
y=Y,
i=I,
j=J,
k=K,
intensity=Z,
colorscale=imps.PLT_3DMESH_COLORSCALE,
hovertemplate="<b>DTE</b>: %{y} <br><b>Strike</b>: %{x} <br><b>"
+ z
+ "</b>: %{z}<extra></extra>",
showscale=False,
flatshading=True,
lighting=lighting_effects,
)
]
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
scene=dict(
xaxis=dict(
title="Strike",
tickfont=dict(size=11),
titlefont=dict(size=12),
),
yaxis=dict(
title="DTE",
),
zaxis=dict(
title=z,
),
),
)
fig.update_layout(
margin=dict(l=0, r=0, t=40, b=20),
template=imps.PLT_3DMESH_STYLE_TEMPLATE,
title=f"{label} Surface for {ticker.upper()}",
title_x=0.5,
hoverlabel=imps.PLT_3DMESH_HOVERLABEL,
scene_camera=dict(
up=dict(x=0, y=0, z=2),
center=dict(x=0, y=0, z=-0.3),
eye=dict(x=1.25, y=1.25, z=0.69),
),
scene=imps.PLT_3DMESH_SCENE,
)
imagefile = "opt-vsurf.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"{label} Surface for {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def spos_command(ticker: str = ""):
"""Net short vs position [Stockgrid]"""
# Debug user input
if imps.DEBUG:
logger.debug("dps-spos %s", ticker)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
ticker = ticker.upper()
stock = yf.download(ticker, progress=False)
if stock.empty:
raise Exception("Stock ticker is invalid")
# Retrieve data
df = stockgrid_model.get_net_short_position(ticker)
# Debug user output
if imps.DEBUG:
logger.debug(df.to_string())
# Output data
title = f"Stocks: [Stockgrid] Net Short vs Position {ticker}"
fig = make_subplots(shared_xaxes=True, specs=[[{"secondary_y": True}]])
fig.add_trace(
go.Scatter(
name="Position ($)",
x=df["dates"].values,
y=df["dollar_dp_position"] * 1_000,
line=dict(color="#fdc708", width=2),
opacity=1,
showlegend=False,
),
secondary_y=True,
)
fig.add_trace(
go.Bar(
name="Net Short Vol. ($)",
x=df["dates"],
y=df["dollar_net_volume"],
opacity=1,
showlegend=False,
),
secondary_y=False,
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
# Set y-axes titles
fig.update_xaxes(dtick="M1", tickformat="%b %d\n%Y")
fig.update_yaxes(title_text="<b>Position</b> ($)", secondary_y=True)
fig.update_traces(hovertemplate="%{y:.2s}")
fig.update_layout(
margin=dict(l=0, r=10, t=40, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=f"Net Short Vol. vs Position for {ticker}",
title_x=0.5,
yaxis_title="<b>Net Short Vol.</b> ($)",
font=imps.PLT_FONT,
yaxis=dict(
side="left",
showgrid=False,
fixedrange=False,
layer="above traces",
titlefont=dict(color="#d81aea"),
tickfont=dict(color="#d81aea"),
nticks=10,
),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
fixedrange=False,
),
xaxis2=dict(
rangeslider=dict(visible=False),
type="date",
fixedrange=False,
),
dragmode="pan",
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
yaxis2=dict(
side="right",
position=0.15,
fixedrange=False,
titlefont=dict(color="#fdc708"),
tickfont=dict(color="#fdc708"),
nticks=10,
),
hovermode="x unified",
)
imagefile = "dps_spos.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": title,
"description": plt_link,
"imagefile": imagefile,
}
def gtrades_command(
ticker: str = "",
gov_type="",
past_transactions_months: int = 10,
raw: bool = False,
):
"""Displays government trades [quiverquant.com]"""
# Debug user input
if imps.DEBUG:
logger.debug(
"gov gtrades %s %s %s %s",
ticker,
gov_type,
past_transactions_months,
raw,
)
if ticker == "":
raise Exception("A ticker is required")
possible_args = ["congress", "senate", "house"]
if gov_type == "":
gov_type = "congress"
elif gov_type not in possible_args:
raise Exception(
"Enter a valid government argument, options are: congress, senate and house"
)
# Retrieve Data
df_gov = quiverquant_model.get_government_trading(gov_type, ticker)
if df_gov.empty:
raise Exception(f"No {gov_type} trading data found")
# Output Data
df_gov = df_gov.sort_values("TransactionDate", ascending=False)
start_date = datetime.now() - timedelta(days=past_transactions_months * 30)
df_gov["TransactionDate"] = pd.to_datetime(df_gov["TransactionDate"])
df_gov = df_gov[df_gov["TransactionDate"] > start_date]
if df_gov.empty:
logger.debug("No recent %s trading data found", gov_type)
raise Exception(f"No recent {gov_type} trading data found")
df_gov["min"] = df_gov["Range"].apply(
lambda x: x.split("-")[0].strip("$").replace(",", "").strip()
)
df_gov["max"] = df_gov["Range"].apply(
lambda x: x.split("-")[1].replace(",", "").strip().strip("$")
if "-" in x
else x.strip("$").replace(",", "").split("\n")[0]
)
df_gov["lower"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: int(float(x["min"]))
if x["Transaction"] == "Purchase"
else -int(float(x["max"])),
axis=1,
)
df_gov["upper"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: int(float(x["max"]))
if x["Transaction"] == "Purchase"
else -1 * int(float(x["min"])),
axis=1,
)
df_gov = df_gov.sort_values("TransactionDate", ascending=True)
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.fill_between(
df_gov["TransactionDate"].unique(),
df_gov.groupby("TransactionDate")["lower"].sum().values / 1000,
df_gov.groupby("TransactionDate")["upper"].sum().values / 1000,
)
ax.set_xlim(
[
df_gov["TransactionDate"].values[0],
df_gov["TransactionDate"].values[-1],
]
)
ax.grid()
ax.set_title(f"{gov_type.capitalize()} trading on {ticker}")
ax.set_xlabel("Date")
ax.set_ylabel("Amount ($1k)")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
plt.gcf().autofmt_xdate()
fig.tight_layout()
imagefile = "gov_gtrades.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
dataBytesIO.seek(0)
plt.close("all")
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
return {
"title": "Stocks: [quiverquant.com] Government Trades",
"imagefile": imagefile,
}
def metric_command(
finance_key: str,
finance_metric: str,
ticker: str = "",
):
"""Display financials bars comparing sectors, industry, analysis, countries, market cap and excluding exchanges.
Parameters
----------
finance_key: str
Select finance key from Yahoo Finance(e.g. financialData, defaultKeyStatistics, summaryProfile)
finance_metric: str
Select finance metric from Yahoo Finance (e.g. operatingCashflow, revenueGrowth, ebitda, freeCashflow)
ticker: str
Company ticker to use as the basis.
"""
logger.info("metrics")
exclude_exchanges: bool = True
limit: int = 10
ticker = ticker.lower()
if ticker:
data = yfinance.utils.get_json(f"https://finance.yahoo.com/quote/{ticker}")
if "summaryProfile" in data:
country = data["summaryProfile"]["country"]
if country not in financedatabase_model.get_countries():
similar_cmd = difflib.get_close_matches(
country,
financedatabase_model.get_countries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
country = similar_cmd[0]
sector = data["summaryProfile"]["sector"]
if sector not in financedatabase_model.get_sectors():
similar_cmd = difflib.get_close_matches(
sector,
financedatabase_model.get_sectors(),
n=1,
cutoff=0.7,
)
if similar_cmd:
sector = similar_cmd[0]
industry = data["summaryProfile"]["industry"]
if industry not in financedatabase_model.get_industries():
similar_cmd = difflib.get_close_matches(
industry,
financedatabase_model.get_industries(),
n=1,
cutoff=0.7,
)
if similar_cmd:
industry = similar_cmd[0]
if "price" in data:
mktcap = data["price"]["marketCap"]
if mktcap < 2_000_000_000:
mktcap = "Small"
elif mktcap > 10_000_000_000:
mktcap = "Large"
else:
mktcap = "Mid"
stocks_data = financedatabase_model.get_stocks_data(
country, sector, industry, mktcap, exclude_exchanges
)
metric_data = {}
for symbol in list(stocks_data.keys()):
if finance_key in stocks_data[symbol] and "quoteType" in stocks_data[symbol]:
stock_name = stocks_data[symbol]["quoteType"]["longName"]
metric = (
stocks_data[symbol][finance_key][finance_metric]
if stocks_data[symbol][finance_key] is not None
and finance_metric in stocks_data[symbol][finance_key]
else None
)
if metric and stock_name:
metric_data[stock_name] = (metric, symbol)
if len(metric_data) > 1:
metric_data = dict(
OrderedDict(
sorted(metric_data.items(), key=lambda t: t[1][0], reverse=True)
)
)
company_names = list()
company_metrics = list()
company_tickers = list()
for name, metric in metric_data.items():
company_names.append(name)
company_metrics.append(metric[0])
company_tickers.append(metric[1])
company_name = np.array(company_names)[:limit]
company_metric = np.array(company_metrics)[:limit]
company_ticker = np.array(company_tickers)[:limit]
magnitude = 0
while max(company_metric) > 1_000 or abs(min(company_metric)) > 1_000:
company_metric = np.divide(company_metric, 1_000)
magnitude += 1
# check if the value is a percentage
if (magnitude == 0) and all(company_metric >= 0) and all(company_metric <= 1):
unit = "%"
company_metric = company_metric * 100
else:
unit = " KMBTP"[magnitude]
colors = [
"#ffed00",
"#ef7d00",
"#e4003a",
"#c13246",
"#822661",
"#48277c",
"#005ca9",
"#00aaff",
"#9b30d9",
"#af005f",
"#5f00af",
"#af87ff",
]
fig = go.Figure()
i = 0
for name, metric, tickers in zip(
company_name[::-1], company_metric[::-1], company_ticker[::-1]
):
if len(name.split(" ")) > 6 and len(name) > 40:
name = (
f'{" ".join(name.split(" ")[:4])}\n{" ".join(name.split(" ")[4:])}'
)
df_name = []
df_metric = []
df_tickers = []
df_name.append(name)
df_metric.append(metric)
df_tickers.append(tickers)
fig.add_trace(
go.Bar(
name=tickers,
y=[name],
x=[metric],
orientation="h",
marker=dict(
color=colors[i],
line=dict(color="rgb(248, 248, 249)", width=1),
),
),
)
i += 1
metric_title = (
"".join(
" " + char if char.isupper() else char.strip()
for char in finance_metric
)
.strip()
.capitalize()
)
benchmark = np.median(company_metric)
if unit != " ":
units = f" [{unit}] "
else:
units = " "
title = f"{metric_title.capitalize()}{units}with benchmark of {benchmark:.2f} {unit}<br>"
title += mktcap + " cap companies " if mktcap else "Companies "
if industry:
title += f"in {industry} industry<br>"
elif sector:
title += f"in {sector} sector<br>"
if country:
title += f"in {country}"
title += " " if (industry or sector) else "<br>"
title += (
"(excl. data from international exchanges)"
if exclude_exchanges
else "(incl. data from international exchanges)"
)
fig.add_vline(
x=benchmark,
fillcolor="grey",
opacity=1,
layer="below",
line_width=3,
line=dict(color="grey", dash="dash"),
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=40, r=0, t=100, b=20),
template=imps.PLT_CANDLE_STYLE_TEMPLATE,
title=title,
colorway=colors,
font=imps.PLT_FONT,
legend={"traceorder": "reversed"},
)
imagefile = "sia_metrics.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": "Stocks - Sector and Industry Analysis",
"description": plt_link,
"imagefile": imagefile,
}
def smile_command(
ticker: str = None,
expiry: str = "",
min_sp: float = None,
max_sp: float = None,
):
"""Options Smile"""
# Debug
if imps.DEBUG:
logger.debug("opt smile %s %s %s %s", ticker, expiry, min_sp, max_sp)
# Check for argument
if ticker is None:
raise Exception("Stock ticker is required")
dates = yfinance_model.option_expirations(ticker)
if not dates:
raise Exception("Stock ticker is invalid")
options = yfinance_model.get_option_chain(ticker, expiry)
calls = options.calls.fillna(0.0)
puts = options.puts.fillna(0.0)
current_price = yfinance_model.get_price(ticker)
min_strike = 0.60 * current_price
max_strike = 1.95 * current_price
if len(calls) > 40:
min_strike = 0.60 * current_price
max_strike = 1.50 * current_price
if min_sp:
min_strike = min_sp
if max_sp:
max_strike = max_sp
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=calls["strike"],
y=calls["impliedVolatility"].interpolate(method="nearest"),
name="Calls",
mode="lines+markers",
marker=dict(
color="#00ACFF",
size=4.5,
),
line=dict(color="#00ACFF", width=2, dash="dash"),
))
fig.add_trace(
go.Scatter(
x=puts["strike"],
y=puts["impliedVolatility"].interpolate(method="nearest"),
name="Puts",
mode="lines+markers",
marker=dict(
color="#e4003a",
size=4.5,
),
line=dict(color="#e4003a", width=2, dash="dash"),
))
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_xaxes(
range=[min_strike, max_strike],
constrain="domain",
)
fig.update_layout(
margin=dict(l=20, r=0, t=60, b=20),
template=imps.PLT_SCAT_STYLE_TEMPLATE,
font=imps.PLT_FONT,
title=
f"<b>Implied Volatility vs. Strike for {ticker.upper()} expiring {expiry}</b>",
title_x=0.5,
legend_title="",
xaxis_title="Strike",
yaxis_title="Implied Volatility",
yaxis=dict(
side="right",
fixedrange=False,
nticks=20,
),
xaxis=dict(
rangeslider=dict(visible=False),
nticks=20,
),
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
dragmode="pan",
)
imagefile = "opt_smile.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title":
f"Implied Volatility vs. Stirke for {ticker.upper()} expiring {expiry}",
"description": plt_link,
"imagefile": imagefile,
}
def qtrcontracts_command(num: int = 20, analysis=""):
"""Displays a look at government contracts [quiverquant.com]"""
# Debug user input
if imps.DEBUG:
logger.debug("gov-qtrcontracts %s %s", num, analysis)
possible_args = ["total", "upmom", "downmom"]
if analysis == "":
analysis = "total"
elif analysis not in possible_args:
raise Exception(
"Enter a valid analysis argument, options are: total, upmom and downmom"
)
# Retrieve Data
df_contracts = quiverquant_model.get_government_trading("quarter-contracts")
if df_contracts.empty:
raise Exception("No quarterly government contracts found")
tickers = quiverquant_model.analyze_qtr_contracts(analysis, num)
# Output Data
if analysis in {"upmom", "downmom"}:
description = tickers.to_string()
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
max_amount = 0
quarter_ticks = []
for symbol in tickers:
amounts = (
df_contracts[df_contracts["Ticker"] == symbol]
.sort_values(by=["Year", "Qtr"])["Amount"]
.values
)
qtr = (
df_contracts[df_contracts["Ticker"] == symbol]
.sort_values(by=["Year", "Qtr"])["Qtr"]
.values
)
year = (
df_contracts[df_contracts["Ticker"] == symbol]
.sort_values(by=["Year", "Qtr"])["Year"]
.values
)
ax.plot(np.arange(0, len(amounts)), amounts / 1_000_000, "-*", lw=2, ms=15)
if len(amounts) > max_amount:
max_amount = len(amounts)
quarter_ticks = [
f"{quarter[0]} - Q{quarter[1]} " for quarter in zip(year, qtr)
]
ax.set_xlim([-0.5, max_amount - 0.5])
ax.set_xticks(np.arange(0, max_amount))
ax.set_xticklabels(quarter_ticks)
ax.grid()
ax.legend(tickers)
titles = {
"upmom": "Highest increasing quarterly Government Contracts",
"downmom": "Highest decreasing quarterly Government Contracts",
}
ax.set_title(titles[analysis])
ax.set_xlabel("Date")
ax.set_ylabel("Amount ($1M)")
fig.tight_layout()
imagefile = "gov_qtrcontracts.png"
dataBytesIO = io.BytesIO()
plt.savefig(dataBytesIO)
dataBytesIO.seek(0)
plt.close("all")
imagefile = imps.image_border(imagefile, base64=dataBytesIO)
output = {
"title": "Stocks: [quiverquant.com] Government Contracts",
"imagefile": imagefile,
"description": description,
}
elif analysis == "total":
tickers.index = [ind + " " * (7 - len(ind)) for ind in tickers.index]
tickers[:] = [str(round(val[0] / 1e9, 2)) for val in tickers.values]
tickers.columns = ["Amount [M]"]
output = {
"title": "Stocks: [quiverquant.com] Government Contracts",
"description": tickers.to_string(),
}
return output
def cc_hist_command(ticker: str = None,
expiry: str = "",
strike: float = 10,
opt_type: str = ""):
"""Plot historical option prices
Parameters
----------
ticker: str
Stock ticker
expiry: str
expiration date
strike: float
Option strike price
put: bool
Calls for call
Puts for put
"""
# Debug
if imps.DEBUG:
logger.info("opt hist %s, %s, %s, %s", ticker, strike, opt_type,
expiry)
# Check for argument
if ticker is None:
raise Exception("Stock ticker is required")
yf_ticker = yf.Ticker(ticker)
dates = list(yf_ticker.options)
if not dates:
raise Exception("Stock ticker is invalid")
options = yf.Ticker(ticker).option_chain(expiry)
if opt_type == "Calls":
options = options.calls
if opt_type == "Puts":
options = options.puts
chain_id = options.loc[options.strike == strike,
"contractSymbol"].values[0]
df_hist = yf.download(chain_id)
df_hist.index.name = "date"
title = f"{ticker.upper()} {strike} {opt_type} expiring {expiry} Historical"
fig = make_subplots(
shared_xaxes=True,
specs=[[{
"secondary_y": True
}]],
)
fig.add_trace(
go.Candlestick(
x=df_hist.index,
open=df_hist.Open,
high=df_hist.High,
low=df_hist.Low,
close=df_hist.Close,
name="OHLC",
increasing_line_color="#00ACFF",
decreasing_line_color="#e4003a",
showlegend=False,
),
secondary_y=True,
)
fig.add_trace(
go.Bar(
x=df_hist.index,
y=df_hist.Volume,
name="Volume",
yaxis="y2",
marker_color="#fdc708",
opacity=0.3,
showlegend=False,
),
secondary_y=False,
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=0, r=0, t=25, b=5),
template=imps.PLT_CANDLE_STYLE_TEMPLATE,
showlegend=False,
title=title,
title_x=0.1,
title_font_size=14,
yaxis2_title="Premium",
yaxis_title="Volume",
font=imps.PLT_FONT,
yaxis=dict(
showgrid=False,
fixedrange=False,
side="left",
titlefont=dict(color="#fdc708", size=12),
tickfont=dict(
color="#fdc708",
size=14,
),
nticks=20,
),
yaxis2=dict(
side="right",
fixedrange=False,
anchor="x",
layer="above traces",
overlaying="y",
nticks=20,
tickfont=dict(size=14, ),
showline=False,
),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
),
dragmode="pan",
)
fig.update_xaxes(rangebreaks=[
dict(bounds=["sat", "mon"]),
], )
imagefile = "opt_hist.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": title,
"description": plt_link,
"imagefile": imagefile,
}
def adx_command(
ticker="",
interval: int = 15,
past_days: int = 0,
length="14",
scalar="100",
drift="1",
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with average directional movement index [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta adx %s %s %s %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
length,
scalar,
drift,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
if not length.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
length = float(length)
if not scalar.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
scalar = float(scalar)
if not drift.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
drift = float(drift)
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
if df_ta.empty:
raise Exception("No Data Found")
ta_data = trend_indicators_model.adx(
df_stock["High"],
df_stock["Low"],
df_stock["Adj Close"],
length,
scalar,
drift,
)
df_ta = df_ta.join(ta_data)
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.07,
row_width=[0.4, 0.6],
specs=[[{
"secondary_y": True
}], [{
"secondary_y": False
}]],
)
title = f"<b>{plot['plt_title']} Average Directional Movement Index</b>"
fig = plot["fig"]
idx = 6 if interval != 1440 else 11
fig.add_trace(
go.Scatter(
name=f"ADX ({length})",
mode="lines",
x=df_ta.index,
y=df_ta.iloc[:, idx].values,
opacity=1,
line=dict(width=2),
),
secondary_y=False,
row=2,
col=1,
)
fig.add_trace(
go.Scatter(
name=f"+DI ({length})",
mode="lines",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 1)].values,
opacity=1,
line=dict(width=1),
),
secondary_y=False,
row=2,
col=1,
)
fig.add_trace(
go.Scatter(
name=f"-DI ({length})",
mode="lines",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 2)].values,
opacity=1,
line=dict(width=1),
),
secondary_y=False,
row=2,
col=1,
)
fig.add_hline(
y=25,
fillcolor="grey",
opacity=1,
layer="below",
line_width=3,
line=dict(color="grey", dash="dash"),
row=2,
col=1,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.01,
title_font_size=12,
dragmode="pan",
)
imagefile = "ta_adx.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title":
f"Stocks: Average-Directional-Movement-Index {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def ad_command(
ticker="",
interval: int = 15,
past_days: int = 0,
is_open: bool = False,
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with accumulation/distribution line [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta ad %s %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
is_open,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
df_ta = df_ta.join(volume_model.ad(df_stock, is_open))
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.07,
row_width=[0.4, 0.6],
specs=[
[{
"secondary_y": True
}],
[{
"secondary_y": False
}],
],
)
title = f"<b>{plot['plt_title']} A/D</b>"
fig = plot["fig"]
fig.add_trace(
go.Scatter(
name="A/D",
x=df_ta.index,
y=df_ta.iloc[:, 6].values
if interval != 1440 else df_ta.iloc[:, 11].values,
mode="lines",
line=dict(width=2),
opacity=1,
),
row=2,
col=1,
)
fig.add_hline(
y=0,
fillcolor="grey",
opacity=1,
layer="below",
line_width=3,
line=dict(color="grey", dash="dash"),
row=2,
col=1,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.1,
title_font_size=14,
dragmode="pan",
)
imagefile = "ta_ad.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Accumulation/Distribution Line {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def aroon_command(
ticker="",
interval: int = 15,
past_days: int = 0,
length="25",
scalar="100",
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with aroon indicator [Yahoo Finance]"""
# Debug
if imps.DEBUG:
logger.debug(
"ta aroon %s %s %s %s %s, %s, %s, %s, %s, %s",
ticker,
interval,
past_days,
length,
scalar,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
if not length.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
length = int(length)
if not scalar.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
scalar = float(scalar)
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
df_ta = df_ta.join(
trend_indicators_model.aroon(df_ta["High"], df_ta["Low"], length,
scalar))
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
rows=3,
cols=1,
shared_xaxes=True,
vertical_spacing=0.02,
row_width=[0.2, 0.2, 0.3],
specs=[
[{
"secondary_y": True
}],
[{
"secondary_y": False
}],
[{
"secondary_y": False
}],
],
)
title = f"<b>{plot['plt_title']} Aroon ({length})</b>"
fig = plot["fig"]
idx = 6 if interval != 1440 else 11
fig.add_trace(
go.Scatter(
name="Aroon DOWN",
x=df_ta.index,
y=df_ta.iloc[:, idx].values,
opacity=1,
),
row=2,
col=1,
secondary_y=False,
)
fig.add_trace(
go.Scatter(
name="Aroon UP",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 1)].values,
opacity=1,
),
row=2,
col=1,
secondary_y=False,
)
fig.add_trace(
go.Scatter(
name="Aroon OSC",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 2)].values,
opacity=1,
),
row=3,
col=1,
secondary_y=False,
)
fig.add_hline(
y=50,
fillcolor="grey",
opacity=1,
layer="below",
line_width=3,
line=dict(color="grey", dash="dash"),
row=2,
col=1,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.1,
title_font_size=14,
dragmode="pan",
yaxis=dict(nticks=10),
yaxis2=dict(nticks=10),
)
imagefile = "ta_aroon.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Aroon-Indicator {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def bbands_command(
ticker="",
interval: int = 15,
past_days: int = 0,
length="20",
n_std: float = 2.0,
ma_mode="sma",
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with bollinger bands [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta bbands %s %s %s %s %s %s %s %s %s %s",
ticker,
past_days,
length,
n_std,
ma_mode,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
possible_ma = [
"dema",
"ema",
"fwma",
"hma",
"linreg",
"midpoint",
"pwma",
"rma",
"sinwma",
"sma",
"swma",
"t3",
"tema",
"trima",
"vidya",
"wma",
"zlma",
]
if ticker == "":
raise Exception("Stock ticker is required")
if not length.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
ta_length = float(length)
n_std = float(n_std)
if ma_mode not in possible_ma:
raise Exception("Invalid ma entered")
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
df_ta = df_ta.join(
volatility_model.bbands(df_ta["Adj Close"], ta_length, n_std, ma_mode))
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(df_ta, ticker, interval, extended_hours,
news)
title = f"<b>{plot['plt_title']} Bollinger Bands ({ma_mode.upper()})</b>"
fig = plot["fig"]
idx = 6 if interval != 1440 else 11
fig.add_trace(
go.Scatter(
name=f"BBU_{length}_{n_std}",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 2)].values,
opacity=1,
mode="lines",
line_color="indigo",
),
secondary_y=True,
row=1,
col=1,
)
fig.add_trace(
go.Scatter(
name=f"BBL_{length}_{n_std}",
x=df_ta.index,
y=df_ta.iloc[:, idx].values,
opacity=1,
mode="lines",
line_color="indigo",
fill="tonexty",
fillcolor="rgba(74, 0, 128, 0.2)",
),
secondary_y=True,
row=1,
col=1,
)
fig.add_trace(
go.Scatter(
name=f"BBM_{length}_{n_std}",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 1)].values,
opacity=1,
line=dict(
width=1.5,
dash="dash",
),
),
secondary_y=True,
row=1,
col=1,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.1,
title_font_size=14,
dragmode="pan",
)
imagefile = "ta_bbands.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Bollinger-Bands {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def ftd_command(ticker: str = "", start="", end=""):
"""Fails-to-deliver data [SEC]"""
# Debug user input
if imps.DEBUG:
logger.debug("dps ftd %s %s %s", ticker, start, end)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
ticker = ticker.upper()
if start == "":
start = datetime.now() - timedelta(days=365)
else:
start = datetime.strptime(start, imps.DATE_FORMAT)
if end == "":
end = datetime.now()
else:
end = datetime.strptime(end, imps.DATE_FORMAT)
# Retrieve data
ftds_data = sec_model.get_fails_to_deliver(ticker, start, end, 0)
# Debug user output
if imps.DEBUG:
logger.debug(ftds_data.to_string())
stock = imps.load(ticker, start)
stock_ftd = stock[stock.index > start]
stock_ftd = stock_ftd[stock_ftd.index < end]
ftd_opacity = 0.4 if (start > (datetime.now() - timedelta(days=30))) else 0.6
ftd_opacity = (
ftd_opacity if (start > (datetime.now() - timedelta(days=120))) else 0.9
)
# Output data
fig = make_subplots(shared_xaxes=True, specs=[[{"secondary_y": True}]])
fig.add_trace(
go.Scatter(
name=ticker,
x=stock_ftd.index,
y=stock_ftd["Adj Close"],
line=dict(color="#fdc708", width=2),
opacity=1,
showlegend=False,
),
secondary_y=True,
)
fig.add_trace(
go.Bar(
name="FTDs",
x=ftds_data["SETTLEMENT DATE"],
y=ftds_data["QUANTITY (FAILS)"],
yaxis="y2",
opacity=ftd_opacity,
),
secondary_y=False,
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=0, r=20, t=30, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=f"{ticker} Failed-to-deliver",
title_x=0.5,
yaxis2_title="<b>Stock Price</b>",
font=imps.PLT_FONT,
yaxis2=dict(
side="left",
fixedrange=False,
showgrid=False,
layer="above traces",
overlaying="y",
titlefont=dict(color="#fdc708"),
tickfont=dict(
color="#fdc708",
size=13,
),
nticks=20,
),
yaxis=dict(
side="right",
position=0.15,
fixedrange=False,
title_text="<b>Shares</b>",
titlefont=dict(color="#d81aea"),
tickfont=dict(
color="#d81aea",
size=13,
),
nticks=20,
),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
fixedrange=False,
),
xaxis2=dict(
rangeslider=dict(visible=False),
type="date",
fixedrange=False,
),
dragmode="pan",
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
hovermode="x unified",
)
imagefile = "dps_ftd.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: [SEC] Failure-to-deliver {ticker}",
"description": plt_link,
"imagefile": imagefile,
}
def rsi_command(
ticker="",
interval: int = 15,
past_days: int = 0,
length="14",
scalar="100",
drift="1",
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with relative strength index [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta rsi %s %s %s %s %s %s",
ticker,
interval,
past_days,
length,
scalar,
drift,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
if not length.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
length = int(length)
if not scalar.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
scalar = float(scalar)
if not drift.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
drift = int(drift)
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
df_ta = df_ta.join(momentum_model.rsi(df_ta["Adj Close"], length, scalar, drift))
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.07,
row_width=[0.5, 0.6],
specs=[
[{"secondary_y": True}],
[{"secondary_y": False}],
],
)
title = f"<b>{plot['plt_title']} RSI</b>"
fig = plot["fig"]
fig.add_trace(
go.Scatter(
name=f"RSI {length}",
mode="lines",
x=df_ta.index,
y=df_ta.iloc[:, 6].values if interval != 1440 else df_ta.iloc[:, 11].values,
opacity=1,
),
row=2,
col=1,
secondary_y=False,
)
fig.add_hrect(
y0=70,
y1=100,
fillcolor="red",
opacity=0.2,
layer="below",
line_width=0,
row=2,
col=1,
)
fig.add_hrect(
y0=0,
y1=30,
fillcolor="green",
opacity=0.2,
layer="below",
line_width=0,
row=2,
col=1,
)
fig.add_hline(
y=70,
fillcolor="green",
opacity=1,
layer="below",
line_width=3,
line=dict(color="red", dash="dash"),
row=2,
col=1,
)
fig.add_hline(
y=30,
fillcolor="green",
opacity=1,
layer="below",
line_width=3,
line=dict(color="green", dash="dash"),
row=2,
col=1,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.1,
title_font_size=14,
dragmode="pan",
yaxis=dict(nticks=15),
yaxis2=dict(nticks=10),
)
imagefile = "ta_rsi.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Relative-Strength-Index {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def stoch_command(
ticker: str = "",
interval: int = 15,
past_days: int = 0,
fast_k="14",
slow_d="3",
slow_k="4",
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with stochastic relative strength average [Yahoo Finance]"""
# Debug
if imps.DEBUG:
logger.debug(
"ta stoch %s %s %s %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
fast_k,
slow_k,
slow_d,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
if not fast_k.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
fast_k = int(fast_k)
if not slow_k.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
slow_k = int(slow_k)
if not slow_d.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
slow_d = int(slow_d)
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
if df_ta.empty:
raise Exception("No Data Found")
df_ta = df_ta.join(
momentum_model.stoch(
df_stock["High"],
df_stock["Low"],
df_stock["Adj Close"],
fast_k,
slow_d,
slow_k,
))
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.07,
row_width=[0.4, 0.6],
specs=[[{
"secondary_y": True
}], [{
"secondary_y": False
}]],
)
title = f"<b>{plot['plt_title']} STOCH RSI</b>"
fig = plot["fig"]
idx = 6 if interval != 1440 else 11
fig.add_trace(
go.Scatter(
name=f"%K {fast_k}, {slow_d}, {slow_k}",
x=df_ta.index,
y=df_ta.iloc[:, idx].values,
line=dict(width=1.8),
mode="lines",
opacity=1,
),
row=2,
col=1,
)
fig.add_trace(
go.Scatter(
name=f"%D {fast_k}, {slow_d}, {slow_k}",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 1)].values,
line=dict(width=1.8, dash="dash"),
opacity=1,
),
row=2,
col=1,
)
fig.add_hrect(
y0=80,
y1=100,
fillcolor="red",
opacity=0.2,
layer="below",
line_width=0,
row=2,
col=1,
)
fig.add_hrect(
y0=0,
y1=20,
fillcolor="green",
opacity=0.2,
layer="below",
line_width=0,
row=2,
col=1,
)
fig.add_hline(
y=80,
fillcolor="green",
opacity=1,
layer="below",
line_width=3,
line=dict(color="red", dash="dash"),
row=2,
col=1,
)
fig.add_hline(
y=20,
fillcolor="green",
opacity=1,
layer="below",
line_width=3,
line=dict(color="green", dash="dash"),
row=2,
col=1,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.01,
title_font_size=14,
dragmode="pan",
)
imagefile = "ta_stoch.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title":
f"Stocks: Stochastic-Relative-Strength-Index {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def kc_command(
ticker="",
interval: int = 15,
past_days: int = 0,
length="20",
scalar="2",
ma_mode="sma",
offset="0",
start="",
end="",
extended_hours: bool = False,
heikin_candles: bool = False,
news: bool = False,
):
"""Displays chart with keltner channel [Yahoo Finance]"""
# Debug
if imps.DEBUG:
# pylint: disable=logging-too-many-args
logger.debug(
"ta kc %s %s %s %s %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
length,
scalar,
ma_mode,
offset,
start,
end,
extended_hours,
heikin_candles,
news,
)
# Check for argument
if not length.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
length = int(length)
if not scalar.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
scalar = float(scalar)
if not offset.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
offset = float(offset)
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
heikin_candles=heikin_candles,
)
if df_stock.empty:
raise Exception("No Data Found")
df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
if df_ta.empty:
raise Exception("No Data Found")
ta_data = volatility_model.kc(
df_stock["High"],
df_stock["Low"],
df_stock["Adj Close"],
length,
scalar,
ma_mode,
offset,
)
df_ta = df_ta.join(ta_data)
# Output Data
if interval != 1440:
df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]
df_ta = df_ta.fillna(0.0)
plot = load_candle.candle_fig(
df_ta,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
shared_xaxes=True,
vertical_spacing=0.07,
)
title = f"<b>{plot['plt_title']} Keltner Channels ({ma_mode.upper()})</b>"
fig = plot["fig"]
idx = 6 if interval != 1440 else 11
fig.add_trace(
go.Scatter(
name="upper",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 2)].values,
opacity=1,
mode="lines",
line_color="indigo",
showlegend=False,
),
row=1,
col=1,
secondary_y=True,
)
fig.add_trace(
go.Scatter(
name="lower",
x=df_ta.index,
y=df_ta.iloc[:, idx].values,
opacity=1,
mode="lines",
line_color="indigo",
fill="tonexty",
fillcolor="rgba(74, 0, 128, 0.2)",
showlegend=False,
),
row=1,
col=1,
secondary_y=True,
)
fig.add_trace(
go.Scatter(
name="mid",
x=df_ta.index,
y=df_ta.iloc[:, (idx + 1)].values,
opacity=1,
line=dict(
width=1.5,
dash="dash",
),
showlegend=False,
),
row=1,
col=1,
secondary_y=True,
)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.02,
title_font_size=14,
dragmode="pan",
)
imagefile = "ta_kc.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Keltner-Channel {ticker.upper()}",
"description": plt_link,
"imagefile": imagefile,
}
def cps_command(
country: str,
mktcap: str = "",
exclude_exchanges: bool = True,
raw: bool = False,
max_sectors_to_display: int = 15,
min_pct_to_display_sector: float = 0.015,
):
"""Display number of companies per sector in a specific country (and market cap). [Source: Finance Database]
Parameters
----------
country: str
Select country to get number of companies by each sector
mktcap: str
Select market cap of companies to consider from Small, Mid and Large
exclude_exchanges : bool
Exclude international exchanges
raw: bool
Output all raw data
max_sectors_to_display: int
Maximum number of sectors to display
min_pct_to_display_sector: float
Minimum percentage to display sector
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
companies_per_sector = financedatabase_model.get_companies_per_sector_in_country(
country, mktcap, exclude_exchanges
)
companies_per_sector = dict(
OrderedDict(
sorted(companies_per_sector.items(), key=lambda t: t[1], reverse=True)
)
)
for key, value in companies_per_sector.copy().items():
if value == 0:
del companies_per_sector[key]
if not companies_per_sector:
raise Exception("No companies found with these parameters!\n")
df = pd.DataFrame.from_dict(companies_per_sector, orient="index")
df.index.name = "Sector"
df.columns = ["Number of companies"]
df["Number of companies"] = df["Number of companies"].astype(int)
title = mktcap + " Cap Companies " if mktcap else "Companies "
title += f"in {country}"
title += " Excl. Exchanges" if exclude_exchanges else " incl. Exchanges"
title = textwrap.fill(title, 40)
title_plot = textwrap.indent(text=title, prefix="<br>")
if raw:
output = {
"title": "Stocks - Sector and Industry Analysis",
"description": f"{df}",
}
else:
if len(companies_per_sector) > 1:
total_num_companies = sum(companies_per_sector.values())
min_companies_to_represent = round(
min_pct_to_display_sector * total_num_companies
)
filter_sectors_to_display = (
np.array(list(companies_per_sector.values()))
> min_companies_to_represent
)
if any(filter_sectors_to_display):
if not all(filter_sectors_to_display):
num_sectors_to_display = np.where(~filter_sectors_to_display)[0][0]
if num_sectors_to_display < max_sectors_to_display:
max_sectors_to_display = num_sectors_to_display
else:
raise Exception(
"The minimum threshold percentage specified is too high, thus it will be ignored."
)
if len(companies_per_sector) > max_sectors_to_display:
companies_per_sector_sliced = dict(
list(companies_per_sector.items())[: max_sectors_to_display - 1]
)
companies_per_sector_sliced["Others"] = sum(
dict(
list(companies_per_sector.items())[max_sectors_to_display - 1 :]
).values()
)
legend, values = zip(*companies_per_sector_sliced.items())
else:
legend, values = zip(*companies_per_sector.items())
fig = go.Figure()
colors = [
"#ffed00",
"#ef7d00",
"#e4003a",
"#c13246",
"#822661",
"#48277c",
"#005ca9",
"#00aaff",
"#9b30d9",
"#af005f",
"#5f00af",
"#af87ff",
]
fig.add_trace(
go.Pie(
labels=legend,
values=values,
textinfo="label+percent",
showlegend=False,
)
)
fig.update_traces(
textposition="outside",
textfont_size=15,
marker=dict(
colors=colors,
line=dict(color="#F5EFF3", width=0.8),
),
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=40, r=0, t=80, b=40),
title=dict(
text=title_plot,
y=1,
x=0.5,
xanchor="center",
yanchor="top",
),
template=imps.PLT_CANDLE_STYLE_TEMPLATE,
colorway=colors,
font=imps.PLT_FONT,
)
elif len(companies_per_sector) == 1:
raise Exception(
f"Only 1 sector found '{list(companies_per_sector.keys())[0]}'. No pie chart will be depicted."
)
else:
raise Exception("No sector found. No pie chart will be depicted.")
imagefile = "sia_cps.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
output = {
"title": "Stocks - Sector and Industry Analysis",
"description": plt_link,
"imagefile": imagefile,
}
return output
def dpotc_command(ticker: str = ""):
"""Dark pools (ATS) vs OTC data [FINRA]"""
# Debug user input
if imps.DEBUG:
logger.debug("dps dpotc %s", ticker)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
ticker = ticker.upper()
stock = yf.download(ticker, progress=False)
if stock.empty:
raise Exception("Stock ticker is invalid")
# Retrieve data
ats, otc = finra_model.getTickerFINRAdata(ticker)
# Debug user output
if imps.DEBUG:
logger.debug(ats.to_string())
logger.debug(otc.to_string())
# Output data
title = f"Stocks: [FINRA] Dark Pools (ATS) vs OTC {ticker}"
if ats.empty and otc.empty:
raise Exception("Stock ticker is invalid")
fig = make_subplots(
rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.07,
row_width=[0.4, 0.6],
specs=[[{
"secondary_y": True
}], [{
"secondary_y": False
}]],
)
if not ats.empty and not otc.empty:
fig.add_trace(
go.Bar(
x=ats.index,
y=(ats["totalWeeklyShareQuantity"] +
otc["totalWeeklyShareQuantity"]),
name="ATS",
opacity=0.8,
),
row=1,
col=1,
secondary_y=False,
)
fig.add_trace(
go.Bar(
x=otc.index,
y=otc["totalWeeklyShareQuantity"],
name="OTC",
opacity=0.8,
yaxis="y2",
offset=0.0001,
),
row=1,
col=1,
)
elif not ats.empty:
fig.add_trace(
go.Bar(
x=ats.index,
y=(ats["totalWeeklyShareQuantity"] +
otc["totalWeeklyShareQuantity"]),
name="ATS",
opacity=0.8,
),
row=1,
col=1,
secondary_y=False,
)
elif not otc.empty:
fig.add_trace(
go.Bar(
x=otc.index,
y=otc["totalWeeklyShareQuantity"],
name="OTC",
opacity=0.8,
yaxis="y2",
secondary_y=False,
),
row=1,
col=1,
)
if not ats.empty:
fig.add_trace(
go.Scatter(
name="ATS",
x=ats.index,
y=ats["totalWeeklyShareQuantity"] /
ats["totalWeeklyTradeCount"],
line=dict(color="#fdc708", width=2),
opacity=1,
showlegend=False,
yaxis="y2",
),
row=2,
col=1,
)
if not otc.empty:
fig.add_trace(
go.Scatter(
name="OTC",
x=otc.index,
y=otc["totalWeeklyShareQuantity"] /
otc["totalWeeklyTradeCount"],
line=dict(color="#d81aea", width=2),
opacity=1,
showlegend=False,
),
row=2,
col=1,
)
else:
fig.add_trace(
go.Scatter(
name="OTC",
x=otc.index,
y=otc["totalWeeklyShareQuantity"] /
otc["totalWeeklyTradeCount"],
line=dict(color="#d81aea", width=2),
opacity=1,
showlegend=False,
),
row=2,
col=1,
)
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=20, r=0, t=10, b=20),
template=imps.PLT_CANDLE_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=f"Dark Pools (ATS) vs OTC (Non-ATS) Data for {ticker}",
title_x=0.5,
yaxis3_title="Shares per Trade",
yaxis_title="Total Weekly Shares",
xaxis2_title="Weeks",
font=imps.PLT_FONT,
yaxis=dict(
fixedrange=False,
side="left",
nticks=20,
),
yaxis2=dict(
fixedrange=False,
showgrid=False,
overlaying="y",
anchor="x",
layer="above traces",
),
yaxis3=dict(
fixedrange=False,
nticks=10,
),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
showspikes=True,
nticks=20,
),
legend=dict(orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1),
barmode="group",
bargap=0.5,
bargroupgap=0,
dragmode="pan",
hovermode="x unified",
spikedistance=1,
hoverdistance=1,
)
imagefile = "dps_dpotc.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": title,
"description": plt_link,
"imagefile": imagefile,
}
def hist_command(
ticker: str = "",
expiry: str = "",
strike: float = 10,
opt_type: str = "",
greek: str = "",
):
"""Plot historical option prices
Parameters
----------
ticker: str
Stock ticker
expiry: str
expiration date
strike: float
Option strike price
put: bool
Calls for call
Puts for put
"""
# Debug
if imps.DEBUG:
logger.info("opt grhist %s %s %s %s %s", ticker, strike, opt_type,
expiry, greek)
# Check for argument
if ticker is None:
raise Exception("Stock ticker is required")
if opt_type == "Puts":
put = bool(True)
if opt_type == "Calls":
put = bool(False)
chain_id = ""
df_hist = syncretism_model.get_historical_greeks(ticker, expiry, chain_id,
strike, put)
title = f"\n{ticker.upper()} {strike} {opt_type} expiring {expiry} Historical {greek.upper()}"
# Output data
fig = make_subplots(shared_xaxes=True, specs=[[{"secondary_y": True}]])
fig.add_trace(
go.Scatter(
name=ticker.upper(),
x=df_hist.index,
y=df_hist.price,
line=dict(color="#fdc708", width=2),
opacity=1,
), )
fig.add_trace(
go.Scatter(
name="Premium",
x=df_hist.index,
y=df_hist["premium"],
opacity=1,
yaxis="y2",
), )
if greek:
fig.add_trace(
go.Scatter(
name=f"{greek.upper()}",
x=df_hist.index,
y=df_hist[greek],
opacity=1,
yaxis="y3",
), )
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_layout(
margin=dict(l=10, r=10, t=30, b=20),
template=imps.PLT_TA_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title=title,
title_x=0.03,
title_font_size=14,
yaxis_title="<b>Stock Price</b>",
font=imps.PLT_FONT,
yaxis=dict(
side="right",
fixedrange=False,
titlefont=dict(color="#fdc708"),
tickfont=dict(color="#fdc708"),
showgrid=False,
position=0.02,
nticks=20,
),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
fixedrange=False,
domain=[0.037, 1],
),
xaxis2=dict(
rangeslider=dict(visible=False),
type="date",
fixedrange=False,
),
xaxis3=dict(
rangeslider=dict(visible=False),
type="date",
fixedrange=False,
),
dragmode="pan",
legend=dict(orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1),
yaxis2=dict(
side="left",
fixedrange=False,
anchor="x",
overlaying="y",
titlefont=dict(color="#d81aea"),
tickfont=dict(color="#d81aea"),
nticks=20,
),
yaxis3=dict(
side="left",
position=0,
fixedrange=False,
showgrid=False,
overlaying="y",
titlefont=dict(color="#00e6c3"),
tickfont=dict(color="#00e6c3"),
nticks=20,
),
hovermode="x unified",
)
imagefile = "opt_hist.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title":
f"{ticker.upper()} {strike} {opt_type} expiring {expiry} Historical",
"description": plt_link,
"imagefile": imagefile,
}
def candle_command(
ticker: str = "",
interval: int = 15,
past_days: int = 0,
extended_hours: bool = False,
start="",
end="",
news: bool = False,
heikin_candles: bool = False,
vwap: bool = False,
):
"""Shows candle plot of loaded ticker or crypto. [Source: Yahoo Finance or Binance API]
Parameters
----------
ticker : Stock Ticker
interval : Chart Minute Interval, 1440 for Daily
past_days: Past Days to Display. Default: 0(Not for Daily)
extended_hours: Display Pre/After Market Hours. Default: False
start: YYYY-MM-DD format
end: YYYY-MM-DD format
news: Display clickable news markers on interactive chart. Default: False
heikin_candles: Heikin Ashi candles. Default: False
"""
logger.info(
"candle %s %s %s %s %s %s %s %s",
ticker,
interval,
past_days,
extended_hours,
start,
end,
news,
heikin_candles,
)
# Retrieve Data
df_stock, start, end, bar_start = load_candle.stock_data(
ticker=ticker,
interval=interval,
past_days=past_days,
extended_hours=extended_hours,
start=start,
end=end,
news=news,
heikin_candles=heikin_candles,
)
df_stock = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]
df_stock = df_stock.join(overlap_model.vwap(df_stock, 0))
# Check that loading a stock was not successful
if df_stock.empty:
raise Exception(f"No data found for {ticker.upper()}.")
# Output Data
if interval != 1440:
df_stock = df_stock.loc[(df_stock.index >= bar_start) & (df_stock.index < end)]
plot = load_candle.candle_fig(
df_stock,
ticker,
interval,
extended_hours,
news,
bar=bar_start,
int_bar=interval,
)
title = f"{plot['plt_title']} Chart"
fig = plot["fig"]
if interval != 1440 and vwap:
fig.add_trace(
go.Scatter(
name="VWAP",
x=df_stock.index,
y=df_stock["VWAP_D"],
opacity=0.65,
line=dict(color="#00e6c3", width=2),
showlegend=True,
),
secondary_y=True,
)
fig.update_layout(
margin=dict(l=0, r=0, t=40, b=20),
template=imps.PLT_CANDLE_STYLE_TEMPLATE,
title=title,
title_x=0.5,
title_font_size=14,
)
imagefile = "candle.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=True)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": title,
"description": plt_link,
"imagefile": imagefile,
}
def options_run(
ticker,
url,
expiry,
dates,
df_bcinfo,
calls,
puts,
df_opt,
current_price,
min_strike,
max_strike,
min_strike2,
max_strike2,
max_pain,
):
"""Options Overview"""
titles, reports, embeds, embeds_img, choices, images_list = [], [], [], [], [], []
fig = go.Figure()
dmax = df_opt[["OI_call", "OI_put"]].values.max()
dmin = df_opt[["OI_call", "OI_put"]].values.min()
fig.add_trace(
go.Scatter(
x=df_opt.index,
y=df_opt["OI_call"],
name="Calls",
mode="lines+markers",
line=dict(color="#00ACFF", width=3),
))
fig.add_trace(
go.Scatter(
x=df_opt.index,
y=df_opt["OI_put"],
name="Puts",
mode="lines+markers",
line=dict(color="#e4003a", width=3),
))
fig.add_trace(
go.Scatter(
x=[current_price, current_price],
y=[dmin, dmax],
mode="lines",
line=dict(color="gold", width=2),
name="Current Price",
))
fig.add_trace(
go.Scatter(
x=[max_pain, max_pain],
y=[dmin, dmax],
mode="lines",
line=dict(color="grey", width=3, dash="dash"),
name=f"Max Pain: {max_pain}",
))
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_xaxes(
range=[min_strike, max_strike],
constrain="domain",
)
fig.update_layout(
margin=dict(l=0, r=0, t=60, b=20),
template=imps.PLT_SCAT_STYLE_TEMPLATE,
title=f"Open Interest for {ticker.upper()} expiring {expiry}",
title_x=0.5,
legend_title="",
xaxis_title="Strike",
yaxis_title="Open Interest (1k)",
xaxis=dict(rangeslider=dict(visible=False), ),
font=imps.PLT_FONT,
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
dragmode="pan",
)
imagefile = "opt-oi.png"
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
reports.append(plt_link)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
if imps.IMAGES_URL or not imps.IMG_HOST_ACTIVE:
image_link_oi = imps.multi_image(imagefile)
images_list.append(imagefile)
else:
image_link_oi = imps.multi_image(imagefile)
calls_df = calls[columns].rename(columns=column_map)
calls_df = calls_df[calls_df["strike"] >= min_strike2]
calls_df = calls_df[calls_df["strike"] <= max_strike2]
calls_df["iv"] = pd.to_numeric(calls_df["iv"].astype(float))
formats = {"iv": "{:.2f}"}
for col, f in formats.items():
calls_df[col] = calls_df[col].map(lambda x: f.format(x))
calls_df = calls_df.fillna("")
calls_df.set_index("strike", inplace=True)
if "^" not in ticker:
if "-" in df_bcinfo.iloc[0, 1]:
iv = f"```diff\n- {df_bcinfo.iloc[0, 1]}\n```"
else:
iv = f"```yaml\n {df_bcinfo.iloc[0, 1]}\n```"
pfix, sfix = f"{ticker.upper()} ", f" expiring {expiry}"
if expiry == dates[0]:
pfix = f"{ticker.upper()} Weekly "
sfix = ""
titles.append(f"{ticker.upper()} Overview", )
titles.append(f"{pfix}Open Interest{sfix}", )
embeds.append(
disnake.Embed(
title=f"{ticker.upper()} Overview",
color=imps.COLOR,
), )
embeds.append(
disnake.Embed(
title=f"{pfix}Open Interest{sfix}",
description=plt_link,
colour=imps.COLOR,
), )
choices.append(
disnake.SelectOption(label=f"{ticker.upper()} Overview",
value="0",
emoji="🟢"), )
choices.append(
disnake.SelectOption(label=f"{pfix}Open Interest{sfix}",
value="1",
emoji="🟢"), )
i, i2, end = 0, 0, 20
df_calls = []
dindex = len(calls_df.index)
while i < dindex:
df_calls = calls_df.iloc[i:end]
df_calls.append(df_calls)
figc = imps.plot_df(
df_calls,
fig_size=(1000, (40 + (40 * 20))),
col_width=[3, 3, 3, 3],
tbl_header=imps.PLT_TBL_HEADER,
tbl_cells=imps.PLT_TBL_CELLS,
font=imps.PLT_TBL_FONT,
row_fill_color=imps.PLT_TBL_ROW_COLORS,
paper_bgcolor="rgba(0, 0, 0, 0)",
)
imagefile = "opt-calls.png"
imagefile = imps.save_image(imagefile, figc)
if imps.IMAGES_URL or not imps.IMG_HOST_ACTIVE:
image_link = imps.multi_image(imagefile)
images_list.append(imagefile)
else:
image_link = imps.multi_image(imagefile)
embeds_img.append(f"{image_link}", )
titles.append(f"{pfix}Calls{sfix}", )
embeds.append(
disnake.Embed(
title=f"{pfix}Calls{sfix}",
colour=imps.COLOR,
), )
i2 += 1
i += 20
end += 20
# Add Calls page field
i, page, puts_page = 2, 0, 3
i3 = i2 + 2
choices.append(
disnake.SelectOption(label="Calls Page 1", value="2", emoji="🟢"), )
for i in range(2, i3):
page += 1
puts_page += 1
embeds[i].add_field(name=f"Calls Page {page}",
value="_ _",
inline=True)
puts_df = puts[columns].rename(columns=column_map)
puts_df = puts_df[puts_df["strike"] >= min_strike2]
puts_df = puts_df[puts_df["strike"] <= max_strike2]
puts_df["iv"] = pd.to_numeric(puts_df["iv"].astype(float))
formats = {"iv": "{:.2f}"}
for col, f in formats.items():
puts_df[col] = puts_df[col].map(lambda x: f.format(x)) # pylint: disable=W0640
puts_df = puts_df.fillna("")
puts_df.set_index("strike", inplace=True)
pfix, sfix = f"{ticker.upper()} ", f" expiring {expiry}"
if expiry == dates[0]:
pfix = f"{ticker.upper()} Weekly "
sfix = ""
# Puts Pages
i, end = 0, 20
df_puts = []
dindex = len(puts_df.index)
while i < dindex:
df_puts = puts_df.iloc[i:end]
df_puts.append(df_puts)
figp = imps.plot_df(
df_puts,
fig_size=(1000, (40 + (40 * 20))),
col_width=[3, 3, 3, 3],
tbl_header=imps.PLT_TBL_HEADER,
tbl_cells=imps.PLT_TBL_CELLS,
font=imps.PLT_TBL_FONT,
row_fill_color=imps.PLT_TBL_ROW_COLORS,
paper_bgcolor="rgba(0, 0, 0, 0)",
)
imagefile = "opt-puts.png"
imagefile = imps.save_image(imagefile, figp)
if imps.IMAGES_URL or not imps.IMG_HOST_ACTIVE:
image_link = imps.multi_image(imagefile)
images_list.append(imagefile)
else:
image_link = imps.multi_image(imagefile)
embeds_img.append(f"{image_link}", )
titles.append(f"{pfix}Puts{sfix}", )
embeds.append(
disnake.Embed(
title=f"{pfix}Puts{sfix}",
colour=imps.COLOR,
), )
i2 += 1
i += 20
end += 20
# Add Puts page field
i, page = 0, 0
puts_page -= 1
i2 += 2
choices.append(
disnake.SelectOption(label="Puts Page 1",
value=f"{puts_page}",
emoji="🟢"), )
for i in range(puts_page, i2):
page += 1
embeds[i].add_field(name=f"Puts Page {page}", value="_ _", inline=True)
# Author/Footer
for i in range(0, i2):
embeds[i].set_author(
name=imps.AUTHOR_NAME,
url=imps.AUTHOR_URL,
icon_url=imps.AUTHOR_ICON_URL,
)
embeds[i].set_footer(
text=imps.AUTHOR_NAME,
icon_url=imps.AUTHOR_ICON_URL,
)
# Set images to Pages
i = 0
img_i = 0
embeds[1].set_image(url=image_link_oi)
for i in range(2, i2):
embeds[i].set_image(url=embeds_img[img_i])
img_i += 1
i += 1
if url:
embeds[0].set_thumbnail(url=f"{url}")
else:
embeds[0].set_thumbnail(url=imps.AUTHOR_ICON_URL)
# Overview Section
if "^" not in ticker:
reports.append(
f"{'':^5}*{df_bcinfo.iloc[0, 0]:^25}*{'':^5}*{df_bcinfo.iloc[1, 0]:^25}*{'':^5}\n"
)
reports.append(
f"{'':^8}{df_bcinfo.iloc[0, 1]:^25}{'':^5}{df_bcinfo.iloc[1, 1]:^25}\n"
)
i, i2 = 2, 3
while i < 11:
text = (
f"{'':^5}*{df_bcinfo.iloc[i, 0]:^25}*{'':^5}*{df_bcinfo.iloc[i2, 0]:^25}*{'':^5}\n"
f"{'':^5}{df_bcinfo.iloc[i, 1]:^30}{'':^5}{df_bcinfo.iloc[i2, 1]:^25}{'':^10}\n"
)
reports.append(text)
i += 1
i2 += 1
embeds[0].add_field(name=f"{df_bcinfo.iloc[0, 0]}",
value=iv,
inline=False)
embeds[0].add_field(
name=f"•{df_bcinfo.iloc[1, 0]}",
value=f"```css\n{df_bcinfo.iloc[1, 1]}\n```",
inline=True,
)
for N in range(2, 6):
embeds[0].add_field(
name=f"_ _ _ _ _ _ _ _ _ _ •{df_bcinfo.iloc[N, 0]}",
value=f"```css\n{df_bcinfo.iloc[N, 1]}\n```",
inline=True,
)
embeds[0].add_field(name="_ _", value="_ _", inline=False)
for N in range(6, 8):
embeds[0].add_field(
name=f"_ _ _ _ _ _ _ _ _ _ •{df_bcinfo.iloc[N, 0]}",
value=f"```css\n{df_bcinfo.iloc[N, 1]}\n```",
inline=True,
)
embeds[0].add_field(name="_ _", value="_ _", inline=False)
for N in range(8, 10):
embeds[0].add_field(
name=f"_ _ _ _ _ _ _ _ _ _ •{df_bcinfo.iloc[N, 0]}",
value=f"```css\n{df_bcinfo.iloc[N, 1]}\n```",
inline=True,
)
embeds[0].add_field(name="_ _", value="_ _", inline=False)
for N in range(10, 12):
embeds[0].add_field(
name=f"_ _ _ _ _ _ _ _ _ _ •{df_bcinfo.iloc[N, 0]}",
value=f"```css\n{df_bcinfo.iloc[N, 1]}\n```",
inline=True,
)
embeds[0].set_footer(text=f"Page 1 of {len(embeds)}")
return titles, reports, embeds, choices, embeds_img, images_list
def cpi_command(start=""):
"""Displays Consumer Prices Index (CPI)"""
# Debug
if imps.DEBUG:
logger.debug(
"econ cpi %s",
start,
)
if start == "":
start = datetime.now() - timedelta(days=900)
else:
start = datetime.strptime(start, imps.DATE_FORMAT)
# Retrieve data
df_d = ("date", "CPI")
series_ids = list(df_d)
data = pd.DataFrame()
for s_id in series_ids:
data = pd.concat(
[
data,
pd.DataFrame(fred_model.get_series_data("CPIAUCSL", start),
columns=[s_id]),
],
axis=1,
)
df = data.dropna()
# Check for argument
if df.empty:
raise Exception("No available data found")
data["date"] = pd.to_datetime(data["date"])
data["CPI"] = data["CPI"].astype(float)
data = data.drop(columns=["CPI"])
end = datetime.now() + timedelta(days=30)
# Debug user output
if imps.DEBUG:
logger.debug(df.to_string())
fig = go.Figure()
fig.add_trace(go.Scatter(
x=df.index,
y=df["CPI"],
))
if imps.PLT_WATERMARK:
fig.add_layout_image(imps.PLT_WATERMARK)
fig.update_xaxes(dtick="M2", tickformat="%b\n%Y")
fig.update_layout(
xaxis_range=[df.index[0], end],
margin=dict(l=0, r=0, t=40, b=50),
template=imps.PLT_SCAT_STYLE_TEMPLATE,
colorway=imps.PLT_TA_COLORWAY,
title="CPI Monthly",
title_x=0.5,
yaxis_title="Consumer Prices",
legend_title="",
font=imps.PLT_FONT,
yaxis=dict(
fixedrange=False,
nticks=20,
),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
),
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
dragmode="pan",
)
imagefile = "econ-cpi.png"
# Check if interactive settings are enabled
plt_link = ""
if imps.INTERACTIVE:
plt_link = imps.inter_chart(fig, imagefile, callback=False)
fig.update_layout(
width=800,
height=500,
)
imagefile = imps.image_border(imagefile, fig=fig)
return {
"title": "Consumer Prices Index",
"description": plt_link,
"imagefile": imagefile,
}
