def view_ma(
ma_type: str,
s_ticker: str,
s_interval: str,
df_stock: pd.DataFrame,
length: List[int],
offset: int,
export: str = "",
) -> pd.DataFrame:
"""Plots MA technical indicator
Parameters
----------
ma_type: str
Type of moving average. Either "EMA" "ZLMA" or "SMA"
s_ticker : str
Ticker
s_interval : str
Interval of data
df_stock : pd.DataFrame
Dataframe of prices
length : List[int]
Length of EMA window
export : str
Format to export data
"""
if s_interval == "1440min":
price_df = pd.DataFrame(df_stock["Adj Close"].values,
columns=["Price"],
index=df_stock.index)
else:
price_df = pd.DataFrame(df_stock["Close"].values,
columns=["Price"],
index=df_stock.index)
l_legend = [s_ticker]
for win in length:
if ma_type == "EMA":
df_ta = overlap_model.ema(s_interval, df_stock, win, offset)
l_legend.append(f"EMA {win}")
elif ma_type == "SMA":
df_ta = overlap_model.sma(s_interval, df_stock, win, offset)
l_legend.append(f"SMA {win}")
elif ma_type == "WMA":
df_ta = overlap_model.wma(s_interval, df_stock, win, offset)
l_legend.append(f"WMA {win}")
elif ma_type == "HMA":
df_ta = overlap_model.hma(s_interval, df_stock, win, offset)
l_legend.append(f"HMA {win}")
elif ma_type == "ZLMA":
df_ta = overlap_model.zlma(s_interval, df_stock, win, offset)
l_legend.append(f"ZLMA {win}")
price_df = price_df.join(df_ta)
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.set_title(f"{s_ticker} {ma_type.upper()}")
ax.plot(price_df.index, price_df["Price"], lw=3, c="k")
ax.set_xlabel("Time")
ax.set_xlim([price_df.index[0], price_df.index[-1]])
ax.set_ylabel(f"{s_ticker} Price")
for idx in range(1, price_df.shape[1]):
ax.plot(price_df.iloc[:, idx])
ax.legend(l_legend)
ax.grid(b=True, which="major", color="#666666", linestyle="-")
if gtff.USE_ION:
plt.ion()
plt.gcf().autofmt_xdate()
fig.tight_layout(pad=1)
plt.show()
print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
f"{ma_type.lower()}{'_'.join([str(win) for win in length])}",
price_df,
)
def sma_command(ticker="", window="", offset="", start="", end=""):
"""Displays chart with simple moving average [Yahoo Finance]"""
# Debug
if cfg.DEBUG:
logger.debug(
"ta-sma %s %s %s %s %s",
ticker,
window,
offset,
start,
end,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
if start == "":
start = datetime.now() - timedelta(days=365)
else:
start = datetime.strptime(start, cfg.DATE_FORMAT)
if end == "":
end = datetime.now()
else:
end = datetime.strptime(end, cfg.DATE_FORMAT)
l_legend = [ticker]
if window == "":
window = [20, 50]
else:
window_temp = list()
for wind in window.split(","):
try:
window_temp.append(float(wind))
except Exception as e:
raise Exception("Window needs to be a float") from e
window = window_temp
ticker = ticker.upper()
stock = helpers.load(ticker, start)
if stock.empty:
raise Exception("Stock ticker is invalid")
# Retrieve Data
price_df = pd.DataFrame(stock["Adj Close"].values,
columns=["Price"],
index=stock.index)
i = 1
for win in window:
sma_data = overlap_model.sma(values=stock["Adj Close"],
length=win,
offset=offset)
price_df = price_df.join(sma_data)
l_legend.append(f"SMA {win}")
i += 1
# Output Data
start = start.strftime("%Y-%m-%d")
end = end.strftime("%Y-%m-%d")
price_df = price_df.loc[(price_df.index >= start) & (price_df.index < end)]
fig = go.Figure()
fig.add_trace(
go.Scatter(
name=f"{ticker}",
x=price_df.index,
y=price_df["Price"],
line=dict(color="#fdc708", width=2),
opacity=1,
), )
for i in range(1, price_df.shape[1]):
trace_name = price_df.columns[i].replace("_", " ")
fig.add_trace(
go.Scatter(
name=trace_name,
x=price_df.index,
y=price_df.iloc[:, i],
opacity=1,
), )
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=cfg.PLT_TA_STYLE_TEMPLATE,
colorway=cfg.PLT_TA_COLORWAY,
title=f"{ticker} SMA",
title_x=0.5,
yaxis_title="Stock Price ($)",
xaxis_title="Time",
yaxis=dict(fixedrange=False, ),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
),
dragmode="pan",
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
)
config = dict({"scrollZoom": True})
imagefile = "ta_sma.png"
# Check if interactive settings are enabled
plt_link = ""
if cfg.INTERACTIVE:
html_ran = helpers.uuid_get()
fig.write_html(f"in/sma_{html_ran}.html", config=config)
plt_link = f"[Interactive]({cfg.INTERACTIVE_URL}/sma_{html_ran}.html)"
fig.update_layout(
width=800,
height=500,
)
imagefile = helpers.image_border(imagefile, fig=fig)
return {
"title": f"Stocks: Simple-Moving-Average {ticker}",
"description": plt_link,
"imagefile": imagefile,
}
async def sma_command(ctx, ticker="", window="", offset="", start="", end=""):
"""Displays chart with simple moving average [Yahoo Finance]"""
try:
# Debug
if cfg.DEBUG:
print(f"!stocks.ta.sma {ticker} {window} {offset} {start} {end}")
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
if start == "":
start = datetime.now() - timedelta(days=365)
else:
start = datetime.strptime(start, cfg.DATE_FORMAT)
if end == "":
end = datetime.now()
else:
end = datetime.strptime(end, cfg.DATE_FORMAT)
l_legend = [ticker]
if window == "":
window = [20, 50]
else:
window_temp = list()
for wind in window.split(","):
try:
window_temp.append(float(wind))
except Exception as e:
raise Exception("Window needs to be a float") from e
window = window_temp
if offset == "":
offset = 0
else:
if not offset.lstrip("-").isnumeric():
raise Exception("Number has to be an integer")
offset = float(offset)
ticker = ticker.upper()
stock = discordbot.helpers.load(ticker, start)
if stock.empty:
raise Exception("Stock ticker is invalid")
# Retrieve Data
price_df = pd.DataFrame(stock["Adj Close"].values,
columns=["Price"],
index=stock.index)
i = 1
for win in window:
sma_data = overlap_model.sma(s_interval="1440min",
df_stock=stock,
length=win,
offset=offset)
price_df = price_df.join(sma_data)
l_legend.append(f"SMA {win}")
i += 1
# Output Data
start = start.strftime("%Y-%m-%d")
end = end.strftime("%Y-%m-%d")
price_df = price_df.loc[(price_df.index >= start)
& (price_df.index < end)]
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.set_title(f"{ticker} SMA")
ax.plot(price_df.index, price_df["Price"], lw=3, c="k")
ax.set_xlabel("Time")
ax.set_xlim([price_df.index[0], price_df.index[-1]])
ax.set_ylabel(f"{ticker} Price")
for idx in range(1, price_df.shape[1]):
ax.plot(price_df.iloc[:, idx])
ax.legend(l_legend)
ax.grid(b=True, which="major", color="#666666", linestyle="-")
plt.gcf().autofmt_xdate()
fig.tight_layout(pad=1)
plt.savefig("ta_sma.png")
uploaded_image = gst_imgur.upload_image("ta_sma.png",
title="something")
image_link = uploaded_image.link
if cfg.DEBUG:
print(f"Image URL: {image_link}")
title = "Stocks: Simple-Moving-Average " + ticker
embed = discord.Embed(title=title, colour=cfg.COLOR)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
embed.set_image(url=image_link)
os.remove("ta_sma.png")
await ctx.send(embed=embed)
except Exception as e:
embed = discord.Embed(
title="ERROR Stocks: Simple-Moving-Average",
colour=cfg.COLOR,
description=e,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed)
def view_ma(
series: pd.Series,
length: List[int] = None,
offset: int = 0,
ma_type: str = "EMA",
s_ticker: str = "",
export: str = "",
external_axes: Optional[List[plt.Axes]] = None,
) -> None:
"""Plots MA technical indicator
Parameters
----------
series : pd.Series
Series of prices
length : List[int]
Length of EMA window
ma_type: str
Type of moving average. Either "EMA" "ZLMA" or "SMA"
s_ticker : str
Ticker
export : str
Format to export data
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
# Define a dataframe for adding EMA series to it
price_df = pd.DataFrame(series)
l_legend = [s_ticker]
if not length:
length = [20, 50]
for win in length:
if ma_type == "EMA":
df_ta = overlap_model.ema(series, win, offset)
l_legend.append(f"EMA {win}")
elif ma_type == "SMA":
df_ta = overlap_model.sma(series, win, offset)
l_legend.append(f"SMA {win}")
elif ma_type == "WMA":
df_ta = overlap_model.wma(series, win, offset)
l_legend.append(f"WMA {win}")
elif ma_type == "HMA":
df_ta = overlap_model.hma(series, win, offset)
l_legend.append(f"HMA {win}")
elif ma_type == "ZLMA":
df_ta = overlap_model.zlma(series, win, offset)
l_legend.append(f"ZLMA {win}")
price_df = price_df.join(df_ta)
plot_data = reindex_dates(price_df)
# This plot has 1 axis
if external_axes is None:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
else:
if len(external_axes) != 1:
console.print("[red]Expected list of one axis item./n[/red]")
return
(ax, ) = external_axes
ax.plot(plot_data.index, plot_data.iloc[:, 1].values)
ax.set_xlim([plot_data.index[0], plot_data.index[-1]])
ax.set_ylabel(f"{s_ticker} Price")
for idx in range(2, plot_data.shape[1]):
ax.plot(plot_data.iloc[:, idx])
ax.set_title(f"{s_ticker} {ma_type.upper()}")
ax.legend(l_legend)
theme.style_primary_axis(
ax,
data_index=plot_data.index.to_list(),
tick_labels=plot_data["date"].to_list(),
)
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
f"{ma_type.lower()}{'_'.join([str(win) for win in length])}",
price_df,
)
async def sma_command(ctx, ticker="", window="", offset="", start="", end=""):
"""Displays chart with simple moving average [Yahoo Finance]"""
try:
# Debug
if cfg.DEBUG:
logger.debug(
"!stocks.ta.sma %s %s %s %s %s",
ticker,
window,
offset,
start,
end,
)
# Check for argument
if ticker == "":
raise Exception("Stock ticker is required")
if start == "":
start = datetime.now() - timedelta(days=365)
else:
start = datetime.strptime(start, cfg.DATE_FORMAT)
if end == "":
end = datetime.now()
else:
end = datetime.strptime(end, cfg.DATE_FORMAT)
l_legend = [ticker]
if window == "":
window = [20, 50]
else:
window_temp = list()
for wind in window.split(","):
try:
window_temp.append(float(wind))
except Exception as e:
raise Exception("Window needs to be a float") from e
window = window_temp
ticker = ticker.upper()
stock = discordbot.helpers.load(ticker, start)
if stock.empty:
raise Exception("Stock ticker is invalid")
# Retrieve Data
price_df = pd.DataFrame(stock["Adj Close"].values,
columns=["Price"],
index=stock.index)
i = 1
for win in window:
sma_data = overlap_model.sma(values=stock["Adj Close"],
length=win,
offset=offset)
price_df = price_df.join(sma_data)
l_legend.append(f"SMA {win}")
i += 1
# Output Data
start = start.strftime("%Y-%m-%d")
end = end.strftime("%Y-%m-%d")
price_df = price_df.loc[(price_df.index >= start)
& (price_df.index < end)]
fig = go.Figure()
fig.add_trace(
go.Scatter(
name=f"{ticker}",
x=price_df.index,
y=price_df["Price"],
line=dict(color="#fdc708", width=2),
opacity=1,
), )
for i in range(1, price_df.shape[1]):
trace_name = price_df.columns[i].replace("_", " ")
fig.add_trace(
go.Scatter(
name=trace_name,
x=price_df.index,
y=price_df.iloc[:, i],
opacity=1,
), )
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=20),
template=cfg.PLT_TA_STYLE_TEMPLATE,
colorway=cfg.PLT_TA_COLORWAY,
title=f"{ticker} SMA",
title_x=0.5,
yaxis_title="Stock Price ($)",
xaxis_title="Time",
yaxis=dict(fixedrange=False, ),
xaxis=dict(
rangeslider=dict(visible=False),
type="date",
),
dragmode="pan",
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
)
config = dict({"scrollZoom": True})
imagefile = "ta_sma.png"
# Check if interactive settings are enabled
plt_link = ""
if cfg.INTERACTIVE:
html_ran = random.randint(69, 69420)
fig.write_html(f"in/sma_{html_ran}.html", config=config)
plt_link = f"[Interactive]({cfg.INTERACTIVE_URL}/sma_{html_ran}.html)"
fig.update_layout(
width=800,
height=500,
)
fig.write_image(imagefile)
img = Image.open(imagefile)
im_bg = Image.open(cfg.IMG_BG)
h = img.height + 240
w = img.width + 520
# Paste fig onto background img and autocrop background
img = img.resize((w, h), Image.ANTIALIAS)
x1 = int(0.5 * im_bg.size[0]) - int(0.5 * img.size[0])
y1 = int(0.5 * im_bg.size[1]) - int(0.5 * img.size[1])
x2 = int(0.5 * im_bg.size[0]) + int(0.5 * img.size[0])
y2 = int(0.5 * im_bg.size[1]) + int(0.5 * img.size[1])
img = img.convert("RGB")
im_bg.paste(img, box=(x1 - 5, y1, x2 - 5, y2))
im_bg.save(imagefile, "PNG", quality=100)
image = Image.open(imagefile)
image = autocrop_image(image, 0)
image.save(imagefile, "PNG", quality=100)
image = disnake.File(imagefile)
print(f"Image {imagefile}")
embed = disnake.Embed(
title=f"Stocks: Simple-Moving-Average {ticker}",
description=plt_link,
colour=cfg.COLOR,
)
embed.set_image(url=f"attachment://{imagefile}")
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
os.remove(imagefile)
await ctx.send(embed=embed, file=image)
except Exception as e:
embed = disnake.Embed(
title="ERROR Stocks: Simple-Moving-Average",
colour=cfg.COLOR,
description=e,
)
embed.set_author(
name=cfg.AUTHOR_NAME,
icon_url=cfg.AUTHOR_ICON_URL,
)
await ctx.send(embed=embed, delete_after=30.0)
def view_ma(
values: pd.Series,
length: List[int] = None,
offset: int = 0,
ma_type: str = "EMA",
s_ticker: str = "",
export: str = "",
) -> pd.DataFrame:
"""Plots MA technical indicator
Parameters
----------
values : pd.Series
Series of prices
length : List[int]
Length of EMA window
ma_type: str
Type of moving average. Either "EMA" "ZLMA" or "SMA"
s_ticker : str
Ticker
export : str
Format to export data
"""
# Define a dataframe for adding EMA values to it
price_df = pd.DataFrame(values)
l_legend = [s_ticker]
if not length:
length = [20, 50]
for win in length:
if ma_type == "EMA":
df_ta = overlap_model.ema(values, win, offset)
l_legend.append(f"EMA {win}")
elif ma_type == "SMA":
df_ta = overlap_model.sma(values, win, offset)
l_legend.append(f"SMA {win}")
elif ma_type == "WMA":
df_ta = overlap_model.wma(values, win, offset)
l_legend.append(f"WMA {win}")
elif ma_type == "HMA":
df_ta = overlap_model.hma(values, win, offset)
l_legend.append(f"HMA {win}")
elif ma_type == "ZLMA":
df_ta = overlap_model.zlma(values, win, offset)
l_legend.append(f"ZLMA {win}")
price_df = price_df.join(df_ta)
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.set_title(f"{s_ticker} {ma_type.upper()}")
ax.plot(values.index, values.values, lw=3, c="k")
ax.set_xlabel("Time")
ax.set_xlim([price_df.index[0], price_df.index[-1]])
ax.set_ylabel(f"{s_ticker} Price")
for idx in range(1, price_df.shape[1]):
ax.plot(price_df.iloc[:, idx])
ax.legend(l_legend)
ax.grid(b=True, which="major", color="#666666", linestyle="-")
if gtff.USE_ION:
plt.ion()
plt.gcf().autofmt_xdate()
fig.tight_layout(pad=1)
plt.show()
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
f"{ma_type.lower()}{'_'.join([str(win) for win in length])}",
price_df,
)