def comp_plot(df):
''' merged is a merged file containing Index(['Bill Date',
# 'Qty', 'Sale Count', 'state', 'positive', 'Daily Cases'], dtype ='object') '''
# Create figure with secondary y-axis
df2 = df.groupby('Bill Date').agg({'Qty': 'sum', 'Pred': 'sum'}).reset_index()
fig = _make_subplots(specs=[[{"secondary_y": False
}]])
fig.add_trace(
_go.Line(x=df2['Bill Date'], y=df2['Qty'], name="Actual Sales"),
secondary_y=False,
)
# Add traces
fig.add_trace(
_go.Line(x=df2['Bill Date'], y=df2['Pred'], name="Predictions"),
secondary_y=False,
)
# Add figure title
fig.update_layout(
title_text="Sales vs Prediction", plot_bgcolor='rgba(0,0,0,0)'
)
# Set x-axis title
fig.update_xaxes(title_text="Dates")
# Set y-axes titles
fig.update_yaxes(title_text="Sales Qty", secondary_y=False)
fig.show()
def sales_plot(merged, over='Qty'):
''' merged is a merged file containing Index(['Bill Date',
# 'Qty', 'Sale Count', 'state', 'positive', 'Daily Cases'], dtype ='object') '''
# Create figure with secondary y-axis
fig = _make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(
_go.Bar(x=merged['Bill Date'], y=merged['Daily Cases'], name="COVID Evolution", opacity=0.50),
secondary_y=True,
)
# Add traces
fig.add_trace(
_go.Line(x=merged['Bill Date'], y=merged[over], name="Sales Qty"),
secondary_y=False,
)
# Add figure title
fig.update_layout(
title_text="COVID vs Sales", plot_bgcolor='rgba(0,0,0,0)'
)
# Set x-axis title
fig.update_xaxes(title_text="Dates")
# Set y-axes titles
fig.update_yaxes(title_text="Sales Qty", secondary_y=False)
fig.update_yaxes(title_text="COVID Evolution", secondary_y=True)
fig.show()
def make_subplots(*args, **kwargs) -> tp.BaseFigure:
"""Makes subplots and passes them to `FigureWidget`."""
return make_figure(_make_subplots(*args, **kwargs))
def make_subplots(*args, **kwargs):
"""Makes subplots and passes them to `FigureWidget`."""
return FigureWidget(_make_subplots(*args, **kwargs))
def chart_perf_summary(df, geometric=True, title=None):
"""
Function to plot the cumulative performance and drawdowns of
multiple assets
Parameters
----------
df : DataFrame | Series
wide dataframe with a datetime index and assets as columns or a
series with a datetime index for a univariate chart
geometric : bool
True to plot geometric returns, False to plot absolute
title : str
Title for plot
>>> import risktools as rt
>>> df = rt.data.open_data('dfwide')
>>> df = df[['CL01', 'CL12', 'CL36']]
>>> df = rt.returns(df, period_return=1)
>>> rt.chart_perf_summary(df, title="Cummulative Returns and Drawdowns")
"""
df = df.copy()
if geometric == True:
ret = df.add(1).cumprod()
else:
ret = df.cumsum()
dd = drawdowns(df, geometric=geometric)
cols = DEFAULT_PLOTLY_COLORS
fig = _make_subplots(rows=2,
cols=1,
subplot_titles=("Cummulative Returns", "Drawdowns"))
for i, c in enumerate(ret.columns):
fig.add_trace(
go.Scatter(
x=ret.index,
y=ret[c],
name=c,
line=dict(width=2, color=cols[i]),
legendgroup="a",
),
row=1,
col=1,
)
for i, c in enumerate(dd.columns):
fig.add_trace(
go.Scatter(
x=dd.index,
y=dd[c],
name=c,
line=dict(width=2, color=cols[i]),
legendgroup="b",
showlegend=False,
),
row=2,
col=1,
)
fig.update_layout(title_text=title)
return fig
def chart_eia_sd(market, key, start_dt="2010-01-01", output="chart", **kwargs):
"""
Function for plotting and returning data from the EIA Supply & Demand Balances for
refined oil products such as mogas, diesel, jet and resid.
Parameters
----------
market : ['mogas', 'diesel', 'jet', 'resid']
Refined product type to build the S&D balance for
key : str
EIA API key
start_dt : str | datetime
Starting date for S&D balance data
output : ['chart','data']
Output as a chart or return data as a dataframe, by default 'chart'
Returns
-------
pandas dataframe or a plotly figure object
Examples
--------
>>> import risktools as rt
>>> fig = rt.chart_eia_sd('mogas', up['eia'])
>>> fig.show()
"""
eia = data.open_data("tickers_eia")
eia = eia[eia.sd_category == market]
df = get_eia_df(eia.tick_eia.to_list(), key=key)
df = df.merge(eia[["tick_eia", "category"]],
left_on=["series_id"],
right_on=["tick_eia"]).drop("tick_eia", axis=1)
df.date = pd.to_datetime(df.date)
df = df.set_index("date").sort_index()
# create list of plotly figure objects using repeating calls to the
# five_year_plot function to loop through later to create subplot
figs = []
for c in df.category.unique():
tf = df.loc[df.category == c, "value"]
figs.append(chart_five_year_plot(tf, title=c))
# calc shape of final subplot
n = len(df.category.unique())
m = 2
n = _ceil(n / m)
fig = _make_subplots(
n,
m,
subplot_titles=(" ", " ", " ", " ", " ", " "),
)
# Copy returns figures from five_year_plot to a single subplot figure
a = 1
b = 1
for i, _ in enumerate(figs):
for j, _ in enumerate(figs[i]["data"]):
fig.add_trace(figs[i]["data"][j], row=a, col=b)
fig["layout"]["annotations"][
(a - 1) * 2 + b -
1]["text"] = figs[i]["layout"]["title"]["text"]
# copy xaxis nticks and tickformat to subplots so that it keeps that formatting.
# if they don't exist, pass
try:
fig["layout"][f"xaxis{i+1}"]["nticks"] = figs[i]["layout"][
"xaxis"]["nticks"]
fig["layout"][f"xaxis{i+1}"]["tickformat"] = figs[i]["layout"][
"xaxis"]["tickformat"]
except:
pass
if b == m:
b = 1
a += 1
else:
b += 1
fig.update_layout(showlegend=False)
# return figs
if output == "chart":
return fig
else:
return df