Below is the code I am using and for some reason when I run it the ETH chart shows the right x axis, but when I select anything else it shows MS.
Below is a snippet of what the data looks like. The rest of the assets have the same date format.
def get_dataset(src, name):
df = src[src.asset == name].copy()
xaxis_dt_format = "%Y-%m-%d %H:%M"
time_dict = {
i: date.strftime(xaxis_dt_format)
for i, date in enumerate(pd.to_datetime(df["date"]))
}
time_df = pd.DataFrame.from_dict(time_dict, orient = "index", columns = ["date"])
time_source = ColumnDataSource(data = time_df)
inc = df.close > df.open
dec = ~inc
inc_source = ColumnDataSource(
data=dict(
x1=df.index[inc],
top1=df.open[inc],
bottom1=df.close[inc],
high1=df.high[inc],
low1=df.low[inc],
Date1=df.date[inc],
)
)
dec_source = ColumnDataSource(
data=dict(
x2=df.index[dec],
top2=df.open[dec],
bottom2=df.close[dec],
high2=df.high[dec],
low2=df.low[dec],
Date2=df.date[dec],
)
)
bb_source = ColumnDataSource(
data=dict(
sma = df["SMA"],
upper_band = df["Upper Band"],
lower_band = df["Lower Band"],
color1 = df["Trade_Ind"],
signals = df["Signals"],
Date3=df.index,
)
)
return inc_source, dec_source, bb_source, time_source
def make_plot(source1, source2, source3, source4, title_name):
# Select the datetime format for the x axis depending on the timeframe
xaxis_dt_format = "%Y-%m-%d %H:%M"
time_df = source4.to_df()
time_df = time_df.rename(columns = {time_df.columns[0]:"Count", "date": "Date"})
time_df.pop("Count")
#time_dict = time_df.to_dict()
fig = figure(
sizing_mode="stretch_both",
tools="xpan,xwheel_zoom,reset,save",
active_drag="xpan",
active_scroll="xwheel_zoom",
x_axis_type="datetime",
#x_range=Range1d(df.index[0], df.index[-1], bounds="auto"),
title=title_name,
)
fig.yaxis[0].formatter = NumeralTickFormatter(format="$5.3f")
# Colour scheme for increasing and descending candles
INCREASING_COLOR = "#17BECF"
DECREASING_COLOR = "#7F7F7F"
width = 0.5
# Plot candles
# High and low
fig.segment(
x0="x1", y0="high1", x1="x1", y1="low1", source=source1, color="black"
)
fig.segment(
x0="x2", y0="high2", x1="x2", y1="low2", source=source2, color="black"
)
# Open and close
r1 = fig.vbar(
x="x1",
width=width,
top="top1",
bottom="bottom1",
source=source1,
fill_color=INCREASING_COLOR,
line_color="black",
)
r2 = fig.vbar(
x="x2",
width=width,
top="top2",
bottom="bottom2",
source=source2,
fill_color=DECREASING_COLOR,
line_color="black",
)
# Add on extra lines (e.g. moving averages) here
fig.line(x = "Date3", y = "sma", source = source3, line_color = "firebrick")
fig.varea(x = "Date3", y1 = "sma", y2 = "upper_band", source = source3, fill_alpha = 0.25)
fig.line(x = "Date3", y = "sma", source = source3)
fig.varea(x = "Date3", y1 = "sma", y2 = "lower_band", source = source3, fill_alpha = 0.25)
fig.line(x = "Date3", y = "sma", source = source3)
fig.circle(x = "Date3", y = "signals", color="color1", source = source3, size=15)
#fig.xaxis[0].formatter=DatetimeTickFormatter(hourmin = ['%H:%M'])
fig.xaxis.major_label_overrides = {
i: date.strftime(xaxis_dt_format)
for i, date in enumerate(pd.to_datetime(time_df["Date"]))
}
fig.xaxis.formatter=DatetimeTickFormatter(days = ['%m/%d'])
# Set up the hover tooltip to display some useful data
fig.add_tools(
HoverTool(
renderers=[r1],
tooltips=[
("Open", "$@top1"),
("High", "$@high1"),
("Low", "$@low1"),
("Close", "$@bottom1"),
("Date", "@Date1{" + xaxis_dt_format + "}"),
],
formatters={"Date1": "datetime"},
)
)
fig.add_tools(
HoverTool(
renderers=[r2],
tooltips=[
("Open", "$@top2"),
("High", "$@high2"),
("Low", "$@low2"),
("Close", "$@bottom2"),
("Date", "@Date2{" + xaxis_dt_format + "}"),
],
formatters={"Date2": "datetime"},
)
)
return fig
def update_plot(attrname, old, new):
currency = currency_select.value
plot.title.text = "2 HR Results for " + currencies[currency]['title']
src1, src2, src3, src4 = get_dataset(df, currencies[currency]['asset'])
source1.data.update(src1.data)
source2.data.update(src2.data)
source3.data.update(src3.data)
source4.data.update(src4.data)
currency = 'ETH'
currencies = {
'XBT': {
'asset': 'XBT',
'title': 'XBT 2 Hour',
},
'ETH': {
'asset': 'ETH',
'title': 'ETH 2 Hour',
},
'LTC': {
'asset': 'LTC',
'title': 'LTC 2 Hour',
},
'XRP':{
'asset': 'XRP',
'title': 'XRP 2 Hour',
}
}
currency_select = Select(value=currency, title='Asset', options=sorted(currencies.keys()))
df = pd.read_csv( "E:\\Python36\\Scripts\\_Algorithms\\BokehTest.csv")
source1, source2, source3, source4 = get_dataset(df, currencies[currency]['asset'])
plot = make_plot(source1, source2, source3, source4, "2 HR Results for " + currencies[currency]['title'])
currency_select.on_change('value', update_plot)
controls = column(currency_select)
curdoc().add_root(row(plot, controls))
curdoc().title = "2 HR Results"
