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import streamlit as st
import plotly.graph_objects as go
from plotly.validators.scatter.marker import SymbolValidator
from plotly.subplots import make_subplots
import numpy as np
from scipy import signal
import calculator
# chart datapoint icons
raw_symbols = SymbolValidator().values
up_arrow = raw_symbols[5]
down_arrow = raw_symbols[6]
def create_candlestick(fig, dates, dataset, title, y_label):
candlestick = go.Candlestick(name=y_label,
x=dates,
open=dataset['Open'],
high=dataset['High'],
low=dataset['Low'],
close=dataset['Close'])
fig.add_trace(candlestick)
fig.update_xaxes(
rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(step="all")
])
)
)
def create_indicators(fig, datasets):
for indicator in datasets:
indicator_data = datasets[indicator]
marker_color="lightskyblue"
marker_symbol = 0
if 'IsBullish' in indicator_data:
if indicator_data['IsBullish']:
marker_color = 'green'
marker_symbol = 5
else:
marker_color = 'red'
marker_symbol = 6
indicator_plot = go.Scatter(name=indicator,
mode="markers",
x=indicator_data['Date'],
y=indicator_data['Values'],
marker_symbol=marker_symbol,
marker_line_color="midnightblue",
marker_color=marker_color,
marker_line_width=2,
marker_size=15,
hovertemplate="%{indicator}: %{y}%{x}<br>number: %{marker.symbol}<extra></extra>")
fig.add_trace(indicator_plot)
def create_lines(fig, dates, datasets, title, y_label):
for key in datasets:
line = go.Scatter(name=key, x=dates, y=datasets[key])
fig.add_trace(line)
def create_markers(fig, dates, dataset, title, y_label, marker_symbol=3, marker_color="blue", marker_size=15):
line = go.Scatter(name=title, x=dates, y=dataset,
mode="markers",
marker_symbol=marker_symbol,
marker_line_color="midnightblue",
marker_color=marker_color,
marker_line_width=2,
marker_size=marker_size)
fig.add_trace(line)
def create_line(fig, dates, dataset, title="title", y_label="values", marker_symbol=4, marker_size=15, color='rgba(0,100,80,0.2)'):
line = go.Scatter(name=title, x=dates, y=dataset, marker_line_color="yellow", fillcolor=color)
fig.add_trace(line)
def create_fill_area(fig, dates, y_low, y_high, title, color='rgba(0,100,80,0.2)'):
# line_low = go.Scatter(name=title, x=dates, y=y_low, fillcolor=color, showlegend=False)
# fig.add_trace(line_low)
# line_high = go.Scatter(name=title, x=dates, y=y_low, fillcolor=color, showlegend=False)
# fig.add_trace(line_high)
fill_area = go.Scatter(
name=title,
x=dates + dates[::-1],
y=y_high + y_low[::-1],
fill='toself',
fillcolor=color,
line=dict(color=color)
)
fig.add_trace(fill_area)
def create_spectrogram(dates, data_list, sampling_frequency=1, num_points_fft=128, overlap_percent=50.0, title="title", log_scale=True):
data_list = calculator.normalize(data_list, 1, -1)
# Spectrogram
w = signal.blackman(num_points_fft)
freqs, bins, pxx = signal.spectrogram(data_list, sampling_frequency, window=w, nfft=num_points_fft, noverlap=int(num_points_fft*overlap_percent/100.0))
dates_subset = [dates[int(bin)] for bin in bins]
if log_scale:
z = 10 * np.log10(pxx)
else:
z = pxx
trace = [go.Heatmap(
x=dates_subset,
y=freqs,
z=z,
colorscale='Jet',
)]
layout = go.Layout(
title=title,
yaxis=dict(title='Frequency'), # x-axis label
xaxis=dict(title='Time'), # y-axis label
)
fig = go.Figure(data=trace, layout=layout)
fig.update_layout(title=title)
st.plotly_chart(fig, use_container_width=True)
return fig
def create_heatmap(dates, data_list, bin_count=10, time_steps=20, title='title'):
time_width = int(len(dates)/time_steps)
dates_subset = [dates[index*time_width] for index in range(time_steps)]
min_val = np.min(data_list)
max_val = np.max(data_list)
delta = (max_val-min_val)
min_val -= 0.2*delta
max_val += 0.2*delta
delta = (max_val-min_val)
bin_width = delta/(bin_count + 1)
bins = np.arange(min_val, max_val, bin_width)
values = np.empty(shape=(time_steps, bin_count))
for time_index in range(time_steps):
data_subset = data_list[time_index*time_width:time_index*time_width+time_width]
counts, res_bins = np.histogram(data_subset, bins=bins)
values[time_index:] = counts
trace = [go.Heatmap(
x=dates_subset,
y=bins,
z=values.transpose(),
colorscale='Jet',
)]
layout = go.Layout(
title=title,
yaxis=dict(title='Values'), # x-axis label
xaxis=dict(title='Time'), # y-axis label
)
fig = go.Figure(data=trace, layout=layout)
fig.update_layout(title=title)
st.plotly_chart(fig, use_container_width=True)
return fig
def add_mouse_indicator(fig, selected_points, min, max):
if any(selected_points):
fig.add_shape(
go.layout.Shape(
type='line',
x0=selected_points[0]['x'],
x1=selected_points[0]['x'],
y0=min,
y1=max,
line=dict(color='red', width=2),
)
)
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