iframe-proxy | Sunbelt Computer Software

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

import dash_core_components as dcc

import dash_html_components as html

import dash_table

from dash import Dash

from dash.dependencies import Input, Output

import stock_pattern_analyzer as spa

from dash_app_functions import get_search_window_sizes, get_symbols, search_most_recent

app = Dash(__name__, meta_tags=[{"name": "viewport", "content": "width=device-width"}])

app.title = "Stock Patterns"

server = app.server

##### Header #####

header_div = html.Div([html.Div([html.H3("📈")], className="one-third column"),

html.Div([html.Div([html.H3("Stock Patterns", style={"margin-bottom": "0px"}),

html.H5("Find historical patterns and use for forecasting",

style={"margin-top": "0px"})])],

className="one-half column", id="title"),

html.Div([html.A(html.Button("Gabor Vecsei"), href="https://www.gaborvecsei.com/")],

className="one-third column",

id="learn-more-button")],

id="header", className="row flex-display", style={"margin-bottom": "25px"})

##### Explanation #####

explanation_div = html.Div([dcc.Markdown("""Select a stock symbol and a time-frame. This tools finds similar patterns in

historical data.

The most similar patters are visualized with an extended *time-frame/'future data'*, which can be an

indication of future price movement for the selected (anchor) stock.""")])

##### Settings container #####

symbol_dropdown_id = "id-symbol-dropdown"

available_symbols = get_symbols()

default_symbol = "AAPL" if "AAPL" in available_symbols else available_symbols[0]

symbol_dropdown = dcc.Dropdown(id=symbol_dropdown_id,

options=[{"label": x, "value": x} for x in available_symbols],

multi=False,

value=default_symbol,

className="dcc_control")

window_size_dropdown_id = "id-window-size-dropdown"

window_sizes = get_search_window_sizes()

window_size_dropdown = dcc.Dropdown(id=window_size_dropdown_id,

options=[{"label": f"{x} days", "value": x} for x in window_sizes],

multi=False,

value=window_sizes[2],

className="dcc_control")

future_size_input_id = "id-future-size-input"

MAX_FUTURE_WINDOW_SIZE = 10

future_size_input = dcc.Input(id=future_size_input_id, type="number", min=0, max=MAX_FUTURE_WINDOW_SIZE, value=5,

className="dcc_control")

top_k_input_id = "id-top-k-input"

MAX_TOP_K_VALUE = 10

top_k_input = dcc.Input(id=top_k_input_id, type="number", min=0, max=MAX_TOP_K_VALUE, value=5, className="dcc_control")

offset_checkbox_id = "id-offset-checkbox"

offset_checkbox = dcc.Checklist(id=offset_checkbox_id, options=[{"label": "Use Offset", "value": "offset"}],

value=["offset"], className="dcc_control")

settings_div = html.Div([html.P("Symbol (anchor)", className="control_label"),

symbol_dropdown,

html.P("Search window size", className="control_label"),

window_size_dropdown,

html.P(f"Future window size (max. {MAX_FUTURE_WINDOW_SIZE})", className="control_label"),

future_size_input,

html.P(f"Patterns to match (max. {MAX_TOP_K_VALUE})", className="control_label"),

top_k_input,

html.P("Offset the matched patterns for easy comparison (to the anchors last market close)",

className="control_label"),

offset_checkbox],

className="pretty_container three columns",

id="id-settings-div")

##### Stats & Graph #####

graph_id = "id-graph"

stats_and_graph_div = html.Div([html.Div(id="id-stats-container", className="row container-display"),

html.Div([dcc.Graph(id=graph_id)], id="id-graph-div", className="pretty_container")],

id="id-graph-container", className="nine columns")

##### Matched Stocks List #####

matched_table_id = "id-matched-list"

table_columns = ["Index",

"Match distance",

"Symbol",

"Pattern Start Date",

"Pattern End Date",

"Pattern Start Close Value ($)",

"Pattern End Close Value ($)",

"Pattern Future Close Value ($)"]

table = dash_table.DataTable(id=matched_table_id, columns=[{"id": c, "name": c} for c in table_columns], page_size=5)

matched_div = html.Div([html.Div([html.H6("Matched (most similar) patterns"), table],

className="pretty_container")],

id="id-matched-list-container",

className="eleven columns")

##### Reference Links #####

css_link = html.A("[1] Style of the page (css)",

href="https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-oil-and-gas")

yahoo_data_link = html.A("[2] Yahoo data", href="https://finance.yahoo.com")

gabor_github_link = html.A("[3] Gabor Vecsei GitHub", href="https://github.com/gaborvecsei")

reference_links_div = html.Div([html.Div([html.H6("References"),

css_link,

html.Br(),

yahoo_data_link,

html.Br(),

gabor_github_link],

className="pretty_container")],

className="four columns")

##### Layout #####

app.layout = html.Div([header_div,

explanation_div,

html.Div([settings_div,

stats_and_graph_div],

className="row flex-display"),

html.Div([matched_div], className="row flex-display"),

reference_links_div],

id="mainContainer",

style={"display": "flex", "flex-direction": "column"})

##### Callbacks #####

@app.callback([Output(graph_id, "figure"),

Output(matched_table_id, "data")],

[Input(symbol_dropdown_id, "value"),

Input(window_size_dropdown_id, "value"),

Input(future_size_input_id, "value"),

Input(top_k_input_id, "value"),

Input(offset_checkbox_id, "value")])

def update_plot_and_table(symbol_value, window_size_value, future_size_value, top_k_value, checkbox_value):

# RetAPI search

ret = search_most_recent(symbol=symbol_value,

window_size=window_size_value,

top_k=top_k_value,

future_size=future_size_value)

# Parse response and build the HTML table rows

table_rows = []

values = []

symbols = []

start_end_dates = []

for i, match in enumerate(ret.matches):

values.append(match.values)

symbols.append(match.symbol)

start_end_dates.append((match.start_date, match.end_date))

row_values = [i + 1,

match.distance,

match.symbol,

match.end_date,

match.start_date,

match.values[-1],

match.values[window_size_value - 1],

match.values[0]]

row_dict = {c: v for c, v in zip(table_columns, row_values)}

table_rows.append(row_dict)

offset_traces = False if len(checkbox_value) == 0 else True

# Visualize the data on a graph

fig = spa.visualize_graph(match_values_list=values,

match_symbols=symbols,

match_str_dates=start_end_dates,

window_size=window_size_value,

future_size=future_size_value,

anchor_symbol=ret.anchor_symbol,

anchor_values=ret.anchor_values,

show_legend=False,

offset_traces=offset_traces)

return fig, table_rows

if __name__ == "__main__":

app.run_server(debug=False, host="0.0.0.0")

Sunbelt Computer Software

PL/B Language Development and Support

Navigation Menu

Repository navigation

dash_app.py

dash_app.py

Sunbelt Computer Software

PL/B Language Development and Support

FilesExpand file tree

dash_app.py

Latest commit

History

dash_app.py

File metadata and controls