various changes

Author: gaborvecsei

assets/favicon.ico

@@ -436,7 +436,6 @@ li {
 /* Tables
 –––––––––––––––––––––––––––––––––––––––––––––––––– */
 table {
-  border-collapse: collapse;
 }
 th,
 td {

assets/s1.css

@@ -1,11 +1,14 @@
 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
 
-from dash_app_functions import get_search_window_sizes, visualize_graph, get_symbols
+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 #####
@@ -60,34 +63,85 @@
 ##### 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", "Start date", "End Date", "Start Close Value ($)",
+                 "End Close Value ($)"]
+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 patterns"), table],
+                                 className="pretty_container")],
+                       id="id-matched-list-container",
+                       className="eleven columns")
+
+##### Reference Links #####
+
+css_link = dcc.Link("[1] Style of the page (css)",
+                    href="https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-oil-and-gas")
+yahoo_data_link = dcc.Link("[2] Yahoo data", href="https://finance.yahoo.com")
+reference_links_div = html.Div([html.Div([html.H6("References"),
+                                          css_link,
+                                          html.Br(),
+                                          yahoo_data_link],
+                                         className="pretty_container")],
+                               className="four columns")
+
 ##### Layout #####
 
 app.layout = html.Div([header_div,
                        html.Div([settings_div,
                                  stats_and_graph_div],
-                                className="row flex-display")],
+                                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"),
+@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")])
-def update_plot(symbol_value, window_size_value, future_size_value, top_k_value):
-    fig = visualize_graph(symbol=symbol_value,
-                          window_size=window_size_value,
-                          future_size=future_size_value,
-                          top_k=top_k_value)
-    return fig
+def update_plot_and_table(symbol_value, window_size_value, future_size_value, top_k_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.start_date, match.end_date, match.values[0],
+                      match.values[-1]]
+        row_dict = {c: v for c, v in zip(table_columns, row_values)}
+        table_rows.append(row_dict)
+
+    # 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)
+
+    return fig, table_rows
 
 
 if __name__ == "__main__":

dash_app.py

@@ -1,7 +1,7 @@
-import requests
 import os
 
-import stock_pattern_analyzer as spa
+import requests
+
 from rest_api_models import (TopKSearchResponse, SearchWindowSizeResponse, DataRefreshResponse,
                              AvailableSymbolsResponse)
 
@@ -33,29 +33,3 @@ def get_last_refresh_date() -> str:
     res = DataRefreshResponse.parse_obj(res)
     date_str = res.date.strftime("%Y/%m/%d, %H:%M:%S")
     return date_str
-
-
-def visualize_graph(symbol: str, window_size: int, future_size: int, top_k: int):
-    try:
-        ret = search_most_recent(symbol=symbol,
-                                 window_size=window_size,
-                                 top_k=top_k,
-                                 future_size=future_size)
-        values = []
-        symbols = []
-        dates = []
-        for m in ret.matches:
-            values.append(m.values)
-            symbols.append(m.symbol)
-            dates.append((m.start_date, m.end_date))
-
-        fig = spa.visualize_graph(match_values_list=values,
-                                  match_symbols=symbols,
-                                  match_str_dates=dates,
-                                  window_size=window_size,
-                                  future_size=future_size,
-                                  anchor_symbol=ret.anchor_symbol,
-                                  anchor_values=ret.anchor_values)
-        return fig
-    except Exception:
-        return None

dash_app_functions.py

@@ -2,6 +2,7 @@
 from pathlib import Path
 
 import numpy  as np
+import pandas as pd
 import uvicorn
 from apscheduler.schedulers.asyncio import AsyncIOScheduler
 from fastapi import FastAPI, HTTPException, Response
@@ -16,8 +17,20 @@
 app.scheduler = AsyncIOScheduler()
 app.last_refreshed = None
 
-AVAILABLE_SEARCH_WINDOW_SIZES = [5, 10, 15, 20]
-TICKER_LIST = ["AAPL", "GME", "AMC", "TSLA"]
+
+def get_sp500_ticker_list() -> set:
+    table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
+    df = table[0]
+    return set(df["Symbol"].values)
+
+
+AVAILABLE_SEARCH_WINDOW_SIZES = list(range(5, 16, 1)) + [20, 25, 30, 45]
+TICKER_LIST = ["AAPL", "GME", "AMC", "TSLA", "hff"]
+# TICKER_LIST = {"AAPL", "MSFT", "AMZN", "BABA", "ROKU", "TDOC", "CRSP", "SQ", "NVTA", "Z", "BIDU", "SPOT", "PRLB",
+#                "TSLA", "GME", "BB", "AMC", "LI", "NIO"}
+# # TODO: handle if symbol is missing + parallell download
+# TICKER_LIST = TICKER_LIST.union(get_sp500_ticker_list())
+TICKER_LIST = sorted(TICKER_LIST)
 PERIOD_YEARS = 2
 
 

rest_api.py

@@ -28,7 +28,12 @@ def __init__(self, ticker_symbols: list, period_years: int = 5, interval: int =
     def fill(self) -> None:
         for symbol in tqdm(self.ticker_symbols):
             ticker = yfinance.Ticker(symbol)
-            ticker_df = ticker.history(period=f"{self.period_years}y", interval=f"{self.interval}d")[::-1]
+            period_str = f"{self.period_years}y"
+            interval_str = f"{self.interval}d"
+            ticker_df = ticker.history(period=period_str, interval=interval_str, rounding=True)[::-1]
+            if ticker_df.empty or len(ticker_df) == 0:
+                self.ticker_symbols.remove(symbol)
+                continue
 
             close_values = ticker_df["Close"].values
             dates = ticker_df.index.values

stock_pattern_analyzer/data.py

@@ -15,7 +15,8 @@ def visualize_graph(match_values_list: List[np.ndarray],
                     window_size: int,
                     future_size: int,
                     anchor_symbol: str,
-                    anchor_values: np.ndarray):
+                    anchor_values: np.ndarray,
+                    show_legend: bool = True) -> graph_objs.Figure:
     fig = graph_objs.Figure()
 
     assert len(match_values_list) == len(match_symbols), "Something is fishy"
@@ -30,7 +31,7 @@ def visualize_graph(match_values_list: List[np.ndarray],
         minmax_values = minmax_scale(original_values)
         trace = graph_objs.Scatter(x=x,
                                    y=minmax_values,
-                                   name=f"{i}) {match_symbol} ({match_start_date}-{match_end_date})",
+                                   name=f"{i}) {match_symbol} ({match_start_date} - {match_end_date})",
                                    mode="lines",
                                    line=dict(color=VALUES_COLOR),
                                    opacity=1 / (i + 1),
@@ -52,8 +53,8 @@ def visualize_graph(match_values_list: List[np.ndarray],
 
     fig.add_vline(x=window_size, line_dash="dash", line_color="black", annotation_text="Last market close")
 
-    fig.update_xaxes(showspikes=True, spikecolor="black", spikesnap="cursor", spikemode="across")
-    fig.update_yaxes(showspikes=True, spikecolor="black", spikethickness=1)
+    # fig.update_xaxes(showspikes=True, spikecolor="black", spikesnap="cursor", spikemode="across")
+    # fig.update_yaxes(showspikes=True, spikecolor="black", spikethickness=1)
 
     x_axis_ticker_labels = list(range(-window_size, future_size + 1))
     fig.update_layout(title=f"Similar patters for {anchor_symbol} based on historical market close data",
@@ -66,6 +67,7 @@ def visualize_graph(match_values_list: List[np.ndarray],
                       autosize=True,
                       plot_bgcolor=FIG_BG_COLOR,
                       paper_bgcolor=FIG_BG_COLOR,
-                      legend=dict(font=dict(size=9), orientation="h"))
+                      legend=dict(font=dict(size=9), orientation="h", yanchor="bottom", y=-0.5),
+                      showlegend=show_legend)
 
     return fig