new fft algo with laplacian range of convergencemain

1 files changed, 77 insertions, 56 deletions

diff --git a/app.py b/app.py
index 4032592..57928df 100644
--- a/app.py
+++ b/app.py
@@ -1,80 +1,101 @@
-from dash import Dash, dcc, html, Input, Output
+from dash import Dash, dcc, html, Input, Output, dash_table
 import plotly.graph_objects as go
 import json
 from datetime import datetime, timedelta
 from ema_algo import Ema_Algo
 from api import fetch_chart_data_yahoo
-from analysis import compute_results
+from analysis import summarize_results
 import pytz
+import pandas as pd
 
 app = Dash(__name__)
 
-# pull stock data from json files
-# timestamps_file = open('timestamps.json', 'r')
-# timestamps_file_data = timestamps_file.read()
-# timestamps_raw = json.loads(timestamps_file_data)
-# timestamps = [datetime.datetime.fromtimestamp(t) for t in timestamps_raw]
+# pull the data from the files
+table_data, algos_to_results = summarize_results('test-1-ema')
 
-# prices_file = open('close_prices.json', 'r')
-# prices = json.loads(prices_file.read())
-
-# intersection_indices = find_intersections(ema_5, ema_13, offset=13) # offset so don't calculate the SMA days
-# interpolated_intersections = [interpolate_intersection(indices, timestamps, ema_5, ema_13) for indices in intersection_indices]
-# intersected_x = []
-# intersected_y = []
-# for x,y in interpolated_intersections:
-#     intersected_x.append(x)
-#     intersected_y.append(y)
+app.layout = html.Div([
+    html.H4('Backtesting using EMA algos [ALPHA VERSION 0.1.0]'),
+    dcc.Dropdown(
+        id='batch_name',
+        options=[
+            {'label': 'Test 1 - EMA', 'value': 'test-1-ema'}
+        ],
+        value='test-1-ema',
+        clearable=False
+    ),
+    html.Hr(),
+    dash_table.DataTable(
+          table_data.to_dict('records'),
+          [{"name": i, "id": i} for i in table_data.columns],
+            style_table={'overflowX': 'auto'},
+            style_cell={'textAlign': 'center'},
+            style_header={
+                'backgroundColor': 'rgb(230, 230, 230)',
+                'fontWeight': 'bold'
+            },
+            style_data={
+                'whiteSpace': 'normal',
+                'height': 'auto'
+            },
+            sort_action='native',
+    ),
 
-app.layout = html.Div(
-    html.H4('Backtesting using EMA algos [ALPHA VERSION 0.0.5]'),
-    dcc.table(id="results_table")
-    dcc.Input(id="file_id", value="SPY", type="text"),
     html.Hr(),
-    dcc.Graph(id="graph"),
+    # dcc.Graph(id="graph"),
     html.P("If bought and sold on these signals, the percent gain/loss would be:"),
-    html.P(id="percent_gain")
+    html.P(id="percent_gain"),
+
+    # for each algo, print the individual results given by the map
+    html.Div(id='algo_results', children=[
+        html.Div([
+            html.Div([
+                  
+            ])
+        ]) for algo_name, result in algos_to_results.items()
+    ])
 
 ])
 
 @app.callback(
-    Output("results_table", "figure"),
-    Output("graph", "figure"),
-    Output("error_message", "children"),
-    Input("file_id", "value"),
-    )
-def display_color(file_id):
+    Input("batch_name", "value"),
+)
+def display_color(batch_name):
         # compute the results to show in the table
-        path = 'test-1-ema'
-        result_summary = compute_results(path, file_id)
+        path = batch_name
+        result_summary = summarize_results(path)
         if not result_summary:
-            return go.Figure(), go.Figure(), "No results found for the given file ID."
+            return "No results found for the given file ID."
+        
+        # update the table data 
+        global table_data
+        table_data = result_summary
+
+        # table = go.Figure(
+        #         data = [go.Table(
+        #                 header=dict(values=["algo name", "algo params", "avg percent gain", "best stock for gain"]),
+        #                 cells=dict(values=result_summary)
+        #         )]
+        # )
+        # data = fetch_chart_data_yahoo('XRP-USD', '1d', None, timedelta(weeks=52))
+        # times = [datetime.fromtimestamp(t).astimezone(pytz.timezone('US/Eastern')) for t in data['timestamps']]
+        # comp_scatter = go.Scatter(name='Price (yahoo)', x=times, y=data['prices'], line=dict(color='rgb(255, 0, 0)'), mode='lines')
+        # fig = go.Figure(
+        #     data = [
+        #         go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 0, 0)'), mode='lines'), 
+        #         comp_scatter
+        #     ] 
+        #     # + algo_graphs + buy_sell_scatters
+        #     ,
+        #     layout = go.Layout(
+        #         title=go.layout.Title(text='Chart for ' + chart_data['name']),
+        #         xaxis=go.layout.XAxis(title='Date (dt=' + url_params['interval'] + ', range=' + url_params['period'] + ')'),
+        #         yaxis=go.layout.YAxis(title='Price ($)')
+        #     )
+        # )
+
         
-        print(result_summary)
 
-        table = go.Figure(
-                data = [go.Table(
-                        header=dict(values=["algo name", "algo params", "avg percent gain", "best stock order"]),
-                        cells=dict(values=result_summary)
-                )]
-        )
-        data = fetch_chart_data_yahoo('XRP-USD', '1d', None, timedelta(weeks=52))
-        times = [datetime.fromtimestamp(t).astimezone(pytz.timezone('US/Eastern')) for t in data['timestamps']]
-        comp_scatter = go.Scatter(name='Price (yahoo)', x=times, y=data['prices'], line=dict(color='rgb(255, 0, 0)'), mode='lines')
-        fig = go.Figure(
-            data = [
-                go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 0, 0)'), mode='lines'), 
-                comp_scatter
-            ] 
-            # + algo_graphs + buy_sell_scatters
-            ,
-            layout = go.Layout(
-                title=go.layout.Title(text='Chart for ' + chart_data['name']),
-                xaxis=go.layout.XAxis(title='Date (dt=' + url_params['interval'] + ', range=' + url_params['period'] + ')'),
-                yaxis=go.layout.YAxis(title='Price ($)')
-            )
-        )
-        return fig, percent_gain, error_style, error_message
+        return