begin infrastructure for automated backtesting, allowing for viewing details about the trial after

1 files changed, 38 insertions, 42 deletions

diff --git a/app.py b/app.py
index 68aacb8..b1d1914 100644
--- a/app.py
+++ b/app.py
@@ -1,11 +1,8 @@
 from dash import Dash, dcc, html, Input, Output
-from analysis import find_intersections, interpolate_intersection 
-from api import fetch_chart_data
-from ema import calc_emas, calculate_profit
 import plotly.graph_objects as go
 import json
-import datetime
-import pandas as pd
+from datetime import datetime
+from ema_algo import Ema_Algo
 
 app = Dash(__name__)
 
@@ -66,49 +63,48 @@ app.layout = html.Div([
     Input("interval_dropdown", "value")
     )
 def display_color(ticker, period, interval):
-        chart_data = fetch_chart_data(ticker, period, interval)
-        error_style = {"color" : "inherit"}
-        error_message = ''
-        if chart_data['error'] == True:
-            # implement a feeback mechanism for ERROR codes
-            error_style = {"color" : "red"}
-            error_message = 'Issue with parameter selection. Please try again.'
-
-        timestamps_raw = chart_data['timestamps']
-        timestamps = [datetime.datetime.fromtimestamp(t) for t in timestamps_raw]
-        prices = chart_data['prices']
+        fd = open('bt-recent.json', 'r')
+        raw_data = fd.read()
+        trial_data = json.loads(raw_data)
+        fd.close()
+
+        chart_data = trial_data['chart_data']
+        backtest_results = trial_data['backtest_results']
 
-        ema_5 = calc_emas(5, prices)
-        ema_13 = calc_emas(13, prices)
-        profit = calculate_profit(ema_5, ema_13, prices, timestamps, 13)
-        buy_info = profit[-2]
-        print(buy_info)
-        buy_x = []
-        buy_y = []
-        for x,y,_ in buy_info:
-            buy_x.append(x)
-            buy_y.append(y)
-
-        sell_info = profit[-1]
-        sell_x = []
-        sell_y = []
-        for x,y,_ in sell_info:
-            sell_x.append(x)
-            sell_y.append(y)
-
-        print("Result Analysis:\n", "Percent gain/loss:\t", profit[0], profit[1], profit[2])
-        percent_gain = profit[0] * 100
+        percent_gain = backtest_results['percent_gain']
+        error_style = {"color" : "red"}
+        error_message = "False error"
 
         # Code to execute no matter what (optional)
+
+        raw_timestamps = chart_data['timestamps']
+        timestamps = [datetime.fromtimestamp(t) for t in raw_timestamps]
+        prices = chart_data['prices']
+
+        # test to see if graphc works, TODO make it abstracted
+        algoEMA = Ema_Algo()
+        algo_graph_data = backtest_results['algo_graph_data']
+        algo_graphs = algoEMA.export_graph(algo_graph_data)
+
+        buy_indices = backtest_results['buy_indices']
+        sell_indices = backtest_results['sell_indices']
+
+        buy_prices, buy_times = [], []
+        for i in buy_indices:
+                buy_prices.append(prices[i])
+                buy_times.append(timestamps[i])
+        sell_prices, sell_times = [], []
+        for i in sell_indices:
+                sell_prices.append(prices[i])
+                sell_times.append(timestamps[i])
+        buy_sell_scatters = [
+                go.Scatter(name='Buys', x=buy_times, y=buy_prices, line=dict(color='rgb(0, 0, 255)'), mode='markers', marker_size=10),
+                go.Scatter(name='Sells', x=sell_times, y=sell_prices, line=dict(color='rgb(255, 255, 0)'), mode='markers', marker_size=10)
+        ]
         fig = go.Figure(
             data = [
                 go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 0, 0)'), mode='lines'), 
-                # go.Scatter(name='5 day EMA', x=timestamps, y=ema_5, line=dict(color='rgb(0, 255, 0)'),  mode='lines'), 
-                # go.Scatter(name='13 day EMA', x=timestamps, y=ema_13, line=dict(color='rgb(0, 0, 255)'),  mode='lines'), 
-                # go.Scatter(name='EMA Intersections', x=intersected_x, y=intersected_y, line=dict(color='rgb(255, 0, 0)'), mode='markers'),
-                go.Scatter(name='Buys', x=buy_x, y=buy_y, line=dict(color='rgb(0, 0, 255)'), mode='markers', marker_size=10),
-                go.Scatter(name='Sells', x=sell_x, y=sell_y, line=dict(color='rgb(255, 255, 0)'), mode='markers', marker_size=10),
-            ],
+            ] + 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=' + interval + ', range=' + period + ')'),