add calculations that determine the percent gain and loss from using this algorithm

1 files changed, 37 insertions, 19 deletions

diff --git a/app.py b/app.py
index 5cd5521..51e0564 100644
--- a/app.py
+++ b/app.py
@@ -1,24 +1,11 @@
 from dash import Dash, dcc, html, Input, Output
-from analysis import calc_emas, find_intersections, interpolate_intersection
+from analysis import calc_emas, find_intersections, interpolate_intersection, calculate_profit
 import plotly.graph_objects as go
 import json
 import datetime
 
 app = Dash(__name__)
 
-
-app.layout = html.Div([
-    html.H4('Interactive color selection with simple Dash example'),
-    html.P("Select color:"),
-    dcc.Dropdown(
-        id="dropdown",
-        options=['Gold', 'MediumTurquoise', 'LightGreen'],
-        value='Gold',
-        clearable=False,
-    ),
-    dcc.Graph(id="graph"),
-])
-
 # pull stock data from json files
 timestamps_file = open('timestamps.json', 'r')
 timestamps_file_data = timestamps_file.read()
@@ -31,7 +18,7 @@ prices = json.loads(prices_file.read())
 ema_5 = calc_emas(5, prices)
 ema_13 = calc_emas(13, prices)
 
-intersection_indices = find_intersections(ema_5, ema_13)
+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 = []
@@ -39,7 +26,36 @@ for x,y in interpolated_intersections:
     intersected_x.append(x)
     intersected_y.append(y)
 
+profit = calculate_profit(ema_5, ema_13, prices, timestamps, 13)
+buy_info = profit[-2]
+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])
+percent_gain = profit[0] * 100
+
+app.layout = html.Div([
+    html.H4('Interactive color selection with simple Dash example'),
+    html.P("Select color:"),
+    dcc.Dropdown(
+        id="dropdown",
+        options=['Gold', 'MediumTurquoise', 'LightGreen'],
+        value='Gold',
+        clearable=False,
+    ),
+    dcc.Graph(id="graph"),
+    html.P("If bought and sold on these signals, the percent gain/loss would be: " + str(round(percent_gain, 4)))
+])
 
 @app.callback(
     Output("graph", "figure"),
@@ -47,10 +63,12 @@ for x,y in interpolated_intersections:
 def display_color(color):
     fig = go.Figure(
         [
-            go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 255, 255)'), mode='lines'), # prices
-            go.Scatter(name='5 day EMA', x=timestamps, y=ema_5, line=dict(color='rgb(0, 255, 0)'),  mode='lines'), # 5 ema line
-            go.Scatter(name='13 day EMA', x=timestamps, y=ema_13, line=dict(color='rgb(0, 0, 255)'),  mode='lines'), # 13 ema line
-            go.Scatter(name='EMA Intersections', x=intersected_x, y=intersected_y, line=dict(color='rgb(255, 0, 0)'), mode='markers') # EMA intersection points
+            go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 255, 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),
         ]
     )
     return fig