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

2 files changed, 77 insertions, 22 deletions

diff --git a/analysis.py b/analysis.py
index 2b5ed59..644fe93 100644
--- a/analysis.py
+++ b/analysis.py
@@ -68,21 +68,21 @@ def interpolate_intersection(intersection_indices, timestamps, prices1, prices2)
 """
 Returns the indices of where two arrays' values intersects
 """
-def find_intersections(prices1, prices2):
+def find_intersections(prices1, prices2, offset=0):
     if len(prices1) != len(prices2):
         print("ERROR IN find_intersections: len of arrs not the same")
         return []
     prev_p1 = prices1[0]
     prev_p2 = prices2[0]
     intersection_indices = set()
-    for i in range(1, len(prices1)):
+    for i in range(1 + offset, len(prices1)):
         next_p1 = prices1[i]
         next_p2 = prices2[i]
         # if the sign (negative to positive) changes, then there was an intersection between these pts
         sub_prev = prev_p1 - prev_p2
         sub_next = next_p1 - next_p2
 
-        if (sub_prev > 0 and sub_next < 0) or (sub_prev < 0 and sub_next > 0): # TODO, consider on the 0 case
+        if (sub_prev > 0 and sub_next < 0) or (sub_prev < 0 and sub_next > 0):
             intersection_indices.add((i-1, i))
         
         if sub_next == 0:
@@ -92,3 +92,40 @@ def find_intersections(prices1, prices2):
         prev_p2 = next_p2
     
     return intersection_indices
+
+def calculate_profit(buy_line, sell_line, prices, timestamps, offset=0, starting_money=10000):
+    if len(buy_line) != len(sell_line):
+        print("ERROR IN find_intersections: len of arrs not the same")
+        return []
+    is_bought = False
+    curr_money = 10000
+    shares_owned = 0
+    buy_info = []
+    sell_info = []
+    for i in range(offset, len(buy_line)):
+        current_b1 = buy_line[i]
+        current_sl = sell_line[i]
+        # if the sign is positive, we want to hold, if it's negative, we want to sell
+        sign_signal = current_b1 - current_sl
+
+        if sign_signal > 0:
+            if not is_bought:
+                # buy the stock
+                shares_owned = curr_money / prices[i]
+                curr_money = 0
+                buy_info.append((timestamps[i], prices[i], i))
+            is_bought = True
+        if sign_signal < 0:
+            if is_bought:
+                # selling the stock
+                curr_money = prices[i] * shares_owned
+                shares_owned = 0
+                sell_info.append((timestamps[i], prices[i], i))
+            is_bought = False
+
+    # TODO: consider end interval
+    total_assets = prices[-1] * shares_owned + curr_money
+    percent_gain = (total_assets - starting_money) / starting_money
+    return (percent_gain, total_assets, buy_info, sell_info)
+
+    
\ No newline at end of file
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