1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
|
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
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]
# 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('Interactive color selection with simple Dash example'),
html.Label("Ticker ", htmlFor="ticker"),
dcc.Input(id="ticker", value="SPY", type="text"),
html.Br(),
html.Label("Period ", htmlFor="period"),
dcc.Dropdown(
id="period_dropdown",
options=["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"],
value = "1y",
),
html.Br(),
html.Label("Interval ", htmlFor="Interval"),
dcc.Dropdown(
id="interval_dropdown",
options=["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "4h", "1d", "5d", "1wk", "1mo", "3mo"],
value = "1d",
),
html.Hr(),
dcc.Graph(id="graph"),
html.P("If bought and sold on these signals, the percent gain/loss would be: TODO"),
html.P(id="percent_gain")
])
@app.callback(
Output("graph", "figure"),
Output("percent_gain", "children"),
Input("ticker", "value"),
Input("period_dropdown", "value"),
Input("interval_dropdown", "value")
)
def display_color(ticker, period, interval):
chart_data = fetch_chart_data(ticker, period, interval)
if chart_data['error'] == True:
# implement a feeback mechanism for ERROR codes
print("TODO: FIX THIS")
timestamps_raw = chart_data['timestamps']
timestamps = [datetime.datetime.fromtimestamp(t) for t in timestamps_raw]
prices = chart_data['prices']
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
# Code to execute no matter what (optional)
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),
],
layout = go.Layout(
title=go.layout.Title(text='Chart for ' + chart_data['name']),
xaxis=go.layout.XAxis(title='Date (dt=' + interval + ', range=' + period + ')'),
yaxis=go.layout.YAxis(title='Price ($)')
)
)
return (fig, percent_gain)
app.run(debug=True)
|
