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from dash import Dash, dcc, html, Input, Output
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
import pytz
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('Backtesting using the EMA method (5 vs 13) [ALPHA VERSION 0.0.2]'),
html.Div(
[
html.Label("Ticker ", htmlFor="ticker"),
dcc.Input(id="ticker", value="SPY", type="text"),
html.Br(),
html.Label("Period ", htmlFor="period_dropdown"),
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_dropdown"),
dcc.Dropdown(
id="interval_dropdown",
options=["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "4h", "1d", "5d", "1wk", "1mo", "3mo"],
value = "1d",
),
html.P(id='error_message'),
],
id='input_params'
),
html.Hr(),
dcc.Graph(id="graph"),
html.P("If bought and sold on these signals, the percent gain/loss would be:"),
html.P(id="percent_gain")
])
@app.callback(
Output("graph", "figure"),
Output("percent_gain", "children"),
Output("input_params", "style"),
Output("error_message", "children"),
Input("ticker", "value"),
Input("period_dropdown", "value"),
Input("interval_dropdown", "value")
)
def display_color(ticker, period, interval):
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']
url_params = trial_data['url_params']
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).astimezone(pytz.timezone('US/Eastern')) 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)
]
data = fetch_chart_data_yahoo('XRP-USD', '1h', 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
app.run(debug=True)
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