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

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)
percent_gain = 0
memo_fig = None

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: " + str(percent_gain))
])

@app.callback(
    Output("graph", "figure"),
    Input("ticker", "value"),
    Input("period_dropdown", "value"),
    Input("interval_dropdown", "value")
    )
def display_color(ticker, period, interval):
    try: 
        chart_data = fetch_chart_data(ticker, period, interval)
    except:
        return memo_fig
    else:
        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]
        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
    finally:
        # Code to execute no matter what (optional)
        fig = go.Figure(
            [
                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),
            ]
        )
        print(ticker, period, interval)
        memo_fig = fig
        return fig


app.run(debug=True)