264 lines
14 KiB
Python
Executable File
264 lines
14 KiB
Python
Executable File
import dash
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import dash_html_components as html
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import dash_core_components as dcc
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import dash_table
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import plotly.express as px
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import yfinance as yf
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from dash.dependencies import Input, Output, State
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import plotly.graph_objects as go
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import numpy as np
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import pandas as pd
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import datetime
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import time
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from pandas_datareader import data
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# Load data
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stock_list = ['MMM', 'ABT', 'ABBV', 'ABMD', 'ACN', 'ATVI', 'ADBE', 'AMD', 'AAP', 'AES', 'AFL', 'A', 'APD', 'AKAM',
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'ALK', 'ALB', 'ARE', 'ALXN', 'ALGN', 'ALLE', 'LNT', 'ALL', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR', 'AEE',
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'AAL', 'AEP', 'AXP', 'AIG', 'AMT', 'AWK', 'AMP', 'ABC', 'AME', 'AMGN', 'APH', 'ADI', 'ANSS', 'ANTM',
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'AON', 'AOS', 'APA', 'AAPL', 'AMAT', 'APTV', 'ADM', 'ANET', 'AJG', 'AIZ', 'T', 'ATO', 'ADSK', 'ADP',
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'AZO', 'AVB', 'AVY', 'BKR', 'BLL', 'BAC', 'BK', 'BAX', 'BDX', 'BRK.B', 'BBY', 'BIO', 'BIIB', 'BLK', 'BA',
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'BKNG', 'BWA', 'BXP', 'BSX', 'BMY', 'AVGO', 'BR', 'BF.B', 'CHRW', 'COG', 'CDNS', 'CZR', 'CPB', 'COF',
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'CAH', 'KMX', 'CCL', 'CARR', 'CTLT', 'CAT', 'CBOE', 'CBRE', 'CDW', 'CE', 'CNC', 'CNP', 'CERN', 'CF',
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'CRL', 'SCHW', 'CHTR', 'CVX', 'CMG', 'CB', 'CHD', 'CI', 'CINF', 'CTAS', 'CSCO', 'C', 'CFG', 'CTXS', 'CLX',
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'CME', 'CMS', 'KO', 'CTSH', 'CL', 'CMCSA', 'CMA', 'CAG', 'COP', 'ED', 'STZ', 'COO', 'CPRT', 'GLW', 'CTVA',
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'COST', 'CCI', 'CSX', 'CMI', 'CVS', 'DHI', 'DHR', 'DRI', 'DVA', 'DE', 'DAL', 'XRAY', 'DVN', 'DXCM',
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'FANG', 'DLR', 'DFS', 'DISCA', 'DISCK', 'DISH', 'DG', 'DLTR', 'D', 'DPZ', 'DOV', 'DOW', 'DTE', 'DUK',
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'DRE', 'DD', 'DXC', 'EMN', 'ETN', 'EBAY', 'ECL', 'EIX', 'EW', 'EA', 'EMR', 'ENPH', 'ETR', 'EOG', 'EFX',
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'EQIX', 'EQR', 'ESS', 'EL', 'ETSY', 'EVRG', 'ES', 'RE', 'EXC', 'EXPE', 'EXPD', 'EXR', 'XOM', 'FFIV', 'FB',
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'FAST', 'FRT', 'FDX', 'FIS', 'FITB', 'FE', 'FRC', 'FISV', 'FLT', 'FMC', 'F', 'FTNT', 'FTV', 'FBHS',
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'FOXA', 'FOX', 'BEN', 'FCX', 'GPS', 'GRMN', 'IT', 'GNRC', 'GD', 'GE', 'GIS', 'GM', 'GPC', 'GILD', 'GL',
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'GPN', 'GS', 'GWW', 'HAL', 'HBI', 'HIG', 'HAS', 'HCA', 'PEAK', 'HSIC', 'HSY', 'HES', 'HPE', 'HLT', 'HOLX',
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'HD', 'HON', 'HRL', 'HST', 'HWM', 'HPQ', 'HUM', 'HBAN', 'HII', 'IEX', 'IDXX', 'INFO', 'ITW', 'ILMN',
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'INCY', 'IR', 'INTC', 'ICE', 'IBM', 'IP', 'IPG', 'IFF', 'INTU', 'ISRG', 'IVZ', 'IPGP', 'IQV', 'IRM',
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'JKHY', 'J', 'JBHT', 'SJM', 'JNJ', 'JCI', 'JPM', 'JNPR', 'KSU', 'K', 'KEY', 'KEYS', 'KMB', 'KIM', 'KMI',
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'KLAC', 'KHC', 'KR', 'LB', 'LHX', 'LH', 'LRCX', 'LW', 'LVS', 'LEG', 'LDOS', 'LEN', 'LLY', 'LNC', 'LIN',
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'LYV', 'LKQ', 'LMT', 'L', 'LOW', 'LUMN', 'LYB', 'MTB', 'MRO', 'MPC', 'MKTX', 'MAR', 'MMC', 'MLM', 'MAS',
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'MA', 'MKC', 'MXIM', 'MCD', 'MCK', 'MDT', 'MRK', 'MET', 'MTD', 'MGM', 'MCHP', 'MU', 'MSFT', 'MAA', 'MHK',
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'TAP', 'MDLZ', 'MPWR', 'MNST', 'MCO', 'MS', 'MOS', 'MSI', 'MSCI', 'NDAQ', 'NTAP', 'NFLX', 'NWL', 'NEM',
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'NWSA', 'NWS', 'NEE', 'NLSN', 'NKE', 'NI', 'NSC', 'NTRS', 'NOC', 'NLOK', 'NCLH', 'NOV', 'NRG', 'NUE',
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'NVDA', 'NVR', 'NXPI', 'ORLY', 'OXY', 'ODFL', 'OMC', 'OKE', 'ORCL', 'OGN', 'OTIS', 'PCAR', 'PKG', 'PH',
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'PAYX', 'PAYC', 'PYPL', 'PENN', 'PNR', 'PBCT', 'PEP', 'PKI', 'PRGO', 'PFE', 'PM', 'PSX', 'PNW', 'PXD',
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'PNC', 'POOL', 'PPG', 'PPL', 'PFG', 'PG', 'PGR', 'PLD', 'PRU', 'PTC', 'PEG', 'PSA', 'PHM', 'PVH', 'QRVO',
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'PWR', 'QCOM', 'DGX', 'RL', 'RJF', 'RTX', 'O', 'REG', 'REGN', 'RF', 'RSG', 'RMD', 'RHI', 'ROK', 'ROL',
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'ROP', 'ROST', 'RCL', 'SPGI', 'CRM', 'SBAC', 'SLB', 'STX', 'SEE', 'SRE', 'NOW', 'SHW', 'SPG', 'SWKS',
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'SNA', 'SO', 'LUV', 'SWK', 'SBUX', 'STT', 'STE', 'SYK', 'SIVB', 'SYF', 'SNPS', 'SYY', 'TMUS', 'TROW',
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'TTWO', 'TPR', 'TGT', 'TEL', 'TDY', 'TFX', 'TER', 'TSLA', 'TXN', 'TXT', 'TMO', 'TJX', 'TSCO', 'TT', 'TDG',
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'TRV', 'TRMB', 'TFC', 'TWTR', 'TYL', 'TSN', 'UDR', 'ULTA', 'USB', 'UAA', 'UA', 'UNP', 'UAL', 'UNH', 'UPS',
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'URI', 'UHS', 'UNM', 'VLO', 'VTR', 'VRSN', 'VRSK', 'VZ', 'VRTX', 'VFC', 'VIAC', 'VTRS', 'V', 'VNO', 'VMC',
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'WRB', 'WAB', 'WMT', 'WBA', 'DIS', 'WM', 'WAT', 'WEC', 'WFC', 'WELL', 'WST', 'WDC', 'WU', 'WRK', 'WY',
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'WHR', 'WMB', 'WLTW', 'WYNN', 'XEL', 'XLNX', 'XYL', 'YUM', 'ZBRA', 'ZBH', 'ZION', 'ZTS']
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stock_list = ['WHR', 'WMB', 'WLTW', 'WYNN', 'XEL', 'XLNX', 'XYL', 'YUM', 'ZBRA', 'ZBH', 'ZION', 'ZTS']
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tickerData = yf.Tickers(stock_list)
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market_cap = data.get_quote_yahoo(stock_list)['marketCap']
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fullTableDf = tickerData.history(period='1d', start='2019-1-1', end='2020-1-25')
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closePrices = fullTableDf['Close']
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selected_stocks_in_graph = [stock_list[0]]
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selected_stocks_in_graph_df = closePrices[selected_stocks_in_graph]
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selected_stock_in_table = stock_list[0]
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selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
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from_time = None
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to_time = None
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# Initialize the app
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app = dash.Dash(__name__)
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app.config.suppress_callback_exceptions = True
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stock_info_in_time_period_df = pd.DataFrame(columns=stock_list, index=["średnia", "cena", "dywidenda", "wolatylność"])
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mean_prices_and_dividends = fullTableDf[['Close', 'Dividends']].mean(axis=0).unstack(level=0)
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mean_prices_and_dividends_and_market_cap = pd.concat([mean_prices_and_dividends, market_cap], axis=1)
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mean_prices_and_dividends_figure = px.scatter(mean_prices_and_dividends_and_market_cap.reset_index(), size="marketCap", x='Dividends', y='Close', text="index", template='plotly_dark')
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def make_gauge(title, min_v, value, max_v):
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gauge_size = 180
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fig = go.Figure(go.Indicator(
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mode="gauge+number",
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value=value,
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domain={'x': [0, 1], 'y': [0, 1]},
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title={'text': title}))
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fig.layout.height = gauge_size
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fig.layout.width = gauge_size
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fig.layout.margin = dict(l=2, r=2, t=10, b=2)
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fig.layout.paper_bgcolor = "#1E1E1E"
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fig.layout.font.color = "white"
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fig.layout.font.family = "Arial"
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return fig
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def get_options(list_stocks):
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dict_list = []
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for i in list_stocks:
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dict_list.append({'label': i, 'value': i})
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return dict_list
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app.layout = html.Div(
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children=[
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html.Div(className='row',
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children=[
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html.Div(className='four columns div-user-controls',
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children=[
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html.P('Wybierz akcje (jedną lub więcej) do porównania na wykresie'),
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html.Div(
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className='div-for-dropdown',
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children=[
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dcc.Dropdown(id='stockselector',
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options=get_options(stock_list),
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multi=True,
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value=selected_stocks_in_graph,
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style={'backgroundColor': '#1E1E1E'},
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className='stockselector'
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)
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],
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style={'color': '#1E1E1E'}),
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html.P('Wybierz jedną akcję aby zobaczyć szczegóły w tabeli'),
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html.Div(
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className='div-for-dropdown',
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children=[
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dcc.Dropdown(id='table_selector',
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options=get_options(stock_list),
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multi=False, value=selected_stock_in_table,
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style={'backgroundColor': '#1E1E1E'},
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className='stockselector'
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),
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html.Button("Pobierz dane", id="btn_data",style={'margin-top': '3rem'}),
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dcc.Download(id="download-data")
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],
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style={'color': '#1E1E1E'}),
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html.P('Wybierz przedział czasu by policzyć średnie i wachania'),
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html.Div(
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className='gauges',
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children=[
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dcc.Graph(id='average_gauge'),
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dcc.Graph(id='volatility_gauge')
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]),
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dash_table.DataTable(
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id='info_in_time_period',
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style_header={'backgroundColor': 'rgb(30, 30, 30)'},
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style_cell={
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'backgroundColor': 'rgb(50, 50, 50)',
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'color': 'white'
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},
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)
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]),
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html.Div(className='eight columns div-for-charts bg-grey',
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children=[
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dcc.Tabs(id='tabs', value='tab-1', children=[
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dcc.Tab(id='tab-1', label='Chart 1', value='tab-1', children=[
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dcc.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
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]),
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dcc.Tab(id='tab-2', label='Chart 2', value='tab-2',children=[
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dcc.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure, config={'displayModeBar': False}, animate=True),
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]),
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]),
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dash_table.DataTable(
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id='stock_price_table',
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style_header={'backgroundColor': 'rgb(30, 30, 30)'},
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style_cell={
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'backgroundColor': 'rgb(50, 50, 50)',
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'color': 'white'
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},
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)
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])
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])
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]
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)
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# Callback for downloading file
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@app.callback(
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Output("download-data", "data"),
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Input("btn_data", "n_clicks"),
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State('table_selector', 'value'),
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prevent_initial_call=True,
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)
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def create_download_file(n_clicks, selected_table):
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global selected_stock_in_table_df
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return dcc.send_data_frame(selected_stock_in_table_df.to_csv, "data-{table}.csv".format(table = selected_table))
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# Callback for timeseries price
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@app.callback(Output('timeseries', 'figure'), [Input('stockselector', 'value')])
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def update_graph(selected_dropdown_value):
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global selected_stocks_in_graph
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global selected_stocks_in_graph_df
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selected_stocks_in_graph = selected_dropdown_value
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selected_stocks_in_graph_df = closePrices[selected_stocks_in_graph]
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figure = px.line(selected_stocks_in_graph_df, template='plotly_dark', title='Stock Prices')
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# figure.update_layout(clickmode='event+select')
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return figure
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@app.callback([Output("stock_price_table", "data"), Output('stock_price_table', 'columns')],
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[Input('table_selector', 'value')])
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def update_table(selected_dropdown_value):
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global selected_stock_in_table
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global selected_stock_in_table_df
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selected_stock_in_table = selected_dropdown_value
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selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
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data = selected_stock_in_table_df.to_dict('records')
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columns = [{"name": i, "id": i} for i in selected_stock_in_table_df.columns]
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return data, columns
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average_gauge = make_gauge('średnia', 0, 0, 400)
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volatility_gauge = make_gauge('wolatylność', 0, 0, 400)
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def round_to_nearest_weekday(date):
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if isinstance(date,pd.Timestamp):
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if date.dayofweek > 5:
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date += datetime.timedelta(days=8-date.dayofweek)
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assert date.dayofweek <= 5
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return date
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elif isinstance(date, str):
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if '.' in date:
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date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f")
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elif ':' in date:
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date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
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else:
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date = datetime.datetime.strptime(date, "%Y-%m-%d")
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if date.isoweekday() > 5:
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date += datetime.timedelta(days=8-date.isoweekday())
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assert date.isoweekday() <= 5
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return date
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@app.callback([Output('average_gauge', 'figure'),
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Output('volatility_gauge', 'figure'),
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Output('info_in_time_period', 'data')],
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Input('timeseries', 'relayoutData'))
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def change_time_period(selectedData):
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global from_time
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global to_time
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global average_gauge
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global volatility_gauge
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global stock_info_in_time_period_df
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if selectedData is not None:
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if "xaxis.range[0]" in selectedData and "xaxis.range[1]" in selectedData:
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from_time = selectedData["xaxis.range[0]"]
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to_time = selectedData["xaxis.range[1]"]
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else:
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from_time = selected_stock_in_table_df.index.min()
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to_time = selected_stock_in_table_df.index.max()
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from_time = round_to_nearest_weekday(from_time)
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to_time = round_to_nearest_weekday(to_time)
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full_table_in_time_period = fullTableDf.loc[from_time:to_time]
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mean = full_table_in_time_period.mean(axis=0)
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mean = mean.xs('Close', level=0)
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std = full_table_in_time_period.std(axis=0)
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std = std.xs('Close', level=0)
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time_period = selected_stock_in_table_df.loc[from_time:to_time]
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mean = time_period.mean(axis=0)
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std = time_period.std(axis=0)
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# TODO: oblicz stock_info_in_time_period_df tutuaj !!!
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average_gauge = make_gauge('średnia', 0, mean['Close'], 400)
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volatility_gauge = make_gauge('wolatylność', 0, std['Close'], 400)
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return average_gauge, volatility_gauge, stock_info_in_time_period_df.T.to_dict('records')
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if __name__ == '__main__':
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app.run_server(debug=True)
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