import streamlit as st
import pandas as pd
import yfinance as yf
import base64
import io
import os
from datetime import datetime, timedelta
from PIL import Image
from plotly import graph_objs as go
from datetime import date
st.set_page_config(layout='wide', initial_sidebar_state='expanded')
st.set_option('deprecation.showPyplotGlobalUse', False)
st.title('ML Wall Street')
st.image('images/img.png')
# @st.cache_data
# Функция для получения данных о ценах акций
def get_stock_data():
dow_tickers = ['UNH', 'MSFT', 'GS', 'HD', 'AMGN', 'MCD', 'CAT', 'CRM', 'V', 'BA', 'HON', 'TRV', 'AAPL', 'AXP', 'JPM', 'IBM', 'JNJ', 'WMT', 'PG', 'CVX', 'MRK', 'MMM', 'NKE', 'DIS', 'KO', 'DOW', 'CSCO', 'INTC', 'VZ', 'WBA']
start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
end_date = datetime.now().strftime('%Y-%m-%d')
dow_data = yf.download(dow_tickers, start=start_date, end=end_date)
return dow_data
data = get_stock_data()
latest_date = data.index[-1].strftime('%Y-%m-%d')
data = data.loc[latest_date, 'Close'].reset_index()
data.columns = ['Ticker', 'Close']
data['Close'] = data['Close'].round(2)
# Добавляем кнопку обновления данных
# if st.button("Обновить данные", type="primary"):
# data = get_stock_data()
# latest_date = data.index[-1].strftime('%Y-%m-%d')
# data = data.loc[latest_date, 'Close'].reset_index()
# data.columns = ['Ticker', 'Close']
# data['Close'] = data['Close'].round(2)
# st.success("Данные успешно обновлены!")
st.markdown(f"Цены актуальны на последнюю дату закрытия торгов {latest_date}
", unsafe_allow_html=True)
col3, col1, col2 = st.columns([0.2, 5.3, 1.8])
with col2:
def image_to_base64(img_path, output_size=(64, 64)):
if os.path.exists(img_path):
with Image.open(img_path) as img:
img = img.resize(output_size)
buffered = io.BytesIO()
img.save(buffered, format="PNG")
return f"data:image/png;base64,{base64.b64encode(buffered.getvalue()).decode()}"
return ""
if 'Logo' not in data.columns:
output_dir = 'downloaded_logos'
data['Logo'] = data['Ticker'].apply(lambda name: os.path.join(output_dir, f'{name}.png'))
# Convert image paths to Base64
data["Logo"] = data["Logo"].apply(image_to_base64)
image_column = st.column_config.ImageColumn(label="")
ticker_column = st.column_config.TextColumn(label="Ticker 💬", help="📍**Тикеры компаний Индекса Dow Jones**")
price_column = st.column_config.TextColumn(label=f"Close 💬", help="📍**Цена за последний день (в USD)**")
data.reset_index(drop=True, inplace=True)
data.index = data.index + 1
data = data[['Logo', 'Ticker', 'Close']]
st.write('')
st.write('')
st.markdown('**Компании Индекса Dow Jones**')
st.dataframe(data, height=1088, column_config={"Logo": image_column, "Ticker":ticker_column, 'Close':price_column})
with col1:
START = "1920-01-01"
TODAY = date.today().strftime("%Y-%m-%d")
# @st.cache_data
def load_data(ticker):
data = yf.download(ticker, START, TODAY)
data.reset_index(inplace=True)
return data
def plot_raw_data(data, text):
fig = go.Figure()
fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Цена закрытия"))
fig.update_layout(title_text=text, xaxis_rangeslider_visible=True)
fig.update_traces(showlegend=True)
st.plotly_chart(fig, use_container_width=True)
data = load_data('^DJI')
last_DJI = data['Close'].iloc[-1]
diff_DJI = data['Close'].iloc[-1] - data['Close'].iloc[-2]
pr_DJI = 100 * diff_DJI / last_DJI
text_DJI = f'🇺🇸 Dow Jones Industrial Average (^DJI) \
{last_DJI:.2f} {diff_DJI:.2f}({pr_DJI:.2f}%)' \
'
DJI - DJI Real Time Price. Currency in USD'
plot_raw_data(data, text_DJI)
check1 = st.checkbox("Исторические данные Dow Jones Industrial Average")
if check1:
st.write(data)
data_500 = load_data('^GSPC')
last_500 = data_500['Close'].iloc[-1]
diff_500 = data_500['Close'].iloc[-1] - data_500['Close'].iloc[-2]
pr_500 = 100 * diff_500 / last_500
text_500 = f'🇺🇸 S&P 500 (^GSPC) \
{last_500:.2f} {diff_500:.2f}({pr_500:.2f}%)' \
'
SNP - SNP Real Time Price. Currency in USD'
plot_raw_data(data_500, text_500)
check4 = st.checkbox("S&P 500")
if check4:
st.write(data_500)
data_SSE = load_data('000001.SS')
last_SSE = data_SSE['Close'].iloc[-1]
diff_SSE = data_SSE['Close'].iloc[-1] - data_SSE['Close'].iloc[-2]
pr_SSE = 100 * diff_SSE / last_SSE
text_SSE = f'🇨🇳 SSE Composite Index (000001.SS) \
{last_SSE:.2f} {diff_SSE:.2f}({pr_SSE:.2f}%)' \
'
Shanghai - Shanghai Delayed Price. Currency in CNY'
plot_raw_data(data_SSE, text_SSE)
check2 = st.checkbox("Исторические данные SSE Composite Index")
if check2:
st.write(data_SSE)
data_IMOEX = load_data('IMOEX.ME')
last_IMOEX = data_IMOEX['Close'].iloc[-1]
diff_IMOEX = data_IMOEX['Close'].iloc[-1] - data_IMOEX['Close'].iloc[-2]
pr_IMOEX = 100 * diff_IMOEX / last_IMOEX
text_IMOEX= f'🇷🇺 MOEX Russia Index (IMOEX.ME) \
{last_IMOEX:.2f} {diff_IMOEX:.2f}({pr_IMOEX:.2f}%)' \
'
MCX - MCX Real Time Price. Currency in RUB'
plot_raw_data(data_IMOEX, text_IMOEX)
check3 = st.checkbox("Исторические данные MOEX Russia Index")
if check3:
st.write(data_IMOEX)