final project

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,145 @@
+import streamlit as st
+import pandas as pd
+import yfinance as yf
+import base64
+import io
+import os
+import torch
+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')
+
+# Загрузка весов модели (выполняется только при первом запуске)
+@st.cache_data
+def load_model_weights():
+    return torch.load('model/model_weights.pth')
+
+# Сохранение весов модели в сессионном состоянии
+if 'model_weights' not in st.session_state:
+    st.session_state.model_weights = load_model_weights()
+
+# @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"<h3 style='text-align: center;'>Цены актуальны на последнюю дату закрытия торгов {latest_date}</h3>", 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) \
+        <span style="font-size: 1.5em;">{last_DJI:.2f}</span> <span style="font-size: 1em; color: crimson;">{diff_DJI:.2f}</span><span style="font-size: 1em; color: crimson;">({pr_DJI:.2f}%)</span>' \
+        '<br><span style="font-size: 0.7em; color: grey;">DJI - DJI Real Time Price. Currency in USD</span>'
+    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) \
+        <span style="font-size: 1.5em;">{last_500:.2f}</span> <span style="font-size: 1em; color: crimson;">{diff_500:.2f}</span><span style="font-size: 1em; color: crimson;">({pr_500:.2f}%)</span>' \
+        '<br><span style="font-size: 0.7em; color: grey;">SNP - SNP Real Time Price. Currency in USD</span>'
+    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) \
+        <span style="font-size: 1.5em;">{last_SSE:.2f}</span> <span style="font-size: 1em; color: crimson;">{diff_SSE:.2f}</span><span style="font-size: 1em; color: crimson;">({pr_SSE:.2f}%)</span>' \
+        '<br><span style="font-size: 0.7em; color: grey;">Shanghai - Shanghai Delayed Price. Currency in CNY</span>'
+    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) \
+        <span style="font-size: 1.5em;">{last_IMOEX:.2f}</span> <span style="font-size: 1em; color: crimson;">{diff_IMOEX:.2f}</span><span style="font-size: 1em; color: crimson;">({pr_IMOEX:.2f}%)</span>' \
+        '<br><span style="font-size: 0.7em; color: grey;">MCX - MCX Real Time Price. Currency in RUB</span>'
+    plot_raw_data(data_IMOEX, text_IMOEX)
+    check3 = st.checkbox("Исторические данные MOEX Russia Index")
+    if check3:
+        st.write(data_IMOEX)