import streamlit as st import yfinance as yf import plotly.graph_objs as go from plotly.subplots import make_subplots from crew import crew_creator from dotenv import load_dotenv load_dotenv() st.set_page_config(layout="wide", page_title="Finance Agent", initial_sidebar_state="expanded") st.sidebar.markdown('

Configuration

', unsafe_allow_html=True) st.markdown("""

AI-Agents Finance Analyst Platform

Welcome to my cutting-edge stock analysis platform, leveraging Artificial Intelligence and Large Language Models (LLMs) to deliver professional-grade investment insights. Our system offers:

Users can obtain a detailed, AI-generated analysis report by simply selecting a stock symbol, specifying a time period, and choosing desired analysis indicators. This platform aims to empower investors with data-driven, AI-enhanced decision-making tools for the complex world of stock market investments.

Please note, this analysis is for informational purposes only and should not be construed as financial or investment advice.

""", unsafe_allow_html=True) stock_symbol = st.sidebar.text_input("Enter Stock Symbol", value="META", placeholder="META, AAPL, NVDA") time_period = st.sidebar.selectbox("Select Time Period", ['1mo', '3mo', '6mo', '1y', '2y', '5y', 'max']) indicators = st.sidebar.multiselect("Select Indicators", ['Moving Averages', 'Volume', 'RSI', 'MACD']) analyze_button = st.sidebar.button("📊 Analyze Stock", help="Click to start the stock analysis") # Initialize session state if 'analyzed' not in st.session_state: st.session_state.analyzed = False st.session_state.stock_info = None st.session_state.stock_data = None st.session_state.result_file_path = None def get_stock_data(stock_symbol, period='1y'): return yf.download(stock_symbol, period=period) def plot_stock_chart(stock_data, indicators): fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.05, row_heights=[0.6, 0.2, 0.2]) # Main price chart fig.add_trace(go.Candlestick(x=stock_data.index, open=stock_data['Open'], high=stock_data['High'], low=stock_data['Low'], close=stock_data['Close'], name='Price'), row=1, col=1) # Add selected indicators if 'Moving Averages' in indicators: fig.add_trace(go.Scatter(x=stock_data.index, y=stock_data['Close'].rolling(window=50).mean(), name='50 MA', line=dict(color='orange')), row=1, col=1) fig.add_trace(go.Scatter(x=stock_data.index, y=stock_data['Close'].rolling(window=200).mean(), name='200 MA', line=dict(color='red')), row=1, col=1) if 'Volume' in indicators: fig.add_trace(go.Bar(x=stock_data.index, y=stock_data['Volume'], name='Volume'), row=2, col=1) if 'RSI' in indicators: delta = stock_data['Close'].diff() gain = (delta.where(delta > 0, 0)).rolling(window=14).mean() loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean() rs = gain / loss rsi = 100 - (100 / (1 + rs)) fig.add_trace(go.Scatter(x=stock_data.index, y=rsi, name='RSI'), row=3, col=1) if 'MACD' in indicators: ema12 = stock_data['Close'].ewm(span=12, adjust=False).mean() ema26 = stock_data['Close'].ewm(span=26, adjust=False).mean() macd = ema12 - ema26 signal = macd.ewm(span=9, adjust=False).mean() fig.add_trace(go.Scatter(x=stock_data.index, y=macd, name='MACD'), row=3, col=1) fig.add_trace(go.Scatter(x=stock_data.index, y=signal, name='Signal'), row=3, col=1) fig.update_layout( title='Stock Analysis', yaxis_title='Price', xaxis_rangeslider_visible=False, height=800, showlegend=True ) fig.update_xaxes( rangeselector=dict( buttons=list([ dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all") ]) ), rangeslider=dict(visible=False), type="date" ) return fig if analyze_button: st.session_state.analyzed = False # Reset analyzed state st.snow() # Fetch stock info and data with st.spinner(f"Fetching data for {stock_symbol}..."): stock = yf.Ticker(stock_symbol) st.session_state.stock_info = stock.info st.session_state.stock_data = get_stock_data(stock_symbol, period=time_period) # Create and run the crew with st.spinner("Running analysis, please wait..."): st.session_state.result_file_path = crew_creator(stock_symbol) st.session_state.analyzed = True # Display stock info if available if st.session_state.stock_info: st.markdown('

Stock Information

', unsafe_allow_html=True) info = st.session_state.stock_info col1, col2, col3 = st.columns(3) with col1: st.markdown(f"**Company Name:** {info.get('longName', 'N/A')}") st.markdown(f"**Sector:** {info.get('sector', 'N/A')}") with col2: st.markdown(f"**Industry:** {info.get('industry', 'N/A')}") st.markdown(f"**Country:** {info.get('country', 'N/A')}") with col3: st.markdown(f"**Current Price:** ${info.get('currentPrice', 'N/A')}") st.markdown(f"**Market Cap:** ${info.get('marketCap', 'N/A')}") # Display CrewAI result if available if st.session_state.result_file_path: st.markdown('

Analysis Result

', unsafe_allow_html=True) # with open(st.session_state.result_file_path, 'r') as file: # result = file.read() st.markdown("---") st.markdown(st.session_state.result_file_path) # Display chart if st.session_state.analyzed and st.session_state.stock_data is not None: st.markdown('

Interactive Stock Chart

', unsafe_allow_html=True) st.plotly_chart(plot_stock_chart(st.session_state.stock_data, indicators), use_container_width=True) st.markdown("---") st.markdown('

Crafted by base234

', unsafe_allow_html=True)