Upload 2 files

app.py

@@ -0,0 +1,156 @@
+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('<p class="medium-font">Configuration</p>', unsafe_allow_html=True)
+
+st.markdown("""
+<div class="analysis-card">
+    <h2 class="analysis-title">AI-Agents Finance Analyst Platform</h2>
+    <p class="analysis-content">
+        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:
+    </p>
+     <ul class="analysis-list">
+        <li class="analysis-list-item">Comprehensive Data Analysis on stocks, and investing.</li>
+        <li class="analysis-list-item">In-depth fundamental and technical analyses</li>
+        <li class="analysis-list-item">Extensive web and news research integration</li>
+        <li class="analysis-list-item">Customizable analysis parameters including time frames and specific indicators</li>
+    </ul>
+    <p class="analysis-content">
+        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.
+    </p>
+    <p class="analysis-content">
+        Please note, this analysis is for informational purposes only and should not be construed as financial or investment advice.
+</div>
+""", unsafe_allow_html=True)
+
+stock_symbol = st.sidebar.text_input("Enter Stock Symbol", value="NVDA", 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('<p class="medium-font">Stock Information</p>', 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('<p class="medium-font">Analysis Result</p>', 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('<p class="medium-font">Interactive Stock Chart</p>', unsafe_allow_html=True)
+    st.plotly_chart(plot_stock_chart(st.session_state.stock_data, indicators), use_container_width=True)
+
+
+st.markdown("---")
+st.markdown('<p class="small-font">Crafted by base234 </p>', unsafe_allow_html=True)

crew.py

@@ -0,0 +1,179 @@
+import os
+import warnings
+from crewai import Agent, Task, Crew
+from crewai_tools import ScrapeWebsiteTool, SerperDevTool
+from crewai import Crew, Process
+from langchain_openai import ChatOpenAI
+
+from dotenv import load_dotenv
+load_dotenv()
+warnings.filterwarnings('ignore')
+
+SERPER_API_KEY = os.getenv('SERPER_API_KEY')
+
+SAMBAVERSE_API_KEY = os.getenv('SAMBANOVA_API_KEY')
+SAMBANOVA_API_URL = "https://api.sambanova.ai/v1"
+
+search_tool = SerperDevTool()
+scrape_tool = ScrapeWebsiteTool()
+
+llm = ChatOpenAI(
+        model="Meta-Llama-3.1-8B-Instruct-8k",
+        temperature=0.5,
+        max_retries=2,
+        base_url=SAMBANOVA_API_URL,  
+        api_key=SAMBAVERSE_API_KEY,
+    )
+
+def crew_creator(stock_selection):
+
+    # data_analyst_agent = Agent(
+    #     role="Data Analyst",
+    #     goal="Monitor and analyze market data in real-time "
+    #         "to identify trends and predict market movements.",
+    #     backstory="Specializing in financial markets, this agent "
+    #             "uses statistical modeling and machine learning "
+    #             "to provide crucial insights. With a knack for data, "
+    #             "the Data Analyst Agent is the cornerstone for "
+    #             "informing trading decisions.",
+    #     verbose=True,
+    #     allow_delegation=True,
+    #     tools = [scrape_tool, search_tool],
+    #     llm=llm,
+    # )
+
+    trading_strategy_agent = Agent(
+        role="Trading Strategy Developer",
+        goal="Develop and test various trading strategies based "
+            "on insights from the Data Analyst Agent.",
+        backstory="Equipped with a deep understanding of financial "
+                "markets and quantitative analysis, this agent "
+                "devises and refines trading strategies. It evaluates "
+                "the performance of different approaches to determine "
+                "the most profitable and risk-averse options.",
+        verbose=True,
+        allow_delegation=True,
+        tools = [scrape_tool, search_tool],
+        llm=llm,
+    )
+
+    # execution_agent = Agent(
+    #     role="Trade Advisor",
+    #     goal="Suggest optimal trade execution strategies "
+    #         "based on approved trading strategies.",
+    #     backstory="This agent specializes in analyzing the timing, price, "
+    #             "and logistical details of potential trades. By evaluating "
+    #             "these factors, it provides well-founded suggestions for "
+    #             "when and how trades should be executed to maximize "
+    #             "efficiency and adherence to strategy.",
+    #     verbose=True,
+    #     allow_delegation=True,
+    #     tools = [scrape_tool, search_tool],
+    #     llm=llm,
+    # )
+
+    # risk_management_agent = Agent(
+    #     role="Risk Advisor",
+    #     goal="Evaluate and provide insights on the risks "
+    #         "associated with potential trading activities.",
+    #     backstory="Armed with a deep understanding of risk assessment models "
+    #             "and market dynamics, this agent scrutinizes the potential "
+    #             "risks of proposed trades. It offers a detailed analysis of "
+    #             "risk exposure and suggests safeguards to ensure that "
+    #             "trading activities align with the firm’s risk tolerance.",
+    #     verbose=True,
+    #     allow_delegation=True,
+    #     tools = [scrape_tool, search_tool],
+    #     llm=llm,
+    # )
+
+    # Task for Data Analyst Agent: Analyze Market Data
+    # data_analysis_task = Task(
+    #     description=(
+    #         "Continuously monitor and analyze market data for "
+    #         "the selected stock ({stock_selection}). "
+    #         "Use statistical modeling and machine learning to "
+    #         "identify trends and predict market movements."
+    #     ),
+    #     expected_output=(
+    #         "Insights and alerts about significant market "
+    #         "opportunities or threats for {stock_selection}."
+    #     ),
+    #     agent=data_analyst_agent,    
+    # )
+
+    # Task for Trading Strategy Agent: Develop Trading Strategies
+    strategy_development_task = Task(
+        description=(
+            "Develop and refine trading strategies based on "
+            "the insights from the Data Analyst and "
+            # "user-defined risk tolerance ({risk_tolerance}). "
+            # "Consider trading preferences ({trading_strategy_preference})."
+        ),
+        expected_output=(
+            "A set of potential trading strategies for {stock_selection} "
+            "that align with the user's risk tolerance."
+        ),
+        agent=trading_strategy_agent,
+    )
+
+    # Task for Trade Advisor Agent: Plan Trade Execution
+    # execution_planning_task = Task(
+    #     description=(
+    #         "Analyze approved trading strategies to determine the "
+    #         "best execution methods for {stock_selection}, "
+    #         "considering current market conditions and optimal pricing."
+    #     ),
+    #     expected_output=(
+    #         "Detailed execution plans suggesting how and when to "
+    #         "execute trades for {stock_selection}."
+    #     ),
+    #     agent=execution_agent,
+    # )
+
+    # Task for Risk Advisor Agent: Assess Trading Risks
+    # risk_assessment_task = Task(
+    #     description=(
+    #         "Evaluate the risks associated with the proposed trading "
+    #         "strategies and execution plans for {stock_selection}. "
+    #         "Provide a detailed analysis of potential risks "
+    #         "and suggest mitigation strategies."
+    #     ),
+    #     expected_output=(
+    #         "A comprehensive risk analysis report detailing potential "
+    #         "risks and mitigation recommendations for {stock_selection}."
+    #     ),
+    #     agent=risk_management_agent,
+    # )
+
+    # Define the crew with agents and tasks
+    financial_trading_crew = Crew(
+        agents=[
+                # data_analyst_agent, 
+                trading_strategy_agent, 
+                # execution_agent, 
+                # risk_management_agent
+            ],
+        
+        tasks=[
+                # data_analysis_task, 
+                strategy_development_task, 
+                # execution_planning_task, 
+                # risk_assessment_task
+            ],
+
+        manager_llm = llm,
+        process=Process.sequential,
+        verbose=True,
+    )
+
+    result = financial_trading_crew.kickoff(inputs={
+        'stock_selection': stock_selection,
+        # 'initial_capital': initial_capital,
+        # 'risk_tolerance': risk_tolerance,
+        # 'trading_strategy_preference': trading_strategy_preference,
+        # 'news_impact_consideration': news_impact_consideration
+    })
+    return str(result)
+
+# print(result)