Update app.py

app.py

@@ -7,15 +7,12 @@ import yfinance as yf
 import plotly.graph_objects as go
 import plotly.express as px
 from datetime import datetime, timedelta
-import groq
 from scipy import stats
-from dotenv import load_dotenv
+import os
+
 # Set page title and configuration
 st.set_page_config(page_title="Theaimart Stock Analysis", page_icon="📊", layout="wide")
 
-# Initialize Groq client
-groq_client = groq.Groq(api_key="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")
-
 # Enhanced Custom CSS
 st.markdown("""
 <style>
@@ -100,32 +97,6 @@ st.markdown("""
 </style>
 """, unsafe_allow_html=True)
 
-
-@st.cache_data(ttl=3600)
-def get_stock_overview(stock):
-    prompt = f"""
-    Provide a concise, factual overview of {stock} stock, including:
-    1. Brief company description
-    2. Key recent developments (last 6 months)
-    3. Major strengths and potential risks
-    Base your response on widely accepted financial data and recent news. 
-    Limit the response to 5 sentences and avoid speculative statements.
-    """
-    try:
-        response = groq_client.chat.completions.create(
-            messages=[
-                {"role": "system", "content": "You are a financial analyst providing brief, factual stock overviews."},
-                {"role": "user", "content": prompt}
-            ],
-            model="mixtral-8x7b-32768",
-            max_tokens=200
-        )
-        return response.choices[0].message.content
-    except Exception as e:
-        st.error(f"Error fetching stock overview: {str(e)}")
-        return "Unable to fetch stock overview at this time."
-
-
 @st.cache_data(ttl=3600)
 def search_google(query):
     url = f"https://www.google.com/search?q={query}"
@@ -145,7 +116,6 @@ def search_google(query):
         st.error(f"Error fetching news: {str(e)}")
         return []
 
-
 @st.cache_data(ttl=3600)
 def fetch_stock_data(ticker, period="2y"):
     try:
@@ -158,7 +128,6 @@ def fetch_stock_data(ticker, period="2y"):
         st.error(f"Error fetching stock data: {str(e)}")
         return pd.DataFrame()
 
-
 def analyze_market_data(stock_data):
     if stock_data.empty:
         return "Insufficient data for analysis."
@@ -182,7 +151,6 @@ def analyze_market_data(stock_data):
     - All-Time Low: ${low_price:.2f}
     """
 
-
 def develop_trading_strategies(stock, risk_tolerance, strategy_preference, stock_data):
     if stock_data.empty:
         return "Insufficient data for strategy development."
@@ -199,20 +167,12 @@ def develop_trading_strategies(stock, risk_tolerance, strategy_preference, stock
     - Sharpe Ratio: {sharpe_ratio:.2f}
     Provide a concise, fact-based strategy in 3-4 sentences, avoiding speculative advice.
     """
-    try:
-        response = groq_client.chat.completions.create(
-            messages=[
-                {"role": "system", "content": "You are a financial expert specializing in trading strategies."},
-                {"role": "user", "content": prompt}
-            ],
-            model="mixtral-8x7b-32768",
-            max_tokens=150
-        )
-        return response.choices[0].message.content
-    except Exception as e:
-        st.error(f"Error developing trading strategy: {str(e)}")
-        return "Unable to develop trading strategy at this time."
-
+    # Here you should implement the logic to develop trading strategies based on the given parameters
+    return f"""
+    - Volatility: {volatility:.2f}
+    - Sharpe Ratio: {sharpe_ratio:.2f}
+    - Strategy: Focus on momentum trading, entering positions during periods of low volatility and high Sharpe ratios. Use stop-loss orders to manage risk and take profit levels to lock in gains.
+    """
 
 def plan_trade_execution(stock, initial_capital, risk_tolerance, stock_data):
     if stock_data.empty:
@@ -243,7 +203,6 @@ def plan_trade_execution(stock, initial_capital, risk_tolerance, stock_data):
     - Max Position Size: {(risk_amount / (last_price - stop_loss)):.0f} shares
     """
 
-
 def assess_trading_risks(stock, stock_data):
     if stock_data.empty:
         return "Insufficient data for risk assessment."
@@ -294,7 +253,6 @@ def assess_trading_risks(stock, stock_data):
 
     return risk_metrics, heatmap
 
-
 def create_candlestick_chart(stock_data):
     fig = go.Figure(data=[go.Candlestick(x=stock_data.index,
                                          open=stock_data['Open'],
@@ -311,7 +269,6 @@ def create_candlestick_chart(stock_data):
     )
     return fig
 
-
 @st.cache_data
 def run_monte_carlo_simulation(stock_data, initial_investment, num_simulations, time_horizon):
     returns = stock_data['Close'].pct_change().dropna()
@@ -329,7 +286,6 @@ def run_monte_carlo_simulation(stock_data, initial_investment, num_simulations,
 
     return simulations
 
-
 # Streamlit app
 st.title("🚀 Theaimart Stock Investment Analysis")
 
@@ -349,8 +305,6 @@ with col2:
 
 news_impact_consideration = st.checkbox("Consider News Impact", value=True)
 
-# ... [previous code remains the same] ...
-
 if st.button("🔍 Run In-Depth Analysis"):
     st.session_state['run_clicked'] = True
 
@@ -375,11 +329,6 @@ if st.session_state.get('run_clicked', False):
             stock_data = fetch_stock_data(stock_selection)
 
             if not stock_data.empty:
-                # Stock Overview
-                st.subheader("📌 Stock Overview")
-                overview = get_stock_overview(stock_selection)
-                st.markdown(f'<div class="overview-card">{overview}</div>', unsafe_allow_html=True)
-
                 # Stock Price Chart
                 st.subheader("📈 Stock Price Trends")
                 st.plotly_chart(create_candlestick_chart(stock_data), use_container_width=True)
@@ -486,4 +435,4 @@ st.markdown("""
 
 # Footer
 st.markdown('<div class="footer">© Theaimart 2024 | Advanced Stock Analysis Tool</div>', unsafe_allow_html=True)
-st.caption("Powered by cutting-edge AI and real-time financial data analysis.")
+st.caption("Powered by cutting-edge AI and real-time financial data analysis.")