import streamlit as st import base64 from datetime import datetime import json import os from anthropic import Anthropic from PIL import Image import io import yaml def load_anthropic_key(): """Load Anthropic API key from environment variables""" try: api_key = os.getenv('ANTHROPIC_API_KEY') if not api_key: st.error("Anthropic API key not found. Please set ANTHROPIC_API_KEY in your environment variables.") return None return api_key except Exception as e: st.error(f"Error loading Anthropic API key: {str(e)}") return None def initialize_anthropic_client(): """Initialize Anthropic client with API key""" api_key = load_anthropic_key() if api_key: return Anthropic(api_key=api_key) return None def create_prompt_template(patterns, indicators): """Creates a structured prompt for the LLM based on the chart and analysis needs""" prompt = """You are an expert financial analyst. Please analyze this financial chart (chart type will be detected automatically) and provide insights in the following structured format: 1. VISUAL ANALYSIS - First identify the type of chart (candlestick, line, OHLC, area, etc.) - Identify and describe the main trend - Note key price levels visible in the chart - Describe any significant patterns: {patterns} - Comment on volume trends if visible - Analyze these technical indicators: {indicators} 2. TECHNICAL INTERPRETATION - Current market structure and trend strength - Key support and resistance levels with price points - Any visible divergences or convergences - Pattern reliability assessment 3. RISK ANALYSIS - Potential risk levels - Risk/reward scenarios - Warning signs or red flags - Market context considerations 4. ACTIONABLE INSIGHTS - Potential trading scenarios - Key price targets - Suggested stop-loss levels - Timeframe considerations 5. SIMPLIFIED EXPLANATION Provide a 2-3 sentence summary in simple terms for novice traders. IMPORTANT: Clearly mark this as AI-generated analysis for informational purposes only. """ return prompt.format( patterns=', '.join(patterns) if patterns else 'all visible patterns', indicators=', '.join(indicators) if indicators else 'visible indicators' ) def detect_chart_type(client, image_data): """Detect chart type using Claude Vision""" try: encoded_image = base64.b64encode(image_data).decode('utf-8') message = client.messages.create( model="claude-3-opus-20240229", max_tokens=50, messages=[{ "role": "user", "content": [ { "type": "text", "text": "What type of financial chart is this? Choose from: Candlestick, Line, OHLC, Area, or Other. Just respond with one word." }, { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": encoded_image } } ] }] ) chart_type = message.content[0].text.strip() return chart_type except Exception as e: st.error(f"Error in chart type detection: {str(e)}") return "Other" def analyze_chart_with_claude(client, image_data, prompt, chart_type=None): """Analyze chart using Claude Vision""" try: encoded_image = base64.b64encode(image_data).decode('utf-8') # If chart type wasn't provided, detect it first if not chart_type: chart_type = detect_chart_type(client, image_data) st.info(f"Detected chart type: {chart_type}") message = client.messages.create( model="claude-3-opus-20240229", max_tokens=1000, messages=[{ "role": "user", "content": [ { "type": "text", "text": prompt.format(chart_type=chart_type) }, { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": encoded_image } } ] }] ) return message.content[0].text, chart_type except Exception as e: st.error(f"Error in Claude analysis: {str(e)}") return None, None def continue_analysis_with_claude(client, question, previous_analysis, image_data=None): """Continue the analysis based on a follow-up question""" try: content = [ { "type": "text", "text": f"""Previous analysis: {previous_analysis} User's follow-up question: {question} Please provide a detailed answer to the follow-up question, maintaining the context of the previous analysis.""" } ] # Add image to the content if available if image_data: encoded_image = base64.b64encode(image_data).decode('utf-8') content.append({ "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": encoded_image } }) message = client.messages.create( model="claude-3-opus-20240229", max_tokens=1000, messages=[{ "role": "user", "content": content }] ) return message.content[0].text except Exception as e: st.error(f"Error in follow-up analysis: {str(e)}") return None def get_trading_education(client, concept): """Get educational content about trading concepts""" try: message = client.messages.create( model="claude-3-opus-20240229", max_tokens=1000, messages=[{ "role": "user", "content": f"""Please explain the trading concept '{concept}' in a clear, educational way. Structure your response as follows: 1. Basic Definition 2. How it Works 3. Key Characteristics 4. When to Look for It 5. Trading Implications 6. Common Mistakes to Avoid 7. Real-World Example If relevant, describe what a typical chart pattern for this concept looks like. Include any important formulas or calculations if applicable. Please make this explanation suitable for beginners while also including enough depth for intermediate traders.""" }] ) return message.content[0].text except Exception as e: st.error(f"Error in getting educational content: {str(e)}") return None def extract_stock_info(analysis_text): """Extract stock name and other metadata from analysis text""" # This is a simple implementation - can be made more sophisticated stock_name = "Unknown" try: # Look for common stock name patterns if "analyzing" in analysis_text.lower(): words = analysis_text.split() for i, word in enumerate(words): if word.lower() == "analyzing": stock_name = words[i + 1].strip("(),.:") except: pass return stock_name def save_chat_history(chat_history, image_data=None, filename=None): """Saves chat history and associated image to JSON and image files""" if not os.path.exists("chat_histories"): os.makedirs("chat_histories") if not os.path.exists("chat_images"): os.makedirs("chat_images") timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") # Get the stock name from the latest analysis stock_name = "Unknown" if chat_history: latest_analysis = chat_history[-1]['analysis'] stock_name = extract_stock_info(latest_analysis) # Create filename with metadata if filename: base_filename = filename else: base_filename = f"{stock_name}_{timestamp}" # Save image if provided image_filename = None if image_data: image_filename = f"{base_filename}.jpg" image_path = os.path.join("chat_images", image_filename) with open(image_path, "wb") as f: f.write(image_data) # Add metadata to chat history chat_data = { 'metadata': { 'stock_name': stock_name, 'date_created': timestamp, 'image_file': image_filename }, 'conversations': chat_history } # Save chat history json_filename = f"{base_filename}.json" filepath = os.path.join("chat_histories", json_filename) with open(filepath, "w") as f: json.dump(chat_data, f) return json_filename def load_chat_history(filename): """Loads chat history and associated image""" filepath = os.path.join("chat_histories", filename) with open(filepath, "r") as f: chat_data = json.load(f) # Load associated image if it exists image_data = None if chat_data.get('metadata', {}).get('image_file'): image_path = os.path.join("chat_images", chat_data['metadata']['image_file']) if os.path.exists(image_path): with open(image_path, "rb") as f: image_data = f.read() return chat_data, image_data def main(): st.set_page_config( page_title="Stock Chart Assistant", layout="wide", initial_sidebar_state="expanded" ) # Initialize Anthropic client client = initialize_anthropic_client() if not client: st.error("Failed to initialize Anthropic client. Please check your API key configuration.") return # Initialize session state if 'chat_history' not in st.session_state: st.session_state.chat_history = [] if 'current_image' not in st.session_state: st.session_state.current_image = None if 'current_analysis' not in st.session_state: st.session_state.current_analysis = None # Tab selection tab1, tab2 = st.tabs(["Chart Analysis", "Learn Trading"]) with tab1: # Initialize variables uploaded_file = None screenshot_taken = False # Sidebar with st.sidebar: st.title("🚀 Chart Analysis AI") upload_option = st.radio( "Choose input method:", ("Upload Image", "Take Screenshot", "Ask Question"), key="analysis_upload_option" # Added unique key ) # File uploader if upload_option == "Upload Image": uploaded_file = st.file_uploader("Upload your chart", type=["png", "jpg", "jpeg"], key="analysis_file_uploader") if uploaded_file: st.session_state.current_image = uploaded_file.getvalue() elif upload_option == "Take Screenshot": if st.button("Take Screenshot", key="analysis_screenshot_button"): st.info("Feature coming soon! For now, please use the Upload Image option.") screenshot_taken = False # Analysis Options st.subheader("Analysis Options") patterns = st.multiselect( "Patterns to Look For", ["Double Top/Bottom", "Head and Shoulders", "Triangle", "Flag", "Wedge", "Channel", "Support/Resistance"], key="analysis_patterns" ) indicators = st.multiselect( "Technical Indicators", ["Moving Averages", "RSI", "MACD", "Bollinger Bands", "Volume", "Stochastic", "ADX"], key="analysis_indicators" ) # Main content area st.title("📈 Stock Chart Analysis Assistant") # Create two columns for layout col1, col2 = st.columns([2, 1]) with col1: if upload_option == "Ask Question": user_question = st.text_input("What would you like to know about your chart?") # Display uploaded image if uploaded_file is not None: st.image(uploaded_file, caption="Uploaded Chart", use_container_width=True) # Continue chat section if st.session_state.current_analysis: st.subheader("Continue Analysis") follow_up = st.text_input("Ask a follow-up question about this chart:") if st.button("Send Follow-up"): if follow_up: with st.spinner("Analyzing..."): follow_up_response = continue_analysis_with_claude( client, follow_up, st.session_state.current_analysis, st.session_state.current_image ) if follow_up_response: st.write(follow_up_response) # Add to chat history st.session_state.chat_history.append({ 'timestamp': datetime.now().isoformat(), 'question': follow_up, 'analysis': follow_up_response }) if st.button("Analyze"): if upload_option == "Ask Question" and user_question: st.info("Question-based analysis feature coming soon!") elif uploaded_file is None and not screenshot_taken: st.warning("Please upload an image or take a screenshot first.") else: with st.spinner("Analyzing chart..."): # Generate prompt prompt = create_prompt_template(patterns, indicators) if uploaded_file: # Process image and get analysis analysis_result, chart_type = analyze_chart_with_claude( client, uploaded_file.getvalue(), prompt ) if analysis_result: # Store current analysis st.session_state.current_analysis = analysis_result # Add to chat history st.session_state.chat_history.append({ 'timestamp': datetime.now().isoformat(), 'chart_type': chart_type, 'analysis': analysis_result }) # Display analysis st.subheader("Analysis Results") st.write(analysis_result) # Risk warning st.warning( "⚠️ This analysis is AI-generated and for informational purposes only. " "Do not make trading decisions solely based on this information." ) with col2: st.subheader("Chat History") # Display chat history for chat in st.session_state.chat_history: timestamp = datetime.fromisoformat(chat['timestamp']).strftime("%Y-%m-%d %H:%M") with st.expander(f"Analysis from {timestamp}"): st.write(chat['analysis']) if 'question' in chat: st.write(f"Follow-up: {chat['question']}") # Save chat options save_name = st.text_input("Save chat as (optional):", key="save_chat_name") if st.button("Save Chat", key="save_chat_button"): if st.session_state.chat_history: filename = save_chat_history( st.session_state.chat_history, st.session_state.current_image, f"{save_name}.json" if save_name else None ) st.success(f"Chat saved as {filename}") else: st.warning("No chat history to save.") with tab2: st.title("📚 Learn Trading") # Search or select trading concept concept = st.text_input("Enter a trading concept you'd like to learn about (e.g., 'evening star pattern', 'RSI', 'MACD'):", key="learn_concept") if st.button("Learn", key="learn_button"): if concept: with st.spinner("Getting educational content..."): education_content = get_trading_education(client, concept) if education_content: st.markdown(education_content) if __name__ == "__main__": main() # Initialize variables uploaded_file = None screenshot_taken = False # Sidebar with st.sidebar: st.title("🚀 Chart Analysis AI") upload_option = st.radio( "Choose input method:", ("Upload Image", "Take Screenshot", "Ask Question") ) # File uploader if upload_option == "Upload Image": uploaded_file = st.file_uploader("Upload your chart", type=["png", "jpg", "jpeg"]) elif upload_option == "Take Screenshot": if st.button("Take Screenshot", key="screenshot"): st.info("Feature coming soon! For now, please use the Upload Image option.") screenshot_taken = False # Analysis Options st.subheader("Analysis Options") patterns = st.multiselect( "Patterns to Look For", ["Double Top/Bottom", "Head and Shoulders", "Triangle", "Flag", "Wedge", "Channel", "Support/Resistance"] ) indicators = st.multiselect( "Technical Indicators", ["Moving Averages", "RSI", "MACD", "Bollinger Bands", "Volume", "Stochastic", "ADX"] ) # Main content area st.title("📈 Stock Chart Analysis Assistant") # Create two columns for layout col1, col2 = st.columns([2, 1]) with col1: if upload_option == "Ask Question": user_question = st.text_input("What would you like to know about your chart?") # Display uploaded image if uploaded_file is not None: st.image(uploaded_file, caption="Uploaded Chart", use_container_width=True) if st.button("Analyze"): if upload_option == "Ask Question" and user_question: st.info("Question-based analysis feature coming soon!") elif uploaded_file is None and not screenshot_taken: st.warning("Please upload an image or take a screenshot first.") else: with st.spinner("Analyzing chart..."): # Generate prompt prompt = create_prompt_template(patterns, indicators) if uploaded_file: # Process image and get analysis analysis_result, chart_type = analyze_chart_with_claude( client, uploaded_file.getvalue(), prompt ) if analysis_result: # Add to chat history st.session_state.chat_history.append({ 'timestamp': datetime.now().isoformat(), 'chart_type': chart_type, 'analysis': analysis_result }) # Display analysis st.subheader("Analysis Results") st.write(analysis_result) # Risk warning st.warning( "⚠️ This analysis is AI-generated and for informational purposes only. " "Do not make trading decisions solely based on this information." ) with col2: st.subheader("Chat History") # Display chat history for chat in st.session_state.chat_history: with st.expander(f"Analysis from {chat['timestamp'][:16]}"): st.write(chat['analysis']) # Save chat options save_name = st.text_input("Save chat as (optional):") if st.button("Save Chat"): if st.session_state.chat_history: filename = save_chat_history( st.session_state.chat_history, f"{save_name}.json" if save_name else None ) st.success(f"Chat saved as {filename}") else: st.warning("No chat history to save.") if __name__ == "__main__": main()