""" Conversational Trading Agent This agent can: 1. Have normal conversations with users 2. Update trading strategies when user provides specific instructions Uses MiniMax extended thinking API for proper thinking/reasoning separation. """ import json import re import requests from typing import List, Optional, Dict, Any from ...core.config import get_settings from ...db.models import Bot SYSTEM_PROMPT = """You are a helpful AI trading assistant named Randebu. You help users manage their trading bots. Your response must be valid JSON with exactly this structure: { "thinking": "Your internal reasoning and analysis (what you're thinking about)", "response": "Your actual response to the user (be concise and helpful)", "strategy_update": null or { "conditions": [{"type": "price_drop" | "price_rise" | "volume_spike" | "price_level", "token": "TOKEN_SYMBOL", "token_address": null, "threshold": number, ...}], "actions": [{"type": "buy" | "sell" | "hold", "amount_percent": number, ...}], "risk_management": {"stop_loss_percent": number, "take_profit_percent": number} } } Guidelines: - "thinking" should be detailed reasoning about the user's request - "response" should be conversational and clear - "strategy_update" should be populated ONLY when the user provides specific trading parameters (percentages, tokens, conditions, etc.) - IMPORTANT: When a token is mentioned, set "token_address": null and ask user to confirm the token address before saving. Your response should say something like: "I need to confirm the token address. Could you provide the contract address for [TOKEN]?" - If no strategy parameters are provided, set "strategy_update" to null - Be friendly, concise, and helpful in your response Example 1 (no strategy update): User: "What can this bot do?" { "thinking": "The user is asking about the bot's capabilities. I should explain the main features.", "response": "Randebu is your AI trading assistant! It can monitor cryptocurrency prices and execute trades based on your configured strategies. Tell me your trading parameters and I'll set them up for you.", "strategy_update": null } Example 2 (token needs confirmation): User: "I want to buy PEPE when it drops 10%" { "thinking": "User wants to buy PEPE. I need the token contract address to proceed. I should ask for confirmation.", "response": "I'd be happy to set up a buy order for PEPE! However, I need to confirm the token contract address. Could you provide the BSC contract address for PEPE? (It usually starts with 0x...)", "strategy_update": { "conditions": [{"type": "price_drop", "token": "PEPE", "token_address": null, "threshold": 10}], "actions": [{"type": "buy", "amount_percent": 100}], "risk_management": null } } Example 3 (with token address provided by user): User: "Buy 0x6982508145454Ce125dDE157d8d64a26D53f60a2 when it drops 10%" { "thinking": "User provided a contract address, I can use it directly.", "response": "Perfect! I've configured your strategy to buy the token when it drops 10%.", "strategy_update": { "conditions": [{"type": "price_drop", "token": "TOKEN", "token_address": "0x6982508145454Ce125dDE157d8d64a26D53f60a2", "threshold": 10}], "actions": [{"type": "buy", "amount_percent": 100}], "risk_management": null } }""" class ConversationalAgent: def __init__(self, api_key: str, model: str = "MiniMax-M2.7", bot_id: str = None): self.api_key = api_key self.model = model self.bot_id = bot_id # Extended thinking endpoint self.thinking_endpoint = "https://api.minimax.io/v1/text/chatcompletion_v2" def chat(self, user_message: str, conversation_history: List[Dict] = None) -> Dict[str, Any]: """Process a user message and return a structured response. Args: user_message: The user's message conversation_history: Optional list of previous messages Returns: Dict with 'response', 'thinking', and 'strategy_updated' """ try: # Build messages array with system prompt and conversation history messages = [{"role": "system", "content": SYSTEM_PROMPT}] # Add conversation history (last 10 messages) if conversation_history: for msg in conversation_history[-10:]: role = "assistant" if msg.get("role") == "assistant" else "user" messages.append({"role": role, "content": msg.get("content", "")}) # Add current user message messages.append({"role": "user", "content": user_message}) # Make API call to extended thinking endpoint resp = requests.post( self.thinking_endpoint, headers={ "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json" }, json={ "model": self.model, "messages": messages, "temperature": 0.7, "max_tokens": 2000, "thinking": { "type": "human", "budget_tokens": 1500 } } ) result = resp.json() # Extract thinking from reasoning_content thinking = None if "choices" in result and len(result["choices"]) > 0: choice = result["choices"][0] if "message" in choice: thinking = choice["message"].get("reasoning_content") # Get the main response content content = result.get("choices", [{}])[0].get("message", {}).get("content", "") # Parse JSON from the content thinking_field = None response_text = content strategy_update = None # Try to extract JSON from the content json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', content, re.DOTALL) if json_match: json_str = json_match.group(1) else: # Try to find JSON object directly json_match = re.search(r'\{.*\}', content, re.DOTALL) if json_match: json_str = json_match.group(0) else: json_str = None if json_str: try: parsed = json.loads(json_str) thinking_field = parsed.get("thinking", "") response_text = parsed.get("response", content) strategy_update = parsed.get("strategy_update") except json.JSONDecodeError: pass # Use defaults # Use the native thinking from API if available, otherwise use parsed thinking final_thinking = thinking or thinking_field # Check if token_address is missing in strategy_update strategy_needs_confirmation = False token_search_results = None if strategy_update: # Extract token name from conditions token_name = None for cond in strategy_update.get("conditions", []): if not cond.get("token_address") and cond.get("token"): token_name = cond.get("token") strategy_needs_confirmation = True break # Search for token if name is found if strategy_needs_confirmation and token_name: try: from ..ave.client import AveCloudClient from ...core.config import get_settings settings = get_settings() ave_client = AveCloudClient( api_key=settings.AVE_API_KEY, plan=settings.AVE_API_PLAN ) # Run async search in sync context import asyncio tokens = asyncio.run(ave_client.get_tokens(query=token_name, chain="bsc", limit=5)) if tokens: token_search_results = [ { "symbol": t.get("symbol", ""), "name": t.get("name", ""), "address": t.get("id") or t.get("contract_address", ""), "chain": t.get("chain", "bsc") } for t in tokens ] except Exception as e: print(f"Token search error: {e}") # Only update strategy if token_address is provided if strategy_update and strategy_needs_confirmation: # Don't auto-save - user needs to confirm token address # Return response but with strategy_update as None return { "response": response_text, "thinking": final_thinking, "strategy_updated": False, "strategy_needs_confirmation": True, "strategy_data": strategy_update, "token_search_results": token_search_results, "success": True } # Update strategy in database if provided if strategy_update and self.bot_id: self._update_strategy(strategy_update) return { "response": response_text, "thinking": final_thinking, "strategy_updated": strategy_update is not None, "strategy_needs_confirmation": False, "success": True } except Exception as e: return { "response": f"I encountered an error: {str(e)}. Please try again.", "thinking": None, "strategy_updated": False, "success": False } def _update_strategy(self, strategy_update: Dict) -> bool: """Update the bot's strategy in the database.""" try: from ...core.database import get_db db = next(get_db()) bot = db.query(Bot).filter(Bot.id == self.bot_id).first() if bot: bot.strategy_config = strategy_update db.commit() return True except Exception as e: print(f"Error updating strategy: {e}") return False def get_conversational_agent(api_key: str = None, model: str = None, bot_id: str = None) -> ConversationalAgent: """Get or create a ConversationalAgent instance.""" if api_key is None: settings = get_settings() api_key = settings.MINIMAX_API_KEY if model is None: settings = get_settings() model = settings.MINIMAX_MODEL return ConversationalAgent(api_key=api_key, model=model, bot_id=bot_id)