feat: implement chat interface with CrewAI integration

- Create MiniMax LLM connector for CrewAI integration
- Implement TradingCrew with trading_designer, strategy_validator, strategy_explainer
- Add strategy parsing from natural language to strategy_config JSON
- Update chat endpoint with CrewAI integration and conversation context
- Add strategy validation logic
- Add explanation generation for user-friendly responses
- Add BotChatRequest/BotChatResponse schemas

Fixes #6

src/backend/app/services/ai_agent/llm_connector.py

@@ -1,13 +1,108 @@
-from typing import Optional
+from typing import Optional, List, Dict, Any
+import httpx
+from crewai import LLM
 
 
-class LLMConnector:
+class MiniMaxLLM(LLM):
+    def __init__(self, api_key: str, model: str = "MiniMax-Text-01", **kwargs):
+        super().__init__(**kwargs)
+        self.api_key = api_key
+        self.model = model
+        self.base_url = "https://api.minimax.chat/v1"
+
+    def _call(self, messages: List[Dict[str, str]], **kwargs) -> str:
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        payload = {
+            "model": self.model,
+            "messages": messages,
+            "temperature": kwargs.get("temperature", 0.7),
+            "max_tokens": kwargs.get("max_tokens", 2048),
+        }
+        with httpx.Client(timeout=60.0) as client:
+            response = client.post(
+                f"{self.base_url}/chat/completions",
+                headers=headers,
+                json=payload,
+            )
+            response.raise_for_status()
+            return response.json()["choices"][0]["message"]["content"]
+
+    def call(self, messages: List[Dict[str, str]], **kwargs) -> str:
+        return self._call(messages, **kwargs)
+
+
+class MiniMaxConnector:
     def __init__(self, api_key: str, model: str = "MiniMax-Text-01"):
         self.api_key = api_key
         self.model = model
 
-    def chat(self, messages: list[dict], **kwargs):
-        raise NotImplementedError("LLM integration not yet implemented")
+    def chat(self, messages: list[dict], **kwargs) -> str:
+        formatted_messages = []
+        for msg in messages:
+            if isinstance(msg, dict):
+                formatted_messages.append(
+                    {
+                        "role": msg.get("role", "user"),
+                        "content": msg.get("content", str(msg)),
+                    }
+                )
+            else:
+                formatted_messages.append({"role": "user", "content": str(msg)})
 
-    def parse_strategy(self, user_message: str) -> dict:
-        raise NotImplementedError("Strategy parsing not yet implemented")
+        llm = MiniMaxLLM(api_key=self.api_key, model=self.model)
+        return llm.call(formatted_messages, **kwargs)
+
+    def parse_strategy(
+        self, user_message: str, conversation_history: list[dict] = None
+    ) -> dict:
+        system_prompt = """You are a trading strategy designer. Parse the user's natural language request into a JSON strategy_config object.
+
+Supported conditions (MVP):
+- price_drop: Token price drops by X% (requires: token, threshold_percent)
+- price_rise: Token price rises by X% (requires: token, threshold_percent)
+- volume_spike: Trading volume increases X% (requires: token, threshold_percent)
+- price_level: Price crosses above/below X (requires: token, price, direction)
+
+Output ONLY valid JSON with this schema:
+{
+    "conditions": [
+        {
+            "type": "price_drop|price_rise|volume_spike|price_level",
+            "params": {
+                "token": "TOKEN_SYMBOL",
+                "threshold_percent": number,  // for price_drop, price_rise, volume_spike
+                "price": number,              // for price_level
+                "direction": "above|below"    // for price_level
+            }
+        }
+    ],
+    "actions": [
+        {
+            "type": "buy|sell|notify",
+            "params": {}
+        }
+    ]
+}
+
+If the user wants a condition not in the supported list, ask for clarification.
+"""
+
+        messages = [{"role": "system", "content": system_prompt}]
+        if conversation_history:
+            for msg in conversation_history:
+                messages.append(
+                    {"role": msg.get("role", "user"), "content": msg.get("content", "")}
+                )
+        messages.append({"role": "user", "content": user_message})
+
+        response = self.chat(messages)
+        try:
+            import json
+
+            result = json.loads(response)
+            return result
+        except json.JSONDecodeError:
+            return {"error": "Failed to parse strategy", "raw_response": response}