fix: properly configure CrewAI LLM with MiniMax api_base

- Use CrewAI's LLM class directly with api_base parameter instead of custom subclass
- Remove broken MiniMaxLLM inheritance from LLM
- Update agent creation to use LLM(model, api_key, api_base) pattern

The issue was that inheriting from CrewAI's LLM class caused the api_base
to be set to None. Now we use CrewAI's LLM directly with the correct parameters.

Fixes #43

src/backend/.env.example

@@ -32,7 +32,7 @@ MINIMAX_API_KEY=your-minimax-api-key
 
 # MiniMax model to use
 # Common options: MiniMax-Text-01, MiniMax-M2.1
-MINIMAX_MODEL=MiniMax-Text-01
+MINIMAX_MODEL=MiniMax-M2.7
 
 # =============================================================================
 # AVE CLOUD API

src/backend/app/api/auth.py

@@ -58,7 +58,7 @@ def get_current_user(
 
 
 @router.post(
-    "/register", response_model=UserResponse, status_code=status.HTTP_201_CREATED
+    "/register", response_model=Token, status_code=status.HTTP_201_CREATED
 )
 def register(user: UserCreate, db: Session = Depends(get_db)):
     existing_user = db.query(User).filter(User.email == user.email).first()
@@ -75,7 +75,10 @@ def register(user: UserCreate, db: Session = Depends(get_db)):
     db.add(db_user)
     db.commit()
     db.refresh(db_user)
-    return db_user
+    
+    # Generate and return access token so frontend can proceed immediately
+    access_token = create_access_token(data={"sub": db_user.id})
+    return Token(access_token=access_token, token_type="bearer")
 
 
 @router.post("/login", response_model=Token)

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

@@ -1,4 +1,4 @@
-from .crew import CrewAgent
-from .llm_connector import LLMConnector
+from .crew import TradingCrew, get_trading_crew
+from .llm_connector import MiniMaxLLM, MiniMaxConnector
 
-__all__ = ["CrewAgent", "LLMConnector"]
+__all__ = ["TradingCrew", "get_trading_crew", "MiniMaxLLM", "MiniMaxConnector"]

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

@@ -1,7 +1,7 @@
 from typing import List, Optional, Dict, Any
-from crewai import Agent, Task, Crew
-from .llm_connector import MiniMaxConnector, MiniMaxLLM
-from ..core.config import get_settings
+from crewai import Agent, Task, Crew, LLM
+from .llm_connector import MiniMaxConnector
+from ...core.config import get_settings
 
 
 class StrategyValidator:
@@ -33,29 +33,24 @@ class StrategyValidator:
                 errors.append(f"Condition {i}: unsupported type '{cond_type}'")
                 continue
 
-            params = condition.get("params", {})
             if cond_type in ["price_drop", "price_rise", "volume_spike"]:
-                if "token" not in params:
+                if "token" not in condition:
                     errors.append(f"Condition {i}: missing 'token'")
-                if "threshold_percent" not in params:
-                    errors.append(f"Condition {i}: missing 'threshold_percent'")
-                elif not isinstance(params["threshold_percent"], (int, float)):
-                    errors.append(
-                        f"Condition {i}: 'threshold_percent' must be a number"
-                    )
-                elif params["threshold_percent"] <= 0:
-                    errors.append(
-                        f"Condition {i}: 'threshold_percent' must be positive"
-                    )
+                if "threshold" not in condition:
+                    errors.append(f"Condition {i}: missing 'threshold'")
+                elif not isinstance(condition["threshold"], (int, float)):
+                    errors.append(f"Condition {i}: 'threshold' must be a number")
+                elif condition["threshold"] <= 0:
+                    errors.append(f"Condition {i}: 'threshold' must be positive")
 
             elif cond_type == "price_level":
-                if "token" not in params:
+                if "token" not in condition:
                     errors.append(f"Condition {i}: missing 'token'")
-                if "price" not in params:
+                if "price" not in condition:
                     errors.append(f"Condition {i}: missing 'price'")
-                if "direction" not in params:
+                if "direction" not in condition:
                     errors.append(f"Condition {i}: missing 'direction'")
-                elif params["direction"] not in ["above", "below"]:
+                elif condition["direction"] not in ["above", "below"]:
                     errors.append(
                         f"Condition {i}: direction must be 'above' or 'below'"
                     )
@@ -85,23 +80,22 @@ class StrategyExplainer:
                 explanations.append("This strategy will trigger when:")
                 for cond in cond_list:
                     cond_type = cond.get("type")
-                    params = cond.get("params", {})
-                    token = params.get("token", "the token")
+                    token = cond.get("token", "the token")
 
                     if cond_type == "price_drop":
-                        pct = params.get("threshold_percent", 0)
+                        pct = cond.get("threshold", 0)
                         explanations.append(f"  - {token} price drops by {pct}%")
                     elif cond_type == "price_rise":
-                        pct = params.get("threshold_percent", 0)
+                        pct = cond.get("threshold", 0)
                         explanations.append(f"  - {token} price rises by {pct}%")
                     elif cond_type == "volume_spike":
-                        pct = params.get("threshold_percent", 0)
+                        pct = cond.get("threshold", 0)
                         explanations.append(
                             f"  - {token} trading volume increases by {pct}%"
                         )
                     elif cond_type == "price_level":
-                        price = params.get("price", 0)
-                        direction = params.get("direction", "unknown")
+                        price = cond.get("price", 0)
+                        direction = cond.get("direction", "unknown")
                         explanations.append(
                             f"  - {token} price crosses {direction} ${price}"
                         )
@@ -126,7 +120,7 @@ class StrategyExplainer:
 
 
 def create_trading_designer_agent(
-    api_key: str, model: str = "MiniMax-Text-01"
+    api_key: str, model: str = "MiniMax-M2.7"
 ) -> Agent:
     connector = MiniMaxConnector(api_key=api_key, model=model)
 
@@ -147,13 +141,13 @@ def create_trading_designer_agent(
         role="Trading Strategy Designer",
         goal="Convert natural language trading requests into precise strategy configurations",
         backstory=system_prompt,
-        llm=MiniMaxLLM(api_key=api_key, model=model),
+        llm=LLM(model=model, api_key=api_key, api_base="https://api.minimax.io/v1"),
         verbose=True,
     )
 
 
 def create_strategy_validator_agent(
-    api_key: str, model: str = "MiniMax-Text-01"
+    api_key: str, model: str = "MiniMax-M2.7"
 ) -> Agent:
     return Agent(
         role="Strategy Validator",
@@ -161,13 +155,13 @@ def create_strategy_validator_agent(
         backstory="""You are a meticulous strategy validator with expertise in trading systems.
         You check that all required parameters are present, values are reasonable, and the 
         strategy makes logical sense. You never approve strategies with missing or invalid data.""",
-        llm=MiniMaxLLM(api_key=api_key, model=model),
+        llm=LLM(model=model, api_key=api_key, api_base="https://api.minimax.io/v1"),
         verbose=True,
     )
 
 
 def create_strategy_explainer_agent(
-    api_key: str, model: str = "MiniMax-Text-01"
+    api_key: str, model: str = "MiniMax-M2.7"
 ) -> Agent:
     return Agent(
         role="Strategy Explainer",
@@ -175,13 +169,13 @@ def create_strategy_explainer_agent(
         backstory="""You are a patient trading strategy explainer. You translate complex 
         strategy configurations into easy-to-understand language. You help users understand 
         exactly what their strategies will do when triggered.""",
-        llm=MiniMaxLLM(api_key=api_key, model=model),
+        llm=LLM(model=model, api_key=api_key, api_base="https://api.minimax.io/v1"),
         verbose=True,
     )
 
 
 class TradingCrew:
-    def __init__(self, api_key: str, model: str = "MiniMax-Text-01"):
+    def __init__(self, api_key: str, model: str = "MiniMax-M2.7"):
         self.api_key = api_key
         self.model = model
         self.validator = StrategyValidator()

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

@@ -1,14 +1,12 @@
 from typing import Optional, List, Dict, Any
 import httpx
-from crewai import LLM
 
 
-class MiniMaxLLM(LLM):
-    def __init__(self, api_key: str, model: str = "MiniMax-Text-01", **kwargs):
-        super().__init__(**kwargs)
+class MiniMaxLLM:
+    def __init__(self, api_key: str, model: str = "MiniMax-M2.7", **kwargs):
         self.api_key = api_key
         self.model = model
-        self.base_url = "https://api.minimax.chat/v1"
+        self.base_url = "https://api.minimax.io/v1"
 
     def _call(self, messages: List[Dict[str, str]], **kwargs) -> str:
         headers = {
@@ -61,9 +59,9 @@ class MiniMaxConnector:
         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_drop: Token price drops by X% (requires: token, threshold)
+- price_rise: Token price rises by X% (requires: token, threshold)
+- volume_spike: Trading volume increases X% (requires: token, threshold)
 - price_level: Price crosses above/below X (requires: token, price, direction)
 
 Output ONLY valid JSON with this schema:
@@ -71,18 +69,17 @@ 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
+            "chain": "bsc",
+            "threshold": number,  // for price_drop, price_rise, volume_spike
             "price": number,     // for price_level
-                "direction": "above|below"    // for price_level
-            }
+            "direction": "above|below",  // for price_level
+            "timeframe": "1h"
         }
     ],
     "actions": [
         {
-            "type": "buy|sell|notify",
-            "params": {}
+            "type": "buy|sell|notify"
         }
     ]
 }

src/backend/app/services/ave/client.py

@@ -90,6 +90,22 @@ class AveCloudClient:
                 return data.get("data", [])
             raise Exception(f"Failed to fetch klines: {data}")
 
+    async def get_token_price(self, token_id: str) -> Optional[Dict[str, Any]]:
+        url = f"{self.DATA_API_URL}/v2/tokens/price"
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                url,
+                headers=self._data_headers(),
+                json={"token_ids": [token_id]},
+                timeout=30.0,
+            )
+            response.raise_for_status()
+            data = response.json()
+            if data.get("status") == 200:
+                prices = data.get("data", {})
+                return prices.get(token_id)
+            return None
+
     async def get_trending_tokens(
         self, chain: Optional[str] = None, limit: int = 20
     ) -> List[Dict[str, Any]]:

src/backend/app/services/backtest/ave_client.py

@@ -1,70 +0,0 @@
-import httpx
-from typing import List, Dict, Any, Optional
-from datetime import datetime
-
-
-class AveCloudClient:
-    BASE_URL = "https://prod.ave-api.com"
-
-    def __init__(self, api_key: str, plan: str = "free"):
-        self.api_key = api_key
-        self.plan = plan
-
-    def _headers(self) -> Dict[str, str]:
-        return {"X-API-KEY": self.api_key}
-
-    async def get_klines(
-        self,
-        token_id: str,
-        interval: str = "1h",
-        limit: int = 100,
-        start_time: Optional[int] = None,
-        end_time: Optional[int] = None,
-    ) -> List[Dict[str, Any]]:
-        url = f"{self.BASE_URL}/v2/klines/token/{token_id}"
-        params = {"interval": interval, "limit": limit}
-        if start_time:
-            params["start_time"] = start_time
-        if end_time:
-            params["end_time"] = end_time
-
-        async with httpx.AsyncClient() as client:
-            response = await client.get(
-                url, headers=self._headers(), params=params, timeout=30.0
-            )
-            response.raise_for_status()
-            data = response.json()
-            if data.get("status") == 200:
-                return data.get("data", [])
-            raise Exception(f"Failed to fetch klines: {data}")
-
-    async def get_token_price(self, token_id: str) -> Optional[Dict[str, Any]]:
-        url = f"{self.BASE_URL}/v2/tokens/price"
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                url,
-                headers=self._headers(),
-                json={"token_ids": [token_id]},
-                timeout=30.0,
-            )
-            response.raise_for_status()
-            data = response.json()
-            if data.get("status") == 200:
-                prices = data.get("data", {})
-                return prices.get(token_id)
-            return None
-
-    async def get_batch_prices(self, token_ids: List[str]) -> Dict[str, Dict[str, Any]]:
-        url = f"{self.BASE_URL}/v2/tokens/price"
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                url,
-                headers=self._headers(),
-                json={"token_ids": token_ids},
-                timeout=30.0,
-            )
-            response.raise_for_status()
-            data = response.json()
-            if data.get("status") == 200:
-                return data.get("data", {})
-            return {}

src/backend/app/services/backtest/engine.py

@@ -2,7 +2,7 @@ import uuid
 import asyncio
 from datetime import datetime
 from typing import Dict, Any, List, Optional
-from .ave_client import AveCloudClient
+from ..ave.client import AveCloudClient
 
 
 class BacktestEngine:
@@ -20,10 +20,15 @@ class BacktestEngine:
         self.strategy_config = config.get("strategy_config", {})
         self.conditions = self.strategy_config.get("conditions", [])
         self.actions = self.strategy_config.get("actions", [])
+        self.risk_management = self.strategy_config.get("risk_management", {})
+        self.stop_loss_percent = self.risk_management.get("stop_loss_percent")
+        self.take_profit_percent = self.risk_management.get("take_profit_percent")
         self.initial_balance = config.get("initial_balance", 10000.0)
         self.current_balance = self.initial_balance
         self.position = 0.0
         self.position_token = ""
+        self.entry_price: Optional[float] = None
+        self.entry_time: Optional[int] = None
         self.trades: List[Dict[str, Any]] = []
         self.running = False
 
@@ -103,11 +108,73 @@ class BacktestEngine:
 
             timestamp = kline.get("timestamp", 0)
 
+            if self.position > 0 and self.entry_price is not None:
+                exit_info = self._check_risk_management(price, timestamp)
+                if exit_info:
+                    await self._execute_risk_exit(price, timestamp, exit_info)
+                    continue
+
             for condition in self.conditions:
                 if self._check_condition(condition, klines, i, price):
                     await self._execute_actions(price, timestamp, condition)
                     break
 
+    def _check_risk_management(
+        self, current_price: float, timestamp: int
+    ) -> Optional[Dict[str, Any]]:
+        if self.position <= 0 or self.entry_price is None:
+            return None
+
+        if self.stop_loss_percent is not None:
+            stop_loss_price = self.entry_price * (1 - self.stop_loss_percent / 100)
+            if current_price <= stop_loss_price:
+                return {"reason": "stop_loss", "price": stop_loss_price}
+
+        if self.take_profit_percent is not None:
+            take_profit_price = self.entry_price * (1 + self.take_profit_percent / 100)
+            if current_price >= take_profit_price:
+                return {"reason": "take_profit", "price": take_profit_price}
+
+        return None
+
+    async def _execute_risk_exit(
+        self, price: float, timestamp: int, exit_info: Dict[str, Any]
+    ):
+        if self.position <= 0:
+            return
+
+        reason = exit_info["reason"]
+        sell_amount = self.position * price
+        self.current_balance += sell_amount
+        self.trades.append(
+            {
+                "type": "sell",
+                "token": self.position_token,
+                "price": price,
+                "amount": sell_amount,
+                "quantity": self.position,
+                "timestamp": timestamp,
+                "exit_reason": reason,
+            }
+        )
+        self.signals.append(
+            {
+                "id": str(uuid.uuid4()),
+                "bot_id": self.bot_id,
+                "run_id": self.run_id,
+                "signal_type": "sell",
+                "token": self.position_token,
+                "price": price,
+                "confidence": 1.0,
+                "reasoning": f"Risk management triggered {reason}",
+                "executed": False,
+                "created_at": datetime.utcnow(),
+            }
+        )
+        self.position = 0
+        self.entry_price = None
+        self.entry_time = None
+
     def _check_condition(
         self,
         condition: Dict[str, Any],
@@ -173,6 +240,8 @@ class BacktestEngine:
                 self.position += amount / price
                 self.current_balance -= amount
                 self.position_token = token
+                self.entry_price = price
+                self.entry_time = timestamp
                 self.trades.append(
                     {
                         "type": "buy",
@@ -209,9 +278,12 @@ class BacktestEngine:
                         "amount": sell_amount,
                         "quantity": self.position,
                         "timestamp": timestamp,
+                        "exit_reason": "manual",
                     }
                 )
                 self.position = 0
+                self.entry_price = None
+                self.entry_time = None
                 self.signals.append(
                     {
                         "id": str(uuid.uuid4()),

src/backend/app/services/simulate/engine.py

@@ -1,8 +1,11 @@
 import uuid
 import asyncio
+import logging
 from datetime import datetime
 from typing import Dict, Any, List, Optional
-from ..backtest.ave_client import AveCloudClient
+from ..ave.client import AveCloudClient
+
+logger = logging.getLogger(__name__)
 
 
 class SimulateEngine:
@@ -20,6 +23,9 @@ class SimulateEngine:
         self.strategy_config = config.get("strategy_config", {})
         self.conditions = self.strategy_config.get("conditions", [])
         self.actions = self.strategy_config.get("actions", [])
+        self.risk_management = self.strategy_config.get("risk_management", {})
+        self.stop_loss_percent = self.risk_management.get("stop_loss_percent")
+        self.take_profit_percent = self.risk_management.get("take_profit_percent")
         self.check_interval = config.get("check_interval", 60)
         self.duration_seconds = config.get("duration_seconds", 3600)
         self.auto_execute = config.get("auto_execute", False)
@@ -29,6 +35,13 @@ class SimulateEngine:
         self.started_at: Optional[datetime] = None
         self.last_price: Optional[float] = None
         self.last_volume: Optional[float] = None
+        self.position: float = 0.0
+        self.position_token: str = ""
+        self.entry_price: Optional[float] = None
+        self.entry_time: Optional[int] = None
+        self.current_balance: float = config.get("initial_balance", 10000.0)
+        self.trades: List[Dict[str, Any]] = []
+        self.errors: List[str] = []
 
     async def run(self) -> Dict[str, Any]:
         self.running = True
@@ -65,7 +78,9 @@ class SimulateEngine:
                         self.last_volume = current_volume
 
                 except Exception as e:
-                    pass
+                    logger.warning(f"Failed to get price for {token_id}: {e}")
+                    self.errors.append(f"Price fetch failed for {token_id}: {str(e)}")
+                    continue
 
                 for _ in range(self.check_interval):
                     if not self.running:
@@ -83,6 +98,8 @@ class SimulateEngine:
 
         self.results = self.results or {}
         self.results["total_signals"] = len(self.signals)
+        self.results["total_errors"] = len(self.errors)
+        self.results["errors"] = self.errors
         self.results["signals"] = self.signals
         self.results["started_at"] = self.started_at
         self.results["ended_at"] = datetime.utcnow()
@@ -94,11 +111,70 @@ class SimulateEngine:
     ):
         timestamp = int(datetime.utcnow().timestamp() * 1000)
 
+        if self.position > 0 and self.entry_price is not None:
+            exit_info = self._check_risk_management(current_price, timestamp)
+            if exit_info:
+                await self._execute_risk_exit(current_price, timestamp, exit_info)
+                return
+
         for condition in self.conditions:
             if self._check_condition(condition, current_price, current_volume):
                 await self._execute_actions(current_price, timestamp, condition)
                 break
 
+    def _check_risk_management(
+        self, current_price: float, timestamp: int
+    ) -> Optional[Dict[str, Any]]:
+        if self.position <= 0 or self.entry_price is None:
+            return None
+
+        if self.stop_loss_percent is not None:
+            stop_loss_price = self.entry_price * (1 - self.stop_loss_percent / 100)
+            if current_price <= stop_loss_price:
+                return {"reason": "stop_loss", "price": stop_loss_price}
+
+        if self.take_profit_percent is not None:
+            take_profit_price = self.entry_price * (1 + self.take_profit_percent / 100)
+            if current_price >= take_profit_price:
+                return {"reason": "take_profit", "price": take_profit_price}
+
+        return None
+
+    async def _execute_risk_exit(
+        self, price: float, timestamp: int, exit_info: Dict[str, Any]
+    ):
+        if self.position <= 0:
+            return
+
+        reason = exit_info["reason"]
+        self.trades.append(
+            {
+                "type": "sell",
+                "token": self.position_token,
+                "price": price,
+                "quantity": self.position,
+                "timestamp": timestamp,
+                "exit_reason": reason,
+            }
+        )
+        self.signals.append(
+            {
+                "id": str(uuid.uuid4()),
+                "bot_id": self.bot_id,
+                "run_id": self.run_id,
+                "signal_type": "sell",
+                "token": self.position_token,
+                "price": price,
+                "confidence": 1.0,
+                "reasoning": f"Risk management triggered {reason}",
+                "executed": self.auto_execute,
+                "created_at": datetime.utcnow(),
+            }
+        )
+        self.position = 0
+        self.entry_price = None
+        self.entry_time = None
+
     def _check_condition(
         self,
         condition: Dict[str, Any],
@@ -146,11 +222,32 @@ class SimulateEngine:
         token = matched_condition.get("token", self.token)
         reasoning = f"Condition {matched_condition.get('type')} triggered"
 
+        for action in self.actions:
+            action_type = action.get("type", "")
+            if action_type == "buy":
+                amount_percent = action.get("amount_percent", 10)
+                amount = self.current_balance * (amount_percent / 100)
+                self.position += amount / price
+                self.position_token = token
+                self.entry_price = price
+                self.entry_time = timestamp
+                self.current_balance -= amount
+                self.trades.append(
+                    {
+                        "type": "buy",
+                        "token": token,
+                        "price": price,
+                        "amount": amount,
+                        "quantity": amount / price,
+                        "timestamp": timestamp,
+                    }
+                )
+
             signal = {
                 "id": str(uuid.uuid4()),
                 "bot_id": self.bot_id,
                 "run_id": self.run_id,
-            "signal_type": "signal",
+                "signal_type": action_type,
                 "token": token,
                 "price": price,
                 "confidence": 0.8,

src/backend/requirements.txt

@@ -6,6 +6,7 @@ pydantic-settings>=2.1.0
 email-validator>=2.0.0
 python-jose[cryptography]>=3.3.0
 passlib[bcrypt]>=1.7.4
+bcrypt>=4.0,<5.0  # Required for passlib compatibility
 crewai>=0.1.0
 anthropic>=0.18.0
 httpx>=0.26.0