feat: implement stop-loss and take-profit in backtest and simulate engines

LLM was outputting nested params structure but engines expect flat fields.
This caused backtesting and simulation to never trigger any trades.

Changes:
- llm_connector.py: Update prompt to output flat condition structure
- crew.py: Update StrategyValidator to validate flat structure
- crew.py: Update StrategyExplainer to read flat structure

Fixes #25

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

@@ -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}"
                         )

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

@@ -61,9 +61,9 @@ 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 +71,17 @@ 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/backtest/engine.py

@@ -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

@@ -20,6 +20,9 @@ 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 +32,12 @@ 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]] = []
 
     async def run(self) -> Dict[str, Any]:
         self.running = True
@@ -94,11 +103,70 @@ 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 +214,32 @@ 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,