feat: klines-based simulation instead of polling

- Fetch historical klines once from AVE API (10 CU per request)
- Process each candle as a time step
- Limit to 500 candles max per simulation
- No continuous polling - processes all data in seconds
- Frontend now selects kline interval (1m, 5m, 15m, 1h)
- Much more efficient API usage

src/backend/app/api/simulate.py

@@ -145,7 +145,7 @@ async def start_simulation(
         "bot_id": bot_id,
         "token": config.token,
         "chain": config.chain,
-        "check_interval": config.check_interval,
+        "kline_interval": config.kline_interval,
         "auto_execute": False,  # Always paper trade
         "strategy_config": bot.strategy_config,
         "ave_api_key": settings.AVE_API_KEY,
@@ -160,7 +160,7 @@ async def start_simulation(
         config={
             "token": config.token,
             "chain": config.chain,
-            "check_interval": config.check_interval,
+            "kline_interval": config.kline_interval,
         },
         signals=[],
     )

src/backend/app/db/schemas.py

@@ -100,7 +100,7 @@ class BacktestResponse(BaseModel):
 class SimulationCreate(BaseModel):
     token: str
     chain: str
-    check_interval: int = 60
+    kline_interval: str = "1m"
 
     @field_validator("chain")
     @classmethod

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

@@ -3,6 +3,7 @@ import asyncio
 import logging
 from datetime import datetime
 from typing import Dict, Any, List, Optional
+
 from ..ave.client import AveCloudClient
 
 logger = logging.getLogger(__name__)
@@ -26,23 +27,38 @@ class SimulateEngine:
         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)
+        # Kline-based settings
+        self.kline_interval = config.get("kline_interval", "1m")
+        self.max_candles = config.get("max_candles", 500)  # Limit candles to process
+        
         self.auto_execute = config.get("auto_execute", False)
         self.token = config.get("token", "")
         self.chain = config.get("chain", "bsc")
         self.running = False
         self.started_at: Optional[datetime] = None
-        self.last_price: Optional[float] = None
+        
+        # Price tracking (for conditions)
+        self.last_close: Optional[float] = None
         self.last_volume: Optional[float] = None
+        
+        # Position tracking (for risk management)
         self.position: float = 0.0
         self.position_token: str = ""
         self.entry_price: Optional[float] = None
         self.entry_time: Optional[int] = None
+        
+        # Portfolio
         self.current_balance: float = config.get("initial_balance", 10000.0)
         self.trades: List[Dict[str, Any]] = []
+        
+        # Error tracking
         self.errors: List[str] = []
         
+        # Kline data
+        self.klines: List[Dict[str, Any]] = []
+        self.last_processed_time: Optional[int] = None
+
     async def run(self) -> Dict[str, Any]:
         self.running = True
         self.status = "running"
@@ -59,71 +75,113 @@ class SimulateEngine:
             self.results = {"error": "Token ID is required"}
             return self.results
 
-        # Run forever until stopped (no end_time limit)
         try:
-            while self.running:
+            # Step 1: Fetch klines (only once for simulation)
-                try:
+            self.klines = await self._fetch_klines(token_id)
-                    price_data = await self.ave_client.get_token_price(token_id)
-                    if price_data:
-                        current_price = float(price_data.get("price", 0))
-                        current_volume = float(price_data.get("volume", 0))
             
-                        if current_price > 0:
+            if not self.klines:
-                            # Only check conditions if we have a previous price to compare
+                self.status = "failed"
-                            if self.last_price is not None:
+                self.results = {"error": "No kline data available"}
-                                await self._check_conditions(
+                return self.results
-                                    current_price, current_volume, price_data
-                                )
-                            # Update last price AFTER checking (so next iteration has comparison data)
-                            self.last_price = current_price
-                            self.last_volume = current_volume
             
-                except Exception as e:
+            logger.info(f"Fetched {len(self.klines)} klines for {token_id}")
-                    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):
+            # Step 2: Process candles (with limit)
+            candles_processed = 0
+            for candle in self.klines:
                 if not self.running:
                     break
-                    await asyncio.sleep(1)
+                if candles_processed >= self.max_candles:
+                    logger.info(f"Reached max candles limit ({self.max_candles})")
+                    break
                 
-            # Simulation was stopped
+                candle_time = int(candle.get("time", 0))
-            self.status = "stopped"
+                
+                # Get OHLCV data from candle
+                close_price = float(candle.get("close", 0))
+                volume = float(candle.get("volume", 0))
+                
+                if close_price > 0:
+                    # Process candle
+                    await self._process_candle(close_price, volume, candle_time)
+                    
+                    # Update last close for next iteration
+                    self.last_close = close_price
+                    self.last_volume = volume
+                    
+                    # Track last processed time
+                    self.last_processed_time = candle_time
+                
+                candles_processed += 1
+            
+            self.status = "completed"
 
         except Exception as e:
+            logger.error(f"Simulation error: {e}")
             self.status = "failed"
             self.results = {"error": str(e)}
+            self.errors.append(str(e))
 
         self.results = self.results or {}
         self.results["total_signals"] = len(self.signals)
+        self.results["total_trades"] = len(self.trades)
         self.results["total_errors"] = len(self.errors)
         self.results["errors"] = self.errors
         self.results["signals"] = self.signals
+        self.results["candles_processed"] = candles_processed if self.running else 0
         self.results["started_at"] = self.started_at
         self.results["ended_at"] = datetime.utcnow()
 
         return self.results
 
-    async def _check_conditions(
+    async def _fetch_klines(
-        self, current_price: float, current_volume: float, price_data: Dict[str, Any]
+        self, 
+        token_id: str, 
+        limit: int = 500
+    ) -> List[Dict[str, Any]]:
+        """Fetch klines from AVE API."""
+        try:
+            klines = await self.ave_client.get_klines(
+                token_id,
+                interval=self.kline_interval,
+                limit=limit
+            )
+            
+            # Sort by time ascending (oldest first)
+            klines = sorted(klines, key=lambda x: x.get("time", 0))
+            return klines
+            
+        except Exception as e:
+            logger.warning(f"Failed to fetch klines for {token_id}: {e}")
+            self.errors.append(f"Kline fetch failed: {str(e)}")
+            return []
+
+    async def _process_candle(
+        self, 
+        close_price: float, 
+        volume: float, 
+        timestamp: int
     ):
-        timestamp = int(datetime.utcnow().timestamp() * 1000)
+        """Process a single candle - check conditions and risk management."""
         
+        # Check risk management first (for open positions)
         if self.position > 0 and self.entry_price is not None:
-            exit_info = self._check_risk_management(current_price, timestamp)
+            exit_info = self._check_risk_management(close_price, timestamp)
             if exit_info:
-                await self._execute_risk_exit(current_price, timestamp, exit_info)
+                await self._execute_risk_exit(close_price, timestamp, exit_info)
-                return
+                return  # Skip condition check if we just exited
 
+        # Check conditions (only if no open position)
+        if self.position == 0:
             for condition in self.conditions:
-            if self._check_condition(condition, current_price, current_volume):
+                if self._check_condition(condition, close_price, volume):
-                await self._execute_actions(current_price, timestamp, condition)
+                    await self._execute_actions(close_price, timestamp, condition)
                     break
 
     def _check_risk_management(
         self, current_price: float, timestamp: int
     ) -> Optional[Dict[str, Any]]:
+        """Check if stop loss or take profit is triggered."""
         if self.position <= 0 or self.entry_price is None:
             return None
 
@@ -142,6 +200,7 @@ class SimulateEngine:
     async def _execute_risk_exit(
         self, price: float, timestamp: int, exit_info: Dict[str, Any]
     ):
+        """Execute stop loss or take profit."""
         if self.position <= 0:
             return
 
@@ -180,32 +239,34 @@ class SimulateEngine:
         current_price: float,
         current_volume: float,
     ) -> bool:
+        """Check if a condition is met based on price movement."""
         cond_type = condition.get("type", "")
         threshold = condition.get("threshold", 0)
-        price_level = condition.get("price")
-        direction = condition.get("direction", "above")
 
         if cond_type == "price_drop":
-            if self.last_price is None or self.last_price <= 0:
+            # Price dropped by threshold % from last close
+            if self.last_close is None or self.last_close <= 0:
                 return False
-            drop_pct = ((self.last_price - current_price) / self.last_price) * 100
+            drop_pct = ((self.last_close - current_price) / self.last_close) * 100
             return drop_pct >= threshold
 
         elif cond_type == "price_rise":
-            if self.last_price is None or self.last_price <= 0:
+            # Price rose by threshold % from last close
+            if self.last_close is None or self.last_close <= 0:
                 return False
-            rise_pct = ((current_price - self.last_price) / self.last_price) * 100
+            rise_pct = ((current_price - self.last_close) / self.last_close) * 100
             return rise_pct >= threshold
 
         elif cond_type == "volume_spike":
+            # Volume increased significantly
             if self.last_volume is None or self.last_volume <= 0:
                 return False
-            volume_increase = (
+            volume_increase = ((current_volume - self.last_volume) / self.last_volume) * 100
-                (current_volume - self.last_volume) / self.last_volume
-            ) * 100
             return volume_increase >= threshold
 
         elif cond_type == "price_level":
+            price_level = condition.get("price")
+            direction = condition.get("direction", "above")
             if price_level is None:
                 return False
             if direction == "above":
@@ -218,6 +279,7 @@ class SimulateEngine:
     async def _execute_actions(
         self, price: float, timestamp: int, matched_condition: Dict[str, Any]
     ):
+        """Execute buy/sell actions based on matched condition."""
         token = matched_condition.get("token", self.token)
         reasoning = f"Condition {matched_condition.get('type')} triggered"
 
@@ -226,18 +288,21 @@ class SimulateEngine:
             if action_type == "buy":
                 amount_percent = action.get("amount_percent", 10)
                 amount = self.current_balance * (amount_percent / 100)
-                self.position += amount / price
+                quantity = amount / price
+                
+                self.position += quantity
                 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,
+                        "quantity": quantity,
                         "timestamp": timestamp,
                     }
                 )
@@ -258,10 +323,12 @@ class SimulateEngine:
             self.signals.append(signal)
 
     def stop(self):
+        """Stop the simulation."""
         self.running = False
         self.status = "stopped"
 
     def get_results(self) -> Dict[str, Any]:
+        """Get simulation results."""
         return {
             "id": self.run_id,
             "status": self.status,
@@ -270,4 +337,5 @@ class SimulateEngine:
         }
 
     def get_signals(self) -> List[Dict[str, Any]]:
+        """Get current signals."""
         return self.signals

src/frontend/src/lib/api/client.ts

@@ -189,7 +189,7 @@ export const api = {
 	},
 
 	simulate: {
-		async start(botId: string, config: { token: string; chain?: string; check_interval: number }): Promise<Simulation> {
+		async start(botId: string, config: { token: string; chain?: string; kline_interval: string }): Promise<Simulation> {
 			const response = await fetch(`${API_URL}/bots/${botId}/simulate`, {
 				method: 'POST',
 				headers: getAuthHeaders(),

src/frontend/src/routes/bot/[id]/simulate/+page.svelte

@@ -9,7 +9,7 @@
 	let botId = $derived($page.params.id);
 	let tokenName = $state('');
 	let tokenAddress = $state('');
-	let intervalSeconds = $state(60);
+	let klineInterval = $state('1m');
 	let isRunning = $state(false);
 
 	onMount(async () => {
@@ -70,7 +70,7 @@
 			const simulation = await api.simulate.start(botId, {
 				token: tokenAddress,
 				chain: 'bsc',
-				check_interval: intervalSeconds
+				kline_interval: klineInterval
 			});
 			setCurrentSimulation(simulation);
 			clearSignals();
@@ -132,11 +132,12 @@
 						</div>
 					</div>
 					<div class="field">
-						<label for="interval">Check Interval</label>
+						<label for="klineInterval">Kline Interval</label>
-						<select id="interval" bind:value={intervalSeconds} disabled={isRunning}>
+						<select id="klineInterval" bind:value={klineInterval} disabled={isRunning}>
-							<option value={10}>Every 10 seconds</option>
+							<option value="1m">1 minute</option>
-							<option value={30}>Every 30 seconds</option>
+							<option value="5m">5 minutes</option>
-							<option value={60}>Every minute</option>
+							<option value="15m">15 minutes</option>
+							<option value="1h">1 hour</option>
 						</select>
 					</div>
 				</div>