TradingView MCP Server

Instructions

Args:
	exchange: Exchange name like KUCOIN, BINANCE, BYBIT, etc.
	timeframe: One of 5m, 15m, 1h, 4h, 1D, 1W, 1M
	volume_multiplier: How many times the volume should be above normal level (default 2.0)
	price_change_min: Minimum price change percentage (default 3.0)
	limit: Number of rows to return (max 50)

Implementation Reference

• src/tradingview_mcp/server.py:996-1093 (handler)

  The handler function for the 'volume_breakout_scanner' tool. It detects coins with significant volume increases relative to average (using volume.SMA20 or estimate) combined with minimum price change thresholds. Processes symbols in batches using tradingview_ta.get_multiple_analysis, calculates volume_ratio and price_change, filters matches, and returns sorted list of breakouts with indicators.

  @mcp.tool()
  def volume_breakout_scanner(exchange: str = "KUCOIN", timeframe: str = "15m", volume_multiplier: float = 2.0, price_change_min: float = 3.0, limit: int = 25) -> list[dict]:
  	"""Detect coins with volume breakout + price breakout.
  	
  	Args:
  		exchange: Exchange name like KUCOIN, BINANCE, BYBIT, etc.
  		timeframe: One of 5m, 15m, 1h, 4h, 1D, 1W, 1M
  		volume_multiplier: How many times the volume should be above normal level (default 2.0)
  		price_change_min: Minimum price change percentage (default 3.0)
  		limit: Number of rows to return (max 50)
  	"""
  	exchange = sanitize_exchange(exchange, "KUCOIN")
  	timeframe = sanitize_timeframe(timeframe, "15m")
  	volume_multiplier = max(1.5, min(10.0, volume_multiplier))
  	price_change_min = max(1.0, min(20.0, price_change_min))
  	limit = max(1, min(limit, 50))
  	
  	# Get symbols
  	symbols = load_symbols(exchange)
  	if not symbols:
  		return []
  	
  	screener = EXCHANGE_SCREENER.get(exchange, "crypto")
  	volume_breakouts = []
  	
  	# Process in batches
  	batch_size = 100
  	for i in range(0, min(len(symbols), 500), batch_size):  # Limit to 500 symbols for performance
  		batch_symbols = symbols[i:i + batch_size]
  		
  		try:
  			analysis = get_multiple_analysis(screener=screener, interval=timeframe, symbols=batch_symbols)
  		except Exception:
  			continue
  			
  		for symbol, data in analysis.items():
  			try:
  				if not data or not hasattr(data, 'indicators'):
  					continue
  					
  				indicators = data.indicators
  				
  				# Get required data
  				volume = indicators.get('volume', 0)
  				close = indicators.get('close', 0)
  				open_price = indicators.get('open', 0)
  				sma20_volume = indicators.get('volume.SMA20', 0)  # 20-period volume average
  				
  				if not all([volume, close, open_price]) or volume <= 0:
  					continue
  				
  				# Calculate price change %
  				price_change = ((close - open_price) / open_price) * 100 if open_price > 0 else 0
  				
  				# Volume ratio calculation
  				# If SMA20 volume not available, use a simple heuristic
  				if sma20_volume and sma20_volume > 0:
  					volume_ratio = volume / sma20_volume
  				else:
  					# Estimate average volume as current volume / 2 (conservative)
  					avg_volume_estimate = volume / 2
  					volume_ratio = volume / avg_volume_estimate if avg_volume_estimate > 0 else 1
  				
  				# Check conditions
  				if (abs(price_change) >= price_change_min and 
  					volume_ratio >= volume_multiplier):
  					
  					# Get additional indicators
  					rsi = indicators.get('RSI', 50)
  					bb_upper = indicators.get('BB.upper', 0)
  					bb_lower = indicators.get('BB.lower', 0)
  					
  					# Volume strength score
  					volume_strength = min(10, volume_ratio)  # Cap at 10x
  					
  					volume_breakouts.append({
  						"symbol": symbol,
  						"changePercent": price_change,
  						"volume_ratio": round(volume_ratio, 2),
  						"volume_strength": round(volume_strength, 1),
  						"current_volume": volume,
  						"breakout_type": "bullish" if price_change > 0 else "bearish",
  						"indicators": {
  							"close": close,
  							"RSI": rsi,
  							"BB_upper": bb_upper,
  							"BB_lower": bb_lower,
  							"volume": volume
  						}
  					})
  					
  			except Exception:
  				continue
  	
  	# Sort by volume strength first, then by price change
  	volume_breakouts.sort(key=lambda x: (x["volume_strength"], abs(x["changePercent"])), reverse=True)
  	
  	return volume_breakouts[:limit]