Yahoo Finance MCP Server

import base64 import io import numpy as np import pandas as pd from mcp.types import ImageContent from yfmcp.types import ChartType def _calculate_volume_profile(df: pd.DataFrame, bins: int = 50) -> pd.Series: """Calculate volume profile by distributing volume across price levels.""" price_min = df["Low"].min() price_max = df["High"].max() # Create price bins price_bins = np.linspace(price_min, price_max, bins + 1) price_centers = (price_bins[:-1] + price_bins[1:]) / 2 # Initialize volume profile volume_profile = pd.Series(0.0, index=price_centers) # Distribute volume for each bar based on price range # Use itertuples() instead of iterrows() for better performance (~14x faster) for row in df.itertuples(): low = row.Low high = row.High volume = row.Volume # Find bins that this bar overlaps with overlapping_bins = (price_centers >= low) & (price_centers <= high) if overlapping_bins.any(): # Distribute volume proportionally based on overlap # Simple approach: distribute evenly across overlapping bins num_bins = overlapping_bins.sum() if num_bins > 0: volume_per_bin = volume / num_bins volume_profile[overlapping_bins] += volume_per_bin return volume_profile def generate_chart(symbol: str, df: pd.DataFrame, chart_type: ChartType) -> ImageContent | str: """Generate a financial chart using mplfinance. Shows candlestick price data with volume, optionally with VWAP or volume profile. Returns base64-encoded WebP image for efficient token usage. """ import matplotlib matplotlib.use("Agg") # Use non-interactive backend import matplotlib.cm as cm import matplotlib.pyplot as plt import mplfinance as mpf # Prepare data for mplfinance (needs OHLCV columns) # Ensure column names match what mplfinance expects df = df[["Open", "High", "Low", "Close", "Volume"]] # Handle volume profile separately as it needs custom layout if chart_type == "volume_profile": # Calculate volume profile volume_profile = _calculate_volume_profile(df) # Create a custom figure with proper layout for side-by-side charts fig = plt.figure(figsize=(18, 10)) # Create gridspec for layout: left side for candlestick+volume, right side for volume profile gs = fig.add_gridspec( 2, 2, width_ratios=[3.5, 1], height_ratios=[3, 1], hspace=0.3, wspace=0.15, left=0.08, right=0.95, top=0.95, bottom=0.1, ) # Left side: candlestick chart (top) and volume bars (bottom) ax_price = fig.add_subplot(gs[0, 0]) ax_volume = fig.add_subplot(gs[1, 0], sharex=ax_price) # Right side: volume profile (aligned with price chart) ax_profile = fig.add_subplot(gs[0, 1], sharey=ax_price) # Plot candlestick and volume using mplfinance on our custom axes style = mpf.make_mpf_style(base_mpf_style="yahoo", rc={"figure.facecolor": "white"}) mpf.plot( df, type="candle", volume=ax_volume, style=style, ax=ax_price, show_nontrading=False, returnfig=False, ) # Plot volume profile as horizontal bars on the right viridis = cm.get_cmap("viridis") colors = viridis(np.linspace(0, 1, len(volume_profile))) ax_profile.barh(volume_profile.index, volume_profile.values, color=colors, alpha=0.7) ax_profile.set_xlabel("Volume", fontsize=10) ax_profile.set_title("Volume Profile", fontsize=12, fontweight="bold", pad=10) ax_profile.grid(True, alpha=0.3, axis="x") ax_profile.set_ylabel("") # Share y-axis label with main chart # Set overall title fig.suptitle(f"{symbol} - Volume Profile", fontsize=16, fontweight="bold", y=0.98) # Save directly to WebP format buf = io.BytesIO() fig.savefig(buf, format="webp", dpi=150, bbox_inches="tight") buf.seek(0) plt.close(fig) else: # Standard mplfinance chart (price_volume or vwap) addplots = [] if chart_type == "vwap": # VWAP = Sum(Price * Volume) / Sum(Volume) typical_price = (df["High"] + df["Low"] + df["Close"]) / 3 vwap = (typical_price * df["Volume"]).cumsum() / df["Volume"].cumsum() addplots.append(mpf.make_addplot(vwap, color="orange", width=2, linestyle="--", label="VWAP")) # Create style style = mpf.make_mpf_style(base_mpf_style="yahoo", rc={"figure.facecolor": "white"}) # Save chart directly to WebP format buf = io.BytesIO() plot_kwargs = { "type": "candle", "volume": True, "style": style, "title": f"{symbol} - {chart_type.replace('_', ' ').title()}", "ylabel": "Price", "ylabel_lower": "Volume", "savefig": {"fname": buf, "format": "webp", "dpi": 150, "bbox_inches": "tight"}, "show_nontrading": False, "returnfig": False, } if addplots: plot_kwargs["addplot"] = addplots mpf.plot(df, **plot_kwargs) buf.seek(0) return ImageContent( type="image", data=base64.b64encode(buf.read()).decode("utf-8"), mimeType="image/webp", )