├── .gitignore ├── .python-version ├── Dockerfile ├── README.md ├── pyproject.toml ├── smithery.yaml ├── src └── mcp_trader │ ├── __init__.py │ ├── data.py │ ├── indicators.py │ ├── server.py │ └── tests │ └── test_relative_strength.py ├── technical-mcp.png └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | # Python-generated files 2 | __pycache__/ 3 | *.py[oc] 4 | build/ 5 | dist/ 6 | wheels/ 7 | *.egg-info 8 | 9 | # Virtual environments 10 | .venv 11 | 12 | # Environment variables 13 | .env 14 | -------------------------------------------------------------------------------- /.python-version: -------------------------------------------------------------------------------- 1 | 3.11 -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # Generated by https://smithery.ai. See: https://smithery.ai/docs/config#dockerfile 2 | # Use a Python image with uv pre-installed 3 | FROM ghcr.io/astral-sh/uv:python3.11-bookworm-slim AS uv 4 | 5 | # Install required packages 6 | RUN apt-get update && apt-get install -yqq \ 7 | libsoup2.4-dev \ 8 | libwebkit2gtk-4.0-dev \ 9 | build-essential \ 10 | python3-dev \ 11 | libpq-dev \ 12 | gettext 13 | 14 | # Install and compile TA-Lib 15 | ENV TALIB_DIR=/usr/local 16 | RUN wget https://github.com/ta-lib/ta-lib/releases/download/v0.6.4/ta-lib-0.6.4-src.tar.gz \ 17 | && tar -xzf ta-lib-0.6.4-src.tar.gz \ 18 | && cd ta-lib-0.6.4/ \ 19 | && ./configure --prefix=$TALIB_DIR \ 20 | && make -j$(nproc) \ 21 | && make install \ 22 | && cd .. \ 23 | && rm -rf ta-lib-0.6.4-src.tar.gz ta-lib-0.6.4/ 24 | 25 | # Set the working directory 26 | WORKDIR /app 27 | 28 | # Enable bytecode compilation 29 | ENV UV_COMPILE_BYTECODE=1 30 | 31 | # Copy pyproject.toml and uv.lock for dependencies 32 | COPY pyproject.toml uv.lock ./ 33 | 34 | # Install the project's dependencies using the lockfile and settings 35 | RUN --mount=type=cache,target=/root/.cache/uv \ 36 | uv sync --frozen --no-install-project --no-dev --no-editable 37 | 38 | # Copy the entire source code into the container 39 | COPY src/ ./src/ 40 | 41 | # Set the environment variable for Tiingo API Key 42 | ENV TIINGO_API_KEY=your_api_key_here 43 | 44 | # Place executables in the environment at the front of the path 45 | ENV PATH="/app/.venv/bin:$PATH" 46 | 47 | # Default command to run the MCP server 48 | CMD ["uv", "run", "mcp-trader"] 49 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MCP Trader Server 2 | 3 | [![smithery badge](https://smithery.ai/badge/mcp-trader)](https://smithery.ai/server/mcp-trader) 4 | 5 | A Model Context Protocol (MCP) server for stock traders. 6 | 7 | ## Features 8 | 9 | ### Tools 10 | 11 | The server provides the following tools for stock analysis and trading: 12 | 13 | - **analyze-stock**: Performs technical analysis on a given stock symbol 14 | 15 | - Required argument: `symbol` (string, e.g. "NVDA") 16 | - Returns comprehensive technical analysis including: 17 | - Moving average trends (20, 50, 200 SMA) 18 | - Momentum indicators (RSI, MACD) 19 | - Volatility metrics (ATR, ADRP) 20 | - Volume analysis 21 | 22 | - **relative-strength**: Calculates a stock's relative strength compared to a benchmark 23 | 24 | - Required argument: `symbol` (string, e.g. "AAPL") 25 | - Optional argument: `benchmark` (string, default: "SPY") 26 | - Returns relative strength metrics across multiple timeframes (21, 63, 126, 252 days) 27 | - Includes performance comparison between the stock and benchmark 28 | 29 | - **volume-profile**: Analyzes volume distribution by price 30 | 31 | - Required argument: `symbol` (string, e.g. "MSFT") 32 | - Optional argument: `lookback_days` (integer, default: 60) 33 | - Returns volume profile analysis including: 34 | - Point of Control (POC) - price level with highest volume 35 | - Value Area (70% of volume range) 36 | - Top volume price levels 37 | 38 | - **detect-patterns**: Identifies chart patterns in price data 39 | 40 | - Required argument: `symbol` (string, e.g. "AMZN") 41 | - Returns detected chart patterns with confidence levels and price targets 42 | 43 | - **position-size**: Calculates optimal position size based on risk parameters 44 | 45 | - Required arguments: 46 | - `symbol` (string, e.g. "TSLA") 47 | - `stop_price` (number) 48 | - `risk_amount` (number) 49 | - `account_size` (number) 50 | - Optional argument: `price` (number, default: current price) 51 | - Returns recommended position size, dollar risk, and potential profit targets 52 | 53 | - **suggest-stops**: Suggests stop loss levels based on technical analysis 54 | - Required argument: `symbol` (string, e.g. "META") 55 | - Returns multiple stop loss suggestions based on: 56 | - ATR-based stops (1x, 2x, 3x ATR) 57 | - Percentage-based stops (2%, 5%, 8%) 58 | - Technical levels (moving averages, recent swing lows) 59 | 60 | ### Technical Analysis Capabilities 61 | 62 | The server leverages several specialized analysis modules: 63 | 64 | - **TechnicalAnalysis**: Core technical indicators and trend analysis 65 | 66 | - Moving averages (SMA 20, 50, 200) 67 | - Momentum indicators (RSI, MACD) 68 | - Volatility metrics (ATR, Average Daily Range Percentage) 69 | - Volume analysis (20-day average volume) 70 | 71 | - **RelativeStrength**: Comparative performance analysis 72 | 73 | - Multi-timeframe relative strength scoring (21, 63, 126, 252 days) 74 | - Performance comparison against benchmark indices 75 | - Outperformance/underperformance classification 76 | 77 | - **VolumeProfile**: Advanced volume analysis 78 | 79 | - Price level volume distribution 80 | - Point of Control (POC) identification 81 | - Value Area calculation (70% of volume) 82 | 83 | - **PatternRecognition**: Chart pattern detection 84 | 85 | - Support/resistance levels 86 | - Common chart patterns (head and shoulders, double tops/bottoms, etc.) 87 | - Confidence scoring for detected patterns 88 | 89 | - **RiskAnalysis**: Position sizing and risk management 90 | - Risk-based position sizing 91 | - Multiple stop loss strategies 92 | - R-multiple profit target calculation 93 | 94 | ### Data Sources 95 | 96 | The server uses the [Tiingo API](https://api.tiingo.com/) for market data: 97 | 98 | - Historical daily OHLCV data 99 | - Adjusted prices for accurate backtesting 100 | - Up to 1 year of historical data by default 101 | 102 | ## Setup 103 | 104 | ### Prerequisites 105 | 106 | - Python 3.11+ 107 | - [uv](https://github.com/astral-sh/uv) 108 | - [ta-lib](https://ta-lib.org/install/) 109 | - [Tiingo API Key](https://api.tiingo.com/) 110 | 111 | ### Environment Variables 112 | 113 | Create a `.env` file: 114 | 115 | ```bash 116 | TIINGO_API_KEY=your_api_key_here 117 | ``` 118 | 119 | ### Installing via Smithery 120 | 121 | To install Trader for Claude Desktop automatically via [Smithery](https://smithery.ai/server/mcp-trader): 122 | 123 | ```bash 124 | npx -y @smithery/cli install mcp-trader --client claude 125 | ``` 126 | 127 | This will: 128 | 129 | 1. Install the MCP Trader server 130 | 2. Configure it with your Tiingo API key 131 | 3. Set up the Claude Desktop integration 132 | 133 | #### Smithery Configuration 134 | 135 | The server includes a `smithery.yaml` configuration file that defines: 136 | 137 | - Required configuration parameters (Tiingo API key) 138 | - Command function to start the MCP server 139 | - Integration with Claude Desktop 140 | 141 | You can customize the Smithery configuration by editing the `smithery.yaml` file. 142 | 143 | ### Installation 144 | 145 | ```bash 146 | uv venv --python 3.11 147 | source .venv/bin/activate # On Windows: .venv\Scripts\activate 148 | uv sync 149 | ``` 150 | 151 | ### Docker Deployment 152 | 153 | The project includes a Dockerfile for containerized deployment: 154 | 155 | ```bash 156 | # Build the Docker image 157 | docker build -t mcp-trader . 158 | 159 | # Run the container with your API key 160 | docker run -e TIINGO_API_KEY=your_api_key_here -p 8000:8000 mcp-trader 161 | ``` 162 | 163 | To run the container in HTTP server mode: 164 | 165 | ```bash 166 | docker run -e TIINGO_API_KEY=your_api_key_here -p 8000:8000 mcp-trader uv run mcp-trader --http 167 | ``` 168 | 169 | ## Configuration 170 | 171 | ### Claude Desktop App 172 | 173 | On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` 174 | 175 | On Windows: `%APPDATA%/Claude/claude_desktop_config.json` 176 | 177 | Development Configuration: 178 | 179 | ```json 180 | { 181 | "mcpServers": { 182 | "stock-analyzer": { 183 | "command": "uv", 184 | "args": [ 185 | "--directory", 186 | "/absolute/path/to/mcp-trader", 187 | "run", 188 | "mcp-trader" 189 | ] 190 | "env": { 191 | "TIINGO_API_KEY": "your_api_key_here" 192 | } 193 | } 194 | } 195 | } 196 | ``` 197 | 198 | ## Development 199 | 200 | ### Build and Run 201 | 202 | ```bash 203 | uv build 204 | uv run mcp-trader 205 | ``` 206 | 207 | ### HTTP Server Mode 208 | 209 | The server can also run as a standalone HTTP server for testing or integration with other applications: 210 | 211 | ```bash 212 | uv run mcp-trader --http 213 | ``` 214 | 215 | This starts an HTTP server on http://localhost:8000 with the following endpoints: 216 | 217 | - **GET /list-tools**: Returns a list of available tools and their schemas 218 | - **POST /call-tool**: Executes a tool with the provided arguments 219 | - Request body format: 220 | ```json 221 | { 222 | "name": "analyze-stock", 223 | "arguments": { 224 | "symbol": "AAPL" 225 | } 226 | } 227 | ``` 228 | - Returns an array of content items (text, images, etc.) 229 | 230 | ### Debugging 231 | 232 | Use the MCP Inspector for debugging: 233 | 234 | ```bash 235 | npx @modelcontextprotocol/inspector uv --directory /path/to/mcp-trader run mcp-trader 236 | ``` 237 | 238 | ## Example Usage 239 | 240 | In Claude Desktop: 241 | 242 | ``` 243 | Analyze the technical setup for NVDA 244 | ``` 245 | 246 | The server will return a technical analysis summary including trend status, momentum indicators, and key metrics. 247 | 248 | ![NVDA Technical Analysis](./technical-mcp.png) 249 | 250 | ## Dependencies 251 | 252 | See pyproject.toml for full dependency list: 253 | 254 | ``` 255 | - aiohttp >=3.11.11 256 | - mcp >=1.2.0 257 | - numpy ==1.26.4 258 | - pandas >=2.2.3 259 | - pandas-ta >=0.3.14b0 260 | - python-dotenv >=1.0.1 261 | - setuptools >=75.8.0 262 | - ta-lib >=0.6.0 263 | ``` 264 | 265 | ## Contributing 266 | 267 | Contributions to MCP Trader are welcome! Here are some ways you can contribute: 268 | 269 | - **Add new tools**: Implement additional technical analysis tools or trading strategies 270 | - **Improve existing tools**: Enhance the accuracy or performance of current tools 271 | - **Add data sources**: Integrate additional market data providers 272 | - **Documentation**: Improve the documentation or add examples 273 | - **Bug fixes**: Fix issues or improve error handling 274 | 275 | ### Development Workflow 276 | 277 | 1. Fork the repository 278 | 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 279 | 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 280 | 4. Push to the branch (`git push origin feature/amazing-feature`) 281 | 5. Open a Pull Request 282 | 283 | ## Future Plans 284 | 285 | The MCP Trader project has several planned enhancements: 286 | 287 | - **Portfolio Analysis**: Tools for analyzing and optimizing portfolios 288 | - **Backtesting**: Capabilities to test trading strategies on historical data 289 | - **Sentiment Analysis**: Integration with news and social media sentiment data 290 | - **Options Analysis**: Tools for analyzing options strategies and pricing 291 | - **Real-time Data**: Support for real-time market data feeds 292 | - **Custom Strategies**: Framework for implementing and testing custom trading strategies 293 | - **Alerts**: Notification system for price and technical indicator alerts 294 | 295 | ## Further Reading 296 | 297 | Learn more about this project through these detailed blog posts: 298 | 299 | - [Building a Stock Analysis Server with MCP, Part 1](https://sethhobson.com/2025/01/building-a-stock-analysis-server-with-mcp-part-1/) - Initial setup, architecture, and core technical analysis features 300 | - [Building a Stock Analysis Server with MCP, Part 2](https://sethhobson.com/2025/03/building-a-stock-analysis-server-with-mcp-part-2/) - Relative Strength, Volume, Pattern Recognition, Risk analysis 301 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "mcp-trader" 3 | version = "0.1.0" 4 | description = "A sample MCP server for traders" 5 | readme = "README.md" 6 | requires-python = ">=3.11" 7 | dependencies = [ 8 | "aiohttp>=3.11.11", 9 | "mcp>=1.2.0", 10 | "numpy==1.26.4", 11 | "pandas>=2.2.3", 12 | "pandas-ta>=0.3.14b0", 13 | "python-dotenv>=1.0.1", 14 | "setuptools>=75.8.0", 15 | "ta-lib>=0.6.0", 16 | ] 17 | [[project.authors]] 18 | name = "Seth Hobson" 19 | email = "seth@major7apps.com" 20 | 21 | [build-system] 22 | requires = [ "hatchling",] 23 | build-backend = "hatchling.build" 24 | 25 | [project.scripts] 26 | mcp-trader = "mcp_trader:main" 27 | -------------------------------------------------------------------------------- /smithery.yaml: -------------------------------------------------------------------------------- 1 | # Smithery configuration file: https://smithery.ai/docs/config#smitheryyaml 2 | 3 | startCommand: 4 | type: stdio 5 | configSchema: 6 | # JSON Schema defining the configuration options for the MCP. 7 | type: object 8 | required: 9 | - tiingoApiKey 10 | properties: 11 | tiingoApiKey: 12 | type: string 13 | description: The API key for the Tiingo API. 14 | commandFunction: 15 | # A function that produces the CLI command to start the MCP on stdio. 16 | |- 17 | (config) => ({ command: 'uv', args: ['run', 'mcp-trader'], env: { TIINGO_API_KEY: config.tiingoApiKey } }) 18 | -------------------------------------------------------------------------------- /src/mcp_trader/__init__.py: -------------------------------------------------------------------------------- 1 | from . import server 2 | import asyncio 3 | 4 | def main(): 5 | """Main entry point for the package.""" 6 | asyncio.run(server.main()) 7 | 8 | # Optionally expose other important items at package level 9 | __all__ = ['main', 'server'] -------------------------------------------------------------------------------- /src/mcp_trader/data.py: -------------------------------------------------------------------------------- 1 | import os 2 | import aiohttp 3 | import pandas as pd 4 | 5 | from datetime import datetime, timedelta 6 | from dotenv import load_dotenv 7 | 8 | load_dotenv() 9 | 10 | 11 | class MarketData: 12 | """Handles all market data fetching operations.""" 13 | 14 | def __init__(self): 15 | self.api_key = os.getenv("TIINGO_API_KEY") 16 | if not self.api_key: 17 | raise ValueError("TIINGO_API_KEY not found in environment") 18 | 19 | self.headers = {"Content-Type": "application/json", "Authorization": f"Token {self.api_key}"} 20 | 21 | async def get_historical_data(self, symbol: str, lookback_days: int = 365) -> pd.DataFrame: 22 | """ 23 | Fetch historical daily data for a given symbol. 24 | 25 | Args: 26 | symbol (str): The stock symbol to fetch data for. 27 | lookback_days (int): Number of days to look back from today. 28 | 29 | Returns: 30 | pd.DataFrame: DataFrame containing historical market data. 31 | 32 | Raises: 33 | ValueError: If the symbol is invalid or no data is returned. 34 | Exception: For other unexpected issues during the fetch operation. 35 | """ 36 | end_date = datetime.now() 37 | start_date = end_date - timedelta(days=lookback_days) 38 | 39 | url = ( 40 | f"https://api.tiingo.com/tiingo/daily/{symbol}/prices?" 41 | f'startDate={start_date.strftime("%Y-%m-%d")}&' 42 | f'endDate={end_date.strftime("%Y-%m-%d")}' 43 | ) 44 | 45 | try: 46 | async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=10)) as session: 47 | async with session.get(url, headers=self.headers) as response: 48 | if response.status == 404: 49 | raise ValueError(f"Symbol not found: {symbol}") 50 | response.raise_for_status() 51 | data = await response.json() 52 | 53 | if not data: 54 | raise ValueError(f"No data returned for {symbol}") 55 | 56 | df = pd.DataFrame(data) 57 | df["date"] = pd.to_datetime(df["date"]) 58 | df.set_index("date", inplace=True) 59 | 60 | df[["open", "high", "low", "close"]] = df[["adjOpen", "adjHigh", "adjLow", "adjClose"]].round(2) 61 | df["volume"] = df["adjVolume"].astype(int) 62 | df["symbol"] = symbol.upper() 63 | 64 | return df 65 | 66 | except aiohttp.ClientError as e: 67 | raise ConnectionError(f"Network error while fetching data for {symbol}: {e}") 68 | except ValueError as ve: 69 | raise ve # Propagate value errors (symbol issues, no data, etc.) 70 | except Exception as e: 71 | raise Exception(f"Unexpected error fetching data for {symbol}: {e}") 72 | -------------------------------------------------------------------------------- /src/mcp_trader/indicators.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import pandas_ta as ta 3 | 4 | from typing import Dict, Any, List 5 | 6 | 7 | class TechnicalAnalysis: 8 | """Technical analysis toolkit with improved performance and readability.""" 9 | 10 | @staticmethod 11 | def add_core_indicators(df: pd.DataFrame) -> pd.DataFrame: 12 | """Add a core set of technical indicators.""" 13 | try: 14 | # Adding trend indicators 15 | df["sma_20"] = ta.sma(df["close"], length=20) 16 | df["sma_50"] = ta.sma(df["close"], length=50) 17 | df["sma_200"] = ta.sma(df["close"], length=200) 18 | 19 | # Adding volatility indicators and volume 20 | daily_range = df["high"].sub(df["low"]) 21 | adr = daily_range.rolling(window=20).mean() 22 | df["adrp"] = adr.div(df["close"]).mul(100) 23 | df["avg_20d_vol"] = df["volume"].rolling(window=20).mean() 24 | 25 | # Adding momentum indicators 26 | df["atr"] = ta.atr(df["high"], df["low"], df["close"], length=14) 27 | df["rsi"] = ta.rsi(df["close"], length=14) 28 | macd = ta.macd(df["close"], fast=12, slow=26, signal=9) 29 | if macd is not None: 30 | df = pd.concat([df, macd], axis=1) 31 | 32 | return df 33 | 34 | except KeyError as e: 35 | raise KeyError(f"Missing column in input DataFrame: {str(e)}") 36 | except Exception as e: 37 | raise Exception(f"Error calculating indicators: {str(e)}") 38 | 39 | @staticmethod 40 | def check_trend_status(df: pd.DataFrame) -> Dict[str, Any]: 41 | """Analyze the current trend status.""" 42 | if df.empty: 43 | raise ValueError("DataFrame is empty. Ensure it contains valid data.") 44 | 45 | latest = df.iloc[-1] 46 | return { 47 | "above_20sma": latest["close"] > latest["sma_20"], 48 | "above_50sma": latest["close"] > latest["sma_50"], 49 | "above_200sma": latest["close"] > latest["sma_200"], 50 | "20_50_bullish": latest["sma_20"] > latest["sma_50"], 51 | "50_200_bullish": latest["sma_50"] > latest["sma_200"], 52 | "rsi": latest["rsi"], 53 | "macd_bullish": latest.get("MACD_12_26_9", 0) 54 | > latest.get("MACDs_12_26_9", 0), 55 | } 56 | 57 | 58 | class RelativeStrength: 59 | """Tools for calculating relative strength metrics.""" 60 | 61 | @staticmethod 62 | async def calculate_rs( 63 | market_data, 64 | symbol: str, 65 | benchmark: str = "SPY", 66 | lookback_periods: List[int] = [21, 63, 126, 252], 67 | ) -> Dict[str, float]: 68 | """ 69 | Calculate relative strength compared to a benchmark across multiple timeframes. 70 | 71 | Args: 72 | market_data: Our market data fetcher instance 73 | symbol (str): The stock symbol to analyze 74 | benchmark (str): The benchmark symbol (default: SPY for S&P 500 ETF) 75 | lookback_periods (List[int]): Periods in trading days to calculate RS (default: [21, 63, 126, 252]) 76 | 77 | Returns: 78 | Dict[str, float]: Relative strength scores for each timeframe 79 | """ 80 | try: 81 | # Get data for both the stock and benchmark 82 | stock_df = await market_data.get_historical_data( 83 | symbol, max(lookback_periods) + 10 84 | ) 85 | benchmark_df = await market_data.get_historical_data( 86 | benchmark, max(lookback_periods) + 10 87 | ) 88 | 89 | # Calculate returns for different periods 90 | rs_scores = {} 91 | 92 | for period in lookback_periods: 93 | # Check if we have enough data for this period 94 | if len(stock_df) <= period or len(benchmark_df) <= period: 95 | # Skip this period if we don't have enough data 96 | continue 97 | 98 | # Calculate the percent change for both 99 | stock_return = ( 100 | stock_df["close"].iloc[-1] / stock_df["close"].iloc[-period] - 1 101 | ) * 100 102 | benchmark_return = ( 103 | benchmark_df["close"].iloc[-1] / benchmark_df["close"].iloc[-period] 104 | - 1 105 | ) * 100 106 | 107 | # Calculate relative strength (stock return minus benchmark return) 108 | relative_performance = stock_return - benchmark_return 109 | 110 | # Convert to a 1-100 score (this is simplified; in practice you might use a more 111 | # sophisticated distribution model based on historical data) 112 | rs_score = min(max(50 + relative_performance, 1), 99) 113 | 114 | rs_scores[f"RS_{period}d"] = round(rs_score, 2) 115 | rs_scores[f"Return_{period}d"] = round(stock_return, 2) 116 | rs_scores[f"Benchmark_{period}d"] = round(benchmark_return, 2) 117 | rs_scores[f"Excess_{period}d"] = round(relative_performance, 2) 118 | 119 | return rs_scores 120 | 121 | except Exception as e: 122 | raise Exception(f"Error calculating relative strength: {str(e)}") 123 | 124 | 125 | class VolumeProfile: 126 | """Tools for analyzing volume distribution by price.""" 127 | 128 | @staticmethod 129 | def analyze_volume_profile(df: pd.DataFrame, num_bins: int = 10) -> Dict[str, Any]: 130 | """ 131 | Create a volume profile analysis by price level. 132 | 133 | Args: 134 | df (pd.DataFrame): Historical price and volume data 135 | num_bins (int): Number of price bins to create (default: 10) 136 | 137 | Returns: 138 | Dict[str, Any]: Volume profile analysis 139 | """ 140 | try: 141 | if len(df) < 20: 142 | raise ValueError("Not enough data for volume profile analysis") 143 | 144 | # Find the price range for the period 145 | price_min = df["low"].min() 146 | price_max = df["high"].max() 147 | 148 | # Create price bins 149 | bin_width = (price_max - price_min) / num_bins 150 | 151 | # Initialize the profile 152 | profile = { 153 | "price_min": price_min, 154 | "price_max": price_max, 155 | "bin_width": bin_width, 156 | "bins": [], 157 | } 158 | 159 | # Calculate volume by price bin 160 | for i in range(num_bins): 161 | bin_low = price_min + i * bin_width 162 | bin_high = bin_low + bin_width 163 | bin_mid = (bin_low + bin_high) / 2 164 | 165 | # Filter data in this price range 166 | mask = (df["low"] <= bin_high) & (df["high"] >= bin_low) 167 | volume_in_bin = df.loc[mask, "volume"].sum() 168 | 169 | # Calculate percentage of total volume 170 | volume_percent = ( 171 | (volume_in_bin / df["volume"].sum()) * 100 172 | if df["volume"].sum() > 0 173 | else 0 174 | ) 175 | 176 | profile["bins"].append( 177 | { 178 | "price_low": round(bin_low, 2), 179 | "price_high": round(bin_high, 2), 180 | "price_mid": round(bin_mid, 2), 181 | "volume": int(volume_in_bin), 182 | "volume_percent": round(volume_percent, 2), 183 | } 184 | ) 185 | 186 | # Find the Point of Control (POC) - the price level with the highest volume 187 | poc_bin = max(profile["bins"], key=lambda x: x["volume"]) 188 | profile["point_of_control"] = round(poc_bin["price_mid"], 2) 189 | 190 | # Find Value Area (70% of volume) 191 | sorted_bins = sorted( 192 | profile["bins"], key=lambda x: x["volume"], reverse=True 193 | ) 194 | cumulative_volume = 0 195 | value_area_bins = [] 196 | 197 | for bin_data in sorted_bins: 198 | value_area_bins.append(bin_data) 199 | cumulative_volume += bin_data["volume_percent"] 200 | if cumulative_volume >= 70: 201 | break 202 | 203 | if value_area_bins: 204 | profile["value_area_low"] = round( 205 | min([b["price_low"] for b in value_area_bins]), 2 206 | ) 207 | profile["value_area_high"] = round( 208 | max([b["price_high"] for b in value_area_bins]), 2 209 | ) 210 | 211 | return profile 212 | 213 | except Exception as e: 214 | raise Exception(f"Error analyzing volume profile: {str(e)}") 215 | 216 | 217 | class PatternRecognition: 218 | """Tools for detecting common chart patterns.""" 219 | 220 | @staticmethod 221 | def detect_patterns(df: pd.DataFrame) -> Dict[str, Any]: 222 | """ 223 | Detect common chart patterns in price data. 224 | 225 | Args: 226 | df (pd.DataFrame): Historical price data 227 | 228 | Returns: 229 | Dict[str, Any]: Detected patterns and their properties 230 | """ 231 | try: 232 | if len(df) < 60: # Need enough data for pattern detection 233 | return { 234 | "patterns": [], 235 | "message": "Not enough data for pattern detection", 236 | } 237 | 238 | patterns = [] 239 | 240 | # We'll use a window of the most recent data for our analysis 241 | recent_df = df.tail(60).copy() 242 | 243 | # Find local minima and maxima 244 | recent_df["is_min"] = ( 245 | recent_df["low"].rolling(window=5, center=True).min() 246 | == recent_df["low"] 247 | ) 248 | recent_df["is_max"] = ( 249 | recent_df["high"].rolling(window=5, center=True).max() 250 | == recent_df["high"] 251 | ) 252 | 253 | # Get the indices, prices, and dates of local minima and maxima 254 | minima = recent_df[recent_df["is_min"]].copy() 255 | maxima = recent_df[recent_df["is_max"]].copy() 256 | 257 | # Double Bottom Detection 258 | if len(minima) >= 2: 259 | for i in range(len(minima) - 1): 260 | for j in range(i + 1, len(minima)): 261 | price1 = minima.iloc[i]["low"] 262 | price2 = minima.iloc[j]["low"] 263 | date1 = minima.iloc[i].name 264 | date2 = minima.iloc[j].name 265 | 266 | # Check if the two bottoms are at similar price levels (within 3%) 267 | if abs(price1 - price2) / price1 < 0.03: 268 | # Check if they're at least 10 days apart 269 | days_apart = (date2 - date1).days 270 | if days_apart >= 10 and days_apart <= 60: 271 | # Check if there's a peak in between that's at least 5% higher 272 | mask = (recent_df.index > date1) & ( 273 | recent_df.index < date2 274 | ) 275 | if mask.any(): 276 | max_between = recent_df.loc[mask, "high"].max() 277 | if max_between > price1 * 1.05: 278 | patterns.append( 279 | { 280 | "type": "Double Bottom", 281 | "start_date": date1.strftime( 282 | "%Y-%m-%d" 283 | ), 284 | "end_date": date2.strftime("%Y-%m-%d"), 285 | "price_level": round( 286 | (price1 + price2) / 2, 2 287 | ), 288 | "confidence": "Medium", 289 | } 290 | ) 291 | 292 | # Double Top Detection (similar logic, but for maxima) 293 | if len(maxima) >= 2: 294 | for i in range(len(maxima) - 1): 295 | for j in range(i + 1, len(maxima)): 296 | price1 = maxima.iloc[i]["high"] 297 | price2 = maxima.iloc[j]["high"] 298 | date1 = maxima.iloc[i].name 299 | date2 = maxima.iloc[j].name 300 | 301 | if abs(price1 - price2) / price1 < 0.03: 302 | days_apart = (date2 - date1).days 303 | if days_apart >= 10 and days_apart <= 60: 304 | mask = (recent_df.index > date1) & ( 305 | recent_df.index < date2 306 | ) 307 | if mask.any(): 308 | min_between = recent_df.loc[mask, "low"].min() 309 | if min_between < price1 * 0.95: 310 | patterns.append( 311 | { 312 | "type": "Double Top", 313 | "start_date": date1.strftime( 314 | "%Y-%m-%d" 315 | ), 316 | "end_date": date2.strftime("%Y-%m-%d"), 317 | "price_level": round( 318 | (price1 + price2) / 2, 2 319 | ), 320 | "confidence": "Medium", 321 | } 322 | ) 323 | 324 | # Check for potential breakouts 325 | close = df["close"].iloc[-1] 326 | recent_high = df["high"].iloc[-20:].max() 327 | recent_low = df["low"].iloc[-20:].min() 328 | 329 | # Resistance breakout 330 | if close > recent_high * 0.99 and close < recent_high * 1.02: 331 | patterns.append( 332 | { 333 | "type": "Resistance Breakout", 334 | "price_level": round(recent_high, 2), 335 | "confidence": "Medium", 336 | } 337 | ) 338 | 339 | # Support breakout (breakdown) 340 | if close < recent_low * 1.01 and close > recent_low * 0.98: 341 | patterns.append( 342 | { 343 | "type": "Support Breakdown", 344 | "price_level": round(recent_low, 2), 345 | "confidence": "Medium", 346 | } 347 | ) 348 | 349 | return {"patterns": patterns} 350 | 351 | except Exception as e: 352 | raise Exception(f"Error detecting patterns: {str(e)}") 353 | 354 | 355 | class RiskAnalysis: 356 | """Tools for risk management and position sizing.""" 357 | 358 | @staticmethod 359 | def calculate_position_size( 360 | price: float, 361 | stop_price: float, 362 | risk_amount: float, 363 | account_size: float, 364 | max_risk_percent: float = 2.0, 365 | ) -> Dict[str, Any]: 366 | """ 367 | Calculate appropriate position size based on risk parameters. 368 | 369 | Args: 370 | price (float): Current stock price 371 | stop_price (float): Stop loss price 372 | risk_amount (float): Amount willing to risk in dollars 373 | account_size (float): Total trading account size 374 | max_risk_percent (float): Maximum percentage of account to risk 375 | 376 | Returns: 377 | Dict[str, Any]: Position sizing recommendations 378 | """ 379 | try: 380 | # Validate inputs 381 | if price <= 0 or account_size <= 0: 382 | raise ValueError("Price and account size must be positive") 383 | 384 | if price <= stop_price and stop_price != 0: 385 | raise ValueError( 386 | "For long positions, stop price must be below entry price" 387 | ) 388 | 389 | # Calculate risk per share 390 | risk_per_share = abs(price - stop_price) 391 | 392 | if risk_per_share == 0: 393 | raise ValueError( 394 | "Risk per share cannot be zero. Entry and stop prices must differ." 395 | ) 396 | 397 | # Calculate position size based on dollar risk 398 | shares_based_on_risk = int(risk_amount / risk_per_share) 399 | 400 | # Calculate maximum position size based on account risk percentage 401 | max_risk_dollars = account_size * (max_risk_percent / 100) 402 | max_shares = int(max_risk_dollars / risk_per_share) 403 | 404 | # Take the smaller of the two 405 | recommended_shares = min(shares_based_on_risk, max_shares) 406 | actual_dollar_risk = recommended_shares * risk_per_share 407 | 408 | # Calculate position cost 409 | position_cost = recommended_shares * price 410 | 411 | # Calculate R-Multiples (potential reward to risk ratios) 412 | r1_target = price + risk_per_share 413 | r2_target = price + 2 * risk_per_share 414 | r3_target = price + 3 * risk_per_share 415 | 416 | return { 417 | "recommended_shares": recommended_shares, 418 | "dollar_risk": round(actual_dollar_risk, 2), 419 | "risk_per_share": round(risk_per_share, 2), 420 | "position_cost": round(position_cost, 2), 421 | "account_percent_risked": round( 422 | (actual_dollar_risk / account_size) * 100, 2 423 | ), 424 | "r_multiples": { 425 | "r1": round(r1_target, 2), 426 | "r2": round(r2_target, 2), 427 | "r3": round(r3_target, 2), 428 | }, 429 | } 430 | 431 | except Exception as e: 432 | raise Exception(f"Error calculating position size: {str(e)}") 433 | 434 | @staticmethod 435 | def suggest_stop_levels(df: pd.DataFrame) -> Dict[str, float]: 436 | """ 437 | Suggest appropriate stop-loss levels based on technical indicators. 438 | 439 | Args: 440 | df (pd.DataFrame): Historical price data with technical indicators 441 | 442 | Returns: 443 | Dict[str, float]: Suggested stop levels 444 | """ 445 | try: 446 | if len(df) < 20: 447 | raise ValueError("Not enough data for stop level analysis") 448 | 449 | latest = df.iloc[-1] 450 | close = latest["close"] 451 | 452 | # Calculate ATR-based stops 453 | atr = latest.get("atr", df["high"].iloc[-20:] - df["low"].iloc[-20:]).mean() 454 | 455 | # Different stop strategies 456 | stops = { 457 | "atr_1x": round(close - 1 * atr, 2), 458 | "atr_2x": round(close - 2 * atr, 2), 459 | "atr_3x": round(close - 3 * atr, 2), 460 | "percent_2": round(close * 0.98, 2), 461 | "percent_5": round(close * 0.95, 2), 462 | "percent_8": round(close * 0.92, 2), 463 | } 464 | 465 | # Add SMA-based stops if available 466 | for sma in ["sma_20", "sma_50", "sma_200"]: 467 | if sma in latest and not pd.isna(latest[sma]): 468 | stops[sma] = round(latest[sma], 2) 469 | 470 | # Check for recent swing low 471 | recent_lows = df["low"].iloc[-20:].sort_values() 472 | if not recent_lows.empty: 473 | stops["recent_swing"] = round(recent_lows.iloc[0], 2) 474 | 475 | return stops 476 | 477 | except Exception as e: 478 | raise Exception(f"Error suggesting stop levels: {str(e)}") 479 | -------------------------------------------------------------------------------- /src/mcp_trader/server.py: -------------------------------------------------------------------------------- 1 | import mcp.types as types 2 | import mcp.server.stdio 3 | import asyncio 4 | 5 | from mcp.server.models import InitializationOptions 6 | from mcp.server import NotificationOptions, Server 7 | 8 | # Add our new imports 9 | from .data import MarketData 10 | from .indicators import ( 11 | TechnicalAnalysis, 12 | RelativeStrength, 13 | VolumeProfile, 14 | PatternRecognition, 15 | RiskAnalysis, 16 | ) 17 | 18 | # Initialize our service instances 19 | market_data = MarketData() 20 | tech_analysis = TechnicalAnalysis() 21 | rs_analysis = RelativeStrength() 22 | volume_analysis = VolumeProfile() 23 | pattern_recognition = PatternRecognition() 24 | risk_analysis = RiskAnalysis() 25 | 26 | # Keep the server initialization 27 | server = Server("mcp-trader") 28 | 29 | 30 | @server.list_tools() 31 | async def handle_list_tools() -> list[types.Tool]: 32 | """List our stock analysis tools.""" 33 | return [ 34 | types.Tool( 35 | name="analyze-stock", 36 | description="Analyze a stock's technical setup", 37 | inputSchema={ 38 | "type": "object", 39 | "properties": { 40 | "symbol": { 41 | "type": "string", 42 | "description": "Stock symbol (e.g., NVDA)", 43 | } 44 | }, 45 | "required": ["symbol"], 46 | }, 47 | ), 48 | types.Tool( 49 | name="relative-strength", 50 | description="Calculate a stock's relative strength compared to benchmark", 51 | inputSchema={ 52 | "type": "object", 53 | "properties": { 54 | "symbol": { 55 | "type": "string", 56 | "description": "Stock symbol to analyze", 57 | }, 58 | "benchmark": { 59 | "type": "string", 60 | "description": "Benchmark symbol (default: SPY)", 61 | "default": "SPY", 62 | }, 63 | }, 64 | "required": ["symbol"], 65 | }, 66 | ), 67 | types.Tool( 68 | name="volume-profile", 69 | description="Analyze volume distribution by price", 70 | inputSchema={ 71 | "type": "object", 72 | "properties": { 73 | "symbol": { 74 | "type": "string", 75 | "description": "Stock symbol to analyze", 76 | }, 77 | "lookback_days": { 78 | "type": "integer", 79 | "description": "Number of days to analyze", 80 | "default": 60, 81 | }, 82 | }, 83 | "required": ["symbol"], 84 | }, 85 | ), 86 | types.Tool( 87 | name="detect-patterns", 88 | description="Detect chart patterns in price data", 89 | inputSchema={ 90 | "type": "object", 91 | "properties": { 92 | "symbol": { 93 | "type": "string", 94 | "description": "Stock symbol to analyze", 95 | } 96 | }, 97 | "required": ["symbol"], 98 | }, 99 | ), 100 | types.Tool( 101 | name="position-size", 102 | description="Calculate optimal position size based on risk parameters", 103 | inputSchema={ 104 | "type": "object", 105 | "properties": { 106 | "symbol": {"type": "string", "description": "Stock symbol"}, 107 | "price": { 108 | "type": "number", 109 | "description": "Entry price (0 for current price)", 110 | }, 111 | "stop_price": {"type": "number", "description": "Stop loss price"}, 112 | "risk_amount": { 113 | "type": "number", 114 | "description": "Dollar amount to risk", 115 | }, 116 | "account_size": { 117 | "type": "number", 118 | "description": "Total account size in dollars", 119 | }, 120 | }, 121 | "required": ["symbol", "stop_price", "risk_amount", "account_size"], 122 | }, 123 | ), 124 | types.Tool( 125 | name="suggest-stops", 126 | description="Suggest stop loss levels based on technical analysis", 127 | inputSchema={ 128 | "type": "object", 129 | "properties": { 130 | "symbol": { 131 | "type": "string", 132 | "description": "Stock symbol to analyze", 133 | } 134 | }, 135 | "required": ["symbol"], 136 | }, 137 | ), 138 | ] 139 | 140 | 141 | @server.call_tool() 142 | async def handle_call_tool( 143 | name: str, arguments: dict | None 144 | ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: 145 | """Handle tool execution requests.""" 146 | if not arguments: 147 | raise ValueError("Missing arguments") 148 | 149 | try: 150 | # Original analyze-stock tool 151 | if name == "analyze-stock": 152 | symbol = arguments.get("symbol") 153 | if not symbol: 154 | raise ValueError("Missing symbol") 155 | 156 | # Fetch data 157 | df = await market_data.get_historical_data(symbol) 158 | 159 | # Add indicators 160 | df = tech_analysis.add_core_indicators(df) 161 | 162 | # Get trend status 163 | trend = tech_analysis.check_trend_status(df) 164 | 165 | analysis = f""" 166 | Technical Analysis for {symbol}: 167 | 168 | Trend Analysis: 169 | - Above 20 SMA: {"✅ " if trend["above_20sma"] else "❌ "} 170 | - Above 50 SMA: {"✅ " if trend["above_50sma"] else "❌ "} 171 | - Above 200 SMA: {"✅ " if trend["above_200sma"] else "❌ "} 172 | - 20/50 SMA Bullish Cross: {"✅ " if trend["20_50_bullish"] else "❌ "} 173 | - 50/200 SMA Bullish Cross: {"✅ " if trend["50_200_bullish"] else "❌ "} 174 | 175 | Momentum: 176 | - RSI (14): {trend["rsi"]:.2f} 177 | - MACD Bullish: {"✅ " if trend["macd_bullish"] else "❌ "} 178 | 179 | Latest Price: ${df["close"].iloc[-1]:.2f} 180 | Average True Range (14): {df["atr"].iloc[-1]:.2f} 181 | Average Daily Range Percentage: {df["adrp"].iloc[-1]:.2f}% 182 | Average Volume (20D): {int(df["avg_20d_vol"].iloc[-1])} 183 | """ 184 | 185 | return [types.TextContent(type="text", text=analysis)] 186 | 187 | # Relative Strength Analysis 188 | elif name == "relative-strength": 189 | symbol = arguments.get("symbol") 190 | benchmark = arguments.get("benchmark", "SPY") 191 | 192 | if not symbol: 193 | raise ValueError("Missing symbol") 194 | 195 | # Calculate relative strength 196 | rs_results = await rs_analysis.calculate_rs(market_data, symbol, benchmark) 197 | 198 | # Format the results 199 | rs_text = f""" 200 | Relative Strength Analysis for {symbol} vs {benchmark}: 201 | 202 | """ 203 | # Check if we have any results 204 | if not rs_results: 205 | rs_text += "Insufficient historical data to calculate relative strength metrics." 206 | return [types.TextContent(type="text", text=rs_text)] 207 | 208 | for period, score in rs_results.items(): 209 | if period.startswith("RS_"): 210 | days = period.split("_")[1] 211 | rs_text += f"- {days} Relative Strength: {score}" 212 | 213 | # Add classification 214 | if score >= 80: 215 | rs_text += " (Strong Outperformance) ⭐⭐⭐" 216 | elif score >= 65: 217 | rs_text += " (Moderate Outperformance) ⭐⭐" 218 | elif score >= 50: 219 | rs_text += " (Slight Outperformance) ⭐" 220 | elif score >= 35: 221 | rs_text += " (Slight Underperformance) ⚠️" 222 | elif score >= 20: 223 | rs_text += " (Moderate Underperformance) ⚠️⚠️" 224 | else: 225 | rs_text += " (Strong Underperformance) ⚠️⚠️⚠️" 226 | 227 | rs_text += "\n" 228 | 229 | rs_text += "\nPerformance Details:\n" 230 | 231 | for period in ["21d", "63d", "126d", "252d"]: 232 | # Check if we have data for this period 233 | if ( 234 | f"Return_{period}" not in rs_results 235 | or f"Benchmark_{period}" not in rs_results 236 | or f"Excess_{period}" not in rs_results 237 | ): 238 | continue 239 | 240 | stock_return = rs_results.get(f"Return_{period}") 241 | benchmark_return = rs_results.get(f"Benchmark_{period}") 242 | excess = rs_results.get(f"Excess_{period}") 243 | 244 | if ( 245 | stock_return is not None 246 | and benchmark_return is not None 247 | and excess is not None 248 | ): 249 | rs_text += f"- {period}: {symbol} {stock_return:+.2f}% vs {benchmark} {benchmark_return:+.2f}% = {excess:+.2f}%\n" 250 | 251 | # If no performance details were added 252 | if "\nPerformance Details:\n" == rs_text.split("\n")[-2] + "\n": 253 | rs_text += "No performance details available due to insufficient historical data.\n" 254 | 255 | return [types.TextContent(type="text", text=rs_text)] 256 | 257 | # Volume Profile Analysis 258 | elif name == "volume-profile": 259 | symbol = arguments.get("symbol") 260 | lookback_days = arguments.get("lookback_days", 60) 261 | 262 | if not symbol: 263 | raise ValueError("Missing symbol") 264 | 265 | # Get historical data 266 | df = await market_data.get_historical_data(symbol, lookback_days + 10) 267 | 268 | # Analyze volume profile 269 | profile = volume_analysis.analyze_volume_profile(df.tail(lookback_days)) 270 | 271 | # Format the results 272 | profile_text = f""" 273 | Volume Profile Analysis for {symbol} (last {lookback_days} days): 274 | 275 | Point of Control (POC): ${profile["point_of_control"]} (Price level with highest volume) 276 | Value Area: ${profile["value_area_low"]} - ${profile["value_area_high"]} (70% of volume) 277 | 278 | Volume by Price Level (High to Low): 279 | """ 280 | 281 | # Sort bins by volume and format 282 | sorted_bins = sorted( 283 | profile["bins"], key=lambda x: x["volume"], reverse=True 284 | ) 285 | for i, bin_data in enumerate(sorted_bins[:5]): # Show top 5 volume levels 286 | profile_text += f"{i + 1}. ${bin_data['price_low']} - ${bin_data['price_high']}: {bin_data['volume_percent']:.1f}% of volume\n" 287 | 288 | return [types.TextContent(type="text", text=profile_text)] 289 | 290 | # Pattern Recognition 291 | elif name == "detect-patterns": 292 | symbol = arguments.get("symbol") 293 | 294 | if not symbol: 295 | raise ValueError("Missing symbol") 296 | 297 | # Get historical data 298 | df = await market_data.get_historical_data(symbol, lookback_days=90) 299 | 300 | # Detect patterns 301 | pattern_results = pattern_recognition.detect_patterns(df) 302 | 303 | # Format the results 304 | if not pattern_results["patterns"]: 305 | pattern_text = f"No significant chart patterns detected for {symbol} in the recent data." 306 | else: 307 | pattern_text = f"Chart Patterns Detected for {symbol}:\n\n" 308 | 309 | for pattern in pattern_results["patterns"]: 310 | pattern_text += f"- {pattern['type']}" 311 | 312 | if "start_date" in pattern and "end_date" in pattern: 313 | pattern_text += ( 314 | f" ({pattern['start_date']} to {pattern['end_date']})" 315 | ) 316 | 317 | pattern_text += f": Price level ${pattern['price_level']}" 318 | 319 | if "confidence" in pattern: 320 | pattern_text += f" (Confidence: {pattern['confidence']})" 321 | 322 | pattern_text += "\n" 323 | 324 | pattern_text += "\nNote: Pattern recognition is not 100% reliable and should be confirmed with other forms of analysis." 325 | 326 | return [types.TextContent(type="text", text=pattern_text)] 327 | 328 | # Position Sizing 329 | elif name == "position-size": 330 | symbol = arguments.get("symbol") 331 | price = arguments.get("price", 0) 332 | stop_price = arguments.get("stop_price") 333 | risk_amount = arguments.get("risk_amount") 334 | account_size = arguments.get("account_size") 335 | 336 | if not all([symbol, stop_price, risk_amount, account_size]): 337 | raise ValueError("Missing required parameters") 338 | 339 | # If price is 0, get the current price 340 | if price == 0: 341 | df = await market_data.get_historical_data(symbol, lookback_days=5) 342 | price = df["close"].iloc[-1] 343 | 344 | # Calculate position size 345 | position_results = risk_analysis.calculate_position_size( 346 | price=price, 347 | stop_price=stop_price, 348 | risk_amount=risk_amount, 349 | account_size=account_size, 350 | ) 351 | 352 | # Format the results 353 | position_text = f""" 354 | Position Sizing for {symbol} at ${price:.2f}: 355 | 356 | 📊 Recommended Position: 357 | - {position_results["recommended_shares"]} shares (${position_results["position_cost"]:.2f}) 358 | - Risk: ${position_results["dollar_risk"]:.2f} ({position_results["account_percent_risked"]:.2f}% of account) 359 | - Risk per share: ${position_results["risk_per_share"]:.2f} 360 | 361 | 🎯 Potential Targets (R-Multiples): 362 | - R1 (1:1): ${position_results["r_multiples"]["r1"]:.2f} 363 | - R2 (2:1): ${position_results["r_multiples"]["r2"]:.2f} 364 | - R3 (3:1): ${position_results["r_multiples"]["r3"]:.2f} 365 | 366 | Remember what Ramada said: "Good trades don't just happen, they're the result of careful planning!" 367 | """ 368 | 369 | return [types.TextContent(type="text", text=position_text)] 370 | 371 | # Suggest Stop Levels 372 | elif name == "suggest-stops": 373 | symbol = arguments.get("symbol") 374 | 375 | if not symbol: 376 | raise ValueError("Missing symbol") 377 | 378 | # Get historical data 379 | df = await market_data.get_historical_data(symbol, lookback_days=60) 380 | 381 | # Add indicators 382 | df = tech_analysis.add_core_indicators(df) 383 | 384 | # Get stop suggestions 385 | stops = risk_analysis.suggest_stop_levels(df) 386 | 387 | latest_close = df["close"].iloc[-1] 388 | 389 | # Format the results 390 | stops_text = f""" 391 | Suggested Stop Levels for {symbol} (Current Price: ${latest_close:.2f}): 392 | 393 | ATR-Based Stops: 394 | - Conservative (1x ATR): ${stops["atr_1x"]:.2f} ({((latest_close - stops["atr_1x"]) / latest_close * 100):.2f}% from current price) 395 | - Moderate (2x ATR): ${stops["atr_2x"]:.2f} ({((latest_close - stops["atr_2x"]) / latest_close * 100):.2f}% from current price) 396 | - Aggressive (3x ATR): ${stops["atr_3x"]:.2f} ({((latest_close - stops["atr_3x"]) / latest_close * 100):.2f}% from current price) 397 | 398 | Percentage-Based Stops: 399 | - Tight (2%): ${stops["percent_2"]:.2f} 400 | - Medium (5%): ${stops["percent_5"]:.2f} 401 | - Wide (8%): ${stops["percent_8"]:.2f} 402 | 403 | Technical Levels: 404 | """ 405 | 406 | if "sma_20" in stops: 407 | stops_text += f"- 20-day SMA: ${stops['sma_20']:.2f} ({((latest_close - stops['sma_20']) / latest_close * 100):.2f}% from current price)\n" 408 | 409 | if "sma_50" in stops: 410 | stops_text += f"- 50-day SMA: ${stops['sma_50']:.2f} ({((latest_close - stops['sma_50']) / latest_close * 100):.2f}% from current price)\n" 411 | 412 | if "sma_200" in stops: 413 | stops_text += f"- 200-day SMA: ${stops['sma_200']:.2f} ({((latest_close - stops['sma_200']) / latest_close * 100):.2f}% from current price)\n" 414 | 415 | if "recent_swing" in stops: 416 | stops_text += f"- Recent Swing Low: ${stops['recent_swing']:.2f} ({((latest_close - stops['recent_swing']) / latest_close * 100):.2f}% from current price)\n" 417 | 418 | return [types.TextContent(type="text", text=stops_text)] 419 | 420 | else: 421 | raise ValueError(f"Unknown tool: {name}") 422 | 423 | except Exception as e: 424 | return [ 425 | types.TextContent( 426 | type="text", text=f"\n\nError: {str(e)}\n\n" 427 | ) 428 | ] 429 | 430 | 431 | # Keep the main function as is 432 | async def main(): 433 | async with mcp.server.stdio.stdio_server() as (read_stream, write_stream): 434 | await server.run( 435 | read_stream, 436 | write_stream, 437 | InitializationOptions( 438 | server_name="mcp-trader", 439 | server_version="0.1.0", 440 | capabilities=server.get_capabilities( 441 | notification_options=NotificationOptions(), 442 | experimental_capabilities={}, 443 | ), 444 | ), 445 | ) 446 | 447 | 448 | # Add a standalone HTTP server for testing 449 | async def run_http_server(): 450 | """Run a standalone HTTP server for testing purposes.""" 451 | from aiohttp import web 452 | 453 | # Initialize our data and analysis modules 454 | global \ 455 | market_data, \ 456 | tech_analysis, \ 457 | rs_analysis, \ 458 | pattern_recognition, \ 459 | volume_analysis, \ 460 | risk_analysis 461 | market_data = MarketData() 462 | tech_analysis = TechnicalAnalysis() 463 | rs_analysis = RelativeStrength() 464 | pattern_recognition = PatternRecognition() 465 | volume_analysis = VolumeProfile() 466 | risk_analysis = RiskAnalysis() 467 | 468 | app = web.Application() 469 | 470 | async def list_tools_handler(request): 471 | tools = await handle_list_tools() 472 | return web.json_response(tools) 473 | 474 | async def call_tool_handler(request): 475 | data = await request.json() 476 | name = data.get("name") 477 | arguments = data.get("arguments", {}) 478 | 479 | result = await handle_call_tool(name, arguments) 480 | 481 | # Convert the result to a JSON-serializable format 482 | response_data = [] 483 | for item in result: 484 | if item.type == "text": 485 | response_data.append({"type": "text", "text": item.text}) 486 | # Add other content types as needed 487 | 488 | return web.json_response(response_data) 489 | 490 | app.router.add_get("/list-tools", list_tools_handler) 491 | app.router.add_post("/call-tool", call_tool_handler) 492 | 493 | print("Starting HTTP server on http://localhost:8000") 494 | runner = web.AppRunner(app) 495 | await runner.setup() 496 | site = web.TCPSite(runner, "localhost", 8000) 497 | await site.start() 498 | 499 | # Keep the server running 500 | while True: 501 | await asyncio.sleep(3600) # Sleep for an hour 502 | 503 | 504 | if __name__ == "__main__": 505 | import sys 506 | 507 | if len(sys.argv) > 1 and sys.argv[1] == "--http": 508 | # Run as HTTP server 509 | asyncio.run(run_http_server()) 510 | else: 511 | # Run as MCP server 512 | asyncio.run(main()) 513 | -------------------------------------------------------------------------------- /src/mcp_trader/tests/test_relative_strength.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import json 3 | import aiohttp 4 | 5 | 6 | async def test_relative_strength(): 7 | """Test the relative-strength tool with NVDA and SPY.""" 8 | async with aiohttp.ClientSession() as session: 9 | # Call the tool 10 | payload = { 11 | "name": "relative-strength", 12 | "arguments": {"symbol": "NVDA", "benchmark": "SPY"}, 13 | } 14 | 15 | async with session.post( 16 | "http://localhost:8000/call-tool", json=payload 17 | ) as response: 18 | if response.status == 200: 19 | result = await response.json() 20 | print(json.dumps(result, indent=2)) 21 | else: 22 | print(f"Error: {response.status}") 23 | print(await response.text()) 24 | 25 | 26 | if __name__ == "__main__": 27 | asyncio.run(test_relative_strength()) 28 | -------------------------------------------------------------------------------- /technical-mcp.png: 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