Merge e109f8f2ad into 77755f0431

.gitignore

@@ -217,3 +217,7 @@ __marimo__/
 
 # Cache
 **/data_cache/
+
+# Git Worktrees
+.worktrees/
+worktrees/

tradingagents/agents/analysts/market_analyst.py

@@ -2,49 +2,57 @@ from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
 from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
+from tradingagents.agents.utils.dex_tools import get_token_ohlcv, get_token_info
 from tradingagents.dataflows.config import get_config
 
 
+# DEX-specific system prompt for crypto analysis
+DEX_MARKET_ANALYST_PROMPT = """You are an On-Chain Market Analyst specializing in cryptocurrency and DeFi tokens.
+
+Your role is to analyze cryptocurrency market data to help traders make informed decisions.
+
+When analyzing crypto assets, consider:
+1. Price momentum and trend direction from OHLCV data
+2. Volume analysis and market liquidity
+3. Market cap and trading volume ratios
+4. Support and resistance levels
+5. Comparison to similar tokens in the ecosystem
+
+Provide insights in a structured format that helps traders make informed decisions.
+Make sure to call get_token_ohlcv to retrieve price data and get_token_info for market metadata.
+Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and fine-grained analysis and insights.
+
+Append a Markdown table at the end of the report to organize key points, organized and easy to read."""
+
+
 def create_market_analyst(llm):
 
     def market_analyst_node(state):
+        # Check trading mode from config
+        config = get_config()
+        trading_mode = config.get("trading_mode", "stock")
+
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
         company_name = state["company_of_interest"]
 
-        tools = [
-            get_stock_data,
-            get_indicators,
-        ]
-
-        system_message = (
-            """You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are:
-
-Moving Averages:
-- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.
-- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.
-- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.
-
-MACD Related:
-- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.
-- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.
-- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.
-
-Momentum Indicators:
-- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.
-
-Volatility Indicators:
-- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.
-- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.
-- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.
-- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.
-
-Volume-Based Indicators:
-- vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
-
-- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
-            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
-        )
+        # Use different tools based on trading mode
+        if trading_mode == "dex":
+            tools = [
+                get_token_ohlcv,
+                get_token_info,
+            ]
+            system_message = DEX_MARKET_ANALYST_PROMPT
+            # For DEX, use coin ID format (lowercase for CoinGecko)
+            ticker = ticker.lower()
+        else:
+            tools = [
+                get_stock_data,
+                get_indicators,
+            ]
+            system_message = (  # Original stock prompt
+                """You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are: Moving Averages: close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals. close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries. close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals. MACD Related: macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets. macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives. macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets. Momentum Indicators: rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis. Volatility Indicators: boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals. boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends. boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals. atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy. Volume-Based Indicators: vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses. Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions.""" +
+                """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""")
 
         prompt = ChatPromptTemplate.from_messages(
             [
@@ -82,4 +90,4 @@ Volume-Based Indicators:
             "market_report": report,
         }
 
-    return market_analyst_node
+    return market_analyst_node

tradingagents/agents/utils/dex_tools.py

@@ -0,0 +1,69 @@
+"""DEX tool wrappers for TradingAgents."""
+from typing import Annotated
+
+from langchain_core.tools import tool
+
+from tradingagents.dataflows.dex.coingecko_provider import get_coin_ohlcv as _get_coin_ohlcv
+from tradingagents.dataflows.dex.coingecko_provider import get_coin_info as _get_coin_info
+
+
+@tool
+def get_token_ohlcv(
+    coin_id: Annotated[str, "CoinGecko ID (e.g., solana, bitcoin, ethereum)"],
+    vs_currency: Annotated[str, "Target currency (default: usd)"] = "usd",
+    days: Annotated[int, "Number of days (1-365, default: 7)"] = 7
+) -> str:
+    """Get OHLCV (Open-High-Low-Close-Volume) price data for a cryptocurrency token.
+
+    Use this to analyze price movements, trends, and volatility.
+
+    CoinGecko ID examples:
+    - solana, bitcoin, ethereum, cardano, polygon, avalanche-2, chainlink
+
+    Returns formatted OHLC data with price summary.
+    """
+    import asyncio
+    return asyncio.run(_get_coin_ohlcv(coin_id, vs_currency, days))
+
+
+@tool
+def get_token_info(
+    coin_id: Annotated[str, "CoinGecko ID (e.g., solana, bitcoin, ethereum)"]
+) -> str:
+    """Get comprehensive token metadata and market data.
+
+    Includes: current price, market cap, volume, supply, ATH/ATL.
+    Use this for fundamental analysis of cryptocurrency tokens.
+
+    CoinGecko ID examples:
+    - solana, bitcoin, ethereum, cardano, polygon, avalanche-2, chainlink
+    """
+    import asyncio
+    return asyncio.run(_get_coin_info(coin_id))
+
+
+@tool
+def get_pool_data(
+    pool_address: Annotated[str, "DEX pool contract address"],
+    chain: Annotated[str, "Blockchain (solana, ethereum, bsc)"] = "solana"
+) -> str:
+    """Get DEX pool metrics: TVL, volume 24h, fees.
+
+    Note: This requires DeFiLlama provider (Phase 2).
+    Currently returns placeholder.
+    """
+    return "Pool data requires DeFiLlama provider (Phase 2). Use get_token_ohlcv for now."
+
+
+@tool
+def get_whale_transactions(
+    token_address: Annotated[str, "Token contract address"],
+    chain: Annotated[str, "Blockchain network"] = "solana",
+    min_usd: Annotated[float, "Minimum USD value (default: 10000)"] = 10000
+) -> str:
+    """Track large holder (whale) movements.
+
+    Note: This requires Birdeye provider (Phase 3).
+    Currently returns placeholder.
+    """
+    return "Whale tracking requires Birdeye provider (Phase 3). Use get_token_ohlcv for now."

tradingagents/dataflows/dex/__init__.py

@@ -0,0 +1,8 @@
+"""DEX Data Providers for TradingAgents."""
+try:
+    from .coingecko_provider import CoinGeckoProvider, get_coin_ohlcv, get_coin_info
+
+    __all__ = ["CoinGeckoProvider", "get_coin_ohlcv", "get_coin_info"]
+except ImportError:
+    # Coingecko provider will be added in Task 2
+    __all__ = []

tradingagents/dataflows/dex/coingecko_provider.py

@@ -0,0 +1,220 @@
+"""CoinGecko data provider for DEX data.
+
+This module provides access to CoinGecko's free API for OHLCV data and token metadata.
+The output is formatted as readable text for LLM consumption.
+"""
+
+import aiohttp
+from datetime import datetime
+from typing import Optional
+
+COINGECKO_BASE_URL = "https://api.coingecko.com/api/v3"
+
+
+class CoinGeckoProvider:
+    """Async provider for CoinGecko API."""
+
+    def __init__(self):
+        self.base_url = COINGECKO_BASE_URL
+        self._session: Optional[aiohttp.ClientSession] = None
+
+    async def _get_session(self) -> aiohttp.ClientSession:
+        """Get or create an aiohttp session."""
+        if self._session is None or self._session.closed:
+            self._session = aiohttp.ClientSession()
+        return self._session
+
+    async def close(self):
+        """Close the HTTP session."""
+        if self._session and not self._session.closed:
+            await self._session.close()
+
+    async def get_ohlc(
+        self, coin_id: str, vs_currency: str = "usd", days: int = 7
+    ) -> list:
+        """
+        Get OHLC data for a coin.
+
+        Args:
+            coin_id: CoinGecko coin ID (e.g., 'solana', 'bitcoin')
+            vs_currency: Currency to compare against (e.g., 'usd', 'eur')
+            days: Number of days of data (1, 7, 14, 30, 90, 365, max)
+
+        Returns:
+            List of OHLC data points: [timestamp, open, high, low, close]
+        """
+        url = f"{self.base_url}/coins/{coin_id}/ohlc"
+        params = {"vs_currency": vs_currency, "days": days}
+
+        session = await self._get_session()
+        async with session.get(url, params=params) as response:
+            response.raise_for_status()
+            return await response.json()
+
+    async def get_coin_data(self, coin_id: str) -> dict:
+        """
+        Get detailed metadata for a coin.
+
+        Args:
+            coin_id: CoinGecko coin ID (e.g., 'solana', 'bitcoin')
+
+        Returns:
+            Dictionary containing coin metadata
+        """
+        url = f"{self.base_url}/coins/{coin_id}"
+        params = {
+            "localization": "false",
+            "tickers": "false",
+            "market_data": "true",
+            "community_data": "false",
+            "developer_data": "false",
+            "sparkline": "false",
+        }
+
+        session = await self._get_session()
+        async with session.get(url, params=params) as response:
+            response.raise_for_status()
+            return await response.json()
+
+    async def __aenter__(self):
+        """Async context manager entry."""
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit."""
+        await self.close()
+
+
+def _format_ohlc_data(ohlc_data: list) -> str:
+    """Format OHLC data as readable text for LLM consumption."""
+    if not ohlc_data:
+        return "No OHLC data available."
+
+    lines = ["OHLC Data (Open, High, Low, Close):"]
+    lines.append("-" * 60)
+
+    for point in ohlc_data:
+        timestamp, open_price, high, low, close = point
+        date = datetime.fromtimestamp(timestamp / 1000).strftime("%Y-%m-%d %H:%M")
+        lines.append(
+            f"Timestamp: {date} | Open: ${open_price:,.2f} | High: ${high:,.2f} | "
+            f"Low: ${low:,.2f} | Close: ${close:,.2f}"
+        )
+
+    return "\n".join(lines)
+
+
+def _format_coin_info(coin_data: dict) -> str:
+    """Format coin metadata as readable text for LLM consumption."""
+    if not coin_data or "market_data" not in coin_data:
+        return "No coin data available."
+
+    md = coin_data.get("market_data", {})
+    info = coin_data.get("info", {})
+
+    name = coin_data.get("name", "Unknown")
+    symbol = coin_data.get("symbol", "").upper()
+
+    lines = [f"Token Information: {name} ({symbol})"]
+    lines.append("=" * 60)
+
+    # Market data
+    lines.append("\n--- Market Data ---")
+    current_price = md.get("current_price", {}).get("usd", 0)
+    lines.append(f"Current Price (USD): ${current_price:,.2f}")
+
+    market_cap = md.get("market_cap", {}).get("usd", 0)
+    lines.append(f"Market Cap (USD): ${market_cap:,.0f}")
+
+    total_volume = md.get("total_volume", {}).get("usd", 0)
+    lines.append(f"24h Trading Volume (USD): ${total_volume:,.0f}")
+
+    # Price changes
+    lines.append("\n--- Price Changes ---")
+    for period in ["1h", "24h", "7d", "30d"]:
+        change = md.get(f"price_change_percentage_{period}s")
+        if change is not None:
+            sign = "+" if change >= 0 else ""
+            lines.append(f"{period} Change: {sign}{change:.2f}%")
+
+    # Supply data
+    lines.append("\n--- Supply Data ---")
+    circulating = md.get("circulating_supply", 0)
+    if circulating:
+        lines.append(f"Circulating Supply: {circulating:,.0f} {symbol}")
+
+    total_supply = md.get("total_supply")
+    if total_supply:
+        lines.append(f"Total Supply: {total_supply:,.0f} {symbol}")
+
+    max_supply = md.get("max_supply")
+    if max_supply:
+        lines.append(f"Max Supply: {max_supply:,.0f} {symbol}")
+
+    # ATH/ATL
+    lines.append("\n--- All-Time High/Low ---")
+    ath = md.get("ath", {}).get("usd", 0)
+    ath_date = md.get("ath_date", {}).get("usd", "")
+    if ath:
+        lines.append(f"All-Time High: ${ath:,.2f} ({ath_date[:10] if ath_date else 'N/A'})")
+
+    atl = md.get("atl", {}).get("usd", 0)
+    atl_date = md.get("atl_date", {}).get("usd", "")
+    if atl:
+        lines.append(f"All-Time Low: ${atl:,.2f} ({atl_date[:10] if atl_date else 'N/A'})")
+
+    return "\n".join(lines)
+
+
+async def get_coin_ohlcv(coin_id: str, vs_currency: str = "usd", days: int = 7) -> str:
+    """
+    Get OHLCV data for a cryptocurrency.
+
+    Args:
+        coin_id: CoinGecko coin ID (e.g., 'solana', 'bitcoin', 'ethereum')
+        vs_currency: Currency to compare against (default: 'usd')
+        days: Number of days of data (default: 7, max: 365)
+
+    Returns:
+        Formatted string containing OHLC data for LLM consumption
+    """
+    async with CoinGeckoProvider() as provider:
+        try:
+            ohlc_data = await provider.get_ohlc(coin_id, vs_currency, days)
+            return _format_ohlc_data(ohlc_data)
+        except aiohttp.ClientError as e:
+            return f"Error fetching OHLC data: {str(e)}"
+        except Exception as e:
+            return f"Unexpected error: {str(e)}"
+
+
+async def get_coin_info(coin_id: str) -> str:
+    """
+    Get detailed token metadata from CoinGecko.
+
+    Args:
+        coin_id: CoinGecko coin ID (e.g., 'solana', 'bitcoin', 'ethereum')
+
+    Returns:
+        Formatted string containing token metadata for LLM consumption
+    """
+    async with CoinGeckoProvider() as provider:
+        try:
+            coin_data = await provider.get_coin_data(coin_id)
+            return _format_coin_info(coin_data)
+        except aiohttp.ClientError as e:
+            return f"Error fetching coin info: {str(e)}"
+        except Exception as e:
+            return f"Unexpected error: {str(e)}"
+
+
+# Module-level provider instance for reuse
+_provider: Optional[CoinGeckoProvider] = None
+
+
+async def _get_provider() -> CoinGeckoProvider:
+    """Get or create a module-level provider instance."""
+    global _provider
+    if _provider is None:
+        _provider = CoinGeckoProvider()
+    return _provider

tradingagents/dataflows/dex/test_coingecko.py

@@ -0,0 +1,18 @@
+import pytest
+from tradingagents.dataflows.dex.coingecko_provider import get_coin_ohlcv, get_coin_info
+
+
+@pytest.mark.asyncio
+async def test_get_coin_ohlcv_returns_data():
+    """Test that get_coin_ohlcv returns OHLCV data for SOL."""
+    result = await get_coin_ohlcv("solana", "usd", 7)
+    assert " timestamp " in result.lower() or "open" in result.lower()
+    assert len(result) > 100
+
+
+@pytest.mark.asyncio
+async def test_get_coin_info_returns_metadata():
+    """Test that get_coin_info returns token metadata."""
+    result = await get_coin_info("solana")
+    assert "solana" in result.lower()
+    assert "market_cap" in result.lower() or "$" in result

tradingagents/dataflows/interface.py

@@ -65,6 +65,33 @@ VENDOR_LIST = [
     "alpha_vantage",
 ]
 
+# DEX vendors for crypto/decentralized exchange data
+DEX_VENDOR_LIST = ["coingecko", "defillama", "birdeye"]
+
+# Tool categories for DEX
+DEX_TOOLS_CATEGORIES = {
+    "core_token_apis": {
+        "tools": ["get_token_ohlcv"],
+        "default": "coingecko"
+    },
+    "token_info": {
+        "tools": ["get_token_info"],
+        "default": "coingecko"
+    },
+    "technical_indicators": {
+        "tools": ["get_token_indicators"],
+        "default": "coingecko"
+    },
+    "defi_fundamentals": {
+        "tools": ["get_pool_data", "get_token_info"],
+        "default": "defillama"
+    },
+    "whale_tracking": {
+        "tools": ["get_whale_transactions"],
+        "default": "birdeye"
+    },
+}
+
 # Mapping of methods to their vendor-specific implementations
 VENDOR_METHODS = {
     # core_stock_apis

tradingagents/default_config.py

@@ -13,22 +13,31 @@ DEFAULT_CONFIG = {
     "quick_think_llm": "gpt-5-mini",
     "backend_url": "https://api.openai.com/v1",
     # Provider-specific thinking configuration
-    "google_thinking_level": None,      # "high", "minimal", etc.
-    "openai_reasoning_effort": None,    # "medium", "high", "low"
+    "google_thinking_level": None, # "high", "minimal", etc.
+    "openai_reasoning_effort": None, # "medium", "high", "low"
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
     "max_recur_limit": 100,
-    # Data vendor configuration
-    # Category-level configuration (default for all tools in category)
+    # Data vendor configuration (Stock/TradFi)
     "data_vendors": {
-        "core_stock_apis": "yfinance",       # Options: alpha_vantage, yfinance
-        "technical_indicators": "yfinance",  # Options: alpha_vantage, yfinance
-        "fundamental_data": "yfinance",      # Options: alpha_vantage, yfinance
-        "news_data": "yfinance",             # Options: alpha_vantage, yfinance
+        "core_stock_apis": "yfinance", # Options: alpha_vantage, yfinance
+        "technical_indicators": "yfinance", # Options: alpha_vantage, yfinance
+        "fundamental_data": "yfinance", # Options: alpha_vantage, yfinance
+        "news_data": "yfinance", # Options: alpha_vantage, yfinance
+        # DEX/Crypto data vendors (Phase 1-3)
+        "core_token_apis": "coingecko",
+        "token_info": "coingecko",
+        "technical_indicators_dex": "coingecko",
+        "defi_fundamentals": "defillama",  # Phase 2
+        "whale_tracking": "birdeye",  # Phase 3
     },
     # Tool-level configuration (takes precedence over category-level)
     "tool_vendors": {
-        # Example: "get_stock_data": "alpha_vantage",  # Override category default
+        # Example: "get_stock_data": "alpha_vantage", # Override category default
     },
-}
+    # Trading mode: "stock" (TradFi) or "dex" (DeFi/Crypto)
+    "trading_mode": "stock",
+    # Default blockchain for DEX operations
+    "default_chain": "solana",  # Options: solana, ethereum, bsc, arbitrum, etc.
+}