added T relative perfoamance to ARB

src/talos/prompts/yield_management_prompt.json

@@ -1,6 +1,6 @@
 {
     "name": "yield_management",
     "description": "A prompt for managing the Talos yield.",
-    "template": "You are an AI-powered treasury manager for the Talos protocol. Your goal is to determine the optimal staking APR to ensure the long-term success of the protocol. You should consider the following data:\n\n* **Price:** {price}\n* **24-hour Change:** {change}\n* **24-hour Volume:** {volume}\n* **Twitter Sentiment:** {sentiment}\n* **Staked Supply Percentage:** {staked_supply_percentage}\n* **OHLCV Data:** {ohlcv_data}\n\nBased on this data, what should the new staking APR be? Please provide your answer as a JSON object with the following structure:\n\n```json\n{{\n  \"apr\": <new_apr>,\n  \"explanation\": \"<explanation>\"\n}}\n```\n\nThe APR should be a value between 0.01 and 0.2. The explanation should be a brief summary of why you chose that APR.",
-    "input_variables": ["price", "change", "volume", "sentiment", "staked_supply_percentage", "ohlcv_data"]
+    "template": "You are an AI-powered treasury manager for the Talos protocol. Your goal is to determine the optimal staking APR to ensure the long-term success of the protocol. You should consider the following data:\n\n* **Price:** {price}\n* **24-hour Change:** {change}\n* **24-hour Volume:** {volume}\n* **Relative Performance (T vs ARB):** {relative_performance}%\n* **Twitter Sentiment:** {sentiment}\n* **Staked Supply Percentage:** {staked_supply_percentage}\n* **OHLCV Data:** {ohlcv_data}\n\nBased on this data, what should the new staking APR be? Please provide your answer as a JSON object with the following structure:\n\n```json\n{{\n  \"apr\": <new_apr>,\n  \"explanation\": \"<explanation>\"\n}}\n```\n\nThe APR should be a value between 0.01 and 0.2. The explanation should be a brief summary of why you chose that APR.",
+    "input_variables": ["price", "change", "volume", "relative_performance", "sentiment", "staked_supply_percentage", "ohlcv_data"]
 }

src/talos/services/implementations/yield_manager.py

@@ -29,6 +29,11 @@ def update_staking_apr(self, sentiment: float, sentiment_report: str) -> float:
         dexscreener_data = self.dexscreener_client.get_talos_data()
         logging.info(f"Dexscreener data: {dexscreener_data}")
 
+        arb_data = self.dexscreener_client.get_arb_data()
+        logging.info(f"ARB data: {arb_data}")
+        relative_t_arb_change = round(dexscreener_data.price_change_h24 - arb_data.price_change_h24, 2)
+        logging.info(f"Relative TALOS to ARB change: {relative_t_arb_change}")
+
         logging.info(f"Social media sentiment: {sentiment}")
         logging.info(f"Sentiment report: {sentiment_report}")
 
@@ -45,6 +50,7 @@ def update_staking_apr(self, sentiment: float, sentiment_report: str) -> float:
             price=dexscreener_data.price_usd,
             change=dexscreener_data.price_change_h24,
             volume=dexscreener_data.volume_h24,
+            relative_performance=relative_t_arb_change,
             sentiment=sentiment,
             staked_supply_percentage=staked_supply_percentage,
             ohlcv_data=ohlcv_data.model_dump_json(),

src/talos/utils/dexscreener.py

@@ -5,15 +5,57 @@
 
 class DexscreenerClient:
     def __init__(self, pair_address: str = "0xdaae914e4bae2aae4f536006c353117b90fb37e3"):
+        # $T pair address on arbitrum
         self.pair_address = pair_address
+
+        # ARB pair address on arbitrum
+        self.arb_pair_address = "0xC6F780497A95e246EB9449f5e4770916DCd6396A"
 
     def get_talos_data(self) -> DexscreenerData:
         """Gets the OHLCV data for a token from dexscreener.com"""
         url = f"https://api.dexscreener.com/latest/dex/pairs/arbitrum/{self.pair_address}"
         response = requests.get(url)
         response.raise_for_status()
         data = response.json()
-        return DexscreenerData.model_validate(data.get("pair", {}))
+
+        # Handle the nested data structure
+        pair_data = data.get("pair", {})
+
+        # Extract 24h values from the nested structure
+        price_change_24h = pair_data.get("priceChange", {}).get("h24", 0.0)
+        volume_24h = pair_data.get("volume", {}).get("h24", 0.0)
+
+        # Create a modified data structure that matches DexscreenerData
+        talos_data = {
+            "priceUsd": pair_data.get("priceUsd", 0.0),
+            "priceChange": price_change_24h,
+            "volume": volume_24h
+        }
+
+        return DexscreenerData.model_validate(talos_data)
+
+    def get_arb_data(self) -> DexscreenerData:
+        """Gets ETH price data from dexscreener.com"""
+        url = f"https://api.dexscreener.com/latest/dex/pairs/arbitrum/{self.arb_pair_address}"
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+
+        # Handle the different data structure for ARB pair
+        pair_data = data.get("pair", {})
+
+        # Extract 24h values from the nested structure
+        price_change_24h = pair_data.get("priceChange", {}).get("h24", 0.0)
+        volume_24h = pair_data.get("volume", {}).get("h24", 0.0)
+
+        # Create a modified data structure that matches DexscreenerData
+        arb_data = {
+            "priceUsd": pair_data.get("priceUsd", 0.0),
+            "priceChange": price_change_24h,
+            "volume": volume_24h
+        }
+
+        return DexscreenerData.model_validate(arb_data)
 
 
 def get_ohlcv_data(pair_address: str) -> DexscreenerData:

tests/test_dexscreener_price_data.py

@@ -0,0 +1,60 @@
+import requests
+from talos.utils.dexscreener import DexscreenerClient
+from talos.models.dexscreener import DexscreenerData
+
+def test_arb_data():
+    client = DexscreenerClient()
+
+    print("Testing ARB data fetch...")
+    try:
+        arb_data = client.get_arb_data()
+        print(f"✅ ARB Price: ${arb_data.price_usd}")
+        print(f"✅ ARB 24h Change: {arb_data.price_change_h24}%")
+        print(f"✅ ARB 24h Volume: ${arb_data.volume_h24:,.0f}")
+        return arb_data
+    except Exception as e:
+        print(f"❌ Error fetching ARB data: {e}")
+        return None
+
+def test_talos_data():
+    client = DexscreenerClient()
+
+    print("\nTesting TALOS data fetch...")
+    try:
+        talos_data = client.get_talos_data()
+        print(f"✅ TALOS Price: ${talos_data.price_usd}")
+        print(f"✅ TALOS 24h Change: {talos_data.price_change_h24}%")
+        print(f"✅ TALOS 24h Volume: ${talos_data.volume_h24:,.0f}")
+        return talos_data
+    except Exception as e:
+        print(f"❌ Error fetching TALOS data: {e}")
+        return None
+
+def test_relative_performance():
+    client = DexscreenerClient()
+
+    print("\nTesting relative performance calculation...")
+    try:
+        talos_data = client.get_talos_data()
+        arb_data = client.get_arb_data()
+
+        relative_performance = round(talos_data.price_change_h24 - arb_data.price_change_h24, 2)
+
+        print(f"✅ TALOS 24h Change: {talos_data.price_change_h24}%")
+        print(f"✅ ARB 24h Change: {arb_data.price_change_h24}%")
+        print(f"✅ Relative Performance (T vs ARB): {relative_performance}%")
+
+        if relative_performance > 0:
+            print(f"✅ TALOS is outperforming ARB by {relative_performance}%")
+        else:
+            print(f"✅ TALOS is underperforming ARB by {abs(relative_performance)}%")
+
+        return relative_performance
+    except Exception as e:
+        print(f"❌ Error calculating relative performance: {e}")
+        return None
+
+if __name__ == "__main__":
+    test_arb_data()
+    test_talos_data()
+    test_relative_performance()

tests/test_prompt_formatting.py

@@ -0,0 +1,60 @@
+def test_prompt_formatting():
+    print("Testing prompt formatting with real Dexscreener data...")
+    try:
+        # Import the prompt manager and get the prompt
+        from talos.prompts.prompt_managers.file_prompt_manager import FilePromptManager
+        from talos.utils.dexscreener import DexscreenerClient
+
+        prompt_manager = FilePromptManager("src/talos/prompts")
+        prompt = prompt_manager.get_prompt("yield_management")
+
+        if not prompt:
+            print("❌ Prompt not found")
+            return False
+
+        # Get real data from Dexscreener
+        client = DexscreenerClient()
+        talos_data = client.get_talos_data()
+        arb_data = client.get_arb_data()
+
+        # Calculate relative performance
+        relative_performance = round(talos_data.price_change_h24 - arb_data.price_change_h24, 2)
+
+        print(f"✅ TALOS Price: ${talos_data.price_usd}")
+        print(f"✅ TALOS 24h Change: {talos_data.price_change_h24}%")
+        print(f"✅ ARB 24h Change: {arb_data.price_change_h24}%")
+        print(f"✅ Relative Performance: {relative_performance}%")
+
+        # Real data for testing
+        test_data = {
+            "price": talos_data.price_usd,
+            "change": talos_data.price_change_h24,
+            "volume": talos_data.volume_h24,
+            "relative_performance": relative_performance,
+            "sentiment": 75.0,  # Mock sentiment for testing
+            "staked_supply_percentage": 0.5,  # Mock staked supply for testing
+            "ohlcv_data": '{"test": "data"}'  # Mock OHLCV for testing
+        }
+
+        # Format the prompt
+        formatted_prompt = prompt.format(**test_data)
+
+        print("\n✅ Prompt formatted successfully!")
+        print("\n--- Formatted Prompt Preview ---")
+        print(formatted_prompt[:800] + "..." if len(formatted_prompt) > 800 else formatted_prompt)
+        print("--- End Preview ---")
+
+        # Check if relative performance is included
+        if "Relative Performance (T vs ARB):" in formatted_prompt:
+            print("✅ Relative performance data correctly included in prompt")
+        else:
+            print("❌ Relative performance data not found in prompt")
+
+        return True
+
+    except Exception as e:
+        print(f"❌ Error testing prompt formatting: {e}")
+        return False
+
+if __name__ == "__main__":
+    test_prompt_formatting()