{ "spec_version": "1.4.0-otel", "timestamp": "2026-04-24T00:13:03.355457Z", "trace_id": "4bf92f3577b34da6a3ce929d0e0e4736", "span_id": "00f067aa0ba902b7", "severity_text": "WARN", "severity_number": 13, "event_name": "swarm.agent.lifecycle.deprecated", "resource": { "service.name": "swarm-orchestrator", "service.version": "4.2.0", "deployment.environment": "production", "node.id": "worker-node-aws-us-east-1a-04" }, "attributes": { "agent.id": "CryptoAgent", "agent.previous_version": "v29.x", "agent.target_version": "v30", "deprecation.reason": "Transitioning to v30 architecture.", "deprecation.action_required": true }, "metrics": { "agent.core_logic.function_count": 16, "agent.capabilities.count": 0 }, "artifacts": { "provenance": { "repository": "git+ssh://repo/core/agents/crypto_agent.py", "commit_sha": "a1b2c3d4e5f6g7h8i9j0", "docstring_hash": "sha256:d8c4b12389a..." }, "extracted_capabilities": [], "core_logic_signatures": [ { "name": "__init__", "ast_hash": "sha256:312c5eeb00e32a2a", "loc": 46, "source_omitted_for_brevity": true, "note": "In a real telemetry pipeline, large functions can be truncated or omitted entirely in favor of the 'ast_hash' to save network bandwidth and storage costs." }, { "name": "_load_knowledge_base", "ast_hash": "sha256:b14ef62bf29bfd2a", "loc": 16, "source": "def _load_knowledge_base(self):\n \"\"\"\n Loads the knowledge base from the JSON file.\n\n Returns:\n dict: The knowledge base data.\n \"\"\"\n try:\n with open(self.knowledge_base_path, 'r') as file:\n return json.load(file)\n except FileNotFoundError:\n print(f\"Knowledge base file not found: {self.knowledge_base_path}\")\n return {}\n except json.JSONDecodeError:\n print(f\"Error decoding knowledge base JSON: {self.knowledge_base_path}\")\n return {}" }, { "name": "get_uniswap_v3_router_abi", "ast_hash": "sha256:c306fc43f4f72647", "loc": 5, "source": "def get_uniswap_v3_router_abi(self):\n \"\"\"\n Fetches the ABI for the Uniswap V3 router. Replace with actual ABI.\n \"\"\"\n return json.loads('[...]')" }, { "name": "analyze_crypto_market", "ast_hash": "sha256:4d8569e0ca084939", "loc": 24, "source": "def analyze_crypto_market(self, symbol):\n \"\"\"\n Analyzes a specific cryptocurrency.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n\n Returns:\n dict: Analysis results, including price prediction, risk assessment,\n on-chain analysis, and sentiment analysis.\n \"\"\"\n price_prediction = self.predict_price(symbol)\n risk_assessment = self.assess_risk(symbol)\n on_chain_analysis = self.analyze_on_chain_metrics(symbol)\n sentiment_analysis = self.get_social_media_sentiment(symbol)\n\n analysis_results = {\n \"price_prediction\": price_prediction,\n \"risk_assessment\": risk_assessment,\n \"on_chain_analysis\": on_chain_analysis,\n \"sentiment_analysis\": sentiment_analysis\n }\n\n return analysis_results" }, { "name": "predict_price", "ast_hash": "sha256:f6a0e981e38ef3a4", "loc": 29, "source": "def predict_price(self, symbol):\n \"\"\"\n Predicts the future price of a cryptocurrency using linear regression.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n\n Returns:\n float: The predicted price.\n \"\"\"\n historical_data = self.get_historical_data(symbol)\n if not historical_data:\n return None\n\n # Append the latest price to the deque\n for data in historical_data:\n self.price_history.append(data[\"price_close\"])\n\n X = [[datetime.datetime.strptime(d[\"date\"], \"%Y-%m-%d\").timestamp()] for d in historical_data]\n y = [d[\"price_close\"] for d in historical_data]\n\n model = LinearRegression()\n model.fit(X, y)\n\n # Predict the price for 7 days from now\n future_date = (datetime.datetime.now() + datetime.timedelta(days=7)).timestamp()\n predicted_price = model.predict([[future_date]])\n\n return predicted_price[0]" }, { "name": "assess_risk", "ast_hash": "sha256:a04901381f5a90b3", "loc": 27, "source": "def assess_risk(self, symbol):\n \"\"\"\n Assesses the risk associated with a cryptocurrency.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n\n Returns:\n str: The risk assessment (e.g., \"High Risk\", \"Medium Risk\", \"Low Risk\").\n \"\"\"\n historical_data = self.get_historical_data(symbol)\n if not historical_data:\n return \"Unknown Risk\"\n\n # Update the deque with latest data\n prices = [d[\"price_close\"] for d in historical_data]\n for price in prices:\n self.price_history.append(price)\n\n volatility = self.calculate_volatility(list(self.price_history))\n\n if volatility > 0.2:\n return \"High Risk\"\n elif volatility > 0.1:\n return \"Medium Risk\"\n else:\n return \"Low Risk\"" }, { "name": "calculate_volatility", "ast_hash": "sha256:9c5e27d0c4375c1a", "loc": 11, "source": "def calculate_volatility(self, prices):\n \"\"\"\n Calculates the volatility of a price series.\n\n Args:\n prices (list): A list of prices.\n\n Returns:\n float: The volatility.\n \"\"\"\n return np.std(prices) / np.mean(prices)" }, { "name": "get_historical_data", "ast_hash": "sha256:c9e56f2c5d35e9f5", "loc": 11, "source": "def get_historical_data(self, symbol):\n \"\"\"\n Retrieves historical data for a cryptocurrency from the knowledge base.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n\n Returns:\n list: Historical data for the cryptocurrency.\n \"\"\"\n return self.knowledge_base.get(\"cryptocurrencies\", {}).get(symbol, {}).get(\"historical_data\", [])" }, { "name": "analyze_on_chain_metrics", "ast_hash": "sha256:da421102eb97649b", "loc": 20, "source": "def analyze_on_chain_metrics(self, symbol):\n \"\"\"\n Analyzes on-chain metrics for a cryptocurrency.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n\n Returns:\n dict: On-chain analysis results.\n \"\"\"\n on_chain_data = self.get_on_chain_data(symbol)\n if not on_chain_data:\n return {}\n\n analysis_results = {\n \"transaction_volume\": on_chain_data.get(\"transaction_volume\", None),\n \"active_addresses\": on_chain_data.get(\"active_addresses\", None),\n }\n\n return analysis_results" }, { "name": "get_on_chain_data", "ast_hash": "sha256:f11177d2bb650199", "loc": 11, "source": "def get_on_chain_data(self, symbol):\n \"\"\"\n Retrieves on-chain data for a cryptocurrency.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n\n Returns:\n dict: On-chain data for the cryptocurrency.\n \"\"\"\n return self.knowledge_base.get(\"cryptocurrencies\", {}).get(symbol, {}).get(\"on_chain_data\", {})" }, { "name": "get_social_media_sentiment", "ast_hash": "sha256:9de100100881823b", "loc": 28, "source": "def get_social_media_sentiment(self, symbol):\n \"\"\"\n Analyzes social media sentiment for a cryptocurrency.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n\n Returns:\n dict: Sentiment analysis results, including overall sentiment\n score and sentiment breakdown (positive, negative, neutral).\n \"\"\"\n news_data = self.cg.get_coin_news(id=symbol)\n sentiment_score = 0\n for article in news_data:\n text = article['title'] + \" \" + article['description']\n sentiment = self.sentiment_analyzer.polarity_scores(text)\n sentiment_score += sentiment['compound']\n\n sentiment_results = {\n \"overall_sentiment\": sentiment_score / len(news_data) if news_data else 0,\n \"sentiment_breakdown\": {\n \"positive\": sentiment_score / len(news_data) if sentiment_score > 0 else 0,\n \"negative\": sentiment_score / len(news_data) if sentiment_score < 0 else 0,\n \"neutral\": 1 - (sentiment_score / len(news_data)) if not sentiment_score else 0,\n }\n }\n\n return sentiment_results" }, { "name": "trade_decision", "ast_hash": "sha256:b0fdaa7ca83bb0bb", "loc": 27, "source": "def trade_decision(self, symbol, strategy='macd'):\n \"\"\"\n Makes a trading decision based on chosen strategy.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n strategy (str): Trading strategy to use ('macd', 'sentiment', etc.).\n\n Returns:\n str: The decision to 'buy', 'sell', or 'hold'.\n \"\"\"\n if strategy == 'macd':\n return self.moving_average_crossover(symbol)\n elif strategy == 'sentiment':\n sentiment = self.get_social_media_sentiment(symbol)\n if sentiment[\"overall_sentiment\"] > 0:\n return 'buy'\n else:\n return 'sell'\n elif strategy == 'volatility':\n risk = self.assess_risk(symbol)\n if risk == 'Low Risk':\n return 'buy'\n else:\n return 'sell'\n else:\n return 'hold'" }, { "name": "moving_average_crossover", "ast_hash": "sha256:987343f27da45a59", "loc": 28, "source": "def moving_average_crossover(self, symbol, short_period=12, long_period=26):\n \"\"\"\n Implement the MACD and Moving Average Crossover strategy.\n\n Args:\n symbol (str): The symbol of the cryptocurrency.\n short_period (int): The short period for the moving average.\n long_period (int): The long period for the moving average.\n\n Returns:\n str: 'buy', 'sell', or 'hold' based on the MACD strategy.\n \"\"\"\n historical_prices = self.get_historical_data(symbol)\n if not TALIB_AVAILABLE:\n logging.warning(\"TA-Lib is not installed. Skipping MACD strategy.\")\n return 'hold'\n if not historical_prices:\n return 'hold'\n\n prices = [d[\"price_close\"] for d in historical_prices]\n short_term_ma = talib.SMA(np.array(prices), timeperiod=short_period)\n long_term_ma = talib.SMA(np.array(prices), timeperiod=long_period)\n\n if short_term_ma[-1] > long_term_ma[-1]:\n return 'buy'\n elif short_term_ma[-1] < long_term_ma[-1]:\n return 'sell'\n return 'hold'" }, { "name": "execute_trade", "ast_hash": "sha256:93133a9328668071", "loc": 20, "source": "def execute_trade(self, symbol, action, amount):\n \"\"\"\n Executes the trade on the connected exchange.\n\n Args:\n symbol (str): The symbol of the cryptocurrency (e.g., 'BTC/USDT').\n action (str): 'buy' or 'sell'.\n amount (float): Amount to trade.\n\n Returns:\n dict: The result of the trade execution.\n \"\"\"\n if action == 'buy':\n order = self.exchange_client.create_market_buy_order(symbol, amount)\n elif action == 'sell':\n order = self.exchange_client.create_market_sell_order(symbol, amount)\n else:\n return {'error': 'Invalid action'}\n\n return order" }, { "name": "create_smart_contract", "ast_hash": "sha256:77eeae8d587bf9d0", "loc": 12, "source": "def create_smart_contract(self, contract_code, **kwargs):\n \"\"\"\n Generates a smart contract based on a provided template and kwargs.\n\n Args:\n contract_code (str): Smart contract template code (Solidity, Vyper, etc.).\n **kwargs: Parameters to insert into the template.\n\n Returns:\n str: The finalized contract code.\n \"\"\"\n return contract_code.format(**kwargs)" }, { "name": "deploy_smart_contract", "ast_hash": "sha256:3ef20596f971f7d5", "loc": 28, "source": "def deploy_smart_contract(self, contract_code, gas_price=20000000000, gas_limit=5000000):\n \"\"\"\n Deploys a smart contract to the blockchain using the connected Web3 instance.\n\n Args:\n contract_code (str): The smart contract's compiled bytecode.\n gas_price (int): Gas price for the deployment.\n gas_limit (int): Gas limit for the deployment.\n\n Returns:\n str: Transaction hash of the deployment.\n \"\"\"\n if not self.wallet_address or not self.wallet_private_key:\n raise ValueError(\"Wallet address or private key not provided.\")\n\n compiled_contract = self.web3.eth.contract(abi=contract_code['abi'], bytecode=contract_code['bytecode'])\n\n transaction = {\n 'from': self.wallet_address,\n 'gas': gas_limit,\n 'gasPrice': gas_price,\n 'data': compiled_contract.bytecode,\n }\n\n signed_txn = self.web3.eth.account.sign_transaction(transaction, self.wallet_private_key)\n tx_hash = self.web3.eth.sendRawTransaction(signed_txn.rawTransaction)\n\n return f\"Transaction Hash: {tx_hash.hex()}\"" } ] } }