{ "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": "AlgoTradingAgent", "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": 10, "agent.capabilities.count": 0 }, "artifacts": { "provenance": { "repository": "git+ssh://repo/core/agents/algo_trading_agent.py", "commit_sha": "a1b2c3d4e5f6g7h8i9j0", "docstring_hash": "sha256:d8c4b12389a..." }, "extracted_capabilities": [], "core_logic_signatures": [ { "name": "__init__", "ast_hash": "sha256:a64d00f80eb28cf0", "loc": 19, "source": "def __init__(self, config: Dict[str, Any] = None, data=None, strategies=None, initial_balance=10000, **kwargs):\n \"\"\"\n Initializes the trading agent.\n\n Args:\n config (Dict[str, Any]): Configuration dictionary for AgentBase compatibility.\n data (pd.DataFrame): Historical market data (e.g., price history).\n strategies (list, optional): List of strategies to be simulated. Defaults to None.\n initial_balance (float, optional): The starting balance for the simulation. Defaults to 10000.\n \"\"\"\n if config is None:\n config = {}\n super().__init__(config, **kwargs)\n \n # Support both legacy init params and config-based init\n self.data = data if data is not None else config.get('data', pd.DataFrame())\n self.strategies = strategies or config.get('strategies', ['momentum', 'mean_reversion', 'arbitrage'])\n self.initial_balance = initial_balance if initial_balance != 10000 else config.get('initial_balance', 10000)\n self.results = {}" }, { "name": "execute", "ast_hash": "sha256:ca8f34b4e72b364f", "loc": 36, "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": "run_simulation", "ast_hash": "sha256:04cc7df9212a2e8b", "loc": 19, "source": "def run_simulation(self, strategy):\n \"\"\"\n Runs the simulation for a given strategy.\n\n Args:\n strategy (str): The name of the strategy to simulate.\n\n Returns:\n dict: The performance metrics for the strategy.\n \"\"\"\n if strategy == 'momentum':\n return self.momentum_trading()\n elif strategy == 'mean_reversion':\n return self.mean_reversion_trading()\n elif strategy == 'arbitrage':\n return self.arbitrage_trading()\n else:\n logging.warning(f\"Unknown strategy: {strategy}\")\n return {}" }, { "name": "momentum_trading", "ast_hash": "sha256:613970cd86b6dff8", "loc": 32, "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": "mean_reversion_trading", "ast_hash": "sha256:923430a3e48feb26", "loc": 30, "source": "def mean_reversion_trading(self):\n \"\"\"\n Simulates mean reversion trading strategy.\n\n Buy when the price is below the mean (moving average), sell when above.\n\n Returns:\n dict: Performance metrics for mean reversion strategy.\n \"\"\"\n balance = self.initial_balance\n position = 0\n trade_log = []\n\n window_size = 20 # Lookback period for moving average\n for i in range(window_size, len(self.data)):\n mean = self.data['Close'][i - window_size:i].mean()\n price = self.data['Close'][i]\n\n if price < mean and position == 0: # Buy signal\n position = balance / price\n balance = 0\n\n elif price > mean and position > 0: # Sell signal\n balance = position * price\n position = 0\n\n trade_log.append({'Balance': balance, 'Position': position})\n\n final_balance = balance if position == 0 else position * self.data['Close'].iloc[-1]\n return self.calculate_performance_metrics(trade_log, final_balance)" }, { "name": "arbitrage_trading", "ast_hash": "sha256:97fc9eb12ebccebd", "loc": 31, "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": "calculate_performance_metrics", "ast_hash": "sha256:85bc14a46b4224da", "loc": 29, "source": "def calculate_performance_metrics(self, trade_log, final_balance):\n \"\"\"\n Calculates performance metrics such as cumulative returns and drawdown.\n\n Args:\n trade_log (list): List of trade actions.\n final_balance (float): The final balance after the simulation.\n\n Returns:\n dict: The performance metrics.\n \"\"\"\n trade_df = pd.DataFrame(trade_log)\n trade_df['Cumulative Balance'] = trade_df['Balance'].cumsum() + trade_df['Position'] * self.data['Close']\n\n total_return = (trade_df['Cumulative Balance'].iloc[-1] - self.initial_balance) / self.initial_balance\n win_rate = np.mean(trade_df['Cumulative Balance'] > 0) * 100\n max_drawdown = self.calculate_max_drawdown(trade_df['Cumulative Balance'])\n\n # Sharpe Ratio\n daily_returns = trade_df['Cumulative Balance'].pct_change().dropna()\n sharpe_ratio = daily_returns.mean() / daily_returns.std() if daily_returns.std() != 0 else 0\n\n return {\n 'Final Balance': final_balance,\n 'Total Return': total_return,\n 'Win Rate': win_rate,\n 'Max Drawdown': max_drawdown,\n 'Sharpe Ratio': sharpe_ratio\n }" }, { "name": "calculate_max_drawdown", "ast_hash": "sha256:cb068d9e3f983750", "loc": 13, "source": "def calculate_max_drawdown(self, cumulative_balance):\n \"\"\"\n Calculates the maximum drawdown of the portfolio.\n\n Args:\n cumulative_balance (pd.Series): Series of cumulative balance values.\n\n Returns:\n float: The maximum drawdown percentage.\n \"\"\"\n rolling_max = cumulative_balance.cummax()\n drawdown = (cumulative_balance - rolling_max) / rolling_max\n return drawdown.min()" }, { "name": "evaluate_strategies", "ast_hash": "sha256:501b47af549f95ef", "loc": 11, "source": "def evaluate_strategies(self):\n \"\"\"\n Evaluates all strategies by running simulations and comparing their performance.\n\n Returns:\n dict: A dictionary of performance metrics for each strategy.\n \"\"\"\n for strategy in self.strategies:\n self.results[strategy] = self.run_simulation(strategy)\n\n return self.results" }, { "name": "plot_performance", "ast_hash": "sha256:4414d9e7b466a93d", "loc": 14, "source": "def plot_performance(self):\n \"\"\"\n Plots the performance of all strategies.\n \"\"\"\n plt.figure(figsize=(10, 6))\n\n for strategy, result in self.results.items():\n plt.plot(result['Final Balance'], label=f'{strategy} (Total Return: {result[\"Total Return\"]*100:.2f}%)')\n\n plt.title('Strategy Performance Comparison')\n plt.xlabel('Time')\n plt.ylabel('Portfolio Value')\n plt.legend(loc='upper left')\n plt.show()" } ] } }