{ "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": "FinancialModelingAgent", "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": 15, "agent.capabilities.count": 0 }, "artifacts": { "provenance": { "repository": "git+ssh://repo/core/agents/financial_modeling_agent.py", "commit_sha": "a1b2c3d4e5f6g7h8i9j0", "docstring_hash": "sha256:d8c4b12389a..." }, "extracted_capabilities": [], "core_logic_signatures": [ { "name": "__init__", "ast_hash": "sha256:cb02ee580798bdfa", "loc": 41, "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": "execute", "ast_hash": "sha256:01185ee8654b4f1b", "loc": 99, "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": "apply_sentiment_adjustment", "ast_hash": "sha256:de4856638427587c", "loc": 22, "source": "def apply_sentiment_adjustment(self, sentiment_score: float):\n \"\"\"\n Adjusts financial assumptions based on a sentiment score.\n Score range: -1.0 (Very Bearish) to 1.0 (Very Bullish)\n \"\"\"\n # Logic:\n # Bullish (>0): Increases growth rate slightly, might lower discount rate (lower risk premium).\n # Bearish (<0): Decreases growth rate, increases discount rate.\n\n # Adjustment Factors\n growth_impact = 0.02 * sentiment_score # +/- 2%\n discount_impact = -0.01 * sentiment_score # Inverse: High sentiment -> Lower risk premium\n\n self.assumptions.growth_rate += growth_impact\n self.assumptions.discount_rate += discount_impact\n self.assumptions.sentiment_adjustment_factor = 1.0 + (0.1 * sentiment_score)\n\n # Sync back to instance vars\n self.growth_rate = self.assumptions.growth_rate\n self.discount_rate = self.assumptions.discount_rate\n\n logging.info(f\"Applied sentiment adjustment (score: {sentiment_score}). New Growth: {self.growth_rate:.4f}, New Discount: {self.discount_rate:.4f}\")" }, { "name": "generate_cash_flows", "ast_hash": "sha256:2e9c868baafbd4a8", "loc": 15, "source": "def generate_cash_flows(self, years=None, cash_flow_input=None):\n \"\"\"\n Generates a forecast of cash flows over a number of years.\n \"\"\"\n years = years or self.forecast_years\n if cash_flow_input is not None:\n if not isinstance(cash_flow_input, (list, np.ndarray)):\n raise ValueError(\"cash_flow_input must be a list or numpy array.\")\n self.cash_flows = np.array(cash_flow_input)\n else:\n cash_flows = []\n for year in range(1, years + 1):\n cash_flows.append(self.initial_cash_flow * (1 + self.growth_rate) ** year)\n self.cash_flows = np.array(cash_flows)\n return self.cash_flows" }, { "name": "calculate_discounted_cash_flows", "ast_hash": "sha256:22a58727b51f86f0", "loc": 10, "source": "def calculate_discounted_cash_flows(self):\n \"\"\"\n Calculates the discounted cash flows (DCF) using the provided discount rate.\n \"\"\"\n if self.cash_flows is None:\n raise ValueError(\"Cash flows have not been generated.\")\n\n discounted_cash_flows = self.cash_flows / (1 + self.discount_rate) ** np.arange(1, len(self.cash_flows) + 1)\n self.discounted_cash_flows = discounted_cash_flows\n return self.discounted_cash_flows" }, { "name": "calculate_terminal_value", "ast_hash": "sha256:33d59bccc1fa04bd", "loc": 16, "source": "def calculate_terminal_value(self):\n \"\"\"\n Calculates the terminal value based on the final year's cash flow and the terminal growth rate.\n \"\"\"\n if self.cash_flows is None:\n raise ValueError(\"Cash flows have not been generated.\")\n\n if self.terminal_valuation_method == 'Gordon Growth':\n terminal_cash_flow = self.cash_flows[-1] * (1 + self.terminal_growth_rate)\n terminal_value = terminal_cash_flow / (self.discount_rate - self.terminal_growth_rate)\n elif self.terminal_valuation_method == 'Exit Multiple':\n terminal_value = self.cash_flows[-1] * self.industry_multiples.get(\"EBITDA\", 10)\n else:\n raise ValueError(\"Invalid terminal valuation method.\")\n self.terminal_value = terminal_value\n return self.terminal_value" }, { "name": "calculate_npv", "ast_hash": "sha256:df49110de6e9a85c", "loc": 14, "source": "def calculate_npv(self):\n \"\"\"\n Calculates the net present value (NPV) of the investment based on discounted cash flows and terminal value.\n \"\"\"\n if self.discounted_cash_flows is None:\n self.calculate_discounted_cash_flows()\n\n if self.terminal_value is None:\n self.calculate_terminal_value()\n\n npv = np.sum(self.discounted_cash_flows) + self.terminal_value / \\\n (1 + self.discount_rate) ** len(self.discounted_cash_flows)\n self.npv = npv\n return self.npv" }, { "name": "perform_sensitivity_analysis", "ast_hash": "sha256:d42ede5acd1dfa30", "loc": 24, "source": "def perform_sensitivity_analysis(self, sensitivity_range, variable='growth_rate'):\n \"\"\"\n Performs sensitivity analysis on a given variable (e.g., growth rate, discount rate).\n \"\"\"\n results = {}\n original_variable = getattr(self, variable)\n for value in sensitivity_range:\n setattr(self, variable, value)\n # Re-generate cash flows if growth rate changes\n if variable == 'growth_rate':\n self.generate_cash_flows()\n self.calculate_discounted_cash_flows()\n self.calculate_terminal_value()\n npv = self.calculate_npv()\n results[value] = npv\n\n # Reset to original state\n setattr(self, variable, original_variable)\n if variable == 'growth_rate':\n self.generate_cash_flows()\n self.calculate_discounted_cash_flows()\n self.calculate_terminal_value()\n\n return results" }, { "name": "fetch_and_calculate_dcf", "ast_hash": "sha256:1d2f07b73f91d5e2", "loc": 19, "source": "def fetch_and_calculate_dcf(self, company_identifier: str, company_name: str = \"Unknown Company\") -> Tuple[float, Dict[str, Any], Dict[str, pd.DataFrame]]:\n \"\"\"\n Fetches financial data, calculates DCF, and generates a comprehensive report.\n \"\"\"\n self.company_name = company_name\n\n financial_data = self._fetch_financial_data(company_identifier)\n # Calculate Free Cash Flow for forecast\n fcf_forecast = self._calculate_fcf_forecast(financial_data)\n\n # Calculate DCF\n intrinsic_value, dcf_details = self.calculate_dcf_from_projections(fcf_forecast)\n\n report_data = {\n 'historical': pd.DataFrame(financial_data['historical']),\n 'forecast': pd.DataFrame(financial_data['forecast']),\n 'dcf_details': dcf_details\n }\n return intrinsic_value, dcf_details, report_data" }, { "name": "_fetch_financial_data", "ast_hash": "sha256:5ef85cd2a51d60bf", "loc": 25, "source": "def _fetch_financial_data(self, company_identifier: str) -> Dict[str, Any]:\n \"\"\"\n Placeholder method to fetch financial data from data sources.\n \"\"\"\n # Mock data\n historical_data = {\n 'revenue': [100, 110, 120, 130, 140],\n 'ebitda': [20, 22, 25, 28, 30],\n 'ebit': [15, 17, 20, 23, 25],\n 'interest_expense': [2, 2, 2, 2, 2],\n 'total_debt': [50, 48, 46, 44, 42],\n 'cash_and_equivalents': [10, 12, 15, 18, 20],\n 'total_assets': [200, 210, 220, 230, 240],\n 'current_assets': [50, 55, 60, 65, 70],\n 'current_liabilities': [30, 32, 34, 36, 38],\n 'ebitda_margin': [0.20, 0.21, 0.22, 0.23, 0.24],\n 'capex': [10, 11, 12, 13, 14],\n }\n forecast_data = {\n 'revenue_growth': [0.10] * 7,\n 'ebitda_margin': [0.25] * 7,\n 'capex_percent_revenue': [0.10] * 7,\n 'working_capital_percent_revenue': [0.05] * 7,\n }\n return {'historical': historical_data, 'forecast': forecast_data}" }, { "name": "_calculate_fcf_forecast", "ast_hash": "sha256:7f1d219cadbd0711", "loc": 38, "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_dcf_from_projections", "ast_hash": "sha256:d4ebd71c09a1aa19", "loc": 37, "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_monte_carlo_simulation", "ast_hash": "sha256:e5613ea328be5316", "loc": 48, "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_financial_ratios", "ast_hash": "sha256:3613d1daf3832ad5", "loc": 49, "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_lbo", "ast_hash": "sha256:e2b7f70a66d8cb91", "loc": 81, "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." } ] } }