From 3d67e8d1e58a0641db1c8cb0567bb7f60e9dbe2f Mon Sep 17 00:00:00 2001 From: furyhawk Date: Fri, 12 Jun 2026 16:39:48 +0800 Subject: [PATCH] Refactor code structure for improved readability and maintainability --- .gitignore | 10 + .python-version | 1 + README.md | 0 agent_a.py | 106 + pyproject.toml | 16 + .../analyzing-financial-statements/SKILL.md | 69 + .../calculate_ratios.py | 346 +++ .../interpret_ratios.py | 377 +++ skills/applying-brand-guidelines/REFERENCE.md | 137 + skills/applying-brand-guidelines/SKILL.md | 171 ++ .../applying-brand-guidelines/apply_brand.py | 432 +++ .../validate_brand.py | 325 +++ skills/creating-financial-models/SKILL.md | 173 ++ skills/creating-financial-models/dcf_model.py | 530 ++++ .../sensitivity_analysis.py | 389 +++ uv.lock | 2400 +++++++++++++++++ 16 files changed, 5482 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 README.md create mode 100644 agent_a.py create mode 100644 pyproject.toml create mode 100644 skills/analyzing-financial-statements/SKILL.md create mode 100644 skills/analyzing-financial-statements/calculate_ratios.py create mode 100644 skills/analyzing-financial-statements/interpret_ratios.py create mode 100644 skills/applying-brand-guidelines/REFERENCE.md create mode 100644 skills/applying-brand-guidelines/SKILL.md create mode 100644 skills/applying-brand-guidelines/apply_brand.py create mode 100644 skills/applying-brand-guidelines/validate_brand.py create mode 100644 skills/creating-financial-models/SKILL.md create mode 100644 skills/creating-financial-models/dcf_model.py create mode 100644 skills/creating-financial-models/sensitivity_analysis.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/agent_a.py b/agent_a.py new file mode 100644 index 0000000..07b8a84 --- /dev/null +++ b/agent_a.py @@ -0,0 +1,106 @@ +import logfire +from pydantic_ai.models.openai import OpenAIResponsesModel +from pydantic_ai.providers.openai import OpenAIProvider +from pydantic_ai import Agent +from pydantic_ai.capabilities import MCP, Thinking, ToolSearch, WebSearch +from pydantic_ai_harness import CodeMode + +# Community packages, alphabetical: +from pydantic_ai_backends import ConsoleCapability +from pydantic_ai_shields import CostTracking, InputGuard, SecretRedaction, ToolGuard +from pydantic_ai_skills import SkillsCapability +from pydantic_ai_summarization import ContextManagerCapability +from pydantic_ai_todo import TodoCapability +from pydantic_deep import MemoryCapability, StuckLoopDetection +from pydantic_deep.deps import DeepAgentDeps +from subagents_pydantic_ai import SubAgentCapability, SubAgentConfig + +# See https://ai.pydantic.dev/logfire/ for setup details. +logfire.configure() +logfire.instrument_pydantic_ai() + +model = OpenAIResponsesModel( + "llama", + provider=OpenAIProvider(base_url="http://localhost:8011/v1"), +) + +agent = Agent( + model, + capabilities=[ + # --- Tool execution & discovery --- + # Wraps every tool into a single run_code, sandboxed by Monty. + CodeMode(), + # Progressive tool discovery for large tool sets; discovered tools fold into run_code. + ToolSearch(), + # --- Reasoning --- + # Provider-adaptive thinking; uses native extended thinking on supporting models. + Thinking(effort="xhigh"), + # --- Context management --- + # Sliding window + LLM compaction. By @vstorm-co: + # https://github.com/vstorm-co/summarization-pydantic-ai + # Pydantic AI also ships `AnthropicCompaction` and `OpenAICompaction` for + # provider-native compaction. + ContextManagerCapability(max_tokens=100_000), + # --- Tools --- + # Connect to any MCP server -- here, the open-source Hacker News server + # (https://github.com/cyanheads/hn-mcp-server). + MCP("https://hn.caseyjhand.com/mcp"), + # Provider-adaptive web search; falls back to a local DuckDuckGo implementation. + WebSearch(), + # Filesystem + shell. By @vstorm-co: https://github.com/vstorm-co/pydantic-ai-backend + ConsoleCapability(), + # --- Memory & persistence --- + # Persistent ./MEMORY.md per agent name. By @vstorm-co: + # https://github.com/vstorm-co/pydantic-deepagents + MemoryCapability(agent_name="harness-example"), + # --- Orchestration --- + # Agent skills (Anthropic's spec) by @DougTrajano: + # https://github.com/DougTrajano/pydantic-ai-skills + # @vstorm-co's pydantic-deep also offers skills loading; the two have different + # spec footprints (Doug's is closer to programmatic skills). + SkillsCapability(directories=["./skills"]), + # Spawn sub-agents with their own toolsets and instructions. By @vstorm-co: + # https://github.com/vstorm-co/subagents-pydantic-ai + SubAgentCapability( + subagents=[ + SubAgentConfig( + name="researcher", + description="Deep research on a topic", + instructions="You are a thorough research assistant.", + ), + ] + ), + # Track tasks and subtasks; in-memory by default, AsyncPostgresStorage available. + # By @vstorm-co: https://github.com/vstorm-co/pydantic-ai-todo + TodoCapability(enable_subtasks=True), + # --- Safety & reliability --- + # The next four are by @vstorm-co: https://github.com/vstorm-co/pydantic-ai-shields + # Per-run cost cap with a callback hook. + CostTracking(budget_usd=5.0), + # Reject prompts that look like prompt-injection attempts. + InputGuard(guard=lambda p: "ignore previous instructions" not in p.lower()), + # Block or require approval per tool name. + ToolGuard(blocked=["rm"], require_approval=["write_file"]), + # Detect API keys/tokens in tool I/O and redact before they reach the model. + SecretRedaction(), + # Bail out if the agent gets stuck calling the same tools in a loop. + # By @vstorm-co: https://github.com/vstorm-co/pydantic-deepagents + StuckLoopDetection(), + ], +) + + +async def main() -> None: + deps = DeepAgentDeps() + result = await agent.run( + "What are your skills.", + # "Find the latest HN stories about AI, pull their comment threads, and summarize the discussions.", + deps=deps, + ) + print(result.output) + + +if __name__ == "__main__": + import asyncio + + asyncio.run(main()) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..108b5bb --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "agent-alpha" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "logfire[asyncpg,fastapi,httpx,sqlite3]>=4.36.0", + "pydantic-ai-backend>=0.2.11", + "pydantic-ai-harness[code-mode]>=0.3.0", + "pydantic-ai-shields>=0.3.4", + "pydantic-ai-skills>=0.11.0", + "pydantic-ai-slim[duckduckgo,mcp,openai]>=1.107.0", + "pydantic-ai-todo>=0.2.4", + "pydantic-deep>=0.3.28", +] diff --git a/skills/analyzing-financial-statements/SKILL.md b/skills/analyzing-financial-statements/SKILL.md new file mode 100644 index 0000000..6e87cb1 --- /dev/null +++ b/skills/analyzing-financial-statements/SKILL.md @@ -0,0 +1,69 @@ +--- +name: analyzing-financial-statements +description: This skill calculates key financial ratios and metrics from financial statement data for investment analysis +--- + +# Financial Ratio Calculator Skill + +This skill provides comprehensive financial ratio analysis for evaluating company performance, profitability, liquidity, and valuation. + +## Capabilities + +Calculate and interpret: +- **Profitability Ratios**: ROE, ROA, Gross Margin, Operating Margin, Net Margin +- **Liquidity Ratios**: Current Ratio, Quick Ratio, Cash Ratio +- **Leverage Ratios**: Debt-to-Equity, Interest Coverage, Debt Service Coverage +- **Efficiency Ratios**: Asset Turnover, Inventory Turnover, Receivables Turnover +- **Valuation Ratios**: P/E, P/B, P/S, EV/EBITDA, PEG +- **Per-Share Metrics**: EPS, Book Value per Share, Dividend per Share + +## How to Use + +1. **Input Data**: Provide financial statement data (income statement, balance sheet, cash flow) +2. **Select Ratios**: Specify which ratios to calculate or use "all" for comprehensive analysis +3. **Interpretation**: The skill will calculate ratios and provide industry-standard interpretations + +## Input Format + +Financial data can be provided as: +- CSV with financial line items +- JSON with structured financial statements +- Text description of key financial figures +- Excel files with financial statements + +## Output Format + +Results include: +- Calculated ratios with values +- Industry benchmark comparisons (when available) +- Trend analysis (if multiple periods provided) +- Interpretation and insights +- Excel report with formatted results + +## Example Usage + +"Calculate key financial ratios for this company based on the attached financial statements" + +"What's the P/E ratio if the stock price is $50 and annual earnings are $2.50 per share?" + +"Analyze the liquidity position using the balance sheet data" + +## Scripts + +- `calculate_ratios.py`: Main calculation engine for all financial ratios +- `interpret_ratios.py`: Provides interpretation and benchmarking + +## Best Practices + +1. Always validate data completeness before calculations +2. Handle missing values appropriately (use industry averages or exclude) +3. Consider industry context when interpreting ratios +4. Include period comparisons for trend analysis +5. Flag unusual or concerning ratios + +## Limitations + +- Requires accurate financial data +- Industry benchmarks are general guidelines +- Some ratios may not apply to all industries +- Historical data doesn't guarantee future performance diff --git a/skills/analyzing-financial-statements/calculate_ratios.py b/skills/analyzing-financial-statements/calculate_ratios.py new file mode 100644 index 0000000..a927e46 --- /dev/null +++ b/skills/analyzing-financial-statements/calculate_ratios.py @@ -0,0 +1,346 @@ +""" +Financial ratio calculation module. +Provides functions to calculate key financial metrics and ratios. +""" + +import json +from typing import Any + + +class FinancialRatioCalculator: + """Calculate financial ratios from financial statement data.""" + + def __init__(self, financial_data: dict[str, Any]): + """ + Initialize with financial statement data. + + Args: + financial_data: Dictionary containing income_statement, balance_sheet, + cash_flow, and market_data + """ + self.income_statement = financial_data.get("income_statement", {}) + self.balance_sheet = financial_data.get("balance_sheet", {}) + self.cash_flow = financial_data.get("cash_flow", {}) + self.market_data = financial_data.get("market_data", {}) + self.ratios = {} + + def safe_divide(self, numerator: float, denominator: float, default: float = 0.0) -> float: + """Safely divide two numbers, returning default if denominator is zero.""" + if denominator == 0: + return default + return numerator / denominator + + def calculate_profitability_ratios(self) -> dict[str, float]: + """Calculate profitability ratios.""" + ratios = {} + + # ROE (Return on Equity) + net_income = self.income_statement.get("net_income", 0) + shareholders_equity = self.balance_sheet.get("shareholders_equity", 0) + ratios["roe"] = self.safe_divide(net_income, shareholders_equity) + + # ROA (Return on Assets) + total_assets = self.balance_sheet.get("total_assets", 0) + ratios["roa"] = self.safe_divide(net_income, total_assets) + + # Gross Margin + revenue = self.income_statement.get("revenue", 0) + cogs = self.income_statement.get("cost_of_goods_sold", 0) + gross_profit = revenue - cogs + ratios["gross_margin"] = self.safe_divide(gross_profit, revenue) + + # Operating Margin + operating_income = self.income_statement.get("operating_income", 0) + ratios["operating_margin"] = self.safe_divide(operating_income, revenue) + + # Net Margin + ratios["net_margin"] = self.safe_divide(net_income, revenue) + + return ratios + + def calculate_liquidity_ratios(self) -> dict[str, float]: + """Calculate liquidity ratios.""" + ratios = {} + + current_assets = self.balance_sheet.get("current_assets", 0) + current_liabilities = self.balance_sheet.get("current_liabilities", 0) + + # Current Ratio + ratios["current_ratio"] = self.safe_divide(current_assets, current_liabilities) + + # Quick Ratio (Acid Test) + inventory = self.balance_sheet.get("inventory", 0) + quick_assets = current_assets - inventory + ratios["quick_ratio"] = self.safe_divide(quick_assets, current_liabilities) + + # Cash Ratio + cash = self.balance_sheet.get("cash_and_equivalents", 0) + ratios["cash_ratio"] = self.safe_divide(cash, current_liabilities) + + return ratios + + def calculate_leverage_ratios(self) -> dict[str, float]: + """Calculate leverage/solvency ratios.""" + ratios = {} + + total_debt = self.balance_sheet.get("total_debt", 0) + shareholders_equity = self.balance_sheet.get("shareholders_equity", 0) + + # Debt-to-Equity Ratio + ratios["debt_to_equity"] = self.safe_divide(total_debt, shareholders_equity) + + # Interest Coverage Ratio + ebit = self.income_statement.get("ebit", 0) + interest_expense = self.income_statement.get("interest_expense", 0) + ratios["interest_coverage"] = self.safe_divide(ebit, interest_expense) + + # Debt Service Coverage Ratio + net_operating_income = self.income_statement.get("operating_income", 0) + total_debt_service = interest_expense + self.balance_sheet.get( + "current_portion_long_term_debt", 0 + ) + ratios["debt_service_coverage"] = self.safe_divide(net_operating_income, total_debt_service) + + return ratios + + def calculate_efficiency_ratios(self) -> dict[str, float]: + """Calculate efficiency/activity ratios.""" + ratios = {} + + revenue = self.income_statement.get("revenue", 0) + total_assets = self.balance_sheet.get("total_assets", 0) + + # Asset Turnover + ratios["asset_turnover"] = self.safe_divide(revenue, total_assets) + + # Inventory Turnover + cogs = self.income_statement.get("cost_of_goods_sold", 0) + inventory = self.balance_sheet.get("inventory", 0) + ratios["inventory_turnover"] = self.safe_divide(cogs, inventory) + + # Receivables Turnover + accounts_receivable = self.balance_sheet.get("accounts_receivable", 0) + ratios["receivables_turnover"] = self.safe_divide(revenue, accounts_receivable) + + # Days Sales Outstanding + ratios["days_sales_outstanding"] = self.safe_divide(365, ratios["receivables_turnover"]) + + return ratios + + def calculate_valuation_ratios(self) -> dict[str, float]: + """Calculate valuation ratios.""" + ratios = {} + + share_price = self.market_data.get("share_price", 0) + shares_outstanding = self.market_data.get("shares_outstanding", 0) + market_cap = share_price * shares_outstanding + + # P/E Ratio + net_income = self.income_statement.get("net_income", 0) + eps = self.safe_divide(net_income, shares_outstanding) + ratios["pe_ratio"] = self.safe_divide(share_price, eps) + ratios["eps"] = eps + + # P/B Ratio + book_value = self.balance_sheet.get("shareholders_equity", 0) + book_value_per_share = self.safe_divide(book_value, shares_outstanding) + ratios["pb_ratio"] = self.safe_divide(share_price, book_value_per_share) + ratios["book_value_per_share"] = book_value_per_share + + # P/S Ratio + revenue = self.income_statement.get("revenue", 0) + ratios["ps_ratio"] = self.safe_divide(market_cap, revenue) + + # EV/EBITDA + ebitda = self.income_statement.get("ebitda", 0) + total_debt = self.balance_sheet.get("total_debt", 0) + cash = self.balance_sheet.get("cash_and_equivalents", 0) + enterprise_value = market_cap + total_debt - cash + ratios["ev_to_ebitda"] = self.safe_divide(enterprise_value, ebitda) + + # PEG Ratio (if growth rate available) + earnings_growth = self.market_data.get("earnings_growth_rate", 0) + if earnings_growth > 0: + ratios["peg_ratio"] = self.safe_divide(ratios["pe_ratio"], earnings_growth * 100) + + return ratios + + def calculate_all_ratios(self) -> dict[str, Any]: + """Calculate all financial ratios.""" + return { + "profitability": self.calculate_profitability_ratios(), + "liquidity": self.calculate_liquidity_ratios(), + "leverage": self.calculate_leverage_ratios(), + "efficiency": self.calculate_efficiency_ratios(), + "valuation": self.calculate_valuation_ratios(), + } + + def interpret_ratio(self, ratio_name: str, value: float) -> str: + """Provide interpretation for a specific ratio.""" + interpretations = { + "current_ratio": lambda v: ( + "Strong liquidity" + if v > 2 + else "Adequate liquidity" + if v > 1.5 + else "Potential liquidity concerns" + if v > 1 + else "Liquidity issues" + ), + "debt_to_equity": lambda v: ( + "Low leverage" + if v < 0.5 + else "Moderate leverage" + if v < 1 + else "High leverage" + if v < 2 + else "Very high leverage" + ), + "roe": lambda v: ( + "Excellent returns" + if v > 0.20 + else "Good returns" + if v > 0.15 + else "Average returns" + if v > 0.10 + else "Below average returns" + if v > 0 + else "Negative returns" + ), + "pe_ratio": lambda v: ( + "Potentially undervalued" + if 0 < v < 15 + else "Fair value" + if 15 <= v < 25 + else "Growth premium" + if 25 <= v < 40 + else "High valuation" + if v >= 40 + else "N/A (negative earnings)" + if v <= 0 + else "N/A" + ), + } + + if ratio_name in interpretations: + return interpretations[ratio_name](value) + return "No interpretation available" + + def format_ratio(self, name: str, value: float, format_type: str = "ratio") -> str: + """Format ratio value for display.""" + if format_type == "percentage": + return f"{value * 100:.2f}%" + elif format_type == "times": + return f"{value:.2f}x" + elif format_type == "days": + return f"{value:.1f} days" + elif format_type == "currency": + return f"${value:.2f}" + else: + return f"{value:.2f}" + + +def calculate_ratios_from_data(financial_data: dict[str, Any]) -> dict[str, Any]: + """ + Main function to calculate all ratios from financial data. + + Args: + financial_data: Dictionary with financial statement data + + Returns: + Dictionary with calculated ratios and interpretations + """ + calculator = FinancialRatioCalculator(financial_data) + ratios = calculator.calculate_all_ratios() + + # Add interpretations + interpretations = {} + for category, category_ratios in ratios.items(): + interpretations[category] = {} + for ratio_name, value in category_ratios.items(): + interpretations[category][ratio_name] = { + "value": value, + "formatted": calculator.format_ratio(ratio_name, value), + "interpretation": calculator.interpret_ratio(ratio_name, value), + } + + return { + "ratios": ratios, + "interpretations": interpretations, + "summary": generate_summary(ratios), + } + + +def generate_summary(ratios: dict[str, Any]) -> str: + """Generate a text summary of the financial analysis.""" + summary_parts = [] + + # Profitability summary + prof = ratios.get("profitability", {}) + if prof.get("roe", 0) > 0: + summary_parts.append( + f"ROE of {prof['roe'] * 100:.1f}% indicates {'strong' if prof['roe'] > 0.15 else 'moderate'} shareholder returns." + ) + + # Liquidity summary + liq = ratios.get("liquidity", {}) + if liq.get("current_ratio", 0) > 0: + summary_parts.append( + f"Current ratio of {liq['current_ratio']:.2f} suggests {'good' if liq['current_ratio'] > 1.5 else 'potential'} liquidity {'position' if liq['current_ratio'] > 1.5 else 'concerns'}." + ) + + # Leverage summary + lev = ratios.get("leverage", {}) + if lev.get("debt_to_equity", 0) >= 0: + summary_parts.append( + f"Debt-to-equity of {lev['debt_to_equity']:.2f} indicates {'conservative' if lev['debt_to_equity'] < 0.5 else 'moderate' if lev['debt_to_equity'] < 1 else 'high'} leverage." + ) + + # Valuation summary + val = ratios.get("valuation", {}) + if val.get("pe_ratio", 0) > 0: + summary_parts.append( + f"P/E ratio of {val['pe_ratio']:.1f} suggests the stock is trading at {'a discount' if val['pe_ratio'] < 15 else 'fair value' if val['pe_ratio'] < 25 else 'a premium'}." + ) + + return " ".join(summary_parts) if summary_parts else "Insufficient data for summary." + + +# Example usage +if __name__ == "__main__": + # Sample financial data + sample_data = { + "income_statement": { + "revenue": 1000000, + "cost_of_goods_sold": 600000, + "operating_income": 200000, + "ebit": 180000, + "ebitda": 250000, + "interest_expense": 20000, + "net_income": 150000, + }, + "balance_sheet": { + "total_assets": 2000000, + "current_assets": 800000, + "cash_and_equivalents": 200000, + "accounts_receivable": 150000, + "inventory": 250000, + "current_liabilities": 400000, + "total_debt": 500000, + "current_portion_long_term_debt": 50000, + "shareholders_equity": 1500000, + }, + "cash_flow": { + "operating_cash_flow": 180000, + "investing_cash_flow": -100000, + "financing_cash_flow": -50000, + }, + "market_data": { + "share_price": 50, + "shares_outstanding": 100000, + "earnings_growth_rate": 0.10, + }, + } + + results = calculate_ratios_from_data(sample_data) + print(json.dumps(results, indent=2)) diff --git a/skills/analyzing-financial-statements/interpret_ratios.py b/skills/analyzing-financial-statements/interpret_ratios.py new file mode 100644 index 0000000..5603b02 --- /dev/null +++ b/skills/analyzing-financial-statements/interpret_ratios.py @@ -0,0 +1,377 @@ +""" +Financial ratio interpretation module. +Provides industry benchmarks and contextual analysis. +""" + +from typing import Any + + +class RatioInterpreter: + """Interpret financial ratios with industry context.""" + + # Industry benchmark ranges (simplified for demonstration) + BENCHMARKS = { + "technology": { + "current_ratio": {"excellent": 2.5, "good": 1.8, "acceptable": 1.2, "poor": 1.0}, + "debt_to_equity": {"excellent": 0.3, "good": 0.5, "acceptable": 1.0, "poor": 2.0}, + "roe": {"excellent": 0.25, "good": 0.18, "acceptable": 0.12, "poor": 0.08}, + "gross_margin": {"excellent": 0.70, "good": 0.50, "acceptable": 0.35, "poor": 0.20}, + "pe_ratio": {"undervalued": 15, "fair": 25, "growth": 35, "expensive": 50}, + }, + "retail": { + "current_ratio": {"excellent": 2.0, "good": 1.5, "acceptable": 1.0, "poor": 0.8}, + "debt_to_equity": {"excellent": 0.5, "good": 0.8, "acceptable": 1.5, "poor": 2.5}, + "roe": {"excellent": 0.20, "good": 0.15, "acceptable": 0.10, "poor": 0.05}, + "gross_margin": {"excellent": 0.40, "good": 0.30, "acceptable": 0.20, "poor": 0.10}, + "pe_ratio": {"undervalued": 12, "fair": 18, "growth": 25, "expensive": 35}, + }, + "financial": { + "current_ratio": {"excellent": 1.5, "good": 1.2, "acceptable": 1.0, "poor": 0.8}, + "debt_to_equity": {"excellent": 1.0, "good": 2.0, "acceptable": 4.0, "poor": 6.0}, + "roe": {"excellent": 0.15, "good": 0.12, "acceptable": 0.08, "poor": 0.05}, + "pe_ratio": {"undervalued": 10, "fair": 15, "growth": 20, "expensive": 30}, + }, + "manufacturing": { + "current_ratio": {"excellent": 2.2, "good": 1.7, "acceptable": 1.3, "poor": 1.0}, + "debt_to_equity": {"excellent": 0.4, "good": 0.7, "acceptable": 1.2, "poor": 2.0}, + "roe": {"excellent": 0.18, "good": 0.14, "acceptable": 0.10, "poor": 0.06}, + "gross_margin": {"excellent": 0.35, "good": 0.25, "acceptable": 0.18, "poor": 0.12}, + "pe_ratio": {"undervalued": 14, "fair": 20, "growth": 28, "expensive": 40}, + }, + "healthcare": { + "current_ratio": {"excellent": 2.3, "good": 1.8, "acceptable": 1.4, "poor": 1.0}, + "debt_to_equity": {"excellent": 0.3, "good": 0.6, "acceptable": 1.0, "poor": 1.8}, + "roe": {"excellent": 0.22, "good": 0.16, "acceptable": 0.11, "poor": 0.07}, + "gross_margin": {"excellent": 0.65, "good": 0.45, "acceptable": 0.30, "poor": 0.20}, + "pe_ratio": {"undervalued": 18, "fair": 28, "growth": 40, "expensive": 55}, + }, + } + + def __init__(self, industry: str = "general"): + """ + Initialize interpreter with industry context. + + Args: + industry: Industry sector for benchmarking + """ + self.industry = industry.lower() + self.benchmarks = self.BENCHMARKS.get(self.industry, self._get_general_benchmarks()) + + def _get_general_benchmarks(self) -> dict[str, Any]: + """Get general industry-agnostic benchmarks.""" + return { + "current_ratio": {"excellent": 2.0, "good": 1.5, "acceptable": 1.0, "poor": 0.8}, + "debt_to_equity": {"excellent": 0.5, "good": 1.0, "acceptable": 1.5, "poor": 2.5}, + "roe": {"excellent": 0.20, "good": 0.15, "acceptable": 0.10, "poor": 0.05}, + "gross_margin": {"excellent": 0.40, "good": 0.30, "acceptable": 0.20, "poor": 0.10}, + "pe_ratio": {"undervalued": 15, "fair": 22, "growth": 30, "expensive": 45}, + } + + def interpret_ratio(self, ratio_name: str, value: float) -> dict[str, Any]: + """ + Interpret a single ratio with context. + + Args: + ratio_name: Name of the ratio + value: Calculated ratio value + + Returns: + Dictionary with interpretation details + """ + interpretation = { + "value": value, + "rating": "N/A", + "message": "", + "recommendation": "", + "benchmark_comparison": {}, + } + + if ratio_name in self.benchmarks: + benchmark = self.benchmarks[ratio_name] + interpretation["benchmark_comparison"] = benchmark + + # Determine rating based on benchmarks + if ratio_name in ["current_ratio", "roe", "gross_margin"]: + # Higher is better + if value >= benchmark["excellent"]: + interpretation["rating"] = "Excellent" + interpretation["message"] = ( + "Performance significantly exceeds industry standards" + ) + elif value >= benchmark["good"]: + interpretation["rating"] = "Good" + interpretation["message"] = ( + f"Above average performance for {self.industry} industry" + ) + elif value >= benchmark["acceptable"]: + interpretation["rating"] = "Acceptable" + interpretation["message"] = "Meets industry standards" + else: + interpretation["rating"] = "Poor" + interpretation["message"] = "Below industry standards - attention needed" + + elif ratio_name == "debt_to_equity": + # Lower is better + if value <= benchmark["excellent"]: + interpretation["rating"] = "Excellent" + interpretation["message"] = "Very conservative capital structure" + elif value <= benchmark["good"]: + interpretation["rating"] = "Good" + interpretation["message"] = "Healthy leverage level" + elif value <= benchmark["acceptable"]: + interpretation["rating"] = "Acceptable" + interpretation["message"] = "Moderate leverage" + else: + interpretation["rating"] = "Poor" + interpretation["message"] = "High leverage - potential risk" + + elif ratio_name == "pe_ratio": + # Context-dependent + if value > 0: + if value < benchmark["undervalued"]: + interpretation["rating"] = "Potentially Undervalued" + interpretation["message"] = ( + f"Trading below typical {self.industry} multiples" + ) + elif value < benchmark["fair"]: + interpretation["rating"] = "Fair Value" + interpretation["message"] = "In line with industry averages" + elif value < benchmark["growth"]: + interpretation["rating"] = "Growth Premium" + interpretation["message"] = "Market pricing in growth expectations" + else: + interpretation["rating"] = "Expensive" + interpretation["message"] = "High valuation relative to industry" + + # Add specific recommendations + interpretation["recommendation"] = self._get_recommendation( + ratio_name, interpretation["rating"] + ) + + return interpretation + + def _get_recommendation(self, ratio_name: str, rating: str) -> str: + """Generate actionable recommendations based on ratio and rating.""" + recommendations = { + "current_ratio": { + "Poor": "Consider improving working capital management, reducing short-term debt, or increasing liquid assets", + "Acceptable": "Monitor liquidity closely and consider building additional cash reserves", + "Good": "Maintain current liquidity management practices", + "Excellent": "Strong liquidity position - consider productive use of excess cash", + }, + "debt_to_equity": { + "Poor": "High leverage increases financial risk - consider debt reduction strategies", + "Acceptable": "Monitor debt levels and ensure adequate interest coverage", + "Good": "Balanced capital structure - maintain current approach", + "Excellent": "Conservative leverage - may consider strategic use of debt for growth", + }, + "roe": { + "Poor": "Focus on improving operational efficiency and profitability", + "Acceptable": "Explore opportunities to enhance returns through operational improvements", + "Good": "Solid returns - continue current strategies", + "Excellent": "Outstanding performance - ensure sustainability of high returns", + }, + "pe_ratio": { + "Potentially Undervalued": "May present buying opportunity if fundamentals are solid", + "Fair Value": "Reasonably priced relative to industry peers", + "Growth Premium": "Ensure growth prospects justify premium valuation", + "Expensive": "Consider valuation risk - ensure fundamentals support high multiple", + }, + } + + if ratio_name in recommendations and rating in recommendations[ratio_name]: + return recommendations[ratio_name][rating] + + return "Continue monitoring this metric" + + def analyze_trend( + self, ratio_name: str, values: list[float], periods: list[str] + ) -> dict[str, Any]: + """ + Analyze trend in a ratio over time. + + Args: + ratio_name: Name of the ratio + values: List of ratio values + periods: List of period labels + + Returns: + Trend analysis dictionary + """ + if len(values) < 2: + return { + "trend": "Insufficient data", + "message": "Need at least 2 periods for trend analysis", + } + + # Calculate trend + first_value = values[0] + last_value = values[-1] + change = last_value - first_value + pct_change = (change / abs(first_value)) * 100 if first_value != 0 else 0 + + # Determine trend direction + if abs(pct_change) < 5: + trend = "Stable" + elif pct_change > 0: + trend = "Improving" if ratio_name != "debt_to_equity" else "Deteriorating" + else: + trend = "Deteriorating" if ratio_name != "debt_to_equity" else "Improving" + + return { + "trend": trend, + "change": change, + "pct_change": pct_change, + "message": f"{ratio_name} has {'increased' if change > 0 else 'decreased'} by {abs(pct_change):.1f}% from {periods[0]} to {periods[-1]}", + "values": list(zip(periods, values, strict=False)), + } + + def generate_report(self, ratios: dict[str, Any]) -> str: + """ + Generate a comprehensive interpretation report. + + Args: + ratios: Dictionary of calculated ratios + + Returns: + Formatted report string + """ + report_lines = [ + f"Financial Analysis Report - {self.industry.title()} Industry Context", + "=" * 70, + "", + ] + + for category, category_ratios in ratios.items(): + report_lines.append(f"\n{category.upper()} ANALYSIS") + report_lines.append("-" * 40) + + for ratio_name, value in category_ratios.items(): + if isinstance(value, (int, float)): + interpretation = self.interpret_ratio(ratio_name, value) + report_lines.append(f"\n{ratio_name.replace('_', ' ').title()}:") + report_lines.append(f" Value: {value:.2f}") + report_lines.append(f" Rating: {interpretation['rating']}") + report_lines.append(f" Analysis: {interpretation['message']}") + report_lines.append(f" Action: {interpretation['recommendation']}") + + return "\n".join(report_lines) + + +def perform_comprehensive_analysis( + ratios: dict[str, Any], + industry: str = "general", + historical_data: dict[str, Any] | None = None, +) -> dict[str, Any]: + """ + Perform comprehensive ratio analysis with interpretations. + + Args: + ratios: Calculated financial ratios + industry: Industry sector for benchmarking + historical_data: Optional historical ratio data for trend analysis + + Returns: + Complete analysis with interpretations and recommendations + """ + interpreter = RatioInterpreter(industry) + analysis = { + "current_analysis": {}, + "trend_analysis": {}, + "overall_health": {}, + "recommendations": [], + } + + # Analyze current ratios + for category, category_ratios in ratios.items(): + analysis["current_analysis"][category] = {} + for ratio_name, value in category_ratios.items(): + if isinstance(value, (int, float)): + analysis["current_analysis"][category][ratio_name] = interpreter.interpret_ratio( + ratio_name, value + ) + + # Perform trend analysis if historical data provided + if historical_data: + for ratio_name, historical_values in historical_data.items(): + if "values" in historical_values and "periods" in historical_values: + analysis["trend_analysis"][ratio_name] = interpreter.analyze_trend( + ratio_name, historical_values["values"], historical_values["periods"] + ) + + # Generate overall health assessment + analysis["overall_health"] = _assess_overall_health(analysis["current_analysis"]) + + # Generate key recommendations + analysis["recommendations"] = _generate_key_recommendations(analysis) + + # Add formatted report + analysis["report"] = interpreter.generate_report(ratios) + + return analysis + + +def _assess_overall_health(current_analysis: dict[str, Any]) -> dict[str, str]: + """Assess overall financial health based on ratio analysis.""" + ratings = [] + for _category, category_analysis in current_analysis.items(): + for _ratio_name, ratio_analysis in category_analysis.items(): + if "rating" in ratio_analysis: + ratings.append(ratio_analysis["rating"]) + + # Simple scoring system + score_map = { + "Excellent": 4, + "Good": 3, + "Acceptable": 2, + "Poor": 1, + "Fair Value": 3, + "Potentially Undervalued": 3, + "Growth Premium": 2, + "Expensive": 1, + } + + scores = [score_map.get(rating, 2) for rating in ratings] + avg_score = sum(scores) / len(scores) if scores else 0 + + if avg_score >= 3.5: + health = "Excellent" + message = "Company shows strong financial health across most metrics" + elif avg_score >= 2.5: + health = "Good" + message = "Overall healthy financial position with some areas for improvement" + elif avg_score >= 1.5: + health = "Fair" + message = "Mixed financial indicators - attention needed in several areas" + else: + health = "Poor" + message = "Significant financial challenges requiring immediate attention" + + return {"status": health, "message": message, "score": f"{avg_score:.1f}/4.0"} + + +def _generate_key_recommendations(analysis: dict[str, Any]) -> list[str]: + """Generate prioritized recommendations based on analysis.""" + recommendations = [] + + # Check for critical issues + for _category, category_analysis in analysis["current_analysis"].items(): + for ratio_name, ratio_analysis in category_analysis.items(): + if ratio_analysis.get("rating") == "Poor": + recommendations.append( + f"Priority: Address {ratio_name.replace('_', ' ')} - {ratio_analysis.get('recommendation', '')}" + ) + + # Add trend-based recommendations + for ratio_name, trend in analysis.get("trend_analysis", {}).items(): + if trend.get("trend") == "Deteriorating": + recommendations.append( + f"Monitor: {ratio_name.replace('_', ' ')} showing negative trend" + ) + + # Add general recommendations if healthy + if not recommendations: + recommendations.append("Continue current financial management practices") + recommendations.append("Consider strategic growth opportunities") + + return recommendations[:5] # Return top 5 recommendations diff --git a/skills/applying-brand-guidelines/REFERENCE.md b/skills/applying-brand-guidelines/REFERENCE.md new file mode 100644 index 0000000..c453dca --- /dev/null +++ b/skills/applying-brand-guidelines/REFERENCE.md @@ -0,0 +1,137 @@ +# Brand Guidelines Reference + +## Quick Reference Card + +### Must-Have Elements +✅ Company logo on first page/slide +✅ Correct brand colors (no variations) +✅ Approved fonts only +✅ Consistent formatting throughout +✅ Professional tone of voice + +### Never Use +❌ Competitor logos or references +❌ Unapproved colors or gradients +❌ Decorative or script fonts +❌ Pixelated or stretched logos +❌ Informal language or slang + +## Color Codes Reference + +### For Digital (RGB/Hex) +| Color Name | Hex Code | RGB | Usage | +|------------|----------|-----|-------| +| Acme Blue | #0066CC | 0, 102, 204 | Primary headers, CTAs | +| Acme Navy | #003366 | 0, 51, 102 | Body text, secondary | +| Success Green | #28A745 | 40, 167, 69 | Positive values | +| Warning Amber | #FFC107 | 255, 193, 7 | Warnings, attention | +| Error Red | #DC3545 | 220, 53, 69 | Errors, negative | +| Neutral Gray | #6C757D | 108, 117, 125 | Muted text | +| Light Gray | #F8F9FA | 248, 249, 250 | Backgrounds | + +### For Print (CMYK) +| Color Name | CMYK | Pantone | +|------------|------|---------| +| Acme Blue | 100, 50, 0, 20 | 2935 C | +| Acme Navy | 100, 50, 0, 60 | 2965 C | + +## Document Templates + +### Email Signature +``` +[Name] +[Title] +Acme Corporation | Innovation Through Excellence +[Phone] | [Email] +www.acmecorp.example +``` + +### Slide Footer +``` +© 2025 Acme Corporation | Confidential | Page [X] +``` + +### Report Header +``` +[Logo] [Document Title] Page [X] of [Y] +``` + +## Accessibility Standards + +### Color Contrast +- Text on white background: Use Acme Navy (#003366) +- Text on blue background: Use white (#FFFFFF) +- Minimum contrast ratio: 4.5:1 for body text +- Minimum contrast ratio: 3:1 for large text + +### Font Sizes +- Minimum body text: 11pt (print), 14px (digital) +- Minimum caption text: 9pt (print), 12px (digital) + +## File Naming Conventions + +### Standard Format +``` +YYYY-MM-DD_DocumentType_Version_Status.ext +``` + +### Examples +- `2025-01-15_QuarterlyReport_v2_FINAL.pptx` +- `2025-01-15_BudgetAnalysis_v1_DRAFT.xlsx` +- `2025-01-15_Proposal_v3_APPROVED.pdf` + +## Common Mistakes to Avoid + +1. **Wrong Blue**: Using generic blue instead of Acme Blue #0066CC +2. **Stretched Logo**: Always maintain aspect ratio +3. **Too Many Colors**: Stick to the approved palette +4. **Inconsistent Fonts**: Don't mix font families +5. **Missing Logo**: Always include on first page +6. **Wrong Date Format**: Use "Month DD, YYYY" +7. **Decimal Places**: Be consistent (currency: 2, percentage: 1) + +## Department-Specific Guidelines + +### Finance +- Always right-align numbers in tables +- Use parentheses for negative values: ($1,234) +- Include data source citations + +### Marketing +- Can use full secondary color palette +- May include approved imagery +- Follow social media specific guidelines when applicable + +### Legal +- Use numbered sections (1.0, 1.1, 1.2) +- Include document control information +- Apply "Confidential" watermark when needed + +## International Considerations + +### Date Formats by Region +- **US**: Month DD, YYYY (January 15, 2025) +- **UK**: DD Month YYYY (15 January 2025) +- **ISO**: YYYY-MM-DD (2025-01-15) + +### Currency Display +- **USD**: $1,234.56 +- **EUR**: €1.234,56 +- **GBP**: £1,234.56 + +## Version History + +| Version | Date | Changes | +|---------|------|---------| +| 2.0 | Jan 2025 | Added digital color codes | +| 1.5 | Oct 2024 | Updated font guidelines | +| 1.0 | Jan 2024 | Initial brand guidelines | + +## Contact for Questions + +**Brand Team** +Email: brand@acmecorp.example +Slack: #brand-guidelines + +**For Exceptions** +Submit request to brand team with business justification \ No newline at end of file diff --git a/skills/applying-brand-guidelines/SKILL.md b/skills/applying-brand-guidelines/SKILL.md new file mode 100644 index 0000000..6d430aa --- /dev/null +++ b/skills/applying-brand-guidelines/SKILL.md @@ -0,0 +1,171 @@ +--- +name: applying-brand-guidelines +description: This skill applies consistent corporate branding and styling to all generated documents including colors, fonts, layouts, and messaging +--- + +# Corporate Brand Guidelines Skill + +This skill ensures all generated documents adhere to corporate brand standards for consistent, professional communication. + +## Brand Identity + +### Company: Acme Corporation +**Tagline**: "Innovation Through Excellence" +**Industry**: Technology Solutions + +## Visual Standards + +### Color Palette + +**Primary Colors**: +- **Acme Blue**: #0066CC (RGB: 0, 102, 204) - Headers, primary buttons +- **Acme Navy**: #003366 (RGB: 0, 51, 102) - Text, accents +- **White**: #FFFFFF - Backgrounds, reverse text + +**Secondary Colors**: +- **Success Green**: #28A745 (RGB: 40, 167, 69) - Positive metrics +- **Warning Amber**: #FFC107 (RGB: 255, 193, 7) - Cautions +- **Error Red**: #DC3545 (RGB: 220, 53, 69) - Negative values +- **Neutral Gray**: #6C757D (RGB: 108, 117, 125) - Secondary text + +### Typography + +**Primary Font Family**: Segoe UI, system-ui, -apple-system, sans-serif + +**Font Hierarchy**: +- **H1**: 32pt, Bold, Acme Blue +- **H2**: 24pt, Semibold, Acme Navy +- **H3**: 18pt, Semibold, Acme Navy +- **Body**: 11pt, Regular, Acme Navy +- **Caption**: 9pt, Regular, Neutral Gray + +### Logo Usage + +- Position: Top-left corner on first page/slide +- Size: 120px width (maintain aspect ratio) +- Clear space: Minimum 20px padding on all sides +- Never distort, rotate, or apply effects + +## Document Standards + +### PowerPoint Presentations + +**Slide Templates**: +1. **Title Slide**: Company logo, presentation title, date, presenter +2. **Section Divider**: Section title with blue background +3. **Content Slide**: Title bar with blue background, white content area +4. **Data Slide**: For charts/graphs, maintain color palette + +**Layout Rules**: +- Margins: 0.5 inches all sides +- Title position: Top 15% of slide +- Bullet indentation: 0.25 inches per level +- Maximum 6 bullet points per slide +- Charts use brand colors exclusively + +### Excel Spreadsheets + +**Formatting Standards**: +- **Headers**: Row 1, Bold, White text on Acme Blue background +- **Subheaders**: Bold, Acme Navy text +- **Data cells**: Regular, Acme Navy text +- **Borders**: Thin, Neutral Gray +- **Alternating rows**: Light gray (#F8F9FA) for readability + +**Chart Defaults**: +- Primary series: Acme Blue +- Secondary series: Success Green +- Gridlines: Neutral Gray, 0.5pt +- No 3D effects or gradients + +### PDF Documents + +**Page Layout**: +- **Header**: Company logo left, document title center, page number right +- **Footer**: Copyright notice left, date center, classification right +- **Margins**: 1 inch all sides +- **Line spacing**: 1.15 +- **Paragraph spacing**: 12pt after + +**Section Formatting**: +- Main headings: Acme Blue, 16pt, bold +- Subheadings: Acme Navy, 14pt, semibold +- Body text: Acme Navy, 11pt, regular + +## Content Guidelines + +### Tone of Voice + +- **Professional**: Formal but approachable +- **Clear**: Avoid jargon, use simple language +- **Active**: Use active voice, action-oriented +- **Positive**: Focus on solutions and benefits + +### Standard Phrases + +**Opening Statements**: +- "At Acme Corporation, we..." +- "Our commitment to innovation..." +- "Delivering excellence through..." + +**Closing Statements**: +- "Thank you for your continued partnership." +- "We look forward to serving your needs." +- "Together, we achieve excellence." + +### Data Presentation + +**Numbers**: +- Use comma separators for thousands +- Currency: $X,XXX.XX format +- Percentages: XX.X% (one decimal) +- Dates: Month DD, YYYY + +**Tables**: +- Headers in brand blue +- Alternating row colors +- Right-align numbers +- Left-align text + +## Quality Standards + +### Before Finalizing + +Always ensure: +1. Logo is properly placed and sized +2. All colors match brand palette exactly +3. Fonts are consistent throughout +4. No typos or grammatical errors +5. Data is accurately presented +6. Professional tone maintained + +### Prohibited Elements + +Never use: +- Clip art or stock photos without approval +- Comic Sans, Papyrus, or decorative fonts +- Rainbow colors or gradients +- Animations or transitions (unless specified) +- Competitor branding or references + +## Application Instructions + +When creating any document: +1. Start with brand colors and fonts +2. Apply appropriate template structure +3. Include logo on first page/slide +4. Use consistent formatting throughout +5. Review against brand standards +6. Ensure professional appearance + +## Scripts + +- `apply_brand.py`: Automatically applies brand formatting to documents +- `validate_brand.py`: Checks documents for brand compliance + +## Notes + +- These guidelines apply to all external communications +- Internal documents may use simplified formatting +- Special projects may have exceptions (request approval) +- Brand guidelines updated quarterly - check for latest version diff --git a/skills/applying-brand-guidelines/apply_brand.py b/skills/applying-brand-guidelines/apply_brand.py new file mode 100644 index 0000000..246146a --- /dev/null +++ b/skills/applying-brand-guidelines/apply_brand.py @@ -0,0 +1,432 @@ +""" +Brand application module for corporate document styling. +Applies consistent branding to Excel, PowerPoint, and PDF documents. +""" + +from typing import Any + + +class BrandFormatter: + """Apply corporate brand guidelines to documents.""" + + # Brand color definitions + COLORS = { + "primary": { + "acme_blue": {"hex": "#0066CC", "rgb": (0, 102, 204)}, + "acme_navy": {"hex": "#003366", "rgb": (0, 51, 102)}, + "white": {"hex": "#FFFFFF", "rgb": (255, 255, 255)}, + }, + "secondary": { + "success_green": {"hex": "#28A745", "rgb": (40, 167, 69)}, + "warning_amber": {"hex": "#FFC107", "rgb": (255, 193, 7)}, + "error_red": {"hex": "#DC3545", "rgb": (220, 53, 69)}, + "neutral_gray": {"hex": "#6C757D", "rgb": (108, 117, 125)}, + "light_gray": {"hex": "#F8F9FA", "rgb": (248, 249, 250)}, + }, + } + + # Font definitions + FONTS = { + "primary": "Segoe UI", + "fallback": ["system-ui", "-apple-system", "sans-serif"], + "sizes": {"h1": 32, "h2": 24, "h3": 18, "body": 11, "caption": 9}, + "weights": {"regular": 400, "semibold": 600, "bold": 700}, + } + + # Company information + COMPANY = { + "name": "Acme Corporation", + "tagline": "Innovation Through Excellence", + "copyright": "© 2025 Acme Corporation. All rights reserved.", + "website": "www.acmecorp.example", + "logo_path": "assets/acme_logo.png", + } + + def __init__(self): + """Initialize brand formatter with standard settings.""" + self.colors = self.COLORS + self.fonts = self.FONTS + self.company = self.COMPANY + + def format_excel(self, workbook_config: dict[str, Any]) -> dict[str, Any]: + """ + Apply brand formatting to Excel workbook configuration. + + Args: + workbook_config: Excel workbook configuration dictionary + + Returns: + Branded workbook configuration + """ + branded_config = workbook_config.copy() + + # Apply header formatting + branded_config["header_style"] = { + "font": { + "name": self.fonts["primary"], + "size": self.fonts["sizes"]["body"], + "bold": True, + "color": self.colors["primary"]["white"]["hex"], + }, + "fill": {"type": "solid", "color": self.colors["primary"]["acme_blue"]["hex"]}, + "alignment": {"horizontal": "center", "vertical": "center"}, + "border": {"style": "thin", "color": self.colors["secondary"]["neutral_gray"]["hex"]}, + } + + # Apply data cell formatting + branded_config["cell_style"] = { + "font": { + "name": self.fonts["primary"], + "size": self.fonts["sizes"]["body"], + "color": self.colors["primary"]["acme_navy"]["hex"], + }, + "alignment": {"horizontal": "left", "vertical": "center"}, + } + + # Apply alternating row colors + branded_config["alternating_rows"] = { + "enabled": True, + "color": self.colors["secondary"]["light_gray"]["hex"], + } + + # Chart color scheme + branded_config["chart_colors"] = [ + self.colors["primary"]["acme_blue"]["hex"], + self.colors["secondary"]["success_green"]["hex"], + self.colors["secondary"]["warning_amber"]["hex"], + self.colors["secondary"]["neutral_gray"]["hex"], + ] + + return branded_config + + def format_powerpoint(self, presentation_config: dict[str, Any]) -> dict[str, Any]: + """ + Apply brand formatting to PowerPoint presentation configuration. + + Args: + presentation_config: PowerPoint configuration dictionary + + Returns: + Branded presentation configuration + """ + branded_config = presentation_config.copy() + + # Slide master settings + branded_config["master"] = { + "background_color": self.colors["primary"]["white"]["hex"], + "title_area": { + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["h1"], + "color": self.colors["primary"]["acme_blue"]["hex"], + "bold": True, + "position": {"x": 0.5, "y": 0.15, "width": 9, "height": 1}, + }, + "content_area": { + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["body"], + "color": self.colors["primary"]["acme_navy"]["hex"], + "position": {"x": 0.5, "y": 2, "width": 9, "height": 5}, + }, + "footer": { + "show_slide_number": True, + "show_date": True, + "company_name": self.company["name"], + }, + } + + # Title slide template + branded_config["title_slide"] = { + "background": self.colors["primary"]["acme_blue"]["hex"], + "title_color": self.colors["primary"]["white"]["hex"], + "subtitle_color": self.colors["primary"]["white"]["hex"], + "include_logo": True, + "logo_position": {"x": 0.5, "y": 0.5, "width": 2}, + } + + # Content slide template + branded_config["content_slide"] = { + "title_bar": { + "background": self.colors["primary"]["acme_blue"]["hex"], + "text_color": self.colors["primary"]["white"]["hex"], + "height": 1, + }, + "bullet_style": {"level1": "•", "level2": "○", "level3": "▪", "indent": 0.25}, + } + + # Chart defaults + branded_config["charts"] = { + "color_scheme": [ + self.colors["primary"]["acme_blue"]["hex"], + self.colors["secondary"]["success_green"]["hex"], + self.colors["secondary"]["warning_amber"]["hex"], + self.colors["secondary"]["neutral_gray"]["hex"], + ], + "gridlines": {"color": self.colors["secondary"]["neutral_gray"]["hex"], "width": 0.5}, + "font": {"name": self.fonts["primary"], "size": self.fonts["sizes"]["caption"]}, + } + + return branded_config + + def format_pdf(self, document_config: dict[str, Any]) -> dict[str, Any]: + """ + Apply brand formatting to PDF document configuration. + + Args: + document_config: PDF document configuration dictionary + + Returns: + Branded document configuration + """ + branded_config = document_config.copy() + + # Page layout + branded_config["page"] = { + "margins": {"top": 1, "bottom": 1, "left": 1, "right": 1}, + "size": "letter", + "orientation": "portrait", + } + + # Header configuration + branded_config["header"] = { + "height": 0.75, + "content": { + "left": {"type": "logo", "width": 1.5}, + "center": { + "type": "text", + "content": document_config.get("title", "Document"), + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["body"], + "color": self.colors["primary"]["acme_navy"]["hex"], + }, + "right": {"type": "page_number", "format": "Page {page} of {total}"}, + }, + } + + # Footer configuration + branded_config["footer"] = { + "height": 0.5, + "content": { + "left": { + "type": "text", + "content": self.company["copyright"], + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["caption"], + "color": self.colors["secondary"]["neutral_gray"]["hex"], + }, + "center": {"type": "date", "format": "%B %d, %Y"}, + "right": {"type": "text", "content": "Confidential"}, + }, + } + + # Text styles + branded_config["styles"] = { + "heading1": { + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["h1"], + "color": self.colors["primary"]["acme_blue"]["hex"], + "bold": True, + "spacing_after": 12, + }, + "heading2": { + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["h2"], + "color": self.colors["primary"]["acme_navy"]["hex"], + "bold": True, + "spacing_after": 10, + }, + "heading3": { + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["h3"], + "color": self.colors["primary"]["acme_navy"]["hex"], + "bold": False, + "spacing_after": 8, + }, + "body": { + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["body"], + "color": self.colors["primary"]["acme_navy"]["hex"], + "line_spacing": 1.15, + "paragraph_spacing": 12, + }, + "caption": { + "font": self.fonts["primary"], + "size": self.fonts["sizes"]["caption"], + "color": self.colors["secondary"]["neutral_gray"]["hex"], + "italic": True, + }, + } + + # Table formatting + branded_config["table_style"] = { + "header": { + "background": self.colors["primary"]["acme_blue"]["hex"], + "text_color": self.colors["primary"]["white"]["hex"], + "bold": True, + }, + "rows": { + "alternating_color": self.colors["secondary"]["light_gray"]["hex"], + "border_color": self.colors["secondary"]["neutral_gray"]["hex"], + }, + } + + return branded_config + + def validate_colors(self, colors_used: list[str]) -> dict[str, Any]: + """ + Validate that colors match brand guidelines. + + Args: + colors_used: List of color codes used in document + + Returns: + Validation results with corrections if needed + """ + results = {"valid": True, "corrections": [], "warnings": []} + + approved_colors = [] + for category in self.colors.values(): + for color in category.values(): + approved_colors.append(color["hex"].upper()) + + for color in colors_used: + color_upper = color.upper() + if color_upper not in approved_colors: + results["valid"] = False + # Find closest brand color + closest = self._find_closest_brand_color(color) + results["corrections"].append( + { + "original": color, + "suggested": closest, + "message": f"Non-brand color {color} should be replaced with {closest}", + } + ) + + return results + + def _find_closest_brand_color(self, color: str) -> str: + """Find the closest brand color to a given color.""" + # Simplified - in reality would calculate color distance + return self.colors["primary"]["acme_blue"]["hex"] + + def apply_watermark(self, document_type: str) -> dict[str, Any]: + """ + Generate watermark configuration for documents. + + Args: + document_type: Type of document (draft, confidential, etc.) + + Returns: + Watermark configuration + """ + watermarks = { + "draft": { + "text": "DRAFT", + "color": self.colors["secondary"]["neutral_gray"]["hex"], + "opacity": 0.1, + "angle": 45, + "font_size": 72, + }, + "confidential": { + "text": "CONFIDENTIAL", + "color": self.colors["secondary"]["error_red"]["hex"], + "opacity": 0.1, + "angle": 45, + "font_size": 60, + }, + "sample": { + "text": "SAMPLE", + "color": self.colors["secondary"]["warning_amber"]["hex"], + "opacity": 0.15, + "angle": 45, + "font_size": 72, + }, + } + + return watermarks.get(document_type, watermarks["draft"]) + + def get_chart_palette(self, num_series: int = 4) -> list[str]: + """ + Get color palette for charts. + + Args: + num_series: Number of data series + + Returns: + List of hex color codes + """ + palette = [ + self.colors["primary"]["acme_blue"]["hex"], + self.colors["secondary"]["success_green"]["hex"], + self.colors["secondary"]["warning_amber"]["hex"], + self.colors["secondary"]["neutral_gray"]["hex"], + self.colors["primary"]["acme_navy"]["hex"], + self.colors["secondary"]["error_red"]["hex"], + ] + + return palette[:num_series] + + def format_number(self, value: float, format_type: str = "general") -> str: + """ + Format numbers according to brand standards. + + Args: + value: Numeric value to format + format_type: Type of formatting (currency, percentage, general) + + Returns: + Formatted string + """ + if format_type == "currency": + return f"${value:,.2f}" + elif format_type == "percentage": + return f"{value:.1f}%" + elif format_type == "large_number": + if value >= 1_000_000: + return f"{value / 1_000_000:.1f}M" + elif value >= 1_000: + return f"{value / 1_000:.1f}K" + else: + return f"{value:.0f}" + else: + return f"{value:,.0f}" if value >= 1000 else f"{value:.2f}" + + +def apply_brand_to_document(document_type: str, config: dict[str, Any]) -> dict[str, Any]: + """ + Main function to apply branding to any document type. + + Args: + document_type: Type of document ('excel', 'powerpoint', 'pdf') + config: Document configuration + + Returns: + Branded configuration + """ + formatter = BrandFormatter() + + if document_type.lower() == "excel": + return formatter.format_excel(config) + elif document_type.lower() in ["powerpoint", "pptx"]: + return formatter.format_powerpoint(config) + elif document_type.lower() == "pdf": + return formatter.format_pdf(config) + else: + raise ValueError(f"Unsupported document type: {document_type}") + + +# Example usage +if __name__ == "__main__": + # Example Excel configuration + excel_config = {"title": "Quarterly Report", "sheets": ["Summary", "Details"]} + + branded_excel = apply_brand_to_document("excel", excel_config) + print("Branded Excel Configuration:") + print(branded_excel) + + # Example PowerPoint configuration + ppt_config = {"title": "Business Review", "num_slides": 10} + + branded_ppt = apply_brand_to_document("powerpoint", ppt_config) + print("\nBranded PowerPoint Configuration:") + print(branded_ppt) diff --git a/skills/applying-brand-guidelines/validate_brand.py b/skills/applying-brand-guidelines/validate_brand.py new file mode 100644 index 0000000..d2c4173 --- /dev/null +++ b/skills/applying-brand-guidelines/validate_brand.py @@ -0,0 +1,325 @@ +#!/usr/bin/env python3 +""" +Brand Validation Script +Validates content against brand guidelines including colors, fonts, tone, and messaging. +""" + +import json +import re +from dataclasses import asdict, dataclass + + +@dataclass +class BrandGuidelines: + """Brand guidelines configuration""" + + brand_name: str + primary_colors: list[str] + secondary_colors: list[str] + fonts: list[str] + tone_keywords: list[str] + prohibited_words: list[str] + tagline: str | None = None + logo_usage_rules: dict | None = None + + +@dataclass +class ValidationResult: + """Result of brand validation""" + + passed: bool + score: float + violations: list[str] + warnings: list[str] + suggestions: list[str] + + +class BrandValidator: + """Validates content against brand guidelines""" + + def __init__(self, guidelines: BrandGuidelines): + self.guidelines = guidelines + + def validate_colors(self, content: str) -> tuple[list[str], list[str]]: + """ + Validate color usage in content (hex codes, RGB, color names) + Returns: (violations, warnings) + """ + violations = [] + warnings = [] + + # Find hex colors + hex_pattern = r"#[0-9A-Fa-f]{6}|#[0-9A-Fa-f]{3}" + found_colors = re.findall(hex_pattern, content) + + # Find RGB colors + rgb_pattern = r"rgb\s*\(\s*\d{1,3}\s*,\s*\d{1,3}\s*,\s*\d{1,3}\s*\)" + found_colors.extend(re.findall(rgb_pattern, content, re.IGNORECASE)) + + approved_colors = self.guidelines.primary_colors + self.guidelines.secondary_colors + + for color in found_colors: + if color.upper() not in [c.upper() for c in approved_colors]: + violations.append(f"Unapproved color used: {color}") + + return violations, warnings + + def validate_fonts(self, content: str) -> tuple[list[str], list[str]]: + """ + Validate font usage in content + Returns: (violations, warnings) + """ + violations = [] + warnings = [] + + # Common font specification patterns + font_patterns = [ + r'font-family\s*:\s*["\']?([^;"\']+)["\']?', + r"font:\s*[^;]*\s+([A-Za-z][A-Za-z\s]+)(?:,|;|\s+\d)", + ] + + found_fonts = [] + for pattern in font_patterns: + matches = re.findall(pattern, content, re.IGNORECASE) + found_fonts.extend(matches) + + for font in found_fonts: + font_clean = font.strip().lower() + # Check if any approved font is in the found font string + if not any(approved.lower() in font_clean for approved in self.guidelines.fonts): + violations.append(f"Unapproved font used: {font}") + + return violations, warnings + + def validate_tone(self, content: str) -> tuple[list[str], list[str]]: + """ + Validate tone and messaging + Returns: (violations, warnings) + """ + violations = [] + warnings = [] + + # Check for prohibited words + content_lower = content.lower() + for word in self.guidelines.prohibited_words: + if word.lower() in content_lower: + violations.append(f"Prohibited word/phrase used: '{word}'") + + # Check for tone keywords (should have at least some) + tone_matches = sum( + 1 for keyword in self.guidelines.tone_keywords if keyword.lower() in content_lower + ) + + if tone_matches == 0 and len(content) > 100: + warnings.append( + f"Content may not align with brand tone. " + f"Consider using terms like: {', '.join(self.guidelines.tone_keywords[:5])}" + ) + + return violations, warnings + + def validate_brand_name(self, content: str) -> tuple[list[str], list[str]]: + """ + Validate brand name usage and capitalization + Returns: (violations, warnings) + """ + violations = [] + warnings = [] + + # Find all variations of the brand name + brand_pattern = re.compile(re.escape(self.guidelines.brand_name), re.IGNORECASE) + matches = brand_pattern.findall(content) + + for match in matches: + if match != self.guidelines.brand_name: + violations.append( + f"Incorrect brand name capitalization: '{match}' " + f"should be '{self.guidelines.brand_name}'" + ) + + return violations, warnings + + def calculate_score(self, violations: list[str], warnings: list[str]) -> float: + """Calculate compliance score (0-100)""" + violation_penalty = len(violations) * 10 + warning_penalty = len(warnings) * 3 + + score = max(0, 100 - violation_penalty - warning_penalty) + return round(score, 2) + + def generate_suggestions(self, violations: list[str], warnings: list[str]) -> list[str]: + """Generate helpful suggestions based on violations and warnings""" + suggestions = [] + + if any("color" in v.lower() for v in violations): + suggestions.append( + f"Use approved colors: Primary: {', '.join(self.guidelines.primary_colors[:3])}" + ) + + if any("font" in v.lower() for v in violations): + suggestions.append(f"Use approved fonts: {', '.join(self.guidelines.fonts)}") + + if any("tone" in w.lower() for w in warnings): + suggestions.append( + f"Incorporate brand tone keywords: {', '.join(self.guidelines.tone_keywords[:5])}" + ) + + if any("brand name" in v.lower() for v in violations): + suggestions.append(f"Always capitalize brand name as: {self.guidelines.brand_name}") + + return suggestions + + def validate(self, content: str) -> ValidationResult: + """ + Perform complete brand validation + Returns: ValidationResult + """ + all_violations = [] + all_warnings = [] + + # Run all validation checks + color_v, color_w = self.validate_colors(content) + all_violations.extend(color_v) + all_warnings.extend(color_w) + + font_v, font_w = self.validate_fonts(content) + all_violations.extend(font_v) + all_warnings.extend(font_w) + + tone_v, tone_w = self.validate_tone(content) + all_violations.extend(tone_v) + all_warnings.extend(tone_w) + + brand_v, brand_w = self.validate_brand_name(content) + all_violations.extend(brand_v) + all_warnings.extend(brand_w) + + # Calculate score and generate suggestions + score = self.calculate_score(all_violations, all_warnings) + suggestions = self.generate_suggestions(all_violations, all_warnings) + + return ValidationResult( + passed=len(all_violations) == 0, + score=score, + violations=all_violations, + warnings=all_warnings, + suggestions=suggestions, + ) + + +def load_guidelines_from_json(filepath: str) -> BrandGuidelines: + """ + Load brand guidelines from JSON file + + Args: + filepath: Path to JSON file containing brand guidelines + + Returns: + BrandGuidelines object + + Raises: + FileNotFoundError: If the file doesn't exist + json.JSONDecodeError: If the file contains invalid JSON + TypeError: If required fields are missing + """ + try: + with open(filepath) as f: + data = json.load(f) + return BrandGuidelines(**data) + except FileNotFoundError as e: + raise FileNotFoundError(f"Brand guidelines file not found: {filepath}") from e + except json.JSONDecodeError as e: + raise json.JSONDecodeError( + f"Invalid JSON in brand guidelines file: {e.msg}", e.doc, e.pos + ) from e + except TypeError as e: + raise TypeError(f"Missing required fields in brand guidelines: {e}") from e + + +def get_acme_corporation_guidelines() -> BrandGuidelines: + """ + Get default Acme Corporation brand guidelines. + + These guidelines match the standards defined in the SKILL.md reference. + Users should customize these for their own organization. + + Returns: + BrandGuidelines object with Acme Corporation standards + """ + return BrandGuidelines( + brand_name="Acme Corporation", + primary_colors=["#0066CC", "#003366", "#FFFFFF"], # Acme Blue, Acme Navy, White + secondary_colors=[ + "#28A745", + "#FFC107", + "#DC3545", + "#6C757D", + "#F8F9FA", + ], # Success Green, Warning Amber, Error Red, Neutral Gray, Light Gray + fonts=["Segoe UI", "system-ui", "-apple-system", "sans-serif"], + tone_keywords=[ + "innovation", + "excellence", + "professional", + "solutions", + "trusted", + "reliable", + ], + prohibited_words=["cheap", "outdated", "inferior", "unprofessional", "sloppy"], + tagline="Innovation Through Excellence", + ) + + +def main(): + """Example usage demonstrating brand validation""" + # Load Acme Corporation brand guidelines + # Users should customize this for their own organization + guidelines = get_acme_corporation_guidelines() + + # Example content to validate (intentionally contains violations for demonstration) + test_content = """ + Welcome to acme corporation! + + We are a cheap solution provider with outdated technology. + + Our innovation and excellence in professional solutions are trusted by many. + + Contact us at: font-family: 'Comic Sans MS' + Color scheme: #FF0000 + Background: rgb(255, 0, 0) + """ + + # Validate + validator = BrandValidator(guidelines) + result = validator.validate(test_content) + + # Print results + print("=" * 60) + print("BRAND VALIDATION REPORT") + print("=" * 60) + print(f"\nOverall Status: {'✓ PASSED' if result.passed else '✗ FAILED'}") + print(f"Compliance Score: {result.score}/100") + + if result.violations: + print(f"\n❌ VIOLATIONS ({len(result.violations)}):") + for i, violation in enumerate(result.violations, 1): + print(f" {i}. {violation}") + + if result.warnings: + print(f"\n⚠️ WARNINGS ({len(result.warnings)}):") + for i, warning in enumerate(result.warnings, 1): + print(f" {i}. {warning}") + + if result.suggestions: + print("\n💡 SUGGESTIONS:") + for i, suggestion in enumerate(result.suggestions, 1): + print(f" {i}. {suggestion}") + + print("\n" + "=" * 60) + + # Return JSON for programmatic use + return asdict(result) + + +if __name__ == "__main__": + main() diff --git a/skills/creating-financial-models/SKILL.md b/skills/creating-financial-models/SKILL.md new file mode 100644 index 0000000..5cdb0bd --- /dev/null +++ b/skills/creating-financial-models/SKILL.md @@ -0,0 +1,173 @@ +--- +name: creating-financial-models +description: This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions +--- + +# Financial Modeling Suite + +A comprehensive financial modeling toolkit for investment analysis, valuation, and risk assessment using industry-standard methodologies. + +## Core Capabilities + +### 1. Discounted Cash Flow (DCF) Analysis +- Build complete DCF models with multiple growth scenarios +- Calculate terminal values using perpetuity growth and exit multiple methods +- Determine weighted average cost of capital (WACC) +- Generate enterprise and equity valuations + +### 2. Sensitivity Analysis +- Test key assumptions impact on valuation +- Create data tables for multiple variables +- Generate tornado charts for sensitivity ranking +- Identify critical value drivers + +### 3. Monte Carlo Simulation +- Run thousands of scenarios with probability distributions +- Model uncertainty in key inputs +- Generate confidence intervals for valuations +- Calculate probability of achieving targets + +### 4. Scenario Planning +- Build best/base/worst case scenarios +- Model different economic environments +- Test strategic alternatives +- Compare outcome probabilities + +## Input Requirements + +### For DCF Analysis +- Historical financial statements (3-5 years) +- Revenue growth assumptions +- Operating margin projections +- Capital expenditure forecasts +- Working capital requirements +- Terminal growth rate or exit multiple +- Discount rate components (risk-free rate, beta, market premium) + +### For Sensitivity Analysis +- Base case model +- Variable ranges to test +- Key metrics to track + +### For Monte Carlo Simulation +- Probability distributions for uncertain variables +- Correlation assumptions between variables +- Number of iterations (typically 1,000-10,000) + +### For Scenario Planning +- Scenario definitions and assumptions +- Probability weights for scenarios +- Key performance indicators to track + +## Output Formats + +### DCF Model Output +- Complete financial projections +- Free cash flow calculations +- Terminal value computation +- Enterprise and equity value summary +- Valuation multiples implied +- Excel workbook with full model + +### Sensitivity Analysis Output +- Sensitivity tables showing value ranges +- Tornado chart of key drivers +- Break-even analysis +- Charts showing relationships + +### Monte Carlo Output +- Probability distribution of valuations +- Confidence intervals (e.g., 90%, 95%) +- Statistical summary (mean, median, std dev) +- Risk metrics (VaR, probability of loss) + +### Scenario Planning Output +- Scenario comparison table +- Probability-weighted expected values +- Decision tree visualization +- Risk-return profiles + +## Model Types Supported + +1. **Corporate Valuation** + - Mature companies with stable cash flows + - Growth companies with J-curve projections + - Turnaround situations + +2. **Project Finance** + - Infrastructure projects + - Real estate developments + - Energy projects + +3. **M&A Analysis** + - Acquisition valuations + - Synergy modeling + - Accretion/dilution analysis + +4. **LBO Models** + - Leveraged buyout analysis + - Returns analysis (IRR, MOIC) + - Debt capacity assessment + +## Best Practices Applied + +### Modeling Standards +- Consistent formatting and structure +- Clear assumption documentation +- Separation of inputs, calculations, outputs +- Error checking and validation +- Version control and change tracking + +### Valuation Principles +- Use multiple valuation methods for triangulation +- Apply appropriate risk adjustments +- Consider market comparables +- Validate against trading multiples +- Document key assumptions clearly + +### Risk Management +- Identify and quantify key risks +- Use probability-weighted scenarios +- Stress test extreme cases +- Consider correlation effects +- Provide confidence intervals + +## Example Usage + +"Build a DCF model for this technology company using the attached financials" + +"Run a Monte Carlo simulation on this acquisition model with 5,000 iterations" + +"Create sensitivity analysis showing impact of growth rate and WACC on valuation" + +"Develop three scenarios for this expansion project with probability weights" + +## Scripts Included + +- `dcf_model.py`: Complete DCF valuation engine +- `sensitivity_analysis.py`: Sensitivity testing framework + +## Limitations and Disclaimers + +- Models are only as good as their assumptions +- Past performance doesn't guarantee future results +- Market conditions can change rapidly +- Regulatory and tax changes may impact results +- Professional judgment required for interpretation +- Not a substitute for professional financial advice + +## Quality Checks + +The model automatically performs: +1. Balance sheet balancing checks +2. Cash flow reconciliation +3. Circular reference resolution +4. Sensitivity bound checking +5. Statistical validation of Monte Carlo results + +## Updates and Maintenance + +- Models use latest financial theory and practices +- Regular updates for market parameter defaults +- Incorporation of regulatory changes +- Continuous improvement based on usage patterns diff --git a/skills/creating-financial-models/dcf_model.py b/skills/creating-financial-models/dcf_model.py new file mode 100644 index 0000000..5d3eedb --- /dev/null +++ b/skills/creating-financial-models/dcf_model.py @@ -0,0 +1,530 @@ +""" +Discounted Cash Flow (DCF) valuation model. +Implements enterprise valuation using free cash flow projections. +""" + +from typing import Any + +import numpy as np + + +class DCFModel: + """Build and calculate DCF valuation models.""" + + def __init__(self, company_name: str = "Company"): + """ + Initialize DCF model. + + Args: + company_name: Name of the company being valued + """ + self.company_name = company_name + self.historical_financials = {} + self.projections = {} + self.assumptions = {} + self.wacc_components = {} + self.valuation_results = {} + + def set_historical_financials( + self, + revenue: list[float], + ebitda: list[float], + capex: list[float], + nwc: list[float], + years: list[int], + ): + """ + Set historical financial data. + + Args: + revenue: Historical revenue + ebitda: Historical EBITDA + capex: Historical capital expenditure + nwc: Historical net working capital + years: Historical years + """ + self.historical_financials = { + "years": years, + "revenue": revenue, + "ebitda": ebitda, + "capex": capex, + "nwc": nwc, + "ebitda_margin": [ebitda[i] / revenue[i] for i in range(len(revenue))], + "capex_percent": [capex[i] / revenue[i] for i in range(len(revenue))], + } + + def set_assumptions( + self, + projection_years: int = 5, + revenue_growth: list[float] = None, + ebitda_margin: list[float] = None, + tax_rate: float = 0.25, + capex_percent: list[float] = None, + nwc_percent: list[float] = None, + terminal_growth: float = 0.03, + ): + """ + Set projection assumptions. + + Args: + projection_years: Number of years to project + revenue_growth: Annual revenue growth rates + ebitda_margin: EBITDA margins by year + tax_rate: Corporate tax rate + capex_percent: Capex as % of revenue + nwc_percent: NWC as % of revenue + terminal_growth: Terminal growth rate + """ + if revenue_growth is None: + revenue_growth = [0.10] * projection_years # Default 10% growth + + if ebitda_margin is None: + # Use historical average if available + if self.historical_financials: + avg_margin = np.mean(self.historical_financials["ebitda_margin"]) + ebitda_margin = [avg_margin] * projection_years + else: + ebitda_margin = [0.20] * projection_years # Default 20% margin + + if capex_percent is None: + capex_percent = [0.05] * projection_years # Default 5% of revenue + + if nwc_percent is None: + nwc_percent = [0.10] * projection_years # Default 10% of revenue + + self.assumptions = { + "projection_years": projection_years, + "revenue_growth": revenue_growth, + "ebitda_margin": ebitda_margin, + "tax_rate": tax_rate, + "capex_percent": capex_percent, + "nwc_percent": nwc_percent, + "terminal_growth": terminal_growth, + } + + def calculate_wacc( + self, + risk_free_rate: float, + beta: float, + market_premium: float, + cost_of_debt: float, + debt_to_equity: float, + tax_rate: float | None = None, + ) -> float: + """ + Calculate Weighted Average Cost of Capital (WACC). + + Args: + risk_free_rate: Risk-free rate (e.g., 10-year treasury) + beta: Equity beta + market_premium: Equity market risk premium + cost_of_debt: Pre-tax cost of debt + debt_to_equity: Debt-to-equity ratio + tax_rate: Tax rate (uses assumption if not provided) + + Returns: + WACC as decimal + """ + if tax_rate is None: + tax_rate = self.assumptions.get("tax_rate", 0.25) + + # Calculate cost of equity using CAPM + cost_of_equity = risk_free_rate + beta * market_premium + + # Calculate weights + equity_weight = 1 / (1 + debt_to_equity) + debt_weight = debt_to_equity / (1 + debt_to_equity) + + # Calculate WACC + wacc = equity_weight * cost_of_equity + debt_weight * cost_of_debt * (1 - tax_rate) + + self.wacc_components = { + "risk_free_rate": risk_free_rate, + "beta": beta, + "market_premium": market_premium, + "cost_of_equity": cost_of_equity, + "cost_of_debt": cost_of_debt, + "debt_to_equity": debt_to_equity, + "equity_weight": equity_weight, + "debt_weight": debt_weight, + "tax_rate": tax_rate, + "wacc": wacc, + } + + return wacc + + def project_cash_flows(self) -> dict[str, list[float]]: + """ + Project future cash flows based on assumptions. + + Returns: + Dictionary with projected financials + """ + years = self.assumptions["projection_years"] + + # Start with last historical revenue if available + if self.historical_financials and "revenue" in self.historical_financials: + base_revenue = self.historical_financials["revenue"][-1] + else: + base_revenue = 1000 # Default base + + projections = { + "year": list(range(1, years + 1)), + "revenue": [], + "ebitda": [], + "ebit": [], + "tax": [], + "nopat": [], + "capex": [], + "nwc_change": [], + "fcf": [], + } + + prev_revenue = base_revenue + prev_nwc = base_revenue * 0.10 # Initial NWC assumption + + for i in range(years): + # Revenue + revenue = prev_revenue * (1 + self.assumptions["revenue_growth"][i]) + projections["revenue"].append(revenue) + + # EBITDA + ebitda = revenue * self.assumptions["ebitda_margin"][i] + projections["ebitda"].append(ebitda) + + # EBIT (assuming depreciation = capex for simplicity) + depreciation = revenue * self.assumptions["capex_percent"][i] + ebit = ebitda - depreciation + projections["ebit"].append(ebit) + + # Tax + tax = ebit * self.assumptions["tax_rate"] + projections["tax"].append(tax) + + # NOPAT + nopat = ebit - tax + projections["nopat"].append(nopat) + + # Capex + capex = revenue * self.assumptions["capex_percent"][i] + projections["capex"].append(capex) + + # NWC change + nwc = revenue * self.assumptions["nwc_percent"][i] + nwc_change = nwc - prev_nwc + projections["nwc_change"].append(nwc_change) + + # Free Cash Flow + fcf = nopat + depreciation - capex - nwc_change + projections["fcf"].append(fcf) + + prev_revenue = revenue + prev_nwc = nwc + + self.projections = projections + return projections + + def calculate_terminal_value( + self, method: str = "growth", exit_multiple: float | None = None + ) -> float: + """ + Calculate terminal value using perpetuity growth or exit multiple. + + Args: + method: 'growth' for perpetuity growth, 'multiple' for exit multiple + exit_multiple: EV/EBITDA exit multiple (if using multiple method) + + Returns: + Terminal value + """ + if not self.projections: + raise ValueError("Must project cash flows first") + + if method == "growth": + # Gordon growth model + final_fcf = self.projections["fcf"][-1] + terminal_growth = self.assumptions["terminal_growth"] + wacc = self.wacc_components["wacc"] + + # FCF in terminal year + terminal_fcf = final_fcf * (1 + terminal_growth) + + # Terminal value + terminal_value = terminal_fcf / (wacc - terminal_growth) + + elif method == "multiple": + if exit_multiple is None: + exit_multiple = 10 # Default EV/EBITDA multiple + + final_ebitda = self.projections["ebitda"][-1] + terminal_value = final_ebitda * exit_multiple + + else: + raise ValueError("Method must be 'growth' or 'multiple'") + + return terminal_value + + def calculate_enterprise_value( + self, terminal_method: str = "growth", exit_multiple: float | None = None + ) -> dict[str, Any]: + """ + Calculate enterprise value by discounting cash flows. + + Args: + terminal_method: Method for terminal value calculation + exit_multiple: Exit multiple if using multiple method + + Returns: + Valuation results dictionary + """ + if not self.projections: + self.project_cash_flows() + + if "wacc" not in self.wacc_components: + raise ValueError("Must calculate WACC first") + + wacc = self.wacc_components["wacc"] + years = self.assumptions["projection_years"] + + # Calculate PV of projected cash flows + pv_fcf = [] + for i, fcf in enumerate(self.projections["fcf"]): + discount_factor = (1 + wacc) ** (i + 1) + pv = fcf / discount_factor + pv_fcf.append(pv) + + total_pv_fcf = sum(pv_fcf) + + # Calculate terminal value + terminal_value = self.calculate_terminal_value(terminal_method, exit_multiple) + + # Discount terminal value + terminal_discount = (1 + wacc) ** years + pv_terminal = terminal_value / terminal_discount + + # Enterprise value + enterprise_value = total_pv_fcf + pv_terminal + + self.valuation_results = { + "enterprise_value": enterprise_value, + "pv_fcf": total_pv_fcf, + "pv_terminal": pv_terminal, + "terminal_value": terminal_value, + "terminal_method": terminal_method, + "pv_fcf_detail": pv_fcf, + "terminal_percent": pv_terminal / enterprise_value * 100, + } + + return self.valuation_results + + def calculate_equity_value( + self, net_debt: float, cash: float = 0, shares_outstanding: float = 100 + ) -> dict[str, Any]: + """ + Calculate equity value from enterprise value. + + Args: + net_debt: Total debt minus cash + cash: Cash and equivalents (if not netted) + shares_outstanding: Number of shares (millions) + + Returns: + Equity valuation metrics + """ + if "enterprise_value" not in self.valuation_results: + raise ValueError("Must calculate enterprise value first") + + ev = self.valuation_results["enterprise_value"] + + # Equity value = EV - Net Debt + equity_value = ev - net_debt + cash + + # Per share value + value_per_share = equity_value / shares_outstanding if shares_outstanding > 0 else 0 + + equity_results = { + "equity_value": equity_value, + "shares_outstanding": shares_outstanding, + "value_per_share": value_per_share, + "net_debt": net_debt, + "cash": cash, + } + + self.valuation_results.update(equity_results) + return equity_results + + def sensitivity_analysis( + self, variable1: str, range1: list[float], variable2: str, range2: list[float] + ) -> np.ndarray: + """ + Perform two-way sensitivity analysis on valuation. + + Args: + variable1: First variable to test ('wacc', 'growth', 'margin') + range1: Range of values for variable1 + variable2: Second variable to test + range2: Range of values for variable2 + + Returns: + 2D array of valuations + """ + results = np.zeros((len(range1), len(range2))) + + # Store original values + orig_wacc = self.wacc_components.get("wacc", 0.10) + orig_growth = self.assumptions.get("terminal_growth", 0.03) + orig_margin = self.assumptions.get("ebitda_margin", [0.20] * 5) + + for i, val1 in enumerate(range1): + for j, val2 in enumerate(range2): + # Update first variable + if variable1 == "wacc": + self.wacc_components["wacc"] = val1 + elif variable1 == "growth": + self.assumptions["terminal_growth"] = val1 + elif variable1 == "margin": + self.assumptions["ebitda_margin"] = [val1] * len(orig_margin) + + # Update second variable + if variable2 == "wacc": + self.wacc_components["wacc"] = val2 + elif variable2 == "growth": + self.assumptions["terminal_growth"] = val2 + elif variable2 == "margin": + self.assumptions["ebitda_margin"] = [val2] * len(orig_margin) + + # Recalculate + self.project_cash_flows() + valuation = self.calculate_enterprise_value() + results[i, j] = valuation["enterprise_value"] + + # Restore original values + self.wacc_components["wacc"] = orig_wacc + self.assumptions["terminal_growth"] = orig_growth + self.assumptions["ebitda_margin"] = orig_margin + + return results + + def generate_summary(self) -> str: + """ + Generate text summary of valuation results. + + Returns: + Formatted summary string + """ + if not self.valuation_results: + return "No valuation results available. Run valuation first." + + summary = [ + f"DCF Valuation Summary - {self.company_name}", + "=" * 50, + "", + "Key Assumptions:", + f" Projection Period: {self.assumptions['projection_years']} years", + f" Revenue Growth: {np.mean(self.assumptions['revenue_growth']) * 100:.1f}% avg", + f" EBITDA Margin: {np.mean(self.assumptions['ebitda_margin']) * 100:.1f}% avg", + f" Terminal Growth: {self.assumptions['terminal_growth'] * 100:.1f}%", + f" WACC: {self.wacc_components['wacc'] * 100:.1f}%", + "", + "Valuation Results:", + f" Enterprise Value: ${self.valuation_results['enterprise_value']:,.0f}M", + f" PV of FCF: ${self.valuation_results['pv_fcf']:,.0f}M", + f" PV of Terminal: ${self.valuation_results['pv_terminal']:,.0f}M", + f" Terminal % of Value: {self.valuation_results['terminal_percent']:.1f}%", + "", + ] + + if "equity_value" in self.valuation_results: + summary.extend( + [ + "Equity Valuation:", + f" Equity Value: ${self.valuation_results['equity_value']:,.0f}M", + f" Shares Outstanding: {self.valuation_results['shares_outstanding']:.0f}M", + f" Value per Share: ${self.valuation_results['value_per_share']:.2f}", + "", + ] + ) + + return "\n".join(summary) + + +# Helper functions for common calculations + + +def calculate_beta(stock_returns: list[float], market_returns: list[float]) -> float: + """ + Calculate beta from return series. + + Args: + stock_returns: Historical stock returns + market_returns: Historical market returns + + Returns: + Beta coefficient + """ + covariance = np.cov(stock_returns, market_returns)[0, 1] + market_variance = np.var(market_returns) + beta = covariance / market_variance if market_variance != 0 else 1.0 + return beta + + +def calculate_fcf_cagr(fcf_series: list[float]) -> float: + """ + Calculate compound annual growth rate of FCF. + + Args: + fcf_series: Free cash flow time series + + Returns: + CAGR as decimal + """ + if len(fcf_series) < 2: + return 0 + + years = len(fcf_series) - 1 + if fcf_series[0] <= 0 or fcf_series[-1] <= 0: + return 0 + + cagr = (fcf_series[-1] / fcf_series[0]) ** (1 / years) - 1 + return cagr + + +# Example usage +if __name__ == "__main__": + # Create model + model = DCFModel("TechCorp") + + # Set historical data + model.set_historical_financials( + revenue=[800, 900, 1000], + ebitda=[160, 189, 220], + capex=[40, 45, 50], + nwc=[80, 90, 100], + years=[2022, 2023, 2024], + ) + + # Set assumptions + model.set_assumptions( + projection_years=5, + revenue_growth=[0.15, 0.12, 0.10, 0.08, 0.06], + ebitda_margin=[0.23, 0.24, 0.25, 0.25, 0.25], + tax_rate=0.25, + terminal_growth=0.03, + ) + + # Calculate WACC + model.calculate_wacc( + risk_free_rate=0.04, beta=1.2, market_premium=0.07, cost_of_debt=0.05, debt_to_equity=0.5 + ) + + # Project cash flows + model.project_cash_flows() + + # Calculate valuation + model.calculate_enterprise_value() + + # Calculate equity value + model.calculate_equity_value(net_debt=200, shares_outstanding=50) + + # Print summary + print(model.generate_summary()) diff --git a/skills/creating-financial-models/sensitivity_analysis.py b/skills/creating-financial-models/sensitivity_analysis.py new file mode 100644 index 0000000..9674aad --- /dev/null +++ b/skills/creating-financial-models/sensitivity_analysis.py @@ -0,0 +1,389 @@ +""" +Sensitivity analysis module for financial models. +Tests impact of variable changes on key outputs. +""" + +from collections.abc import Callable +from typing import Any + +import numpy as np +import pandas as pd + + +class SensitivityAnalyzer: + """Perform sensitivity analysis on financial models.""" + + def __init__(self, base_model: Any): + """ + Initialize sensitivity analyzer. + + Args: + base_model: Base financial model to analyze + """ + self.base_model = base_model + self.base_output = None + self.sensitivity_results = {} + + def one_way_sensitivity( + self, + variable_name: str, + base_value: float, + range_pct: float, + steps: int, + output_func: Callable, + model_update_func: Callable, + ) -> pd.DataFrame: + """ + Perform one-way sensitivity analysis. + + Args: + variable_name: Name of variable to test + base_value: Base case value + range_pct: +/- percentage range to test + steps: Number of steps in range + output_func: Function to calculate output metric + model_update_func: Function to update model with new value + + Returns: + DataFrame with sensitivity results + """ + # Calculate range + min_val = base_value * (1 - range_pct) + max_val = base_value * (1 + range_pct) + test_values = np.linspace(min_val, max_val, steps) + + results = [] + for value in test_values: + # Update model + model_update_func(value) + + # Calculate output + output = output_func() + + results.append( + { + "variable": variable_name, + "value": value, + "pct_change": (value - base_value) / base_value * 100, + "output": output, + "output_change": output - self.base_output if self.base_output else 0, + } + ) + + # Reset to base + model_update_func(base_value) + + return pd.DataFrame(results) + + def two_way_sensitivity( + self, + var1_name: str, + var1_base: float, + var1_range: list[float], + var2_name: str, + var2_base: float, + var2_range: list[float], + output_func: Callable, + model_update_func: Callable, + ) -> pd.DataFrame: + """ + Perform two-way sensitivity analysis. + + Args: + var1_name: First variable name + var1_base: First variable base value + var1_range: Range of values for first variable + var2_name: Second variable name + var2_base: Second variable base value + var2_range: Range of values for second variable + output_func: Function to calculate output + model_update_func: Function to update model (takes var1, var2) + + Returns: + DataFrame with two-way sensitivity table + """ + results = np.zeros((len(var1_range), len(var2_range))) + + for i, val1 in enumerate(var1_range): + for j, val2 in enumerate(var2_range): + # Update both variables + model_update_func(val1, val2) + + # Calculate output + results[i, j] = output_func() + + # Reset to base + model_update_func(var1_base, var2_base) + + # Create DataFrame + df = pd.DataFrame( + results, + index=[f"{var1_name}={v:.2%}" if v < 1 else f"{var1_name}={v:.1f}" for v in var1_range], + columns=[ + f"{var2_name}={v:.2%}" if v < 1 else f"{var2_name}={v:.1f}" for v in var2_range + ], + ) + + return df + + def tornado_analysis( + self, variables: dict[str, dict[str, Any]], output_func: Callable + ) -> pd.DataFrame: + """ + Create tornado diagram data showing relative impact of variables. + + Args: + variables: Dictionary of variables with base, low, high values + output_func: Function to calculate output + + Returns: + DataFrame sorted by impact magnitude + """ + # Store base output + self.base_output = output_func() + + tornado_data = [] + + for var_name, var_info in variables.items(): + # Test low value + var_info["update_func"](var_info["low"]) + low_output = output_func() + + # Test high value + var_info["update_func"](var_info["high"]) + high_output = output_func() + + # Reset to base + var_info["update_func"](var_info["base"]) + + # Calculate impact + impact = high_output - low_output + low_delta = low_output - self.base_output + high_delta = high_output - self.base_output + + tornado_data.append( + { + "variable": var_name, + "base_value": var_info["base"], + "low_value": var_info["low"], + "high_value": var_info["high"], + "low_output": low_output, + "high_output": high_output, + "low_delta": low_delta, + "high_delta": high_delta, + "impact": abs(impact), + "impact_pct": abs(impact) / self.base_output * 100, + } + ) + + # Sort by impact + df = pd.DataFrame(tornado_data) + df = df.sort_values("impact", ascending=False) + + return df + + def scenario_analysis( + self, + scenarios: dict[str, dict[str, float]], + variable_updates: dict[str, Callable], + output_func: Callable, + probability_weights: dict[str, float] | None = None, + ) -> pd.DataFrame: + """ + Analyze multiple scenarios with different variable combinations. + + Args: + scenarios: Dictionary of scenarios with variable values + variable_updates: Functions to update each variable + output_func: Function to calculate output + probability_weights: Optional probability for each scenario + + Returns: + DataFrame with scenario results + """ + results = [] + + for scenario_name, variables in scenarios.items(): + # Update all variables for this scenario + for var_name, value in variables.items(): + if var_name in variable_updates: + variable_updates[var_name](value) + + # Calculate output + output = output_func() + + # Get probability if provided + prob = ( + probability_weights.get(scenario_name, 1 / len(scenarios)) + if probability_weights + else 1 / len(scenarios) + ) + + results.append( + { + "scenario": scenario_name, + "probability": prob, + "output": output, + **variables, # Include all variable values + } + ) + + # Reset model (simplified - should restore all base values) + + df = pd.DataFrame(results) + + # Calculate expected value + df["weighted_output"] = df["output"] * df["probability"] + expected_value = df["weighted_output"].sum() + + # Add summary row + summary = pd.DataFrame( + [ + { + "scenario": "Expected Value", + "probability": 1.0, + "output": expected_value, + "weighted_output": expected_value, + } + ] + ) + + df = pd.concat([df, summary], ignore_index=True) + + return df + + def breakeven_analysis( + self, + variable_name: str, + variable_update: Callable, + output_func: Callable, + target_value: float, + min_search: float, + max_search: float, + tolerance: float = 0.01, + ) -> float: + """ + Find breakeven point where output equals target. + + Args: + variable_name: Variable to adjust + variable_update: Function to update variable + output_func: Function to calculate output + target_value: Target output value + min_search: Minimum search range + max_search: Maximum search range + tolerance: Convergence tolerance + + Returns: + Breakeven value of variable + """ + # Binary search for breakeven + low = min_search + high = max_search + + while (high - low) > tolerance: + mid = (low + high) / 2 + variable_update(mid) + output = output_func() + + if abs(output - target_value) < tolerance: + return mid + elif output < target_value: + low = mid + else: + high = mid + + return (low + high) / 2 + + +def create_data_table( + row_variable: tuple[str, list[float], Callable], + col_variable: tuple[str, list[float], Callable], + output_func: Callable, +) -> pd.DataFrame: + """ + Create Excel-style data table for two variables. + + Args: + row_variable: (name, values, update_function) + col_variable: (name, values, update_function) + output_func: Function to calculate output + + Returns: + DataFrame formatted as data table + """ + row_name, row_values, row_update = row_variable + col_name, col_values, col_update = col_variable + + results = np.zeros((len(row_values), len(col_values))) + + for i, row_val in enumerate(row_values): + for j, col_val in enumerate(col_values): + row_update(row_val) + col_update(col_val) + results[i, j] = output_func() + + df = pd.DataFrame( + results, + index=pd.Index(row_values, name=row_name), + columns=pd.Index(col_values, name=col_name), + ) + + return df + + +# Example usage +if __name__ == "__main__": + # Mock model for demonstration + class SimpleModel: + def __init__(self): + self.revenue = 1000 + self.margin = 0.20 + self.multiple = 10 + + def calculate_value(self): + ebitda = self.revenue * self.margin + return ebitda * self.multiple + + # Create model and analyzer + model = SimpleModel() + analyzer = SensitivityAnalyzer(model) + + # One-way sensitivity + results = analyzer.one_way_sensitivity( + variable_name="Revenue", + base_value=model.revenue, + range_pct=0.20, + steps=5, + output_func=model.calculate_value, + model_update_func=lambda x: setattr(model, "revenue", x), + ) + + print("One-Way Sensitivity Analysis:") + print(results) + + # Tornado analysis + variables = { + "Revenue": { + "base": 1000, + "low": 800, + "high": 1200, + "update_func": lambda x: setattr(model, "revenue", x), + }, + "Margin": { + "base": 0.20, + "low": 0.15, + "high": 0.25, + "update_func": lambda x: setattr(model, "margin", x), + }, + "Multiple": { + "base": 10, + "low": 8, + "high": 12, + "update_func": lambda x: setattr(model, "multiple", x), + }, + } + + tornado = analyzer.tornado_analysis(variables, model.calculate_value) + print("\nTornado Analysis:") + print(tornado[["variable", "impact", "impact_pct"]]) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..a422368 --- /dev/null +++ b/uv.lock @@ -0,0 +1,2400 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version < '3.13'", +] + +[[package]] +name = "agent-alpha" +version = "0.1.0" +source = { virtual = "." } +dependencies = [ + { name = "logfire", extra = ["asyncpg", "fastapi", "httpx", "sqlite3"] }, + { name = "pydantic-ai-backend" }, + { name = "pydantic-ai-harness", extra = ["code-mode"] }, + { name = 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