Merge pull request #1124 from adamvangrover/adam-scaffolding-expansion-2483806575072651795

feat(architecture): Expand baseline scaffolds for adam modules

Author: adamvangrover

.jules/learnings.md

@@ -1 +1,2 @@
 - When acting as the daily financial agent under sandbox constraints without live web access, plausible synthetic data should be generated via a Python script to populate the required markdown reports and JSONL schemas (e.g., `MarketMayhemLedger`).
+- When expanding or migrating architecture modules, always build redundantly and additively (e.g., maintaining old import paths to prevent breaking changes or using default arguments for newly added parameters). This ensures that every piece remains portable and modular during active refactoring.

adam_finance/__init__.py

@@ -0,0 +1,113 @@
+from typing import Dict, Any, List
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def calculate_dcf(financials: Dict[str, Any], risk_free_rate: float = 0.04, scenario: Dict[str, Any] = None) -> Dict[str, Any]:
+    """
+    Performs a simplified Discounted Cash Flow (DCF) analysis.
+
+    Args:
+        financials: Dict containing 'fcf' (Free Cash Flow), 'growth_rate', 'beta'.
+        risk_free_rate: The current risk-free rate (e.g., 10y Treasury).
+        scenario: Optional dict for Scenario Injection (e.g., {'growth_rate': 0.02, 'market_risk_premium': 0.08})
+
+    Returns:
+        Dict with 'wacc', 'terminal_growth', 'intrinsic_value', 'share_price'.
+    """
+    logger.info("Running DCF Analysis...")
+
+    if scenario:
+        logger.info(f"Injecting Scenario: {scenario}")
+
+    # Extract or Default (Scenario overrides financials)
+    scenario = scenario or {}
+
+    fcf = scenario.get("fcf", financials.get("fcf", 1000))
+    growth_rate = scenario.get("growth_rate", financials.get("growth_rate", 0.05))
+    beta = scenario.get("beta", financials.get("beta", 1.2))
+    shares_outstanding = financials.get("shares_outstanding", 500)
+
+    # Market Assumptions (Scenario overrides defaults)
+    market_risk_premium = scenario.get("market_risk_premium", 0.05)
+    cost_of_debt = scenario.get("cost_of_debt", 0.06)
+    tax_rate = scenario.get("tax_rate", 0.21)
+
+    # Capital Structure
+    debt_equity_ratio = financials.get("debt_equity_ratio", 0.5)
+
+    # 1. Calculate WACC
+    # Scenario injection for risk_free_rate is handled by the argument, but check scenario dict too
+    rfr = scenario.get("risk_free_rate", risk_free_rate)
+
+    cost_of_equity = rfr + beta * market_risk_premium
+    wacc = (cost_of_equity * (1 / (1 + debt_equity_ratio))) + \
+           (cost_of_debt * (1 - tax_rate) * (debt_equity_ratio / (1 + debt_equity_ratio)))
+
+    # 2. Project FCF (5 years)
+    projected_fcf = []
+    current_fcf = fcf
+    for _ in range(5):
+        current_fcf *= (1 + growth_rate)
+        projected_fcf.append(current_fcf)
+
+    # 3. Terminal Value
+    terminal_growth = 0.025
+    terminal_value = (projected_fcf[-1] * (1 + terminal_growth)) / (wacc - terminal_growth)
+
+    # 4. Discount to Present
+    enterprise_value = 0
+    for t, cash_flow in enumerate(projected_fcf, 1):
+        enterprise_value += cash_flow / ((1 + wacc) ** t)
+
+    enterprise_value += terminal_value / ((1 + wacc) ** 5)
+
+    # 5. Equity Value
+    net_debt = financials.get("net_debt", 2000)
+    equity_value = enterprise_value - net_debt
+    intrinsic_share_price = equity_value / shares_outstanding
+
+    return {
+        "wacc": round(wacc, 4),
+        "terminal_growth": terminal_growth,
+        "intrinsic_value": round(equity_value, 2),
+        "intrinsic_share_price": round(intrinsic_share_price, 2),
+        "method": "5-Year DCF / Gordon Growth"
+    }
+
+
+def calculate_multiples(financials: Dict[str, Any], peer_group: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Performs Relative Valuation using trading multiples.
+    """
+    logger.info("Running Multiples Analysis...")
+
+    ev = financials.get("enterprise_value", 10000)
+    ebitda = financials.get("ebitda", 2000)
+
+    current_ev_ebitda = ev / ebitda if ebitda else 0
+
+    # Peer Analysis
+    peer_multiples = [p.get("ev_ebitda", 10.0) for p in peer_group]
+    peer_median = sum(peer_multiples) / len(peer_multiples) if peer_multiples else 10.0
+
+    premium_discount = (current_ev_ebitda - peer_median) / peer_median
+
+    return {
+        "current_ev_ebitda": round(current_ev_ebitda, 2),
+        "peer_median_ev_ebitda": round(peer_median, 2),
+        "premium_discount_pct": round(premium_discount * 100, 2),
+        "verdict": "Overvalued" if premium_discount > 0.1 else "Undervalued" if premium_discount < -0.1 else "Fairly Valued"
+    }
+
+
+def get_price_targets(intrinsic_price: float, volatility: float) -> Dict[str, float]:
+    """
+    Generates Bull, Base, and Bear case price targets.
+    """
+    return {
+        "bear_case": round(intrinsic_price * (1 - volatility * 2), 2),
+        "base_case": round(intrinsic_price, 2),
+        "bull_case": round(intrinsic_price * (1 + volatility * 2), 2)
+    }

adam_finance/valuation.py

@@ -40,7 +40,7 @@ class ProofOfThoughtLogger:
     """
     Enforces W3C PROV-O compliance strictly on every LLM-generated JSON payload.
     """
-    def log_payload(self, payload: Dict[str, Any]) -> Dict[str, Any]:
+    def log_payload(self, payload: Dict[str, Any], derivation_path: str = "unknown", source_data_object: str = "unknown") -> Dict[str, Any]:
         """Logs and structures a payload according to PROV-O."""
         content_hash = hashlib.sha256(str(payload).encode('utf-8')).hexdigest()
 
@@ -49,7 +49,9 @@ def log_payload(self, payload: Dict[str, Any]) -> Dict[str, Any]:
             "wasGeneratedBy": "Adam_Swarm_Agent",
             "generatedAtTime": time.time(),
             "content_hash": content_hash,
-            "payload": payload
+            "payload": payload,
+            "derivation_path": derivation_path,
+            "source_data_object": source_data_object
         }
         return prov_o_log
 

adam_governance/state_control.py

@@ -1,10 +1,11 @@
 """
 Purpose: Provide base class scaffold for asynchronous neural swarm processing and fast heuristics.
-Dependencies: typing
+Dependencies: typing, inspect
 Outputs: MetaOrchestrator base class.
 """
 
 from typing import Any, List, Dict
+import inspect
 
 class MetaOrchestrator:
     """
@@ -40,3 +41,50 @@ def map_tool(self, tool_name: str, schema: Dict[str, Any]) -> None:
     def get_schema(self, tool_name: str) -> Dict[str, Any]:
         """Retrieve the FastMCP schema for a given tool."""
         return self.mappings.get(tool_name, {})
+
+    def map_schema_from_tool(self, tool_func: Any) -> None:
+        """
+        Dynamically map an API boundary tool (python function) to a FastMCP JSON schema
+        by inspecting its signature.
+        """
+        if not callable(tool_func):
+            raise TypeError("tool_func must be callable")
+
+        name = tool_func.__name__
+        sig = inspect.signature(tool_func)
+
+        properties = {}
+        required = []
+
+        for param_name, param in sig.parameters.items():
+            if param_name == 'self':
+                continue
+
+            param_type = "string"  # Default type
+            if param.annotation != inspect.Parameter.empty:
+                if param.annotation == int:
+                    param_type = "integer"
+                elif param.annotation == float:
+                    param_type = "number"
+                elif param.annotation == bool:
+                    param_type = "boolean"
+                elif param.annotation == list or getattr(param.annotation, '__origin__', None) == list:
+                    param_type = "array"
+                elif param.annotation == dict or getattr(param.annotation, '__origin__', None) == dict:
+                    param_type = "object"
+
+            properties[param_name] = {"type": param_type}
+
+            if param.default == inspect.Parameter.empty:
+                required.append(param_name)
+
+        schema = {
+            "type": "object",
+            "properties": properties,
+            "required": required
+        }
+
+        if tool_func.__doc__:
+            schema["description"] = tool_func.__doc__.strip()
+
+        self.mappings[name] = schema

adam_swarm/orchestrator.py

@@ -32,6 +32,22 @@ <h3 class="mono" style="color: #00f3ff;">SUPERVISED LEARNING</h3>
                         <a href="#" class="cyber-btn" style="background: rgba(0, 243, 255, 0.1); border-color: #00f3ff; color: #00f3ff;">REVIEW MARKERS &rarr;</a>
                     </div>
                 </div>
+
+                <div class="module-card">
+                    <h3 class="mono" style="color: #f59e0b;">STATISTICAL EVALUATION</h3>
+                    <p>Deterministic Pipeline Accuracy</p>
+                    <div style="background: #111; padding: 10px; margin-top: 10px; border-radius: 4px; border: 1px solid #333;">
+                        <code style="color: #f59e0b; font-size: 0.8rem;">> Margin of Error: 0.042<br>> Iteration Need: HIGH<br>> Target Threshold: 0.05</code>
+                    </div>
+                </div>
+
+                <div class="module-card">
+                    <h3 class="mono" style="color: #a855f7;">MILESTONE MARKERS</h3>
+                    <p>Session Process Efficiency</p>
+                    <div style="background: #111; padding: 10px; margin-top: 10px; border-radius: 4px; border: 1px solid #333;">
+                        <code style="color: #a855f7; font-size: 0.8rem;">> Opt. Process Found: DCF Calc<br>> Conviction vs Cmplx Ratio: 3.14<br>> Markers Logged: 17</code>
+                    </div>
+                </div>
             </div>
         </main>
     </div>

showcase/terminal_dashboard.html

@@ -1,6 +1,7 @@
 import pytest
 import os
 import sys
+from typing import Any
 
 # Ensure project root is in PYTHONPATH for testing
 SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -37,12 +38,14 @@ def test_fastmcp_tool_mapper():
 def test_proof_of_thought_logger():
     logger = ProofOfThoughtLogger()
     payload = {"key": "value"}
-    result = logger.log_payload(payload)
+    result = logger.log_payload(payload, derivation_path="path/to/data", source_data_object="source_123")
 
     assert result["entity"] == "LLM_Output"
     assert result["wasGeneratedBy"] == "Adam_Swarm_Agent"
     assert "content_hash" in result
     assert result["payload"] == payload
+    assert result["derivation_path"] == "path/to/data"
+    assert result["source_data_object"] == "source_123"
 
 def test_swarm_to_graph_boundary_abstract():
     # Attempting to instantiate the ABC should raise TypeError
@@ -56,6 +59,9 @@ class MockGraphInput:
                 edges = []
             return MockGraphInput()
 
+        def validate_boundary(self, data: Any) -> bool:
+            return True
+
     class MockSwarmOutput:
         data = "test_data"
         confidence = 0.99
@@ -88,3 +94,28 @@ def test_margin_of_error():
 
     no_iter = evaluate_iteration_need(0.01, 0.05)
     assert no_iter is False
+
+def test_fastmcp_mapper_dynamic():
+    mapper = FastMCPToolMapper()
+
+    def my_tool(arg1: str, arg2: int = 5) -> str:
+        """A test tool."""
+        return arg1 * arg2
+
+    mapper.map_schema_from_tool(my_tool)
+    schema = mapper.get_schema("my_tool")
+
+    assert schema["type"] == "object"
+    assert schema["description"] == "A test tool."
+    assert "arg1" in schema["properties"]
+    assert schema["properties"]["arg1"]["type"] == "string"
+    assert "arg2" in schema["properties"]
+    assert schema["properties"]["arg2"]["type"] == "integer"
+    assert schema["required"] == ["arg1"]
+
+from adam_finance.valuation import calculate_dcf
+def test_adam_finance_valuation():
+    financials = {"fcf": 1000, "growth_rate": 0.05, "beta": 1.2, "shares_outstanding": 500, "debt_equity_ratio": 0.5, "net_debt": 2000}
+    res = calculate_dcf(financials)
+    assert "wacc" in res
+    assert "intrinsic_value" in res