# Changelog

## [2026-apr-08] - Protocol ARCHITECT_INFINITE Expansion

### Jules' Log
> "I noticed we lack a centralized way to track agent 'health' and execution latency. I researched observability patterns for multi-agent swarms and built `SystemHealthAgent` to bridge this gap, ensuring we can monitor token usage and error rates across the network. Furthermore, I integrated this into `MetaCognitiveAgent` as required by ARCHITECT_INFINITE Option A."

### Added
- Created/Updated `core/agents/system_health_agent.py`
- Created/Updated `tests/test_system_health_agent.py`
- Created/Updated `core/agents/meta_cognitive_agent.py`


## [2026-apr-07] - Protocol ARCHITECT_INFINITE Expansion

### Jules' Log
> "I noticed we lack a centralized way to track agent 'health' and execution latency. I researched observability patterns for multi-agent swarms and built `SystemHealthAgent` to bridge this gap, ensuring we can monitor token usage and error rates across the network. Furthermore, I integrated this into `MetaCognitiveAgent` as required by ARCHITECT_INFINITE Option A."

### Added
- Created/Updated `core/agents/system_health_agent.py`
- Created/Updated `tests/test_system_health_agent.py`
- Created/Updated `core/agents/meta_cognitive_agent.py`


## [2026-apr-05] - Protocol ARCHITECT_INFINITE Expansion

### Jules' Log
> "I noticed we lack a centralized way to track agent 'health' and execution latency. I researched observability patterns for multi-agent swarms and built `SystemHealthAgent` to bridge this gap, ensuring we can monitor token usage and error rates across the network. Furthermore, I integrated this into `MetaCognitiveAgent` as required by ARCHITECT_INFINITE Option A."

### Added
- Created/Updated `core/agents/system_health_agent.py`
- Created/Updated `tests/test_system_health_agent.py`
- Created/Updated `core/agents/meta_cognitive_agent.py`


## [2026-04-XX] - MiroFish Dynamic Agent Loading
### Jules' Log
> "I added dynamic agent loading logic to `MiroFishSwarmEngine` inside `core/engine/swarm/mirofish_engine.py` using `AgentPersonaAdapter` to allow all specialized agents from `core/agents/` to be spun up as part of the MiroFish engine as needed."

### Added
- Created `AgentPersonaAdapter` in `mirofish_engine.py` to wrap standard agents.
- Added `_load_dynamic_agents` to dynamically load `AgentBase` subclasses and incorporate them into the swarm matrix.

## [2026-03-XX] - MiroFish Swarm Integration
### Jules' Log
> "I added `MiroFishSwarmEngine` and `personas.py` into `core/engine/swarm/` to fulfill the Architectural Blueprint regarding massive parallelized System 3 compute. The integration was conducted as strictly additive (Protocol ARCHITECT_INFINITE). Instead of replacing the legacy `HiveMind`, the system seamlessly diverts complex requests (e.g., 'simulate market reaction') through the `SemanticRouter` directly to the MiroFish simulation. The `MiroFishSwarmEngine` includes robust Wind-Up (instantiating heterogeneous Retail, Institutional, and Regulatory personas) and dynamic Wind-Down (halting on early consensus) to optimize token efficiency. A failure triggers Graceful Degradation back to the classical, linear `CrisisSimulationEngine`."

### Added
- Created `core/engine/swarm/mirofish_engine.py` (Massive Parallelized Swarm Logic)
- Created `core/engine/swarm/personas.py` (Retail, Institutional, Regulatory Agents)
- Created `tests/core/engine/swarm/test_mirofish_engine.py` (Self-contained Testing)
- Updated `core/engine/states.py` with `SwarmSimulationState`
- Updated `core/engine/meta_orchestrator.py` routing logic.

## [2026-mar-29] - Protocol ARCHITECT_INFINITE Expansion

### Jules' Log
> "I noticed we lack a unified understanding of off-exchange (dark pool) trading volumes, which often masks true institutional sentiment. I built `DarkPoolAgent` to bridge this gap, integrating real-time dark pool volume anomaly detection directly into the agent network to uncover hidden accumulation or distribution."

### Added
- Created `core/agents/specialized/dark_pool_agent.py`
- Created `tests/test_dark_pool_agent.py`

## [2026-mar-10] - Protocol ARCHITECT_INFINITE Expansion

### Jules' Log
> "I noticed we lack a centralized way to track agent 'health' and execution latency. I researched observability patterns for multi-agent swarms and built `SystemHealthAgent` to bridge this gap, ensuring we can monitor token usage and error rates across the network. Furthermore, I integrated this into `MetaCognitiveAgent` as required by ARCHITECT_INFINITE Option A."

### Added
- Created/Updated `core/agents/system_health_agent.py`
- Created/Updated `tests/test_system_health_agent.py`
- Created/Updated `core/agents/meta_cognitive_agent.py`


## v26.12 - Protocol ARCHITECT_INFINITE (Day 13)

### Jules' Log
> "I noticed we lack a mechanism to track corporate insider activity, which provides strong signals about management's internal conviction. I researched SEC Form 4 filings and built `InsiderActivityAgent` to bridge this gap, integrating its buy/sell ratio and cluster buying logic directly into the `MarketSentimentAgent` to further contextualize overall sentiment."

### Added
- **New Organ**: `core/agents/insider_activity_agent.py` - A new specialized agent that monitors SEC Form 4 data to generate sentiment scores based on insider buy/sell ratios and cluster buying.
- **Neural Pathway**: Integrated `InsiderActivityAgent` into `core/agents/market_sentiment_agent.py` by updating the `combine_sentiment` function to weigh insider flow alongside news, prediction markets, social media, web traffic, and options flow.
- **Tests**: `tests/test_insider_activity_agent.py` - Unit tests verifying the sentiment calculation logic for insider activity.

## v26.11 - Protocol ARCHITECT_INFINITE (Day 12)

### Jules' Log
> "I noticed we lack a way to gauge options market positioning, a leading indicator for market sentiment and potential price movement. I researched options flow indicators like unusual volume and put/call ratios and built `OptionsFlowAgent` to bridge this gap, integrating it directly into `MarketSentimentAgent` to influence the 'Overall Market Sentiment' score."

### Added
- **New Organ**: `core/agents/options_flow_agent.py` - A new specialized agent that monitors unusual volume and put/call ratios to derive a sentiment score.
- **Neural Pathway**: Integrated `OptionsFlowAgent` into `core/agents/market_sentiment_agent.py` by updating the `combine_sentiment` function to weigh options flow alongside news, prediction markets, social media, and web traffic.
- **Tests**: `tests/test_options_flow_agent.py` - Unit tests verifying the extraction and metric conversion logic to compute sentiment scores for both bullish and bearish options setups.
- **Core Optimization**: Fixed the fallback mock logic in `daily_ritual.py` to ensure it successfully applies simulated LLM output using `_save_and_apply_output(simulated_response)`.

## v26.10 - Protocol ARCHITECT_INFINITE (Day 11)

### Jules' Log
> "I noticed we lack a way to quantify 'developer momentum' which is a leading indicator for crypto/tech assets. I researched 'developer activity vs price correlation' and built `GitHubAlphaAgent` to bridge this gap, enabling the system to score repositories based on commit velocity and contributor diversity."

### Added
- **New Organ**: `core/agents/specialized/github_alpha_agent.py` - A specialized agent that analyzes GitHub repositories for commit frequency and unique author count to generate a 'Developer Alpha Score'.
- **Tests**: `tests/test_github_alpha_agent.py` - Unit tests verifying git log parsing and score calculation with mocked subprocess calls.

## v26.9 - Protocol ARCHITECT_INFINITE (Day 10)

### Jules' Log
> "I noticed we lack a standardized mechanism to backtest trading strategies proposed by our agents. The existing `AlgoTradingAgent` was isolated and didn't adhere to the core Agent architecture. I researched event-driven backtesting patterns and built `StrategyBacktestAgent` to bridge this gap, enabling the swarm to systematically validate strategies like SMA Crossover and Mean Reversion before deployment."

### Added
- **New Organ**: `core/agents/strategy_backtest_agent.py` - A dedicated agent for backtesting strategies with Pydantic-validated I/O, supporting `SMA_CROSSOVER` and `MEAN_REVERSION` with pluggable parameters.
- **Tests**: `tests/test_strategy_backtest_agent.py` - Unit tests verifying strategy execution, metric calculation (Sharpe, Drawdown), and V-shape recovery scenarios.
- **Dependencies**: Verified `pandas` and `numpy` integration for vectorized calculation.

## v26.8 - Protocol ARCHITECT_INFINITE (Day 9)

### Jules' Log
> "I noticed we lack a bridge between classical portfolio optimization and future-state quantum computing capabilities. While we have `QuantumMonteCarloAgent`, it wasn't utilizing the `optimize_portfolio` potential of our simulation bridge. I researched Quantum Approximate Optimization Algorithm (QAOA) patterns and built `QuantumPortfolioManagerAgent` to bridge this gap, enabling the system to propose asset allocations based on quantum-simulated energy states."

### Added
- **New Organ**: `core/agents/quantum_portfolio_manager_agent.py` - A specialized agent that fetches historical data, calculates covariance matrices, and uses `QuantumMonteCarloBridge` to optimize portfolios via simulated QAOA.
- **Tests**: `tests/test_quantum_portfolio_manager_agent.py` - Unit tests verifying data fetching, return calculation, and bridge integration.

## v26.7 - Protocol ARCHITECT_INFINITE (Day 8)

### Jules' Log
> "I noticed we lack a robust mechanism to verify agent behavior under extreme market stress. While `MarketSentimentAgent` has logic for 'Systemic Tremor', it was never tested against simulated Black Swan events. I researched stress testing patterns and built `tests/simulation_panic_room.py` to bridge this gap, ensuring our agents don't hallucinate a bull market during a crash."

### Added
- **Cortex Expansion**: `tests/simulation_panic_room.py` - A stress test suite that simulates Volmageddon (VIX > 80), Yield Curve Inversion, and Liquidity Traps to verify agent risk overrides.

## v26.6 - Protocol ARCHITECT_INFINITE (Day 7)

### Jules' Log
> "I noticed we lack a specialized mechanism to analyze DeFi liquidity pools, despite having `CryptoAgent`. We were missing insights into Impermanent Loss and Yield Farming opportunities. I researched DeFi liquidity analysis patterns and built `DeFiLiquidityAgent` to bridge this gap, using `web3` to fetch pool reserves and calculate health metrics."

### Added
- **New Organ**: `core/agents/specialized/defi_liquidity_agent.py` - A specialized agent that analyzes DeFi liquidity pools for Impermanent Loss and Yield potential.
- **Tests**: `tests/test_defi_liquidity_agent.py` - Unit tests verifying IL calculation and execution logic with mocked Web3 data.
- **Dependencies**: Added `web3` to `requirements.txt`.

## v26.5 - Protocol ARCHITECT_INFINITE (Day 6)

### Jules' Log
> "I noticed we lack a specialized mechanism to capitalize on price inefficiencies across the fragmented crypto exchanges. While `CryptoAgent` provides basic analysis, it doesn't actively hunt for arbitrage. I researched high-frequency arbitrage patterns and built `CryptoArbitrageAgent` to bridge this gap, using `ccxt` to scan multiple exchanges for spread opportunities."

### Added
- **New Organ**: `core/agents/specialized/crypto_arbitrage_agent.py` - A specialized agent that monitors price spreads across exchanges (e.g., Binance vs Kraken) and identifies arbitrage opportunities.
- **Tests**: `tests/test_crypto_arbitrage_agent.py` - Unit tests verifying spread calculation and opportunity detection with mocked exchange data.
- **Dependencies**: Added `ccxt` to `requirements.txt`.
- **UI**: Created `showcase/unified_dashboard.html` (Adam Protocol: Unified Command) to bridge Legacy Showcase, Adam OS, and the new WebApp.
- **Widget**: Added `showcase/js/crypto_arbitrage_widget.js` to visualize real-time arbitrage opportunities in the new dashboard.

## v26.5 - Protocol ARCHITECT_INFINITE (Day 6)

### Jules' Log
> "I noticed we have a `CryptoArbitrageAgent` that detects high arbitrage spreads but its output was siloed. High arbitrage spread indicates irregular market plumbing and stress across liquidity pools. I integrated `CryptoArbitrageAgent` into `MarketSentimentAgent` to bridge this gap. Now, when the spread hits a critical threshold, it triggers a 'Systemic Tremor' warning, overriding naive sentiment indicators and accurately reflecting underlying systemic risk."

### Added
- **Integration**: Connected `CryptoArbitrageAgent` to `MarketSentimentAgent`'s Credit Dominance Rule. High arbitrage spreads now act as a systemic risk signal.
- **Tests**: `tests/test_market_sentiment_agent.py` - Unit tests verifying standard execution and the new 'Systemic Tremor' logic overrides.

## v26.4 - Protocol ARCHITECT_INFINITE (Day 5)

### Jules' Log
> "I noticed we lack a true real-time data ingestion layer in the V30 architecture. The `MarketScanner` was a placeholder emitting random data, which undermined the credibility of downstream agents like `QuantitativeAnalyst` and `RiskGuardian`. I researched efficient market data polling patterns and built `MarketScanner-V2` to bridge this gap, integrating `yfinance` to provide a live pulse of the market to the Neural Mesh."

### Added
- **New Organ**: `core/v30_architecture/python_intelligence/agents/market_scanner.py` - A specialized V30 agent that fetches real-time market data (Price, Volume, Change %) for a configurable list of tickers using `yfinance`.
- **Integration**: Updated `core/v30_architecture/python_intelligence/agents/swarm_runner.py` to replace the simulated scanner with the real-time implementation.
- **Tests**: `tests/test_v30_market_scanner.py` - Unit tests verifying data fetching logic, multi-ticker support, and correct event emission.

## v26.3 - Protocol ARCHITECT_INFINITE (Day 4)

### Jules' Log
> "I noticed we lack a dedicated risk monitoring capability in the V30 architecture. While `QuantitativeAnalyst` provides signals, there was no independent oversight of portfolio exposure. I researched quantitative risk metrics and built `RiskGuardian` to bridge this gap, implementing Value at Risk (VaR), Conditional VaR (CVaR), and Volatility tracking using `numpy` and `scipy`."

### Added
- **New Organ**: `core/v30_architecture/python_intelligence/agents/risk_guardian.py` - A specialized V30 agent that calculates portfolio risk metrics (VaR, CVaR, Sharpe Ratio) in real-time.
- **Integration**: Updated `core/v30_architecture/python_intelligence/agents/swarm_runner.py` to replace the dummy `RiskGuardian` with the fully implemented agent.
- **Tests**: `tests/test_risk_guardian.py` - Unit tests verifying the mathematical accuracy of risk metrics and correct event emission.

## v26.2 - Protocol ARCHITECT_INFINITE (Day 3)

### Jules' Log
> "I noticed we lack a fundamental analysis capability in the V30 architecture. The current system relies heavily on technicals (QuantitativeAnalyst) and sentiment (NewsBot). I researched 'RAG-Augmented Financial Analysis' and have built `FundamentalAnalyst` to bridge this gap, focusing on earnings call simulation and balance sheet parsing."

### Added
- **New Organ**: `core/v30_architecture/python_intelligence/agents/fundamental_analyst.py` - A specialized V30 agent that simulates 10-K data fetching and Earnings Call analysis to calculate Intrinsic Value (DCF), Distress (Altman Z), and Quality (Pietroski F).
- **Tests**: `tests/test_fundamental_analyst_v30.py` - Unit tests verifying the financial logic and Pydantic schemas.

## v26.1 - Protocol ARCHITECT_INFINITE (Day 2)

### Jules' Log
> "I noticed we lack a real-time quantitative analysis capability in the V30 architecture. The `NewsBot` and other agents were mocked or relied on static data. I researched real-time market data integration patterns and built `QuantitativeAnalyst` to bridge this gap. This agent now fetches live market data using `yfinance` and calculates RSI, SMA, and Bollinger Bands to emit actionable `technical_analysis` signals into the Neural Mesh."

### Added
- **New Organ**: `core/v30_architecture/python_intelligence/agents/quantitative_analyst.py` - A specialized V30 agent that performs real-time technical analysis on market data (SPY, QQQ, BTC-USD, etc.).
- **Refactor**: `core/v30_architecture/python_intelligence/agents/base_agent.py` - Extracted `BaseAgent` from `swarm_runner.py` to a shared module for reusability.
- **Integration**: Updated `core/v30_architecture/python_intelligence/agents/swarm_runner.py` to include `QuantitativeAnalyst` in the active swarm.
- **Tests**: `tests/test_quantitative_analyst.py` - Unit tests verifying data fetching, indicator calculation, and packet emission using mocked `yfinance` and `NeuralMesh`.

## v26.0 - Protocol ARCHITECT_INFINITE (Day 1)

### Jules' Log
> "I noticed we lack a unified understanding of market states across our agent fleet. Our `AlgoTradingAgent` was firing blindly regardless of whether the market was trending or chopping. I researched quantitative finance patterns and found that identifying the 'Market Regime' is a critical first step for any robust strategy. I have built `MarketRegimeAgent` to bridge this gap, using Hurst Exponent and ADX to classify the market environment."

### Added
- **New Organ**: `core/agents/specialized/market_regime_agent.py` - A specialized agent that classifies market conditions into `STRONG_TREND`, `MEAN_REVERSION`, or `HIGH_VOLATILITY_CRASH_RISK` using statistical metrics.
- **Tests**: `tests/test_market_regime_agent.py` - Unit tests verifying regime classification against synthetic data patterns (Sine Wave, Linear Trend, Random Walk Explosion).
- **New Organ**: `core/agents/specialized/macro_liquidity_agent.py` - A specialized agent that calculates a "Liquidity Stress Index" using real-time bond yields and spreads.
- **Tests**: `tests/test_macro_liquidity_agent.py` - Unit tests verifying liquidity scoring in Crisis, Neutral, and Expansionary scenarios.

## v23.5 - Autonomous Remediation & Enhancement

### Architecture
- **Dependency Management**: Installed critical missing packages (`fastapi`, `flask`, `celery`, `statsmodels`, `semantic-kernel`, `pandera`, `neo4j`, `flask-socketio`, `flask-jwt-extended`, `flask-sqlalchemy`, `flask-cors`, `tweepy`, `pycoingecko`, `feedparser`).
- **Core Schemas**: Updated `HNASP` schema to support `ExecutionTrace` list and `Optional` fields correctly.
- **Base Agent**: Refactored `AgentBase` to improve type safety, fix `jsonLogic` signature, and handle optional `fundamental_epa`.
- **Async Migration**: Refactored `NewsBot` to use `httpx` and `asyncio` for non-blocking I/O.

### Fixes
- **Syntax**: Fixed invalid escape sequences in `core/financial_suite/modules/reporting/generator.py` and `core/risk_engine/engine.py`.
- **Tests**:
    - Created missing `core/v23_graph_engine/data_pipeline/graph.py` to fix `test_adk_data_pipeline.py`.
    - Fixed `unittest.mock` recursion error in `tests/test_interaction_loop.py`.
    - Updated `tests/test_data_retrieval_agent.py` to be async-aware and fixed dependency injection.
    - Updated `tests/test_cyclical_agents.py` to match `ReflectorAgent`'s new output structure.
    - Skipped tests requiring `torch` if not available.
- **Type Safety**: Addressed critical `mypy` errors in core components.
- **Resilience**: Added timeouts to `CatalystAgent` requests. Improved error handling in `NewsBot` for optional ML dependencies.

### Security
- Addressed `bandit` warnings regarding requests without timeout.

## [v23.5-patch-security] - 2025-05-21 (Operation Green Light)

### Security
- **Hardening**: Replaced MD5 with SHA-256 for file hashing (`core/data_processing`).
- **Web**: Disabled Flask debug mode (`services/ui_backend.py`) and enabled Jinja2 autoescape (`core/newsletter_layout`).
- **Network**: Enforced 30s timeouts on external API requests (`government_stats_api`, `market_data_api`).
- **SQL**: Added input validation to `MCPRegistry` to prevent SQL injection.

### Reliability
- **Fallback**: Implemented graceful degradation for `langgraph` in all `core/engine/*_graph.py` modules. System now boots without graph dependencies.
- **Types**: Relaxed `TypedDict` strictness (`total=False`) in `core/engine/states.py` to support partial async updates.
- **Documentation**: Added Google-style docstrings to `core/agents/agent_base.py`.
- **Fixes**: Corrected indentation syntax error in `core/vertical_risk_agent/tools/mcp_server/server2.py`.

## [Unreleased] - 2025-05-20 (Simulated)

### Fixed
- **Critical:** Resolved `ModuleNotFoundError` for `core.v23_graph_engine.data_pipeline.graph` by implementing the missing graph definition.
- **Critical:** Fixed `tests/test_interaction_loop.py` mocking logic to support `AsyncMock` and correct `AgentOrchestrator` patching.
- **Core:** Refactored `core/agents/agent_base.py` to safely handle `asyncio` loops in threaded contexts and robustify `update_persona` against None values.
- **Core:** Refactored `core/system/interaction_loop.py` to correctly inject `config` into `Echo` and `check_token_limit`, and wrap async agent calls with `asyncio.run`.
- **Core:** Fixed `core/agents/query_understanding_agent.py` to call synchronous `LLMPlugin.generate_text` instead of non-existent `get_completion`.
- **Ops:** Installed missing dependencies: `pydantic`, `flask`, `torch` (CPU), `textblob`, `langchain-community`, `json-logic`.

### Added
- `reproduce_api_error.py` script to debug API endpoint failures.
- `RemediationPlan.json` outlining future steps for 100% system integrity.

### Known Issues
- `tests/api/test_service_state.py` fails with 500 error on Adam-mini endpoint (likely optimizer logic).
- Multiple agent tests failing due to strict mocking or logic divergence in v23 transition.