# Adam System Requirements This document provides a comprehensive and authoritative overview of the functional and non-functional requirements for the Adam Financial Analysis System. It is intended to be a single source of truth for developers, project managers, and other stakeholders. This document is a living document and will be updated as the system evolves. ## 1. Functional Requirements ### 1.1. Agent Capabilities The system shall be composed of a network of specialized agents, each responsible for a specific domain of expertise. The following agents must be implemented: #### Core Analysis Agents - **Market Sentiment Agent:** Analyzes market sentiment from news, social media, and other sources using advanced NLP and emotion analysis. - **Macroeconomic Analysis Agent:** Analyzes macroeconomic data (e.g., GDP, inflation, interest rates) and trends to assess the health of the economy. - **Geopolitical Risk Agent:** Assesses geopolitical risks and their potential impact on financial markets by analyzing news and political developments. - **Industry Specialist Agent:** Provides in-depth analysis of specific industry sectors (e.g., technology, healthcare, energy). - **Fundamental Analysis Agent:** Conducts fundamental analysis of companies by analyzing financial statements, evaluating management, and performing valuation modeling (e.g., DCF). - **Technical Analysis Agent:** Performs technical analysis of financial instruments, including chart pattern recognition and technical indicator analysis. - **Risk Assessment Agent:** Assesses and manages investment risks, including market risk, credit risk, and liquidity risk. - **Prediction Market Agent:** Gathers and analyzes data from prediction markets to gauge expectations about future events. - **Alternative Data Agent:** Explores and integrates alternative data sources (e.g., web traffic, satellite imagery) for novel insights. - **SNC Analyst Agent:** Specializes in the analysis of Shared National Credits (SNCs). - **Crypto Agent:** Specializes in the analysis of crypto assets and on-chain data. - **Legal Agent:** Provides analysis of legal documents, monitors regulatory changes, and assesses legal risks. #### Advanced and Meta-Agents - **Behavioral Economics Agent:** Analyzes market data and user interactions for signs of cognitive biases (e.g., herding, confirmation bias) and irrational behavior. - **Meta-Cognitive Agent:** Acts as a quality control layer, monitoring the reasoning and outputs of other agents to ensure logical consistency, coherence, and alignment with core principles. - **Discussion Chair Agent:** Leads and moderates discussions in multi-agent simulations (e.g., Investment Committee Simulation) and makes final decisions. #### System and Utility Agents - **Agent Forge:** Automates the creation, configuration, and deployment of new specialized agents. - **Prompt Tuner:** Refines and optimizes prompts used for LLM communication and analysis to improve performance and accuracy. - **Code Alchemist:** Enhances code generation, validation, and deployment within the system's development environment. - **Lingua Maestro:** Handles multi-language translation and communication to process global data sources. - **Sense Weaver:** Handles multi-modal inputs and outputs, allowing the system to process information from various formats (e.g., text, images, charts). - **Data Visualization Agent:** Generates interactive and informative visualizations to aid in understanding complex data. - **Natural Language Generation Agent:** Generates human-readable reports, summaries, and narratives from structured data. - **Machine Learning Model Training Agent:** Trains, evaluates, and updates machine learning models used by other agents. ### 1.2. Simulation Modules The system shall provide a suite of simulation tools to model and analyze complex financial scenarios. These simulations orchestrate the interaction of multiple agents to derive insights. The following simulation modules must be implemented: - **Credit Rating Assessment Simulation:** Simulates the credit rating process for a company, leveraging agents like the Fundamental Analysis Agent and the Risk Assessment Agent. - **Investment Committee Simulation:** Simulates the investment decision-making process of an investment committee, with the Discussion Chair Agent moderating the discussion between various analysis agents. - **Portfolio Optimization Simulation:** Simulates the optimization of an investment portfolio based on user-defined goals and risk tolerance, using agents like the Portfolio Optimization Agent (if available) and the Risk Assessment Agent. - **Stress Testing Simulation:** Simulates the impact of various stress scenarios (e.g., market crashes, interest rate hikes) on a portfolio or financial institution. - **Merger & Acquisition (M&A) Simulation:** Simulates the evaluation and execution of an M&A transaction, involving agents for fundamental analysis, legal analysis, and risk assessment. - **Regulatory Compliance Simulation:** Simulates the process of ensuring compliance with financial regulations. - **Fraud Detection Simulation:** Simulates the detection of fraudulent activities in financial data. ### 1.3. User Interface and User Experience (UI/UX) Requirements The system shall provide a web-based user interface that is intuitive, interactive, and informative. The UI requirements are based on the v19.2 mockups and have been updated to reflect the capabilities of the v21.0 system. #### 1.3.1. Dashboard The dashboard shall provide a high-level overview of the market and the user's portfolio. - **Market Summary:** - Shall display key market indices (e.g., S&P 500, Dow Jones) with current values, percentage changes, and sparkline charts. - Shall include a sentiment indicator (e.g., gauge or bar chart) showing overall market sentiment. - **v21.0 Enhancement:** Shall include a "Cognitive Bias Indicator" powered by the `Behavioral Economics Agent`, showing the prevalence of market-wide biases like herding or FOMO. - **v21.0 Enhancement:** Shall provide "XAI-powered explanations" for market movements, with the `Meta-Cognitive Agent` reviewing these explanations for logical consistency. - **Portfolio Overview:** - Shall display the user's portfolio with asset allocation, performance over time, and key risk metrics. - **Investment Ideas:** - Shall display a list of investment ideas with rationale, conviction rating, and risk assessment. - **v21.0 Enhancement:** Each investment idea shall include a "Behavioral Score" indicating potential cognitive biases influencing the recommendation. - **Alerts:** - Shall display a list of user-defined alerts. - **Simulation Results:** - Shall display a summary of recent simulation runs with links to detailed reports. #### 1.3.2. Analysis Tools The system shall provide a suite of tools for in-depth financial analysis. - **Fundamental Analysis:** Tools for company valuation and financial statement analysis. - **Technical Analysis:** Interactive charting tools with a wide range of technical indicators. - **Risk Assessment:** Tools for assessing market, credit, and other financial risks. - **Financial Modeling:** Tools for building and analyzing financial models. - **Legal Analysis:** Tools for analyzing legal documents and regulatory changes. - **v21.0 Enhancement: Behavioral Analysis:** A new section where users can analyze specific assets or their own portfolio for cognitive biases. #### 1.3.3. General UI/UX Principles - **Transparency:** The UI shall make it easy for users to understand the reasoning behind the system's analysis and recommendations. - **v21.0 Enhancement: Meta-Cognitive Review:** Analytical outputs (e.g., reports, recommendations) shall include a "Meta-Cognitive Review" section that displays a confidence score and a summary of the `Meta-Cognitive Agent`'s findings on the logical consistency of the analysis. - **Interactivity:** The UI shall be highly interactive, with features like drill-down charts, customizable tables, and real-time data updates. - **Customization:** Users shall be able to customize the dashboard, create custom alerts, and set their own investment preferences. ## 2. Future-Proofing and Non-Functional Requirements To ensure the long-term viability and scalability of the Adam system, the following requirements are defined. ### 2.1. New Agent Capabilities The following agents are planned for future development to expand the system's analytical capabilities. #### 2.1.1. Regulatory Compliance Agent - **Scope:** This agent shall be responsible for monitoring and analyzing regulatory changes and ensuring the system's outputs and recommendations are compliant with relevant financial regulations. - **Capabilities:** - **Regulatory Monitoring:** Shall monitor regulatory databases (e.g., SEC Edgar, Federal Register) and legal news sources for changes in financial regulations across specified jurisdictions. - **Compliance Analysis:** Shall analyze investment recommendations, portfolio holdings, and trading strategies to identify potential compliance issues (e.g., violations of insider trading rules, concentration limits). - **Compliance Reporting:** Shall generate compliance reports that highlight potential issues and recommend corrective actions. - **Data Sources:** - Integration with legal and regulatory databases (e.g., Westlaw, LexisNexis, SEC Edgar). - News feeds focused on legal and regulatory news. - **Interaction:** - Shall be integrated into the `Investment Committee Simulation` to provide a compliance check on investment decisions. - Shall be available as an analysis tool for users to check the compliance of their own portfolios. #### 2.1.2. Anomaly Detection Agent - **Scope:** This agent shall be responsible for detecting anomalies and potential fraudulent activities in financial data. - **Capabilities:** - **Transaction Monitoring:** Shall analyze transaction data for patterns that may indicate fraudulent activity (e.g., money laundering, market manipulation). - **Outlier Detection:** Shall identify outliers and anomalies in market data, financial statements, and other datasets that may warrant further investigation. - **Fraud Alerting:** Shall generate alerts when potential fraudulent activities or significant anomalies are detected. - **Data Sources:** - Real-time transaction feeds. - Market data feeds. - Company financial data. - **Interaction:** - Shall work in conjunction with the `Risk Assessment Agent` to provide a more comprehensive view of risk. - The `Fraud Detection Simulation` will be built around this agent's capabilities. ### 2.2. Core System Enhancements The following enhancements to the core system architecture are required to improve performance, scalability, and intelligence. #### 2.2.1. Enhanced Machine Learning Integration - **Requirement:** The system shall integrate more sophisticated machine learning and deep learning models for predictive modeling and pattern recognition. - **Specifics:** - **Time Series Forecasting:** Implement and evaluate deep learning models (e.g., LSTMs, Transformers) for forecasting stock prices, market indices, and other financial time series data. - **Advanced NLP:** Utilize transformer-based models (e.g., BERT, GPT-3) for more nuanced sentiment analysis, text summarization, and named entity recognition from financial documents. - **Reinforcement Learning:** Explore the use of reinforcement learning for developing adaptive trading and portfolio optimization strategies. - **Integration:** The `Machine Learning Model Training Agent` shall be responsible for training, evaluating, and deploying these advanced models. #### 2.2.2. Real-Time Data Integration - **Requirement:** The system shall incorporate real-time data feeds for more dynamic analysis and decision-making. - **Specifics:** - **Low-Latency Market Data:** Integrate with low-latency market data providers to receive real-time stock prices, order book data, and trade data. - **Streaming News and Social Media:** Implement a streaming data pipeline for ingesting and analyzing news articles and social media posts as they are published. - **Impact:** This will enable the `Technical Analysis Agent`, `Market Sentiment Agent`, and `Anomaly Detection Agent` to operate on up-to-the-minute data. #### 2.2.3. Distributed Architecture - **Requirement:** The system shall be deployable across a distributed network to improve performance, scalability, and resilience. - **Specifics:** - **Containerization:** The system and its components (agents, services) shall be containerized using Docker. - **Orchestration:** A container orchestration platform (e.g., Kubernetes) shall be used to manage the deployment, scaling, and networking of the containerized services. - **Microservices:** The monolithic components of the system shall be refactored into microservices where appropriate, to allow for independent scaling and development. #### 2.2.4. Explainable AI (XAI) Enhancements - **Requirement:** The system shall provide more detailed and comprehensive explanations for its decisions and recommendations. - **Specifics:** - **SHAP and LIME Integration:** Integrate XAI libraries like SHAP and LIME to provide feature importance scores for machine learning models. - **Causal Inference:** Move beyond correlation to causal inference models to provide more robust explanations of market phenomena. The `Meta-Cognitive Agent` should be enhanced to incorporate causal reasoning. - **Visual Explanations:** The `Data Visualization Agent` shall be enhanced to generate visual explanations of model predictions and agent reasoning processes. ### 2.3. API and External Integration The system's API shall be expanded to support a wider range of use cases and integrations. #### 2.3.1. API Expansion - **Requirement:** The API shall be expanded to provide programmatic access to more of the system's capabilities. - **Specifics:** - **Agent Endpoints:** Provide API endpoints for directly interacting with specific agents (e.g., get a fundamental analysis report for a specific company). - **Simulation Endpoints:** Provide API endpoints for initiating and monitoring simulations. - **Streaming API:** Provide a WebSocket or other streaming API for receiving real-time data and alerts. #### 2.3.2. Integration with External Systems - **Requirement:** The system shall be able to integrate with external portfolio management and trading platforms. - **Specifics:** - **Portfolio Management Integration:** Implement connectors for popular portfolio management platforms (e.g., BlackRock's Aladdin) to allow for the import of user portfolios and the export of analysis results. - **Trade Execution Integration:** Implement connectors for brokerage APIs to allow for the seamless execution of trading strategies developed within the system. ## 3. Documentation Notes ### 3.1. Document Purpose This document is intended to be the single source of truth for all functional and non-functional requirements for the Adam system. It should be kept up-to-date and should be the first point of reference for any questions about the system's intended behavior or future direction. ### 3.2. Recommended Archival of Outdated Documents To reduce confusion and ensure that this document remains the single source of truth, it is recommended that the following outdated documents be archived: - `docs/architecture.md` (v19.0): The content of this document is outdated and has been superseded by the information in this requirements document and the `docs/SYSTEM_OVERVIEW.md`. - `CONTRIBUTING.md` (v17.0): The contribution guidelines should be updated to be version-agnostic and should be moved to a more prominent location, such as the root of the repository or the `docs` directory. - `UI Mockups.md` (v19.2): The UI/UX requirements in this document have been updated and incorporated into section 1.3 of this document. By archiving these documents, we can ensure that all stakeholders are working from the most current and accurate information.