# Adam v17.0 Example Configuration File

# This file provides a template for configuring Adam v17.0,
# showcasing best practices, naming conventions, and proposed
# hierarchies for different deployment scenarios.

# Knowledge Graph Configuration
knowledge_graph:
  path: data/knowledge_graph.json  # Path to the knowledge graph file
  # format: json  # Optional: Specify the format of the knowledge graph file (default: json)
  # storage: local  # Optional: Specify the storage type (local, cloud, etc.)
  #... other knowledge graph-specific configurations...

# API Configuration
api:
  host: 0.0.0.0  # Host for the API server (0.0.0.0 for all interfaces)
  port: 5000  # Port for the API server
  # authentication:
  #   enabled: true  # Enable or disable API authentication
  #   method: token  # Authentication method (e.g., token, API key)
  #   #... other authentication-related configurations...
  #... other API-specific configurations...

# Agent Configuration
agents:
  market_sentiment_agent:
    # Configuration for the Market Sentiment Agent
    enabled: true  # Enable or disable the agent
    data_sources:
      - news_api
      - twitter_api
    sentiment_analysis_method: lexicon_based  # or machine_learning_based
    #... other agent-specific configuration...

  macroeconomic_analysis_agent:
    # Configuration for the Macroeconomic Analysis Agent
    enabled: true  # Enable or disable the agent
    data_sources:
      - fred_api
      - ecb_api
    macroeconomic_model: var  # or dsge
    #... other agent-specific configuration...

  #... other agents...

# Data Sources Configuration
data_sources:
  news_api:
    api_key: YOUR_NEWS_API_KEY
    #... other data source-specific configuration...

  twitter_api:
    consumer_key: YOUR_CONSUMER_KEY
    consumer_secret: YOUR_CONSUMER_SECRET
    access_token: YOUR_ACCESS_TOKEN
    access_token_secret: YOUR_ACCESS_TOKEN_SECRET
    #... other data source-specific configuration...

  #... other data sources...

# Analysis Modules Configuration
analysis_modules:
  fundamental_analysis:
    # Configuration for the Fundamental Analysis Module
    enabled: true  # Enable or disable the module
    valuation_method: dcf  # or comparables
    #... other module-specific configuration...

  technical_analysis:
    # Configuration for the Technical Analysis Module
    enabled: true  # Enable or disable the module
    indicators:
      - rsi
      - macd
    #... other module-specific configuration...

  #... other analysis modules...

# Reporting Configuration
reporting:
  format: json  # or csv, html, etc.
  #... other reporting configurations...

# Logging Configuration
logging:
  level: INFO  # or DEBUG, WARNING, ERROR, CRITICAL
  #... other logging configurations...

# Deployment Scenarios

# Standalone Module Deployment
# To deploy a specific module as a standalone service,
# enable only that module and its required agents and data sources.
# For example, to deploy only the Fundamental Analysis module:
#
# analysis_modules:
#   fundamental_analysis:
#     enabled: true
#   technical_analysis:
#     enabled: false
#   #... other analysis modules...
#
# agents:
#   fundamental_analyst_agent:
#     enabled: true
#   #... other agents...
#
# data_sources:
#   financial_statements_api:
#     #... configuration...
#   #... other data sources...

# Cross-Platform Deployment
# Adam v17.0 is designed to be platform-agnostic.
# You can deploy it on various operating systems (Windows, macOS, Linux)
# and cloud platforms (AWS, Azure, GCP) by adjusting the
# relevant configurations in this file and the deployment scripts.

# Cross-Language and Engine Integration
# Adam v17.0 can be integrated with different language models and engines
# by modifying the agent configurations and API integrations.
# For example, you can configure an agent to use a different LLM
# by specifying the engine and API key in its configuration.

# Customization
# You can further customize Adam v17.0 by modifying the Python code
# and configuration files directly. For example, you can:
#
# * Add new agents or modules.
# * Modify existing agent behavior or analysis methods.
# * Integrate with new data sources or APIs.
# * Create custom reporting formats.
#
# Refer to the User Guide and API Documentation for more detailed
# instructions and examples.