Open PTC Agent
This notebook is a demo usage of PTC Agent.
Make sure you have installed the dependencies and configured the api keys in the .env file before running the notebook.
import asyncio
import nest_asyncio
from datetime import datetime
from dotenv import load_dotenv
load_dotenv(".env", override=True)
from utils import show_prompt, format_messages, print_agent_config, print_sandbox_tree, display_sandbox_images, display_result, export_sandbox_files, dump_messages
# Enable async in Jupyter
nest_asyncio.apply()
from IPython.display import Image, display
from src.agent import AgentConfig, PTCAgent
from src.ptc_core.session import SessionManager
# Load configuration (async)
config = await AgentConfig.load()
config.validate_api_keys()
%load_ext autoreload
%autoreload 2Create the PTC Agent
# Create session with Daytona sandbox
core_config = config.to_core_config()
session = SessionManager.get_session("demo-notebook", core_config)
await session.initialize()
# Create the agent via PTCAgent
ptc_agent = PTCAgent(config)
agent = ptc_agent.create_agent(
sandbox=session.sandbox,
mcp_registry=session.mcp_registry,
subagent_names=["research", "general-purpose"]
)
# Show the agent graph
display(Image(agent.get_graph().draw_mermaid_png()))[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mCreating new session [0m [36mconversation_id[0m=[35mdemo-notebook[0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mCreated session [0m [36mconversation_id[0m=[35mdemo-notebook[0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mInitializing session [0m [36mconversation_id[0m=[35mdemo-notebook[0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mInitialized MCPRegistry [0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mSkipping disabled MCP servers [0m [36mdisabled_servers[0m=[35m['tavily', 'alphavantage'][0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mConnecting to MCP servers [0m [36mserver_count[0m=[35m2[0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mInitialized MCPServerConnector[0m [36mserver[0m=[35mtickertick[0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mInitialized MCPServerConnector[0m [36mserver[0m=[35myfinance[0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mConnecting to MCP server [0m [36mserver[0m=[35mtickertick[0m
[2m2025-12-01 04:46:12[0m [[32m[1minfo [0m] [1mConnecting to MCP server [0m [36mserver[0m=[35myfinance[0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mDiscovered tools [0m [36mserver[0m=[35mtickertick[0m [36mtools[0m=[35m['get_ticker_news', 'get_broad_ticker_news', 'get_news_from_source', 'get_news_for_multiple_tickers', 'get_curated_news', 'get_entity_news', 'search_tickers'][0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mConnected to MCP server [0m [36mserver[0m=[35mtickertick[0m [36mtool_count[0m=[35m7[0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mDiscovered tools [0m [36mserver[0m=[35myfinance[0m [36mtools[0m=[35m['get_stock_history', 'get_income_statement', 'get_balance_sheet', 'get_cash_flow', 'get_options_chain', 'get_company_info', 'get_analyst_recommendations', 'get_institutional_holders', 'get_dividends_and_splits', 'get_earnings_data', 'get_earnings_dates', 'get_insider_transactions', 'get_mutualfund_holders', 'get_sustainability_data', 'get_news', 'get_insider_roster', 'get_multiple_stocks_history', 'compare_financials', 'get_multiple_stocks_dividends', 'get_multiple_stocks_earnings', 'compare_valuations'][0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mConnected to MCP server [0m [36mserver[0m=[35myfinance[0m [36mtool_count[0m=[35m21[0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mMCP servers connected [0m [36mservers[0m=[35m['tickertick', 'yfinance'][0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mInitialized PTCSandbox [0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mSetting up sandbox [0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mChecking for snapshot [0m [36msnapshot_name[0m=[35mopen-ptc-v1-6f8ea14e[0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mUsing existing snapshot [0m [36msnapshot_name[0m=[35mopen-ptc-v1-6f8ea14e[0m
[2m2025-12-01 04:46:13[0m [[32m[1minfo [0m] [1mCreating sandbox from snapshot[0m [36msnapshot_name[0m=[35mopen-ptc-v1-6f8ea14e[0m
[2m2025-12-01 04:46:15[0m [[32m[1minfo [0m] [1mSandbox created from snapshot [0m [36msnapshot_name[0m=[35mopen-ptc-v1-6f8ea14e[0m
[2m2025-12-01 04:46:15[0m [[32m[1minfo [0m] [1mSandbox ready from snapshot [0m [36msandbox_id[0m=[35m00863413-e271-4844-94b4-f7fe0713c78b[0m [36msnapshot[0m=[35mopen-ptc-v1-6f8ea14e[0m
[2m2025-12-01 04:46:15[0m [[32m[1minfo [0m] [1mSetting up workspace structure[0m
[2m2025-12-01 04:46:15[0m [[32m[1minfo [0m] [1mSandbox working directory: /home/daytona[0m
[2m2025-12-01 04:46:15[0m [[32m[1minfo [0m] [1mCreated directory: /home/daytona/tools[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mCreated directory: /home/daytona/results[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mCreated directory: /home/daytona/tools/docs[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mCreated directory: /home/daytona/code[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mCreated directory: /home/daytona/data[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mUploaded MCP server file [0m [36mlocal_path[0m=[35mmcp_servers/tickertick_mcp_server.py[0m [36msandbox_path[0m=[35m/home/daytona/mcp_servers/tickertick_mcp_server.py[0m [36mserver[0m=[35mtickertick[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mUploaded MCP server file [0m [36mlocal_path[0m=[35mmcp_servers/yfinance_mcp_server.py[0m [36msandbox_path[0m=[35m/home/daytona/mcp_servers/yfinance_mcp_server.py[0m [36mserver[0m=[35myfinance[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mInstalling tool modules [0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mTransformed MCP server command for sandbox[0m [36moriginal_args[0m=[35m['run', 'python', 'mcp_servers/tickertick_mcp_server.py'][0m [36moriginal_command[0m=[35muv[0m [36msandbox_args[0m=[35m['run', 'python', '/home/daytona/mcp_servers/tickertick_mcp_server.py'][0m [36msandbox_command[0m=[35muv[0m [36mserver[0m=[35mtickertick[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mTransformed MCP server command for sandbox[0m [36moriginal_args[0m=[35m['run', 'python', 'mcp_servers/yfinance_mcp_server.py'][0m [36moriginal_command[0m=[35muv[0m [36msandbox_args[0m=[35m['run', 'python', '/home/daytona/mcp_servers/yfinance_mcp_server.py'][0m [36msandbox_command[0m=[35muv[0m [36mserver[0m=[35myfinance[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mGenerating tool module [0m [36mserver[0m=[35mtickertick[0m [36mtool_count[0m=[35m7[0m
[2m2025-12-01 04:46:16[0m [[32m[1minfo [0m] [1mGenerating tool module [0m [36mserver[0m=[35myfinance[0m [36mtool_count[0m=[35m21[0m
[2m2025-12-01 04:46:19[0m [[32m[1minfo [0m] [1mMCP client module installed [0m [36mpath[0m=[35m/home/daytona/tools/mcp_client.py[0m
[2m2025-12-01 04:46:19[0m [[32m[1minfo [0m] [1mTool module installed [0m [36mpath[0m=[35m/home/daytona/tools/tickertick.py[0m [36mserver[0m=[35mtickertick[0m [36mtool_count[0m=[35m7[0m
[2m2025-12-01 04:46:19[0m [[32m[1minfo [0m] [1mTool module installed [0m [36mpath[0m=[35m/home/daytona/tools/yfinance.py[0m [36mserver[0m=[35myfinance[0m [36mtool_count[0m=[35m21[0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mTool modules installation complete[0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mStarting internal MCP servers [0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mCreating MCP server session [0m [36mcommand[0m=[35m'uv run python mcp_servers/tickertick_mcp_server.py'[0m [36mserver[0m=[35mtickertick[0m [36msession[0m=[35mmcp-tickertick[0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mMCP server session configured [0m [36mserver[0m=[35mtickertick[0m [36msession[0m=[35mmcp-tickertick[0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mCreating MCP server session [0m [36mcommand[0m=[35m'uv run python mcp_servers/yfinance_mcp_server.py'[0m [36mserver[0m=[35myfinance[0m [36msession[0m=[35mmcp-yfinance[0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mMCP server session configured [0m [36mserver[0m=[35myfinance[0m [36msession[0m=[35mmcp-yfinance[0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mInternal MCP server configuration complete[0m [36mservers[0m=[35m['tickertick', 'yfinance'][0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mSandbox setup complete [0m [36msandbox_id[0m=[35m00863413-e271-4844-94b4-f7fe0713c78b[0m
[2m2025-12-01 04:46:20[0m [[32m[1minfo [0m] [1mSession initialized [0m [36mconversation_id[0m=[35mdemo-notebook[0m
[2m2025-12-01 04:46:21[0m [[32m[1minfo [0m] [1mInitialized PTCAgent with deepagent[0m [36mmodel[0m=[35mgpt-5.1-codex-mini[0m [36mprovider[0m=[35mopenai[0m
[2m2025-12-01 04:46:21[0m [[32m[1minfo [0m] [1mInitialized DaytonaBackend [0m [36mroot_dir[0m=[35m/home/daytona[0m [36mvirtual_mode[0m=[35mTrue[0m
[2m2025-12-01 04:46:21[0m [[32m[1minfo [0m] [1mUsing custom filesystem tools (overriding middleware)[0m [36mtools[0m=[35m['read_file', 'write_file', 'edit_file', 'glob', 'grep'][0m
[2m2025-12-01 04:46:21[0m [[32m[1minfo [0m] [1mVision tool enabled [0m [36mtool[0m=[35mview_image[0m
[2m2025-12-01 04:46:21[0m [[32m[1minfo [0m] [1mViewImageMiddleware enabled with strict validation and sandbox support[0m
[2m2025-12-01 04:46:21[0m [[32m[1minfo [0m] [1mBackground subagent execution enabled[0m [36mbackground_tools[0m=[35m['wait', 'check_task_progress'][0m [36mtimeout[0m=[35m60.0[0m
[2m2025-12-01 04:46:21[0m [[32m[1minfo [0m] [1mCreating deepagent [0m [36msubagent_count[0m=[35m2[0m [36mtool_count[0m=[35m10[0m [36muse_custom_filesystem_tools[0m=[35mTrue[0m
print_agent_config(config, session, ptc_agent=ptc_agent)======================================================================
AGENT CONFIGURATION SUMMARY
======================================================================
📦 LLM CONFIGURATION
----------------------------------------
Name: gpt-5.1-codex-mini
Model ID: gpt-5.1-codex-mini
Provider: openai
Parameters: {'reasoning': {'effort': 'low', 'summary': 'auto'}}
🔌 MCP SERVERS
----------------------------------------
tickertick: 7 tools
['get_ticker_news', 'get_broad_ticker_news', 'get_news_from_source', 'get_news_for_multiple_tickers', 'get_curated_news', 'get_entity_news', 'search_tickers']
yfinance: 21 tools
['get_stock_history', 'get_income_statement', 'get_balance_sheet', 'get_cash_flow', 'get_options_chain', 'get_company_info', 'get_analyst_recommendations', 'get_institutional_holders', 'get_dividends_and_splits', 'get_earnings_data', 'get_earnings_dates', 'get_insider_transactions', 'get_mutualfund_holders', 'get_sustainability_data', 'get_news', 'get_insider_roster', 'get_multiple_stocks_history', 'compare_financials', 'get_multiple_stocks_dividends', 'get_multiple_stocks_earnings', 'compare_valuations']
Total: 2 servers, 28 tools
🛠️ NATIVE TOOLS
----------------------------------------
execute_code, Bash, read_file, write_file, edit_file, glob, grep, view_image, wait, check_task_progress
🤖 SUBAGENTS
----------------------------------------
research: tavily_search, think_tool
general-purpose: execute_code, read_file, write_file, edit_file, glob, grep, view_image
======================================================================
Runtime system prompt
# Get the formatted tool summary from MCP registry
tool_summary = ptc_agent._get_tool_summary(session.mcp_registry)
subagent_summary = ptc_agent._get_subagent_summary()
# Build the complete system prompt
system_prompt = ptc_agent._build_system_prompt(tool_summary, subagent_summary)
# Show the complete prompt
print(f"Total prompt length: {len(system_prompt)} characters")
show_prompt(system_prompt, title="PTCAgent Runtime System Prompt", border_style="green")Total prompt length: 15450 characters
╭──────────────────────────────────────── PTCAgent Runtime System Prompt ─────────────────────────────────────────╮ │ │ │ For context, today's date is 2025-12-01. │ │ │ │ <task_workflow> │ │ # Task Workflow │ │ │ │ Follow this workflow for all task requests: │ │ │ │ 1. **Save the request**: Use write_file() to save the user's task description to `/results/task_request.md` │ │ 2. **Plan**: Create a todo list with write_todos to break down the task into focused steps │ │ 3. **Execute**: Delegate subtasks to sub-agents using the task() tool, or execute directly │ │ 4. **Write Output**: Write comprehensive results to `/results/` directory (see Output Guidelines below) │ │ 5. **Verify**: Read `/results/task_request.md` to confirm you've addressed all aspects of the original │ │ request │ │ │ │ ## Task Planning Guidelines │ │ - Batch similar subtasks into a single TODO to minimize overhead │ │ - For simple tasks, execute directly or use 1 sub-agent │ │ - For comparisons or multi-faceted tasks, delegate to multiple parallel sub-agents │ │ - Each sub-agent should handle one specific aspect and return findings │ │ </task_workflow> │ │ │ │ <workspace_paths> │ │ # Workspace Structure │ │ │ │ Working directory: `/home/daytona` │ │ │ │ ## Standard Directories │ │ │ │ | Directory | Purpose | │ │ |-----------|---------| │ │ | `results/` | Final deliverables (reports, visualizations) | │ │ | `data/` | Intermediate storage (tool results, API responses) | │ │ | `tools/` | MCP tool modules (auto-generated) | │ │ | `code/` | Execution history (exec_0001.py, etc.) | │ │ │ │ ## Path Rules by Tool │ │ │ │ **execute_code (Python)** - Use RELATIVE paths: │ │ ```python │ │ with open('results/output.json', 'w') as f: # Correct │ │ with open('data/temp.json', 'w') as f: # Correct │ │ ``` │ │ │ │ **File tools (Read/Write/Edit)** - Use RELATIVE paths: │ │ ``` │ │ file_path = "results/report.md" # Correct │ │ file_path = "config.yaml" # Correct │ │ ``` │ │ │ │ **Bash tool** - Use ABSOLUTE paths (working_dir is `/home/daytona`): │ │ ```bash │ │ ls /home/daytona/results/ # Correct │ │ cat /home/daytona/data/output.json # Correct │ │ ls results/ # Also works (relative to working_dir) │ │ ``` │ │ │ │ **NEVER use these path patterns**: │ │ - `/workspace/...` - Wrong prefix, becomes `/home/daytona/workspace/...` │ │ - Absolute paths starting with `/results/` or `/data/` in Bash - these don't exist at root level │ │ </workspace_paths> │ │ │ │ <tool_discovery> │ │ # Tool Discovery │ │ │ │ You have access to MCP servers with specialized tools. Use the `execute_code` tool to run Python code that │ │ invokes these tools. │ │ │ │ │ │ tickertick: Financial news API for ticker news, curated news, and entity news from Tickertick │ │ Instructions: Use for getting financial news about specific tickers, curated market news, news from │ │ specific sources, or news about entities like people and companies. │ │ - Module: tools/tickertick.py │ │ - Tools: 7 tools available │ │ - Import: from tools.tickertick import <tool_name> │ │ - Documentation: tools/docs/tickertick/*.md │ │ │ │ yfinance: Yahoo Finance API for stock prices, financial statements, options chains, and company data │ │ Instructions: Use for historical stock prices, financial statements (income, balance sheet, cash flow), │ │ options chains, company info, analyst recommendations, and institutional holders. Best for fundamental │ │ analysis, historical data, and comparing multiple stocks. Returns large datasets ideal for code-based │ │ post-processing. │ │ - Module: tools/yfinance.py │ │ - Tools: 21 tools available │ │ - Import: from tools.yfinance import <tool_name> │ │ - Documentation: tools/docs/yfinance/*.md │ │ │ │ **Note**: Check `tools/docs/{server_name}/{tool_name}.md` for exact function signatures before use. │ │ │ │ ## How to Use MCP Tools │ │ │ │ Import and use MCP tools in your execute_code calls: │ │ │ │ ```python │ │ from tools.{server_name} import {tool_name} │ │ │ │ result = tool_name(param="value") │ │ print(result) │ │ ``` │ │ │ │ ## Discovery Workflow │ │ │ │ Before using any MCP tool, discover its exact signature using this workflow: │ │ │ │ ### Step 1: Search (glob/grep) │ │ │ │ Find what's available before reading anything: │ │ │ │ ```python │ │ glob("tools/docs/{server_name}/*.md") # List all tool docs for a server │ │ glob("tools/*.py") # List all MCP tool modules │ │ grep("{keyword}", path="tools/docs/") # Search docs by keyword │ │ ``` │ │ │ │ ### Step 2: Read Documentation FIRST │ │ │ │ **ALWAYS read the individual .md doc file first** - each tool has its own documentation: │ │ │ │ ```python │ │ read_file("tools/docs/{server_name}/{tool_name}.md") │ │ ``` │ │ │ │ Tool doc files contain: │ │ - Function signature with all parameters │ │ - Parameter types and descriptions │ │ - Usage examples │ │ - Return type information │ │ │ │ ### Step 3: Fallback to Code (only if needed) │ │ │ │ Only read the .py module if documentation is missing or incomplete: │ │ │ │ ```python │ │ # Use offset/limit to read specific sections, not the entire file │ │ read_file("tools/{server_name}.py", offset=50, limit=30) │ │ ``` │ │ │ │ ## Quick Reference │ │ │ │ | Goal | Command | │ │ |------|---------| │ │ | List server's tools | `glob("tools/docs/{server_name}/*.md")` | │ │ | Read tool documentation | `read_file("tools/docs/{server_name}/{tool_name}.md")` | │ │ | Search all docs | `grep("{keyword}", path="tools/docs/")` | │ │ | Find tool in module | `grep("def {tool_name}", path="tools/{server_name}.py")` | │ │ | List saved results | `glob("results/*")` | │ │ │ │ ## Anti-Patterns to AVOID │ │ │ │ - DON'T read entire .py files to find one tool's signature - read the .md doc instead │ │ - DON'T guess tool parameters without checking documentation first │ │ - DON'T use Bash find/grep when glob/grep tools exist │ │ - DON'T read large files without offset/limit │ │ </tool_discovery> │ │ │ │ <output_guidelines> │ │ # Output Guidelines │ │ │ │ When writing output reports to `/results/`, follow these structure patterns: │ │ │ │ **For comparisons:** │ │ 1. Introduction │ │ 2. Overview of topic A │ │ 3. Overview of topic B │ │ 4. Detailed comparison │ │ 5. Conclusion │ │ │ │ **For lists/rankings:** │ │ Simply list items with details - no introduction needed: │ │ 1. Item 1 with explanation │ │ 2. Item 2 with explanation │ │ 3. Item 3 with explanation │ │ │ │ **For summaries/overviews:** │ │ 1. Overview of topic │ │ 2. Key concept 1 │ │ 3. Key concept 2 │ │ 4. Key concept 3 │ │ 5. Conclusion │ │ │ │ **General guidelines:** │ │ - Use clear section headings (## for sections, ### for subsections) │ │ - Write in paragraph form by default - be text-heavy, not just bullet points │ │ - Do NOT use self-referential language ("I found...", "I researched...") │ │ - Write as a professional report without meta-commentary │ │ - Each section should be comprehensive and detailed │ │ - Use bullet points only when listing is more appropriate than prose │ │ </output_guidelines> │ │ │ │ <citation_rules> │ │ # Citation Rules │ │ │ │ Use numbered citations throughout your report: │ │ - Cite sources inline using [1], [2], [3] format │ │ - Assign each unique URL a single citation number │ │ - End report with ### Sources section listing each numbered source │ │ - Number sources sequentially without gaps (1,2,3,4...) │ │ - Format: [1] Source Title: URL (each on separate line for proper list rendering) │ │ │ │ **Example:** │ │ │ │ Some important finding [1]. Another key insight [2]. │ │ │ │ ### Sources │ │ [1] AI Research Paper: https://example.com/paper │ │ [2] Industry Analysis: https://example.com/analysis │ │ </citation_rules> │ │ │ │ <subagent_coordination> │ │ # Sub-Agent Task Coordination │ │ │ │ Your role is to coordinate work by delegating tasks from your TODO list to specialized sub-agents. │ │ │ │ ## Available Sub-Agents │ │ │ │ - **research**: Delegate research to the sub-agent researcher. Give this researcher one specific topic or │ │ question at a time. The researcher will search the web and provide findings with citations. │ │ Tools: tavily_search, think_tool │ │ - **general-purpose**: Delegate complex tasks to the general-purpose sub-agent. This agent has access to all │ │ filesystem tools (read, write, edit, glob, grep, bash) and can execute Python code with MCP tools. Use for │ │ multi-step operations, data processing, file manipulation, or any task requiring full tool access. │ │ Tools: execute_code, read_file, write_file, edit_file, glob, grep, view_image │ │ │ │ ## Delegation Strategy │ │ │ │ **Simple tasks** -> 1 sub-agent or direct execution: │ │ - "List the top 10 coffee shops in San Francisco" │ │ - "What is quantum computing?" │ │ - "Fetch data for symbol X" │ │ │ │ **Comparisons** -> 1 sub-agent per element: │ │ - "Compare OpenAI vs Anthropic vs DeepMind" -> 3 parallel sub-agents │ │ - "Compare Python vs JavaScript" -> 2 parallel sub-agents │ │ │ │ **Multi-faceted tasks** -> 1 sub-agent per aspect: │ │ - "Analyze topic X: aspect A, aspect B, aspect C" -> 3 parallel sub-agents │ │ - "Process data: fetch, transform, visualize" -> 3 parallel sub-agents │ │ │ │ ## Parallel Execution │ │ - Use at most 3 parallel sub-agents per iteration │ │ - Make multiple task() calls in a single response to enable parallel execution │ │ - Each sub-agent returns findings independently │ │ │ │ ## Background Subagent Execution │ │ │ │ When you invoke subagents via the `task()` tool, they run in the **background** and are assigned sequential │ │ IDs (Task-1, Task-2, Task-3, etc.): │ │ │ │ 1. **Immediate Response**: You receive confirmation with a Task ID (e.g., "Task-1 deployed") │ │ 2. **Continue Working**: You can proceed with other tasks while subagents execute │ │ 3. **Monitor Progress**: Use `check_task_progress()` to see subagent activity │ │ 4. **Collect Results**: Use `wait()` or complete your work to trigger result collection │ │ 5. **Synthesis**: Incorporate subagent results into your final response │ │ │ │ ### Management Tools │ │ │ │ **`wait(task_number=None, timeout=60.0)`** │ │ - Wait for specific task: `wait(task_number=1)` - waits for Task-1 │ │ - Wait for all tasks: `wait()` - waits for all pending background tasks │ │ - Custom timeout: `wait(task_number=2, timeout=120)` - wait up to 2 minutes │ │ │ │ **`check_task_progress(task_number=None)`** │ │ - Check specific task: `check_task_progress(task_number=1)` - shows Task-1 status │ │ - Check all tasks: `check_task_progress()` - overview of all background tasks │ │ - Shows: status (running/completed), tool calls made, current activity, elapsed time │ │ │ │ ### Important Guidelines │ │ │ │ 1. **Avoid Duplicate Assignments**: Before assigning a new task, check if a similar task is already running │ │ using `check_task_progress()`. Do not assign the same or overlapping tasks to multiple subagents. │ │ │ │ 2. **Use Task IDs**: Reference subagents by their Task-N ID when checking progress or waiting for specific │ │ results. │ │ │ │ 3. **Independent Tasks Only**: Background execution works best for independent tasks. For tasks with │ │ dependencies, wait for prerequisite tasks to complete first. │ │ │ │ 4. **Result Collection Options**: │ │ - **Explicit wait**: Call `wait()` or `wait(task_number=N)` when you need results │ │ - **Implicit collection**: When you finish your work, pending results are automatically collected │ │ │ │ ### Example Flow │ │ ``` │ │ User: "Research topics A, B, C and synthesize findings" │ │ │ │ 1. task() -> Research topic A → "Task-1 deployed" │ │ 2. task() -> Research topic B → "Task-2 deployed" │ │ 3. task() -> Research topic C → "Task-3 deployed" │ │ 4. check_task_progress() → Shows 3 tasks running │ │ 5. [Continue other work or wait...] │ │ 6. wait() → Collects all results │ │ 7. Synthesize findings into final response │ │ ``` │ │ │ │ ### Task Status Indicators │ │ - `[RUNNING]`: Task is actively executing │ │ - `[DONE]`: Task finished successfully │ │ - `[ERROR]`: Task encountered an error │ │ │ │ ## Processing Sub-Agent Results │ │ │ │ When a sub-agent returns results: │ │ 1. **View images/charts**: If the sub-agent mentions images, charts, or visualizations, use `view_image` to │ │ view them │ │ - Check for image URLs in the response │ │ - Check for image paths in `data/` or `results/` directories │ │ 2. **Read referenced files if needed**: If the sub-agent saved intermediate results, read them from: │ │ - `data/` - Raw data, large datasets │ │ - `results/task_*.md` - Textual analysis │ │ - `code/` - Code executed by the sub-agent │ │ 3. **Synthesize**: Combine findings from all sub-agents into a cohesive response │ │ │ │ ## Task Limits │ │ - Stop after 3 delegation rounds if task cannot be completed │ │ - Stop when you have sufficient results to fulfill the request │ │ - Bias towards focused execution over exhaustive exploration │ │ </subagent_coordination> │ │ │ │ <data_processing> │ │ # Data Processing │ │ │ │ **Core principle**: Always dump data first, then process. This enables iterative inspection and reprocessing │ │ without re-fetching. │ │ │ │ ## Workflow: Dump First, Then Process │ │ │ │ 1. **Dump raw result** → Save to `data/` immediately after tool call │ │ 2. **Inspect structure** → Use bash/jq or code to peek at schema │ │ 3. **Extract what you need** → Process the saved file, not the raw result │ │ 4. **Return summary** → Only summaries flow to LLM │ │ │ │ This approach: │ │ - Preserves raw data for reprocessing │ │ - Enables quick inspection with bash/jq │ │ - Minimizes token usage │ │ - Allows iterative refinement │ │ │ │ ## Inspecting Large Data │ │ │ │ ### Peek at JSON Structure │ │ ```python │ │ import json │ │ │ │ # Quick structure inspection │ │ with open('data/large_file.json') as f: │ │ data = json.load(f) │ │ │ │ # Show top-level keys and types │ │ for key, value in data.items(): │ │ print(f"{key}: {type(value).__name__}", end="") │ │ if isinstance(value, list): │ │ print(f" ({len(value)} items)") │ │ elif isinstance(value, dict): │ │ print(f" ({len(value)} keys)") │ │ else: │ │ print() │ │ ``` │ │ │ │ ### Using jq for Quick Inspection (Bash) │ │ ```bash │ │ # View structure │ │ jq 'keys' data/file.json │ │ │ │ # First item of array │ │ jq '.[0]' data/file.json │ │ │ │ # Count items │ │ jq 'length' data/file.json │ │ │ │ # Extract specific field │ │ jq '.items[].name' data/file.json │ │ ``` │ │ │ │ ### Display Key Statistics │ │ ```python │ │ import json │ │ from collections import Counter │ │ │ │ with open('data/results.json') as f: │ │ data = json.load(f) │ │ │ │ # For list data │ │ if isinstance(data, list): │ │ print(f"Total records: {len(data)}") │ │ if data and isinstance(data[0], dict): │ │ print(f"Fields: {list(data[0].keys())}") │ │ # Count by category if applicable │ │ if 'type' in data[0]: │ │ types = Counter(item['type'] for item in data) │ │ print(f"By type: {dict(types)}") │ │ ``` │ │ │ │ ## Finding Specific Sections │ │ │ │ ### Search within JSON │ │ ```python │ │ # Find items matching criteria │ │ matches = [item for item in data if 'keyword' in item.get('name', '').lower()] │ │ print(f"Found {len(matches)} matches") │ │ ``` │ │ │ │ ### Using grep for Quick Search │ │ ```bash │ │ # Find lines containing pattern │ │ grep -n "error" data/logs.json │ │ │ │ # Context around match │ │ grep -B2 -A2 "target_id" data/results.json │ │ ``` │ │ │ │ ## When to Dump to `data/` Directory │ │ │ │ ALWAYS dump tool results to `data/` when: │ │ - **Structured data**: JSON, dictionaries, lists with >10 items │ │ - **Large text**: Responses >500 characters │ │ - **Tabular data**: DataFrames, CSV-like data with >5 rows │ │ - **API responses**: Raw API responses (preserve for reprocessing) │ │ - **Intermediate results**: Data that may be referenced later │ │ │ │ ## Storage and Summary Pattern │ │ │ │ ```python │ │ import json │ │ │ │ # 1. Get data from tool │ │ result = some_mcp_tool(query="...") │ │ │ │ # 2. Dump full result │ │ with open('data/tool_result_001.json', 'w') as f: │ │ json.dump(result, f, indent=2) │ │ │ │ # 3. Return ONLY summary │ │ items = result.get('items', []) │ │ print(f""" │ │ ## Result Summary │ │ - Saved to: data/tool_result_001.json │ │ - Records: {len(items)} │ │ - Fields: {list(items[0].keys()) if items else 'N/A'} │ │ - Sample: {items[0] if items else 'empty'} │ │ """) │ │ ``` │ │ </data_processing> │ │ │ │ <image_upload> │ │ # Image Auto-Upload │ │ │ │ Images saved to `results/` are automatically uploaded to cloud storage. The URL is returned in the response. │ │ │ │ **CRITICAL**: Use RELATIVE paths, NOT absolute paths: │ │ - CORRECT: `plt.savefig('results/chart.png')` -> auto-uploaded, URL returned │ │ - WRONG: `plt.savefig('/results/chart.png')` -> NOT uploaded! │ │ │ │ Example: │ │ ```python │ │ import matplotlib.pyplot as plt │ │ plt.figure(figsize=(10, 6)) │ │ plt.plot(data) │ │ plt.savefig('results/chart.png') # Use relative path! │ │ print("Chart saved") │ │ ``` │ │ │ │ The response includes: `` - reference this URL in your report. │ │ </image_upload> │ │ │ │ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Sandbox workspace preview
# Display sandbox file tree BEFORE agent execution
print("=" * 50)
print("Sandbox File Tree (BEFORE)")
print("=" * 50)
print("./")
print_sandbox_tree(session.sandbox)==================================================
Sandbox File Tree (BEFORE)
==================================================
./
├── code/
├── data/
├── mcp_servers/
│ ├── tickertick_mcp_server.py
│ └── yfinance_mcp_server.py
├── results/
└── tools/
├── docs/
│ ├── tickertick/
│ │ ├── get_broad_ticker_news.md
│ │ ├── get_curated_news.md
│ │ ├── get_entity_news.md
│ │ ├── get_news_for_multiple_tickers.md
│ │ ├── get_news_from_source.md
│ │ ├── get_ticker_news.md
│ │ └── search_tickers.md
│ └── yfinance/
│ ├── compare_financials.md
│ ├── compare_valuations.md
│ ├── get_analyst_recommendations.md
│ ├── get_balance_sheet.md
│ ├── get_cash_flow.md
│ ├── get_company_info.md
│ ├── get_dividends_and_splits.md
│ ├── get_earnings_data.md
│ ├── get_earnings_dates.md
│ ├── get_income_statement.md
│ ├── get_insider_roster.md
│ ├── get_insider_transactions.md
│ ├── get_institutional_holders.md
│ ├── get_multiple_stocks_dividends.md
│ ├── get_multiple_stocks_earnings.md
│ ├── get_multiple_stocks_history.md
│ ├── get_mutualfund_holders.md
│ ├── get_news.md
│ ├── get_options_chain.md
│ ├── get_stock_history.md
│ └── get_sustainability_data.md
├── __init__.py
├── mcp_client.py
├── tickertick.py
└── yfinance.py
Agent execution
# You may changes this to your own query.
# By default, two custom finance related mcp servers are available.
# Make sure you configure any additional MCP servers if you want to test it outside of the default scope.
query = """compare the stock price of nvidia and amd over the past year. create a 2 by 2 chart that shows the price, volume, some key fundemental data comparison"""result = await agent.ainvoke(
{
"messages": [
{
"role": "user",
"content": query
}
],
},
)
format_messages(result["messages"])[2m2025-12-01 04:48:07[0m [[32m[1minfo [0m] [1mOrchestrator invoking agent [0m [36mhas_messages[0m=[35mTrue[0m [36miteration[0m=[35m1[0m
[2m2025-12-01 04:48:10[0m [[32m[1minfo [0m] [1mWriting file [0m [36mfile_path[0m=[35mresults/task_request.md[0m [36mnormalized_path[0m=[35m/home/daytona/results/task_request.md[0m [36msize[0m=[35m143[0m
[2m2025-12-01 04:48:11[0m [[32m[1minfo [0m] [1mWrote 143 bytes to /home/daytona/results/task_request.md[0m
[2m2025-12-01 04:48:11[0m [[32m[1minfo [0m] [1mFile written successfully [0m [36mbytes_written[0m=[35m143[0m [36mfile_path[0m=[35m/results/task_request.md[0m
[2m2025-12-01 04:48:16[0m [[32m[1minfo [0m] [1mGlobbing files [0m [36mnormalized_path[0m=[35m/home/daytona/.}[0m [36mpath[0m=[35m.}[0m [36mpattern[0m=[35mtools/docs/yfinance/*.md[0m
[2m2025-12-01 04:48:16[0m [[32m[1minfo [0m] [1mFound 0 files matching **/tools/docs/yfinance/*.md[0m
[2m2025-12-01 04:48:16[0m [[32m[1minfo [0m] [1mNo files found [0m [36mpath[0m=[35m.}[0m [36mpattern[0m=[35mtools/docs/yfinance/*.md[0m
[2m2025-12-01 04:48:18[0m [[32m[1minfo [0m] [1mGlobbing files [0m [36mnormalized_path[0m=[35m/home/daytona[0m [36mpath[0m=[35m.[0m [36mpattern[0m=[35mtools/docs/yfinance/*.md[0m
[2m2025-12-01 04:48:18[0m [[32m[1minfo [0m] [1mFound 21 files matching **/tools/docs/yfinance/*.md[0m
[2m2025-12-01 04:48:18[0m [[32m[1minfo [0m] [1mGlob completed successfully [0m [36mmatches[0m=[35m21[0m [36mpath[0m=[35m.[0m [36mpattern[0m=[35mtools/docs/yfinance/*.md[0m
[2m2025-12-01 04:48:21[0m [[32m[1minfo [0m] [1mReading file [0m [36mfile_path[0m=[35mtools/docs/yfinance/get_multiple_stocks_history.md[0m [36mlimit[0m=[35m400[0m [36mnormalized_path[0m=[35m/home/daytona/tools/docs/yfinance/get_multiple_stocks_history.md[0m [36moffset[0m=[35m0[0m
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[2m2025-12-01 04:48:23[0m [[32m[1minfo [0m] [1mReading file [0m [36mfile_path[0m=[35mtools/docs/yfinance/get_company_info.md[0m [36mlimit[0m=[35m400[0m [36mnormalized_path[0m=[35m/home/daytona/tools/docs/yfinance/get_company_info.md[0m [36moffset[0m=[35m0[0m
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[2m2025-12-01 04:48:27[0m [[32m[1minfo [0m] [1mExecuting code in sandbox [0m [36mcode_length[0m=[35m576[0m
[2m2025-12-01 04:48:27[0m [[32m[1minfo [0m] [1mExecuting code [0m [36mauto_install[0m=[35mTrue[0m [36mcode_hash[0m=[35mac70cff8c67c4b94[0m [36mcode_length[0m=[35m576[0m [36mexecution_id[0m=[35mexec_0001[0m
[2m2025-12-01 04:48:30[0m [[32m[1minfo [0m] [1mCode execution completed [0m [36mcharts_captured[0m=[35m0[0m [36mduration[0m=[35m3.075885057449341[0m [36mexecution_id[0m=[35mexec_0001[0m [36mfiles_created[0m=[35m0[0m [36msuccess[0m=[35mTrue[0m
[2m2025-12-01 04:48:31[0m [[32m[1minfo [0m] [1mCode executed successfully [0m [36mimages_uploaded[0m=[35m0[0m [36mstdout_length[0m=[35m51[0m
[2m2025-12-01 04:48:41[0m [[32m[1minfo [0m] [1mReading file [0m [36mfile_path[0m=[35mdata/nvda_info.json[0m [36mlimit[0m=[35m400[0m [36mnormalized_path[0m=[35m/home/daytona/data/nvda_info.json[0m [36moffset[0m=[35m1[0m
[2m2025-12-01 04:48:42[0m [[32m[1minfo [0m] [1mRead file range [0m [36mfile_path[0m=[35m/home/daytona/data/nvda_info.json[0m [36mlimit[0m=[35m400[0m [36mlines_read[0m=[35m290[0m [36moffset[0m=[35m1[0m
[2m2025-12-01 04:48:42[0m [[32m[1minfo [0m] [1mFile read successfully [0m [36mfile_path[0m=[35mdata/nvda_info.json[0m [36mlines[0m=[35m290[0m [36msize[0m=[35m10517[0m
[2m2025-12-01 04:48:43[0m [[32m[1minfo [0m] [1mReading file [0m [36mfile_path[0m=[35mdata/amd_info.json[0m [36mlimit[0m=[35m200[0m [36mnormalized_path[0m=[35m/home/daytona/data/amd_info.json[0m [36moffset[0m=[35m1[0m
[2m2025-12-01 04:48:44[0m [[32m[1minfo [0m] [1mRead file range [0m [36mfile_path[0m=[35m/home/daytona/data/amd_info.json[0m [36mlimit[0m=[35m200[0m [36mlines_read[0m=[35m200[0m [36moffset[0m=[35m1[0m
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[2m2025-12-01 04:48:46[0m [[32m[1minfo [0m] [1mReading file [0m [36mfile_path[0m=[35mdata/amd_info.json[0m [36mlimit[0m=[35m200[0m [36mnormalized_path[0m=[35m/home/daytona/data/amd_info.json[0m [36moffset[0m=[35m200[0m
[2m2025-12-01 04:48:46[0m [[32m[1minfo [0m] [1mRead file range [0m [36mfile_path[0m=[35m/home/daytona/data/amd_info.json[0m [36mlimit[0m=[35m200[0m [36mlines_read[0m=[35m81[0m [36moffset[0m=[35m200[0m
[2m2025-12-01 04:48:46[0m [[32m[1minfo [0m] [1mFile read successfully [0m [36mfile_path[0m=[35mdata/amd_info.json[0m [36mlines[0m=[35m81[0m [36msize[0m=[35m2821[0m
[2m2025-12-01 04:49:00[0m [[32m[1minfo [0m] [1mExecuting code in sandbox [0m [36mcode_length[0m=[35m3247[0m
[2m2025-12-01 04:49:00[0m [[32m[1minfo [0m] [1mExecuting code [0m [36mauto_install[0m=[35mTrue[0m [36mcode_hash[0m=[35md8efc999caa0c6f3[0m [36mcode_length[0m=[35m3247[0m [36mexecution_id[0m=[35mexec_0002[0m
[2m2025-12-01 04:49:03[0m [[32m[1minfo [0m] [1mCode execution completed [0m [36mcharts_captured[0m=[35m0[0m [36mduration[0m=[35m2.260283946990967[0m [36mexecution_id[0m=[35mexec_0002[0m [36mfiles_created[0m=[35m1[0m [36msuccess[0m=[35mTrue[0m
[2m2025-12-01 04:49:04[0m [[32m[1minfo [0m] [1mUploaded image to storage: charts/exec_0002/nvda_amd_comparison.png[0m
[2m2025-12-01 04:49:04[0m [[32m[1minfo [0m] [1mCode executed successfully [0m [36mimages_uploaded[0m=[35m1[0m [36mstdout_length[0m=[35m46[0m
[2m2025-12-01 04:49:08[0m [[32m[1minfo [0m] [1mExecuting code in sandbox [0m [36mcode_length[0m=[35m905[0m
[2m2025-12-01 04:49:08[0m [[32m[1minfo [0m] [1mExecuting code [0m [36mauto_install[0m=[35mTrue[0m [36mcode_hash[0m=[35m852885017b0e3952[0m [36mcode_length[0m=[35m905[0m [36mexecution_id[0m=[35mexec_0003[0m
[2m2025-12-01 04:49:10[0m [[32m[1minfo [0m] [1mCode execution completed [0m [36mcharts_captured[0m=[35m0[0m [36mduration[0m=[35m1.442831039428711[0m [36mexecution_id[0m=[35mexec_0003[0m [36mfiles_created[0m=[35m0[0m [36msuccess[0m=[35mTrue[0m
[2m2025-12-01 04:49:10[0m [[32m[1minfo [0m] [1mCode executed successfully [0m [36mimages_uploaded[0m=[35m0[0m [36mstdout_length[0m=[35m172[0m
[2m2025-12-01 04:49:25[0m [[32m[1minfo [0m] [1mWriting file [0m [36mfile_path[0m=[35mresults/nvidia_amd_comparison.md[0m [36mnormalized_path[0m=[35m/home/daytona/results/nvidia_amd_comparison.md[0m [36msize[0m=[35m3140[0m
[2m2025-12-01 04:49:25[0m [[32m[1minfo [0m] [1mWrote 3140 bytes to /home/daytona/results/nvidia_amd_comparison.md[0m
[2m2025-12-01 04:49:25[0m [[32m[1minfo [0m] [1mFile written successfully [0m [36mbytes_written[0m=[35m3166[0m [36mfile_path[0m=[35m/results/nvidia_amd_comparison.md[0m
[2m2025-12-01 04:49:32[0m [[32m[1minfo [0m] [1mReading file [0m [36mfile_path[0m=[35mresults/task_request.md[0m [36mlimit[0m=[35m100[0m [36mnormalized_path[0m=[35m/home/daytona/results/task_request.md[0m [36moffset[0m=[35m1[0m
[2m2025-12-01 04:49:32[0m [[32m[1minfo [0m] [1mRead file range [0m [36mfile_path[0m=[35m/home/daytona/results/task_request.md[0m [36mlimit[0m=[35m100[0m [36mlines_read[0m=[35m1[0m [36moffset[0m=[35m1[0m
[2m2025-12-01 04:49:32[0m [[32m[1minfo [0m] [1mFile read successfully [0m [36mfile_path[0m=[35mresults/task_request.md[0m [36mlines[0m=[35m1[0m [36msize[0m=[35m143[0m
╭─────────────────────────────────────────────────── 🧑 Human ────────────────────────────────────────────────────╮ │ compare the stock price of nvidia and amd over the past year. create a 2 by 2 chart that shows the price, │ │ volume, some key fundemental data comparison │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: write_file │ │ Args: { │ │ "file_path": "results/task_request.md", │ │ "content": "Compare the stock price of Nvidia and AMD over the past year and create a 2x2 chart showing │ │ price, volume, and key fundamental data comparison." │ │ } │ │ ID: call_k3htR9isyxxvSLiWVsuwVEau │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ Wrote 143 bytes to /results/task_request.md │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: write_todos │ │ Args: { │ │ "todos": [ │ │ { │ │ "content": "Collect the past year of price, volume, and fundamental data for NVDA and AMD via yfinance", │ │ "status": "in_progress" │ │ }, │ │ { │ │ "content": "Produce a 2x2 chart comparing price, volume, and key fundamentals for both stocks", │ │ "status": "pending" │ │ }, │ │ { │ │ "content": "Draft a professional comparison report summarizing findings and cite data sources", │ │ "status": "pending" │ │ } │ │ ] │ │ } │ │ ID: call_AAwmNFQuHCg4f8VZCGDLCsiJ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ Updated todo list to [{'content': 'Collect the past year of price, volume, and fundamental data for NVDA and │ │ AMD via yfinance', 'status': 'in_progress'}, {'content': 'Produce a 2x2 chart comparing price, volume, and key │ │ fundamentals for both stocks', 'status': 'pending'}, {'content': 'Draft a professional comparison report │ │ summarizing findings and cite data sources', 'status': 'pending'}] │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: glob │ │ Args: { │ │ "pattern": "tools/docs/yfinance/*.md", │ │ "path": ".}" │ │ } │ │ ID: call_v1iCD3wdCIZtwy9MhbvEeQnC │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ No files matching pattern 'tools/docs/yfinance/*.md' found in '.}' │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: glob │ │ Args: { │ │ "pattern": "tools/docs/yfinance/*.md", │ │ "path": "." │ │ } │ │ ID: call_ci3EBGPumiHxv21EqtWh5OOV │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ Found 21 file(s) matching 'tools/docs/yfinance/*.md': │ │ /tools/docs/yfinance/compare_valuations.md │ │ /tools/docs/yfinance/compare_financials.md │ │ /tools/docs/yfinance/get_multiple_stocks_dividends.md │ │ /tools/docs/yfinance/get_insider_transactions.md │ │ /tools/docs/yfinance/get_multiple_stocks_earnings.md │ │ /tools/docs/yfinance/get_sustainability_data.md │ │ /tools/docs/yfinance/get_mutualfund_holders.md │ │ /tools/docs/yfinance/get_insider_roster.md │ │ /tools/docs/yfinance/get_news.md │ │ /tools/docs/yfinance/get_multiple_stocks_history.md │ │ /tools/docs/yfinance/get_earnings_dates.md │ │ /tools/docs/yfinance/get_income_statement.md │ │ /tools/docs/yfinance/get_institutional_holders.md │ │ /tools/docs/yfinance/get_dividends_and_splits.md │ │ /tools/docs/yfinance/get_options_chain.md │ │ /tools/docs/yfinance/get_analyst_recommendations.md │ │ /tools/docs/yfinance/get_stock_history.md │ │ /tools/docs/yfinance/get_earnings_data.md │ │ /tools/docs/yfinance/get_balance_sheet.md │ │ /tools/docs/yfinance/get_cash_flow.md │ │ /tools/docs/yfinance/get_company_info.md │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: read_file │ │ Args: { │ │ "file_path": "tools/docs/yfinance/get_multiple_stocks_history.md", │ │ "offset": 0, │ │ "limit": 400 │ │ } │ │ ID: call_wIwnrDppFCV3liA7PTyWIp6k │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ 1→# get_multiple_stocks_history(tickers: List, period: str = 1y, interval: str = 1d) │ │ 2→ │ │ 3→ │ │ 4→ Get historical data for multiple stocks in a single call. │ │ 5→ │ │ 6→ VERY HIGH PTC VALUE: Returns N × 252 rows per year for N stocks. │ │ 7→ Ideal for portfolio analysis, correlation studies, and comparative performance. │ │ 8→ │ │ 9→ Args: │ │ 10→ tickers: List of ticker symbols (e.g., ["AAPL", "MSFT", "GOOGL"]) │ │ 11→ period: Data period - 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max │ │ 12→ interval: Data interval - 1d, 5d, 1wk, 1mo, 3mo │ │ 13→ │ │ 14→ Returns: │ │ 15→ dict: { │ │ 16→ "period": str, │ │ 17→ "interval": str, │ │ 18→ "data": {ticker: {"count": int, "history": list}}, │ │ 19→ "successful_tickers": list, │ │ 20→ "total_data_points": int, │ │ 21→ "errors": list # Optional, only if errors occurred │ │ 22→ } │ │ 23→ Use pd.DataFrame(result["data"]["AAPL"]["history"]) to convert to DataFrame. │ │ 24→ │ │ 25→ │ │ 26→## Parameters │ │ 27→ │ │ 28→- `tickers` (array) - **Required** │ │ 29→ │ │ 30→- `period` (string) - Optional │ │ 31→ │ │ 32→- `interval` (string) - Optional │ │ 33→ │ │ 34→## Returns │ │ 35→ │ │ 36→**Type:** `dict` │ │ 37→ │ │ 38→dict: { │ │ 39→ "period": str, │ │ 40→ "interval": str, │ │ 41→ "data": {ticker: {"count": int, "history": list}}, │ │ 42→ "successful_tickers": list, │ │ 43→ "total_data_points": int, │ │ 44→ "errors": list # Optional, only if errors occurred │ │ 45→ } │ │ 46→ Use pd.DataFrame(result["data"]["AAPL"]["history"]) to convert to DataFrame. │ │ 47→ │ │ 48→## Example │ │ 49→ │ │ 50→```python │ │ 51→from tools.yfinance import get_multiple_stocks_history │ │ 52→ │ │ 53→result = get_multiple_stocks_history(tickers=[]) │ │ 54→print(result) │ │ 55→``` │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: read_file │ │ Args: { │ │ "file_path": "tools/docs/yfinance/get_company_info.md", │ │ "offset": 0, │ │ "limit": 400 │ │ } │ │ ID: call_T3BTr2PClpYVgE0MDyYv7sYz │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ 1→# get_company_info(ticker: str) │ │ 2→ │ │ 3→ │ │ 4→ Get comprehensive company information (sector, industry, market cap, ratios, etc.) │ │ 5→ │ │ 6→ MEDIUM PTC VALUE: Returns 100+ fields of company metadata. │ │ 7→ Useful for screening, sector analysis, and fundamental overview. │ │ 8→ │ │ 9→ Args: │ │ 10→ ticker: Stock ticker symbol (e.g., "AAPL", "MSFT") │ │ 11→ │ │ 12→ Returns: │ │ 13→ Dictionary with company information │ │ 14→ │ │ 15→ │ │ 16→## Parameters │ │ 17→ │ │ 18→- `ticker` (string) - **Required** │ │ 19→ │ │ 20→## Returns │ │ 21→ │ │ 22→**Type:** `dict` │ │ 23→ │ │ 24→Dictionary with company information │ │ 25→ │ │ 26→## Example │ │ 27→ │ │ 28→```python │ │ 29→from tools.yfinance import get_company_info │ │ 30→ │ │ 31→result = get_company_info(ticker="example") │ │ 32→print(result) │ │ 33→``` │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: execute_code │ │ Args: { │ │ "code": "from tools.yfinance import get_multiple_stocks_history, get_company_info\nimport json\n\nhistory = │ │ get_multiple_stocks_history(tickers=[\"NVDA\", \"AMD\"], period=\"1y\", interval=\"1d\")\nwith │ │ open('data/nvidia_amd_history.json', 'w') as f:\n json.dump(history, f, indent=2)\n\nnvda_info = │ │ get_company_info(ticker=\"NVDA\")\namd_info = get_company_info(ticker=\"AMD\")\nwith │ │ open('data/nvda_info.json', 'w') as f:\n json.dump(nvda_info, f, indent=2)\nwith open('data/amd_info.json', │ │ 'w') as f:\n json.dump(amd_info, f, indent=2)\n\nprint(\"Historical and company info saved for NVDA and │ │ AMD.\")" │ │ } │ │ ID: call_InqfzgB1BQgozjydPkG7PRqx │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ SUCCESS │ │ Historical and company info saved for NVDA and AMD. │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: write_todos │ │ Args: { │ │ "todos": [ │ │ { │ │ "content": "Collect the past year of price, volume, and fundamental data for NVDA and AMD via yfinance", │ │ "status": "completed" │ │ }, │ │ { │ │ "content": "Produce a 2x2 chart comparing price, volume, and key fundamentals for both stocks", │ │ "status": "in_progress" │ │ }, │ │ { │ │ "content": "Draft a professional comparison report summarizing findings and cite data sources", │ │ "status": "pending" │ │ } │ │ ] │ │ } │ │ ID: call_wlA9epTEN4DuLSoUb6WHWMxP │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ Updated todo list to [{'content': 'Collect the past year of price, volume, and fundamental data for NVDA and │ │ AMD via yfinance', 'status': 'completed'}, {'content': 'Produce a 2x2 chart comparing price, volume, and key │ │ fundamentals for both stocks', 'status': 'in_progress'}, {'content': 'Draft a professional comparison report │ │ summarizing findings and cite data sources', 'status': 'pending'}] │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: read_file │ │ Args: { │ │ "file_path": "data/nvda_info.json", │ │ "offset": 1, │ │ "limit": 400 │ │ } │ │ ID: call_f3GqaA4IZCHfzBgap3aB4jkH │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ 1→{ │ │ 2→ "ticker": "NVDA", │ │ 3→ "info": { │ │ 4→ "address1": "2788 San Tomas Expressway", │ │ 5→ "city": "Santa Clara", │ │ 6→ "state": "CA", │ │ 7→ "zip": "95051", │ │ 8→ "country": "United States", │ │ 9→ "phone": "408 486 2000", │ │ 10→ "website": "https://www.nvidia.com", │ │ 11→ "industry": "Semiconductors", │ │ 12→ "industryKey": "semiconductors", │ │ 13→ "industryDisp": "Semiconductors", │ │ 14→ "sector": "Technology", │ │ 15→ "sectorKey": "technology", │ │ 16→ "sectorDisp": "Technology", │ │ 17→ "longBusinessSummary": "NVIDIA Corporation, a computing infrastructure company, provides graphics │ │ and compute and networking solutions in the United States, Singapore, Taiwan, China, Hong Kong, and │ │ internationally. The Compute & Networking segment includes its Data Centre accelerated computing platforms and │ │ artificial intelligence solutions and software; networking; automotive platforms and autonomous and electric │ │ vehicle solutions; Jetson for robotics and other embedded platforms; and DGX Cloud computing services. The │ │ Graphics segment offers GeForce GPUs for gaming and PCs, the GeForce NOW game streaming service and related │ │ infrastructure, and solutions for gaming platforms; Quadro/NVIDIA RTX GPUs for enterprise workstation graphics; │ │ virtual GPU or vGPU software for cloud-based visual and virtual computing; automotive platforms for │ │ infotainment systems; and Omniverse software for building and operating industrial AI and digital twin │ │ applications. It also customized agentic solutions designed in collaboration with NVIDIA to accelerate │ │ enterprise AI adoption. The company's products are used in gaming, professional visualization, data center, and │ │ automotive markets. It sells its products to original equipment manufacturers, original device manufacturers, │ │ system integrators and distributors, independent software vendors, cloud service providers, consumer internet │ │ companies, add-in board manufacturers, distributors, automotive manufacturers and tier-1 automotive suppliers, │ │ and other ecosystem participants. NVIDIA Corporation was incorporated in 1993 and is headquartered in Santa │ │ Clara, California.", │ │ 18→ "fullTimeEmployees": 36000, │ │ 19→ "companyOfficers": [ │ │ 20→ { │ │ 21→ "maxAge": 1, │ │ 22→ "name": "Mr. Jen-Hsun Huang", │ │ 23→ "age": 61, │ │ 24→ "title": "Co-Founder, CEO, President & Director", │ │ 25→ "yearBorn": 1963, │ │ 26→ "fiscalYear": 2025, │ │ 27→ "totalPay": 11054945, │ │ 28→ "exercisedValue": 0, │ │ 29→ "unexercisedValue": 0 │ │ 30→ }, │ │ 31→ { │ │ 32→ "maxAge": 1, │ │ 33→ "name": "Ms. Colette M. Kress", │ │ 34→ "age": 57, │ │ 35→ "title": "Executive VP & CFO", │ │ 36→ "yearBorn": 1967, │ │ 37→ "fiscalYear": 2025, │ │ 38→ "totalPay": 1512641, │ │ 39→ "exercisedValue": 0, │ │ 40→ "unexercisedValue": 0 │ │ 41→ }, │ │ 42→ { │ │ 43→ "maxAge": 1, │ │ 44→ "name": "Ms. Debora Shoquist", │ │ 45→ "age": 69, │ │ 46→ "title": "Executive Vice President of Operations", │ │ 47→ "yearBorn": 1955, │ │ 48→ "fiscalYear": 2025, │ │ 49→ "totalPay": 1379071, │ │ 50→ "exercisedValue": 0, │ │ 51→ "unexercisedValue": 0 │ │ 52→ }, │ │ 53→ { │ │ 54→ "maxAge": 1, │ │ 55→ "name": "Mr. Timothy S. Teter J.D.", │ │ 56→ "age": 57, │ │ 57→ "title": "Executive VP, General Counsel & Secretary", │ │ 58→ "yearBorn": 1967, │ │ 59→ "fiscalYear": 2025, │ │ 60→ "totalPay": 1362989, │ │ 61→ "exercisedValue": 0, │ │ 62→ "unexercisedValue": 0 │ │ 63→ }, │ │ 64→ { │ │ 65→ "maxAge": 1, │ │ 66→ "name": "Mr. Ajay K. Puri", │ │ 67→ "age": 69, │ │ 68→ "title": "Executive Vice President of Worldwide Field Operations", │ │ 69→ "yearBorn": 1955, │ │ 70→ "fiscalYear": 2025, │ │ 71→ "totalPay": 2313851, │ │ 72→ "exercisedValue": 0, │ │ 73→ "unexercisedValue": 0 │ │ 74→ }, │ │ 75→ { │ │ 76→ "maxAge": 1, │ │ 77→ "name": "Mr. Chris A. Malachowsky", │ │ 78→ "title": "Co-Founder", │ │ 79→ "fiscalYear": 2025, │ │ 80→ "totalPay": 320000, │ │ 81→ "exercisedValue": 0, │ │ 82→ "unexercisedValue": 0 │ │ 83→ }, │ │ 84→ { │ │ 85→ "maxAge": 1, │ │ 86→ "name": "Mr. Donald F. Robertson Jr.", │ │ 87→ "age": 55, │ │ 88→ "title": "VP & Chief Accounting Officer", │ │ 89→ "yearBorn": 1969, │ │ 90→ "fiscalYear": 2025, │ │ 91→ "exercisedValue": 0, │ │ 92→ "unexercisedValue": 0 │ │ 93→ }, │ │ 94→ { │ │ 95→ "maxAge": 1, │ │ 96→ "name": "Prof. William J. Dally Ph.D.", │ │ 97→ "age": 63, │ │ 98→ "title": "Chief Scientist & Senior VP of Research", │ │ 99→ "yearBorn": 1961, │ │ 100→ "fiscalYear": 2025, │ │ 101→ "exercisedValue": 0, │ │ 102→ "unexercisedValue": 0 │ │ 103→ }, │ │ 104→ { │ │ 105→ "maxAge": 1, │ │ 106→ "name": "Mr. Toshiya Hari", │ │ 107→ "title": "Vice President of Investor Relations & Strategic Finance", │ │ 108→ "fiscalYear": 2025, │ │ 109→ "exercisedValue": 0, │ │ 110→ "unexercisedValue": 0 │ │ 111→ }, │ │ 112→ { │ │ 113→ "maxAge": 1, │ │ 114→ "name": "Ms. Mylene Mangalindan", │ │ 115→ "title": "VP of Corporate Communications", │ │ 116→ "fiscalYear": 2025, │ │ 117→ "exercisedValue": 0, │ │ 118→ "unexercisedValue": 0 │ │ 119→ } │ │ 120→ ], │ │ 121→ "auditRisk": 5, │ │ 122→ "boardRisk": 10, │ │ 123→ "compensationRisk": 4, │ │ 124→ "shareHolderRightsRisk": 8, │ │ 125→ "overallRisk": 8, │ │ 126→ "governanceEpochDate": 1761955200, │ │ 127→ "compensationAsOfEpochDate": 1767139200, │ │ 128→ "irWebsite": "http://phx.corporate-ir.net/phoenix.zhtml?c=116466&p=irol-IRHome", │ │ 129→ "executiveTeam": [], │ │ 130→ "maxAge": 86400, │ │ 131→ "priceHint": 2, │ │ 132→ "previousClose": 180.26, │ │ 133→ "open": 178.99, │ │ 134→ "dayLow": 176.5, │ │ 135→ "dayHigh": 179.29, │ │ 136→ "regularMarketPreviousClose": 180.26, │ │ 137→ "regularMarketOpen": 178.99, │ │ 138→ "regularMarketDayLow": 176.5, │ │ 139→ "regularMarketDayHigh": 179.29, │ │ 140→ "dividendRate": 0.04, │ │ 141→ "dividendYield": 0.02, │ │ 142→ "exDividendDate": 1764806400, │ │ 143→ "payoutRatio": 0.0099, │ │ 144→ "fiveYearAvgDividendYield": 0.06, │ │ 145→ "beta": 2.269, │ │ 146→ "trailingPE": 43.811882, │ │ 147→ "forwardPE": 42.961166, │ │ 148→ "volume": 88821795, │ │ 149→ "regularMarketVolume": 88821795, │ │ 150→ "averageVolume": 191772396, │ │ 151→ "averageVolume10days": 239390050, │ │ 152→ "averageDailyVolume10Day": 239390050, │ │ 153→ "bid": 170.03, │ │ 154→ "ask": 177.02, │ │ 155→ "bidSize": 1, │ │ 156→ "askSize": 1, │ │ 157→ "marketCap": 4301985153024, │ │ 158→ "fiftyTwoWeekLow": 86.62, │ │ 159→ "fiftyTwoWeekHigh": 212.19, │ │ 160→ "allTimeHigh": 212.19, │ │ 161→ "allTimeLow": 0.033333, │ │ 162→ "priceToSalesTrailing12Months": 22.987812, │ │ 163→ "fiftyDayAverage": 186.8298, │ │ 164→ "twoHundredDayAverage": 153.668, │ │ 165→ "trailingAnnualDividendRate": 0.04, │ │ 166→ "trailingAnnualDividendYield": 0.0002219017, │ │ 167→ "currency": "USD", │ │ 168→ "tradeable": false, │ │ 169→ "enterpriseValue": 4251314028544, │ │ 170→ "profitMargins": 0.53007, │ │ 171→ "floatShares": 23328729000, │ │ 172→ "sharesOutstanding": 24305000000, │ │ 173→ "sharesShort": 237153736, │ │ 174→ "sharesShortPriorMonth": 210957056, │ │ 175→ "sharesShortPreviousMonthDate": 1760486400, │ │ 176→ "dateShortInterest": 1763078400, │ │ 177→ "sharesPercentSharesOut": 0.0098, │ │ 178→ "heldPercentInsiders": 0.0433, │ │ 179→ "heldPercentInstitutions": 0.69321996, │ │ 180→ "shortRatio": 1.27, │ │ 181→ "shortPercentOfFloat": 0.010199999, │ │ 182→ "impliedSharesOutstanding": 24305000000, │ │ 183→ "bookValue": 4.892, │ │ 184→ "priceToBook": 36.18152, │ │ 185→ "lastFiscalYearEnd": 1737849600, │ │ 186→ "nextFiscalYearEnd": 1769385600, │ │ 187→ "mostRecentQuarter": 1761436800, │ │ 188→ "earningsQuarterlyGrowth": 0.653, │ │ 189→ "netIncomeToCommon": 99198001152, │ │ 190→ "trailingEps": 4.04, │ │ 191→ "forwardEps": 4.12, │ │ 192→ "lastSplitFactor": "10:1", │ │ 193→ "lastSplitDate": 1717977600, │ │ 194→ "enterpriseToRevenue": 22.717, │ │ 195→ "enterpriseToEbitda": 37.724, │ │ 196→ "52WeekChange": 0.2767799, │ │ 197→ "SandP52WeekChange": 0.1326145, │ │ 198→ "lastDividendValue": 0.01, │ │ 199→ "lastDividendDate": 1757548800, │ │ 200→ "quoteType": "EQUITY", │ │ 201→ "currentPrice": 177.0, │ │ 202→ "targetHighPrice": 352.0, │ │ 203→ "targetLowPrice": 140.0, │ │ 204→ "targetMeanPrice": 250.66142, │ │ 205→ "targetMedianPrice": 250.0, │ │ 206→ "recommendationMean": 1.32812, │ │ 207→ "recommendationKey": "strong_buy", │ │ 208→ "numberOfAnalystOpinions": 56, │ │ 209→ "totalCash": 60608000000, │ │ 210→ "totalCashPerShare": 2.494, │ │ 211→ "ebitda": 112696000512, │ │ 212→ "totalDebt": 10821999616, │ │ 213→ "quickRatio": 3.605, │ │ 214→ "currentRatio": 4.468, │ │ 215→ "totalRevenue": 187141996544, │ │ 216→ "debtToEquity": 9.102, │ │ 217→ "revenuePerShare": 7.668, │ │ 218→ "returnOnAssets": 0.53528, │ │ 219→ "returnOnEquity": 1.07359, │ │ 220→ "grossProfits": 131092996096, │ │ 221→ "freeCashflow": 53282873344, │ │ 222→ "operatingCashflow": 83158999040, │ │ 223→ "earningsGrowth": 0.667, │ │ 224→ "revenueGrowth": 0.625, │ │ 225→ "grossMargins": 0.7005, │ │ 226→ "ebitdaMargins": 0.60220003, │ │ 227→ "operatingMargins": 0.63168997, │ │ 228→ "financialCurrency": "USD", │ │ 229→ "symbol": "NVDA", │ │ 230→ "language": "en-US", │ │ 231→ "region": "US", │ │ 232→ "typeDisp": "Equity", │ │ 233→ "quoteSourceName": "Nasdaq Real Time Price", │ │ 234→ "triggerable": true, │ │ 235→ "customPriceAlertConfidence": "HIGH", │ │ 236→ "longName": "NVIDIA Corporation", │ │ 237→ "corporateActions": [], │ │ 238→ "preMarketTime": 1764582494, │ │ 239→ "regularMarketTime": 1764352802, │ │ 240→ "exchange": "NMS", │ │ 241→ "messageBoardId": "finmb_32307", │ │ 242→ "exchangeTimezoneName": "America/New_York", │ │ 243→ "exchangeTimezoneShortName": "EST", │ │ 244→ "gmtOffSetMilliseconds": -18000000, │ │ 245→ "market": "us_market", │ │ 246→ "esgPopulated": false, │ │ 247→ "regularMarketChangePercent": -1.8084958, │ │ 248→ "regularMarketPrice": 177.0, │ │ 249→ "prevName": "Usual Stablecoin", │ │ 250→ "nameChangeDate": "2025-11-30", │ │ 251→ "averageAnalystRating": "1.3 - Strong Buy", │ │ 252→ "cryptoTradeable": false, │ │ 253→ "marketState": "PRE", │ │ 254→ "hasPrePostMarketData": true, │ │ 255→ "firstTradeDateMilliseconds": 917015400000, │ │ 256→ "preMarketChange": -1.9299927, │ │ 257→ "preMarketChangePercent": -1.0905145, │ │ 258→ "preMarketPrice": 175.05, │ │ 259→ "regularMarketChange": -3.2599945, │ │ 260→ "regularMarketDayRange": "176.5 - 179.29", │ │ 261→ "fullExchangeName": "NasdaqGS", │ │ 262→ "averageDailyVolume3Month": 191772396, │ │ 263→ "fiftyTwoWeekLowChange": 90.38, │ │ 264→ "fiftyTwoWeekLowChangePercent": 1.0434079, │ │ 265→ "fiftyTwoWeekRange": "86.62 - 212.19", │ │ 266→ "fiftyTwoWeekHighChange": -35.190002, │ │ 267→ "fiftyTwoWeekHighChangePercent": -0.16584194, │ │ 268→ "fiftyTwoWeekChangePercent": 27.67799, │ │ 269→ "dividendDate": 1766707200, │ │ 270→ "earningsTimestamp": 1763586000, │ │ 271→ "earningsTimestampStart": 1763586000, │ │ 272→ "earningsTimestampEnd": 1763586000, │ │ 273→ "earningsCallTimestampStart": 1763589600, │ │ 274→ "earningsCallTimestampEnd": 1763589600, │ │ 275→ "isEarningsDateEstimate": false, │ │ 276→ "epsTrailingTwelveMonths": 4.04, │ │ 277→ "epsForward": 4.12, │ │ 278→ "epsCurrentYear": 4.68543, │ │ 279→ "priceEpsCurrentYear": 37.77668, │ │ 280→ "fiftyDayAverageChange": -9.829803, │ │ 281→ "fiftyDayAverageChangePercent": -0.05261368, │ │ 282→ "twoHundredDayAverageChange": 23.332, │ │ 283→ "twoHundredDayAverageChangePercent": 0.15183383, │ │ 284→ "sourceInterval": 15, │ │ 285→ "exchangeDataDelayedBy": 0, │ │ 286→ "shortName": "NVIDIA Corporation", │ │ 287→ "displayName": "NVIDIA", │ │ 288→ "trailingPegRatio": 0.6751 │ │ 289→ } │ │ 290→} │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: read_file │ │ Args: { │ │ "file_path": "data/amd_info.json", │ │ "offset": 1, │ │ "limit": 200 │ │ } │ │ ID: call_KlSNNyDUqK1xR44YK9vR4Jha │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ 1→{ │ │ 2→ "ticker": "AMD", │ │ 3→ "info": { │ │ 4→ "address1": "2485 Augustine Drive", │ │ 5→ "city": "Santa Clara", │ │ 6→ "state": "CA", │ │ 7→ "zip": "95054", │ │ 8→ "country": "United States", │ │ 9→ "phone": "408 749 4000", │ │ 10→ "website": "https://www.amd.com", │ │ 11→ "industry": "Semiconductors", │ │ 12→ "industryKey": "semiconductors", │ │ 13→ "industryDisp": "Semiconductors", │ │ 14→ "sector": "Technology", │ │ 15→ "sectorKey": "technology", │ │ 16→ "sectorDisp": "Technology", │ │ 17→ "longBusinessSummary": "Advanced Micro Devices, Inc. operates as a semiconductor company worldwide. │ │ It operates in three segments: Data Center, Client and Gaming, and Embedded. The company offers artificial │ │ intelligence (AI) accelerators, x86 microprocessors, and graphics processing units (GPUs) as standalone devices │ │ or as incorporated into accelerated processing units, chipsets, and data center and professional GPUs; and │ │ embedded processors and semi-custom system-on-chip (SoC) products, microprocessor and SoC development services │ │ and technology, data processing units, field programmable gate arrays (FPGA), system on modules, smart network │ │ interface cards, and adaptive SoC products. It provides processors under the AMD Ryzen, AMD Ryzen AI, AMD Ryzen │ │ PRO, AMD Ryzen Threadripper, AMD Ryzen Threadripper PRO, AMD Athlon, and AMD PRO A-Series brands; graphics │ │ under the AMD Radeon graphics and AMD Embedded Radeon graphics; professional graphics under the AMD Radeon Pro │ │ graphics brand; and AI and general-purpose compute infrastructure for hyperscale providers. The company offers │ │ data center graphics under the AMD Instinct accelerators and Radeon PRO V-series brands; server microprocessors │ │ under the AMD EPYC brand; low power solutions under the AMD Athlon, AMD Geode, AMD Ryzen, AMD EPYC, and AMD │ │ R-Series and G-Series brands; FPGA products under the Virtex-6, Virtex-7, Virtex UltraScale+, Kintex-7, Kintex │ │ UltraScale, Kintex UltraScale+, Artix-7, Artix UltraScale+, Spartan-6, and Spartan-7 brands; adaptive SOCs │ │ under the Zynq-7000, Zynq UltraScale+ MPSoC, Zynq UltraScale+ RFSoCs, Versal HBM, Versal Premium, Versal Prime, │ │ Versal AI Core, Versal AI Edge, Vitis, and Vivado brands; and compute and network acceleration board products │ │ under the Alveo and Pensando brands. It serves original equipment and design manufacturers, public cloud │ │ service providers, system integrators, distributors, and add-in-board manufacturers. The company was │ │ incorporated in 1969 and is headquartered in Santa Clara, California.", │ │ 18→ "fullTimeEmployees": 28000, │ │ 19→ "companyOfficers": [ │ │ 20→ { │ │ 21→ "maxAge": 1, │ │ 22→ "name": "Dr. Lisa T. Su Ph.D.", │ │ 23→ "age": 54, │ │ 24→ "title": "Chair, President & CEO", │ │ 25→ "yearBorn": 1970, │ │ 26→ "fiscalYear": 2024, │ │ 27→ "totalPay": 3062598, │ │ 28→ "exercisedValue": 47792792, │ │ 29→ "unexercisedValue": 29753624 │ │ 30→ }, │ │ 31→ { │ │ 32→ "maxAge": 1, │ │ 33→ "name": "Ms. Jean X. Hu Ph.D.", │ │ 34→ "age": 61, │ │ 35→ "title": "Executive VP, CFO, Treasurer & Interim Chief Accounting Officer", │ │ 36→ "yearBorn": 1963, │ │ 37→ "fiscalYear": 2024, │ │ 38→ "totalPay": 1429427, │ │ 39→ "exercisedValue": 0, │ │ 40→ "unexercisedValue": 97947 │ │ 41→ }, │ │ 42→ { │ │ 43→ "maxAge": 1, │ │ 44→ "name": "Mr. Mark D. Papermaster", │ │ 45→ "age": 62, │ │ 46→ "title": "CTO and Executive VP of Technology & Engineering", │ │ 47→ "yearBorn": 1962, │ │ 48→ "fiscalYear": 2024, │ │ 49→ "totalPay": 1694362, │ │ 50→ "exercisedValue": 15503578, │ │ 51→ "unexercisedValue": 20029298 │ │ 52→ }, │ │ 53→ { │ │ 54→ "maxAge": 1, │ │ 55→ "name": "Ms. Ava M. Hahn J.D.", │ │ 56→ "age": 51, │ │ 57→ "title": "Senior VP, General Counsel & Corporate Secretary", │ │ 58→ "yearBorn": 1973, │ │ 59→ "fiscalYear": 2024, │ │ 60→ "totalPay": 2978858, │ │ 61→ "exercisedValue": 0, │ │ 62→ "unexercisedValue": 0 │ │ 63→ }, │ │ 64→ { │ │ 65→ "maxAge": 1, │ │ 66→ "name": "Mr. Forrest E. Norrod", │ │ 67→ "age": 59, │ │ 68→ "title": "Executive VP & GM of Data Center Solutions Business Unit", │ │ 69→ "yearBorn": 1965, │ │ 70→ "fiscalYear": 2024, │ │ 71→ "totalPay": 1428233, │ │ 72→ "exercisedValue": 9752057, │ │ 73→ "unexercisedValue": 4760712 │ │ 74→ }, │ │ 75→ { │ │ 76→ "maxAge": 1, │ │ 77→ "name": "Mr. Keivan Keshvari", │ │ 78→ "title": "Senior Vice President of Global Operations", │ │ 79→ "fiscalYear": 2024, │ │ 80→ "exercisedValue": 0, │ │ 81→ "unexercisedValue": 0 │ │ 82→ }, │ │ 83→ { │ │ 84→ "maxAge": 1, │ │ 85→ "name": "Mr. Hasmukh Ranjan", │ │ 86→ "title": "Senior VP & Chief Information Officer", │ │ 87→ "fiscalYear": 2024, │ │ 88→ "exercisedValue": 0, │ │ 89→ "unexercisedValue": 0 │ │ 90→ }, │ │ 91→ { │ │ 92→ "maxAge": 1, │ │ 93→ "name": "Ms. Ruth Cotter", │ │ 94→ "title": "Senior VP & Chief Administrative Officer", │ │ 95→ "fiscalYear": 2024, │ │ 96→ "exercisedValue": 0, │ │ 97→ "unexercisedValue": 0 │ │ 98→ }, │ │ 99→ { │ │ 100→ "maxAge": 1, │ │ 101→ "name": "Mr. Matthew D. Ramsay", │ │ 102→ "title": "Vice President of Financial Strategy & Investor Relations", │ │ 103→ "fiscalYear": 2024, │ │ 104→ "exercisedValue": 0, │ │ 105→ "unexercisedValue": 0 │ │ 106→ }, │ │ 107→ { │ │ 108→ "maxAge": 1, │ │ 109→ "name": "Mr. Paul Darren Grasby", │ │ 110→ "age": 54, │ │ 111→ "title": "Executive VP, Chief Sales Officer & President AMD EMEA", │ │ 112→ "yearBorn": 1970, │ │ 113→ "fiscalYear": 2024, │ │ 114→ "totalPay": 921473, │ │ 115→ "exercisedValue": 1077205, │ │ 116→ "unexercisedValue": 432646 │ │ 117→ } │ │ 118→ ], │ │ 119→ "auditRisk": 7, │ │ 120→ "boardRisk": 4, │ │ 121→ "compensationRisk": 9, │ │ 122→ "shareHolderRightsRisk": 3, │ │ 123→ "overallRisk": 6, │ │ 124→ "governanceEpochDate": 1761955200, │ │ 125→ "compensationAsOfEpochDate": 1735603200, │ │ 126→ "irWebsite": "http://phx.corporate-ir.net/phoenix.zhtml?c=74093&p=irol-IRHome", │ │ 127→ "executiveTeam": [], │ │ 128→ "maxAge": 86400, │ │ 129→ "priceHint": 2, │ │ 130→ "previousClose": 214.24, │ │ 131→ "open": 216.395, │ │ 132→ "dayLow": 214.0322, │ │ 133→ "dayHigh": 218.3, │ │ 134→ "regularMarketPreviousClose": 214.24, │ │ 135→ "regularMarketOpen": 216.395, │ │ 136→ "regularMarketDayLow": 214.0322, │ │ 137→ "regularMarketDayHigh": 218.3, │ │ 138→ "exDividendDate": 798940800, │ │ 139→ "payoutRatio": 0.0, │ │ 140→ "beta": 1.913, │ │ 141→ "trailingPE": 113.89005, │ │ 142→ "forwardPE": 42.652943, │ │ 143→ "volume": 14756280, │ │ 144→ "regularMarketVolume": 14756280, │ │ 145→ "averageVolume": 59143650, │ │ 146→ "averageVolume10days": 49552640, │ │ 147→ "averageDailyVolume10Day": 49552640, │ │ 148→ "bid": 215.0, │ │ 149→ "ask": 218.07, │ │ 150→ "bidSize": 1, │ │ 151→ "askSize": 2, │ │ 152→ "marketCap": 354147893248, │ │ 153→ "fiftyTwoWeekLow": 76.48, │ │ 154→ "fiftyTwoWeekHigh": 267.08, │ │ 155→ "allTimeHigh": 267.08, │ │ 156→ "allTimeLow": 1.61, │ │ 157→ "priceToSalesTrailing12Months": 11.057792, │ │ 158→ "fiftyDayAverage": 218.7962, │ │ 159→ "twoHundredDayAverage": 150.51225, │ │ 160→ "trailingAnnualDividendRate": 0.0, │ │ 161→ "trailingAnnualDividendYield": 0.0, │ │ 162→ "currency": "USD", │ │ 163→ "tradeable": false, │ │ 164→ "enterpriseValue": 350774886400, │ │ 165→ "profitMargins": 0.10323, │ │ 166→ "floatShares": 1617491831, │ │ 167→ "sharesOutstanding": 1628041540, │ │ 168→ "sharesShort": 40737550, │ │ 169→ "sharesShortPriorMonth": 38861080, │ │ 170→ "sharesShortPreviousMonthDate": 1760486400, │ │ 171→ "dateShortInterest": 1763078400, │ │ 172→ "sharesPercentSharesOut": 0.025, │ │ 173→ "heldPercentInsiders": 0.00449, │ │ 174→ "heldPercentInstitutions": 0.69945997, │ │ 175→ "shortRatio": 0.69, │ │ 176→ "shortPercentOfFloat": 0.0251, │ │ 177→ "impliedSharesOutstanding": 1628041540, │ │ 178→ "bookValue": 37.34, │ │ 179→ "priceToBook": 5.825656, │ │ 180→ "lastFiscalYearEnd": 1735344000, │ │ 181→ "nextFiscalYearEnd": 1766880000, │ │ 182→ "mostRecentQuarter": 1758931200, │ │ 183→ "earningsQuarterlyGrowth": 0.612, │ │ 184→ "netIncomeToCommon": 3131000064, │ │ 185→ "trailingEps": 1.91, │ │ 186→ "forwardEps": 5.1, │ │ 187→ "lastSplitFactor": "2:1", │ │ 188→ "lastSplitDate": 966902400, │ │ 189→ "enterpriseToRevenue": 10.952, │ │ 190→ "enterpriseToEbitda": 57.941, │ │ 191→ "52WeekChange": 0.5312544, │ │ 192→ "SandP52WeekChange": 0.1326145, │ │ 193→ "quoteType": "EQUITY", │ │ 194→ "currentPrice": 217.53, │ │ 195→ "targetHighPrice": 380.0, │ │ 196→ "targetLowPrice": 178.0, │ │ 197→ "targetMeanPrice": 283.56512, │ │ 198→ "targetMedianPrice": 290.0, │ │ 199→ "recommendationMean": 1.54902, │ │ 200→ "recommendationKey": "buy", │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: read_file │ │ Args: { │ │ "file_path": "data/amd_info.json", │ │ "offset": 200, │ │ "limit": 200 │ │ } │ │ ID: call_aCIcY8emp4hRoqKDW8ZgNMiK │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ 200→ "recommendationKey": "buy", │ │ 201→ "numberOfAnalystOpinions": 43, │ │ 202→ "totalCash": 7242999808, │ │ 203→ "totalCashPerShare": 4.449, │ │ 204→ "ebitda": 6054000128, │ │ 205→ "totalDebt": 3870000128, │ │ 206→ "quickRatio": 1.176, │ │ 207→ "currentRatio": 2.308, │ │ 208→ "totalRevenue": 32027000832, │ │ 209→ "debtToEquity": 6.366, │ │ 210→ "revenuePerShare": 19.733, │ │ 211→ "returnOnAssets": 0.025980001, │ │ 212→ "returnOnEquity": 0.05317, │ │ 213→ "grossProfits": 16480000000, │ │ 214→ "freeCashflow": 3245499904, │ │ 215→ "operatingCashflow": 6408000000, │ │ 216→ "earningsGrowth": 0.603, │ │ 217→ "revenueGrowth": 0.356, │ │ 218→ "grossMargins": 0.51457, │ │ 219→ "ebitdaMargins": 0.18902999, │ │ 220→ "operatingMargins": 0.13736, │ │ 221→ "financialCurrency": "USD", │ │ 222→ "symbol": "AMD", │ │ 223→ "language": "en-US", │ │ 224→ "region": "US", │ │ 225→ "typeDisp": "Equity", │ │ 226→ "quoteSourceName": "Nasdaq Real Time Price", │ │ 227→ "triggerable": true, │ │ 228→ "customPriceAlertConfidence": "HIGH", │ │ 229→ "marketState": "PRE", │ │ 230→ "longName": "Advanced Micro Devices, Inc.", │ │ 231→ "shortName": "Advanced Micro Devices, Inc.", │ │ 232→ "corporateActions": [], │ │ 233→ "preMarketTime": 1764582484, │ │ 234→ "regularMarketTime": 1764352802, │ │ 235→ "regularMarketChangePercent": 1.5356578, │ │ 236→ "regularMarketPrice": 217.53, │ │ 237→ "priceEpsCurrentYear": 54.87722, │ │ 238→ "fiftyDayAverageChange": -1.2662048, │ │ 239→ "fiftyDayAverageChangePercent": -0.0057871426, │ │ 240→ "twoHundredDayAverageChange": 67.017746, │ │ 241→ "twoHundredDayAverageChangePercent": 0.4452644, │ │ 242→ "sourceInterval": 15, │ │ 243→ "exchangeDataDelayedBy": 0, │ │ 244→ "averageAnalystRating": "1.5 - Buy", │ │ 245→ "cryptoTradeable": false, │ │ 246→ "averageDailyVolume3Month": 59143650, │ │ 247→ "fiftyTwoWeekLowChange": 141.04999, │ │ 248→ "fiftyTwoWeekLowChangePercent": 1.8442727, │ │ 249→ "fiftyTwoWeekRange": "76.48 - 267.08", │ │ 250→ "fiftyTwoWeekHighChange": -49.549988, │ │ 251→ "fiftyTwoWeekHighChangePercent": -0.1855249, │ │ 252→ "fiftyTwoWeekChangePercent": 53.125443, │ │ 253→ "earningsTimestamp": 1762290000, │ │ 254→ "earningsTimestampStart": 1762290000, │ │ 255→ "earningsTimestampEnd": 1762290000, │ │ 256→ "earningsCallTimestampStart": 1762293600, │ │ 257→ "earningsCallTimestampEnd": 1762293600, │ │ 258→ "isEarningsDateEstimate": false, │ │ 259→ "epsTrailingTwelveMonths": 1.91, │ │ 260→ "epsForward": 5.1, │ │ 261→ "epsCurrentYear": 3.96394, │ │ 262→ "exchange": "NMS", │ │ 263→ "messageBoardId": "finmb_168864", │ │ 264→ "exchangeTimezoneName": "America/New_York", │ │ 265→ "exchangeTimezoneShortName": "EST", │ │ 266→ "gmtOffSetMilliseconds": -18000000, │ │ 267→ "market": "us_market", │ │ 268→ "esgPopulated": false, │ │ 269→ "hasPrePostMarketData": true, │ │ 270→ "firstTradeDateMilliseconds": 322151400000, │ │ 271→ "preMarketChange": -2.2899933, │ │ 272→ "preMarketChangePercent": -1.0532094, │ │ 273→ "preMarketPrice": 215.14, │ │ 274→ "regularMarketChange": 3.2899933, │ │ 275→ "regularMarketDayRange": "214.0322 - 218.3", │ │ 276→ "fullExchangeName": "NasdaqGS", │ │ 277→ "displayName": "Advanced Micro Devices", │ │ 278→ "trailingPegRatio": 0.4826 │ │ 279→ } │ │ 280→} │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: execute_code │ │ Args: { │ │ "code": "import json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom datetime import │ │ datetime\n\nwith open('data/nvidia_amd_history.json') as f:\n history = json.load(f)\n\nnvda_df = │ │ pd.DataFrame(history['data']['NVDA']['history'])\namd_df = │ │ pd.DataFrame(history['data']['AMD']['history'])\n\nfor df in (nvda_df, amd_df):\n df['date'] = │ │ pd.to_datetime(df['date'])\n df.sort_values('date', inplace=True)\n\n\nfig, axes = plt.subplots(2, 2, │ │ figsize=(14, 10), constrained_layout=True)\n\n# Price lines\naxes[0, 0].plot(nvda_df['date'], nvda_df['close'], │ │ label='NVDA', color='#76b900', linewidth=2)\naxes[0, 0].plot(amd_df['date'], amd_df['close'], label='AMD', │ │ color='#ff6600', linewidth=2)\naxes[0, 0].set_title('Closing Price: Past Year')\naxes[0, │ │ 0].set_ylabel('USD')\naxes[0, 0].legend()\naxes[0, 0].grid(True)\n\n# Volume\naxes[0, 1].plot(nvda_df['date'], │ │ nvda_df['volume'] / 1e6, label='NVDA', color='#76b900')\naxes[0, 1].plot(amd_df['date'], amd_df['volume'] / │ │ 1e6, label='AMD', color='#ff6600')\naxes[0, 1].set_title('Daily Volume (Millions)')\naxes[0, │ │ 1].set_ylabel('Shares (M)')\naxes[0, 1].legend()\naxes[0, 1].grid(True)\n\n# Fundamental metrics\nwith │ │ open('data/nvda_info.json') as f:\n nvda_info = json.load(f)['info']\nwith open('data/amd_info.json') as │ │ f:\n amd_info = json.load(f)['info']\n\nvaluation_metrics = ['trailingPE', 'forwardPE', │ │ 'priceToBook']\nprofit_metrics = ['profitMargins', 'grossMargins', 'revenueGrowth']\n\nval_data = {\n │ │ 'Metric': [\"Trailing P/E\", \"Forward P/E\", \"Price / Book\"],\n 'NVDA': ,\n 'AMD': ,\n}\nprof_data = │ │ {\n 'Metric': [\"Profit Margin\", \"Gross Margin\", \"Revenue Growth\"],\n 'NVDA': ,\n 'AMD': │ │ ,\n}\n\nval_df = pd.DataFrame(val_data)\nprof_df = pd.DataFrame(prof_data)\n\nx = range(len(val_df))\nwidth = │ │ 0.35\naxes[1, 0].bar(, val_df['NVDA'], width=width, label='NVDA', color='#76b900')\naxes[1, 0].bar(, │ │ val_df['AMD'], width=width, label='AMD', color='#ff6600')\naxes[1, 0].set_xticks(x)\naxes[1, │ │ 0].set_xticklabels(val_df['Metric'])\naxes[1, 0].set_title('Valuation Metrics')\naxes[1, 0].legend()\naxes[1, │ │ 0].grid(axis='y')\n\nx2 = range(len(prof_df))\naxes[1, 1].bar(, prof_df['NVDA'], width=width, label='NVDA', │ │ color='#76b900')\naxes[1, 1].bar(, prof_df['AMD'], width=width, label='AMD', color='#ff6600')\naxes[1, │ │ 1].set_xticks(x2)\naxes[1, 1].set_xticklabels(prof_df['Metric'])\naxes[1, 1].set_title('Profitability & │ │ Growth')\naxes[1, 1].legend()\naxes[1, 1].grid(axis='y')\n\n# Formatting percentages for bottom-right │ │ bars\naxes[1, 1].set_ylim(0, max(max(prof_df['NVDA']), max(prof_df['AMD'])) * 1.2)\naxes[1, │ │ 1].set_ylabel('Ratio / Growth')\n\n# Save figure\nfig.suptitle('NVDA vs AMD: Price, Volume, and Fundamentals', │ │ fontsize=16)\nplt.savefig('results/nvda_amd_comparison.png')\nprint('Chart saved to │ │ results/nvda_amd_comparison.png')" │ │ } │ │ ID: call_P7j34BiDbPA7oZhO4tqcd3Vy │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ SUCCESS │ │ Chart saved to results/nvda_amd_comparison.png │ │ Files created: results/nvda_amd_comparison.png │ │ │ │ Uploaded images: │ │ !(https://zexi-dev.s3.us-east-1.amazonaws.com/charts/exec_0002/nvda_amd_comparison.png) │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: execute_code │ │ Args: { │ │ "code": "import pandas as pd\nimport json\n\nwith open('data/nvidia_amd_history.json') as f:\n history = │ │ json.load(f)\n\nnvda = pd.DataFrame(history['data']['NVDA']['history'])\namd = │ │ pd.DataFrame(history['data']['AMD']['history'])\nfor df in (nvda, amd):\n df['date'] = │ │ pd.to_datetime(df['date'])\n df.sort_values('date', inplace=True)\n\ndef summarize(df):\n first = │ │ df.iloc[0]\n last = df.iloc[-1]\n start_price = first['close']\n end_price = last['close']\n │ │ pct_change = (end_price / start_price - 1) * 100\n avg_vol = df['volume'].mean()\n return start_price, │ │ end_price, pct_change, avg_vol\n\nnvda_stats = summarize(nvda)\namd_stats = summarize(amd)\n\nprint('NVDA │ │ Start:', nvda_stats[0], 'End:', nvda_stats[1], 'Change %:', nvda_stats[2])\nprint('NVDA Avg Volume:', │ │ nvda_stats[3])\nprint('AMD Start:', amd_stats[0], 'End:', amd_stats[1], 'Change %:', amd_stats[2])\nprint('AMD │ │ Avg Volume:', amd_stats[3])" │ │ } │ │ ID: call_KMSJsOGE5hk9k9AYmWxVCfmL │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ SUCCESS │ │ NVDA Start: 138.21 End: 177.0 Change %: 28.065986542218347 │ │ NVDA Avg Volume: 223143054.0 │ │ AMD Start: 137.18 End: 217.53 Change %: 58.57267823297856 │ │ AMD Avg Volume: 49078907.2 │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: write_todos │ │ Args: { │ │ "todos": [ │ │ { │ │ "content": "Collect the past year of price, volume, and fundamental data for NVDA and AMD via yfinance", │ │ "status": "completed" │ │ }, │ │ { │ │ "content": "Produce a 2x2 chart comparing price, volume, and key fundamentals for both stocks", │ │ "status": "completed" │ │ }, │ │ { │ │ "content": "Draft a professional comparison report summarizing findings and cite data sources", │ │ "status": "in_progress" │ │ } │ │ ] │ │ } │ │ ID: call_dh1nt0WwSDxEpKBhmtvwKw0x │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ Updated todo list to [{'content': 'Collect the past year of price, volume, and fundamental data for NVDA and │ │ AMD via yfinance', 'status': 'completed'}, {'content': 'Produce a 2x2 chart comparing price, volume, and key │ │ fundamentals for both stocks', 'status': 'completed'}, {'content': 'Draft a professional comparison report │ │ summarizing findings and cite data sources', 'status': 'in_progress'}] │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: write_file │ │ Args: { │ │ "file_path": "results/nvidia_amd_comparison.md", │ │ "content": "## Introduction\nThe following analysis reviews the last twelve months of trading for NVIDIA │ │ (NVDA) and Advanced Micro Devices (AMD), focusing on price action, liquidity, and select fundamental metrics │ │ that highlight valuation and profitability dynamics. The accompanying 2\u00d72 chart aggregates these │ │ dimensions\u2014price, volume, and two fundamental comparison panels\u2014providing a visual reference for the │ │ textual conclusions.\n\n## Price Performance Highlights\nNVIDIA closed the period around $177, which represents │ │ a roughly 28.1% gain from one year ago; AMD finished nearer to $217, marking a 58.6% increase over the same │ │ span [1][2]. The price plots (Figure 1, top-left) show NVDA maintaining a steadier uptrend following the spring │ │ pullback, while AMD has exhibited steeper swings tied to cyclical demand in its data center and gaming end │ │ markets.\n\n## Volume Dynamics\nAverage daily trading volume for NVDA (\u2248223 million shares) greatly │ │ exceeds AMD\u2019s (\u224849 million shares), reflecting NVDA\u2019s larger float and heavier institutional │ │ interest; both series are plotted on the top-right panel in millions of shares per session [1][2]. AMD\u2019s │ │ lower relative liquidity magnifies the visible amplitude of price moves when volume spikes occur, whereas │ │ NVDA\u2019s deeper market absorbs similar flows with more muted intraday swings.\n\n## Fundamental │ │ Comparison\nThe lower-left panel contrasts valuation metrics: NVDA trades at about 43.8\u00d7 trailing EPS, │ │ 43.0\u00d7 forward EPS, and a 36.2 price/book multiple, while AMD sits at roughly 113.9\u00d7 trailing EPS, │ │ 42.7\u00d7 forward EPS, and 5.8 price/book [1][2]. The lower-right panel highlights profitability and growth: │ │ NVDA delivers a profit margin near 53%, gross margin of 70%, and ~62.5% revenue growth, compared with │ │ AMD\u2019s 10.3% profit margin, 51.5% gross margin, and ~35.6% revenue growth [1][2]. These contrasts │ │ underscore NVDA\u2019s premium valuation tied to superior margin expansion and revenue acceleration, whereas │ │ AMD is priced with a higher trailing P/E but offers a materially cheaper book value multiple.\n\n## │ │ Chart\n\n**Figure │ │ 1:** Top-left and -right panels depict the 1-year closing price and scaled daily volume for NVDA and AMD. │ │ Bottom-left and -right panels provide side-by-side views of valuation ratios and profitability/growth metrics │ │ used in the comparison.\n\n## Conclusion\nOver the past year, AMD outpaced NVDA in price gain percentage but │ │ remains well behind NVDA in absolute market cap and profitability metrics. NVDA commands premium multiples │ │ justified by stronger margins and faster revenue growth, while AMD trades with a more aggressive trailing P/E │ │ yet a significantly lower price/book ratio. The 2\u00d72 chart synthesizes these differences, providing │ │ side-by-side context for investors weighing relative momentum, liquidity, and fundamental positioning across │ │ the two leading semiconductor firms.\n\n### Sources\n[1] NVIDIA Corporation stock quote and fundamentals: │ │ https://finance.yahoo.com/quote/NVDA\n[2] Advanced Micro Devices stock quote and fundamentals: │ │ https://finance.yahoo.com/quote/AMD" │ │ } │ │ ID: call_FCqQaZAVs3POgBVwUqLFdkcb │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ Wrote 3166 bytes to /results/nvidia_amd_comparison.md │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: write_todos │ │ Args: { │ │ "todos": [ │ │ { │ │ "content": "Collect the past year of price, volume, and fundamental data for NVDA and AMD via yfinance", │ │ "status": "completed" │ │ }, │ │ { │ │ "content": "Produce a 2x2 chart comparing price, volume, and key fundamentals for both stocks", │ │ "status": "completed" │ │ }, │ │ { │ │ "content": "Draft a professional comparison report summarizing findings and cite data sources", │ │ "status": "completed" │ │ } │ │ ] │ │ } │ │ ID: call_oJnMCmp3KDDi2Lo7islYwhmf │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ Updated todo list to [{'content': 'Collect the past year of price, volume, and fundamental data for NVDA and │ │ AMD via yfinance', 'status': 'completed'}, {'content': 'Produce a 2x2 chart comparing price, volume, and key │ │ fundamentals for both stocks', 'status': 'completed'}, {'content': 'Draft a professional comparison report │ │ summarizing findings and cite data sources', 'status': 'completed'}] │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Call: read_file │ │ Args: { │ │ "file_path": "results/task_request.md", │ │ "offset": 1, │ │ "limit": 100 │ │ } │ │ ID: call_lcdWGpjBucl6wvN7jCB7xajC │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮ │ 1→Compare the stock price of Nvidia and AMD over the past year and create a 2x2 chart showing price, │ │ volume, and key fundamental data comparison. │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮ │ Delivered a comprehensive comparison of NVIDIA vs. AMD covering 12-month price movement, volume, and │ │ fundamentals. Key takeaways: │ │ - Price: NVDA gained ~28% vs. AMD’s ~59% over the past year, with NVDA’s trend smoother and AMD’s moves more │ │ volatile. │ │ - Volume: NVDA averages ~223M shares/day versus AMD’s ~49M, which helps explain their differing intraday price │ │ behavior. │ │ - Fundamentals: NVDA trades at lower trailing/forward P/E and a much higher P/B multiple, but it also posts far │ │ superior profit margins (53% vs. 10%), gross margins, and revenue growth. │ │ │ │ Created a 2×2 visualization (price, volume, valuation ratios, profitability/growth) and saved it under │ │ `results/nvda_amd_comparison.png` (embedded in the report). The full textual comparison with citations and the │ │ chart is available in `/results/nvidia_amd_comparison.md`. │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
# Display sandbox file tree AFTER agent execution
print("=" * 50)
print("Sandbox File Tree (AFTER)")
print("=" * 50)
print("./")
print_sandbox_tree(session.sandbox)==================================================
Sandbox File Tree (AFTER)
==================================================
./
├── code/
│ ├── exec_0001.py
│ ├── exec_0002.py
│ └── exec_0003.py
├── data/
│ ├── amd_info.json
│ ├── nvda_info.json
│ └── nvidia_amd_history.json
├── mcp_servers/
│ ├── tickertick_mcp_server.py
│ └── yfinance_mcp_server.py
├── results/
│ ├── nvda_amd_comparison.png
│ ├── nvidia_amd_comparison.md
│ └── task_request.md
└── tools/
├── __pycache__/
│ ├── __init__.cpython-312.pyc
│ ├── mcp_client.cpython-312.pyc
│ └── yfinance.cpython-312.pyc
├── docs/
│ ├── tickertick/
│ │ ├── get_broad_ticker_news.md
│ │ ├── get_curated_news.md
│ │ ├── get_entity_news.md
│ │ ├── get_news_for_multiple_tickers.md
│ │ ├── get_news_from_source.md
│ │ ├── get_ticker_news.md
│ │ └── search_tickers.md
│ └── yfinance/
│ ├── compare_financials.md
│ ├── compare_valuations.md
│ ├── get_analyst_recommendations.md
│ ├── get_balance_sheet.md
│ ├── get_cash_flow.md
│ ├── get_company_info.md
│ ├── get_dividends_and_splits.md
│ ├── get_earnings_data.md
│ ├── get_earnings_dates.md
│ ├── get_income_statement.md
│ ├── get_insider_roster.md
│ ├── get_insider_transactions.md
│ ├── get_institutional_holders.md
│ ├── get_multiple_stocks_dividends.md
│ ├── get_multiple_stocks_earnings.md
│ ├── get_multiple_stocks_history.md
│ ├── get_mutualfund_holders.md
│ ├── get_news.md
│ ├── get_options_chain.md
│ ├── get_stock_history.md
│ └── get_sustainability_data.md
├── __init__.py
├── mcp_client.py
├── tickertick.py
└── yfinance.py
# Display any images generated by the agent
display_sandbox_images(session.sandbox, "results")📊 results/nvda_amd_comparison.png
✅ Displayed 1 image(s)
# Display code file generated by agent
print("./")
print_sandbox_tree(session.sandbox, path="code/")./
├── exec_0001.py
├── exec_0002.py
└── exec_0003.py
# Display results generated by agent
print("./")
print_sandbox_tree(session.sandbox, path="results/")./
├── nvda_amd_comparison.png
├── nvidia_amd_comparison.md
└── task_request.md
Export Sandbox Files
Export all files from the sandbox to your local filesystem for inspection or archival.
# Export all sandbox files to local filesystem
# This downloads code/, data/, and results/ directories with timestamp organization
export_result = export_sandbox_files(session.sandbox)
# Dump agent messages to the same output directory for debugging
if export_result.success:
messages_path = dump_messages(result["messages"], export_result.output_directory)
print(f"Messages saved to: {messages_path}")
# Display the summary
print(export_result.summary())📂 Discovering files in 'code'...
Found 3 file(s)
📂 Discovering files in 'data'...
Found 3 file(s)
📂 Discovering files in 'results'...
Found 3 file(s)
✅ Export completed successfully!
Exported 9 file(s) to: output/20251201_045302
Messages saved to: /Users/chen/projects/open-ptc-agent/output/20251201_045302/messages.json
Export Summary (20251201_045302)
============================================================
Output Directory: /Users/chen/projects/open-ptc-agent/output/20251201_045302
Total Files: 9
Successfully Exported: 9
Failed: 0
Total Size: 301.39 KB
Directories Processed:
- code
- data
- results
# Alternative: Export only specific directories
# Uncomment and run if you only want to export results
# export_result = export_sandbox_files(
# session.sandbox,
# directories=['results'],
# output_base="my_exports",
# timestamp_format="%Y-%m-%d_%H-%M"
# )
# print(export_result.summary())# Cleanup session when done
await session.cleanup()[2m2025-12-01 04:53:09[0m [[32m[1minfo [0m] [1mCleaning up session [0m [36mconversation_id[0m=[35mdemo-notebook[0m
[2m2025-12-01 04:53:09[0m [[32m[1minfo [0m] [1mCleaning up sandbox [0m [36msandbox_id[0m=[35m00863413-e271-4844-94b4-f7fe0713c78b[0m
[2m2025-12-01 04:53:09[0m [[32m[1minfo [0m] [1mSandbox deleted [0m [36msandbox_id[0m=[35m00863413-e271-4844-94b4-f7fe0713c78b[0m
[2m2025-12-01 04:53:09[0m [[32m[1minfo [0m] [1mDisconnecting from all MCP servers[0m
[2m2025-12-01 04:53:09[0m [[32m[1minfo [0m] [1mDisconnecting from MCP server [0m [36mserver[0m=[35mtickertick[0m
[2m2025-12-01 04:53:09[0m [[32m[1minfo [0m] [1mDisconnecting from MCP server [0m [36mserver[0m=[35myfinance[0m
[2m2025-12-01 04:53:09[0m [[32m[1minfo [0m] [1mSession cleaned up [0m [36mconversation_id[0m=[35mdemo-notebook[0m