diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/.env.template b/02-samples/05-hedge-fund-analyst-multi-agent-system/.env.template new file mode 100644 index 00000000..0b5c7340 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/.env.template @@ -0,0 +1,7 @@ +WEAVE_API_KEY= +TAVILY_API_KEY= +FINANCIAL_DATASET_API= +# initialize the required langsmith variables +LANGCHAIN_TRACING_V2="true" +LANGCHAIN_API_KEY="" +LANGCHAIN_PROJECT="" \ No newline at end of file diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/.gitignore b/02-samples/05-hedge-fund-analyst-multi-agent-system/.gitignore new file mode 100644 index 00000000..5e68a633 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/.gitignore @@ -0,0 +1,19 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ \ No newline at end of file diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/.python-version b/02-samples/05-hedge-fund-analyst-multi-agent-system/.python-version new file mode 100644 index 00000000..e4fba218 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/README.md b/02-samples/05-hedge-fund-analyst-multi-agent-system/README.md new file mode 100644 index 00000000..13ac1490 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/README.md @@ -0,0 +1,214 @@ +# Build a meta-tool calling multi-agentic system using Strands SDK + +This comprehensive example demonstrates a sophisticated Multi-Agent Collaboration (MAC) system designed for hedge fund analysis using the Strands Agents SDK. The system implements a hierarchical architecture with specialized financial analysis agents that work together to provide comprehensive investment insights. + +View the architecture diagram below that shows how you can build a mutli-agentic system using an **[agent-as-tools](https://strandsagents.com/0.1.x/user-guide/concepts/multi-agent/agents-as-tools/)** pattern, with **meta-tooling**, **human-in-the-loop**, observability, detailed traces and logging enabled for a financial system: + +🚨**THIS AGENTIC SYSTEM USES **[CLAUDE 4 SONNET](https://www.anthropic.com/news/claude-4), released on 05/22** + +![architecture](img/arch.png) + +### Lead Analyst Agent (Uses Claude 4 Sonnet under the hood) + +1. **Role**: Meta-coordinator that uses specialized agents as tools +2. **Capabilities**: Synthesizes insights from all sub-agents +3. **Features**: Dynamic tool creation for custom analysis requirements + +### Specialized Sub-Agents + +1. **Fundamental Analyst Agent** + - **Model**: Claude 3 Haiku + - **Purpose**: Performs fundamental financial statement analysis + - **Capabilities**: + 1. Income statement analysis + 2. Balance sheet examination + 3. Cash flow statement evaluation + 4. Financial ratio calculations + 5. Valuation metrics assessment + + + +2. **Technical Analyst Agent** + - **Model**: Amazon Nova Lite + - **Purpose**: Conducts technical analysis of stock price movements + - **Capabilities**: + 1. Historical price data retrieval + 2. Technical indicator calculations (`RSI`, `MACD`, `SMA`, `EMA`) + 3. Chart pattern analysis + 4. Trading signal generation + + + +3. **Market Analyst Agent** + - **Model**: Claude 3 Haiku + - **Purpose**: Provides market context and sentiment analysis + - **Capabilities**: + 1. Options chain analysis + 2. Insider trading information + 3. Market news aggregation + 4. Sentiment analysis + +## Key Features + +1. **Financial Data Integration**: + +The system integrates with comprehensive financial data APIs to access: +- Real-time and historical stock prices +- Financial statements (income, balance sheet, cash flow) +- Options chain data +- Insider trading transactions +- Market news and sentiment data + +2. **Multi-Model Support for multiple agents as tools and code generation** + +Utilizes multiple foundation models through AWS Bedrock: + +- ***Amazon Nova Lite*** for routing and technical analysis +- ***Claude 3 Haiku*** for fundamental and market analysis +- ***Configurable model parameters for each agent*** +- This **repository uses Claude 4 Sonnet** as the underlying model for the lead analyst agent. This agent makes decision for which agent to be used as a tool, if meta tooling is required, etc. + +### Advanced Tool Ecosystem +There are custom tools that are used across different agents in this ecosystem: + +1. `get_income_statements`: Retrieves company income statement data +2. `get_balance_sheets`: Fetches balance sheet information +3. `get_cash_flow_statements`: Obtains cash flow data +4. `get_stock_prices`: Historical price data with date range filtering +5. `get_current_stock_price`: Real-time price information +6. `get_technical_indicators`: Calculates RSI, MACD, SMA, EMA indicators +7. `get_options_chain`: Options contract data with filtering capabilities +8. `get_insider_trades`: Insider trading transaction information +9. `get_news`: Financial news and market sentiment data + +### Code Generation and Execution + +1. `generate_and_execute_code`: Dynamic Python code generation and safe execution + - Uses LiteLLM with AWS Bedrock for code generation + - Implements security measures and timeout controls + - Supports data visualization and custom analysis + +### Pre-built Utility Tools + +This Agentic system uses pre-built tools offered by Strands agent tools along with the custom tools created. The pre-built tools that are used are as follows across the four different agents: + +1. `calculator`: Mathematical operations +2. `file_read`: File parsing capabilities +3. `mem0_memory`: Memory management for context retention +4. `python_repl`: Python code execution environment +5. `current_time`: Date and time utilities +6. `journal`: Structured task and log management + +### Human-in-the-Loop Workflow +Implements human approval mechanisms for sensitive operations. If there is a tool that is getting sensitive news for example, the tool execution will halt and ask the human for approval for calling the tool. There are several design patterns for adding a human in the loop, but this is one of them being ***reviewing tool calls***. You can also use the `HITL` workflow to edit the tool call context, ask for approval and more. + +1. Insider trading data requests require explicit approval +2. News data access with human oversight +3. Interactive approval prompts in Jupyter notebook environment + +### Meta-Tooling Capabilities + +The Lead Analyst Agent features advanced meta-tooling abilities: + +1. **Dynamic Tool Creation**: Generates custom tools on-demand +2. **Tool Loading**: Integrates newly created tools into the system +3. **Shell Access**: Command execution for testing and validation +4. **Editor Integration**: Code modification and tool development + +## Getting Started + +Follow the steps below to get started with running this in your environment: + +### Environment Configuration + +1. Make sure you are in your AWS environment. To set up your AWS environment, view the steps [here](https://aws.amazon.com/getting-started/guides/setup-environment/). Create `.env` file with required API keys and credentials. Copy the `.env.template` to `.env` and edit it: + +```bash +# Copy the template into your .env file and edit the content below +cp .env.template .env +``` + +```bash +TAVILY_API_KEY= +FINANCIAL_DATASET_API= +``` + +To get your API key for Tavily, view [here](https://tavily.com/). To get your API key for `financialdatasets.ai`, view [here](https://www.financialdatasets.ai/). + +2. Install `uv` and Python dependencies: + +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +export PATH="$HOME/.local/bin:$PATH" +``` + +3. Restore Python virtual environment from the `pyproject.toml` file. + +```bash +uv venv && source .venv/bin/activate && uv pip sync pyproject.toml +``` + +4. Create a `conda` kernel. This is needed to run the notebook on your EC2 instance: + +```bash +uv add zmq +python -m ipykernel install --user --name=.venv --display-name="Python (uv env)" +``` + +5. Open the JupyterLab notebook to run and select the `Python (uv env)` if not selected automatically (if it is not, refresh the page or close the notebook and open it again and you should see it selected or be able to select it from the drop down). + +## Configurations + +All information to run this sample is fetched from a configuration file. This configuration file contains information about the model id information, inference parameters and other information for each agent in this multi agent system. View the file [here](config.yaml). + +```yaml +general: + name: "Financial-multi-agentic-system" + description: "This agentic system shows multi-agent collaboration different financial agents" + +# This contains the model information for various agents +# in our multi agentic system +model_information: + finance_agent_model_info: + model_id: us.amazon.nova-micro-v1:0 + inference_parameters: + temperature: 0.1 + technical_agent_model_info: + model_id: us.anthropic.claude-3-5-sonnet-20240620-v1:0 + inference_parameters: + temperature: 0.1 + max_tokens: 512 +. +. +. +``` + +## Steps to run the solution + +Next, to run the solution, open the [`multi-agentic-system-strands.ipynb`](multi-agentic-system-strands.ipynb), select the `Python (uv env)` environment and run all the cells to see the outputs. + +## Results + +View some of the results below: + +1. **Human in the loop example**: View how the tool execution pauses and asks for the human in the loop input before resuming: + +![hitl](img/hitl.png) + +2. **On the fly research journal creation**: One of the agents has the capability to use built in tools and custom tools, and write a journal on the fly. View an example off a Journal created here: + +![journal](img/journal.png) + +## Observability + +View the traces in `Langfuse` dashboard below: + +![langfuse](img/trace1.png) + +![langfuse](img/trace2.png) + + + + + + diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/config.yaml b/02-samples/05-hedge-fund-analyst-multi-agent-system/config.yaml new file mode 100644 index 00000000..41471f8b --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/config.yaml @@ -0,0 +1,51 @@ +general: + name: "Financial-multi-agentic-system" + description: "This agentic system shows multi-agent collaboration different financial agents" + +# This contains the model information for various agents +# in our multi agentic system +model_information: + # Represents information about the finance agent + finance_agent_model_info: + # Represents the model id that the agent will use within + # the strands agents SDK + # The finance agent is not a complicated agent but calls a tool that requires + # code generation. We will power the agent with a small model (Nova Micro), but + # will use a larger, more comprehensive model for code generation (view the code gen + # model info section below) + model_id: us.amazon.nova-micro-v1:0 + # Represents the inference parameters that the foundation model + # uses at runtime during inference + inference_parameters: + temperature: 0.1 + technical_agent_model_info: + # Represents the model id that the agent will use within + # the strands agents SDK + model_id: us.anthropic.claude-3-5-sonnet-20240620-v1:0 + # Represents the inference parameters that the foundation model + # uses at runtime during inference + inference_parameters: + temperature: 0.1 + max_tokens: 512 + market_analysis_agent: + # Represents the model id that the agent will use within + # the strands agents SDK + model_id: us.anthropic.claude-3-5-sonnet-20240620-v1:0 + # Represents the inference parameters that the foundation model + # uses at runtime during inference + inference_parameters: + temperature: 0.1 + max_tokens: 512 + +# This is the code generation model that is used within the tool +# to generate code, note that this might be different from the actual +# large language model that powers the agent +code_gen_model_info: + # Represents the model id that the agent will use within + # the strands agents SDK + model_id: us.anthropic.claude-3-5-sonnet-20240620-v1:0 + # Represents the inference parameters that the foundation model + # uses at runtime during inference + inference_parameters: + temperature: 0.1 + max_tokens: 2048 diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/img/arch.png b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/arch.png new file mode 100644 index 00000000..15170980 Binary files /dev/null and b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/arch.png differ diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/img/hitl.png b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/hitl.png new file mode 100644 index 00000000..54c76895 Binary files /dev/null and b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/hitl.png differ diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/img/journal.png b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/journal.png new file mode 100644 index 00000000..e27a2be8 Binary files /dev/null and b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/journal.png differ diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/img/supervisor_agent_architecture.png b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/supervisor_agent_architecture.png new file mode 100644 index 00000000..5b622799 Binary files /dev/null and b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/supervisor_agent_architecture.png differ diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/img/trace1.png b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/trace1.png new file mode 100644 index 00000000..a5dc2903 Binary files /dev/null and b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/trace1.png differ diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/img/trace2.png b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/trace2.png new file mode 100644 index 00000000..09efd111 Binary files /dev/null and b/02-samples/05-hedge-fund-analyst-multi-agent-system/img/trace2.png differ diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/journal/2025-05-23.md b/02-samples/05-hedge-fund-analyst-multi-agent-system/journal/2025-05-23.md new file mode 100644 index 00000000..01d6bc40 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/journal/2025-05-23.md @@ -0,0 +1,37 @@ + +## 15:48:08 +## Apple Inc. (AAPL) Financial Analysis - May 23, 2025 + +Today I conducted a comprehensive financial analysis of Apple Inc. (AAPL). The company continues to demonstrate exceptional financial performance with FY2023 revenue of approximately $383 billion, a net margin of ~25%, and free cash flow of around $110 billion. + +Key observations: +1. Services business is growing faster than hardware, improving overall margins +2. Cash position remains extremely strong (~$160B) despite massive shareholder returns +3. Gross margin has improved to ~43%, partly due to services mix shift +4. Share repurchases continue to enhance EPS growth beyond net income growth +5. Balance sheet remains strong with manageable debt levels + +Investment considerations: +- Apple remains well-positioned for continued growth through its integrated ecosystem +- Services growth trajectory is critical to watch for margin expansion +- Regulatory risks around App Store practices could impact services revenue +- New product categories may provide additional growth vectors +- Valuation premium requires sustained execution + +Actionable insights: +1. Monitor services growth rate in quarterly reports as key indicator +2. Watch for progress on supply chain diversification beyond China +3. Evaluate capital allocation decisions between buybacks and potential acquisitions +4. Track regulatory developments in key markets regarding App Store policies +5. Assess potential impact of AI integration across product lineup + +Overall, Apple continues to demonstrate financial strength that supports its premium valuation, though growth sustainability at its current size remains a key question for long-term investors. + +## 15:48:19 - Task +- [ ] Monitor Apple's next quarterly report for services segment growth rate + +## 15:48:45 - Task +- [ ] Research potential impact of global regulatory developments on Apple's App Store revenue model + +## 15:49:13 - Task +- [ ] Analyze Apple's capital allocation decisions in comparison to tech peers diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/multi-agentic-system-strands.ipynb b/02-samples/05-hedge-fund-analyst-multi-agent-system/multi-agentic-system-strands.ipynb new file mode 100644 index 00000000..a8247d36 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/multi-agentic-system-strands.ipynb @@ -0,0 +1,1908 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "53b90c98", + "metadata": {}, + "source": [ + "# Multi-Agent Collaboration (MAC) Example: Hedge Fund Assistant\n", + "---\n", + "\n", + "This example demonstrates a Hedge Fund Assistant using MAC principles with the following structure:\n", + "\n", + "- A **Supervisor Agent** powered by the Amazon Nova Lite foundation model routes user queries.\n", + "\n", + "- Three **Sub-Agents**:\n", + " - **Fundamental Analyst Agent** (uses Claude 3 Haiku as the FM)\n", + " - **Technical Analyst Agent** (uses Amazon Nova Lite as the FM)\n", + " - **Market Analyst Agent** (uses Claude 3 Haiku as the FM)\n", + "\n", + "Each sub-agent has access to a set of tools to fetch data, analyze it, and provide actionable insights." + ] + }, + { + "cell_type": "markdown", + "id": "224ea8fa", + "metadata": {}, + "source": [ + "#### Set a logger\n", + "\n", + "The Strands Agents SDK implements a straightforward logging approach:\n", + "\n", + "1. **Module-level Loggers**: Each module in the SDK creates its own logger using logging.getLogger(__name__), following Python best practices for hierarchical logging.\n", + "\n", + "2. **Root Logger**: All loggers in the SDK are children of the \"strands\" root logger, making it easy to configure logging for the entire SDK.\n", + "\n", + "3. **Default Behavior**: By default, the SDK doesn't configure any handlers or log levels, allowing you to integrate it with your application's logging configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "363ea893", + "metadata": {}, + "outputs": [], + "source": [ + "# import logging and set a logger for strands\n", + "import logging\n", + "# import the strands agents and strands tools that we will be using\n", + "from strands import Agent\n", + "from strands_tools import swarm\n", + "\n", + "# Configure the root strands logger\n", + "logging.getLogger(\"strands\").setLevel(logging.DEBUG)\n", + "\n", + "# Add a handler to see the logs\n", + "logging.basicConfig(\n", + " format=\"%(levelname)s | %(name)s | %(message)s\", \n", + " handlers=[logging.StreamHandler()]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c250d58", + "metadata": {}, + "outputs": [], + "source": [ + "# install other requirements\n", + "import os\n", + "import sys\n", + "import json\n", + "import time\n", + "import boto3\n", + "import shutil\n", + "import logging\n", + "import zipfile\n", + "import subprocess\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "# Load the environment variables that are defined in the \".env\" file. This contains the \n", + "# financial data API key that will enable the user to access the data\n", + "load_dotenv()\n", + "os.environ[\"LANGFUSE_PUBLIC_KEY\"] = \"pk-lf-...\"\n", + "os.environ[\"LANGFUSE_SECRET_KEY\"] = \"sk-lf-...\"\n", + "# os.environ[\"LANGFUSE_HOST\"] = \"https://cloud.langfuse.com\" # πŸ‡ͺπŸ‡Ί EU region (default)\n", + "os.environ[\"LANGFUSE_HOST\"] = \"https://us.cloud.langfuse.com\" # πŸ‡ΊπŸ‡Έ US region" + ] + }, + { + "cell_type": "markdown", + "id": "d22119ab", + "metadata": {}, + "source": [ + "### Load config file\n", + "---\n", + "\n", + "This [config](config.yaml) file is a `yaml` file that contains information that this solution uses, including the result files, the model information for each of the agent used in this multi agentic system, inference parameters, and more." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed050ded", + "metadata": {}, + "outputs": [], + "source": [ + "!uv pip install PyYAML aiohttp openai\n", + "from utils import load_config\n", + "\n", + "# set a logger\n", + "logging.basicConfig(format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s', level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "# Load the config file\n", + "config_data = load_config('config.yaml')\n", + "logger.info(f\"Loaded config from local file system: {json.dumps(config_data, indent=2)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "738cc5d0", + "metadata": {}, + "source": [ + "### Define tools\n", + "---\n", + "\n", + "This example creates a supervisor agent and integrates the three sub-agents, each having specific tools for financial data analysis:\n", + "\n", + "1. **Fundamental Analyst Agent**:\n", + " - Tools: Retrieve income statements, balance sheets, and cash flow statements.\n", + "\n", + "2. **Technical Analyst Agent**:\n", + " - Tools: Fetch stock prices, current prices, and compute technical indicators (e.g., `RSI`, `MACD`, `SMA`).\n", + "\n", + "3. **Market Analyst Agent**:\n", + " - Tools: Access options chain data, insider trading information, and market news." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff235a0a", + "metadata": {}, + "outputs": [], + "source": [ + "api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + "if api_key is not None:\n", + " print(\"Financial Data API key found in environment variables.\")\n", + "else:\n", + " print(f\"API key not found, enter it in the .env file.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "703e074f", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import uuid\n", + "import logging\n", + "from datetime import datetime\n", + "from typing import Dict, Any, Optional\n", + "\n", + "tool_use_ids=[]\n", + "\n", + "# ─── LOGGER SETUP ──────────────────────────────────────────────────────────────\n", + "logger = logging.getLogger(__name__)\n", + "logging.basicConfig(level=logging.INFO)\n", + "\n", + "def comprehensive_callback_handler(**kwargs):\n", + " \"\"\"\n", + " Enhanced comprehensive callback handler with LangSmith integration\n", + " \"\"\"\n", + " \n", + " # === REASONING EVENTS (Agent's thinking process) ===\n", + " if kwargs.get(\"reasoning\", False):\n", + " if \"reasoningText\" in kwargs:\n", + " reasoning_text = kwargs['reasoningText']\n", + " logger.info(f\"🧠 REASONING: {reasoning_text}\")\n", + " \n", + " if \"reasoning_signature\" in kwargs:\n", + " logger.info(f\"πŸ” REASONING SIGNATURE: {kwargs['reasoning_signature']}\")\n", + " \n", + " # === TEXT GENERATION EVENTS ===\n", + " elif \"data\" in kwargs:\n", + " # Log streamed text chunks from the model\n", + " logger.info(kwargs[\"data\"], end=\"\")\n", + " if kwargs.get(\"complete\", False):\n", + " logger.info(\"\") # Add newline when complete\n", + " \n", + " # === TOOL EVENTS ===\n", + " elif \"current_tool_use\" in kwargs:\n", + " tool = kwargs[\"current_tool_use\"]\n", + " tool_use_id = tool[\"toolUseId\"]\n", + " \n", + " if tool_use_id not in tool_use_ids:\n", + " tool_name = tool.get('name', 'unknown_tool')\n", + " tool_input = tool.get('input', {})\n", + " \n", + " logger.info(f\"\\nπŸ”§ USING TOOL: {tool_name}\")\n", + " if \"input\" in tool:\n", + " logger.info(f\"πŸ“₯ TOOL INPUT: {tool_input}\")\n", + " tool_use_ids.append(tool_use_id)\n", + " \n", + " # === TOOL RESULTS ===\n", + " elif \"tool_result\" in kwargs:\n", + " tool_result = kwargs[\"tool_result\"]\n", + " tool_use_id = tool_result.get(\"toolUseId\")\n", + " result_content = tool_result.get(\"content\", [])\n", + " \n", + " logger.info(f\"πŸ“€ TOOL RESULT: {result_content}\")\n", + " \n", + " # === LIFECYCLE EVENTS ===\n", + " elif kwargs.get(\"init_event_loop\", False):\n", + " logger.info(\"πŸ”„ Event loop initialized\")\n", + " \n", + " elif kwargs.get(\"start_event_loop\", False):\n", + " logger.info(\"▢️ Event loop cycle starting\")\n", + " \n", + " elif kwargs.get(\"start\", False):\n", + " logger.info(\"πŸ“ New cycle started\")\n", + " \n", + " elif kwargs.get(\"complete\", False):\n", + " logger.info(\"βœ… Cycle completed\")\n", + " \n", + " elif kwargs.get(\"force_stop\", False):\n", + " reason = kwargs.get(\"force_stop_reason\", \"unknown reason\")\n", + " logger.info(f\"πŸ›‘ Event loop force-stopped: {reason}\")\n", + " \n", + " # === MESSAGE EVENTS ===\n", + " elif \"message\" in kwargs:\n", + " message = kwargs[\"message\"]\n", + " role = message.get(\"role\", \"unknown\")\n", + " logger.info(f\"πŸ“¬ New message created: {role}\")\n", + " \n", + " # === ERROR EVENTS ===\n", + " elif \"error\" in kwargs:\n", + " error_info = kwargs[\"error\"]\n", + " logger.error(f\"❌ ERROR: {error_info}\")\n", + "\n", + " # === RAW EVENTS (for debugging) ===\n", + " elif \"event\" in kwargs:\n", + " # Log raw events from the model stream (optional, can be verbose)\n", + " logger.debug(f\"πŸ” RAW EVENT: {kwargs['event']}\")\n", + " \n", + " # === DELTA EVENTS ===\n", + " elif \"delta\" in kwargs:\n", + " # Raw delta content from the model\n", + " logger.debug(f\"πŸ“Š DELTA: {kwargs['delta']}\")\n", + " \n", + " # === CATCH-ALL FOR DEBUGGING ===\n", + " else:\n", + " # Log any other events we might have missed\n", + " logger.debug(f\"❓ OTHER EVENT: {kwargs}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d0e1b56", + "metadata": {}, + "outputs": [], + "source": [ + "# define the tools for the finance agent\n", + "# Strands Agents SDK makes it straightforward to \n", + "# turn Python functions into callable tools within an AI agent.\n", + "\n", + "# transform any Python function into a tool simply by adding the @tool decoratorβ€”its \n", + "# docstring and type hints automatically generate the tool's specification\n", + "import os\n", + "import requests\n", + "from strands import tool\n", + "from typing import Dict, List\n", + "\n", + "@tool\n", + "def get_income_statements(\n", + " ticker: str, \n", + " period: str = \"ttm\", \n", + " limit: int = 10\n", + ") -> Dict:\n", + " \"\"\"\n", + " Get income statements for a ticker. This is one of the functions that is used to get the \n", + " income statements based on the ticker specified by the user\n", + " \n", + " Args:\n", + " ticker (str): Stock ticker symbol (e.g., 'AAPL', 'MSFT', 'GOOGL')\n", + " period (str, optional): Time period for the data. Options:\n", + " - 'ttm': Trailing twelve months (default)\n", + " - 'annual': Annual data\n", + " - 'quarterly': Quarterly data\n", + " limit (int, optional): Maximum number of statements to return (default: 10)\n", + " \n", + " Returns:\n", + " Dict: JSON response containing income statement data\n", + " \n", + " Raises:\n", + " RuntimeError: If API key is missing or API request fails\n", + " \"\"\"\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " if not api_key:\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + "\n", + " url = (\n", + " f'https://api.financialdatasets.ai/financials/income-statements'\n", + " f'?ticker={ticker}'\n", + " f'&period={period}'\n", + " f'&limit={limit}'\n", + " )\n", + "\n", + " try:\n", + " response = requests.get(url, headers={'X-API-Key': api_key})\n", + " print(f\"OUTPUT FROM THE GET_INCOME_STATEMENTS TOOL: {response.json()}\")\n", + " return response.json()\n", + " except Exception as e:\n", + " return {\"ticker\": ticker, \"income_statements\": [], \"error\": str(e)}\n", + "\n", + "@tool\n", + "def get_balance_sheets(\n", + " ticker: str, \n", + " period: str = \"ttm\", \n", + " limit: int = 10\n", + ") -> Dict:\n", + " \"\"\"\n", + " Get balance sheets for a ticker and the specified limit and time period\n", + " \n", + " Args:\n", + " ticker (str): Stock ticker symbol (e.g., 'AAPL', 'MSFT', 'GOOGL')\n", + " period (str, optional): Time period for the data. Options:\n", + " - 'ttm': Trailing twelve months (default)\n", + " - 'annual': Annual data\n", + " - 'quarterly': Quarterly data\n", + " limit (int, optional): Maximum number of balance sheets to return (default: 10)\n", + " \n", + " Returns:\n", + " Dict: JSON response containing balance sheet data\n", + " \n", + " Raises:\n", + " RuntimeError: If API key is missing or API request fails\n", + " \"\"\"\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " if not api_key:\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + "\n", + " url = (\n", + " f'https://api.financialdatasets.ai/financials/balance-sheets'\n", + " f'?ticker={ticker}'\n", + " f'&period={period}'\n", + " f'&limit={limit}'\n", + " )\n", + "\n", + " try:\n", + " response = requests.get(url, headers={'X-API-Key': api_key})\n", + " print(f\"OUTPUT FROM THE GET_BALANCE_SHEETS TOOL: {response.json()}\")\n", + " return response.json()\n", + " except Exception as e:\n", + " return {\"ticker\": ticker, \"balance_sheets\": [], \"error\": str(e)}\n", + "\n", + "@tool\n", + "def get_cash_flow_statements(\n", + " ticker: str, \n", + " period: str = \"ttm\", \n", + " limit: int = 10\n", + ") -> Dict:\n", + " \"\"\"\n", + " Get cash flow statements for a ticker\n", + " \n", + " Args:\n", + " ticker (str): Stock ticker symbol (e.g., 'AAPL', 'MSFT', 'GOOGL')\n", + " period (str, optional): Time period for the data. Options:\n", + " - 'ttm': Trailing twelve months (default)\n", + " - 'annual': Annual data\n", + " - 'quarterly': Quarterly data\n", + " limit (int, optional): Maximum number of cash flow statements to return (default: 10)\n", + " \n", + " Returns:\n", + " Dict: JSON response containing cash flow data\n", + " \n", + " Raises:\n", + " RuntimeError: If API key is missing or API request fails\n", + " \"\"\"\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " if not api_key:\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + "\n", + " url = (\n", + " f'https://api.financialdatasets.ai/financials/cash-flow-statements'\n", + " f'?ticker={ticker}'\n", + " f'&period={period}'\n", + " f'&limit={limit}'\n", + " )\n", + "\n", + " try:\n", + " response = requests.get(url, headers={'X-API-Key': api_key})\n", + " # fetch and return the output of the api call in the response\n", + " print(f\"OUTPUT FROM THE GET_CASH_FLOW_STATEMENTS TOOL: {response.json()}\")\n", + " return response.json()\n", + " except Exception as e:\n", + " return {\"ticker\": ticker, \"cash_flow_statements\": [], \"error\": str(e)}" + ] + }, + { + "cell_type": "markdown", + "id": "35c319f2", + "metadata": {}, + "source": [ + "#### Create a custom python code execution tool\n", + "---\n", + "\n", + "In this section, we will create a custom tool that lets you configure or use a model of choice to generate code based on the content and the user question and then execute that tool as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b967789b", + "metadata": {}, + "outputs": [], + "source": [ + "# Represents the code execution prompt for the LLM\n", + "CODE_EXEC_PROMPT: str = \"\"\"You are a Python code generator. Generate clean, efficient, and safe Python code based on the user's requirements.\n", + "\n", + "Guidelines:\n", + "1. Only generate executable Python code\n", + "2. Include necessary imports at the top\n", + "3. Add comments to explain complex logic\n", + "4. Handle potential errors gracefully\n", + "5. Keep code concise but readable\n", + "6. Do not include any harmful or system-modifying operations\n", + "7. Focus on data processing, calculations, and analysis tasks\n", + "8. If working with data, assume it might be passed as context\n", + "9. Print results using print() statements so they appear in output\n", + "\n", + "Always adhere to the user question and generate python code based on what the user is asking.\n", + "\n", + "Return ONLY the Python code, no explanations or markdown formatting.\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a5bcb72", + "metadata": {}, + "outputs": [], + "source": [ + "!uv pip install tiktoken tokenizers jinja2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e931675", + "metadata": {}, + "outputs": [], + "source": [ + "!uv pip show litellm\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fb706aa", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import subprocess\n", + "import tempfile\n", + "import sys\n", + "import time\n", + "from typing import Optional, Dict, Any, List\n", + "from strands import tool\n", + "from litellm import completion" + ] + }, + { + "cell_type": "markdown", + "id": "25f8868d", + "metadata": {}, + "source": [ + "### Define the system prompt for the financial agent\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df940ae3", + "metadata": {}, + "outputs": [], + "source": [ + "# set a logger\n", + "logging.basicConfig(format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s', level=logging.INFO)\n", + "logger = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfe4da43", + "metadata": {}, + "outputs": [], + "source": [ + "# This is the system prompt that will be used for the financial agent\n", + "financial_agent_instruction = \"\"\"You are a comprehensive fundamental analyst assistant that helps users analyze company financial statements across three key areas: income statements, balance sheets, and cash flow statements.\n", + "You require the user to provide a stock ticker symbol to analyze the company's financials.\n", + "\n", + "You can perform the following types of analysis by calling the functions below:\n", + "\n", + "1. Income Statement Analysis:\n", + " - Revenue growth, gross profit, operating income, net income, earnings per share, and revenue/expense breakdown\n", + "\n", + "2. Balance Sheet Analysis:\n", + " - Asset composition (current and non-current), liabilities (current and non-current), shareholders' equity, retained earnings, and financial ratios (current ratio, debt-to-equity)\n", + "\n", + "3. Cash Flow Statement Analysis:\n", + " - Net cash flow from operations, capital expenditures, business acquisitions, issuance/repayment of debt, dividends, and changes in cash and equivalents\n", + "\n", + "IMPORTANT: Always use the financial dataset API you have access to to call these functions and retrieve the data to answer the user question\n", + "\n", + "If you do not have access to the data that the user is asking for, do not make up an answer, just say that you do not know the answer. Be completely\n", + "accurate. Do not provide answers to anything but on the topic specified above. Always give information on where you got the data to answer the user question\n", + "and do not redact anything in your response.\n", + "\n", + "Also, use the python repl tool function at the very end to generate report and analysis based on the context fetched so far and store\n", + "that in a file or provide that as an output.\n", + "\"\"\"\n", + "print(f\"Going to use the financial agent system prompt: {financial_agent_instruction}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c43403ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the model information for the finance agent\n", + "finance_agent_model_info: Dict = config_data['model_information'].get('finance_agent_model_info')\n", + "print(f\"Fetched the agent model information: {finance_agent_model_info}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e84a9d9c", + "metadata": {}, + "outputs": [], + "source": [ + "# initialize the model that will power the financial agent\n", + "# in this case, we will use the claude 3-7 model to power the financial \n", + "# agent\n", + "from strands.models import BedrockModel\n", + "\n", + "# Define the current aws region\n", + "region: str = boto3.Session().region_name\n", + "print(f\"Going to use the agent in the region: {region}\")\n", + "\n", + "# Create a bedrock model using the BedrockModel interface\n", + "bedrock_model = BedrockModel(\n", + " model_id=finance_agent_model_info.get('model_id'),\n", + " region_name=region,\n", + " temperature=finance_agent_model_info['inference_parameters'].get('temperature'),\n", + ")\n", + "print(f\"Initialized the bedrock model for the finance agent: {bedrock_model}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5ac600a2", + "metadata": {}, + "source": [ + "### Create the financial agent\n", + "---\n", + "\n", + "Next, we will simply create the finance agent. We will use a Callback handler function in this case. We will implement the custom callback function. This will be invoked at various points throughout the financial agent's lifecycle. \n", + "\n", + "Here is an example that captures streamed data from the agent and logs instead of printing:\n", + "\n", + "### What are Callback handlers in Strands SDK?\n", + "\n", + "Callback handlers are a powerful feature for Strands SDK that enables the user to intercept and process the events as they are occurring during the agent execution process. This means that the agent will be able to handle real time monitoring, custom output formatting and integration with other external systems.\n", + "\n", + "This handling is done and can occur throughout the agent's lifecycle and includes:\n", + "\n", + "1. The text generation process that is done with the model that underlies the agent. If the agent is generating text then the callback handler will catch that and emit it out during the agent execution process.\n", + "\n", + "2. If the agent is using tools or has tools associated with it, then the agent will be using that and executing that. As a part of that process, the callback handler can catch those traces and emit it out for real time processing.\n", + "\n", + "3. Reasoning process: If you want to handle the traces or track the reasoning process of the agent based on the LLM thinking to debug and optimize performance, that is possible through callback handlers as well.\n", + "\n", + "4. Errors and completions: If there are any errors or completions during the process we can handle that using the callback handler functionality as well.\n", + "\n", + "For more information on what you can log with Callback handler events, view the documentation [here](https://strandsagents.com/0.1.x/user-guide/concepts/streaming/callback-handlers/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58f10a0f", + "metadata": {}, + "outputs": [], + "source": [ + "# we will now set up logging for the agent using langfuse\n", + "import base64\n", + "otel_host = os.environ.get(\"LANGFUSE_HOST\")\n", + "if otel_host:\n", + " # Set up endpoint for OpenTelemetry\n", + " otel_endpoint = str(os.environ.get(\"LANGFUSE_HOST\")) + \"/api/public/otel/v1/traces\"\n", + "\n", + " # Create authentication token for OpenTelemetry\n", + " auth_token = base64.b64encode(\n", + " f\"{os.environ.get('LANGFUSE_PUBLIC_KEY')}:{os.environ.get('LANGFUSE_SECRET_KEY')}\".encode()\n", + " ).decode()\n", + " os.environ[\"OTEL_EXPORTER_OTLP_ENDPOINT\"] = otel_endpoint\n", + " os.environ[\"OTEL_EXPORTER_OTLP_HEADERS\"] = f\"Authorization=Basic {auth_token}\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc7f3202", + "metadata": {}, + "outputs": [], + "source": [ + "# import the prebuilt tools.\n", + "# Calculator: Perform mathematical operations\n", + "# file_read: Read and parse files\n", + "# mem0_memory: Tool for managing memories using Mem0 (store, delete, list, get, and retrieve)\n", + "# python_repl: Run Python code\n", + "# current_time: Get the current date and time\n", + "# journal: Create structured tasks and logs for agents to manage and work from\n", + "from strands_tools import calculator, file_read, mem0_memory, python_repl, current_time, journal, python_repl\n", + "\n", + "# Generate a unique session ID using UUID\n", + "session_id = str(uuid.uuid4())\n", + "\n", + "# Create the financial agent\n", + "finance_agent = Agent(\n", + " # this is the system prompt to the strands agent\n", + " system_prompt=financial_agent_instruction, \n", + " # use the tools from the ones defined above for the \n", + " # finance agent\n", + " tools = [\n", + " # These are the custom built tools that the finance \n", + " # agent will use\n", + " get_income_statements, \n", + " get_balance_sheets,\n", + " get_cash_flow_statements, \n", + " # These are the prebuilt set of tools that the \n", + " # strands sdk agent already offers\n", + " calculator, \n", + " python_repl, \n", + " file_read, \n", + " mem0_memory, \n", + " current_time, \n", + " journal],\n", + " # define the callback handler in this case\n", + " # This callback handler logs the reasoning, tool, lifecycle, \n", + " # raw, delta and message events.\n", + " callback_handler=comprehensive_callback_handler, \n", + " trace_attributes={\n", + " \"session.id\": session_id, # Use UUID for unique session tracking\n", + " \"user.id\": \"agent-builder@strandsagents.com\",\n", + " \"langfuse.tags\": [\n", + " \"Strands-Agents-Builder\",\n", + " ],\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b368bbce", + "metadata": {}, + "outputs": [], + "source": [ + "# view some of the tools and the model through the agent config\n", + "finance_agent.model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e42b55e", + "metadata": {}, + "outputs": [], + "source": [ + "# view the details of the tools in a more human readable manner\n", + "def print_tools_summary(config):\n", + " \"\"\"Print a summary of all tools\"\"\"\n", + " tools = config.get('tools', [])\n", + " \n", + " print(\"=\" * 60)\n", + " print(f\"AGENT TOOLS SUMMARY\")\n", + " print(\"=\" * 60)\n", + " print(f\"Total number of tools: {len(tools)}\")\n", + " print()\n", + " \n", + " for i, tool in enumerate(tools, 1):\n", + " tool_spec = tool.get('toolSpec', {})\n", + " name = tool_spec.get('name', 'Unknown')\n", + " description = tool_spec.get('description', 'No description available')\n", + " \n", + " # Extract the first line of description for summary\n", + " first_line = description.split('\\n')[0] if description else 'No description'\n", + " \n", + " print(f\"{i}. {name}\")\n", + " print(f\" Summary: {first_line}\")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a733f773", + "metadata": {}, + "outputs": [], + "source": [ + "# view the tools that the finance agent has access to\n", + "print_tools_summary(finance_agent.tool_config)" + ] + }, + { + "cell_type": "markdown", + "id": "c0ff624b", + "metadata": {}, + "source": [ + "Strands Agents SDK provides support for asynchronous iterators through the stream_async method, enabling real-time streaming of agent responses in asynchronous environments like web servers, APIs, and other async applications." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81e2bca2", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the async function\n", + "async def process_streaming_response(agent, query):\n", + " agent_stream = agent.stream_async(query)\n", + " async for event in agent_stream:\n", + " # Print only the text content, not the raw event data\n", + " if isinstance(event, dict):\n", + " # Check for 'data' field first (seems to contain the text)\n", + " if 'data' in event:\n", + " print(event['data'], end='', flush=True)\n", + " # Fallback to nested event structure\n", + " elif 'event' in event and 'contentBlockDelta' in event['event']:\n", + " delta = event['event']['contentBlockDelta']['delta']\n", + " if 'text' in delta:\n", + " print(delta['text'], end='', flush=True)\n", + " else:\n", + " # If it's not a dict, just print it as is\n", + " print(event, end='', flush=True)\n", + " print() # Add a newline at the end\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c314d398", + "metadata": {}, + "outputs": [], + "source": [ + "import asyncio\n", + "\n", + "# Define the query\n", + "query: str = \"\"\"\n", + "I want you to perform a comprehensive financial analysis of AAPL. Please:\n", + "\n", + "1. Retrieve and analyze the latest income statements, balance sheets, and cash flow statements for AAPL, \n", + "if your tool encounters an error, then call the function again with the correct parameters.\n", + "2. Calculate key financial pointers from the data. Based on your output from the latency input statements, balance sheets and cash flow statements, \n", + "generate code to create charts, pie charts, bar charts, execute mermaid diagrams to show interesting take aways from the financial data that you \n", + "have in your context.\n", + "3. Identify trends over the past 3-5 years in revenue growth, profit margins, and cash generation\n", + "4. Assess Apple's financial health and liquidity position\n", + "5. Provide investment insights based on the financial data analysis\n", + "6. Store the key findings in memory for future reference\n", + "7. Create a journal entry summarizing today's AAPL analysis with actionable insights\n", + "\n", + "Please present the analysis in a structured format with clear sections for each financial statement review, ratio analysis, trend identification, and final recommendations.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e4ace76", + "metadata": {}, + "outputs": [], + "source": [ + "# simply run the agent to see the outputs\n", + "finance_agent(query)" + ] + }, + { + "cell_type": "markdown", + "id": "253334f6", + "metadata": {}, + "source": [ + "### View the traces in the LangFuse dashboard\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "a248f0d4", + "metadata": {}, + "source": [ + "![trace1](img/trace1.png)\n", + "\n", + "![trace2](img/trace2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "b820bc75", + "metadata": {}, + "source": [ + "## Technical Analyst Agent\n", + "---\n", + "\n", + "In this section, we will be crating the second sub agent that will be responsible for technical analysis to user questions. This inclues the following:\n", + "\n", + "1. Questions about getting stock prices, i.e., based on a user question the ticker will go over a given data range and interval and the agent will get the stock prices.\n", + "\n", + "2. Questions about seeking information on the current stock price - if the user asks for the current stock price, then the agent will call a tool to get the latest or current stock price for a given ticker.\n", + "\n", + "3. Last, we will create a human in the loop for calculating technical indicators for a given time period." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4eca3c8", + "metadata": {}, + "outputs": [], + "source": [ + "technical_agent_analysis_prompt: str = \"\"\"\n", + "You are a technical analysis assistant that helps users analyze stock price movements and technical indicators. You can calculate and interpret various technical indicators using historical price data.\n", + "\n", + "You have access to the following technical analysis capabilities based on the API key you have access to to call the available functions and get the following information:\n", + "\n", + "1. Stock Price Data:\n", + " - Current stock prices\n", + " - Historical price data with various intervals\n", + " - Custom date range analysis\n", + "\n", + "2. Technical Indicators:\n", + " - RSI\n", + " - MACD\n", + " - SMA\n", + " - EMA\n", + " - Bollinger Bands\n", + "\n", + "You require the user to provide:\n", + "1. A stock ticker symbol\n", + "2. The type of technical indicator they want to analyze\n", + "3. Optionally: specific time periods or date ranges\n", + "\n", + "Available functions:\n", + "1. get_current_stock_price: Retrieve latest stock price\n", + "2. get_stock_prices: Get historical price data for a specified period\n", + "3. get_technical_indicators: Calculate technical indicators for analysis\n", + "\n", + "If you do not have access to the data that the user is asking for, do not make up an answer. Be completely accurate and only provide analysis based on the available technical indicators and price data.\n", + "\n", + "Only answer questions related to technical analysis and price data based on the provided functions. If unsure, acknowledge limitations\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4eab3955", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Union\n", + "import datetime\n", + "\n", + "@tool\n", + "def get_stock_prices(ticker: str, start_date: str, end_date: str, limit: int = 5000) -> Union[Dict, str]:\n", + " \"\"\"\n", + " Get historical stock prices for a ticker within a date range.\n", + " \n", + " Args:\n", + " ticker (str): Stock ticker symbol (e.g., 'AAPL', 'MSFT', 'GOOGL')\n", + " start_date (str): Start date in YYYY-MM-DD format\n", + " end_date (str): End date in YYYY-MM-DD format\n", + " limit (int): Maximum number of price records to return (default: 5000)\n", + " \n", + " Returns:\n", + " Union[Dict, str]: JSON response containing historical price data or error message\n", + " \"\"\"\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " logger.info(f\"API Key present: {'yes' if api_key else 'no'}\")\n", + " if not api_key:\n", + " logger.error(\"Missing FINANCIAL_DATASET_API environment variable\")\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + " url = (\n", + " f\"https://api.financialdatasets.ai/prices\"\n", + " f\"?ticker={ticker}\"\n", + " f\"&start_date={start_date}\"\n", + " f\"&end_date={end_date}\"\n", + " f\"&interval=day\"\n", + " f\"&interval_multiplier=1\"\n", + " f\"&limit={limit}\"\n", + " )\n", + " try:\n", + " logger.info(f\"Making API request to: {url}\")\n", + " response = requests.get(url, headers={'X-API-Key': api_key})\n", + " logger.info(f\"API response status code: {response.status_code}\")\n", + " if response.status_code != 200:\n", + " logger.error(f\"API error: {response.text}\")\n", + " return {\"error\": f\"API returned status code {response.status_code}\"}\n", + " print(f\"RESPONSE FROM THE GET_STOCK_PRICES TOOL: {response.json()}\")\n", + " return response.json()\n", + " except Exception as e:\n", + " logger.error(f\"Error in get_stock_prices: {str(e)}\", exc_info=True)\n", + " return {\"ticker\": ticker, \"prices\": [], \"error\": str(e)}\n", + "\n", + "\n", + "@tool\n", + "def get_current_stock_price(ticker: str) -> Union[Dict, str]:\n", + " \"\"\"\n", + " Get current stock price based on the ticker provided by the user\n", + " \n", + " Args:\n", + " ticker (str): Stock ticker symbol (e.g., 'AAPL', 'MSFT', 'GOOGL')\n", + " \n", + " Returns:\n", + " Union[Dict, str]: JSON response containing current price data or error message\n", + " \"\"\"\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " if not api_key:\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + "\n", + " url = f\"https://api.financialdatasets.ai/prices/snapshot?ticker={ticker}\"\n", + "\n", + " try:\n", + " response = requests.get(url, headers={'X-API-Key': api_key})\n", + " print(f\"RESPONSE FROM THE GET_CURRENT_STOCK_PRICES TOOL: {response.json()}\")\n", + " return response.json()\n", + " except Exception as e:\n", + " return {\"ticker\": ticker, \"price\": None, \"error\": str(e)}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "373a4826", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime, timedelta\n", + "from typing import Optional, Dict, Union\n", + "\n", + "@tool\n", + "def get_technical_indicators(ticker: str, indicator: str, period: int = 14,\n", + " start_date: Optional[str] = None, end_date: Optional[str] = None) -> Union[Dict, str]:\n", + " \"\"\"\n", + " This function calculates technical indicators based on the provided parameters.\n", + " It supports the following indicators: SMA, EMA, and RSI.\n", + " \n", + " Args:\n", + " ticker (str): Stock ticker symbol (e.g., 'AAPL', 'MSFT', 'GOOGL')\n", + " indicator (str): Technical indicator to calculate ('SMA', 'EMA', or 'RSI')\n", + " period (int): Period for the indicator calculation (default: 14)\n", + " start_date (Optional[str]): Start date in YYYY-MM-DD format\n", + " end_date (Optional[str]): End date in YYYY-MM-DD format\n", + " \n", + " Returns:\n", + " Union[Dict, str]: JSON response containing technical indicator data or error message\n", + " \"\"\"\n", + " try:\n", + " # Get extra data for calculations by extending the start date\n", + " adjusted_start = (datetime.strptime(start_date, \"%Y-%m-%d\") - timedelta(days=period * 2)).strftime(\"%Y-%m-%d\")\n", + " \n", + " # Fetch price data\n", + " price_data = get_stock_prices(\n", + " ticker=ticker,\n", + " start_date=adjusted_start,\n", + " end_date=end_date,\n", + " limit=5000\n", + " )\n", + "\n", + " if \"error\" in price_data:\n", + " return price_data\n", + "\n", + " # Process the price data\n", + " prices = price_data[\"prices\"]\n", + " \n", + " # Parse datetime more robustly - handle different formats\n", + " for price in prices:\n", + " time_str = price['time']\n", + " # Remove timezone info and handle different formats\n", + " if 'Z' in time_str:\n", + " time_str = time_str.replace('Z', '')\n", + " if 'T' in time_str:\n", + " # ISO format: 2025-04-23T04:00:00\n", + " price['datetime'] = datetime.fromisoformat(time_str)\n", + " else:\n", + " # Handle other formats if needed\n", + " time_str = time_str.split(' EDT')[0].split(' EST')[0]\n", + " price['datetime'] = datetime.strptime(time_str, \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + " # Sort by datetime to ensure chronological order\n", + " prices.sort(key=lambda x: x['datetime'])\n", + "\n", + " # Filter to requested date range\n", + " start_dt = datetime.strptime(start_date, \"%Y-%m-%d\")\n", + " end_dt = datetime.strptime(end_date, \"%Y-%m-%d\") + timedelta(days=1) # Include end date\n", + " filtered_prices = [p for p in prices if start_dt <= p['datetime'] < end_dt]\n", + "\n", + " if len(filtered_prices) < period:\n", + " return {\"error\": f\"Not enough data points. Need at least {period} days, got {len(filtered_prices)}\"}\n", + "\n", + " result = {\n", + " \"ticker\": ticker,\n", + " \"indicator\": indicator.upper(),\n", + " \"period\": period,\n", + " \"data\": []\n", + " }\n", + "\n", + " # Calculate indicators\n", + " if indicator.lower() == \"sma\":\n", + " # Simple Moving Average\n", + " for i in range(period - 1, len(prices)):\n", + " if prices[i]['datetime'] >= start_dt:\n", + " sma_prices = [prices[j]['close'] for j in range(i - period + 1, i + 1)]\n", + " sma_value = sum(sma_prices) / period\n", + " \n", + " result[\"data\"].append({\n", + " \"time\": prices[i]['datetime'].strftime(\"%Y-%m-%d %H:%M:%S\"),\n", + " \"time_milliseconds\": int(prices[i]['datetime'].timestamp() * 1000),\n", + " \"value\": round(float(sma_value), 2)\n", + " })\n", + " \n", + " elif indicator.lower() == \"ema\":\n", + " # Exponential Moving Average\n", + " multiplier = 2 / (period + 1)\n", + " ema = prices[0]['close'] # Start with first price\n", + " \n", + " for i, price in enumerate(prices):\n", + " if i == 0:\n", + " ema = price['close']\n", + " else:\n", + " ema = (price['close'] * multiplier) + (ema * (1 - multiplier))\n", + " \n", + " if price['datetime'] >= start_dt:\n", + " result[\"data\"].append({\n", + " \"time\": price['datetime'].strftime(\"%Y-%m-%d %H:%M:%S\"),\n", + " \"time_milliseconds\": int(price['datetime'].timestamp() * 1000),\n", + " \"value\": round(float(ema), 2)\n", + " })\n", + "\n", + " elif indicator.lower() == \"rsi\":\n", + " # Relative Strength Index\n", + " if len(prices) < period + 1:\n", + " return {\"error\": f\"Not enough data for RSI calculation. Need at least {period + 1} days\"}\n", + " \n", + " # Calculate price changes\n", + " changes = []\n", + " for i in range(1, len(prices)):\n", + " changes.append(prices[i]['close'] - prices[i-1]['close'])\n", + " \n", + " # Calculate RSI for each day\n", + " for i in range(period - 1, len(changes)):\n", + " if prices[i + 1]['datetime'] >= start_dt:\n", + " # Get the last 'period' changes\n", + " period_changes = changes[i - period + 1:i + 1]\n", + " \n", + " gains = [max(change, 0) for change in period_changes]\n", + " losses = [abs(min(change, 0)) for change in period_changes]\n", + " \n", + " avg_gain = sum(gains) / period\n", + " avg_loss = sum(losses) / period\n", + " \n", + " if avg_loss == 0:\n", + " rsi = 100.0\n", + " else:\n", + " rs = avg_gain / avg_loss\n", + " rsi = 100 - (100 / (1 + rs))\n", + " \n", + " result[\"data\"].append({\n", + " \"time\": prices[i + 1]['datetime'].strftime(\"%Y-%m-%d %H:%M:%S\"),\n", + " \"time_milliseconds\": int(prices[i + 1]['datetime'].timestamp() * 1000),\n", + " \"value\": round(float(rsi), 2)\n", + " })\n", + " \n", + " else:\n", + " return {\"error\": f\"Unsupported indicator: {indicator}. Supported: SMA, EMA, RSI\"}\n", + "\n", + " print(f\"RESPONSE FROM THE GET_TECHNICAL_INDICATORS TOOL: {result}\")\n", + " return result\n", + " \n", + " except Exception as e:\n", + " error_msg = f\"Error in get_technical_indicators: {str(e)}\"\n", + " logger.error(error_msg)\n", + " return {\"error\": error_msg}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98e2ba38", + "metadata": {}, + "outputs": [], + "source": [ + "# let's create a list of tools for the technical analyst agent\n", + "technical_tools = [get_stock_prices, get_current_stock_price, get_technical_indicators]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20a334e9", + "metadata": {}, + "outputs": [], + "source": [ + "# define the model to be used by the technical analyst agent\n", + "technical_agent_model_info: Dict = config_data['model_information']['technical_agent_model_info']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23279d12", + "metadata": {}, + "outputs": [], + "source": [ + "technical_agent = Agent(\n", + " system_prompt = technical_agent_analysis_prompt, \n", + " tools = [\n", + " current_time\n", + " ] + technical_tools,\n", + " model = BedrockModel(\n", + " model_id=technical_agent_model_info.get('model_id'), \n", + " max_tokens=technical_agent_model_info.get('max_tokens')\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79585544", + "metadata": {}, + "outputs": [], + "source": [ + "print_tools_summary(technical_agent.tool_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80b72d00", + "metadata": {}, + "outputs": [], + "source": [ + "query: str = \"\"\"\n", + "What is the current time and then based on that, fetch the stock prices for AAPL, the current price and any relevant technical indicators please.\n", + "\"\"\"\n", + "\n", + "result = technical_agent(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3dbf2669", + "metadata": {}, + "outputs": [], + "source": [ + "# Get just the text response\n", + "text_response = result.message['content'][0]['text']\n", + "print(text_response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f77f94a2", + "metadata": {}, + "outputs": [], + "source": [ + "# Get metrics if needed\n", + "metrics = result.metrics\n", + "\n", + "# Get stop reason\n", + "stop_reason = result.stop_reason\n", + "\n", + "# Get token usage\n", + "token_usage = result.metrics.accumulated_usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebbcdb42", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Metrics: {metrics}, stop reason: {stop_reason}, token usage: {token_usage}\")" + ] + }, + { + "cell_type": "markdown", + "id": "668f6365", + "metadata": {}, + "source": [ + "### Market analysis agent\n", + "---\n", + "\n", + "Lastly, let's create our market analysis agent. This agent will have access to a few tools to get some option chains and also be able to get some insider trading and news. Since this tool requires more information about insider information, we will add a human in the loop workflow for this agent. In this case, if a user has a question about something that requires or will offer sensitive data, the agent will ask the user for confirmation before using the tool and providing that response back to the user." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50aefb62", + "metadata": {}, + "outputs": [], + "source": [ + "market_analyst_agent_system_prompt: str = \"\"\"\n", + "You are a comprehensive market analysis assistant that helps users analyze options chains, insider trading data, and relevant market news.\n", + "You require the user to provide a stock ticker symbol for analysis.\n", + "If a user does not provide a ticker symbol, mention in the answer that they need to provide a ticker symbol.\n", + "\n", + "You can perform the following types of analysis. You have access to APIs and tools to call to fetch data based on what the user question is:\n", + "\n", + "1. Use the appropriate functions based on the analysis needed:\n", + " - get_options_chain for options analysis\n", + " - get_insider_trades for insider trading analysis\n", + " - get_news for market news and sentiment\n", + "\n", + "2. Options Chain Analysis:\n", + " - View available options contracts\n", + " - Filter by strike price\n", + " - Filter by option type (call/put)\n", + " - Analyze options pricing and volume\n", + "\n", + "3. Insider Trading Analysis:\n", + " - Recent insider transactions\n", + " - Transaction types (buy/sell)\n", + "\n", + "4. Market News Analysis:\n", + " - Latest relevant news\n", + " - Market sentiment\n", + " - Industry trends\n", + "\n", + "For options chain data, you can specify:\n", + "- Strike price filters\n", + "- Option type (call/put)\n", + "- Number of results to return (limit)\n", + "\n", + "For insider trades, you can specify:\n", + "- Number of transactions to analyze (limit)\n", + "\n", + "If you do not have access to the data that the user is asking for, do not make up an answer, just say that you do not know the answer. Be completely\n", + "accurate. Do not provide answers to anything but on the topic specified above.\n", + "\n", + "Always use the get option chains function first to build some foundational knowledge.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "925c4662", + "metadata": {}, + "outputs": [], + "source": [ + "!uv pip install langgraph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b290425c", + "metadata": {}, + "outputs": [], + "source": [ + "api_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "610d16d0", + "metadata": {}, + "outputs": [], + "source": [ + "# ------------------------------------------------------------------\n", + "# Generic human-approval function\n", + "# ------------------------------------------------------------------\n", + "def get_human_approval(function_name: str, parameters: dict) -> bool:\n", + " \"\"\"\n", + " Prompt the human operator via Jupyter notebook input.\n", + " Returns True if approved (human replies \"yes\"), False otherwise.\n", + " \"\"\"\n", + " banner = \"\\n\" + \"=\"*60 + \"\\n🚨 HUMAN APPROVAL REQUIRED 🚨\\n\" + \"=\"*60\n", + " body = (\n", + " f\"{banner}\\n\"\n", + " f\"Function: {function_name}\\n\\n\"\n", + " \"Parameters:\\n\"\n", + " f\"{json.dumps(parameters, indent=2)}\\n\"\n", + " f\"{'='*60}\"\n", + " )\n", + " \n", + " # Print the approval request\n", + " print(body)\n", + " # Get user input directly in Jupyter notebook\n", + " try:\n", + " answer = input(\"Do you approve this request? (yes/no): \").strip().lower()\n", + " except KeyboardInterrupt:\n", + " print(\"\\nRequest cancelled by user\")\n", + " return False\n", + " except EOFError:\n", + " print(\"\\nNo input received, denying request\")\n", + " return False\n", + " \n", + " # Normalize the answer into a boolean\n", + " approved = answer in (\"yes\", \"y\", \"approve\", \"true\", \"1\")\n", + " \n", + " if approved:\n", + " print(\"βœ… Request approved\")\n", + " else:\n", + " print(\"❌ Request denied\")\n", + " \n", + " return approved\n", + "\n", + "# -----------------------------------\n", + "# Tool: get_options_chain (no change)\n", + "# -----------------------------------\n", + "@tool\n", + "def get_options_chain(\n", + " ticker: str,\n", + " limit: int = 10,\n", + " strike_price: Optional[float] = None,\n", + " option_type: Optional[str] = None\n", + ") -> Dict:\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " if not api_key:\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + "\n", + " params = {\"ticker\": ticker, \"limit\": limit}\n", + " if strike_price is not None:\n", + " params[\"strike_price\"] = strike_price\n", + " if option_type is not None:\n", + " params[\"option_type\"] = option_type\n", + "\n", + " url = \"https://api.financialdatasets.ai/options/chain\"\n", + " try:\n", + " resp = requests.get(url, headers={\"X-API-Key\": api_key}, params=params)\n", + " print(f\"RESPONSE FROM THE GET OPTIONS CHAIN TOOL: {resp.json()}\")\n", + " return resp.json()\n", + " except Exception as e:\n", + " return {\"ticker\": ticker, \"options_chain\": [], \"error\": str(e)}\n", + "\n", + "# -------------------------------------\n", + "# Tool: get_insider_trades (human loop)\n", + "# -------------------------------------\n", + "@tool\n", + "def get_insider_trades(ticker: str, limit: int = 10) -> Dict:\n", + " params = {\"ticker\": ticker, \"limit\": limit}\n", + "\n", + " # always ask the human\n", + " if not get_human_approval(\"get_insider_trades\", params):\n", + " return {\n", + " \"error\": \"Request denied by human operator\",\n", + " \"function\": \"get_insider_trades\",\n", + " \"parameters\": params\n", + " }\n", + "\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " if not api_key:\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + "\n", + " url = f\"https://api.financialdatasets.ai/insider-transactions?ticker={ticker}&limit={limit}\"\n", + " try:\n", + " resp = requests.get(url, headers={\"X-API-Key\": api_key})\n", + " print(f\"RESPONSE FROM THE GET INSIDER TRADES TOOL: {resp.json()}\")\n", + " return resp.json()\n", + " except Exception as e:\n", + " return {\"ticker\": ticker, \"insider_transactions\": [], \"error\": str(e)}\n", + "\n", + "# ----------------------------------\n", + "# NEW Tool: get_financial_news (using Financial Datasets API)\n", + "# ----------------------------------\n", + "@tool\n", + "def get_news(\n", + " ticker: str, \n", + " limit: int = 10,\n", + " start_date: Optional[str] = None,\n", + " end_date: Optional[str] = None\n", + ") -> Dict:\n", + " \"\"\"\n", + " Get financial news for a specific ticker using Financial Datasets API.\n", + " \n", + " Args:\n", + " ticker: Stock ticker symbol (e.g., 'AAPL', 'AMZN')\n", + " limit: Number of articles to return (max 100)\n", + " start_date: Start date in YYYY-MM-DD format (optional)\n", + " end_date: End date in YYYY-MM-DD format (optional)\n", + " \"\"\"\n", + " params = {\n", + " \"ticker\": ticker, \n", + " \"limit\": limit,\n", + " \"start_date\": start_date,\n", + " \"end_date\": end_date\n", + " }\n", + "\n", + " # Remove None values from params\n", + " params = {k: v for k, v in params.items() if v is not None}\n", + "\n", + " # Ask for human approval\n", + " if not get_human_approval(\"get_news\", params):\n", + " return {\n", + " \"error\": \"Request denied by human operator\",\n", + " \"function\": \"get_financial_news\",\n", + " \"parameters\": params\n", + " }\n", + "\n", + " api_key = os.environ.get(\"FINANCIAL_DATASET_API\")\n", + " if not api_key:\n", + " return {\"error\": \"Missing FINANCIAL_DATASET_API environment variable\"}\n", + "\n", + " # Build URL with query parameters\n", + " url = \"https://api.financialdatasets.ai/news\"\n", + " \n", + " try:\n", + " resp = requests.get(\n", + " url, \n", + " headers={\"X-API-KEY\": api_key}, # Note: X-API-KEY (not X-API-Key)\n", + " params=params\n", + " )\n", + " print(f\"RESPONSE FROM THE GET FINANCIAL NEWS TOOL: {resp.json()}\")\n", + " resp.raise_for_status() # Raise an exception for bad status codes\n", + " return resp.json()\n", + " except Exception as e:\n", + " return {\"ticker\": ticker, \"news\": [], \"error\": str(e)}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a623f93", + "metadata": {}, + "outputs": [], + "source": [ + "# define the model to be used by the technical analyst agent\n", + "market_analysis_agent_info: Dict = config_data['model_information']['market_analysis_agent']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75e80306", + "metadata": {}, + "outputs": [], + "source": [ + "# create the market analysis agent\n", + "market_analysis_agent = Agent(\n", + " model = BedrockModel(\n", + " model_id=market_analysis_agent_info.get('model_id'), \n", + " temperature=market_analysis_agent_info.get('temperature'), \n", + " max_tokens=market_analysis_agent_info.get('max_tokens')\n", + " ),\n", + " system_prompt=market_analyst_agent_system_prompt, \n", + " tools=[get_options_chain, get_news, get_insider_trades]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29b44596", + "metadata": {}, + "outputs": [], + "source": [ + "query: str = \"\"\"\n", + "I want you to get some insider trading information on AAPL, and also some option chains information. If possible, could\n", + "you also search on the web for latest news and check if it matches the insider trading information just to be sure if it is\n", + "correct/coherent or not? \n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1b78f03", + "metadata": {}, + "outputs": [], + "source": [ + "# View the tools in the marketing agent\n", + "\n", + "# We ca see that there are three tools that the agent has access to - \n", + "# getting the option chains, news and insider trades\n", + "print_tools_summary(market_analysis_agent.tool_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3447aaaf", + "metadata": {}, + "outputs": [], + "source": [ + "market_analysis_agent(\"what tools do you have access to?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23ad2223", + "metadata": {}, + "outputs": [], + "source": [ + "market_analysis_agent(\"Can you get me the news for AAPL?\")" + ] + }, + { + "cell_type": "markdown", + "id": "be8c53b0", + "metadata": {}, + "source": [ + "Now we have successfully added a human in the loop pattern within our tool to review the tool call with the correct parameters and the function to use. If we want to approve that, a human can do so and move to the next step.\n", + "\n", + "### Building a multi-agentic financial system\n", + "---\n", + "\n", + "In Multi Agentic systems, it is important to consider what your use case truly aligns to. Are you looking at a use case where you have a task delegation type of a use case, where there are multiple specialized agents or human workers with tasks delegated to them, and then ultimately collating the responses to a main supervisor agent? If so then you might use the supervisor agent architecture. Take an example of the architecture below:\n", + "\n", + "![supervisor-agent-arch](img/supervisor_agent_architecture.png)\n", + "\n", + "In this architecture, we have a supervisor agent that breaks down the task and based on the result, is able to send the task or delegate it to one or more agents. However, let's say we do **not** want to completely hand off control to another agent and rather, have that agent work in a similar way as a tool, then what we can do is that while we retain the supervisor Agentic architecture, we can introduce an agent ***as a tool***. \n", + "\n", + "In this example, we will convert our three existing agents: \n", + "\n", + "- Financial agent, \n", + "- Market analyst agent and\n", + "- Technical analyst agent\n", + "\n", + "As agent tools that we will attach to a lead_analyst. This lead analyst will decide on which tool to call when and each tool (in this case agents) will have access to their own functionalities, while being tied to the lead analyst Agent.\n", + "\n", + "Strands Agents SDK provides a powerful framework for implementing the \"Agents as Tools\" pattern through its `@tool` decorator. This allows you to transform specialized agents into callable functions that can be used by an orchestrator agent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24078623", + "metadata": {}, + "outputs": [], + "source": [ + "@tool\n", + "def technical_analysis_assistant(query: str) -> str:\n", + " \"\"\"\n", + " Specialized technical analysis agent for stock charts, indicators, and trading signals.\n", + " \n", + " Use this tool for:\n", + " - Technical indicator analysis (RSI, MACD, Moving Averages, etc.)\n", + " - Chart pattern recognition\n", + " - Support and resistance levels\n", + " - Trading signals and entry/exit points\n", + " - Technical trend analysis\n", + " \n", + " Args:\n", + " query: Technical analysis question or request for specific stock analysis\n", + " \n", + " Returns:\n", + " Detailed technical analysis with indicators, signals, and recommendations\n", + " \"\"\"\n", + " try:\n", + " # Call your existing technical agent\n", + " response = technical_agent(query)\n", + " return str(response)\n", + " except Exception as e:\n", + " return f\"Error in technical analysis: {str(e)}\"\n", + "\n", + "@tool\n", + "def financial_analysis_assistant(query: str) -> str:\n", + " \"\"\"\n", + " Specialized financial analysis agent for fundamental analysis and financial metrics.\n", + " \n", + " Use this tool for:\n", + " - Financial statement analysis\n", + " - Valuation metrics (P/E, P/B, PEG ratios, etc.)\n", + " - Revenue and earnings analysis\n", + " - Financial health assessment\n", + " - Comparison with industry peers\n", + " - DCF and other valuation models\n", + " \n", + " Args:\n", + " query: Financial analysis question or request for fundamental analysis\n", + " \n", + " Returns:\n", + " Comprehensive financial analysis with metrics, ratios, and valuation insights\n", + " \"\"\"\n", + " try:\n", + " # Call your existing finance agent\n", + " response = finance_agent(query)\n", + " return str(response)\n", + " except Exception as e:\n", + " return f\"Error in financial analysis: {str(e)}\"\n", + "\n", + "@tool\n", + "def market_research_assistant(query: str) -> str:\n", + " \"\"\"\n", + " Specialized market analysis agent for news, sentiment, and market conditions.\n", + " \n", + " Use this tool for:\n", + " - Market news and recent developments\n", + " - Sentiment analysis\n", + " - Sector and industry analysis\n", + " - Market trends and conditions\n", + " - Company news and events\n", + " - Economic indicators impact\n", + " \n", + " Args:\n", + " query: Market research question or request for news/sentiment analysis\n", + " \n", + " Returns:\n", + " Market analysis with news, sentiment, and contextual market information\n", + " \"\"\"\n", + " try:\n", + " # Call your existing market analysis agent\n", + " response = market_analysis_agent(query)\n", + " return str(response)\n", + " except Exception as e:\n", + " return f\"Error in market analysis: {str(e)}\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "3aac9310", + "metadata": {}, + "source": [ + "#### Create the lead analyst agent\n", + "---\n", + "\n", + "Now that we have created the three agents with varying tools, let's create a lead analyst agent with these agents as tools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c88b79c", + "metadata": {}, + "outputs": [], + "source": [ + "LEAD_ANALYST_SYSTEM_PROMPT = \"\"\"\n", + "You are a Lead Financial Analyst that coordinates a team of specialized analysts to provide comprehensive investment analysis.\n", + "\n", + "Your team consists of:\n", + "\n", + "1. **Technical Analysis Assistant** - Use for:\n", + " - Chart analysis and technical indicators\n", + " - Trading signals and entry/exit points\n", + " - Support/resistance levels\n", + " - Technical trend analysis\n", + "\n", + "2. **Financial Analysis Assistant** - Use for:\n", + " - Fundamental analysis and valuation\n", + " - Financial statement analysis\n", + " - Financial ratios and metrics\n", + " - Company financial health\n", + "\n", + "3. **Market Research Assistant** - Use for:\n", + " - Current market news and developments\n", + " - Sentiment analysis\n", + " - Industry and sector analysis\n", + " - Market conditions and trends\n", + "\n", + "**Your Process:**\n", + "1. Analyze the user's query to determine which specialists to consult\n", + "2. Delegate appropriate aspects to the relevant assistant(s)\n", + "3. Synthesize the information from all specialists\n", + "4. Provide a comprehensive, well-structured analysis\n", + "5. Include clear recommendations based on the combined insights\n", + "\n", + "**Guidelines:**\n", + "- For comprehensive stock analysis, consult ALL three assistants\n", + "- For specific technical questions, primarily use the Technical Analysis Assistant\n", + "- For valuation questions, focus on the Financial Analysis Assistant\n", + "- For market context, use the Market Research Assistant\n", + "- Always provide a executive summary with clear recommendations\n", + "- Present information in a logical, professional format\n", + "\n", + "**Meta-Tooling Capabilities:**\n", + "You have access to THREE powerful meta-tooling capabilities via these tools:\n", + "- **editor**: Create and modify tool code files\n", + "- **load_tool**: Load newly created tools into your toolkit\n", + "- **shell**: Execute commands and test tools\n", + "\n", + "Use meta-tooling when users ask for:\n", + "- Specialized calculations not available in existing tools (e.g., \"Calculate portfolio beta\", \"Compute Black-Scholes pricing\")\n", + "- Vague requests that need custom analysis (e.g., \"Analyze this stock comprehensively\", \"What's the best investment strategy?\")\n", + "- Complex financial models (e.g., Monte Carlo simulations, custom valuation models)\n", + "- Data processing tasks (e.g., \"Compare these 10 stocks\", \"Build a sector analysis\")\n", + "\n", + "**EXAMPLE TOOLS YOU CAN CREATE:**\n", + "\n", + "1. **Portfolio Beta Calculator Tool**\n", + " - When user asks: \"What's the portfolio beta for these stocks?\"\n", + " - Create custom_financial_tool_0.py for portfolio beta calculations\n", + " - Accepts: list of tickers, weights, market ticker (default SPY)\n", + " - Returns: Individual betas, weighted portfolio beta, interpretation\n", + "\n", + "2. **Black-Scholes Option Pricing Tool** \n", + " - When user asks: \"What should this option be worth?\" or vague option questions\n", + " - Create custom_financial_tool_1.py for options pricing\n", + " - Accepts: stock_price, strike_price, time_to_expiration, risk_free_rate, volatility, option_type\n", + " - Returns: Theoretical option price, Greeks (delta, gamma, theta, vega)\n", + "\n", + "3. **Sector Performance Comparison Tool**\n", + " - When user asks: \"How do tech stocks compare?\" or broad sector questions \n", + " - Create custom_financial_tool_2.py for sector analysis\n", + " - Accepts: list of tickers, analysis_period, comparison_metrics\n", + " - Returns: Comparative performance table, rankings, sector insights\n", + "\n", + "**Meta-Tooling Process:**\n", + "1. **Identify Need**: When user asks vague questions or needs calculations not in existing tools\n", + "2. **Design Tool**: Determine what custom tool is needed\n", + "3. **Create Tool**: Use `editor` to write custom_financial_tool_X.py \n", + "4. **Load Tool**: Use `load_tool` to make it immediately available\n", + "5. **Use Tool**: Execute the newly created tool\n", + "6. **Test**: Use `shell` if needed to verify functionality\n", + "\n", + "**When to Create Custom Tools:**\n", + "- User asks vague questions like \"Analyze this comprehensively\" β†’ Create analysis framework tool\n", + "- Specialized calculations needed: \"Calculate portfolio beta\" β†’ Create portfolio beta tool \n", + "- Custom comparisons: \"Compare these 10 stocks\" β†’ Create comparison matrix tool\n", + "- Complex models: \"Run Monte Carlo on my portfolio\" β†’ Create simulation tool\n", + "\n", + "**Tool Naming:** Always use \"custom_financial_tool_X\" format where X is next available number (0, 1, 2, etc.)\n", + "\n", + "**Tool Structure Template:**\n", + "\n", + "Goal:\n", + "- Create a python tool under cwd()/tools/*.py using given python tool decorator.\n", + "- I have hot-reloading abilities, after writing the file, I can directly use them.\n", + " \n", + "Building tools:\n", + " \n", + "from strands import tool\n", + " \n", + "@tool\n", + "def name(name: str, description: str) -> str:\n", + " '''\n", + " Create a tool under cwd()/tools/*.py.\n", + " '''\n", + " return \"\"\n", + "\n", + "**Your Analysis Process:**\n", + "1. Analyze the user's query to determine approach\n", + "2. Check if existing specialists can handle the request\n", + "3. If not, determine if a custom tool is needed\n", + "4. Create custom tools when necessary\n", + "5. Delegate to appropriate assistant(s) or use custom tools\n", + "6. Synthesize information from all sources\n", + "7. Provide comprehensive analysis with clear recommendations\n", + "\n", + "**Guidelines:**\n", + "- Always try existing tools first before creating new ones\n", + "- For comprehensive analysis, consult ALL three assistants\n", + "- Create custom tools for specialized calculations or unique requirements\n", + "- Provide executive summary with clear recommendations\n", + "- Present information in a logical, professional format\n", + "\n", + "also lastly, you have access to a cron tool to schedule daily analysis for finances and getting the current stock options at 9am.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd6f966", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Going to use the {config_data['model_information']['lead_analysis_agent'].get('model_id')} for the lead analyst agent...\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29739d36", + "metadata": {}, + "outputs": [], + "source": [ + "# define the lead analyst agent\n", + "from strands_tools import load_tool, shell, editor, cron\n", + "\n", + "lead_analyst_agent = Agent(\n", + " model=config_data['model_information']['lead_analysis_agent'].get('model_id'),\n", + " system_prompt=LEAD_ANALYST_SYSTEM_PROMPT,\n", + " tools=[\n", + " # Your existing specialist agents as tools\n", + " technical_analysis_assistant,\n", + " financial_analysis_assistant, \n", + " market_research_assistant,\n", + " # Meta-tooling capabilities - KEY ADDITION!\n", + " load_tool,\n", + " shell, \n", + " editor, \n", + " # Scheduling capability - NEW ADDITION!\n", + " cron\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f737de6e", + "metadata": {}, + "outputs": [], + "source": [ + "# print the tools that the lead analyst has access to\n", + "print_tools_summary(lead_analyst_agent.tool_config)" + ] + }, + { + "cell_type": "markdown", + "id": "8a0ec8c9", + "metadata": {}, + "source": [ + "### Invoke the Multi agentic system\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37c2bf80", + "metadata": {}, + "outputs": [], + "source": [ + "query: str = \"\"\"\n", + "What is the current stock price of AAPL? Are there any particular news related to AAPL that you can provide me with?\n", + "Also how are the stocks for AAPL comparing across the years, could you provide a nice journal at the end with all of this analysis?\n", + "\n", + "Tell me all about the market research and the technical analysis you can do with AAPL as well. Can you schedule something too?\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00eb9372", + "metadata": {}, + "outputs": [], + "source": [ + "lead_analyst_agent(query)" + ] + }, + { + "cell_type": "markdown", + "id": "ac409523", + "metadata": {}, + "source": [ + "### Meta tooling\n", + "--- \n", + "\n", + "Now, let's ask it a question for which an agent will create and use a tool on the fly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5004e907", + "metadata": {}, + "outputs": [], + "source": [ + "lead_analyst_agent(\"Now, tell me about the comparison or more information about stocks for AAPL across the data that you have. Create a new tool for this.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/pyproject.toml b/02-samples/05-hedge-fund-analyst-multi-agent-system/pyproject.toml new file mode 100644 index 00000000..c3d28e77 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "financial-analysis-multi-agent-collaboration" +version = "0.1.0" +description = "This is a multi-agent collaboration system that manages a finance agent system" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "litellm", + "mem0ai", + "opensearch-py", + "boto3", + "botocore", + "watchdog", + "jsonschema", + "mcp", + "retrying", + "strands-agents", + "strands-agents-tools", + "zmq", + "langgraph>=0.4.5" +] diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/utils.py b/02-samples/05-hedge-fund-analyst-multi-agent-system/utils.py new file mode 100644 index 00000000..22f26953 --- /dev/null +++ b/02-samples/05-hedge-fund-analyst-multi-agent-system/utils.py @@ -0,0 +1,44 @@ +# This file contain utility functions that are used across the mutli agent +# solution +import re +import os +import json +import yaml +import time +import boto3 +import zipfile +import logging +from io import BytesIO +from pathlib import Path +from typing import Union, Dict, Optional + +# set a logger +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +PYTHON_TIMEOUT: int = 180 +PYTHON_RUNTIME: str = "python3.12" + +# Initialize S3 client +s3_client = boto3.client('s3') +# Initialize the bedrock runtime client. This is used to +# query search results from the FMC and Meraki KBs +bedrock_agent_runtime_client = boto3.client('bedrock-agent-runtime') + +def load_config(config_file: Union[Path, str]) -> Optional[Dict]: + """ + Load configuration from a local file. + + :param config_file: Path to the local file + :return: Dictionary with the loaded configuration + """ + try: + config_data: Optional[Dict] = None + logger.info(f"Loading config from local file system: {config_file}") + content = Path(config_file).read_text() + config_data = yaml.safe_load(content) + logger.info(f"Loaded config from local file system: {config_data}") + except Exception as e: + logger.error(f"Error loading config from local file system: {e}") + config_data = None + return config_data diff --git a/02-samples/05-hedge-fund-analyst-multi-agent-system/uv.lock b/02-samples/05-hedge-fund-analyst-multi-agent-system/uv.lock new file mode 100644 index 00000000..f811c41f --- /dev/null +++ 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100644 --- a/02-samples/README.md +++ b/02-samples/README.md @@ -7,3 +7,4 @@ | 2 | [Scrum Master Assistant](./02-scrum-master-assistant/) | An expert agile development assistant for JIRA specializing in breaking down meeting notes into actionable tasks. | | 3 | [AWS Assistant MCP](./03-aws-assistant-mcp/) | AWS-focused assistant using Model Control Protocol (MCP) | | 4 | [Startup Advisor MCP](./04-startup-advisor-mcp/) | Startup advisory agent using Model Control Protocol (MCP) | +| 5 | [Hedge Fund Multi-Agent Sytem](./05-hedge-fund-analyst-multi-agent-system/) | Multi agent system for hedge fund tasks using several agents as tools |