Sectors Financial API

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### Install langchain-community

Source: https://docs.sectors.app/recipes/generative-ai-python/06-memory-ai

Install the necessary package for SQL chat message history. Recommended to do this in a virtual environment.

```bash
pip install langchain-community
```

--------------------------------

### Get Format Instructions

Source: https://docs.sectors.app/recipes/generative-ai-python/04-structured-output

Retrieve the formatting instructions from the PydanticOutputParser, which can be used in prompts to guide LLM output.

```bash
>>> parser.get_format_instructions()
'The output should be formatted as a JSON instance that conforms to the JSON schema below. As an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}\nthe object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted. Here is the output schema:\n```\n{"description": "Information about a company\'s stock", "properties": {"symbol": {"description": "The stock symbol", "title": "Symbol", "type": "string"}, "name": {"description": "The name of the company for which the stock symbol represents", "title": "Name", "type": "string"}, "sector": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "description": "The sector of the company", "title": "Sector"}, "industry": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "description": "The industry of the company", "title": "Industry"}, "market_cap": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "description": "The market capitalization of the company", "title": "Market Cap"}}, "required": ["symbol", "name"]}\n```'
```

--------------------------------

### Install necessary Python packages

Source: https://docs.sectors.app/recipes/generative-ai-python/02-tool-use

Install the required libraries for making HTTP requests and using Langchain with Groq.

```bash
pip install requests
pip install langchain
pip install langchain-groq
```

--------------------------------

### Direct Fields Query Examples

Source: https://docs.sectors.app/api-references/v2/singapore/screener/sgx-companies

Examples demonstrating how to query direct fields using standard operators.

```APIDOC
## Direct Fields Query Examples

### Description
Examples demonstrating how to query direct fields using standard operators.

### Method
GET

### Endpoint
/v2/singapore/screener

### Parameters
#### Query Parameters
- **where** (string) - Required - Filtering conditions for the screener.

### Request Example
```http
GET /v2/singapore/screener?where=market_cap > 500000000000000
GET /v2/singapore/screener?where=company_name like '%energi%'
GET /v2/singapore/screener?where=sector = 'Financials' and listing_date > '2005-01-01'
```

### Response
#### Success Response (200)
Returns a list of companies matching the query criteria.

#### Response Example
(Response structure depends on the specific query and available data)
```

--------------------------------

### Install Dependencies

Source: https://docs.sectors.app/recipes/sectors-for-ai-agents/04-human-agent

Installs the necessary Python libraries for making API requests and handling asynchronous operations.

```bash
!pip install requests nest_asyncio -q
```

--------------------------------

### Install Sectors Financial API Skill

Source: https://docs.sectors.app/recipes/generative-ai-python/01-agent-skills-guide

Use this command to install the Sectors Financial API skill from ClawHub.

```bash
openclaw skills install sectors-api
```

--------------------------------

### Install openai-agents Package

Source: https://docs.sectors.app/recipes/generative-ai-python/03-multiagent-workflows

Install the necessary package for building agent workflows.

```bash
pip install openai-agents
```

--------------------------------

### Example Log Format

Source: https://docs.sectors.app/recipes/api-security/01-securing-api-usage

This is an example of a structured log entry for API usage. Ensure sensitive information is not logged.

```text
[2023-10-15 14:23:45] INFO: GET /api/v2/company/report/<company-name> - Status: 200 - User ID: 123xx45
```

--------------------------------

### Install LangGraph

Source: https://docs.sectors.app/recipes/generative-ai-python/05-conversational

Install the LangGraph library using pip. This is required for building more complex agentic systems.

```bash
pip install langgraph
```

--------------------------------

### Install Python Packages

Source: https://docs.sectors.app/recipes/stock-investing-and-finance/02-benchmarking-idx-banking-stocks-sectors-api

Installs the required Python packages for data analysis and visualization. Run this command in your environment before proceeding.

```bash
!pip install requests pandas matplotlib numpy -q
```

--------------------------------

### Install Skill via Natural Language

Source: https://docs.sectors.app/recipes/generative-ai-python/01-agent-skills-guide

Alternatively, instruct your OpenClaw agent to install the skill using natural language.

```text
Install the sectors-api skill
```

--------------------------------

### Install Required Python Libraries

Source: https://docs.sectors.app/recipes/quick-start-in-python/01-list-all-subsectors

Install the requests, pandas, and altair libraries using pip. These are necessary for making HTTP requests, data manipulation, and visualization respectively.

```python
pip install requests pandas altair
```

--------------------------------

### Install Required Python Libraries

Source: https://docs.sectors.app/recipes/quick-start-in-python/00-your-first-request

Install the 'requests', 'pandas', and 'altair' libraries using pip. These are necessary for making HTTP requests, data manipulation, and data visualization, respectively.

```bash
pip install requests
pip install pandas
pip install altair
```

--------------------------------

### Clone Sectors Agent Skills Repository

Source: https://docs.sectors.app/recipes/generative-ai-python/01-agent-skills-guide

Clone the repository to your local machine to get started with the Sectors agent skills.

```bash
git clone https://github.com/supertypeai/sectors-agent-skills/
cd sectors-agent-skills
```

--------------------------------

### Import and Initialize ChatGroq with Structured Output

Source: https://docs.sectors.app/recipes/generative-ai-python/03-structured-output

Imports necessary libraries, loads environment variables, defines a Pydantic model for stock information, and initializes `ChatGroq` with a specified model and API key, then configures it for structured output.

```python
import os
from dotenv import load_dotenv
from typing import Optional

from pydantic import BaseModel, Field

from langchain_groq import ChatGroq

load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")

class Stock(BaseModel):
    """Information about a company's stock"""

    symbol: str = Field(description="The stock symbol")
    name: str = Field(description="The name of the company for which the stock symbol represents")
    sector: Optional[str] = Field(default=None, description="The sector of the company")
    industry: Optional[str] = Field(default=None, description="The industry of the company")
    market_cap: Optional[int] = Field(default=None, description="The market capitalization of the company")


llm = ChatGroq(
    temperature=0,
    # model_name="llama3-groq-70b-8192-tool-use-preview",
    model_name="openai/gpt-oss-120b",
    groq_api_key=GROQ_API_KEY,
)

structured_llm = llm.with_structured_output(Stock)
print(type(structured_llm))
```

--------------------------------

### Set Request Timeout in Python

Source: https://docs.sectors.app/recipes/api-security/01-securing-api-usage

Configure a timeout for requests to prevent indefinite hanging. This example sets a 5-second timeout for a GET request.

```python
try:
    response = requests.get(API_URL, headers=headers, timeout=5)  # 5 seconds timeout
    response.raise_for_status()
except requests.exceptions.Timeout:
    print("Request timed out. Try again later.")
```

--------------------------------

### Get SGX Share Buybacks

Source: https://docs.sectors.app/api-references/v2/singapore/transaction/share-buybacks

Fetches SGX share buyback records. You can filter results by date range (start and end dates) and SGX symbol.

```APIDOC
## GET /v2/sgx/buybacks/

### Description
Returns SGX share buyback records. Each row includes the purchase date, buyback type, price range, total value, total shares purchased, treasury shares after purchase, and mandate details.

<Note>SGX symbol: 3 characters (letters or digits). E.g. `D05`, `U11`, `Z74`.</Note>

<Note>Date filters: both `start` and `end` are independent and optional — omit either side to leave that bound unconstrained. Future `end` dates return 400.</Note>

### Method
GET

### Endpoint
/v2/sgx/buybacks/

### Parameters
#### Query Parameters
- **start** (string) - Optional - Start date for filtering buyback records (YYYY-MM-DD).
- **end** (string) - Optional - End date for filtering buyback records (YYYY-MM-DD).
- **symbol** (string) - Optional - SGX stock symbol (3 characters, e.g., D05).
```

--------------------------------

### Create PromptTemplate with Format Instructions

Source: https://docs.sectors.app/recipes/generative-ai-python/03-structured-output

Construct a PromptTemplate that includes the format instructions from the parser, a query variable, and uses partial variables for the instructions.

```python
from langchain_core.prompts import PromptTemplate

prompt = PromptTemplate(
    template="Answer the user query.\n{format_instructions}\n{query}\n",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)
```

--------------------------------

### Get Stock Suspensions

Source: https://docs.sectors.app/api-references/v2/indonesia/news/suspensions

Fetches a paginated list of historical stock suspensions for IDX-listed companies. Supports filtering by symbol, start date, and end date.

```APIDOC
## GET /v2/suspensions/

### Description
Returns a paginated list of historical IDX-listed stock suspensions, including the date a stock was suspended, the official reason, and a link to the IDX PDF notice. Filter by `symbol` to look up a specific company's suspension history, or by `start` / `end` to scope to a date window.

<Note>Date filters: both `start` and `end` are independent and optional — omit either side to leave that bound unconstrained. Future `end` dates return 400.</Note>

### Method
GET

### Endpoint
/v2/suspensions/

### Parameters
#### Query Parameters
- **symbol** (string) - Optional - Filter by company symbol.
- **start** (string) - Optional - Start date for the suspension date window (YYYY-MM-DD).
- **end** (string) - Optional - End date for the suspension date window (YYYY-MM-DD).

### Response
#### Success Response (200)
- **data** (array) - List of suspension records.
  - **symbol** (string) - The stock symbol.
  - **suspension_date** (string) - The date the stock was suspended (YYYY-MM-DD).
  - **reason** (string) - The official reason for the suspension.
  - **notice_url** (string) - URL to the official IDX PDF notice.
- **next_page_token** (string) - Token for paginating to the next page of results.
```

--------------------------------

### Set up LLM and Prompt for Financial Advisor

Source: https://docs.sectors.app/recipes/generative-ai-python/06-memory-ai

Initializes the LLM and defines a chat prompt template for a financial advisor. Pay attention to the variable names used for history and the user's question.

```python
import os
from dotenv import load_dotenv

from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate,
from langchain_core.runnables import RunnableWithMessageHistory
from langchain_core.chat_history import InMemoryChatMessageHistory

load_dotenv()

GROQ_API_KEY = os.getenv("GROQ_API_KEY")
llm = ChatGroq(model="openai/gpt-oss-120b")

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You're a financial stock advisor with adept knowledge of the Indonesian stock exchange (IDX) and adept at analysing, summarizing, inferring trends from financial information",
        ),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{question}"),
    ]
)

chain = prompt | llm
```

--------------------------------

### Accessing Annual Financial Data

Source: https://docs.sectors.app/api-references/v2/indonesia/screener/companies

Use bracket notation with the year to access annual financial data for companies. For example, to get the price-to-earnings ratio for 2024, use `pe[2024]`.

```text
forecast_revenue_estimate[2025]
```

```text
pe[2024]
```

```text
pb[2024]
```

```text
ps[2024]
```

```text
pcf[2024]
```

```text
peg[2024]
```

```text
enterprise_to_ebitda[2024]
```

```text
enterprise_to_revenue[2024]
```

```text
pb_peer_avg[2024]
```

```text
pe_peer_avg[2024]
```

```text
ps_peer_avg[2024]
```

```text
debt_to_asset_ratio[2024]
```

```text
debt_to_equity_ratio[2024]
```

```text
cash_flow_to_debt_ratio[2024]
```

```text
interest_coverage_ratio[2024]
```

```text
current_ratio[2024]
```

```text
operating_cash_flow_margin[2024]
```

```text
fixed_asset_turnover[2024]
```

```text
total_asset_turnover[2024]
```

```text
roa[2024]
```

```text
roe[2024]
```

```text
net_profit_margin[2024]
```

```text
gross_profit_margin[2024]
```

```text
operating_profit_margin[2024]
```

```text
capital_adequacy_ratio[2024]
```

```text
casa_ratio[2024]
```

```text
leverage_ratio[2024]
```

```text
loan_to_deposit_ratio[2024]
```

```text
liquidity_coverage_ratio[2024]
```

```text
efficiency_ratio[2024]
```

```text
net_interest_margin[2024]
```

```text
cost_to_income_ratio[2024]
```

--------------------------------

### Start Claude Code with Skill

Source: https://docs.sectors.app/recipes/generative-ai-python/01-agent-skills-guide

Launch Claude Code from the agent skills directory or explicitly reference the SKILL.md file.

```bash
claude --skill ./SKILL.md

```

--------------------------------

### Fetch Data from Sectors API

Source: https://docs.sectors.app/recipes/build-python-app/sectorscan/01-sectorscan-part1

A reusable function to make GET requests to the Sectors Financial API. Ensure you have the 'requests' library installed. Replace 'Your API Key' with your actual API key.

```python
import requests

def fetch_data(url):
  api_key = "Your API Key"

  headers = {
      "Authorization": api_key
  }

  response = requests.get(url, headers = headers)

  if response.status_code == 200:
      return response.json()
  else:
      # Handle error
      raise Exception(f"API request failed with status code {response.status_code}")
```

--------------------------------

### Get Quarterly Financials

Source: https://docs.sectors.app/api-references/v2/indonesia/report/quarterly-financials

Fetches quarterly financial data for a specified IDX symbol. The available fields may differ based on the company's sector. For example, financial-sector companies will have additional metrics.

```APIDOC
## GET /v2/indonesia/report/quarterly-financials

### Description
Returns quarterly financial data for a given IDX symbol. Fields vary by sector — financial-sector companies (banks, insurance) have additional metrics like `net_interest_income`, `gross_loan`, `total_deposit`.

### Method
GET

### Endpoint
`/v2/indonesia/report/quarterly-financials`

### Parameters
#### Query Parameters
- **symbol** (string) - Required - The IDX symbol of the company (e.g., `BMRI`, `BBCA`, `TLKM`). Optionally followed by `.jk` (case-insensitive).
- **report_date** (string) - Required - The specific report date for the quarterly financials. Use the [Quarterly Financial Dates](../helper-list/company-quarterly-dates) endpoint to get valid `report_date` values for a symbol.

### Response
#### Success Response (200)
- **symbol** (string) - The IDX symbol.
- **report_date** (string) - The date of the financial report.
- **sector_specific_metrics** (object) - Contains additional financial metrics relevant to the company's sector (e.g., `net_interest_income`, `gross_loan`, `total_deposit` for financial sector companies).
- **general_metrics** (object) - Contains standard financial metrics applicable across sectors.

### Costs
1 API credit per quarter returned.
```

--------------------------------

### Get SGX Filings

Source: https://docs.sectors.app/api-references/v2/singapore/news/sgx-filings

Retrieves a paginated list of SGX filings. You can filter the results by start and end dates, transaction type, and holder type. Pagination is supported using limit and offset parameters.

```APIDOC
## GET /v2/singapore/news/sgx-filings

### Description
Retrieves a paginated list of SGX filings. You can filter the results by start and end dates, transaction type, and holder type. Pagination is supported using limit and offset parameters.

### Method
GET

### Endpoint
/v2/singapore/news/sgx-filings

### Parameters
#### Query Parameters
- **start** (string) - Optional - Start date in `YYYY-MM-DD` format. Filters on `timestamp`.
- **end** (string) - Optional - End date in `YYYY-MM-DD` format. Future dates return 400.
- **limit** (integer) - Optional - Number of results to return. Maximum: 30. Default: 20.
- **offset** (integer) - Optional - Number of results to skip for pagination. Default: 0.
- **transaction_type** (string) - Optional - Filter by transaction type (case-insensitive). Enum: award, buy, others, sell, transfer.
- **holder_type** (string) - Optional - Filter by holder type (case-insensitive). Enum: insider, institution.

### Response
#### Success Response (200)
- **results** (array) - Array of SGX filing items.
- **pagination** (object) - Pagination details for the results.

#### Response Example
{
  "results": [
    {
      "symbol": "STI",
      "timestamp": "2023-10-27",
      "transaction_type": "buy",
      "holder_name": "DBS Bank Ltd",
      "holder_type": "institution",
      "holding_before": 1000000,
      "holding_after": 1100000,
      "amount_transaction": 100000,
      "transaction_value": 123456.78,
      "price_per_share": 1.2345678,
      "share_percentage_before": 0.1,
      "share_percentage_after": 0.11,
      "share_percentage_transaction": 0.01
    }
  ],
  "pagination": {
    "total_count": 100,
    "showing": 20,
    "limit": 20,
    "offset": 0,
    "has_next": true
  }
}

#### Error Response (429)
- **code** (integer) - Error code.
- **message** (string) - Error message.

#### Response Example
{
  "code": 429,
  "message": "Rate limit exceeded. Consider upgrading."
}
```

--------------------------------

### Setup Retriever Utility

Source: https://docs.sectors.app/recipes/generative-ai-python/05-conversational

Sets up a utility function to retrieve data from an endpoint using an API key. Handles HTTP errors and returns JSON data.

```python
import os
import json
import requests

from dotenv import load_dotenv
from typing import List

load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
SECTORS_API_KEY = os.getenv("SECTORS_API_KEY")

def retrieve_from_endpoint(url: str) -> dict:
    headers = {"Authorization": SECTORS_API_KEY}

    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        data = response.json()
    except requests.exceptions.HTTPError as err:
        raise SystemExit(err)
    return json.dumps(data)
```

--------------------------------

### Get Commodity Price

Source: https://docs.sectors.app/api-references/v2/mining/commodities-trade/commodity-price

Retrieves historical price data for a specified commodity. You can filter by start and end years. Requesting more than 3 years will result in a 400 error. This operation costs 1 API credit.

```APIDOC
## GET /mining/commodities/{commodity_name}/price

### Description
Retrieves historical price data for a specified commodity.

### Method
GET

### Endpoint
/mining/commodities/{commodity_name}/price

### Parameters
#### Path Parameters
- **commodity_name** (string) - Required - The commodity name (e.g., `Gold`, `Coal`). Get valid names from the [List Commodities](../commodities-trade/commodities) endpoint.

#### Query Parameters
- **start_year** (integer) - Optional - Start year (e.g., `2022`). Defaults to current year − 2.
- **end_year** (integer) - Optional - End year inclusive (e.g., `2024`). Defaults to current year. Maximum 3-year range from `start_year`.

### Response
#### Success Response (200)
- **name** (string) - Commodity name.
- **date** (string) - Price date (monthly).
- **price_usd_per_ton** (number) - Price in USD per metric ton.

#### Response Example
```json
[
  {
    "name": "Gold",
    "date": "2024-01-01",
    "price_usd_per_ton": 63488000
  },
  {
    "name": "Gold",
    "date": "2024-02-01",
    "price_usd_per_ton": 66244000
  }
]
```

#### Error Response (400)
- **error** (string) - Invalid year parameters or date range exceeds 3 years.

#### Error Response (404)
- **error** (string) - Commodity not found or no data in specified range.

#### Error Response (429)
- **code** (integer) - 429
- **message** (string) - Rate limit exceeded. Consider upgrading.
```

--------------------------------

### Initialize LLM and Agent Executor for Tool Use

Source: https://docs.sectors.app/recipes/generative-ai-python/02-tool-use

Sets up the ChatPromptTemplate, ChatGroq LLM, and AgentExecutor for handling tool-based queries. Note: The model 'llama3-groq-70b-8192-tool-use-preview' is deprecated and replaced with 'openai/gpt-oss-120b'. Ensure GROQ_API_KEY is set in your environment.

```python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_groq import ChatGroq
from langchain_classic.agents import create_tool_calling_agent, AgentExecutor

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            """Answer the following queries, being as factual and analytical 
            as you can. If you need the start and end dates but they are not 
            explicitly provided, infer from the query. Whenever you return a 
            list of names, return also the corresponding values for each name. 
            If the volume was about a single day, the start and end 
            parameter should be the same."""
        ),
        ("human", "{input}"),
        # msg containing previous agent tool invocations 
        # and corresponding tool outputs
        MessagesPlaceholder("agent_scratchpad"),
    ]
)

llm = ChatGroq(
    temperature=0,
    # model_name="llama3-groq-70b-8192-tool-use-preview",
    model_name="openai/gpt-oss-120b",
    groq_api_key=GROQ_API_KEY,
)

# agent = create_tool_calling_agent(llm, tools, prompt)

agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
```

--------------------------------

### Get SGX Share Buybacks

Source: https://docs.sectors.app/api-references/v2/singapore/transaction/share-buybacks

Fetches SGX share buyback records. You can filter by a date range using `start` and `end` query parameters. Both parameters are optional. Providing a future `end` date will result in an error.

```APIDOC
## GET /singapore/transaction/share-buybacks

### Description
Returns SGX share buyback records. Each row includes the purchase date, buyback type, price range, total value, total shares purchased, treasury shares after purchase, and mandate details.

### Method
GET

### Endpoint
`/singapore/transaction/share-buybacks`

### Query Parameters
- **start** (string) - Optional - The start date for filtering buyback records (YYYY-MM-DD).
- **end** (string) - Optional - The end date for filtering buyback records (YYYY-MM-DD).

### Request Example
```json
{
  "example": "/singapore/transaction/share-buybacks?start=2023-01-01&end=2023-12-31"
}
```

### Response
#### Success Response (200)
- **purchase_date** (string) - The date of the share purchase.
- **buyback_type** (string) - The type of buyback.
- **price_range** (string) - The price range of the shares bought back.
- **total_value** (number) - The total value of the shares bought back.
- **total_shares_purchased** (integer) - The total number of shares purchased.
- **treasury_shares_after_purchase** (integer) - The number of treasury shares after the purchase.
- **mandate_details** (string) - Details about the buyback mandate.

#### Response Example
```json
{
  "example": [
    {
      "purchase_date": "2023-10-26",
      "buyback_type": "Market Purchase",
      "price_range": "3.10 - 3.12",
      "total_value": 15600.00,
      "total_shares_purchased": 5000,
      "treasury_shares_after_purchase": 100000,
      "mandate_details": "Mandate A"
    }
  ]
}
```
```

--------------------------------

### Detailed Chat Prompt Template

Source: https://docs.sectors.app/recipes/generative-ai-python/02-tool-use

A Python example demonstrating a detailed ChatPromptTemplate for an LLM. It includes system instructions, human input, and a placeholder for agent scratchpad, with the current date dynamically inserted into the system prompt.

```python
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            ""
            Answer the following queries, being as factual and analytical as you can. 
            If you need the start and end dates but they are not explicitly provided, 
            infer from the query. Whenever you return a list of names, return also the 
            corresponding values for each name. If the volume was about a single day, 
            the start and end parameter should be the same. Note that the endpoint for 
            performance since IPO has only one required parameter, which is the stock. 
            Today's date is 
            "
            + get_today_date(),
        ),
        ("human", "{input}"),
        # msg containing previous agent tool invocations and corresponding tool outputs
        MessagesPlaceholder("agent_scratchpad"),
    ]
)
```

--------------------------------

### Get Top Companies by Transaction Volume

Source: https://docs.sectors.app/recipes/generative-ai-python/02-tool-use

This snippet shows an AI agent invoking a tool to retrieve the top companies by transaction volume within a specified date range. It requires start date, end date, and the number of top companies (top_n) as parameters.

```sh
> Entering new AgentExecutor chain...
Invoking: `get_top_companies_by_tx_volume` with `{'start_date': '2024-07-24', 'end_date': '2024-07-31', 'top_n': 3}`


{"2024-07-24": [{"symbol": "GOTO.JK", "company_name": "PT GoTo Gojek Tokopedia Tbk", "volume": 2660984600, 
"price": 54}, {"symbol": "BSBK.JK", "company_name": "PT Wulandari Bangun Laksana Tbk", "volume": 1833364900, 
"price": 83}, {"symbol": "DOOH.JK", "company_name": "PT Era Media Sejahtera Tbk", "volume": 511754100, 
"price": 53}], "2024-07-25": [{"symbol": "BSBK.JK", "company_name": "PT Wulandari Bangun Laksana Tbk", 
"volume": 1513766400, "price": 76}, {"symbol": "GOTO.JK", "company_name": "PT GoTo Gojek Tokopedia Tbk", 
"volume": 911091700, "price": 54}, {"symbol": "BBKP.JK", "company_name": "PT Bank KB Bukopin Tbk", 
"volume": 432596400, "price": 56}], "2024-07-26": [{"symbol": "BSBK.JK", "company_name": "PT Wulandari Bangun Laksana Tbk", 
"volume": 2408506000, "price": 72}, {"symbol": "GOTO.JK", "company_name": "PT GoTo Gojek Tokopedia Tbk", 
"volume": 913457100, "price": 53}, {"symbol": "MSJA.JK", "company_name": "PT Multi Spunindo Jaya Tbk.", 
"volume": 450977300, "price": 342}], "2024-07-29": [{"symbol": "GOTO.JK", "company_name": "PT GoTo Gojek Tokopedia Tbk", 
"volume": 1621366300, "price": 54}, {"symbol": "ATLA.JK", "company_name": "PT Atlantis Subsea Indonesia Tbk", 
"volume": 1255103900, "price": 50}, {"symbol": "BSBK.JK", "company_name": "PT Wulandari Bangun Laksana Tbk", 
"volume": 754254000, "price": 69}], "2024-07-30": [{"symbol": "GOTO.JK", "company_name": "PT GoTo Gojek Tokopedia Tbk", 
"volume": 1236811400, "price": 55}, {"symbol": "BSBK.JK", "company_name": "PT Wulandari Bangun Laksana Tbk", 
"volume": 797879400, "price": 69}, {"symbol": "BIPI.JK", "company_name": "PT Astrindo Nusantara Infrastruktur Tbk.", 
"volume": 629061500, "price": 59}]}

> Finished chain.

Answer: 
The top 3 companies by transaction volume over the last 7 days are PT GoTo Gojek Tokopedia Tbk, 
PT Wulandari Bangun Laksana Tbk, and PT Era Media Sejahtera Tbk.
```

--------------------------------

### Get Daily Stock Transaction Data

Source: https://docs.sectors.app/recipes/generative-ai-python/02-tool-use

This snippet shows an AI agent invoking a tool to retrieve daily stock transaction data for a specific stock symbol within a given date range. It requires the stock symbol, start date, and end date as arguments.

```sh
> Entering new AgentExecutor chain...

To determine if we are seeing an uptrend or downtrend in BBCA\'s closing prices between June 1st and June 30th, 2024, we need 
to analyze the closing prices over this period. 

First, let\'s get the daily transaction data for BBCA for the specified period:

```json
{
  "name": "get_daily_tx",
  "arguments": {
    "stock": "BBCA",
    "start_date": "2024-06-01",
    "end_date": "2024-06-30"
  }
}
```

> Finished chain.
Answer: 

After analyzing the data, we can see that the closing prices have been increasing over the period, with the 
closing price on June 30th being higher than on June 1st. This indicates an uptrend in BBCA\'s closing prices 
during June 2024.

The uptrend could be due to various factors such as positive news or events related to the company, an increase 
in investor confidence, or a general upward trend in the market.
```

--------------------------------

### Successful Response Example (200 OK)

Source: https://docs.sectors.app/api-references/news/news

Example of a successful response returning a list of news articles. This includes article details like ID, creation date, title, body, source, and financial dimensions.

```json
[
    {
        "id": 5642,
        "created_at": "2025-06-28T04:21:45.467814Z",
        "title": "Bank Indonesia Records 43.77% Increase in Financial Surplus in 2024",
        "body": "Bank Indonesia reported a financial surplus of IDR 52.19 trillion in 2024, a 43.77% increase from IDR 36.3 trillion in 2023, driven by high revenue of IDR 228.66 trillion and expenses of IDR 161.3 trillion.",
        "source": "https://insight.kontan.co.id/news/surplus-bank-indonesia-naik-berkat-kebijakan-moneter",
        "timestamp": "2025-06-28T04:35:00Z",
        "sector": "financials",
        "sub_sector": [
            "banks"
        ],
        "tags": [
            "Bank Indonesia",
            "Financial Ratios",
            "Capital Allocation",
            "Profit",
            "Revenue",
            "Bullish"
        ],
        "tickers": [],
        "dimension": {
            "future": 0,
            "dividend": 0,
            "ownership": 0,
            "technical": 0,
            "valuation": 0,
            "financials": 2,
            "management": 0,
            "sustainability": 0
        }
    },
    {
        "id": 5646,
        "created_at": "2025-06-28T04:32:52.825906Z",
        "title": "Indonesian Banks Host Large Events to Attract New Customers and Boost Digital Transactions",
        "body": "Several national banks in Indonesia are hosting large events, such as concerts and marathons, to attract new customers and increase digital transactions. Bank Tabungan Negara (BTN) is expecting a 15-20% increase in transactions and digital service activations through its Jakarta International Marathon event.",
        "source": "https://insight.kontan.co.id/news/bank-menjaring-nasabah-lewat-event-lari-hingga-konser-musik",
        "timestamp": "2025-06-27T06:30:00Z",
        "sector": "financials",
        "sub_sector": [
            "banks"
        ],
        "tags": [
            "Bank Indonesia",
            "Digital Payments",
            "Financial Ratios",
            "Loan Growth",
            "Bullish"
        ],
        "tickers": [
            "BTPN.JK"
        ],
        "dimension": {
            "future": 2,
            "dividend": 0,
            "ownership": 0,
            "technical": 0,
            "valuation": 0,
            "financials": 1,
            "management": 0,
            "sustainability": 0
        }
    },
    ...
  ]
```

--------------------------------

### Get IDX Historical Market Cap Data

Source: https://docs.sectors.app/api-references/transaction/idx-mc

Fetches historical market cap data for the IDX within a specified date range (up to 90 days). Supports custom start and end dates, with default values provided if not specified. Note: This is a v1 endpoint and is discontinued.

```APIDOC
## GET /v1/idx/market-cap

### Description
Retrieves historical market cap data for the IDX (Indonesia Stock Exchange) for a specified interval, with a maximum of 90 days of data.

### Method
GET

### Endpoint
/v1/idx/market-cap

### Parameters
#### Header Parameters
- **Authorization** (string) - Required - Authorization header that should be filled with your Sectors Financial API key.

#### Query Parameters
- **start** (string) - Optional - Start date for which the IDX historical market cap data is to be retrieved. Format: YYYY-MM-DD. Defaults to 30 days before the `end` date, or 30 days before today if `end` is not provided. Must be no earlier than 90 days before the `end` date.
- **end** (string) - Optional - End date for which the IDX historical market cap data is to be retrieved. Format: YYYY-MM-DD. Defaults to today. Must be no later than 90 days after the `start` date.

### Request Example
```json
{
  "example": ""
}
```

### Response
#### Success Response (200)
- **payload** (IDXMarketCapData[]) - An array of IDX market cap data objects.
  - **date** (string) - The date of the total IDX market capitalization data.
  - **idx_total_market_cap** (integer) - The total IDX market capitalization on the given date in IDR.

#### Response Example
```json
[
  {
    "date": "2024-01-02",
    "idx_total_market_cap": 11741696124656268
  },
  {
    "date": "2024-01-03",
    "idx_total_market_cap": 11682715125038308
  }
]
```

#### Error Responses
- **400 Bad Request**
  - `{"error": "Use a valid date format of YYYY-MM-DD."}`
  - `{"error": "Starting date must not be earlier than January 1, 2021."}`
  - `{"error": "Start date must not be later than end date."}`
  - `{"error": "Dates cannot be in the future. Today's date is 2025-04-28."}`
- **429 Too Many Requests**
  - `{"code": 429, "message": "Rate limit exceeded. Consider upgrading."}`
```

--------------------------------

### Get Daily Transaction Data

Source: https://docs.sectors.app/api-references/transaction/daily-transaction

Fetches daily transaction data for a specific stock ticker within a defined date range. The endpoint supports filtering by start and end dates, with a default range of 30 days if not specified. It's important to note that this v1 endpoint is discontinued and will return a 410 Gone status.

```APIDOC
## GET /v1/transaction/daily

### Description
Retrieves daily transaction data for a given ticker within a specified date range (up to 90 days).

### Method
GET

### Endpoint
`/v1/transaction/daily`

### Parameters
#### Header Parameters
- **Authorization** (string) - Required - Authorization header that should be filled with your Sectors Financial API key.

#### Path Parameters
- **ticker** (string) - Required - Ticker of the company for which the daily transaction data is to be retrieved. Examples: `ASII`, `BBCA`, `BBRI`. Format: 4 letters, optionally followed by '.jk', case-insensitive.

#### Query Parameters
- **start** (string) - Optional - Start date for the data retrieval in `YYYY-MM-DD` format. Must be no earlier than 90 days before the `end` date. Defaults to 30 days before `end` date or 30 days before today if `end` is not provided.
- **end** (string) - Optional - End date for the data retrieval in `YYYY-MM-DD` format. Must be no later than 90 days after the `start` date. Defaults to 30 days after `start` date or today if `start` is not provided.

### Response
#### Success Response (200)
- **payload** (DailyTransactionData[]) - An array of daily transaction data objects.
  - **DailyTransactionData.symbol** (string) - The ticker of the company.
  - **DailyTransactionData.date** (string) - The date of the transaction.
  - **DailyTransactionData.close** (integer) - The closing price of the given ticker in IDR.
  - **DailyTransactionData.volume** (integer) - The transaction volume of the given ticker.
  - **DailyTransactionData.market_cap** (integer) - The market capitalization of the given ticker in IDR.

#### Error Response (400)
- **error** (string) - Possible error messages include: "Given stock ticker does not exist in the requested date.", "Please provide a valid stock ticker.", "Invalid query parameters: <Bad Param>.", "Use a valid date format of YYYY-MM-DD."

#### Error Response (429)
- **code** (integer) - 429
- **message** (string) - Rate limit exceeded. Consider upgrading.

### Response Example (200)
```json
[
    {
        "symbol": "GOTO.JK",
        "date": "2024-05-21",
        "close": 0,
        "volume": 0,
        "market_cap": 0
    }
]
```

### Response Example (400)
```json
{
  "error": "Given stock ticker does not exist in the requested date."
}
```

### Response Example (429)
```json
{
  "code": 429,
  "message": "Rate limit exceeded. Consider upgrading."
}
```
```

--------------------------------

### Setting up LLMChain with ConversationBufferMemory

Source: https://docs.sectors.app/recipes/generative-ai-python/06-memory-ai

This snippet shows how to initialize an LLMChain with a ChatGroq model, a PromptTemplate, and ConversationBufferMemory to enable conversational context.

```python
from langchain_classic.memory import ConversationBufferMemory
from langchain_groq import ChatGroq
from langchain_core.prompts.prompt import PromptTemplate
from langchain_classic.chains import LLMChain

load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")

llm = ChatGroq(model="openai/gpt-oss-120b")

template = """The following is a friendly conversation between a human and a helpful AI assistant. 
    If the AI does not know the answer to a question, it truthfully says it does not know.
    Current conversation:
    {history}
    Human: {input}
    AI Assistant:"""


prompt = PromptTemplate(input_variables=["history", "input"], template=template)

conversation = LLMChain(
    llm=llm, 
    prompt=prompt, 
    verbose=True, 
    memory=ConversationBufferMemory(memory_key="history")
)
```

--------------------------------

### Handle Diverse Outputs: Stocks and Conversational Responses

Source: https://docs.sectors.app/recipes/generative-ai-python/04-structured-output

Demonstrates invoking the structured output chain with different types of prompts. The first example generates fictional stock data, while the second handles a simple conversational query, showcasing the flexibility of the `FinalResponse` schema.

```python
create = "Create two fictional companies that contain the word 'Super' in their name and operating in the technology sector. Make up the other details about the companies."
generated = runnable.invoke(create)
print(generated.output)
# Stocks(stocks=[Stock(symbol='SPR', name='SuperTech Innovations', sector='Technology', industry='Technology', market_cap=500000000), Stock(symbol='SUP', name='SuperNet Solutions', sector='Technology', industry='Technology', market_cap=750000000)])

question = "how are you this evening?"
generic = runnable.invoke(question)
print(generic.output)
# response="I'm doing well, thank you for asking. How can I assist you further this evening?"
```

--------------------------------

### Create Prompt and Chain

Source: https://docs.sectors.app/recipes/generative-ai-python/04-structured-output

Construct a prompt using PromptTemplate, incorporating the format instructions, and chain it with an LLM and the PydanticOutputParser for structured output generation.

```python
prompt = PromptTemplate(
    template="Answer the user query.\n{format_instructions}\n{query}\n",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

runnable = prompt | llm | parser
```
Tokens:
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