Understanding market participant behavior is an important aspect of stock market analysis. Price movement alone does not always explain the underlying activity because institutional transactions, broker concentration, and accumulation patterns can provide additional information regarding market sentiment.
This project develops an IDX Broker Smart Money Analyzer using Indonesia Stock Exchange API data to analyze broker activity and identify potential smart money behavior. The system integrates several analytical components:
Global market overview analysis
Sector and subsector company mapping
Broker activity detection
Broker accumulation analysis
The objective of this project is to transform IDX market data into a structured report that helps evaluate transaction activity, broker dominance, buyer and seller composition, and accumulation conditions.
The system is implemented using Python with API integration and Pandas for data processing. The analysis uses IDX RapidAPI endpoints to retrieve market information and convert raw responses into an analytical summary.
The first stage prepares the analysis environment by importing required libraries and configuring the IDX RapidAPI connection. The system defines API authentication parameters including API host, API key, request headers, and base URL for data retrieval.
Cell 1 — Library and API Configuration
import requests
import pandas as pd
import json
API_HOST = "indonesia-stock-exchange-idx.p.rapidapi.com"
API_KEY = "YOUR_API_KEY"
headers = {
"Content-Type": "application/json",
"x-rapidapi-host": API_HOST,
"x-rapidapi-key": API_KEY
}
BASE_URL = "https://indonesia-stock-exchange-idx.p.rapidapi.com"
print("API Ready")This stage establishes the API connection required for collecting stock market information.
The second stage retrieves global market overview data. The system accesses the IDX market overview endpoint to obtain general market information that can provide broader context before performing stock-level analysis.
Cell 2 — Global Market Overview
url = f"{BASE_URL}/api/global/market-overview"
response = requests.get(
url,
headers=headers
)
market_data = response.json()
market_dataThis module provides an overview of current market conditions from IDX API data.
The third stage performs sector and subsector company analysis. The system retrieves company information based on predefined sector and subsector identifiers. This process helps identify companies belonging to specific market classifications.
Cell 3 — Company Sector and Subsector Analysis
sector_id = 7
subsector_id = 19
url = (
f"{BASE_URL}/api/sectors/"
f"{sector_id}/subsectors/"
f"{subsector_id}/companies"
)
response = requests.get(
url,
headers=headers
)
company_data = response.json()
company_dataThis module maps companies based on sector classification to support market segmentation analysis.
The fourth stage analyzes broker activity using IDX broker activity data. The system retrieves broker transactions using parameters including investor type, transaction type, date range, market board, and transaction limit.
The analysis focuses on broker-level trading behavior to identify transaction concentration and market participant activity.
Cell 4 — Broker Activity Detector
broker = "DH"
params = {
"from": "2026-01-02",
"to": "2026-01-02",
"investorType": "INVESTOR_TYPE_ALL",
"transactionType": "TRANSACTION_TYPE_NET",
"page": 1,
"limit": 50,
"marketBoard": "MARKET_BOARD_REGULER"
}
url = (
f"{BASE_URL}/api/market-detector/"
f"broker-activity/{broker}"
)
response = requests.get(
url,
headers=headers,
params=params
)
broker_data = response.json()
broker_dataThis module collects broker transaction data that becomes the foundation for smart money analysis.
The final stage creates the Broker Smart Money Report. The system extracts two main datasets:
Bandar detector information
Broker summary information
The report summarizes important indicators including:
Total transaction value
Transaction volume
Number of buyers
Number of sellers
Top 1 broker dominance
Top 3 broker dominance
Top 5 broker dominance
Accumulation status
Cell 5 — Broker Smart Money Report
bandar = broker_data["data"]["data"]["bandar_detector"]
summary = broker_data["data"]["data"]["broker_summary"]
report = pd.DataFrame({
"Parameter": [
"Total Nilai Transaksi",
"Volume Transaksi",
"Jumlah Buyer",
"Jumlah Seller",
"Dominasi Top 1 Broker",
"Dominasi Top 3 Broker",
"Dominasi Top 5 Broker",
"Status Akumulasi"
],
"Hasil": [
f"Rp {bandar['value']:,}",
f"{bandar['volume']:,} lot",
bandar["total_buyer"],
bandar["total_seller"],
bandar["top1"]["accdist"],
bandar["top3"]["accdist"],
bandar["top5"]["accdist"],
bandar["avg"]["accdist"]
]
})
reportThis final report converts broker activity data into a simplified smart money monitoring dashboard.
Conclusion
This project successfully developed an IDX Broker Smart Money Analyzer using IDX API integration to evaluate market participant activity through broker transaction analysis.
The system combines global market overview, sector company information, broker activity detection, and broker accumulation analysis into a structured analytical workflow.
The broker activity module provides insight into transaction value, trading volume, buyer and seller composition, and broker concentration. The accumulation analysis helps identify whether broker activity indicates accumulation or distribution patterns.
By transforming complex IDX API responses into a simplified report, the system improves the efficiency of monitoring broker behavior and understanding potential smart money movement.
However, broker activity analysis should not be used as a standalone investment decision tool. Additional evaluation using company fundamentals, valuation analysis, technical indicators, and broader market conditions remains necessary.
Overall, the IDX Broker Smart Money Analyzer demonstrates how API-based financial analytics can support systematic stock market research and provide deeper insight into institutional trading behavior.