This project develops an automated IDX market intelligence system using Python and RapidAPI. The system combines three important market analysis components:

1. Subsector Analysis

2. Broker Activity Monitoring

3. Pump and Dump Detection

The objective of this project is to build a simple framework for monitoring market structure, tracking broker transaction activity, and identifying potential abnormal price movements in Indonesian stocks.

The project uses Python libraries such as requests for API communication, pandas for data processing, and JSON handling for transforming API responses into structured datasets.

The complete workflow is organized into five cells, starting from API configuration, data retrieval, pump-dump analysis, and finally generating a market intelligence dashboard.

Cell 1 - Setup Library and API Configuration

# =====================================
# CELL 1 : SETUP
# =====================================

import requests
import pandas as pd
import json
from datetime import datetime


API_KEY = "YOUR_API_KEY"

HOST = "indonesia-stock-exchange-idx.p.rapidapi.com"


HEADERS = {
    "Content-Type": "application/json",
    "x-rapidapi-host": HOST,
    "x-rapidapi-key": API_KEY
}



def call_api(url, params=None):

    try:

        response = requests.get(
            url,
            headers=HEADERS,
            params=params,
            timeout=30
        )


        print("Status :", response.status_code)


        try:
            return response.json()

        except:

            print(response.text)
            return None


    except Exception as e:

        print("ERROR :",e)

        return None

Explanation

Cell 1 prepares the Python environment and creates the basic API request function.

The program imports:

• requests for sending HTTP requests.
  

• pandas for data processing.
  

• json for handling JSON responses.
  

• datetime for recording analysis time.
  

The call_api() function acts as the main communication layer between Python and the IDX API. It sends GET requests with configured headers, checks the response status, and converts valid responses into JSON format.

Error handling is included to prevent the program from stopping when API requests fail or when the returned response is not valid JSON.

Cell 2 - Get Subsector Information

# =====================================
# CELL 2 : SUBSECTORS
# =====================================


sector_id = 7


subsector_url = (
    f"https://{HOST}/api/sectors/{sector_id}/subsectors"
)



subsector_json = call_api(
    subsector_url
)



if subsector_json:


    subsector_data = (
        subsector_json.get("data")
        if isinstance(subsector_json,dict)
        else subsector_json
    )


    subsector_df = pd.DataFrame(
        subsector_data
    )


    print(
        "Jumlah Subsector:",
        len(subsector_df)
    )


    display(
        subsector_df.head()
    )

Explanation

Cell 2 retrieves subsector information from a selected IDX sector.

The selected sector is defined using:

sector_id = 7

The program then accesses the endpoint:

/api/sectors/{sector_id}/subsectors

to retrieve the list of subsectors associated with that sector.

The returned JSON response is converted into a pandas DataFrame:

subsector_df

which allows further analysis and filtering.

Subsector analysis provides a more detailed market view compared with analyzing only broad sectors. Investors can identify which industries contribute to a sector's overall movement.

Cell 3 - Broker Activity Analysis

# =====================================
# CELL 3 : BROKER ACTIVITY
# =====================================


broker_code = "DH"


broker_url = (
"https://indonesia-stock-exchange-idx.p.rapidapi.com"
f"/api/market-detector/broker-activity/{broker_code}"
)



params = {

    "limit":50,
    "marketBoard":"MARKET_BOARD_ALL",
    "page":1,
    "investorType":"INVESTOR_TYPE_ALL",
    "from":"2026-01-02",
    "to":"2026-01-02",
    "transactionType":"TRANSACTION_TYPE_NET"

}



broker_json = call_api(
    broker_url,
    params
)



if broker_json:


    broker_data = (
        broker_json.get("data")
        if isinstance(broker_json,dict)
        else broker_json
    )


    broker_df = pd.DataFrame(
        broker_data
    )


    print(
        "Jumlah Data Broker:",
        len(broker_df)
    )


    display(
        broker_df.head()
    )

Explanation

Cell 3 analyzes broker transaction activity.

The selected broker is:

broker_code = "DH"

The API endpoint retrieves broker activity data:

/api/market-detector/broker-activity/{broker_code}

with several parameters:

• Market board: all boards

• Investor type: all investors

• Transaction type: net transaction

• Date range analysis
  

The response is converted into:

broker_df

which contains broker trading activity data.

Broker activity analysis is useful for monitoring transaction concentration and identifying potential accumulation or distribution behavior from specific market participants.

Cell 4 - Pump and Dump Detection

# =====================================
# CELL 4 : PUMP DUMP DETECTION
# =====================================


symbol = "BUMI"

days = 14


pumpdump_url = (
    "https://indonesia-stock-exchange-idx.p.rapidapi.com"
    f"/api/analysis/bandar/pump-dump/{symbol}"
)



params = {
    "days": days
}



pumpdump_json = call_api(
    pumpdump_url,
    params
)



if pumpdump_json:


    print(
        json.dumps(
            pumpdump_json,
            indent=2
        )
    )


    if pumpdump_json.get("success"):


        data = pumpdump_json["data"]


        pumpdump_df = pd.DataFrame(
            [{
                "Symbol":
                    data.get("symbol"),

                "Company":
                    data.get("company_name"),

                "Analysis Date":
                    data.get("analysis_date"),

                "Risk Score":
                    data.get("risk_score"),

                "Status":
                    data.get("status"),

                "Recommendation":
                    data.get("recommendation"),

                "Safe Entry Price":
                    data.get("safe_entry_price"),

                "Confidence":
                    data.get("confidence")
            }]
        )


        display(
            pumpdump_df
        )

Explanation

Cell 4 performs pump and dump detection analysis.

The analyzed stock is:

symbol = "BUMI"

with an analysis period:

days = 14

The program accesses:

/api/analysis/bandar/pump-dump/{symbol}

to retrieve risk analysis related to abnormal price movements.

The returned data contains several important metrics:

• Symbol

• Company name

• Analysis date

• Risk score

• Status

• Recommendation

• Safe entry price

• Confidence level
  

The system also extracts additional information:

Pump Indicators

pump_indicators

which evaluates possible price movement signals.

Historical Pattern

historical_pattern

which provides historical behavior related to similar events.

Warning Information

The program displays warnings returned by the API to highlight possible risks.

Cell 5 - Market Intelligence Dashboard

# =====================================
# CELL 5 : MARKET INTELLIGENCE DASHBOARD
# =====================================


dashboard = {}


dashboard["Analysis Time"] = datetime.now().strftime(
    "%Y-%m-%d %H:%M:%S"
)



if "subsector_df" in globals():

    dashboard["Total Subsector"] = len(
        subsector_df
    )

else:

    dashboard["Total Subsector"] = 0



if "broker_df" in globals():

    dashboard["Broker Activity Data"] = len(
        broker_df
    )

else:

    dashboard["Broker Activity Data"] = 0



if "data" in globals():

    dashboard["Symbol"] = data.get(
        "symbol"
    )

    dashboard["Status"] = data.get(
        "status"
    )

    dashboard["Risk Score"] = data.get(
        "risk_score"
    )

    dashboard["Recommendation"] = data.get(
        "recommendation"
    )

    dashboard["Confidence"] = data.get(
        "confidence"
    )

    dashboard["Safe Entry"] = data.get(
        "safe_entry_price"
    )


dashboard_df = pd.DataFrame(
    dashboard.items(),
    columns=[
        "Metric",
        "Value"
    ]
)


display(
    dashboard_df
)

Explanation

Cell 5 combines all previous analysis results into a simple market intelligence dashboard.

The dashboard summarizes:

• Analysis time

• Total subsector records

• Broker activity data count

• Stock symbol

• Risk score

• Recommendation

• Confidence level

• Safe entry price
  

The program also creates a simple market signal based on pump-dump risk:

• Low risk / watchlist

• Monitor

• High risk
  

The signal is generated using the API status and risk score values.

Conclusion

This project demonstrates how Python and RapidAPI can be used to build an automated IDX market intelligence system.

The system integrates:

1. Subsector Analysis
   
   To understand detailed industry classification.
   

2. Broker Activity Monitoring
   
   To observe transaction behavior from selected brokers.
   

3. Pump and Dump Detection
   
   To identify potential abnormal market movements and risk conditions.
   

The dashboard provides a structured approach for monitoring market activity and generating early risk signals. However, the output should not be interpreted as a direct trading recommendation. Additional evaluation using fundamental analysis, technical analysis, liquidity conditions, and broader market factors remains necessary before making investment decisions.