Economic Calendar IPO Calendar Python Complete Data Analysis Guide

Introduction

economic calendar IPO calendar Python is an essential approach for anyone who wants to track market-moving events and upcoming investment opportunities. Financial markets are heavily influenced by macroeconomic announcements such as interest rate decisions, inflation data, and employment reports. At the same time, IPO (Initial Public Offering) schedules provide insight into new companies entering the market.

In this tutorial, we will build a complete workflow using Python in Google Colab to fetch and analyze both economic calendar data and IPO calendar data from an API.

You will learn step-by-step how to:

• Connect to a financial API

• Retrieve economic event data

• Fetch upcoming IPO schedules

• Inspect and process JSON responses

• Analyze the importance of market events

This guide is perfect for beginners and developers who want to build real-world financial data pipelines.

Link API
https://rapidapi.com/user/yasimpratama88

Cell 1 — Import Library

import requests

This cell imports the requests library, which allows Python to send HTTP requests to external APIs.

Cell 2 — Setup API

API_KEY = "YOUR_RAPIDAPI_KEY_HERE"

headers = {
    "Content-Type": "application/json",
    "x-rapidapi-host": "indonesia-stock-exchange-idx.p.rapidapi.com",
    "x-rapidapi-key": API_KEY
}

This cell sets up authentication to access the API.

Cell 3 — Get Economic Calendar

url_economic = "https://indonesia-stock-exchange-idx.p.rapidapi.com/api/calendar/economic"

response_economic = requests.get(url_economic, headers=headers)
data_economic = response_economic.json()

print("Economic Status:", response_economic.status_code)

This retrieves economic event data such as:

• Inflation reports
  

• Interest rate decisions
  

• GDP announcements
  

Cell 4 — Get IPO Calendar

url_ipo = "https://indonesia-stock-exchange-idx.p.rapidapi.com/api/calendar/ipo"

response_ipo = requests.get(url_ipo, headers=headers)
data_ipo = response_ipo.json()

print("IPO Status:", response_ipo.status_code)

This fetches IPO schedules including:

• Company name

• Listing date

• Offering details
  

Cell 5 — Check Data Structure

import json

print("=== ECONOMIC DATA ===")
print(json.dumps(data_economic, indent=2))

print("\n=== IPO DATA ===")
print(json.dumps(data_ipo, indent=2))

This helps you understand the JSON structure returned by the API.

Cell 6 — Extract Data

economic_events = data_economic.get("data", [])
ipo_list = data_ipo.get("data", [])

This safely extracts the main data from the API response.

Cell 7 — Basic Metrics

print("\n=== BASIC METRICS ===")

econ_count = len(economic_events)
ipo_count = len(ipo_events)

print("Economic Events:", econ_count)
print("IPO Events     :", ipo_count)

This cell gives a quick overview of how much data is available.

Cell 8 — IPO Analysis

print("\n=== IPO ANALYSIS ===")

active_ipo = [i for i in ipo_events if i.get("ipo_active")]
active_count = len(active_ipo)

print("Active IPO:", active_count)

if active_count > 3:
    print("🔥 Many IPOs → strong market expansion")
elif active_count > 0:
    print("📊 Moderate IPO activity")
else:
    print("⚠️ No IPO → low market expansion")

This cell displays sample IPO data.

Cell 9 — Economic Analysis

print("\n=== ECONOMIC ANALYSIS ===")

if econ_count > 7:
    print("⚠️ High economic events → potential volatility")
elif econ_count > 3:
    print("📊 Moderate economic activity")
else:
    print("✅ Low economic pressure")

This shows key economic events and their impact.

Cell 10 — Combined Market Analysis

print("\n=== COMBINED MARKET ANALYSIS ===")

if econ_count > 7 and active_count > 3:
    print("🔥 HIGH ACTIVITY → volatile but many opportunities")
elif econ_count > 5:
    print("⚠️ Volatile market due to economic events")
elif active_count > 2:
    print("📈 Growth market driven by IPO activity")
else:
    print("📊 Normal market condition")

This compares economic activity vs IPO activity.

Result:

Cell 11 — Market Scoring

print("\n=== MARKET SCORING ===")

score = (econ_count * 2) + (active_count * 3)

print("Market Score:", score)

if score > 20:
    print("🔥 VERY ACTIVE MARKET")
elif score > 10:
    print("📊 MODERATE MARKET")
else:
    print("⚠️ LOW ACTIVITY")

This cell creates a simple scoring system for market activity.

Result:

Cell 12 — Final Insight

print("\n=== FINAL INSIGHT ===")

insight_found = False

if econ_count > 7:
    print("⚠️ High macro risk")
    insight_found = True

if active_count > 2:
    print("📈 IPO opportunities available")
    insight_found = True

if econ_count < 3 and active_count == 0:
    print("📊 Market is quiet → low opportunity")
    insight_found = True

if not insight_found:
    print("📊 Market is stable / normal condition")

This provides a final summary of market conditions.

Result:

Conclusion

economic calendar IPO calendar Python allows you to go beyond basic stock analysis by incorporating real-world financial events into your data workflow. By combining economic indicators and IPO schedules, you gain a broader and more strategic understanding of market behavior.

This tutorial demonstrated a complete pipeline—from API connection to structured data extraction and market analysis. With this foundation, you can expand into advanced applications such as trading signals, financial dashboards, or predictive analytics.

The most important takeaway is this: mastering how to collect and analyze financial event data gives you a strong advantage in building intelligent, data-driven financial systems.