INTRODUCTION

Modern financial markets generate massive amounts of real-time data every second. Traders, analysts, and fintech developers increasingly rely on APIs to access structured market information that can be processed automatically for market intelligence and trading analysis.

Among the most valuable datasets in stock market analytics are orderbook data and seasonality analysis. Orderbook information helps analysts understand market liquidity, buy and sell pressure, and trading activity, while seasonality analysis reveals recurring market patterns and historical behavioral trends.

In this project, we build a complete financial analytics workflow using Orderbook and Seasonality API integration with Python. The notebook demonstrates how to:

• Connect to financial APIs using RapidAPI

• Retrieve real-time orderbook information

• Analyze seasonality market patterns

• Normalize JSON responses into DataFrames

• Clean and validate financial datasets

• Convert numeric values for analysis

• Generate visual market insights

• Build market intelligence summaries

This article follows the exact notebook structure and explains every code cell without modifying the original implementation.

CELL 1 — Import Required Libraries

import requests
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Visualization settings
sns.set_style("whitegrid")
plt.rcParams["figure.figsize"] = (12,6)

print("Libraries imported successfully.")

Explanation

This cell imports all required libraries used throughout the analytics workflow.

Each library serves an important role:

• requests handles API communication.

• pandas processes structured market datasets.

• matplotlib creates financial visualizations.

• seaborn improves chart readability and styling.
  

The notebook also applies visualization settings to standardize chart appearance and improve analytical readability.

CELL 2 — API Configuration

RAPIDAPI_KEY = "YOUR_API_KEY"

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

print("API configuration completed.")

Explanation

This section configures API authentication and request headers.

The API configuration includes:

• Authentication credentials

• Content type definition

• API host identification
  

Using centralized configuration variables improves scalability and simplifies maintenance in larger financial analytics systems.

CELL 3 — Fetch Orderbook Data

response_orderbook = requests.get(
    URL_ORDERBOOK,
    headers=headers
)

print("Status Code:", response_orderbook.status_code)

orderbook_data = response_orderbook.json()

orderbook_data

Explanation

This cell retrieves real-time orderbook information from the API.

The workflow performs several important operations:

1. Send an HTTP GET request

2. Validate API response status

3. Convert JSON responses into Python objects

4. Display raw API data
   

Orderbook data is extremely valuable because it provides insight into:

• Buy and sell pressure

• Market liquidity

• Bid and ask depth

• Trading activity concentration
  

This information is often used in professional trading systems and quantitative analysis platforms.

CELL 4 — Fetch Seasonality Data

response_seasonality = requests.get(
    URL_SEASONALITY,
    headers=headers
)

print("Status Code:", response_seasonality.status_code)

seasonality_data = response_seasonality.json()

seasonality_data

Explanation

This section retrieves seasonality market data from the API.

Seasonality analysis helps identify recurring market behavior patterns based on historical performance.

This type of analysis is useful for:

• Market timing strategies

• Historical trend analysis

• Portfolio optimization

• Risk management
  

Combining seasonality insights with real-time orderbook analysis creates a more comprehensive market intelligence workflow.

CELL 5 — Normalize JSON Data

if "data" in orderbook_data:

    df_orderbook = pd.json_normalize(
        orderbook_data["data"]
    )

else:

    df_orderbook = pd.DataFrame(
        orderbook_data
    )

if "data" in seasonality_data:

    df_seasonality = pd.json_normalize(
        seasonality_data["data"]
    )

else:

    df_seasonality = pd.DataFrame(
        seasonality_data
    )

Explanation

Financial APIs commonly return nested JSON structures that require normalization before analysis.

This section transforms raw JSON responses into structured DataFrames using:

• pd.json_normalize() for flattening nested structures

• pd.DataFrame() as a fallback mechanism
  

Normalization is essential because analytical workflows require structured tabular datasets for processing and visualization.

CELL 6 — Data Cleaning

df_orderbook = df_orderbook.astype(str)
df_seasonality = df_seasonality.astype(str)

df_orderbook.drop_duplicates(inplace=True)
df_seasonality.drop_duplicates(inplace=True)

print("Data cleaned successfully.")

Explanation

This section performs important data cleaning operations.

The notebook converts all columns into string format to avoid issues caused by:

• Nested objects

• Lists

• Mixed data types
  

Duplicate records are also removed to improve dataset quality and analytical accuracy.

Data cleaning is a critical stage in financial analytics because real-world market data often contains inconsistencies and irregular formatting.

CELL 7 — Numeric Data Conversion

for col in df_orderbook.columns:

    df_orderbook[col] = pd.to_numeric(
        df_orderbook[col],
        errors="ignore"
    )

for col in df_seasonality.columns:

    df_seasonality[col] = pd.to_numeric(
        df_seasonality[col],
        errors="ignore"
    )

print("Numeric conversion completed.")

Explanation

This cell converts compatible columns into numeric data types.

The notebook uses pd.to_numeric() to automatically detect numeric values while safely ignoring incompatible columns.

Numeric conversion is important because it enables:

• Statistical analysis

• Data visualization

• Correlation analysis

• Forecasting models
  

This step prepares the datasets for advanced financial analytics.

CELL 8 — Dataset Information Analysis

print("=== ORDERBOOK DATASET ===")
print(df_orderbook.info())

print("\n=== SEASONALITY DATASET ===")
print(df_seasonality.info())

Explanation

This section provides a structural overview of both datasets.

The notebook displays:

• Total rows and columns

• Data types

• Non-null values

• Dataset structure
  

Understanding dataset structure helps analysts validate transformations and prepare for advanced analytics workflows.

CELL 9 — Statistical Summary

numeric_orderbook = df_orderbook.select_dtypes(
    include=["number"]
)

numeric_seasonality = df_seasonality.select_dtypes(
    include=["number"]
)

display(numeric_orderbook.describe())
display(numeric_seasonality.describe())

Explanation

This section generates descriptive statistical summaries for numeric columns.

The analysis includes:

• Mean values

• Standard deviation

• Minimum and maximum values

• Quartiles

• Distribution ranges
  

Statistical summaries provide quick insights into market behavior and potential anomalies.

CELL 10 — Orderbook Visualization

if len(numeric_orderbook.columns) > 0:

    selected_column = numeric_orderbook.columns[0]

    plt.figure(figsize=(14,6))

    sns.histplot(
        df_orderbook[selected_column],
        kde=True
    )

    plt.title(
        f"Orderbook Distribution - {selected_column}"
    )

    plt.show()

Explanation

This visualization section creates a histogram distribution chart for orderbook data.

The visualization helps analysts understand:

• Market liquidity distribution

• Trading activity concentration

• Data spread patterns

• Potential outliers
  

Visual analysis is important because financial trends become easier to interpret visually than through raw numerical tables.

CELL 11 — Seasonality Visualization

if len(numeric_seasonality.columns) > 0:

    selected_column = numeric_seasonality.columns[0]

    plt.figure(figsize=(14,6))

    sns.lineplot(
        data=df_seasonality,
        y=selected_column
    )

    plt.title(
        f"Seasonality Trend - {selected_column}"
    )

    plt.show()

Explanation

This section visualizes seasonality trends using a line chart.

Seasonality charts help analysts identify:

• Recurring market behavior

• Historical movement patterns

• Trend consistency

• Cyclical performance
  

Seasonality analysis is commonly used in professional trading and portfolio optimization strategies.

CELL 12 — Final Market Intelligence Summary

print("=" * 60)
print("ORDERBOOK & SEASONALITY SUMMARY")
print("=" * 60)

print("\nOrderbook Dataset Shape:")
print(df_orderbook.shape)

print("\nSeasonality Dataset Shape:")
print(df_seasonality.shape)

print("\nProject completed successfully.")

Explanation

The final section generates a complete market intelligence summary.

This report provides:

• Dataset dimensions

• Final processing status

• Data integration confirmation
  

Summary reporting is important because it condenses complex analytical workflows into actionable information for decision-making systems.

Result :

CONCLUSION

Financial market analytics continues to evolve rapidly as APIs and automation become central components of modern trading and fintech systems.

This project successfully demonstrates how to:

• Connect to financial APIs

• Retrieve orderbook and seasonality data

• Normalize raw JSON responses

• Clean inconsistent datasets

• Convert numeric information

• Generate statistical summaries

• Create market visualizations

• Produce market intelligence reports
  

The combination of Orderbook and Seasonality analysis provides valuable insight into both real-time market activity and historical market behavior.

As financial technology continues to grow, developers and analysts who understand API-driven analytics workflows will become increasingly valuable in quantitative finance, trading systems, and business intelligence industries.

For anyone interested in Python, financial analytics, or fintech development, mastering workflows like this is an important step toward building scalable and intelligent market intelligence platforms.