Integrating Nigerian (NG) Stock Market Data via iTick API – Real-time Quotes & Historical K-Line

#python #api #tutorial

As a developer who has worked extensively with overseas financial data APIs, I recently took on a project requiring access to Nigerian Stock Exchange (NGX / NSENG) data. I encountered quite a few challenges and learned some valuable lessons along the way.

Unlike the mature API ecosystems of A-share or U.S. markets, documentation and Chinese-language resources for the Nigerian market are almost non-existent — even English materials are very limited. In this article, I’ll share my real-world experience using the iTick API to fetch real-time quotes and historical K-line data for NG stocks. I hope this can save time for others facing similar needs.

1. Preparation: API Setup Basics

1.1 Registration & API Token

iTick is currently one of the more reliable local financial data providers for the Nigerian market, offering both real-time and historical data.

First, go to the official website Register a developer account, complete email verification, and you’ll receive your API_TOKEN (sometimes still called API_KEY in older docs). This token is the core credential for all subsequent API calls — keep it secure.

1.2 Environment Setup

I’m using Python 3.8+. You only need two main libraries for basic usage:

pip install requests pandas mplfinance

requests — for HTTP calls
pandas — for data structuring
mplfinance — optional, for candlestick charting

2.实战1 – Fetching Real-time Quotes

Current endpoint (2026):

GET /stock/quote?region=NG&code=xxxx

Important: The market code for Nigeria is now NG (not NSENG anymore).

Clean & Reusable Implementation

import requests
import time
from typing import Dict, Optional
import pandas as pd


class ITickNGAPI:
    """
    iTick API wrapper for Nigerian (NG) stock market data – 2026 version
    """
    def __init__(self, token: str):
        self.base_url = "https://api.itick.org"
        self.token = token
        self.headers = {
            "accept": "application/json",
            "token": self.token
        }

    def get_realtime_quote(self, stock_code: str) -> Optional[Dict]:
        """
        Get real-time quote for a single stock
        :param stock_code: e.g. "DANGCEM"
        :return: quote dictionary or None if failed
        """
        for retry in range(3):
            try:
                url = f"{self.base_url}/stock/quote"
                params = {
                    "region": "NG",
                    "code": stock_code.upper()      # must be uppercase
                }

                response = requests.get(
                    url,
                    headers=self.headers,
                    params=params,
                    timeout=10
                )

                if response.status_code == 200:
                    result = response.json()
                    if result.get("code") == 0 and "data" in result:
                        quote = result["data"]
                        return {
                            "Symbol": quote["s"],
                            "Last Price": quote["ld"],
                            "Open": quote["o"],
                            "High": quote["h"],
                            "Low": quote["l"],
                            "Volume": quote["v"],
                            "Change": quote["ch"],
                            "Change %": quote["chp"],
                            "Timestamp": quote["t"],
                            "Trading Status": quote["ts"]   # 0=normal, 1=suspended, etc.
                        }
                    else:
                        print(f"No real-time data found for {stock_code} or API error")
                        return None
                else:
                    print(f"Request failed – status {response.status_code} – retry {retry+1}")
                    time.sleep(1.5)

            except requests.exceptions.Timeout:
                print(f"Timeout – retry {retry+1}")
                time.sleep(1.5)
            except Exception as e:
                print(f"Exception: {str(e)}")
                return None

        return None


# ────────────────────────────────────────────────
if __name__ == "__main__":
    MY_TOKEN = "your_itick_token_here"
    api = ITickNGAPI(MY_TOKEN)

    quote = api.get_realtime_quote("DANGCEM")
    if quote:
        print("=== Nigerian (NG) Real-time Quote ===")
        for k, v in quote.items():
            print(f"{k: <14}: {v}")

Sample Output

=== Nigerian (NG) Real-time Quote ===
Symbol        : DANGCEM
Last Price    : 312.5
Open          : 310.0
High          : 315.0
Low           : 308.0
Volume        : 156800
Change        : 2.5
Change %      : 0.81
Timestamp     : 1765526889000
Trading Status: 0

3.实战2 – Fetching Historical K-Line Data

Current endpoint (2026):

GET /stock/kline?region=NG&code=xxxx&kType=8&limit=100

Common kType values:

8 → Daily
9 → Weekly
10 → Monthly
1~7 → Minute bars (1min, 5min, 15min, etc.)

Implementation – Historical K-lines

# Add this method to the ITickNGAPI class above

def get_historical_klines(
    self,
    stock_code: str,
    k_type: int = 8,            # 8 = daily
    limit: int = 100,
    end_timestamp: int = None   # optional end time (ms)
) -> Optional[pd.DataFrame]:
    """
    Fetch historical k-line data
    """
    try:
        url = f"{self.base_url}/stock/kline"
        params = {
            "region": "NG",
            "code": stock_code.upper(),
            "kType": k_type,
            "limit": limit
        }
        if end_timestamp:
            params["et"] = end_timestamp

        response = requests.get(
            url, headers=self.headers, params=params, timeout=12
        )

        if response.status_code == 200:
            result = response.json()
            if result.get("code") == 0 and isinstance(result.get("data"), list):
                data = result["data"]
                if not data:
                    print(f"No historical data for {stock_code}")
                    return None

                df = pd.DataFrame(data)
                df = df[["t", "o", "h", "l", "c", "v"]]
                df.rename(columns={
                    "t": "timestamp_ms",
                    "o": "Open",
                    "h": "High",
                    "l": "Low",
                    "c": "Close",
                    "v": "Volume"
                }, inplace=True)

                # Convert UTC ms → Beijing time
                df["datetime"] = pd.to_datetime(df["timestamp_ms"], unit="ms", utc=True)
                df["datetime"] = df["datetime"].dt.tz_convert("Asia/Shanghai")
                df["date"] = df["datetime"].dt.strftime("%Y-%m-%d %H:%M:%S")

                # Reorder columns
                df = df[["date", "Open", "High", "Low", "Close", "Volume", "timestamp_ms"]]

                return df
            else:
                print("Unexpected API response format")
                return None
        else:
            print(f"K-line request failed – status {response.status_code}")
            return None

    except Exception as e:
        print(f"Error fetching k-line: {str(e)}")
        return None


# ────────────────────────────────────────────────
if __name__ == "__main__":
    # ... (api instance already created)

    df = api.get_historical_klines("DANGCEM", k_type=8, limit=60)

    if df is not None:
        print("\n=== Historical Daily K-line (first 5 rows) ===")
        print(df.head())

        # Optional: candlestick chart
        import mplfinance as mpf
        plot_df = df.copy()
        plot_df["date"] = pd.to_datetime(plot_df["date"])
        plot_df.set_index("date", inplace=True)

        mpf.plot(
            plot_df,
            type='candle',
            volume=True,
            title='DANGCEM (NG) Daily',
            ylabel='Price (NGN)',
            figratio=(16,9),
            style='yahoo'
        )

4. Important Notes (2026 Edition)

Market code is NG — not NSENG anymore
Authentication now uses plain token header (no more Bearer api_key:secret)
All timestamps are millisecond Unix timestamps in UTC
Free tier has rate limits — add sleep or upgrade plan for production use
Prices are in Nigerian Naira (NGN)

5. Summary & Recommendations

Integrating Nigerian stock data is definitely less plug-and-play than U.S. or Chinese markets, but once you understand the quirks (market code change, timestamp handling, rate limits), it becomes manageable.

Key takeaways: