对接印尼金融市场数据K线、实时行情与IPO新股

一、印尼金融市场数据特点与价值

印度尼西亚作为东南亚最大经济体，其金融市场具有以下显著特点：

• 快速增长：雅加达综合指数(JKSE)过去5年年化增长率达12%
• 特色板块：棕榈油、煤炭、镍矿等资源类股票交易活跃
• IPO活跃：2023年印尼IPO数量位居东南亚首位
• 交易时段：上午9:30-12:00，下午13:30-16:00（UTC+7）

二、环境准备与基础配置

1. API密钥获取

# 配置示例
API_KEY = "your_indonesia_api_key"  # 通过官网申请或联系客服获取
BASE_URL = "https://api.stocktv.top"
INDONESIA_ID = 48  # 印尼国家代码

2. 安装必要库

pip install requests websocket-client pandas plotly python-dotenv

3. 安全配置（推荐）

from dotenv import load_dotenv
import os

load_dotenv()
API_KEY = os.getenv('STOCKTV_API_KEY')  # 从环境变量读取

三、K线数据专业对接方案

1. 多周期K线获取

def get_idn_kline(stock_code, interval="1d", exchange="IDX", limit=100):
    """
    获取印尼股票K线数据
    :param stock_code: 股票代码(如BBCA)
    :param interval: 时间间隔(1m/5m/15m/1h/1d)
    :param exchange: 交易所(IDX)
    :param limit: 数据条数
    """
    url = f"{BASE_URL}/stock/kline"
    params = {
        "symbol": stock_code,
        "exchange": exchange,
        "interval": interval,
        "countryId": INDONESIA_ID,
        "limit": limit,
        "key": API_KEY
    }
    response = requests.get(url, params=params)
    data = response.json()
    
    # 转换为DataFrame并处理时区
    df = pd.DataFrame(data['data'])
    df['time'] = pd.to_datetime(df['time'], unit='ms')
    df['time'] = df['time'].dt.tz_localize('UTC').dt.tz_convert('Asia/Jakarta')
    return df

# 获取BBCA银行日K数据
bbca_kline = get_idn_kline("BBCA", interval="1d")

2. 专业K线可视化（使用Plotly）

import plotly.graph_objects as go

def plot_idn_candlestick(df, title):
    fig = go.Figure(data=[go.Candlestick(
        x=df['time'],
        open=df['open'],
        high=df['high'],
        low=df['low'],
        close=df['close'],
        increasing_line_color='green',
        decreasing_line_color='red',
        name='K线'
    )])
    
    # 添加MA5均线
    df['MA5'] = df['close'].rolling(5).mean()
    fig.add_trace(go.Scatter(
        x=df['time'],
        y=df['MA5'],
        name='MA5',
        line=dict(color='orange', width=1)
    ))
    
    fig.update_layout(
        title=f'{title} - 印尼市场',
        xaxis_title='雅加达时间(WIB)',
        yaxis_title='价格(IDR)',
        xaxis_rangeslider_visible=False,
        template="plotly_white"
    )
    
    fig.show()

plot_idn_candlestick(bbca_kline, "BBCA银行日K线")

四、实时行情数据对接

1. WebSocket实时数据订阅

import websocket
import json
import threading

class IDNMarketRealtime:
    def __init__(self):
        self.symbol_names = {
            "BBCA": "Bank Central Asia",
            "TLKM": "Telkom Indonesia",
            "UNVR": "Unilever Indonesia"
        }
    
    def on_message(self, ws, message):
        data = json.loads(message)
        
        # 处理股票实时数据
        if data.get('type') == 'stock':
            symbol = data['symbol']
            name = self.symbol_names.get(symbol, symbol)
            print(f"[{data['time']}] {name}: {data['last']} "
                  f"({data.get('chgPct', 0):.2f}%) 成交量: {data['volume']}")
        
        # 处理指数数据
        elif data.get('type') == 'index':
            print(f"指数更新 {data['name']}: {data['last']} "
                  f"({data.get('chgPct', 0):.2f}%)")
    
    def start(self):
        ws = websocket.WebSocketApp(
            f"wss://ws-api.stocktv.top/connect?key={API_KEY}",
            on_message=self.on_message,
            on_open=self.on_open
        )
        
        # 启动独立线程运行WebSocket
        self.ws_thread = threading.Thread(target=ws.run_forever)
        self.ws_thread.start()
    
    def on_open(self, ws):
        # 订阅印尼龙头股和IDX指数
        subscribe_msg = {
            "action": "subscribe",
            "countryId": INDONESIA_ID,
            "symbols": list(self.symbol_names.keys()),
            "indices": ["JKSE"]  # 雅加达综合指数
        }
        ws.send(json.dumps(subscribe_msg))

# 启动实时行情服务
realtime = IDNMarketRealtime()
realtime.start()

2. 实时数据存储与分析

from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from datetime import datetime

Base = declarative_base()

class RealtimeIDNStock(Base):
    __tablename__ = 'realtime_idn_stocks'
    
    id = Column(Integer, primary_key=True)
    symbol = Column(String(10))
    name = Column(String(50))
    price = Column(Float)
    volume = Column(Integer)
    timestamp = Column(DateTime)
    created_at = Column(DateTime, default=datetime.utcnow)

# 初始化数据库
engine = create_engine('sqlite:///indonesia_market.db')
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)

def save_realtime_data(data):
    session = Session()
    try:
        record = RealtimeIDNStock(
            symbol=data['symbol'],
            name=realtime.symbol_names.get(data['symbol']),
            price=data['last'],
            volume=data.get('volume', 0),
            timestamp=datetime.fromtimestamp(data['timestamp'])
        )
        session.add(record)
        session.commit()
    except Exception as e:
        print(f"数据存储失败: {e}")
        session.rollback()
    finally:
        session.close()

# 在on_message回调中添加：
# save_realtime_data(data)

五、印尼IPO新股数据深度对接

1. 获取IPO日历与详情

def get_idn_ipo_list(status="upcoming"):
    """
    获取印尼IPO列表
    :param status: upcoming(即将上市)/recent(近期上市)
    """
    url = f"{BASE_URL}/stock/getIpo"
    params = {
        "countryId": INDONESIA_ID,
        "status": status,
        "key": API_KEY
    }
    response = requests.get(url, params=params)
    return response.json()

# 获取即将上市的IPO
upcoming_ipos = get_idn_ipo_list()
print("即将上市的印尼IPO:")
for ipo in upcoming_ipos['data'][:3]:
    print(f"- {ipo['company']} ({ipo['symbol']})")
    print(f"  行业: {ipo.get('industry', 'N/A')}")
    print(f"  发行价: {ipo['ipoPrice']} IDR")
    print(f"  上市日期: {ipo['date']}")

# 获取近期IPO表现
recent_ipos = get_idn_ipo_list("recent")
print("\n近期IPO表现:")
for ipo in recent_ipos['data'][:3]:
    change_pct = (ipo['last'] - ipo['ipoPrice']) / ipo['ipoPrice'] * 100
    print(f"- {ipo['company']}: {ipo['ipoPrice']} → {ipo['last']} "
          f"({change_pct:.2f}%)")

2. IPO数据分析可视化

import plotly.express as px

def analyze_idn_ipos():
    ipos = get_idn_ipo_list("recent")['data']
    df = pd.DataFrame(ipos)
    
    # 计算收益率
    df['return_pct'] = (df['last'] - df['ipoPrice']) / df['ipoPrice'] * 100
    
    # 按行业分析
    fig = px.box(df, x='industry', y='return_pct', 
                 title="印尼IPO分行业表现",
                 labels={'return_pct':'收益率(%)', 'industry':'行业'})
    fig.show()
    
    # 发行规模与收益率关系
    fig = px.scatter(df, x='ipoValue', y='return_pct',
                     hover_data=['company', 'symbol'],
                     title="发行规模与收益率",
                     labels={'ipoValue':'发行规模(十亿IDR)', 'return_pct':'收益率(%)'})
    fig.show()
    
    return df

ipo_analysis = analyze_idn_ipos()

六、生产环境最佳实践

1. 错误处理与重试机制

from tenacity import retry, stop_after_attempt, wait_exponential
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@retry(stop=stop_after_attempt(3),
       wait=wait_exponential(multiplier=1, min=4, max=10),
       before_sleep=lambda retry_state: logger.warning(
           f"重试 {retry_state.attempt_number} 次，原因: {retry_state.outcome.exception()}")
       )
def safe_api_call(url, params):
    try:
        response = requests.get(url, params=params, timeout=10)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f"API请求失败: {e}")
        raise

2. 性能优化方案

from functools import lru_cache
import redis

# 初始化Redis
r = redis.Redis(host='localhost', port=6379, db=0)

@lru_cache(maxsize=100)
def get_stock_info_cached(symbol):
    """带缓存的股票信息查询"""
    cache_key = f"idn:stock:{symbol}:info"
    cached = r.get(cache_key)
    if cached:
        return json.loads(cached)
    
    url = f"{BASE_URL}/stock/queryStocks"
    params = {
        "symbol": symbol,
        "countryId": INDONESIA_ID,
        "key": API_KEY
    }
    data = safe_api_call(url, params)
    r.setex(cache_key, 3600, json.dumps(data))  # 缓存1小时
    return data

# 批量获取K线数据优化
from concurrent.futures import ThreadPoolExecutor, as_completed

def batch_get_kline(symbols, interval="1d"):
    """并发获取多只股票K线数据"""
    results = {}
    with ThreadPoolExecutor(max_workers=5) as executor:
        future_to_symbol = {
            executor.submit(get_idn_kline, sym, interval): sym 
            for sym in symbols
        }
        for future in as_completed(future_to_symbol):
            symbol = future_to_symbol[future]
            try:
                results[symbol] = future.result()
            except Exception as e:
                logger.error(f"获取{symbol}数据失败: {e}")
    return results

# 示例：批量获取印尼银行股数据
bank_stocks = ["BBCA", "BBRI", "BMRI"]
bank_data = batch_get_kline(bank_stocks)

七、总结与资源

核心要点回顾

1. K线数据：支持从1分钟到日线的多周期获取，专业可视化方案
2. 实时行情：WebSocket低延迟连接，支持个股和指数
3. IPO数据：完整的上市日历与表现追踪，支持行业分析

扩展资源

• 印尼证券交易所(IDX)官网
• 印尼金融市场日历
• StockTV完整API文档

特别提示：印尼市场有特殊的交易规则需要注意：

1. 价格波动限制(Auto Rejection)：多数股票为±20%
2. 结算周期：T+2
3. 股息税：10%
4. 关注伊斯兰教法合规股票清单(特别适合伊斯兰金融投资者)