一、印度金融市场数据特点
印度作为全球增长最快的主要经济体之一，其金融市场具有以下显著特征：
- 双交易所体系：国家证券交易所(NSE)和孟买证券交易所(BSE)
- 高流动性品种：Nifty 50指数成分股、银行股等
- 独特交易机制：T+2结算制度，上午9:15至下午3:30交易时间(IST)
- 丰富IPO市场：2023年印度IPO数量位居全球前列
二、环境配置与基础对接
1. API密钥获取与配置
# 配置StockTV API
API_KEY = "your_api_key_here" # 通过官网或客服获取
BASE_URL = "https://api.stocktv.top"
# 印度市场特定参数
INDIA_COUNTRY_ID = 14 # 印度国家代码
NSE_EXCHANGE_ID = 46 # NSE交易所代码
BSE_EXCHANGE_ID = 74 # BSE交易所代码2. 安装必要库
pip install requests websocket-client pandas plotly三、印度K线数据专业对接
1. 多周期K线获取接口
import pandas as pd
def get_india_kline(symbol, exchange, interval="15m"):
"""
获取印度股票K线数据
:param symbol: 股票代码(如RELIANCE)
:param exchange: 交易所(NSE/BSE)
:param interval: 时间间隔(1m/5m/15m/1h/1d)
"""
url = f"{BASE_URL}/stock/kline"
params = {
"symbol": symbol,
"exchange": exchange,
"interval": interval,
"countryId": INDIA_COUNTRY_ID,
"key": API_KEY
}
response = requests.get(url, params=params)
data = response.json()
# 转换为Pandas DataFrame
df = pd.DataFrame(data['data'])
df['time'] = pd.to_datetime(df['time'], unit='ms') # 转换印度时区(IST)
df['time'] = df['time'].dt.tz_localize('UTC').dt.tz_convert('Asia/Kolkata')
return df
# 获取Reliance Industries的15分钟K线(NSE)
reliance_kline = get_india_kline("RELIANCE", "NSE", "15m")2. 专业级K线可视化
import plotly.graph_objects as go
def plot_advanced_kline(df):
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'
)])
fig.update_layout(
title='印度股票K线图',
xaxis_title='印度标准时间(IST)',
yaxis_title='价格(INR)',
xaxis_rangeslider_visible=False,
template="plotly_dark"
)
# 添加成交量柱状图
fig.add_trace(go.Bar(
x=df['time'],
y=df['volume'],
name='成交量',
marker_color='rgba(100, 100, 255, 0.6)',
yaxis='y2'
))
fig.update_layout(yaxis2=dict(
title='成交量',
overlaying='y',
side='right'
))
fig.show()
plot_advanced_kline(reliance_kline)四、印度市场实时数据对接
1. WebSocket实时行情订阅
import websocket
import json
import threading
class IndiaMarketData:
def __init__(self):
self.symbol_map = {} # 存储symbol到股票名称的映射
def on_message(self, ws, message):
data = json.loads(message)
# 处理实时行情更新
if data.get('type') == 'stock':
symbol = data['symbol']
print(f"实时行情 {self.symbol_map.get(symbol, symbol)}: "
f"最新价 {data['last']} 成交量 {data['volume']}")
# 处理指数更新
elif data.get('type') == 'index':
print(f"指数更新 {data['name']}: {data['last']} ({data['chgPct']}%)")
def subscribe_symbols(self, ws):
# 订阅Nifty 50成分股(示例)
nifty_stocks = ["RELIANCE", "TCS", "HDFCBANK", "INFY"]
for symbol in nifty_stocks:
self.symbol_map[symbol] = get_stock_name(symbol)
# 订阅请求
subscribe_msg = {
"action": "subscribe",
"countryId": INDIA_COUNTRY_ID,
"symbols": nifty_stocks,
"indices": ["NSEI"] # Nifty 50指数
}
ws.send(json.dumps(subscribe_msg))
def start(self):
ws = websocket.WebSocketApp(
f"wss://ws-api.stocktv.top/connect?key={API_KEY}",
on_message=self.on_message,
on_open=lambda ws: self.subscribe_symbols(ws)
)
# 启动WebSocket连接
wst = threading.Thread(target=ws.run_forever)
wst.start()
# 辅助函数：获取股票名称
def get_stock_name(symbol):
url = f"{BASE_URL}/stock/queryStocks"
params = {
"symbol": symbol,
"countryId": INDIA_COUNTRY_ID,
"key": API_KEY
}
response = requests.get(url, params=params)
return response.json()['data'][0]['name']
# 启动实时数据服务
india_data = IndiaMarketData()
india_data.start()2. 实时数据存储方案
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 RealTimeData(Base):
__tablename__ = 'india_realtime_data'
id = Column(Integer, primary_key=True)
symbol = Column(String(20))
exchange = Column(String(10))
last_price = Column(Float)
volume = Column(Integer)
timestamp = Column(DateTime)
created_at = Column(DateTime, default=datetime.utcnow)
# 初始化数据库连接
engine = create_engine('sqlite:///india_market.db')
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
def save_realtime_data(data):
session = Session()
try:
record = RealTimeData(
symbol=data['symbol'],
exchange=data.get('exchange', 'NSE'),
last_price=data['last'],
volume=data['volume'],
timestamp=datetime.fromtimestamp(data['timestamp'])
)
session.add(record)
session.commit()
except Exception as e:
print(f"保存数据失败: {e}")
session.rollback()
finally:
session.close()
# 在on_message回调中调用
# save_realtime_data(data)五、印度IPO新股数据深度对接
1. 获取IPO日历与详情
def get_india_ipo_list(status="upcoming"):
"""
获取印度IPO列表
:param status: upcoming(即将上市)/recent(近期上市)
"""
url = f"{BASE_URL}/stock/getIpo"
params = {
"countryId": INDIA_COUNTRY_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_india_ipo_list("upcoming")
print("即将上市的IPO:")
for ipo in upcoming_ipos['data'][:5]:
print(f"{ipo['company']} ({ipo['symbol']}) - 发行价: ₹{ipo['ipoPrice']}")
# 获取近期上市的IPO表现
recent_ipos = get_india_ipo_list("recent")
print("\n近期IPO表现:")
for ipo in recent_ipos['data'][:5]:
change = (ipo['last'] - ipo['ipoPrice']) / ipo['ipoPrice'] * 100
print(f"{ipo['company']}: 发行价 ₹{ipo['ipoPrice']} → 当前 ₹{ipo['last']} ({change:.2f}%)")2. IPO数据分析与可视化
import plotly.express as px
def analyze_ipo_performance():
# 获取过去6个月的IPO数据
ipos = get_india_ipo_list("recent")['data']
df = pd.DataFrame(ipos)
# 计算首日/首周涨跌幅
df['listing_gain'] = (df['listingPrice'] - df['ipoPrice']) / df['ipoPrice'] * 100
df['current_gain'] = (df['last'] - df['ipoPrice']) / df['ipoPrice'] * 100
# 绘制散点图
fig = px.scatter(df, x='listing_gain', y='current_gain',
hover_data=['company', 'symbol'],
title="印度IPO表现分析",
labels={'listing_gain':'首日涨幅(%)', 'current_gain':'当前涨幅(%)'})
# 添加参考线
fig.add_hline(y=0, line_dash="dash")
fig.add_vline(x=0, line_dash="dash")
fig.show()
return df
ipo_analysis = analyze_ipo_performance()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}")
raise2. 性能优化方案
import redis
from functools import lru_cache
# 初始化Redis连接
r = redis.Redis(host='localhost', port=6379, db=0)
@lru_cache(maxsize=100)
def get_stock_info(symbol):
"""缓存股票基本信息"""
cache_key = f"stock:{symbol}:info"
cached = r.get(cache_key)
if cached:
return json.loads(cached)
url = f"{BASE_URL}/stock/queryStocks"
params = {
"symbol": symbol,
"countryId": INDIA_COUNTRY_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
def batch_get_kline(symbols, interval):
"""批量获取K线数据，减少API调用次数"""
results = {}
with ThreadPoolExecutor(max_workers=5) as executor:
future_to_symbol = {
executor.submit(get_india_kline, sym, "NSE", 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七、总结与资源
关键要点回顾
- K线数据：支持多周期获取，专业级可视化方案
- 实时行情：WebSocket低延迟连接，支持NSE/BSE双交易所
- IPO数据：完整的新股上市日历与表现追踪
扩展资源
特别提示：印度市场有特殊的节假日安排和交易规则，建议在实现中考虑：
- 处理IST时区转换(UTC+5:30)
- 关注SEBI监管政策变化
- 对IPO锁定期等特殊规则进行额外处理
python
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