Skip to content
All library documents

Keeping Foreign Exchange Market Data APIs Reliable for High-Frequency Trading

Code TqSdk

Summary

This practical article discusses market data reliability as an operational concern for high-frequency foreign exchange systems. It describes failure modes such as delayed quotes, dropped connections, and anomalous prices, which can interfere with short-term signals and trading decisions. Its central guidance is to judge a feed by its stability and accuracy as well as by how easy it is to connect.

The proposed safeguards are automatic reconnection with controlled retry frequency, validation of required fields and price values before passing updates to strategy logic, and request-rate limits or batched subscriptions to reduce the chance of throttling. Provider choice should reflect trading frequency: latency and connection stability receive priority for high-frequency use, while lower-frequency users may also weigh cost and ease of use. The article relies on a personal account and does not provide a systematic benchmark or independent measurements, so its claims about outcomes are anecdotal rather than broadly established.

Key ideas

  • Dropped connections and delayed or invalid quotes can disrupt fast trading decisions.
  • Automatic reconnection can restore a feed, while controlled retries help avoid excessive reconnect attempts.
  • Validate essential instrument and price fields before updates reach strategy logic.
  • Control request rates and batch subscriptions to reduce the risk of API throttling.
  • Provider selection should reflect trading frequency, and the article offers anecdotal rather than systematic evidence.

Tags

ForexHigh-frequency tradingExecutionMarket microstructure
Full text
# dualthrust.py


```py
#!/usr/bin/env python
#  -*- coding: utf-8 -*-
__author__ = 'limin'

'''
Dual Thrust策略 (难度：中级)
参考: https://www.shinnytech.com/blog/dual-thrust
注: 该示例策略仅用于功能示范, 实盘时请根据自己的策略/经验进行修改
'''

from tqsdk import TqApi, TqAuth, TargetPosTask

SYMBOL = "DCE.jd2011"  # 合约代码
NDAY = 5  # 天数
K1 = 0.2  # 上轨K值
K2 = 0.2  # 下轨K值

api = TqApi(auth=TqAuth("快期账户", "账户密码"))
print("策略开始运行")

quote = api.get_quote(SYMBOL)
klines = api.get_kline_serial(SYMBOL, 24 * 60 * 60)  # 86400使用日线
target_pos = TargetPosTask(api, SYMBOL)


def dual_thrust(quote, klines):
    current_open = klines.iloc[-1]["open"]
    HH = max(klines.high.iloc[-NDAY - 1:-1])  # N日最高价的最高价
    HC = max(klines.close.iloc[-NDAY - 1:-1])  # N日收盘价的最高价
    LC = min(klines.close.iloc[-NDAY - 1:-1])  # N日收盘价的最低价
    LL = min(klines.low.iloc[-NDAY - 1:-1])  # N日最低价的最低价
    range = max(HH - LC, HC - LL)
    buy_line = current_open + range * K1  # 上轨
    sell_line = current_open - range * K2  # 下轨
    print("当前开盘价: %f, 上轨: %f, 下轨: %f" % (current_open, buy_line, sell_line))
    return buy_line, sell_line


buy_line, sell_line = dual_thrust(quote, klines)  # 获取上下轨

while True:
    api.wait_update()
    if api.is_changing(klines.iloc[-1], ["datetime", "open"]):  # 新产生一根日线或开盘价发生变化: 重新计算上下轨
        buy_line, sell_line = dual_thrust(quote, klines)

    if api.is_changing(quote, "last_price"):
        if quote.last_price > buy_line:  # 高于上轨
            print("高于上轨,目标持仓 多头3手")
            target_pos.set_target_volume(3)  # 交易
        elif quote.last_price < sell_line:  # 低于下轨
            print("低于下轨,目标持仓 空头3手")
            target_pos.set_target_volume(-3)  # 交易
        else:
            print('未穿越上下轨,不调整持仓')

```

Shown in full with attribution under the source's licence. Licence: Apache-2.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.