QMT期货双向网格策略开发：双向网格构建与动态资金风控\n\n网格交易（Grid Trading）是一种利用价格震荡进行低买高卖的经典量化策略。在期货市场（如股指期货）中，由于自带杠杆且支持双向交易，构建一个双向网格策略并配合动态资金风控，可以有效捕捉震荡行情红利，同时规避单边突破带来的爆仓风险。\n\n本文将详细介绍如何在国信证券QMT平台上，利用Python API开发一个适用于股指期货的双向网格交易策略。\n\n---\n\n## 一、 核心设计思路\n\n1. 双向网格构建：以初始基准价为中心，向上布置多档“卖出开空”网格，向下布置多档“买入开多”网格。\n2. 动态资金管理：根据账户可用资金（m_dAvailable）动态计算每档网格的下单手数，避免盲目满仓。\n3. 动态风控（整体止损）：实时监控账户总资产（m_dBalance），当最大回撤超过设定阈值，或总资产低于安全线时，触发一键平仓并暂停策略。\n\n---\n\n## 二、 关键 QMT API 接口\n\n* ContextInfo.set_account(account)：设定交易账号，绑定主推。\n* get_trade_detail_data(account, type, datatype)：获取账户资金（ACCOUNT）和持仓（POSITION）明细。\n* passorder(opType, orderType, accountid, orderCode, prType, price, volume, ContextInfo)：发送期货委托指令。\n\n---\n\n## 三、 策略源码实现 (Python)\n\n请在QMT策略编辑器中新建Python策略，并粘贴以下代码：\n\npython\n#encoding:gbk\nimport numpy as np\n\ndef init(ContextInfo):\n # 1. 基础参数设置\n ContextInfo.account = '您的期货资金账号' # 请替换为真实的模拟或实盘期货账号\n ContextInfo.set_account(ContextInfo.account)\n ContextInfo.symbol = ContextInfo.stockcode + '.' + ContextInfo.market\n \n # 2. 网格参数\n ContextInfo.grid_step = 15.0 # 网格间距(点数)\n ContextInfo.grid_levels = 5 # 单侧网格层数\n ContextInfo.base_price = 0.0 # 基准价格(运行中动态更新)\n \n # 3. 资金与风控参数\n ContextInfo.risk_ratio = 0.1 # 单笔交易所占可用资金比例\n ContextInfo.max_drawdown_limit = 0.05 # 账户最大回撤止损阈值(5%)\n ContextInfo.init_balance = 0.0 # 初始资金\n ContextInfo.max_balance = 0.0 # 历史最高资金\n ContextInfo.is_stopped = False # 策略风控停止标志\n \n print('期货双向网格策略初始化完成！')\n\ndef handlebar(ContextInfo):\n if ContextInfo.is_stopped:\n return\n \n # 1. 获取最新行情\n full_tick = ContextInfo.get_full_tick([ContextInfo.symbol])\n if not full_tick or ContextInfo.symbol not in full_tick:\n return\n current_price = full_tick[ContextInfo.symbol]['lastPrice']\n \n # 2. 获取账户资金与持仓信息\n acct_info = get_trade_detail_data(ContextInfo.account, 'FUTURE', 'ACCOUNT')\n if not acct_info:\n return\n account_obj = acct_info[0]\n \n current_balance = account_obj.m_dBalance\n available_cash = account_obj.m_dAvailable\n \n # 初始化资金记录\n if ContextInfo.init_balance == 0.0:\n ContextInfo.init_balance = current_balance\n ContextInfo.max_balance = current_balance\n ContextInfo.base_price = current_price\n print(f'策略启动，基准价: {ContextInfo.base_price}, 初始资金: {ContextInfo.init_balance}')\n return\n \n # 3. 动态资金风控逻辑\n if current_balance > ContextInfo.max_balance:\n ContextInfo.max_balance = current_balance\n \n # 计算当前回撤\n drawdown = (ContextInfo.max_balance - current_balance) / ContextInfo.max_balance\n if drawdown >= ContextInfo.max_drawdown_limit:\n print(f'【风控触发】当前回撤 {drawdown*100:.2f}% 超过限制，执行一键平仓！')\n liquidate_all(ContextInfo)\n ContextInfo.is_stopped = True\n return\n \n # 4. 动态计算下单手数 (基于可用资金与合约乘数)\n detail = ContextInfo.get_instrumentdetail(ContextInfo.symbol)\n multiplier = detail.get('VolumeMultiple', 1.0)\n # 假设保证金比例为 15%\n margin_rate = 0.15\n single_margin_limit = available_cash * ContextInfo.risk_ratio\n order_volume = int(single_margin_limit / (current_price * multiplier * margin_rate))\n order_volume = max(1, order_volume) # 至少下单1手\n \n # 5. 网格交易逻辑\n pos_info = get_trade_detail_data(ContextInfo.account, 'FUTURE', 'POSITION')\n long_pos = 0\n short_pos = 0\n for pos in pos_info:\n if pos.m_strInstrumentID == ContextInfo.stockcode:\n # 区分多空持仓\n if pos.m_nDirection == 48: # 多头\n long_pos = pos.m_nVolume\n elif pos.m_nDirection == 49: # 空头\n short_pos = pos.m_nVolume\n \n # 计算当前价格偏离基准价的网格层数\n price_deviation = current_price - ContextInfo.base_price\n current_level = int(price_deviation / ContextInfo.grid_step)\n \n # 向上突破：开空或平多\n if current_level >= 1 and short_pos < ContextInfo.grid_levels:\n # 价格上涨，分批开空\n target_short = min(current_level, ContextInfo.grid_levels)\n if short_pos < target_short:\n diff = target_short - short_pos\n print(f'价格上涨至 {current_price}，触发开空网格，下单 {diff * order_volume} 手')\n passorder(3, 1101, ContextInfo.account, ContextInfo.symbol, 5, -1, diff * order_volume, ContextInfo)\n ContextInfo.base_price = current_price # 更新基准价\n \n # 向下突破：开多或平空\n elif current_level <= -1 and long_pos < ContextInfo.grid_levels:\n # 价格下跌，分批开多\n target_long = min(abs(current_level), ContextInfo.grid_levels)\n if long_pos < target_long:\n diff = target_long - long_pos\n print(f'价格下跌至 {current_price}，触发开多网格，下单 {diff * order_volume} 手')\n passorder(0, 1101, ContextInfo.account, ContextInfo.symbol, 5, -1, diff * order_volume, ContextInfo)\n ContextInfo.base_price = current_price # 更新基准价\n\ndef liquidate_all(ContextInfo):\n # 一键平仓函数\n pos_info = get_trade_detail_data(ContextInfo.account, 'FUTURE', 'POSITION')\n for pos in pos_info:\n if pos.m_strInstrumentID == ContextInfo.stockcode and pos.m_nVolume > 0:\n if pos.m_nDirection == 48: # 平多\n print(f'平多仓: {pos.m_nVolume}手')\n passorder(6, 1101, ContextInfo.account, ContextInfo.symbol, 5, -1, pos.m_nVolume, ContextInfo)\n elif pos.m_nDirection == 49: # 平空\n print(f'平空仓: {pos.m_nVolume}手')\n passorder(8, 1101, ContextInfo.account, ContextInfo.symbol, 5, -1, pos.m_nVolume, ContextInfo)\n\n\n---\n\n## 四、 策略要点解析\n\n1. 动态手数计算：策略通过 ContextInfo.get_instrumentdetail 获取合约乘数，结合当前可用资金和预设的 risk_ratio 动态计算下单手数。当账户资金增长时，下单手数自动放大；资金缩水时，手数自动缩小。\n2. 最大回撤风控：在 handlebar 中，策略会记录历史最高资产净值。一旦当前净值相比最高净值回撤达到 5%（max_drawdown_limit），将立即调用 liquidate_all 函数进行一键平仓，并停止策略运行，防止单边趋势行情导致网格爆仓。\n3. 基准价动态更新：每次网格触发交易后，基准价 ContextInfo.base_price 会更新为当前成交价，从而实现网格的动态平移，适应价格中枢的变化。