Python版商品期货跨期对冲策略

移植自JavaScript版本的「商品期货跨期对冲 - 百行代码实现」，本策略为简单的教学策略，意图展示Python语言的商品期货策略设计。主要用于学习策略编写、参考设计思路。

            python
            
            
class Hedge:
    '对冲控制类'
    def __init__(self, q, e, initAccount, symbolA, symbolB, hedgeSpread, coverSpread):
        self.q = q 
        self.initAccount = initAccount
        self.status = 0
        self.symbolA = symbolA
        self.symbolB = symbolB
        self.e = e
        self.isBusy = False 
        self.hedgeSpread = hedgeSpread
        self.coverSpread = coverSpread
        self.opAmount = OpAmount 
        
    def poll(self):
        if (self.isBusy or not exchange.IO("status")) or not ext.IsTrading(self.symbolA):
            Sleep(1000)
            return 

        insDetailA = exchange.SetContractType(self.symbolA)
        if not insDetailA:
            return 

        tickerA = exchange.GetTicker()
        if not tickerA:
            return 

        insDetailB = exchange.SetContractType(self.symbolB)
        if not insDetailB:
            return 

        tickerB = exchange.GetTicker()
        if not tickerB:
            return 

        LogStatus(_D(), "A卖B买", _N(tickerA["Buy"] - tickerB["Sell"]), "A买B卖", _N(tickerA["Sell"] - tickerB["Buy"]))
        action = 0

        if self.status == 0:
            if (tickerA["Buy"] - tickerB["Sell"]) > self.hedgeSpread:
                Log("开仓 A卖B买", tickerA["Buy"], tickerB["Sell"], "#FF0000")
                action = 1
            elif (tickerB["Buy"] - tickerA["Sell"]) > self.hedgeSpread:
                Log("开仓 B卖A买", tickerB["Buy"], tickerA["Sell"], "#FF0000")
                action = 2
        elif self.status == 1 and (tickerA["Sell"] - tickerB["Buy"]) <= self.coverSpread:
            Log("平仓 A买B卖", tickerA["Sell"], tickerB["Buy"], "#FF0000")
            action = 2
        elif self.status == 2 and (tickerB["Sell"] - tickerA["Buy"]) <= self.coverSpread:
            Log("平仓 B买A卖", tickerB["Sell"] - tickerA["Buy"], "#FF0000")
            action = 1 

        if action == 0:
            return 
        
        self.isBusy = True
        tasks = []
        if action == 1:
            tasks.append([self.symbolA, "sell" if self.status == 0 else "closebuy"])
            tasks.append([self.symbolB, "buy" if self.status == 0 else "closesell"])
        elif action == 2:
            tasks.append([self.symbolA, "buy" if self.status == 0 else "closesell"])
            tasks.append([self.symbolB, "sell" if self.status == 0 else "closebuy"])

        def callBack(task, ret):
            def callBack(task, ret):
                self.isBusy = False
                if task["action"] == "sell":
                    self.status = 2
                elif task["action"] == "buy":
                    self.status = 1
                else:
                    self.status = 0
                    account = _C(exchange.GetAccount)
                    LogProfit(account["Balance"] - self.initAccount["Balance"], account)
            self.q.pushTask(self.e, tasks[1][0], tasks[1][1], self.opAmount, callBack)

        self.q.pushTask(self.e, tasks[0][0], tasks[0][1], self.opAmount, callBack)


def main():
    SetErrorFilter("ready|login|timeout")
    Log("正在与交易服务器连接...")
    while not exchange.IO("status"):
        Sleep(1000)

    Log("与交易服务器连接成功")
    initAccount = _C(exchange.GetAccount)
    Log(initAccount)
    n = 0 

    def callBack(task, ret):
        Log(task["desc"], "成功" if ret else "失败")

    q = ext.NewTaskQueue(callBack)

    if CoverAll:
        Log("开始平掉所有残余仓位...")
        ext.NewPositionManager().CoverAll()
        Log("操作完成")

    t = Hedge(q, exchange, initAccount, SA, SB, HedgeSpread, CoverSpread)
    while True:
        q.poll()
        t.poll()

只是移植一下代码，感觉有点太简单了，我们继续来做一些改造，给策略加上图表。

在LogStatus函数调用的位置之前加上以下代码，把实时的价格差做成K线统计出来，self.preBarTime是Hedge类增加的一个成员，用来记录最新BAR的时间戳，画图我们使用「画线类库」，直接调用画图接口，很简单就可以画出图表。

            python
            
            

        # 计算差价K线
        r = exchange.GetRecords()
        if not r:
            return 
        diff = tickerB["Last"] - tickerA["Last"]
        if r[-1]["Time"] != self.preBarTime:
            # 更新
            self.records.append({"Time": r[-1]["Time"], "High": diff, "Low": diff, "Open": diff, "Close": diff, "Volume": 0})
            self.preBarTime = r[-1]["Time"]
        if diff > self.records[-1]["High"]:
            self.records[-1]["High"] = diff
        if diff < self.records[-1]["Low"]:
            self.records[-1]["Low"] = diff
        self.records[-1]["Close"] = diff
        ext.PlotRecords(self.records, "diff:B-A")
        ext.PlotHLine(self.hedgeSpread if diff > 0 else -self.hedgeSpread, "hedgeSpread")
        ext.PlotHLine(self.coverSpread if diff > 0 else -self.coverSpread, "coverSpread")

回测时的效果：

接下来，我们再加入交互功能，让策略在运行时可以修改HedgeSpread和CoverSpread参数，控制对冲开仓差价、平仓差价。还需要一个一键平仓的按钮。我们在策略编辑页面增加这几个控件。

然后在策略的主循环中，q.poll(),t.poll()调用之后，加上交互控制代码。

            python
            
            
    while True:
        q.poll()
        t.poll()
        # 以下交互控制代码
        cmd = GetCommand()
        if cmd:
            arr = cmd.split(":")
            if arr[0] == "AllCover":
                p.CoverAll()
            elif arr[0] == "SetHedgeSpread":
                t.SetHedgeSpread(float(arr[1]))
            elif arr[0] == "SetCoverSpread":
                t.SetCoverSpread(float(arr[1]))

Python版商品期货跨期对冲策略 (升级图表、交互功能)

策略用于教学，实盘根据自身需求优化调整。
如有问题，欢迎留言。