Python version of Commodity Futures Intertemporal Bollinger Hedge Strategy
Tags: python, strategy, cryptocurrency, fmzquant
Published: 2024-06-17 01:24:52

The previously written intertemporal arbitrage strategy requires manual input of the hedging spread for opening and closing positions. Judging the price difference is more subjective. In this article, we will change the previous hedging strategy to the strategy of using the BOLL indicator to open and close positions.
```
class Hedge:
    'Hedging control class'
    def __init__(self, q, e, initAccount, symbolA, symbolB, maPeriod, atrRatio, opAmount):
        self.q = q 
        self.initAccount = initAccount
        self.status = 0
        self.symbolA = symbolA
        self.symbolB = symbolB
        self.e = e
        self.isBusy = False 
        
        self.maPeriod = maPeriod
        self.atrRatio = atrRatio
        self.opAmount = opAmount
        self.records = []
        self.preBarTime = 0
        
    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 

        recordsA = exchange.GetRecords()
        if not recordsA:
            return 

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

        recordsB = exchange.GetRecords()
        if not recordsB:
            return 

        # Calculate the spread price K line
        if recordsA[-1]["Time"] != recordsB[-1]["Time"]:
            return 

        minL = min(len(recordsA), len(recordsB))
        rA = recordsA.copy()
        rB = recordsB.copy()

        rA.reverse()
        rB.reverse()
        count = 0
        
        arrDiff = []
        for i in range(minL):
            arrDiff.append(rB[i]["Close"] - rA[i]["Close"])
        arrDiff.reverse()
        if len(arrDiff) < self.maPeriod:
            return 

        # Calculate Bollinger Bands indicator
        boll = TA.BOLL(arrDiff, self.maPeriod, self.atrRatio)

        ext.PlotLine("upper trail", boll[0][-2], recordsA[-2]["Time"])
        ext.PlotLine("middle trail", boll[1][-2], recordsA[-2]["Time"])
        ext.PlotLine("lower trail", boll[2][-2], recordsA[-2]["Time"])
        ext.PlotLine("Closing price spread", arrDiff[-2], recordsA[-2]["Time"])

        LogStatus(_D(), "upper trail:", boll[0][-1], "\n", "middle trail:", boll[1][-1], "\n", "lower trail:", boll[2][-1], "\n", "Current closing price spread:", arrDiff[-1])
        
        action = 0
        # Signal trigger
        if self.status == 0:
            if arrDiff[-1] > boll[0][-1]:
                Log("Open position A buy B sell", ", A latest price:", recordsA[-1]["Close"], ", B latest price:", recordsB[-1]["Close"], "#FF0000")
                action = 2
                # Add chart markers
                ext.PlotFlag(recordsA[-1]["Time"], "A buy B sell", "Positive")
            elif arrDiff[-1] < boll[2][-1]:
                Log("Open position A sell B buy", ", A latest price:", recordsA[-1]["Close"], ", B latest price:", recordsB[-1]["Close"], "#FF0000")
                action = 1
                # Add chart markers
                ext.PlotFlag(recordsA[-1]["Time"], "A sell B buy", "Negative")
        elif self.status == 1 and arrDiff[-1] > boll[1][-1]:
            Log("Close position A buy B sell", ", A latest price:", recordsA[-1]["Close"], ", B latest price:", recordsB[-1]["Close"], "#FF0000")
            action = 2
            # Add chart markers
            ext.PlotFlag(recordsA[-1]["Time"], "A buy B sell", "Close Negative")
        elif self.status == 2 and arrDiff[-1] < boll[1][-1]:
            Log("Close position A sell B buy", ", A latest price:", recordsA[-1]["Close"], ", B latest price:", recordsB[-1]["Close"], "#FF0000")
            action = 1 
            # Add chart markers
            ext.PlotFlag(recordsA[-1]["Time"], "A sell B buy", "Close Positive")


        # Execute specific instructions
        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("Connecting to the trading server...")
    while not exchange.IO("status"):
        Sleep(1000)

    Log("Successfully connected to the trading server")
    initAccount = _C(exchange.GetAccount)
    Log(initAccount)

    def callBack(task, ret):
        Log(task["desc"], "success" if ret else "failure")

    q = ext.NewTaskQueue(callBack)
    p = ext.NewPositionManager()
    if CoverAll:
        Log("Start closing all remaining positions...")
        p.CoverAll()
        Log("Operation complete")

    t = Hedge(q, exchange, initAccount, SA, SB, MAPeriod, ATRRatio, OpAmount)
    while True:
        q.poll()
        t.poll()
```
Strategy parameter setting:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/e7ccytuc86t0i6cnpx26.png)

The overall strategy framework is basically the same as the [Python version of commodity futures intertemporal hedging strategy](https://www.fmz.com/digest-topic/5740), except that the corresponding BOLL indicator parameters are added. When the strategy is running, the K-line data of the two contracts is obtained, and then the price difference is calculated to calculate the spread. The array is used as data of the TA.BOLL function to calculate the Bollinger Bands. When the spread exceeds the Bollinger Band's upper rail, it will be hedged, and when it touches the lower rail, it will be opposed operating. When holding a position, touch the middle rail to close the positions.

Backtest:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/uhvluolpeudbm1lzip8a.png)

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qdtqtqp4nntqsj9utbco.png)

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/bnsb1r986h8jz656ovre.png)

This article is mainly used for study purpose only.
Complete strategy: https://www.fmz.com/strategy/213826

From: https://www.fmz.com/digest-topic/5780