[原创] 从魔改韭菜收割机探讨高频策略设计

前几期文章我们分析了原版的现货版韭菜收割机高频策略的思路以及代码实现。

韭菜收割机策略剖析(1)
韭菜收割机策略剖析(2)

对于币圈量化的很多用户都比较关注print money大佬的策略,print money大佬的策略是在币安USDT合约交易的。从观察以及众多关注者的分析可知,该高频策略类似韭菜收割机的原理(草神也说过高频策略原理比较趋近)。但是肯定有精妙之处能实现策略有一个稳定的胜率和适当的盈亏比。

所以技痒的小编也忍不住魔改了一把,虽说魔改过的策略效果被大神们的策略碾压至渣渣。但是也算是对于高频策略的学习实践了,有兴趣的FMZer同学一起来探讨、学习下吧。

魔改过的韭菜收割机

var TickInterval = 100

function LeeksReaper() {
    var self = {}
    self.numTick = 0
    self.lastTradeId = 0
    self.vol = 0
    self.askPrice = 0
    self.bidPrice = 0
    self.orderBook = {
        Asks: [],
        Bids: []
    }
    self.prices = []
    self.tradeOrderId = 0
    self.account = null
    self.buyPrice = 0
    self.sellPrice = 0
    self.state = 0
    self.depth = null

    self.updateTrades = function() {
        var trades = _C(exchange.GetTrades)
        if (self.prices.length == 0) {
            while (trades.length == 0) {
                trades = trades.concat(_C(exchange.GetTrades))
            }
            for (var i = 0; i < 15; i++) {
                self.prices[i] = trades[trades.length - 1].Price
            }
        }
        self.vol = 0.7 * self.vol + 0.3 * _.reduce(trades, function(mem, trade) {
            // Huobi not support trade.Id
            if ((trade.Id > self.lastTradeId) || (trade.Id == 0 && trade.Time > self.lastTradeId)) {
                self.lastTradeId = Math.max(trade.Id == 0 ? trade.Time : trade.Id, self.lastTradeId)
                mem += trade.Amount
            }
            return mem
        }, 0)

    }
    self.updateOrderBook = function() {
        var orderBook = _C(exchange.GetDepth)
        self.depth = orderBook
        self.buyPrice = orderBook.Bids[pendingLevel].Price
        self.sellPrice = orderBook.Asks[pendingLevel].Price
        self.orderBook = orderBook
        if (orderBook.Bids.length < 3 || orderBook.Asks.length < 3) {
            return
        }
        self.bidPrice = orderBook.Bids[0].Price * 0.618 + orderBook.Asks[0].Price * 0.382 + 0.01
        self.askPrice = orderBook.Bids[0].Price * 0.382 + orderBook.Asks[0].Price * 0.618 - 0.01
        self.prices.shift()
        self.prices.push(_N((orderBook.Bids[0].Price + orderBook.Asks[0].Price) * 0.15 +
            (orderBook.Bids[1].Price + orderBook.Asks[1].Price) * 0.1 +
            (orderBook.Bids[2].Price + orderBook.Asks[2].Price) * 0.1 +
            (orderBook.Bids[3].Price + orderBook.Asks[3].Price) * 0.075 +
            (orderBook.Bids[4].Price + orderBook.Asks[4].Price) * 0.05 +
            (orderBook.Bids[5].Price + orderBook.Asks[5].Price) * 0.025))
    }

    self.updateAccount = function() {
        var account = exchange.GetAccount()
        if (!account) {
            return
        }
        self.account = account
        LogProfit(parseFloat(account.Info.totalWalletBalance), account)
    }

    self.CancelAll = function() {
        while (1) {
            var orders = _C(exchange.GetOrders)
            if (orders.length == 0) {
                break
            }
            for (var i = 0; i < orders.length; i++) {
                exchange.CancelOrder(orders[i].Id)
            }
            Sleep(100)
        }
    }

    self.poll = function() {
        self.numTick++
        self.updateTrades()
        self.updateOrderBook()
        var pos = _C(exchange.GetPosition)

        var burstPrice = self.prices[self.prices.length - 1] * burstThresholdPct
        var bull = false
        var bear = false
        LogStatus(_D(), "\n", 'Tick:', self.numTick, 'self.vol:', self.vol, ', lastPrice:', self.prices[self.prices.length - 1], ', burstPrice: ', burstPrice)

        if (self.numTick > 2 && (
                self.prices[self.prices.length - 1] - _.max(self.prices.slice(-6, -1)) > burstPrice ||
                self.prices[self.prices.length - 1] - _.max(self.prices.slice(-6, -2)) > burstPrice && self.prices[self.prices.length - 1] > self.prices[self.prices.length - 2]
            )) {
            bull = true
        } else if (self.numTick > 2 && (
                self.prices[self.prices.length - 1] - _.min(self.prices.slice(-6, -1)) < -burstPrice ||
                self.prices[self.prices.length - 1] - _.min(self.prices.slice(-6, -2)) < -burstPrice && self.prices[self.prices.length - 1] < self.prices[self.prices.length - 2]
            )) {
            bear = true            
        }

        if (pos.length != 0) {
            if (pos[0].Type == PD_LONG) {
                self.state = 1
            } else {
                self.state = 2
            }
        } else {
            self.state = 0
        }


        if ((!bull && !bear)) {
            return
        }

        if (bull) {
            var price = (self.state == 0 || self.state == 1) ? self.buyPrice : self.depth.Bids[coverPendingLevel].Price
            var amount = (self.state == 0 || self.state == 1) ? pendingAmount : pos[0].Amount
            exchange.SetDirection("buy")
            exchange.Buy(price, amount)
        } else if (bear) {
            var price = (self.state == 0 || self.state == 2) ? self.sellPrice : self.depth.Asks[coverPendingLevel].Price
            var amount = (self.state == 0 || self.state == 2) ? pendingAmount : pos[0].Amount
            exchange.SetDirection("sell")
            exchange.Sell(price, amount)                    
        }
        self.numTick = 0
        Sleep(TickInterval)
        self.CancelAll()
        self.updateAccount()
    }

    while (!self.account) {
        self.updateAccount()
        Sleep(500)
    }
    Log("self.account:", self.account)

    return self
}

function main() {
    LogProfitReset()
    exchange.SetPrecision(pricePrecision, amountPrecision)
    exchange.SetContractType("swap")
    var reaper = LeeksReaper()  
    while (true) {
        reaper.poll()
        Sleep(100)
    }
}

策略修改思路

策略是计划使用在币安USDT合约市场交易,币安合约支持单向持仓。所以策略就按照单向持仓的特性修改设计(单向持仓更加方便策略修改),不考虑平仓,只考虑买卖。这样思路也比较贴近现货版的韭菜收割机。

策略基本保留了原版的短期价格趋势突破判定标准,短期价格突破幅度由参数burstThresholdPct控制,根据这个判定条件来判断短期价格为bull(牛),还是bear(熊)。

策略剔除了原版一些模块,比如平衡模块。较大的改动是把下单改为了在订单薄中挂单,等待成交。
期望在多空博弈激烈的混乱盘口中用较低的成本开仓,追随短期趋势,并且在短期趋势反转时平仓,继续反向挂单开仓。

策略删除了其它没用的代码所以非常简短,也很简单。虽然策略是个不赚钱的策略,甚至亏钱,但是作为FMZer学习高频策略,观察高频策略的行为、观察市场微观规律等是一个可以上手的模型。程序化交易、量化交易需要通过大量的实践、经验、理论作为基础。

实盘跑一会儿

可以看到,行情不活跃的时候开平仓是比较困难的。

策略优化

目前,还没有找到好的优化方向。
有兴趣的同学请踊跃发言,一起探讨。

策略地址:https://www.fmz.com/strategy/260806

本策略仅仅用于学习,行情平淡实盘可能亏损。

免责声明:信息仅供参考,不构成投资及交易建议。投资者据此操作,风险自担。
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