一、網(wǎng)格策略

網(wǎng)格策略，總結(jié)為四個字就是：高拋低吸。
例如：首次買入10萬元，價(jià)格每下跌10，買入1萬元，價(jià)格每上漲20，賣出5000元。
其最大的好處就是：行情越是震蕩，獲利空間越大。（反脆弱性）

二、生成模擬交易數(shù)據(jù)

隨機(jī)生成交易數(shù)據(jù)的代碼如下：

function generateSimulateData(base,num,lowest,maxDelta){
    var ret = [base];
    var price = base;
    var deltas = [];
    var total = base;
    var min = base;
    var max = base
    for(var i=1; i<num; ++i){
        var sign = Math.floor(Math.random()*10%2)>0.5?1:-1;
        var delta = Math.floor(Math.random()*maxDelta)*sign;
        price += delta;
        if( price <= lowest){
            price = lowest;
        }
        deltas.push(delta);
        ret.push(price);

        if( price > max){
            max = price;
        }
        if( price < min){
            min = price;
        }

        total += price;
    }
    var open = base;
    var close = ret[num-1];
    var avg = total/num;
    var situation = (close/open-1)*100;
    //console.log(`open=${open}, close=${close}, avg=${avg}, max=${max}, min=${min} situation=${situation.toFixed(2)}%`);
    return {s:situation.toFixed(2),prices:ret};
}

假設(shè)我們要生成100個具有如下特征的數(shù)據(jù)：開盤價(jià)為1000，價(jià)格之間的波動幅度為0-4之間，最多跌到200（會不會跌到200，不一定）可以這樣調(diào)用該函數(shù)：

let simulateData = generateSimulateData(1000,100,200,5);
console.log("{s:<漲跌幅>,prices:<具體數(shù)據(jù)>}")

三、網(wǎng)格變種策略 v1.0

前提：

• 資金無窮大
• 交易以當(dāng)前價(jià)格瞬間完成，不影響后續(xù)市場價(jià)格

策略做法：

• 每秒交易一次。
• 當(dāng)前價(jià)格與上次成功交易價(jià)格相同時(shí)，不交易。
• 當(dāng)前價(jià)格大于持倉價(jià)格時(shí)，買入1。
• 當(dāng)前價(jià)格小于持倉價(jià)格時(shí)，買入2。
• 不進(jìn)行賣出操作，

策略目標(biāo)：策略的漲幅大于BTC每日漲幅，跌幅小于每日跌幅。

那么策略核心代碼如下：

for (let i=1; i<simulatePrices.length; ++i ){
  let curPrice = simulatePrices[i];
  if( myLastPrice==curPrice ){
    continue;
  }
  if( curPrice>=myAvgPrice ){
    myTotal += curPrice;
    myBuyTimes += 1;
  }
  else {
    myTotal += curPrice*2;
    myBuyTimes += 2;
  }
  myAvgPrice = myTotal/myBuyTimes;
}

模擬數(shù)據(jù)下收益情況

以generateSimulateData(1000,86400,200,5);構(gòu)造365次數(shù)據(jù)，最終的平均漲跌幅如下：
originAvg，模擬數(shù)據(jù)的一天平均漲跌幅。
myAvg，使用本策略后一天的平均收益率。

originalSituation:-17.50%, mySituation:56.31%
originalSituation:42.40%, mySituation:-13.41%
originalSituation:4.60%, mySituation:-14.07%
originalSituation:-64.30%, mySituation:88.32%
originalSituation:-3.20%, mySituation:11.48%
originalSituation:-0.40%, mySituation:-2.58%
originalSituation:11.30%, mySituation:-10.25%
originalSituation:56.80%, mySituation:-23.02%
originalSituation:-52.50%, mySituation:31.71%
originalSituation:-37.50%, mySituation:50.15%
originalSituation:26.20%, mySituation:-26.08%
originalSituation:-55.50%, mySituation:143.93%
originalSituation:56.70%, mySituation:-26.36%
....
originAvg:6.55% , myAvg:9.33%

從上述的數(shù)據(jù)來看，很明顯，這個策略并沒有達(dá)到我們要漲多跌少的目標(biāo)。
因此，需要對策略做相應(yīng)優(yōu)化，那么...
下文再見！

代碼

function generateSimulateData(base,num,lowest,maxDelta){
    let ret = [base];
    let price = base;
    let deltas = [];
    let total = base;
    let min = base;
    let max = base
    for(let i=1; i<num; ++i){
        let sign = Math.floor(Math.random()*10%2)>0.5?1:-1;
        let delta = Math.floor(Math.random()*maxDelta)*sign;
        price += delta;
        if( price <= lowest){
            price = lowest;
        }
        deltas.push(delta);
        ret.push(price);

        if( price > max){
            max = price;
        }
        if( price < min){
            min = price;
        }

        total += price;
    }
    let open = base;
    let close = ret[num-1];
    let avg = total/num;
    let situation = (close/open-1)*100;
    //console.log(`open=${open}, close=${close}, avg=${avg}, max=${max}, min=${min} situation=${situation.toFixed(2)}%`);
    return {s:situation.toFixed(2),prices:ret};
}

function myStrategy_1(simulatePrices){
    let myTotal = simulatePrices[0];
    let myLastPrice = simulatePrices[0];
    let myAvgPrice = myTotal;
    let myBuyTimes = 1;

    for (let i=1; i<simulatePrices.length; ++i ){
        let curPrice = simulatePrices[i];

        // console.log(`curPrice:${curPrice}, myCount = ${myBuyTimes}, myTotal = ${myTotal}, myAvgPrice = ${myAvgPrice}`);
        if( myLastPrice==curPrice ){
            continue;
        }

        if( curPrice>=myAvgPrice ){
            myTotal += curPrice;
            myBuyTimes += 1;
        }
        //
        else {
            myTotal += curPrice*2;
            myBuyTimes += 2;
        }

        
        myAvgPrice = myTotal/myBuyTimes;
    }
    // console.log(`myCount = ${myBuyTimes}, myTotal = ${myTotal}, myAvgPrice = ${myAvgPrice}`);

    let s = (simulatePrices[simulatePrices.length-1]/myAvgPrice-1)*100
    //console.log(`myCount = ${myBuyTimes}, myTotal = ${myTotal}, myAvgPrice = ${myAvgPrice}, situation = ${s.toFixed(2)}%`);
    return s.toFixed(2);
}

function main(){
    let originTotal = 0;
    let myTotal = 0;
    for( let i=0; i<365; ++i){
        let simulateData = generateSimulateData(1000,86400,200,5);
        let simulatePrices = simulateData["prices"];
        let originalSituation = Number(simulateData["s"]);
        let mySituation = Number(myStrategy_1(simulatePrices));
        console.log(`originalSituation:${originalSituation.toFixed(2)}%, mySituation:${mySituation.toFixed(2)}%`);
        originTotal += originalSituation;
        myTotal += mySituation;
    }
    originTotal /= 365;
    myTotal /= 365;
    console.log(`originAvg:${originTotal.toFixed(2)}% , myAvg:${myTotal.toFixed(2)}%`);
}
main();