In the last article, we made a simple grid strategy together. In this article, we upgraded and expanded this strategy into a multi species spot grid strategy, and let this strategy be tested in practice. The purpose is not to find a "holy grail", but to discuss various problems and solutions when designing strategies. This article will explain some of my experience in designing this strategy. The content of this article is slightly complicated and it requires a certain foundation in programming.

Design thinking based on strategic needs

This article, like the previous one, still discusses design based on the FMZ Quant (FMZ.COM).

  • Multi-species
    To put it bluntly, I think this grid strategy can not only do BTC_USDT, but also LTC_USDT/EOS_USDT/DOGE_USDT/ETC_USDT/ETH_USDT. Anyway, the spot trading pairs and the varieties that want to run are all traded on the grid at the same time. Hmm~ It feels good to capture the volatile market of multiple species.
    The requirement sounds very simple, and the problem comes when designing.
  • 1. First, the market quotations of multiple varieties are obtained. This is the first problem to be solved. After consulting the exchange's API documentation, I found that most exchanges provide aggregated market interfaces.
    OK, use the aggregated market interface to obtain data.
  • 2. The second problem encountered is account assets. Because it is a multi species strategy, it is necessary to consider the management of each trading pair asset separately. And we need to get data for all assets at once, and record them. Why do we need to get the account asset data? Why do we need to separate the records of each pair?
    Because you need to judge the available assets when placing an order. Is it necessary to obtain it before judging it?
    And you need to calculate the profitis, is it also necessary to record an initial account asset data first, then obtain the current account asset data and compare it with the initial one to calculate the profit and loss?
    Fortunately, the asset account interface of the exchange usually returns all currency asset data, we only need to obtain it once, and then process the data.
  • 3. Strategy parameter design. The parameter design of multi species is quite different from the parameter design of single-variety, although the trading logic of each variety of multi-variety is the same, it is possible that the parameters during trading are different. For example, in the grid strategy, you may want to trade 0.01 BTC each time when doing BTC_USDT trading pair, but it is obviously inappropriate to use this parameter (trading 0.01 coins) when doing DOGE_USDT. Of course, you can also deal with the USDT amount. But there will still be problems. What if you want to trade 1000U for BTC_USDT and 10U for DOGE_USDT? The demand can never be satisfied.
    There may be someone who will think about this problem and then ask: "I can set several sets of parameters to control the parameters of different trading pairs to be done separately."
    This is still not flexible enough to meet the needs, how many sets of parameters are good to set? Three sets of parameters are set, what if I want to make 4 varieties? Do I have to modify the strategy and increase the parameters?…
    Therefore, when designing the parameters of the multi species strategy, it is necessary to fully consider the needs of such differentiated parameters. One solution is to design the parameters as ordinary strings or JSON strings.
    For example: ETHUSDT:100:0.002|LTCUSDT:20:0.1 Among them, "|" divides the data of each species, which means that ETHUSDT:100:0.002 controls the ETH_USDT trading pair, and LTCUSDT:20:0.1 controls the LTC_USDT trading pair. The middle "|" is used to divide.
    ETHUSDT:100:0.002, where ETHUSDT indicates what the trading pair you want to do, 100 is the grid spacing, 0.002 is the number of ETH coins traded in each grid, and the ":" is to divide these data (of course, these parameter rules are made by the strategy designer, you can design anything according to your needs).
    These strings contain the parameter information of each species you want to do. Parse these strings in the strategy, and assign values ​​to the variables of the strategy to control the trading logic of each species. How to parse it? Still use the above example. function main() { var net = [] // The recorded grid parameters, use the data when running to the grid trading logic var params = "ETHUSDT:100:0.002|LTCUSDT:20:0.1" var arrPair = params.split("|") _.each(arrPair, function(pair) { var arr = pair.split(":") var symbol = arr[0] // Trading pair name var diff = parseFloat(arr[1]) // Grid spacing var amount = parseFloat(arr[2]) // Grid order volume net.push({symbol : symbol, diff : diff, amount : amount}) }) Log("Grid parameter data:", net) } /upload/asset/28da0abb2a353a9007021.png Looking at this, the parameters are parsed. Of course, you can also use JSON strings directly, which is simpler. function main() { var params = '[{"symbol":"ETHUSDT","diff":100,"amount":0.002},{"symbol":"LTCUSDT","diff":20,"amount":0.1}]' var net = JSON.parse(params) // The recorded grid parameters, use the data when running to the grid trading logic _.each(net, function(pair) { Log("Trading pairs:", pair.symbol, pair) }) } /upload/asset/28eb2f9d3b2dc88e0bfe5.png
  • 4. Data durability
    There is also a big difference between the strategies that can be applied into practice and the tutorial strategies. The tutorial strategies in the previous article are only a preliminary test of strategy logic and design, and there are more issues to consider when it comes to the real world. In real bot, it is possible to start and stop the real trading. At this time, all the data during the real bot operation will be lost. So how to make the real bot restart to continue running in the previous status after it stopped?
    Here, it is necessary to save the key data persistently when the real bot is running, so that the data can be read and continued to run when it is restarted.
    You can use the _G() function on the FMZ Quantitative Trading Platform, or use the database operation function DBExec(), and you can check the FMZ API documentation for details. For example, we design a tail sweep function and use the _G() function to save grid data. var net = null function main() { // Strategy main functions // Read the stored net first net = _G("net")// ...} function onExit() { _G("net", net) Log("Perform tail-sweeping processing and save data", "#FF0000") } function onexit() { // The exit sweep function defined by the platform system, triggered the execution when the real bot is clicked to stop onExit() } function onerror() { // The abnormal exit function defined by the platform system, triggered the execution when the program is abnormal onExit() }
  • 5. Limits such as order quantity precision, order price precision, minimum order quantity, and minimum order amount, etc. The backtesting system does not impose such strict restrictions on the order amount and order accuracy, but each exchange can have strict standards for the price and order amount when placing an order in the real bot, and these restrictions are not the same in different exchanges. Therefore, there are beginners who test in the backtesting system without problems. Once the real bot is launched, there are various problems when the trading is triggered, and then the content of the error message is not read, and various crazy phenomena appear. For multi-species cases, this requirement is more complicated. For a single-species strategy, you can design a parameter to specify information such as accuracy, but when designing a multi-species strategy, it is obvious that writing this information into the parameters will make the parameters very bloated. At this time, you need to check the API documentation of the exchange to see if there is an interface information related to trading pairs in the exchange documentation. If there are, you can design an automatic access interface in the strategy to obtain information such as accuracy, and configure it into the trading pair information involved in the trading (in short, the accuracy or something is obtained from the exchange automatically, and then adapted to the variables related to the strategy parameters).
  • 6. Adaptation for different exchanges
    Why put this question at the end?
    Because the solutions to these problems we talked about above will bring about the last problem, because our strategy plans to use the aggregated market interface, access to the exchange trading pair accuracy and other data adaptive, access to account information to deal with each trading pair separately, these solutions can vary greatly from exchange to exchange.
    There are differences in interface calls and mechanisms. For spot exchanges, the difference is smaller if the grid strategy is extended to the futures version. The differences in the mechanism of various exchanges are even greater. One solution is to design an FMZ template class library. Write the design in the class library to implement these differences. Reduce the coupling between the strategy itself and the exchange.
    The disadvantage of this is that you need to write a template class library, and implement it specifically for each exchange difference in this template.

Design a template class library

Based on the above analysis, a template class library is designed to reduce the coupling between the strategy and the exchange mechanism and interface.

We can design this template class library like this (part of the code is omitted):

function createBaseEx(e, funcConfigure) {
    var self = {}
    self.e = e 

    self.funcConfigure = funcConfigure = e.GetName()
    self.type ="Futures_") ? "Futures" : "Spot"
    self.label = e.GetLabel()

    // Interfaces to be implemented
    self.interfaceGetTickers = null   // Create a function to asynchronously obtain a thread of aggregated market data
    self.interfaceGetAcc = null       // Create a function that asynchronously obtains account data thread
    self.interfaceGetPos = null       // Get a position
    self.interfaceTrade = null        // Create concurrent orders
    self.waitTickers = null           // Waiting for concurrent market data 
    self.waitAcc = null               // Waiting for account concurrent data
    self.waitTrade = null             // Waiting for order concurrent data
    self.calcAmount = null            // Calculate the order volume based on data such as trading pair accuracy
    self.init = null                  // Initialization work, obtaining data such as accuracy

    // Execute the configuration function to configure the object

    // Check whether the interfaces agreed by configList are implemented
    _.each(configList, function(funcName) {
        if (!self[funcName]) {
            throw "interface" + funcName + "unimplemented"

    return self

$.createBaseEx = createBaseEx
$.getConfigureFunc = function(exName) {
    dicRegister = {
        "Futures_OKCoin" : funcConfigure_Futures_OKCoin,    // Implementation of OK futures
        "Huobi" : funcConfigure_Huobi,
        "Futures_Binance" : funcConfigure_Futures_Binance,
        "Binance" : funcConfigure_Binance,
        "WexApp" : funcConfigure_WexApp,                    // Implementation of wexApp
    return dicRegister

In the template, it is written for specific exchanges, take FMZ's simulated bot WexApp as an example:

function funcConfigure_WexApp(self) {
    var formatSymbol = function(originalSymbol) {
        // BTC_USDT
        var arr = originalSymbol.split("_")
        var baseCurrency = arr[0]
        var quoteCurrency = arr[1]
        return [originalSymbol, baseCurrency, quoteCurrency]

    self.interfaceGetTickers = function interfaceGetTickers() {
        self.routineGetTicker = HttpQuery_Go("")

    self.waitTickers = function waitTickers() {
        var ret = []
        var arr = JSON.parse(self.routineGetTicker.wait()).data
        _.each(arr, function(ele) {
                bid1: parseFloat(, 
                bid1Vol: parseFloat(-1),
                ask1: parseFloat(ele.sell), 
                ask1Vol: parseFloat(-1),
                symbol: formatSymbol([0],
                type: "Spot", 
        return ret 

    self.interfaceGetAcc = function interfaceGetAcc(symbol, updateTS) {
        if (self.updateAccsTS != updateTS) {
            self.routineGetAcc = self.e.Go("GetAccount")

    self.waitAcc = function waitAcc(symbol, updateTS) {
        var arr = formatSymbol(symbol)
        var ret = null 
        if (self.updateAccsTS != updateTS) {
            ret = self.routineGetAcc.wait().Info
            self.bufferGetAccRet = ret 
        } else {
            ret = self.bufferGetAccRet
        if (!ret) {
            return null 
        var acc = {symbol: symbol, Stocks: 0, FrozenStocks: 0, Balance: 0, FrozenBalance: 0, originalInfo: ret}
        _.each(, function(ele) {
            if (ele.currency == arr[1]) {
                // baseCurrency
                acc.Stocks = parseFloat(
                acc.FrozenStocks = parseFloat(ele.frozen)
            } else if (ele.currency == arr[2]) {
                // quoteCurrency
                acc.Balance = parseFloat(
                acc.FrozenBalance = parseFloat(ele.frozen)
        return acc

    self.interfaceGetPos = function interfaceGetPos(symbol, price, initSpAcc, nowSpAcc) {
        var symbolInfo = self.getSymbolInfo(symbol)
        var sumInitStocks = initSpAcc.Stocks + initSpAcc.FrozenStocks
        var sumNowStocks = nowSpAcc.Stocks + nowSpAcc.FrozenStocks
        var diffStocks = _N(sumNowStocks - sumInitStocks, symbolInfo.amountPrecision)
        if (Math.abs(diffStocks) < symbolInfo.min / price) {
            return []
        return [{symbol: symbol, amount: diffStocks, price: null, originalInfo: {}}]

    self.interfaceTrade = function interfaceTrade(symbol, type, price, amount) {
        var tradeType = ""
        if (type == self.OPEN_LONG || type == self.COVER_SHORT) {
            tradeType = "bid"
        } else {
            tradeType = "ask"
        var params = {
            "market": symbol,
            "side": tradeType,
            "amount": String(amount),
            "price" : String(-1),
            "type" : "market"
        self.routineTrade = self.e.Go("IO", "api", "POST", "/api/v1/private/order", self.encodeParams(params))

    self.waitTrade = function waitTrade() {
        return self.routineTrade.wait()

    self.calcAmount = function calcAmount(symbol, type, price, amount) {
        // Obtain trading pair information
        var symbolInfo = self.getSymbolInfo(symbol)
        if (!symbol) {
            throw symbol + ", the trading pair information cannot be checked"
        var tradeAmount = null 
        var equalAmount = null  // Number of coins recorded
        if (type == self.OPEN_LONG || type == self.COVER_SHORT) {
            tradeAmount = _N(amount * price, parseFloat(symbolInfo.pricePrecision))
            // Check the minimum trading volume
            if (tradeAmount < symbolInfo.min) {
                Log(, " tradeAmount:", tradeAmount, "less than", symbolInfo.min)
                return false 
            equalAmount = tradeAmount / price
        } else {
            tradeAmount = _N(amount, parseFloat(symbolInfo.amountPrecision))
            // Check the minimum trading volume
            if (tradeAmount < symbolInfo.min / price) {
                Log(, " tradeAmount:", tradeAmount, "less than", symbolInfo.min / price)
                return false 
            equalAmount = tradeAmount
        return [tradeAmount, equalAmount]

    self.init = function init() {   // Functions that deal with conditions such as accuracy automatically
        var ret = JSON.parse(HttpQuery(""))
        _.each(, function(symbolInfo) {
                symbol: symbolInfo.pair,
                amountPrecision: parseFloat(symbolInfo.basePrecision),
                pricePrecision: parseFloat(symbolInfo.quotePrecision),
                multiplier: 1,
                min: parseFloat(symbolInfo.minQty),
                originalInfo: symbolInfo

Then using this template in a strategy is simple:

function main() {
    var fuExName = exchange.GetName()
    var fuConfigureFunc = $.getConfigureFunc()[fuExName]
    var ex = $.createBaseEx(exchange, fuConfigureFunc)

    var arrTestSymbol = ["LTC_USDT", "ETH_USDT", "EOS_USDT"]
    var ts = new Date().getTime()

    // Test to get tickers
    var tickers = ex.getTickers()
    Log("tickers:", tickers)

    // Test to obtain account information
    ex.goGetAcc(symbol, ts)

    _.each(arrTestSymbol, function(symbol) {        
        _.each(tickers, function(ticker) {
            if (symbol == ticker.originalSymbol) {
                // print ticker data
                Log(symbol, ticker)

        // print asset data
        var acc = ex.getAcc(symbol, ts)
        Log("acc:", acc.symbol, acc)

Strategy real bot

It is very simple to design and write a strategy based on the above template. The entire strategy is about 300+ lines and implements a digital currency spot multi-species grid strategy.



It's losing money currentlyT_T, the source code will not be released for the time being.

Here are a few registration codes, if you are interested, you can use the wexApp to try:

Buy address:
Registration code: 

Just over 200 U, when I just started running, I encountered a big one-sided market, but I recovered slowly. The biggest advantage of spot grids is: "I Can fall asleep!"
The stability is not bad. It has not been modified since May 27th, and futures grid has not dared to try temporarily.

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