Quantum footprint chart is an advanced chart analysis tool, the English name is "Footprint Charts". It shows the trading activity of each price in a single K line. In addition to providing price information, it also provides information such as transaction volume, active buying, and active selling. It is a multi-dimensional technical chart that reveals the true distribution ratio of each price based on trading volume, explains the complex relationship between trading volume, price and time, and can provide more reference for traders.

What is Quantum Footprint

Simply put, Quantum Footprint provides market transparency by micro-analysing what happens to buyers and sellers at various price levels. What do you see in the graph of calorie footprint diagram:

  • K-line price
  • Active Buy Volume (ASK)
  • Active Sell Volume (BID)
  • Equilibrium ratio

As shown in the figure above, this is a demonstration version of the quantitative energy footprint chart implemented on the trading platform FMZ.COM. It is calculated based on the actual tick market. Detailed data are attached to the K line. When the mouse hovers over On the K line, the quantitative energy footprint data can be presented. The data in the blue square is its calculation result, which is divided into two columns in total, and the left column is all the price points of the current K line, which are arranged in order from large to small. The right column is the trading volume for each price level, subdivided into buying trading volume and selling trading volume, separated by "x". On the left of "x" is the volume of active buying, on the right of "x" is the volume of active selling.

The role of Quantum Energy Footprint

Think about what caused the price increase? The answer is simple... buyers and sellers. If there are more buyers than sellers, the price will rise; if there are more sellers than buyers, the price will fall. If the current number of sellers is approximately equal to the number of buying prices, then the market will show a volatile trend, that is, the market is in a balanced state. Once large buyers or sellers appear, this balance will be broken, and then the energy footprint map will show an extreme ratio of active buying and selling.

The energy footprint is dynamically displayed based on Tick data, so it is especially suitable for short-term intraday trading. Traders can accurately see the active trading volume on each price to explain the reasons or laws of price fluctuations and customize their own trading strategies. For example: if the buying volume on the left is much larger than the selling volume on the right, it means that market participants are actively buying, and the price may increase in the future; otherwise, if the selling volume on the right is much larger than the buying volume on the left, it means With market participants actively selling, prices may fall in the future.

Principle of Quantum Energy Footprint

The calculation principle of the Quantum Energy Footprint comes from the volume and market data. The current volume is calculated in real time according to the price changes of the market. If the market price rises, the volume is recorded as an active buying; if the market price falls, Then record the volume as actively selling. In the FMZ energy footprint graph, Tick data will be calculated in real time to accurately calculate the turnover of each Tick.

Quantum Energy Footprint Code Implementation

start: 2020-03-10 00:00:00
end: 2020-03-10 23:59:00
period: 1h
exchanges: [{"eid":"Futures_CTP","currency":"FUTURES"}]
mode: 1

var NewFuturesTradeFilter = function (period) {
    var self = {} // Create an object
    self.c = Chart({ // Create "Chart" chart
        tooltip: {
            xDateFormat:'%Y-%m-%d %H:%M:%S, %A',
        series: [{
            name: exchange.GetName(),
            data: []
    self.c.reset() // clear chart data
    self.pre = null // used to record the last data
    self.records = []
    self.feed = function (ticker) {
        if (!self.pre) {// If the previous data is not true
            self.pre = ticker // Assign the latest data
        var action ='' // Mark as empty string
        Log('ticker', ticker)
        Log('pre', self.pre)
        if (ticker.Last >= self.pre.Sell) {// If the last price of the latest data is greater than or equal to the selling price of the previous data
            action ='buy' // mark as buy
        } else if (ticker.Last <= self.pre.Buy) {// If the last price of the latest data is less than or equal to the bid price of the previous data
            action ='sell' // mark as sell
        } else {
            if (ticker.Last >= ticker.Sell) {// If the last price of the latest data is greater than or equal to the selling price of the latest data
                action ='buy' // mark as buy
            } else if (ticker.Last <= ticker.Buy) {// If the last price of the latest data is less than or equal to the buying price of the latest data
                action ='sell' // mark as "sell"
            } else {
                action ='both' // Mark as "both"
        // reset volume
        if (ticker.Volume <self.pre.Volume) {// If the volume of the latest data is less than the volume of the previous data
            self.pre.Volume = 0 // Assign the volume of the previous data to 0
        var amount = ticker.Volume-self.pre.Volume // the volume of the latest data minus the volume of the previous data
        if (action !='' && amount> 0) {// If the tag is not an empty string and the action is greater than 0
            var epoch = parseInt(ticker.Time / period) * period // Calculate the K-line timestamp and round it
            var bar = null
            var pos = undefined
            if (
                self.records.length == 0 || // If the K-line length is 0 or the last K-line timestamp is less than "epoch"
                self.records[self.records.length-1].time <epoch
            ) {
                bar = {
                    time: epoch,
                    data: {},
                    open: ticker.Last,
                    high: ticker.Last,
                    low: ticker.Last,
                    close: ticker.Last
                } // Assign the latest data to bar
                self.records.push(bar) // Add bar to the records array
            } else {// reassign bar
                bar = self.records[self.records.length-1] // the last bar of the previous data
                bar.high = Math.max(bar.high, ticker.Last) // the maximum price of the last bar of the previous data and the maximum value of the last price of the latest data
                bar.low = Math.min(bar.low, ticker.Last) // The minimum price of the last bar of the previous data and the minimum value of the last price of the latest data
                bar.close = ticker.Last // last price of latest data
                pos = -1
            if (typeof[ticker.Last] ==='undefined') {// If the data is empty
      [ticker.Last] = {// reassign value
                    buy: 0,
                    sell: 0
            if (action =='both') {// If the token is equal to both
      [ticker.Last]['buy'] += amount // buy accumulation
      [ticker.Last]['sell'] += amount // sell accumulated
            } else {
      [ticker.Last][action] += amount // mark accumulation
            var tips =''
            Object.keys( // Put the keys in the object into an array
                .sort() // sort
                .reverse() // Reverse the order in the array
                .forEach(function (p) {// traverse the array
                    tips +='<br>' + p + '' +[p].sell +'x' +[p].buy
            self.c.add( // Add data
                0, {
                    x: bar.time,
                    high: bar.high,
                    low: bar.low,
                    close: bar.close,
                    tips: tips
        self.pre = ticker // reassign
    return self // return object

// program entry
function main() {
    Log(_C(exchange.SetContractType,'MA888')) // subscription data
    var filt = NewFuturesTradeFilter(60000) // Create an object
    while (true) {// Enter loop mode
        var ticker = exchange.GetTicker() // Get exchange tick data
        if (ticker) {// If the Tick data is successfully obtained
            filt.feed(ticker) // Start processing data

Quantum Footprint Code Download

The quantitative code footprint chart strategy code has been released on the platform, and ordinary users can use it without setting.

To sum up

In actual use, the energy footprint map can also analyze the flow of funds from the perspective of volume. Whether it is to analyze the direction of large-scale trends or the short-term direction of the day, it plays a vital role. However, it should be noted that the so-called capital flow here does not refer to the entry and exit of funds, but rather reflects the market's willingness to buy and sell and the game behavior of the main players and retail investors.

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