Adaptive Grid Trading Strategy Based on Quantitative Trading Platform

Author: ChaoZhang, Date: 2024-02-21 10:55:21



This strategy is an adaptive grid trading strategy based on quantitative trading platforms. It sets up automatic or manual grid trading ranges and places buy and sell orders at equal intervals within the range to implement grid trading. When the price breaks through the upper or lower limit of the grid, the strategy automatically adjusts the grid range.

Strategy Principle

  1. Set upper and lower limit prices for the grid. Automatically calculate prices within a certain interval of the highest and lowest historical prices as the upper and lower limits, or manually set fixed upper and lower limit prices.

  2. Calculate the price interval for each grid based on the upper and lower limit prices and the number of grids.

  3. Arrange multiple buy and sell points at equal intervals between the upper and lower limit prices as the grid.

  4. When the market price breaks through the lower limit of the grid, place a buy order at the next grid below the grid where the latest unclosed order is located; when the market price breaks through the upper limit of the grid, place a sell order at the grid above the grid where the latest unclosed order is located.

  5. Thus, continue to buy and sell operations within the upper and lower bounds of the grid. When the price trend reverses, the previous orders will gradually take profit or stop loss.

Advantage Analysis

  1. Grid trading can profit in range-bound and oscillating markets.

  2. Adaptive adjustment of grid range can automatically adjust based on market fluctuations without manual intervention.

  3. The amount of capital investment can be preset to allocate risks across grids.

  4. The logic is simple and easy to understand, and the parameters are flexible to adjust.

Risk Analysis

  1. Breaking through the upper and lower limits may lead to losses

    • Solution: Reasonably set stop loss position.
  2. Trending markets may lead to repeated losses

    • Solution: Identify trends and timely suspend trading.
  3. Improper parameter settings

    • Solution: Adjust grid quantity and price interval parameters.

Optimization Directions

  1. Use machine learning to predict price fluctuation range and trends to dynamically adjust grid parameters.

  2. Switch to trend trading in trending markets to avoid grid trading losses.

  3. Incorporate risk control measures based on capital utilization rate, rate of return etc.

  4. Diversify across asset varieties to increase capital utilization.


This strategy is an adaptive grid strategy with automatically adjustable parameters, suitable for stocks, cryptocurrencies and foreign exchange products with fluctuating and range-bound movements. With adjusted Parameters, it can adapt to different market conditions and has practical value.

start: 2024-01-01 00:00:00
end: 2024-01-24 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]


strategy("Grid Bot Backtesting", overlay=false, pyramiding=3000, close_entries_rule="ANY",, initial_capital=100.0, currency="USD", commission_type=strategy.commission.percent, commission_value=0.025)
i_autoBounds    = input(group="Grid Bounds", title="Use Auto Bounds?", defval=true, type=input.bool)                             // calculate upper and lower bound of the grid automatically? This will theorhetically be less profitable, but will certainly require less attention
i_boundSrc      = input(group="Grid Bounds", title="(Auto) Bound Source", defval="Hi & Low", options=["Hi & Low", "Average"])     // should bounds of the auto grid be calculated from recent High & Low, or from a Simple Moving Average
i_boundLookback = input(group="Grid Bounds", title="(Auto) Bound Lookback", defval=250, type=input.integer, maxval=500, minval=0) // when calculating auto grid bounds, how far back should we look for a High & Low, or what should the length be of our sma
i_boundDev      = input(group="Grid Bounds", title="(Auto) Bound Deviation", defval=0.10, type=input.float, maxval=1, minval=-1)  // if sourcing auto bounds from High & Low, this percentage will (positive) widen or (negative) narrow the bound limits. If sourcing from Average, this is the deviation (up and down) from the sma, and CANNOT be negative.
i_upperBound    = input(group="Grid Bounds", title="(Manual) Upper Boundry(상단 가격)", defval=0.285, type=input.float)                      // for manual grid bounds only. The upperbound price of your grid
i_lowerBound    = input(group="Grid Bounds", title="(Manual) Lower Boundry(하단 가격)", defval=0.225, type=input.float)                      // for manual grid bounds only. The lowerbound price of your grid.
i_gridQty       = input(group="Grid Lines",  title="Grid Line Quantity(그리드 수)", defval=30, maxval=999, minval=1, type=input.integer)       // how many grid lines are in your grid
initial_balance = input(group="Trading option", title="Initial balance(투자금액)", defval=100, step=0.01)

start_time = input(group="Trading option",defval=timestamp('15 March 2023 06:00'), title='Start Time', type = input.time)
end_time = input(group="Trading option",defval=timestamp('31 Dec 2035 20:00'), title='End Time', type = input.time)
isAfterStartDate = true

tradingtime= (timenow - start_time)/(86400000*30)

f_getGridBounds(_bs, _bl, _bd, _up) =>
    if _bs == "Hi & Low"
        _up ? highest(close, _bl) * (1 + _bd) : lowest(close, _bl)  * (1 - _bd)
        avg = sma(close, _bl)
        _up ? avg * (1 + _bd) : avg * (1 - _bd)

f_buildGrid(_lb, _gw, _gq) =>
    gridArr = array.new_float(0)
    for i=0 to _gq-1
        array.push(gridArr, _lb+(_gw*i))

f_getNearGridLines(_gridArr, _price) =>
    arr = array.new_int(3)
    for i = 0 to array.size(_gridArr)-1
        if array.get(_gridArr, i) > _price
            array.set(arr, 0, i == array.size(_gridArr)-1 ? i : i+1)
            array.set(arr, 1, i == 0 ? i : i-1)

var upperBound      = i_autoBounds ? f_getGridBounds(i_boundSrc, i_boundLookback, i_boundDev, true) : i_upperBound  // upperbound of our grid
var lowerBound      = i_autoBounds ? f_getGridBounds(i_boundSrc, i_boundLookback, i_boundDev, false) : i_lowerBound // lowerbound of our grid
var gridWidth       = (upperBound - lowerBound)/(i_gridQty-1)                                                       // space between lines in our grid
var gridLineArr     = f_buildGrid(lowerBound, gridWidth, i_gridQty)                                                 // an array of prices that correspond to our grid lines
var orderArr        = array.new_bool(i_gridQty, false)                                                              // a boolean array that indicates if there is an open order corresponding to each grid line

var closeLineArr    = f_getNearGridLines(gridLineArr, close)                                                        // for plotting purposes - an array of 2 indices that correspond to grid lines near price
var nearTopGridLine = array.get(closeLineArr, 0)                                                                    // for plotting purposes - the index (in our grid line array) of the closest grid line above current price
var nearBotGridLine = array.get(closeLineArr, 1)                                                                    // for plotting purposes - the index (in our grid line array) of the closest grid line below current price
if isAfterStartDate
    for i = 0 to (array.size(gridLineArr) - 1)
        if close < array.get(gridLineArr, i) and not array.get(orderArr, i) and i < (array.size(gridLineArr) - 1)
            buyId = i
            array.set(orderArr, buyId, true)
            strategy.entry(id=tostring(buyId), long=true, qty=(initial_balance/(i_gridQty-1))/close, comment="#"+tostring(buyId))
        if close > array.get(gridLineArr, i) and i != 0
            if array.get(orderArr, i-1)
                sellId = i-1
                array.set(orderArr, sellId, false)
                strategy.close(id=tostring(sellId), comment="#"+tostring(sellId))

    if i_autoBounds
        upperBound  := f_getGridBounds(i_boundSrc, i_boundLookback, i_boundDev, true)
        lowerBound  := f_getGridBounds(i_boundSrc, i_boundLookback, i_boundDev, false)
        gridWidth   := (upperBound - lowerBound)/(i_gridQty-1)
        gridLineArr := f_buildGrid(lowerBound, gridWidth, i_gridQty)

    closeLineArr    := f_getNearGridLines(gridLineArr, close)
    nearTopGridLine := array.get(closeLineArr, 0)
    nearBotGridLine := array.get(closeLineArr, 1)

var table table =,6,8, frame_color = color.rgb(255, 255, 255),frame_width = 2,border_width = 2, border_color=color.rgb(255, 255, 255))

table.cell(table,0,0,"상단 라인 :",,0),text_color =color.white)    
table.cell(table,0,1,"하단 라인 :",,0),text_color =color.white)
table.cell(table,0,2,"그리드 수 :",,0),text_color =color.white)
table.cell(table,0,3,"투자금액 :",text_color =color.white,,0))
table.cell(table,0,4,"그리드당 투자금액 :",text_color =color.white,,0))
table.cell(table,1,0, tostring(upperBound, '###.#####')+ "  USDT",, 0),text_color =color.white)    
table.cell(table,1,1, tostring(lowerBound, '###.#####')+ "  USDT",, 0),text_color =color.white)
table.cell(table,1,2, tostring(i_gridQty, '###'),, 0),text_color =color.white)
table.cell(table,1,3, tostring(initial_balance,'###.##')+ "  USDT",, 0),text_color =color.white)
table.cell(table,1,4, tostring(initial_balance/i_gridQty,'###.##')+ "  USDT",, 0),text_color =color.white)

table.cell(table,2,0,"현재 포지션 :",text_color =color.white,,0))
table.cell(table,2,1,"현재 포지션 평단가 :",text_color =color.white,,0))
table.cell(table,2,2,"현재 포지션 수익 :",,0),text_color =color.white)
table.cell(table,2,3,"현재 포지션 수익 % :",,0),text_color =color.white)
table.cell(table,2,4,"현재 포지션 수수료 :",text_color =color.white,,0))

table.cell(table,3,0, tostring(strategy.position_size) +   syminfo.basecurrency + "\n"  + tostring(strategy.position_size*strategy.position_avg_price/1, '###.##') + "USDT" ,text_color =color.white,, 0))
table.cell(table,3,1, text=strategy.position_size>0 ? tostring(strategy.position_avg_price,'###.####')+ "  USDT" : "NOT TRADING",text_color =color.white,, 0))
table.cell(table,3,2, tostring(strategy.openprofit, '###.##')+ "  USDT",text_color =color.white,bgcolor=strategy.openprofit > 0 ? color.teal : color.maroon)
table.cell(table,3,3, tostring(strategy.openprofit/initial_balance*100, '###.##')+ "%",text_color =color.white,bgcolor=strategy.openprofit > 0 ? color.teal : color.maroon)
table.cell(table,3,4, "-" + tostring(strategy.position_avg_price*strategy.position_size*0.025/100,'###.##')+ "  USDT",text_color =color.white,, 0))

table.cell(table,4,0,"그리드 수익 :",text_color =color.white,,0))
table.cell(table,4,1,"그리드 수익률 :",text_color =color.white,,0))
table.cell(table,4,2,"총 수익 :",,0),text_color =color.white)    
table.cell(table,4,3,"총 수익률 :",,0),text_color =color.white)
table.cell(table,4,4,"현재 자산 :",,0),text_color =color.white)

table.cell(table,5,0, tostring(strategy.netprofit, '###.#####')+ "USDT", text_color =color.white,bgcolor=strategy.netprofit > 0 ? color.teal : color.maroon)
table.cell(table,5,1, tostring((strategy.netprofit)/initial_balance*100/tradingtime, '####.##') + "%",text_color =color.white,bgcolor=strategy.netprofit > 0 ? color.teal : color.maroon)
table.cell(table,5,2, tostring(strategy.netprofit+strategy.openprofit, '###.##') + "  USDT",text_color =color.white,bgcolor=strategy.netprofit+strategy.openprofit > 0 ? color.teal : color.maroon)
table.cell(table,5,3, tostring((strategy.netprofit+strategy.openprofit)/initial_balance*100, '####.##') + "%",text_color =color.white,bgcolor=strategy.netprofit+strategy.openprofit > 0 ? color.teal : color.maroon)
table.cell(table,5,4, tostring(initial_balance+strategy.netprofit+strategy.openprofit, '###.##')+ "  USDT", text_color =color.white,, 0))

// plot(strategy.initial_capital+ strategy.netprofit+strategy.openprofit, "총 수익 USDT",color=color.rgb(81, 137, 128))
// plot(initial_balance, "투자금액",color=color.rgb(81, 137, 128))