该策略运用海洋理论中的网格交易方法,在设定的价格范围内均匀分布网格线,根据价格与网格线的关系进行买入和卖出操作。策略具有自动计算网格价格区间、均匀分布网格线等特点,可以有效控制风险。
该策略首先根据用户选择或默认设置计算价格网格的上下限,即网格的最高价和最低价。计算方式有两种,一是查找回测周期内的最高价和最低价,二是计算一定周期的均线。然后根据用户设置的网格数量,均匀分布网格线。
交易信号的产生依赖于价格与网格线的关系。当价格低于下方网格线时,在该网格线处按固定数量开仓做多;当价格高于上方网格线时,在该网格线处按固定数量平仓。这样随着价格的波动,仓位也在网格内波动,实现获利。
具体来说,策略维护一个网格线价格数组和一个bool数组表示每个网格线是否有挂单。当价格低于某一网格线且该线未有挂单时,在该线价位做多;当价格高于某一网格线且下方网格线有挂单时,在下方网格线处平仓。通过这种方式,实现网格交易。
自动计算网格区间,避免手动设置的困难。可以选择不同计算方式。
均匀分布网格线,避免网格密集导致过度交易。网格线数量可调。
采用网格交易方法,可以有效控制风险,价格波动在网格内则一直可以获利。
对价格没有方向预期,适用于震荡行情。
手续费率和仓位数可自定义设置,适应不同交易品种。
可视化展示网格线,容易掌握交易情况。
突破网格区间风险。价格突破网格上下限会导致亏损扩大。
过于宽松的网格间距风险。网格过宽难以获利,但过窄又增加手续费。需权衡。
持仓时间过长风险。长时间持仓难以获利 yet增加手续费损失。
参数设置不当风险。如回测周期或均线周期等参数设置不当,会影响网格区间计算。
市场系统性风险。该策略更适合震荡行情,不适合长期单边行情。
优化网格参数设置。综合考虑行情特点、交易成本等因素,优化网格数量、回测周期等参数。
网格区间动态调整。当市场发生较大变化时,可以引入动态调整网格区间的机制。
加入止损机制。设定合理的止损线,避免亏损过大。止损线也可以动态调整。
结合其他指标过滤交易。如布林线,趋势指标等,避免不适宜的交易。
优化资金利用效率。加入冷热分析,在波动较小时减少交易。
该策略利用网格交易原理,实现了风险可控的震荡行情交易。策略具有自动计算网格、均匀分布网格等优点,可以通过调整参数适应不同市场环境。风险可控且易于操作。但策略也存在一定局限性,需要持续优化以适应市场变化。总体来说,该策略为网格交易提供了一种较为标准化和可参数化的实现思路。
/*backtest
start: 2023-09-12 00:00:00
end: 2023-10-12 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
strategy("(IK) Grid Script", overlay=true, pyramiding=14, close_entries_rule="ANY", default_qty_type=strategy.cash, initial_capital=100.0, currency="USD", commission_type=strategy.commission.percent, commission_value=0.1)
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", 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", 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", defval=8, maxval=15, minval=3, type=input.integer) // how many grid lines are in your grid
strategy.initial_capital = 50000
f_getGridBounds(_bs, _bl, _bd, _up) =>
if _bs == "Hi & Low"
_up ? highest(close, _bl) * (1 + _bd) : lowest(close, _bl) * (1 - _bd)
else
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))
gridArr
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)
break
arr
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
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=(strategy.initial_capital/(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)