基于RSI的EA，Pine转mq5
不需要实现指标和图表物件，只需要实现下单功能，实现EA。//@version=5
const int maxDistanceToLastBar = 5000
const int labelCooldown = 8
const int KDELimit = 300
strategy("RSI短线", shorttitle = "RSI短线",overlay = true, max_labels_count = 500)
rsiLengthInput = input.int(14, minval = 1, title="RSI Length", group="RSI Settings")
rsiSourceInput = input.source(close, "Source", group="RSI Settings")
highPivotLen = input.int(21,"High Pivot Length", minval = 1, group = "Pivots", display = display.none)
lowPivotLen = input.int(21, "Low Pivot Length", minval = 1, group = "Pivots", display = display.none)
activationThresholdStr = input.string("Medium", "Activation Threshold", options = ["High", "Medium", "Low"], group = "KDE", tooltip = "Determines the amount of arrows shown. Higher options will result in more arrows being rendered.")
string KDEKernel = input.string("Gaussian", "Kernel", options=['Uniform', 'Gaussian', 'Sigmoid'], group = "KDE", tooltip = "The kernel function for KDE calculation. Gaussian is a commonly used kernel and is based on normal distribution.")
float KDEBandwidth = input.float(2.71828, "Bandwidth", group = "KDE", tooltip = "This setting sets the smoothness of the KDE function output.")
int KDEStep = input.int(100, "Nº Bins", minval = 1, group = "KDE", tooltip = "The number of elements the KDE Probability array will have. Higher settings will result in greater precision.")
activationThreshold = (activationThresholdStr == "High" ? 0.4 : activationThresholdStr == "Medium" ? 0.25 : 0.15)
probMode = "Sum"
minPadding =false
bearishColor = input.color(#f23646, "High Pivots", group = "Style", inline = "col", display = display.none)
neutralColor = input.color(color.gray, "Neutral", group = "Style", inline = "col", display = display.none)
bullishColor = input.color(#089981, "Low Pivots", group = "Style", inline = "col", display = display.none)
RSILabelsEnabled = input.bool(true, "RSI Labels", group = "Style")
KDELabelsEnabled = input.bool(true, "KDE Labels", group = "Style")
rsi = ta.rsi(rsiSourceInput, rsiLengthInput)
//#region KDE
gaussian (float distance, float bandwidth = 1.0) => 1.0 / math.sqrt(2.0 * math.pi) * math.pow(math.e, -0.5 * math.pow(distance / bandwidth, 2.0))
uniform (float distance, float bandwidth = 1.0) => (math.abs(distance) > bandwidth) ? 0.0 : 0.5
sigmoid (float distance, float bandwidth = 1.0) => 2.0 / math.pi * (1.0 / (math.pow(math.e, (distance / bandwidth)) + math.pow(math.e, -(distance / bandwidth))))
kde (array<float> arr, string kernel, float bandwidth, int steps) =>
arrSize = arr.size()
arrRange = arr.range()
arrMin = arr.min() - (minPadding ? (arrRange / 2.0) : 0)
stepCount = arrRange / steps
densityRange = array.new<float>(steps * 2)
for i = 0 to (steps * 2) - 1
densityRange.set(i, arrMin + i * stepCount)
xArr = array.new<float>()
yArr = array.new<float>()
for i = 0 to densityRange.size() - 1
float temp = 0
for j = 0 to arr.size() - 1
temp += gaussian(densityRange.get(i) - arr.get(j), 1.0 / bandwidth)
xArr.push(densityRange.get(i))
yArr.push(1.0 / arrSize * temp)
//#endregion
//#region Pivots
prefixSum (array<float> arr, int l, int r) =>
arr.get(r) - (l == 0 ? 0 : arr.get(l - 1))
float MidKDEHigh = na
float MidKDELow = na
var array<float> KDEHighX = na
var array<float> KDEHighY = na
var array<float> KDEHighYSum = array.new<float>()
var array<float> KDELowX = na
var array<float> KDELowY = na
var array<float> KDELowYSum = array.new<float>()
highPivot = ta.pivothigh(highPivotLen, highPivotLen)
lowPivot = ta.pivotlow(lowPivotLen, lowPivotLen)
var highPivotRSIs = array.new<float>()
var lowPivotRSIs = array.new<float>()
if not na(highPivot)
if highPivotRSIs.size() > KDELimit
highPivotRSIs.remove(0)
highPivotRSIs.push(rsi)
= kde(highPivotRSIs, KDEKernel, KDEBandwidth, KDEStep)
KDEHighX := KDEHighX1
KDEHighY := KDEHighY1
KDEHighYSum.clear()
temp = 0.0
for i = 0 to KDEHighY.size() - 1
temp += KDEHighY.get(i)
KDEHighYSum.push(temp)
MidKDEHigh := array.get(KDEHighX, array.indexof(KDEHighY, array.max(KDEHighY)))
if not na(lowPivot)
if lowPivotRSIs.size() > KDELimit
lowPivotRSIs.remove(0)
lowPivotRSIs.push(rsi)
= kde(lowPivotRSIs, KDEKernel, KDEBandwidth, KDEStep)
KDELowX := KDELowX1
KDELowY := KDELowY1
KDELowYSum.clear()
temp = 0.0
for i = 0 to KDELowY.size() - 1
temp += KDELowY.get(i)
KDELowYSum.push(temp)
MidKDELow := array.get(KDELowX, array.indexof(KDELowY, array.max(KDELowY)))
//#endregion
float lowProb = na
float maxLowProb = na
float highProb = na
float maxHighProb = na
if last_bar_index - maxDistanceToLastBar < bar_index
if highPivotRSIs.size() > 0
highXIndexL = array.binary_search_leftmost(KDEHighX, rsi)
highXIndexR = math.min(array.binary_search_rightmost(KDEHighX, rsi), KDEHighX.size() - 1)
nearestIndex = (math.abs(rsi - KDEHighX.get(highXIndexL)) < math.abs(rsi - KDEHighX.get(highXIndexR))) ? highXIndexL : highXIndexR
highProb := prefixSum(KDEHighYSum, 0, nearestIndex)
if lowPivotRSIs.size() > 0
lowXIndexL = array.binary_search_leftmost(KDELowX, rsi)
lowXIndexR = math.min(array.binary_search_rightmost(KDELowX, rsi), KDELowX.size() - 1)
nearestIndex = (math.abs(rsi - KDELowX.get(lowXIndexL)) < math.abs(rsi - KDELowX.get(lowXIndexR))) ? lowXIndexL : lowXIndexR
lowProb := prefixSum(KDELowYSum, nearestIndex, KDELowYSum.size() - 1)
//#region Draw Pivots
color curColor = na
if (not na(KDELowY)) and (not na(KDEHighY))
if lowProb > KDELowY.sum() * (1.0 - activationThreshold)
curColor := bullishColor
else if highProb > KDEHighY.sum() * (1.0 - activationThreshold)
curColor := bearishColor
long = na(curColor) and curColor == bullishColor and barstate.isconfirmed
short = na(curColor) and curColor == bearishColor and barstate.isconfirmed
if long
strategy.entry("Long", strategy.long)
if short
strategy.entry("Short", strategy.short)
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