高级量化交易策略：VWMA结合机器学习增强型RSI的动态交易系统

/*backtest
start: 2024-07-08 00:00:00
end: 2025-07-04 08:00:00
period: 4d
basePeriod: 4d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=5
strategy("VWMA + ML RSI Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)

// === VWMA INPUTS ===
length = input.int(200, minval=1, title="VWMA Length")
src = input.source(hlc3, title="Source")
mult = input.float(3.0, minval=0.001, maxval=50, title="Multiplier")

// === VWMA CALCULATION ===
basis = ta.vwma(src, length)
plot(basis, title="VWMA Basis", color=color.fuchsia, linewidth=2)

// === ML RSI Actual Integration ===
rsiLength = input.int(14, title="RSI Length")
smoothingLength = input.int(3, "Smoothing Length")
mlMaType = input.string("ALMA", "MA Type", options=["SMA", "EMA", "DEMA", "TEMA", "WMA", "VWMA", "SMMA", "HMA", "LSMA", "ALMA"])
almaSigma = input.int(4, "ALMA Sigma")

// === Moving Average Function ===
calcMovingAverage(type, src, length, sigma) =>
    float result = na
    if type == "SMA"
        result := ta.sma(src, length)
    else if type == "EMA"
        result := ta.ema(src, length)
    else if type == "DEMA"
        e1 = ta.ema(src, length)
        e2 = ta.ema(e1, length)
        result := 2 * e1 - e2
    else if type == "TEMA"
        e1 = ta.ema(src, length)
        e2 = ta.ema(e1, length)
        e3 = ta.ema(e2, length)
        result := 3 * (e1 - e2) + e3
    else if type == "WMA"
        norm = 0.0
        sum = 0.0
        for i = 0 to length - 1
            weight = (length - i)
            norm := norm + weight
            sum := sum + src[i] * weight
        result := sum / norm
    else if type == "VWMA"
        result := ta.vwma(src, length)
    else if type == "SMMA"
        result := ta.rma(src, length)
    else if type == "HMA"
        result := ta.hma(src, length)
    else if type == "LSMA"
        result := ta.linreg(src, length, 0)
    else if type == "ALMA"
        result := ta.alma(src, length, 0.85, sigma)
    result

// === Final ML RSI ===
baseRsi = ta.rsi(close, rsiLength)
smoothedRsi = calcMovingAverage(mlMaType, baseRsi, smoothingLength, almaSigma)
finalRsi = smoothedRsi
plot(finalRsi, title="ML RSI", color=color.orange)

// === Buy Condition Flags ===
buyReady = close > basis and finalRsi > 60

// Delayed condition trackers
var bool waitingForRsi = false
var bool waitingForClose = false

if close > basis and finalRsi <= 60
    waitingForRsi := true
else if finalRsi > 60 and close <= basis
    waitingForClose := true

// Reset flags when both conditions meet
if buyReady
    waitingForRsi := false
    waitingForClose := false

// Final Buy Condition
shouldBuy = buyReady or (waitingForRsi and finalRsi > 60 and close > basis) or (waitingForClose and close > basis and finalRsi > 60)

// === Strategy Entry ===
if shouldBuy and strategy.position_size == 0
    strategy.entry("Long", strategy.long)

// === Take Profit and Stop Loss ===
takeLevel = strategy.position_avg_price * 1.015
stopLevel = strategy.position_avg_price * 0.985

// === Exit Conditions ===
sellCondition = close < basis and finalRsi < 40

if strategy.position_size > 0
    strategy.exit("TP/SL", from_entry="Long", limit=takeLevel, stop=stopLevel)
    if sellCondition
        strategy.close("Long")

// === Buy Signal Plot ===
if shouldBuy and strategy.position_size == 0
    label.new(bar_index, low, "BUY", style=label.style_label_up, color=color.green, textcolor=color.white)

// === Sell Signal Plot ===
if sellCondition and strategy.position_size > 0
    label.new(bar_index, high, "SELL", style=label.style_label_down, color=color.red, textcolor=color.white)

// === Plotting Levels for Visuals ===
hline(60, "Buy ML RSI Threshold", color=color.green)
hline(40, "Sell ML RSI Threshold", color=color.red)

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