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動的指数 動的平均 多階層利益傾向 戦略に従った

作者: リン・ハーンチャオチャン,日付: 2024年12月12日 14:29:53
タグ:BBATRCMOTPMO

チャンデ・モメント・オシレーター (MO)

波動性フィルターとしてのボリンジャー帯: 上部帯 = MA + (K * StdDev) 下帯 = MA - (K * StdDev)

入国条件:

  • ローング: 価格がゆっくり VIDYA の上を突破し,急上昇する VIDYA のトレンドと価格がボリンジャーバンド上を突破する
  • ショート: 価格が低速VIDYAを下回る速度のVIDYAトレンドと価格がボリンジャーバンドの下値を下回る低速VIDYAの下値

複数の層の利益取りのメカニズムには以下のものがある.

  1. ATRに基づく収益
  2. 収益率を計算する
  3. ショート・トレード利得率の倍数

戦略 の 利点

  1. ダイナミック適応性: VIDYA インジケーターは,伝統的な移動平均値よりもより敏感で,市場の変動に自動的に調整されます.
  2. 堅牢なリスク管理: 多段階の収益メカニズムにより,異なる価格レベルでの利益が確保される
  3. 格差した処理: 市場特性に適合する 長期・短期ポジションの異なる収益戦略
  4. 波動性フィルタリング:ボリンジャー帯は,偽のブレイクシグナルをフィルタリングするのに役立ちます.
  5. 柔軟なパラメータ: 異なる市場条件に調整可能なパラメータ

戦略リスク

  1. 変動市場リスク: 変動市場において誤った信号を生む可能性があります.
  2. スリップ効果:複数のテイク・プロフィートレベルで価格実行偏差が発生する可能性があります.
  3. パラメータ依存性: 異なる市場環境では パラメータを頻繁に調整する必要がある場合があります.
  4. システム複雑性: 多層の収益メカニズムは戦略の複雑性を高める
  5. ポジション管理のプレッシャー: 多重な利益引き上げレベルはポジション管理を複雑にする可能性があります

オプティマイゼーションの方向性

  1. ダイナミックパラメータ調整: 市場状況の自動調整のための適応パラメータシステムを開発する
  2. 市場環境認識: パラメータの切り替えのための市場状況識別モジュールを追加
  3. ストップ・ロスの最適化: リスク管理を改善するために動的ストップ・ロスのメカニズムを実装する
  4. シグナルフィルタリング: 信号信頼性の向上のために音量および他の補助指標を追加
  5. ポジション管理: よりスマートなポジション割り当てアルゴリズムを開発

概要

この戦略は,VIDYA指標のダイナミックな適応性とボリンジャーバンドの変動性フィルタリングを組み合わせて,包括的なトレンドフォローシステムを作成する.多層次利益採取メカニズムと差別化されたロング/ショートハンドリングは強い利益の可能性とリスク管理を提供します.しかし,ユーザーは市場の環境の変化を監視し,それに合わせてパラメータを調整し,強力なマネーマネジメントシステムを確立する必要があります.さらなる戦略最適化はパラメータ適応,市場環境認識,リスク管理の強化に焦点を当てるべきです.


/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-10 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © PresentTrading

// This strategy, "VIDYA ProTrend Multi-Tier Profit," is a trend-following system that utilizes fast and slow VIDYA indicators 
// to identify entry and exit points based on the direction and strength of the trend. 
// It incorporates Bollinger Bands as a volatility filter and features a multi-step take profit mechanism, 
// with adjustable ATR-based and percentage-based profit targets for both long and short positions. 
// The strategy allows for more aggressive take profit settings for short trades, making it adaptable to varying market conditions.

//@version=5
strategy("VIDYA ProTrend Multi-Tier Profit", overlay=true, precision=3, commission_value= 0.1, commission_type=strategy.commission.percent, slippage= 1, currency=currency.USD, default_qty_type = strategy.percent_of_equity, default_qty_value = 10, initial_capital=10000)


// User-defined inputs
tradeDirection = input.string(title="Trading Direction", defval="Both", options=["Long", "Short", "Both"])
fastVidyaLength = input.int(10, title="Fast VIDYA Length", minval=1)
slowVidyaLength = input.int(30, title="Slow VIDYA Length", minval=1)
minSlopeThreshold = input.float(0.05, title="Minimum VIDYA Slope Threshold", step=0.01)

// Bollinger Bands Inputs
bbLength = input.int(20, title="Bollinger Bands Length", minval=1)
bbMultiplier = input.float(1.0, title="Bollinger Bands Multiplier", step=0.1)

// Multi-Step Take Profit Settings
group_tp = "Multi-Step Take Profit"
useMultiStepTP = input.bool(true, title="Enable Multi-Step Take Profit", group=group_tp)
tp_direction = input.string(title="Take Profit Direction", defval="Both", options=["Long", "Short", "Both"], group=group_tp)
atrLengthTP =  input.int(14, title="ATR Length", group=group_tp)


// ATR-based Take Profit Steps
atrMultiplierTP1 = input.float(2.618, title="ATR Multiplier for TP 1", group=group_tp)
atrMultiplierTP2 = input.float(5.0, title="ATR Multiplier for TP 2", group=group_tp)
atrMultiplierTP3 = input.float(10.0, title="ATR Multiplier for TP 3", group=group_tp)

// Short Position Multiplier for Take Profit Percentages
shortTPPercentMultiplier = input.float(1.5, title="Short TP Percent Multiplier", group=group_tp)

// Percentage-based Take Profit Steps (Long)
tp_level_percent1 = input.float(title="Take Profit Level 1 (%)", defval=3.0, group=group_tp)
tp_level_percent2 = input.float(title="Take Profit Level 2 (%)", defval=8.0, group=group_tp)
tp_level_percent3 = input.float(title="Take Profit Level 3 (%)", defval=17.0, group=group_tp)

// Percentage-based Take Profit Allocation (Long)
tp_percent1 = input.float(title="Take Profit Percent 1 (%)", defval=12.0, group=group_tp)
tp_percent2 = input.float(title="Take Profit Percent 2 (%)", defval=8.0, group=group_tp)
tp_percent3 = input.float(title="Take Profit Percent 3 (%)", defval=10.0, group=group_tp)

// ATR-based Take Profit Percent Allocation (Long)
tp_percentATR1 = input.float(title="ATR TP Percent 1 (%)", defval=10.0, group=group_tp)
tp_percentATR2 = input.float(title="ATR TP Percent 2 (%)", defval=10.0, group=group_tp)
tp_percentATR3 = input.float(title="ATR TP Percent 3 (%)", defval=10.0, group=group_tp)

// Short position percentage allocations using the multiplier
tp_percent1_short = tp_percent1 * shortTPPercentMultiplier
tp_percent2_short = tp_percent2 * shortTPPercentMultiplier
tp_percent3_short = tp_percent3 * shortTPPercentMultiplier

tp_percentATR1_short = tp_percentATR1 * shortTPPercentMultiplier
tp_percentATR2_short = tp_percentATR2 * shortTPPercentMultiplier
tp_percentATR3_short = tp_percentATR3 * shortTPPercentMultiplier

// VIDYA Calculation Function
calcVIDYA(src, length) =>
    alpha = 2 / (length + 1)
    momm = ta.change(src)
    m1 = momm >= 0.0 ? momm : 0.0
    m2 = momm < 0.0 ? -momm : 0.0
    sm1 = math.sum(m1, length)
    sm2 = math.sum(m2, length)
    chandeMO = nz(100 * (sm1 - sm2) / (sm1 + sm2))
    k = math.abs(chandeMO) / 100
    var float vidya = na
    vidya := na(vidya[1]) ? src : (alpha * k * src + (1 - alpha * k) * vidya[1])
    vidya

// Calculate VIDYAs
fastVIDYA = calcVIDYA(close, fastVidyaLength)
slowVIDYA = calcVIDYA(close, slowVidyaLength)

// Bollinger Bands Calculation
[bbUpper, bbBasis, bbLower] = ta.bb(close, bbLength, bbMultiplier)

// Manual Slope Calculation (price difference over time)
calcSlope(current, previous, length) =>
    (current - previous) / length

// Slope of fast and slow VIDYA (comparing current value with value 'length' bars ago)
fastSlope = calcSlope(fastVIDYA, fastVIDYA[fastVidyaLength], fastVidyaLength)
slowSlope = calcSlope(slowVIDYA, slowVIDYA[slowVidyaLength], slowVidyaLength)

// Conditions for long entry with Bollinger Bands filter
longCondition = close > slowVIDYA and fastVIDYA > slowSlope and fastSlope > minSlopeThreshold and slowSlope > 1/2*minSlopeThreshold and close > bbUpper

// Conditions for short entry with Bollinger Bands filter
shortCondition = close < slowVIDYA and fastSlope < slowSlope and fastSlope < -minSlopeThreshold and slowSlope < -1/2*minSlopeThreshold and close < bbLower

// Exit conditions (opposite crossovers or flat slopes)
exitLongCondition = fastSlope < -minSlopeThreshold and slowSlope < -1/2*minSlopeThreshold or shortCondition
exitShortCondition = fastSlope > minSlopeThreshold and slowSlope > 1/2*minSlopeThreshold or longCondition

// Entry and Exit logic with trading direction
if (longCondition) and (strategy.position_size == 0) and (tradeDirection == "Long" or tradeDirection == "Both")
    strategy.entry("Long", strategy.long)

if (exitLongCondition) and strategy.position_size > 0 and (tradeDirection == "Long" or tradeDirection == "Both")
    strategy.close("Long")

if (shortCondition) and (strategy.position_size == 0) and (tradeDirection == "Short" or tradeDirection == "Both")
    strategy.entry("Short", strategy.short)

if (exitShortCondition) and strategy.position_size < 0 and (tradeDirection == "Short" or tradeDirection == "Both")
    strategy.close("Short")


if useMultiStepTP
    if strategy.position_size > 0 and (tp_direction == "Long" or tp_direction == "Both")
        // ATR-based Take Profit (Long)
        tp_priceATR1_long = strategy.position_avg_price + atrMultiplierTP1 * ta.atr(atrLengthTP)
        tp_priceATR2_long = strategy.position_avg_price + atrMultiplierTP2 * ta.atr(atrLengthTP)
        tp_priceATR3_long = strategy.position_avg_price + atrMultiplierTP3 * ta.atr(atrLengthTP)
        
        // Percentage-based Take Profit (Long)
        tp_pricePercent1_long = strategy.position_avg_price * (1 + tp_level_percent1 / 100)
        tp_pricePercent2_long = strategy.position_avg_price * (1 + tp_level_percent2 / 100)
        tp_pricePercent3_long = strategy.position_avg_price * (1 + tp_level_percent3 / 100)

        // Execute ATR-based exits for Long
        strategy.exit("TP ATR 1 Long", from_entry="Long", qty_percent=tp_percentATR1, limit=tp_priceATR1_long)
        strategy.exit("TP ATR 2 Long", from_entry="Long", qty_percent=tp_percentATR2, limit=tp_priceATR2_long)
        strategy.exit("TP ATR 3 Long", from_entry="Long", qty_percent=tp_percentATR3, limit=tp_priceATR3_long)
        
        // Execute Percentage-based exits for Long
        strategy.exit("TP Percent 1 Long", from_entry="Long", qty_percent=tp_percent1, limit=tp_pricePercent1_long)
        strategy.exit("TP Percent 2 Long", from_entry="Long", qty_percent=tp_percent2, limit=tp_pricePercent2_long)
        strategy.exit("TP Percent 3 Long", from_entry="Long", qty_percent=tp_percent3, limit=tp_pricePercent3_long)

    if strategy.position_size < 0 and (tp_direction == "Short" or tp_direction == "Both")
        // ATR-based Take Profit (Short) - using the same ATR levels as long
        tp_priceATR1_short = strategy.position_avg_price - atrMultiplierTP1 * ta.atr(atrLengthTP)
        tp_priceATR2_short = strategy.position_avg_price - atrMultiplierTP2 * ta.atr(atrLengthTP)
        tp_priceATR3_short = strategy.position_avg_price - atrMultiplierTP3 * ta.atr(atrLengthTP)
        
        // Percentage-based Take Profit (Short) - using the same levels, but more aggressive percentages
        tp_pricePercent1_short = strategy.position_avg_price * (1 - tp_level_percent1 / 100)
        tp_pricePercent2_short = strategy.position_avg_price * (1 - tp_level_percent2 / 100)
        tp_pricePercent3_short = strategy.position_avg_price * (1 - tp_level_percent3 / 100)

        // Execute ATR-based exits for Short (using the percentage multiplier for short)
        strategy.exit("TP ATR 1 Short", from_entry="Short", qty_percent=tp_percentATR1_short, limit=tp_priceATR1_short)
        strategy.exit("TP ATR 2 Short", from_entry="Short", qty_percent=tp_percentATR2_short, limit=tp_priceATR2_short)
        strategy.exit("TP ATR 3 Short", from_entry="Short", qty_percent=tp_percentATR3_short, limit=tp_priceATR3_short)
        
        // Execute Percentage-based exits for Short
        strategy.exit("TP Percent 1 Short", from_entry="Short", qty_percent=tp_percent1_short, limit=tp_pricePercent1_short)
        strategy.exit("TP Percent 2 Short", from_entry="Short", qty_percent=tp_percent2_short, limit=tp_pricePercent2_short)
        strategy.exit("TP Percent 3 Short", from_entry="Short", qty_percent=tp_percent3_short, limit=tp_pricePercent3_short)
// Plot VIDYAs
plot(fastVIDYA, color=color.green, title="Fast VIDYA")
plot(slowVIDYA, color=color.red, title="Slow VIDYA")


関連性

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