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Tendencia de las ganancias de varios niveles de índice variable promedio dinámico siguiendo la estrategia

El autor:¿ Qué pasa?, Fecha: 2024-12-12 14:29:53
Las etiquetas:- ¿ Qué?El ATROCMTPEl MO

Oscilador de impulso de cambio (MO)

Las bandas de Bollinger como filtro de volatilidad: La banda superior = MA + (K * StdDev) La banda inferior = MA - (K * StdDev)

Condiciones de entrada:

  • Largo: el precio se rompe por encima de VIDYA lento con tendencia al alza de VIDYA rápido y el precio por encima de la banda superior de Bollinger
  • Corto: el precio se rompe por debajo de VIDYA lento con tendencia a la baja de VIDYA rápido y el precio por debajo de la banda inferior de Bollinger

El mecanismo de obtención de beneficios en varios niveles incluye:

  1. Obtención de beneficios basados en ATR
  2. Profitos obtenidos en porcentaje
  3. Multiplicador para los porcentajes de ganancias de operaciones a corto plazo

Ventajas estratégicas

  1. Adaptabilidad dinámica: el indicador VIDYA se ajusta automáticamente a la volatilidad del mercado, es más sensible que las medias móviles tradicionales
  2. Gestión robusta del riesgo: Mecanismo de obtención de beneficios de varios niveles que bloquea las ganancias a diferentes niveles de precios
  3. Tratamiento diferenciado: estrategias de obtención de beneficios diferentes para posiciones largas y cortas alineadas con las características del mercado
  4. Filtración de la volatilidad: Las bandas de Bollinger ayudan a filtrar las falsas señales de ruptura
  5. Parámetros flexibles: Parámetros ajustables para las diferentes condiciones del mercado

Riesgos estratégicos

  1. Riesgo de mercado irregular: puede generar señales falsas en mercados variables
  2. Impacto del deslizamiento: pueden producirse desviaciones en la ejecución del precio en varios niveles de obtención de beneficios.
  3. Dependencia de parámetros: los diferentes entornos de mercado pueden requerir ajustes frecuentes de parámetros.
  4. Complejidad del sistema: el mecanismo de obtención de beneficios de múltiples niveles aumenta la complejidad de la estrategia
  5. Presión de gestión de la posición: los múltiples niveles de obtención de beneficios pueden complicar la gestión de la posición

Direcciones de optimización

  1. Ajuste dinámico de parámetros: Desarrollar un sistema de parámetros adaptativos para el ajuste automático de las condiciones del mercado
  2. Reconocimiento del entorno de mercado: añadir un módulo de identificación de las condiciones de mercado para el cambio de parámetros
  3. Optimización de pérdidas de parada: Implementar un mecanismo dinámico de pérdida de parada para mejorar el control del riesgo
  4. Filtración de la señal: añadir volumen y otros indicadores auxiliares para mejorar la fiabilidad de la señal
  5. Gestión de posiciones: Desarrollar algoritmos de asignación de posiciones más inteligentes

Resumen de las actividades

Esta estrategia crea un sistema integral de seguimiento de tendencias al combinar la adaptabilidad dinámica del indicador VIDYA con el filtrado de volatilidad de Bollinger Bands. El mecanismo de toma de ganancias de múltiples niveles y el manejo largo / corto diferenciado proporcionan un fuerte potencial de ganancia y control de riesgos. Sin embargo, los usuarios deben monitorear los cambios en el entorno del mercado, ajustar los parámetros en consecuencia y establecer sistemas de gestión de dinero robustos.


/*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")


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