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Dual Timeframe Dynamic Support Trading System

Author: ChaoZhang, Date: 2024-12-05 16:44:56
Tags: SMAEMA

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Overview

This strategy is a dual timeframe dynamic support trading system that combines SMA and EMA crossover signals on weekly and daily timeframes. The system utilizes support bands formed between moving averages to identify market trends and trading opportunities, enhancing trading accuracy through signal confirmation from two different time periods. The strategy employs percentage-based position management and accounts for trading costs and slippage.

Strategy Principles

The core principle revolves around monitoring moving average crossovers and relative positions across two timeframes:

  1. Long timeframe (weekly) uses 20-week SMA and 21-week EMA, short timeframe (daily) uses 50-day SMA and 51-day EMA
  2. In the long timeframe, long signals are generated when EMA crosses above SMA, and positions are closed on downward crosses
  3. In the short timeframe, long signals occur when EMA crosses above SMA and short-term EMA is above long-term EMA
  4. All long positions are closed when short timeframe generates short signals or long timeframe shows downward crosses
  5. The strategy operates within specified time ranges with automatic position closure outside these ranges

Strategy Advantages

  1. Multiple confirmation mechanism: Reduces false signals through dual timeframe confirmation
  2. Dynamic support band: Support bands between moving averages adapt to market changes
  3. Comprehensive risk management: Includes consideration of trading costs and slippage with percentage-based position sizing
  4. Strong adaptability: Support bands automatically adjust to market volatility
  5. Clear operational rules: Well-defined entry and exit conditions, easy to implement and backtest

Strategy Risks

  1. Choppy market risk: May generate frequent false signals in sideways markets
  2. Lag risk: Moving averages have inherent lag, potentially missing optimal entry points
  3. Parameter sensitivity: Strategy performance heavily depends on moving average period selection
  4. Market environment dependency: Performs better in trending markets but may struggle in highly volatile conditions
  5. Position sizing risk: Fixed percentage positioning may present excessive risk in certain market conditions

Optimization Directions

  1. Incorporate volatility indicators: Consider adding ATR for dynamic position sizing
  2. Optimize parameter selection: Backtest different moving average periods to optimize system performance
  3. Add market environment filters: Implement trend strength indicators to filter unsuitable market conditions
  4. Enhance stop-loss mechanisms: Consider adding trailing or fixed stops for better risk control
  5. Improve position management: Dynamically adjust position sizes based on signal strength and market volatility

Conclusion

This strategy builds a relatively robust trading system by combining moving average crossover signals from different timeframes. It identifies market trends through the support band concept and uses multiple confirmation mechanisms to improve trading accuracy. The strategy design considers various practical trading factors, including trading costs, slippage, and time management. While inherent risks exist, the suggested optimization directions can further enhance the strategy’s stability and profitability.

/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-04 00:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Demo GPT - Bull Market Support Band", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_value=0.1, slippage=3)

start_date = input(timestamp("2018-01-01 00:00 +0000"), title="Start Date")
end_date = input(timestamp("2069-12-31 00:00 +0000"), title="End Date")

lsmaLength = input.int(20, title="Long SMA Length", minval=1)
lemaLength = input.int(21, title="Long EMA Length", minval=1)
customLongTimeframe = input.timeframe("W", title="Long Timeframe")  // Khung thời gian dài
ssmaLength = input.int(50, title="Short SMA Length", minval=1)
semaLength = input.int(51, title="Short EMA Length", minval=1)
customShortTimeframe = input.timeframe("D", title="Short Timeframe")  // Khung thời gian ngắn

source = close

// Tính toán SMA và EMA cho khung thời gian dài
smaLong = ta.sma(source, lsmaLength)
emaLong = ta.ema(source, lemaLength)
outSmaLong = request.security(syminfo.tickerid, customLongTimeframe, smaLong)
outEmaLong = request.security(syminfo.tickerid, customLongTimeframe, emaLong)

// Tính toán SMA và EMA cho khung thời gian ngắn
smaShort = ta.sma(source, ssmaLength)
emaShort = ta.ema(source, semaLength)
outSmaShort = request.security(syminfo.tickerid, customShortTimeframe, smaShort)
outEmaShort = request.security(syminfo.tickerid, customShortTimeframe, emaShort)

// Plot các chỉ báo trên biểu đồ
smaPlotLong = plot(outSmaLong, color=color.new(color.red, 0), title='20w SMA (Long)')
emaPlotLong = plot(outEmaLong, color=color.new(color.green, 0), title='21w EMA (Long)')
smaPlotShort = plot(outSmaShort, color=color.new(color.red, 0), title='20d SMA (Short)')
emaPlotShort = plot(outEmaShort, color=color.new(color.green, 0), title='21d EMA (Short)')

// Fill vùng giữa các đường SMA và EMA
fill(smaPlotLong, emaPlotLong, color=color.new(color.orange, 75), fillgaps=true)
fill(smaPlotShort, emaPlotShort, color=color.new(color.orange, 75), fillgaps=true)

// Điều kiện long và short cho khung thời gian dài
longConditionLong = ta.crossover(outEmaLong, outSmaLong)
shortConditionLong = ta.crossunder(outEmaLong, outSmaLong)

// Điều kiện long và short cho khung thời gian ngắn
longConditionShort = ta.crossover(outEmaShort, outSmaShort) and (outEmaShort > outEmaLong)
shortConditionShort = ta.crossunder(outEmaShort, outSmaShort) and (outEmaShort > outEmaLong) // Điều kiện short khi EMA ngắn hạn cắt xuống dưới SMA ngắn hạn và EMA ngắn hạn cao hơn EMA dài hạn

// Kiểm tra điều kiện trong khoảng thời gian được chỉ định
inDateRange = true

// Nếu khung ngắn hạn xuất hiện tín hiệu short, ưu tiên đóng tất cả các lệnh Long
if shortConditionShort and inDateRange
    strategy.close_all()

// Nếu khung dài có tín hiệu short, đóng tất cả các lệnh Long
if shortConditionLong and inDateRange
    strategy.close_all()

// Nếu khung ngắn hạn có tín hiệu long và không có tín hiệu short từ khung dài, vào lệnh Long
if longConditionShort and not shortConditionLong and not shortConditionShort and inDateRange
    strategy.entry("Long", strategy.long)

// Đóng tất cả các lệnh khi không trong khoảng thời gian được chọn
if not inDateRange
    strategy.close_all()
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