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Multi-Moving Average Crossover Trend Following Strategy with Volatility Filter

Author: ChaoZhang, Date: 2024-07-29 13:37:09
Tags: MAEMASMAWMAVWMASMMARMA

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Overview

This strategy is a trend-following trading system based on multiple moving average crossovers and volatility filtering. It utilizes three moving averages of different periods to identify market trends and uses a fourth moving average as a benchmark for bull/bear market determination. The strategy also incorporates a volatility indicator as a trading filter to avoid trading in low volatility environments. It supports both long and short positions and provides flexible position management and stop-loss mechanisms.

Strategy Principles

  1. Moving Average Selection: The strategy uses three main moving averages (short-term, medium-term, and long-term) to determine trends. Users can choose from six predefined moving averages, each of which can be individually configured with parameters including calculation period, data source, and type (e.g., SMA, EMA).

  2. Trend Determination:

    • Bullish Trend: When the short-term MA is above the long-term MA, and the medium-term MA crosses above the long-term MA.
    • Bearish Trend: When the short-term MA is below the long-term MA, and the medium-term MA crosses below the long-term MA.

  3. Bull/Bear Market Determination: An optional fourth moving average can be used as a dividing line for bull and bear markets. Only long positions are allowed when the price is above this line, and only short positions when below.

  4. Volatility Filter: A volatility indicator based on highest and lowest prices is used. The strategy only generates trading signals when volatility exceeds a user-defined threshold.

  5. Entry Logic:

    • Long Entry: Enter a long position when a bullish trend is confirmed, volatility conditions are met, and the price is above the long-term MA.
    • Short Entry: Enter a short position when a bearish trend is confirmed, volatility conditions are met, and the price is below the long-term MA.

  6. Exit Logic:

    • Partial Exit: Close a certain percentage of the position when the trend reverses (medium-term MA crosses the long-term MA again).
    • Full Exit: Close all positions in the opposite direction when the price crosses the bull/bear market dividing line.

  7. Stop Loss: Uses a fixed percentage stop loss, which can be customized by the user.

  8. Position Management: Uses a fixed percentage of account equity for each trade, which can be customized by the user.

Strategy Advantages

  1. Multi-dimensional Trend Analysis: By using multiple moving averages, the strategy can capture market trends more comprehensively, reducing false signals.

  2. Flexible Parameter Configuration: Users can adjust various parameters flexibly according to the characteristics of different markets and trading instruments, including MA type, period, and data source.

  3. Volatility Filtering: By incorporating a volatility indicator, the strategy can avoid trading in low volatility environments, improving signal quality.

  4. Bull/Bear Market Adaptation: The optional bull/bear market determination mechanism allows the strategy to better adapt to different market environments, reducing counter-trend trades.

  5. Dynamic Position Management: The equity-based position management method automatically adjusts trading size as the account size changes.

  6. Multi-layered Risk Control: Includes multiple risk control mechanisms such as volatility filtering, trend confirmation, partial position closure, and fixed stop loss.

  7. Bi-directional Trading: Supports both long and short positions, allowing for trading opportunities in various market conditions.

  8. Visual Aids: The strategy plots various moving averages and trade signal labels on the chart, facilitating intuitive analysis and backtesting.

Strategy Risks

  1. Lagging Nature: Moving averages are inherently lagging indicators, which may lead to slightly delayed entry and exit timing, affecting profitability.

  2. Poor Performance in Ranging Markets: In sideways, choppy markets, the strategy may generate frequent false signals, leading to overtrading and losses.

  3. Parameter Sensitivity: The strategy’s performance is highly dependent on parameter settings, and different markets and timeframes may require different parameter combinations.

  4. Drawdown Risk: During trend reversals, the strategy may not exit positions entirely in a timely manner, potentially leading to significant drawdowns.

  5. Over-reliance on Technical Indicators: The strategy is entirely based on technical indicators, ignoring fundamental factors, which may lead to poor performance during major news or events.

  6. Money Management Risk: The fixed percentage position sizing method may result in excessive risk exposure during consecutive losses.

  7. Stop Loss Setting: The fixed percentage stop loss may not be suitable for all market environments and could lead to premature exits during high volatility periods.

Strategy Optimization Directions

  1. Adaptive Parameters: Introduce adaptive mechanisms to dynamically adjust moving average parameters and volatility thresholds based on market conditions.

  2. Multi-timeframe Analysis: Incorporate information from longer and shorter timeframes to improve trend determination accuracy.

  3. Volatility Indicator Optimization: Consider using more sophisticated volatility indicators such as ATR or Bollinger Bandwidth for more accurate market condition assessment.

  4. Momentum Indicator Integration: Combine momentum indicators like RSI or MACD to optimize entry and exit timing.

  5. Improved Stop Loss Mechanism: Implement trailing stops or ATR-based dynamic stop losses to better adapt to market volatility.

  6. Market Sentiment Integration: Incorporate market sentiment indicators like VIX to optimize strategy performance in different market environments.

  7. Position Management Optimization: Implement dynamic position sizing based on volatility or current profit/loss for better risk control.

  8. Fundamental Filter Addition: Consider fundamental factors such as important economic data releases or company earnings reports to avoid trading during high-risk periods.

  9. Machine Learning Optimization: Use machine learning algorithms to optimize parameter combinations and decision rules, improving strategy adaptability.

  10. Backtesting and Forward Testing: Conduct more comprehensive backtesting and forward testing in different markets and time periods to verify strategy robustness.

Conclusion

The Multi-Moving Average Crossover Trend Following Strategy with Volatility Filter is a comprehensive and flexible trading system that combines multiple moving averages, volatility indicators, and trend-following principles. Through multi-dimensional trend analysis and strict risk control, the strategy has the potential to capture persistent trends in various market environments. However, users need to pay attention to parameter optimization and market adaptability issues, and consider introducing more advanced technical indicators and risk management techniques to further enhance strategy performance. Overall, this is a solid strategy framework that provides a good foundation for further research and optimization.

/*backtest
start: 2023-07-23 00:00:00
end: 2024-07-28 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy(title="WODIsMA Strategy", shorttitle="WMA_Strategy", overlay=true, overlay=true, pyramiding=2, default_qty_value=6, default_qty_type=strategy.fixed, initial_capital=1000, currency=currency.USD)

// 用户输入参数
capital_pct = input.float(20, title="每笔订单使用的资金百分比(%)", minval=0.1, maxval=100, group="Position") / 100
close_pct = input.float(20, title="每次平仓使用的百分比(%)", minval=0, maxval=100, group="Position") / 100
stop_loss_user = input.float(10, title="止损百分比(%)", minval=0, maxval=100, group="Position") / 100
allow_long = input.bool(true, title="是否做多", group="Position")
allow_short = input.bool(true, title="是否做空", group="Position")

// 用户选择的移动平均线
short_term_ma = input.string("MA 0", title="短期趋势均线", options=["MA 0", "MA 1", "MA 2", "MA 3", "MA 4", "MA 5"], group="TrendIdentify")
mid_term_ma = input.string("MA 1", title="中期趋势均线", options=["MA 0", "MA 1", "MA 2", "MA 3", "MA 4", "MA 5"], group="TrendIdentify")
long_term_ma = input.string("MA 2", title="长期趋势均线", options=["MA 0", "MA 1", "MA 2", "MA 3", "MA 4", "MA 5"], group="TrendIdentify")
bull_bear_ma = input.string("MA 3", title="牛熊趋势均线", options=["MA 0", "MA 1", "MA 2", "MA 3", "MA 4", "MA 5"], group="TrendIdentify")
enable_bull_bear = input.bool(false, title="是否启用牛熊趋势线", group="TrendIdentify")
// 波动率指标参数
volatility_k = input.int(60, title="波动率数值K线数" , group="volatility")
volatility_threshold = input.float(1, minval=0, title="波动率值 0则不使用(%)", group="volatility")

// 定义不同类型的移动平均线函数
ma(source, length, type) =>
    switch type
        "SMA" => ta.sma(source, length)
        "EMA" => ta.ema(source, length)
        "SMMA (RMA)" => ta.rma(source, length)
        "WMA" => ta.wma(source, length)
        "VWMA" => ta.vwma(source, length)

// 定义每根均线的输入参数和颜色
length0 = input.int(16, minval=1, title="Length 0", group="MA 0")
source0 = input.source(hl2, title="Source 0", group="MA 0")
type0 = input.string("SMA", title="Type 0", options=["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"], group="MA 0")
timeframe0 = input.timeframe("", title="Timeframe 0", group="MA 0")
color0 = input.color(color.gray, title="Color 0", group="MA 0")
show0 = input.bool(true, title="Show MA 0", group="MA 0")

length1 = input.int(48, minval=1, title="Length 1", group="MA 1")
source1 = input.source(hl2, title="Source 1", group="MA 1")
type1 = input.string("SMA", title="Type 1", options=["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"], group="MA 1")
timeframe1 = input.timeframe("", title="Timeframe 1", group="MA 1")
color1 = input.color(color.aqua, title="Color 1", group="MA 1")
show1 = input.bool(true, title="Show MA 1", group="MA 1")

length2 = input.int(144, minval=1, title="Length 2", group="MA 2")
source2 = input.source(hl2, title="Source 2", group="MA 2")
type2 = input.string("SMA", title="Type 2", options=["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"], group="MA 2")
timeframe2 = input.timeframe("", title="Timeframe 2", group="MA 2")
color2 = input.color(color.orange, title="Color 2", group="MA 2")
show2 = input.bool(true, title="Show MA 2", group="MA 2")

length3 = input.int(432, minval=1, title="Length 3", group="MA 3")
source3 = input.source(hl2, title="Source 3", group="MA 3")
type3 = input.string("SMA", title="Type 3", options=["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"], group="MA 3")
timeframe3 = input.timeframe("", title="Timeframe 3", group="MA 3")
color3 = input.color(color.green, title="Color 3", group="MA 3")
show3 = input.bool(true, title="Show MA 3", group="MA 3")

length4 = input.int(91, minval=1, title="Length 4", group="MA 4")
source4 = input.source(hl2, title="Source 4", group="MA 4")
type4 = input.string("SMA", title="Type 4", options=["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"], group="MA 4")
timeframe4 = input.timeframe("D", title="Timeframe 4", group="MA 4")
color4 = input.color(color.rgb(159, 110, 208), title="Color 4", group="MA 4") // 浅紫色
style4 = input.string("step", title="Style 4", options=["line", "step"], group="MA 4")
show4 = input.bool(false, title="Show MA 4", group="MA 4")

length5 = input.int(182, minval=1, title="Length 5", group="MA 5")
source5 = input.source(hl2, title="Source 5", group="MA 5")
type5 = input.string("SMA", title="Type 5", options=["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"], group="MA 5")
timeframe5 = input.timeframe("D", title="Timeframe 5", group="MA 5")
color5 = input.color(color.red, title="Color 5", group="MA 5")
style5 = input.string("step", title="Style 5", options=["line", "step"], group="MA 5")
show5 = input.bool(true, title="Show MA 5", group="MA 5")

// 计算每根均线的值
value0 = request.security(syminfo.tickerid, timeframe0, ma(source0, length0, type0))
value1 = request.security(syminfo.tickerid, timeframe1, ma(source1, length1, type1))
value2 = request.security(syminfo.tickerid, timeframe2, ma(source2, length2, type2))
value3 = request.security(syminfo.tickerid, timeframe3, ma(source3, length3, type3))
value4 = request.security(syminfo.tickerid, timeframe4, ma(source4, length4, type4))
value5 = request.security(syminfo.tickerid, timeframe5, ma(source5, length5, type5))

// 绘制每根均线
plot(show0 ? value0 : na, title="MA 0", color=color0)
plot(show1 ? value1 : na, title="MA 1", color=color1)
plot(show2 ? value2 : na, title="MA 2", color=color2)
plot(show3 ? value3 : na, title="MA 3", color=color3)
plot(show4 ? value4 : na, title="MA 4", color=color4, style=style4 == "step" ? plot.style_stepline : plot.style_line, linewidth=2)
plot(show5 ? value5 : na, title="MA 5", color=color5, style=style5 == "step" ? plot.style_stepline : plot.style_line, linewidth=2)

// 添加策略部分

// 选择均线值
get_ma_value(ma_name) =>
    if (ma_name == "MA 0")
        value0
    else if (ma_name == "MA 1")
        value1
    else if (ma_name == "MA 2")
        value2
    else if (ma_name == "MA 3")
        value3
    else if (ma_name == "MA 4")
        value4
    else
        value5

short_ma_value = get_ma_value(short_term_ma)
mid_ma_value = get_ma_value(mid_term_ma)
long_ma_value = get_ma_value(long_term_ma)
bull_bear_ma_value = get_ma_value(bull_bear_ma)

// 计算波动率
high_close = ta.highest(high, volatility_k)
low_close = ta.lowest(low, volatility_k)
volatility = 100 * (high_close - low_close) / low_close

// 波动率条件背景色
volatilityCondition = (volatility > volatility_threshold)
volatilityConditionBG = (volatility > volatility_threshold) and volatility_threshold != 0

bgcolor(volatilityConditionBG ? color.new(color.green, 90) : na, title="Volatility Background")

// 策略信号
long_condition = (short_ma_value > long_ma_value and ta.crossover(mid_ma_value, long_ma_value))
short_condition = (short_ma_value < long_ma_value and ta.crossunder(mid_ma_value, long_ma_value))

var float stop_level_long = na
var float stop_level_short = na

// 执行策略
if (volatilityCondition and allow_long and (not enable_bull_bear or close > bull_bear_ma_value)) 
    if (long_condition and close > long_ma_value)  // 判断是否立即触发止损
        strategy.entry("Long", strategy.long, qty=capital_pct * strategy.equity / close)
        label.new(bar_index, low*0.996, text="BUY", color=color.green, textcolor=color.white, style=label.style_label_up, size=size.small)

if (volatilityCondition and allow_short and (not enable_bull_bear or close < bull_bear_ma_value)) 
    if (short_condition and close < long_ma_value)  // 判断是否立即触发止损
        strategy.entry("Short", strategy.short, qty=capital_pct * strategy.equity / close)
        label.new(bar_index, high*1.004, text="SELL", color=color.red, textcolor=color.white, style=label.style_label_down, size=size.small)

// 部分平仓逻辑
if (enable_bull_bear)
    // 当当前价格处在牛熊趋势均线之下时
    if (close < bull_bear_ma_value)
        // 平所有多仓
        if (strategy.position_size > 0)
            strategy.close("Long", comment="平所有多仓")
            label.new(bar_index, low*0.996, text="CLOSE", color=color.gray, textcolor=color.white, style=label.style_label_up, size=size.small)
        // 当短期均线在长期均线之上时，中期均线向上穿过长期均线，平空
        if (short_ma_value > long_ma_value and ta.crossover(mid_ma_value, long_ma_value) and volatilityCondition)
            if (strategy.position_size < 0)
                strategy.close("Short", qty=close_pct * strategy.position_size, comment="部分平空")
                label.new(bar_index, high*1.004, text="CLOSE", color=color.gray, textcolor=color.white, style=label.style_label_down, size=size.small)

    // 当当前价格处在牛熊趋势均线之上时
    if (close > bull_bear_ma_value)
        // 平所有空仓
        if (strategy.position_size < 0)
            strategy.close("Short", comment="平所有空仓")
            label.new(bar_index, high*1.004, text="CLOSE", color=color.gray, textcolor=color.white, style=label.style_label_up, size=size.small)
        // 当短期均线在长期均线之下时，中期均线向下穿过长期均线，平多
        if (short_ma_value < long_ma_value and ta.crossunder(mid_ma_value, long_ma_value) and volatilityCondition)
            if (strategy.position_size > 0)
                strategy.close("Long", qty=close_pct * strategy.position_size, comment="部分平多")
                label.new(bar_index, low*0.996, text="CLOSE", color=color.gray, textcolor=color.white, style=label.style_label_down, size=size.small)
else if (not enable_bull_bear and not (allow_long and allow_short))
    // 当短期均线在长期均线之上时，中期均线向上穿过长期均线，平空
    if (short_ma_value > long_ma_value and ta.crossover(mid_ma_value, long_ma_value) and volatilityCondition)
        if (strategy.position_size < 0)
            strategy.close("Short", qty=close_pct * strategy.position_size, comment="部分平空")
            label.new(bar_index, low*0.996, text="CLOSE", color=color.gray, textcolor=color.white, style=label.style_label_up, size=size.small)

    // 当短期均线在长期均线之下时，中期均线向下穿过长期均线，平多
    if (short_ma_value < long_ma_value and ta.crossunder(mid_ma_value, long_ma_value) and volatilityCondition)
        if (strategy.position_size > 0)
            strategy.close("Long", qty=close_pct * strategy.position_size, comment="部分平多")
            label.new(bar_index, high*1.004, text="CLOSE", color=color.gray, textcolor=color.white, style=label.style_label_down, size=size.small)

// 止损处理
if (strategy.position_size > 0)
    stop_level_long_user = strategy.position_avg_price * (1 - stop_loss_user)
    strategy.exit("Stop Loss", from_entry="Long", stop=stop_level_long_user)
else if (strategy.position_size < 0)
    stop_level_short_user = strategy.position_avg_price * (1 + stop_loss_user)
    strategy.exit("Stop Loss", from_entry="Short", stop=stop_level_short_user)
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