This strategy is a short-term trading strategy based on the Hull Moving Average indicator. The strategy uses the golden cross and dead cross of the Hull Moving Average lines to generate trading signals, belonging to a trend-following strategy.
This strategy is mainly based on the Hull Moving Average indicator. The Hull Moving Average line consists of two moving averages. First it calculates the median moving average line nma of the price, with a period of hullperiod. Then it calculates the fast moving average line n2ma, with a period of half of the nma’s. When n2ma crosses above nma, a buy signal is generated. When n2ma crosses below nma, a sell signal is generated.
To filter out some false signals, the strategy also introduces the Hull Line (Hull_Line). The Hull Line is a linear regression result of the difference between nma and n2ma. When there is divergence between the price and the Hull Line, the strategy will skip the trading signal.
Specifically, the strategy rules are as follows:
Calculate the nma, with period hullperiod
Calculate the n2ma, with period half of the nma period
Calculate the difference diff between n2ma and nma
Moving average the diff with period sqrt(hullperiod),得到and get the Hull Line Hull_Line
When price crosses above Hull Line, a buy signal is generated
When price crosses below Hull Line, a sell signal is generated
If there is divergence between price and Hull Line, skip the signal
Enter with a certain percentage of the position, adopt exit stop loss
The advantages of this strategy include:
Based on Hull Moving Average, it can quickly capture the trend and follow the trend
Use Hull Line to filter false signals and improve signal quality
Good risk-reward ratio and drawdown, suitable for short-term trading
Flexible parameter tuning, adaptable to different market environments
Adopt reversal stop loss, can stop loss in time and control risks
Combine seasonality to avoid systemic risks in specific time periods
This strategy also has some risks:
Trend following strategy, cannot trade all day long
Larger losses when trend reverses
Lagging of moving averages, cannot timely capture turning points
High trading frequency leads to higher trading costs
Inappropriate parameter settings may lead to lower profitability in range-bound markets
To control these risks, we can take the following measures:
Adopt martingale stop loss strategy to control single loss
Optimize parameters and test robustness in different market environments
Combine trend judging indicators to avoid chasing trends during reversals
Increase holding time to lower trading frequency
This strategy can also be optimized in the following aspects:
Combine momentum indicators to locate the starting point of trends for better entry
Add machine learning models to assist in judging trend direction and strength
Adopt adaptive parameter setting to adjust parameters based on real-time market dynamics
Configure multi-timeframe Hull systems, with different position sizes for different timeframes
Combine volume indicators to avoid false breakouts with insufficient momentum
Add volatility-based position sizing model to dynamically adjust position sizes based on volatility
The Hull Moving Average Swing Trading Strategy is an efficient short-term trend following strategy overall. It uses the Hull Moving Average system to determine the trend direction for the purpose of following the trend. Compared with single moving average systems, it has higher signal quality and Parameters flexibility. The advantage of this strategy lies in quickly capturing trend changes with relatively small drawdowns. The weakness is the inability to cope with trend reversals. We can use Parameter optimization, stop loss strategies, adding auxiliary models etc. to control risks and make the strategy robust in more market environments.
/*backtest
start: 2023-09-06 00:00:00
end: 2023-10-06 00:00:00
period: 6h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
// Hull Moving Average Swing Trader by SEASIDE420
strategy("Hull Moving Average Swing Trader", shorttitle="HMA_Swing_Trader", default_qty_type=strategy.percent_of_equity, default_qty_value=100, calc_on_order_fills=true, calc_on_every_tick=true, pyramiding=0)
hullperiod = input(title="HullMA Period", type=input.integer, defval=210, minval=1)
price = input(open, type=input.source, title="Price data")
FromMonth = input(defval=1, title="From Month", minval=1, maxval=12)
FromDay = input(defval=1, title="From Day", minval=1, maxval=31)
FromYear = input(defval=2020, title="From Year", minval=2017)
ToMonth = input(defval=1, title="To Month", minval=1, maxval=12)
ToDay = input(defval=1, title="To Day", minval=1, maxval=31)
ToYear = input(defval=9999, title="To Year", minval=2017)
start = timestamp(FromYear, FromMonth, FromDay, 00, 00)
finish = timestamp(ToYear, ToMonth, ToDay, 23, 59)
window() => true
n2ma = 2 * wma(price, round(hullperiod / 2))
nma = wma(price, hullperiod)
diff = n2ma - nma
sqn = round(sqrt(hullperiod))
n2ma1 = 2 * wma(price[1], round(hullperiod / 2))
nma1 = wma(price[1], hullperiod)
diff1 = n2ma1 - nma1
n1 = wma(diff, sqn)
n2 = wma(diff1, sqn)
Hull_Line = n1 / n1 * n2
Hull_retracted = if n1 > n2
Hull_retracted = Hull_Line - 2
else
Hull_retracted = Hull_Line + 2
c1 = Hull_retracted + n1 - price
c2 = Hull_retracted - n2 + price
c4 = n1 > n2 ? color.green : color.red
c2p = plot(c2, color=color.black, linewidth=1)
c3p = plot(price, color=color.black, linewidth=1)
fill(c3p, c2p, color=c4, transp=75)
//plot(cross(c1, c2) ? c1 : na, style=plot.style_circles, color=c4, linewidth=4)
if price < c2
strategy.close("BUY", when=window())
if price > c2
strategy.close("SELL", when=window())
if price > c2 and price[1] > c1
strategy.entry("BUY", strategy.long, when=window())
if price < c1 and price[1] < c2
strategy.entry("SELL", strategy.short, when=window()) // /L'-,
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