Skip to content

Latest commit

 

History

History
175 lines (111 loc) · 6.07 KB

双均线金叉死叉策略Dual-Moving-Average-Crossover-Strategy.md

File metadata and controls

175 lines (111 loc) · 6.07 KB
Raw

Name

双均线金叉死叉策略Dual-Moving-Average-Crossover-Strategy

Author

ChaoZhang

Strategy Description

[trans]

该策略通过计算两个不同周期的移动平均线,并根据它们的金叉死叉形成买入和卖出信号。

策略原理

该策略首先允许用户选择移动平均线的类型和长度。类型包括SMA、EMA、VWMA等,长度则决定了均线的周期。

然后根据用户选择计算出两条移动平均线。如果快线从下方上穿慢线,形成金叉,则产生买入信号。如果快线从上方下穿慢线,形成死叉,则产生卖出信号。

这样,当短期平均价格高于长期平均价格时,被视为市场处于上涨趋势,应该买入。当短期价格低于长期价格时,被视为市场处于下跌趋势,应该卖出。

优势分析

  • 策略逻辑简单清晰,易于理解实现。
  • 移动平均线能有效过滤市场噪音,识别趋势。
  • 可灵活选择移动平均线类型和参数,适应不同品种和周期。
  • 容易通过多种指标组合进行优化。

风险分析

  • 当市场处于震荡时,可能产生多次错误信号。
  • 参数选择不当可能导致策略表现不佳。
  • 信号产生滞后,无法及时捕捉转折点。
  • 面临突发事件的行情冲击风险。

可通过适当优化参数,组合其他指标生成信号,设置止损止盈等方式来控制风险。

优化方向

  • 测试不同类型和长度的参数,寻找最优参数组合。
  • 增加其他指标过滤,如量价指标、波动率指标等。
  • 增加止损止盈逻辑,降低回撤。
  • 结合趋势判断指标,避免不适宜的交易环境。
  • 优化资金管理策略,如仓位管理、风险预算等。

总结

该策略整体思路简单清晰,通过计算双均线形成交易信号,可根据市场环境灵活调整参数,和其他策略组合优化,但需要注意防范震荡市场的风险,合理进行资金管理。整体来说是一个值得考虑的选择。

||

This strategy generates trading signals based on the crossover of two moving averages with different periods.

Strategy Logic

The strategy allows users to choose the type and length of moving averages. The types include SMA, EMA, VWMA, etc. The length determines the period of the moving averages.

Two moving averages are calculated based on the user's selection. If the faster line crosses above the slower line, a golden cross is formed and a buy signal is generated. If the faster line crosses below the slower line, a death cross is formed and a sell signal is generated.

When the short-term average price is above the long-term average price, it is considered an uptrend and long positions should be taken. When the short-term price is below the long-term price, it is considered a downtrend and short positions should be taken.

Advantage Analysis

  • The strategy logic is simple and clear, easy to understand and implement.
  • Moving averages can effectively filter market noise and identify trends.
  • The MA type and parameters can be flexibly chosen to adapt to different products and timeframes.
  • Easy to optimize by combining various indicators.

Risk Analysis

  • May generate multiple false signals when the market is ranging.
  • Inappropriate parameter selection may lead to poor strategy performance.
  • Signals are lagging, unable to timely capture turning points.
  • Exposed to sudden price shocks.

Risks can be managed by optimizing parameters, combining other indicators for signal generation, implementing stop loss/take profit, etc.

Optimization Directions

  • Test different types and length of MAs to find optimal parameters.
  • Add other indicators for signal filtering, e.g. volume, volatility indicators.
  • Add stop loss/take profit logic to reduce drawdown.
  • Incorporate trend evaluation to avoid unsuitable market conditions.
  • Optimize money management such as position sizing, risk budgeting.

Conclusion

The strategy has a simple and clear logic of generating signals with dual MAs crossover. It allows flexible parameter tuning and combinations with other strategies for optimization, but risks of ranging markets should be monitored and money management is crucial. Overall it is a strategy worth considering.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 30 MA length
v_input_2 true Type
v_input_3_close 0 Source: close

Source (PineScript)

/*backtest
start: 2023-09-09 00:00:00
end: 2023-09-13 00:00:00
period: 10m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy(title = "Noro's MAs Tests", shorttitle = "MAs tests", overlay=true, default_qty_type = strategy.percent_of_equity, default_qty_value=100.0, pyramiding=0)


len = input(30, defval = 30, minval = 2, maxval = 1000, title = "MA length")
type = input(1, defval = 1, minval = 1, maxval = 7, title = "Type")
src = input(close, defval = close, title = "Source")

//DEMA
dema = 2 * ema(src, len) - ema(ema(close, len), len)

//TEMA
xPrice = close
xEMA1 = ema(src, len)
xEMA2 = ema(xEMA1, len)
xEMA3 = ema(xEMA2, len)
tema = 3 * xEMA1 - 3 * xEMA2 + xEMA3

//KAMA
xvnoise = abs(src - src[1])
nfastend = 0.20
nslowend = 0.05
nsignal = abs(src - src[len])
nnoise = sum(xvnoise, len)
nefratio = iff(nnoise != 0, nsignal / nnoise, 0)
nsmooth = pow(nefratio * (nfastend - nslowend) + nslowend, 2) 
kama = nz(kama[1]) + nsmooth * (src - nz(kama[1]))

//PriceChannel
lasthigh = highest(src, len)
lastlow = lowest(src, len)
center = (lasthigh + lastlow) / 2

ma = type == 1 ? sma(src, len) : type == 2 ? ema(src, len) : type == 3 ? vwma(src, len) : type == 4 ? dema : type == 5 ? tema : type == 6 ? kama : type == 7 ? center : 0

plot(ma, color = blue, linewidth = 3, transp = 0)

trend = low > ma ? 1 : high < ma ? -1 : trend[1]

longCondition = trend == 1 and trend[1] == -1
if (longCondition)
    strategy.entry("Long", strategy.long)

shortCondition = trend == -1 and trend[1] == 1
if (shortCondition)
    strategy.entry("Short", strategy.short)

Detail

https://www.fmz.com/strategy/427090

Last Modified

2023-09-17 22:35:07