ChaoZhang
一种多因子量化交易策略,该策略综合考虑了均线因子和振荡指标因子,以控制风险提高稳定性。本文将详细介绍该交易策略的原理、优势以及可能存在的风险。
策略原理
本策略主要由三大模块组成:
- 均线因子
使用5条不同周期的EMA均线(8日、13日、21日、34日、55日)构建趋势滤波器。均线由短至长排列,只有当短周期均线上穿长周期均线时,才具有趋势特征并产生交易信号。
- 振荡指标因子
同时结合RSI和Stochastic两大振荡指标对突破进行验证,避免在震荡市场中产生大量假突破。
RSI的参数为14,当RSI在40-70区间时符合做多条件,在30-60区间时符合做空条件。
Stochastic参数为(14,3,3),当K线在20-80区间时符合做多条件,在5-95区间时符合做空条件。
- 入场及出场逻辑
只有当均线因子和振荡指标因子同时符合条件时,才会触发入场信号;当任一因子不再符合条件时,则产生出场信号。
整个策略采用严格的多因子过滤机制,在保持较高胜率的同时也确保了交易信号的稳定可靠。
策略优势
- 多因子设计有效过滤了市场噪音,避免了过度交易
- 同时考虑趋势因子和反转因子,兼具趋势跟踪和点位交易的优点
- 结合均线与振荡指标,可以在趋势中捕捉反转点位
- 优化空间大,可以通过调整参数获得更好的策略效果
风险提示
- 多因子策略信号频率较低,可能错失部分交易机会
- 均线产生滞后,应结合较短周期指标进行验证
- 振荡指标容易产生虚假信号,应仅作为辅助因子
- 需要定期优化参数以适应市场环境的变化
总结
本策略成功融合了趋势跟踪和反转交易的优点,多因子模型有效控制了风险,可以获得稳定的超额收益。这是一种非常实用的量化交易策略模型,值得人工智能社区进行深入研究与应用。
策略源码
/*backtest
start: 2022-09-12 00:00:00
end: 2022-11-15 00:00:00
period: 2d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=3
strategy(title = "Combined Strategy", default_qty_type = strategy.percent_of_equity, default_qty_value = 100, commission_type=strategy.commission.percent, commission_value = .0020, pyramiding = 0, slippage = 3, overlay = true)
//----------//
// MOMENTUM //
//----------//
ema8 = ema(close, 8)
ema13 = ema(close, 13)
ema21 = ema(close, 21)
ema34 = ema(close, 34)
ema55 = ema(close, 55)
plot(ema8, color=red, style=line, title="8", linewidth=1)
plot(ema13, color=orange, style=line, title="13", linewidth=1)
plot(ema21, color=yellow, style=line, title="21", linewidth=1)
plot(ema34, color=aqua, style=line, title="34", linewidth=1)
plot(ema55, color=lime, style=line, title="55", linewidth=1)
longEmaCondition = ema8 > ema13 and ema13 > ema21 and ema21 > ema34 and ema34 > ema55
exitLongEmaCondition = ema13 < ema55
shortEmaCondition = ema8 < ema13 and ema13 < ema21 and ema21 < ema34 and ema34 < ema55
exitShortEmaCondition = ema13 > ema55
// ---------- //
// OSCILLATORS //
// ----------- //
rsi = rsi(close, 14)
longRsiCondition = rsi < 70 and rsi > 40
exitLongRsiCondition = rsi > 70
shortRsiCondition = rsi > 30 and rsi < 60
exitShortRsiCondition = rsi < 30
// Stochastic
length = 14, smoothK = 3, smoothD = 3
kFast = stoch(close, high, low, 14)
dSlow = sma(kFast, smoothD)
longStochasticCondition = kFast < 80
exitLongStochasticCondition = kFast > 95
shortStochasticCondition = kFast > 20
exitShortStochasticCondition = kFast < 5
//----------//
// STRATEGY //
//----------//
longCondition = longEmaCondition and longRsiCondition and longStochasticCondition and strategy.position_size == 0
exitLongCondition = (exitLongEmaCondition or exitLongRsiCondition or exitLongStochasticCondition) and strategy.position_size > 0
if (longCondition)
strategy.entry("LONG", strategy.long)
if (exitLongCondition)
strategy.close("LONG")
shortCondition = shortEmaCondition and shortRsiCondition and shortStochasticCondition and strategy.position_size == 0
exitShortCondition = (exitShortEmaCondition or exitShortRsiCondition or exitShortStochasticCondition) and strategy.position_size < 0
if (shortCondition)
strategy.entry("SHORT", strategy.short)
if (exitShortCondition)
strategy.close("SHORT")