这个策略是一个基于随机慢速指标(Stochastic Slow Oscillator)的交易策略,同时结合了移动平均线(Moving Average)、相对强弱指数(RSI)和人工智能(AI)技术。该策略通过对随机慢速指标的交叉信号进行判断,同时考虑价格相对于200日移动平均线的位置,以及人工智能模型生成的信号,来确定买卖信号。策略还设置了止盈和止损位,以控制风险。
该策略通过结合随机慢速指标、移动平均线、相对强弱指数和人工智能技术,构建了一个多因子交易策略。策略利用随机指标捕捉超买超卖信号,同时使用趋势过滤器和智能化信号生成,提高了策略的稳健性和适应性。尽管策略存在一定的风险,如指标失效和模型不确定性,但通过优化指标参数、改进人工智能模型、采用动态风控措施等方法,可以进一步提升策略的性能和风险控制能力。
/*backtest
start: 2024-03-12 00:00:00
end: 2024-04-11 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=5
strategy("Stochastic Slow Strategy with More Entries and AI", overlay=true)
length = input.int(30, minval=1)
OverBought = input(40)
OverSold = input(19)
smoothK = input.int(18, minval=1)
smoothD = input.int(7, minval=1)
minKValue = input(12, title="Minimum K Value")
// Stochastic calculations
k = ta.sma(ta.stoch(close, high, low, length), smoothK)
d = ta.sma(k, smoothD)
co = ta.crossover(k, d)
cu = ta.crossunder(k, d)
// Trend filter (200-period simple moving average)
ema200 = ta.sma(close, 200)
// Define la función para calcular la señal de la red neuronal recurrente
rnn_signal(price_series) =>
// Aquí implementa tu modelo de red neuronal recurrente para generar la señal
// Puedes usar bibliotecas externas o implementar tu propio modelo aquí
// Ejemplo de señal aleatoria
signal = ta.rsi(price_series, 14) > 50 ? 1 : -1
// Devuelve la señal generada por la red neuronal recurrente
signal
// Calcula la señal utilizando la función definida anteriormente
ai_signal = rnn_signal(close)
// Entry conditions
longCondition = ta.crossover(close, ema200) and k < OverSold and k > minKValue and ai_signal == 1
shortCondition = ta.crossunder(close, ema200) and k > OverBought and k > minKValue and ai_signal == -1
if (not na(k) and not na(d))
if (co and k < OverSold and k > minKValue)
strategy.entry("LONG", strategy.long, comment="LONG")
strategy.exit("ExitLong", "LONG", profit = close * 500, loss = close * 200)
if (cu and k > OverBought and k > minKValue)
strategy.entry("SHORT", strategy.short, comment="SHORT")
strategy.exit("ExitShort", "SHORT", profit = close * 500, loss = close * 200)
if (longCondition)
strategy.entry("LongEntry", strategy.long, comment="LongEntry")
strategy.exit("ExitLongEntry", "LongEntry", profit = close * 500, loss = close * 200)
if (shortCondition)
strategy.entry("ShortEntry", strategy.short, comment="ShortEntry")
strategy.exit("ExitShortEntry", "ShortEntry", profit = close * 500, loss = close * 200)
// Plotting
plot(ema200, color=color.blue, title="200 SMA")