ChaoZhang
概述
本策略结合了双EMA黄金交叉、标准化ATR噪音过滤器和ADX趋势指标,旨在为交易者提供更可靠的买入信号。该策略综合多个指标过滤虚假信号,识别更可靠的交易机会。
策略原理
该策略使用8周期和20周期的EMA构建双EMA黄金交叉系统。当短周期EMA上穿长周期EMA时生成买入信号。
此外,策略还设置了多个辅助指标进行过滤:
14周期ATR,经过标准化处理,过滤掉市场中过小的价格波动。
14周期ADX,用来识别趋势的力度。只有在强势趋势中才会考虑交易信号。
14周期成交量SMA,过滤掉成交量较小的时间点。
4/14周期Super Trend指标,判断多空市场方向。
在满足趋势方向、ATR标准化值、ADX值和成交量条件后,EMA黄金交叉才会最终触发买入信号。
策略优势
- 多指标组合,可靠性较高
该策略集成了EMA、ATR、ADX、Super Trend等多个指标,通过指标互补形成较强的信号过滤体系,可靠性较高。
- 参数可调节空间大
ATR标准化值阈值、ADX阈值、持仓周期等参数都可根据实际情况优化调整,策略灵活度较高。
- 可区分多空市场
通过Super Trend指标判断多空市,针对多空市场使用不同的参数标准,避免错失机会。
策略风险
- 参数优化难度大
策略参数组合复杂,优化难度较大,需要大量回测找到最优参数。
- 指标错触发风险
尽管有多重过滤,由于指标本质带有滞后性,仍有错触发风险。需要充分考虑止损理论。
- 交易频率偏低
受到多重指标和滤波的影响,策略交易频率会比较低,可能长期无交易的情况。
策略优化方向
- 优化参数组合
通过大量回测数据找到指标参数的最优组合。
- 增加机器学习
基于大量历史数据,运用机器学习算法自动优化策略参数,实现策略的自适应性。
- 考虑更多市场因素
结合更多指标判断市场结构、情绪等因素,丰富策略的多样性。
总结
本策略综合考虑了趋势、波动性和量价因素,通过多指标过滤和参数调节形成交易体系。综合而言,该策略可靠性较高,可通过进一步优化其参数组合和建模方式提升策略的交易效率。
策略源码
/*backtest
start: 2023-11-29 00:00:00
end: 2023-12-06 00:00:00
period: 5m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//Description:
//This strategy is a refactored version of an EMA cross strategy with a normalized ATR filter and ADX control.
//It aims to provide traders with signals for long positions based on market conditions defined by various indicators.
//How it Works:
//1. EMA: Uses short (8 periods) and long (20 periods) EMAs to identify crossovers.
//2. ATR: Uses a 14-period ATR, normalized to its 20-period historical range, to filter out noise.
//3. ADX: Uses a 14-period RMA to identify strong trends.
//4. Volume: Filters trades based on a 14-period SMA of volume.
//5. Super Trend: Uses a Super Trend indicator to identify the market direction.
//How to Use:
//- Buy Signal: Generated when EMA short crosses above EMA long, and other conditions like ATR and market direction are met.
//- Sell Signal: Generated based on EMA crossunder and high ADX value.
//Originality and Usefulness:
//This script combines EMA, ATR, ADX, and Super Trend indicators to filter out false signals and identify more reliable trading opportunities.
//USD Strength is not working, just simulated it as PSEUDO CODE: [close>EMA(50)]
//Strategy Results:
//- Account Size: $1000
//- Commission: Not considered
//- Slippage: Not considered
//- Risk: Less than 5% per trade
//- Dataset: Aim for more than 100 trades for sufficient sample size
//Note: This script should be used for educational purposes and should not be considered as financial advice.
//Chart:
//- The script's output is plotted as Buy and Sell signals on the chart.
//- No other scripts are included for clarity.
//- Have tested with 30mins period
//- You are encouraged to play with parameters, let me know if you
//@version=5
strategy("Advanced EMA Cross with Normalized ATR Filter, Controlling ADX", shorttitle="ALP V5", overlay=true )
// Initialize variables
var bool hasBought = false
var int barCountSinceBuy = 0
// Define EMA periods
emaShort = ta.ema(close, 8)
emaLong = ta.ema(close, 20)
// Define ATR parameters
atrLength = 14
atrValue = ta.atr(atrLength)
maxHistoricalATR = ta.highest(atrValue, 20)
minHistoricalATR = ta.lowest(atrValue, 20)
normalizedATR = (atrValue - minHistoricalATR) / (maxHistoricalATR - minHistoricalATR)
// Define ADX parameters
adxValue = ta.rma(close, 14)
adxHighLevel = 30
isADXHigh = adxValue > adxHighLevel
// Initialize risk management variables
var float stopLossPercent = na
var float takeProfitPercent = na
// Calculate USD strength
// That's not working as usd strenght, since I couldn't manage to get usd strength
//I've just simulated it as if the current close price is above 50 days average (it's likely a bullish trend), usd is strong (usd_strenth variable is positive)
usd_strength = close / ta.ema(close, 50) - 1
// Adjust risk parameters based on USD strength
if (usd_strength > 0)
stopLossPercent := 3
takeProfitPercent := 6
else
stopLossPercent := 4
takeProfitPercent := 8
// Initialize position variable
var float positionPrice = na
// Volume filter
minVolume = ta.sma(volume, 14) * 1.5
isVolumeHigh = volume > minVolume
// Market direction using Super Trend indicator
[supertrendValue, supertrendDirection] = ta.supertrend(4, 14)
bool isBullMarket = supertrendDirection < 0
bool isBearMarket = supertrendDirection > 0
// Buy conditions for Bull and Bear markets
buyConditionBull = isBullMarket and ta.crossover(emaShort, emaLong) and normalizedATR > 0.2
buyConditionBear = isBearMarket and ta.crossover(emaShort, emaLong) and normalizedATR > 0.5
buyCondition = buyConditionBull or buyConditionBear
// Sell conditions for Bull and Bear markets
sellConditionBull = isBullMarket and (ta.crossunder(emaShort, emaLong) or isADXHigh)
sellConditionBear = isBearMarket and (ta.crossunder(emaShort, emaLong) or isADXHigh)
sellCondition = sellConditionBull or sellConditionBear
// Final Buy and Sell conditions
if (buyCondition)
strategy.entry("Buy", strategy.long)
positionPrice := close
hasBought := true
barCountSinceBuy := 0
if (hasBought)
barCountSinceBuy := barCountSinceBuy + 1
// Stop-loss and take-profit levels
longStopLoss = positionPrice * (1 - stopLossPercent / 100)
longTakeProfit = positionPrice * (1 + takeProfitPercent / 100)
// Final Sell condition
finalSellCondition = sellCondition and hasBought and barCountSinceBuy >= 3 and isVolumeHigh
if (finalSellCondition)
strategy.close("Buy")
positionPrice := na
hasBought := false
barCountSinceBuy := 0
// Implement stop-loss and take-profit
strategy.exit("Stop Loss", "Buy", stop=longStopLoss)
strategy.exit("Take Profit", "Buy", limit=longTakeProfit)
// Plot signals
plotshape(series=buyCondition, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="Buy")
plotshape(series=finalSellCondition, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="Sell")