ChaoZhang
Overview
This strategy calculates the deviation of gold price from its 21-day exponential moving average to determine overbought and oversold situations in the market. It adopts a momentum trading approach with stop loss mechanism to control risk when deviation reaches certain thresholds in terms of standard deviation.
Strategy Logic
- Calculate 21-day EMA as the baseline
- Compute deviation of price from EMA
- Standardize deviation into Z-Score
- Go long when Z-Score crosses over 0.5; Go short when Z-Score crosses below -0.5
- Close position when Z-Score falls back to 0.5/-0.5 threshold
- Set stop loss when Z-Score goes over 3 or below -3
Advantage Analysis
The advantages of this strategy are:
- EMA as dynamic support/resistance to capture trends
- Stddev and Z-Score effectively gauge overbought/oversold levels, reducing false signals
- Exponential EMA puts more weight on recent prices, making it more sensitive
- Z-Score standardizes deviation for unified判断 rules
- Stop loss mechanism controls risk and limits losses
Risk Analysis
Some risks to consider:
- EMA can generate wrong signals when price gaps or breaks out
- Stddev/Z-Score thresholds need proper tuning for best performance
- Improper stop loss setting could lead to unnecessary losses
- Black swan events may trigger stop loss and miss trend opportunity
Solutions: 1. Optimize EMA parameter to identify major trends 2. Backtest to find optimal Stddev/Z-Score thresholds 3. Test stop loss rationality with trailing stops 4. Reassess market post-event, adjust strategy accordingly
Optimization Directions
Some ways to improve the strategy:
- Use volatility indictors like ATR instead of simple Stddev to gauge risk appetite
- Test different types of moving averages for better baseline
- Optimize EMA parameter to find best period
- Optimize Z-Score thresholds for improved performance
- Add volatility-based stops for more intelligent risk control
Conclusion
Overall this is a solid trend following strategy. It uses EMA to define trend direction and standardized deviation to clearly identify overbought/oversold levels for trade signals. Reasonable stop loss controls risk while letting profits run. Further parameter tuning and adding conditions can make this strategy more robust for practical application.
/*backtest
start: 2024-01-20 00:00:00
end: 2024-02-19 00:00:00
period: 4h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=5
strategy("GC Momentum Strategy with Stoploss and Limits", overlay=true)
// Input for the length of the EMA
ema_length = input.int(21, title="EMA Length", minval=1)
// Exponential function parameters
steepness = 2
// Calculate the EMA
ema = ta.ema(close, ema_length)
// Calculate the deviation of the close price from the EMA
deviation = close - ema
// Calculate the standard deviation of the deviation
std_dev = ta.stdev(deviation, ema_length)
// Calculate the Z-score
z_score = deviation / std_dev
// Long entry condition if Z-score crosses +0.5 and is below 3 standard deviations
long_condition = ta.crossover(z_score, 0.5)
// Short entry condition if Z-score crosses -0.5 and is above -3 standard deviations
short_condition = ta.crossunder(z_score, -0.5)
// Exit long position if Z-score converges below 0.5 from top
exit_long_condition = ta.crossunder(z_score, 0.5)
// Exit short position if Z-score converges above -0.5 from below
exit_short_condition = ta.crossover(z_score, -0.5)
// Stop loss condition if Z-score crosses above 3 or below -3
stop_loss_long = ta.crossover(z_score, 3)
stop_loss_short = ta.crossunder(z_score, -3)
// Enter and exit positions based on conditions
if (long_condition)
strategy.entry("Long", strategy.long)
if (short_condition)
strategy.entry("Short", strategy.short)
if (exit_long_condition)
strategy.close("Long")
if (exit_short_condition)
strategy.close("Short")
if (stop_loss_long)
strategy.close("Long")
if (stop_loss_short)
strategy.close("Short")
// Plot the Z-score on the chart
plot(z_score, title="Z-score", color=color.blue, linewidth=2)
// Optional: Plot zero lines for reference
hline(0.5, "Upper Threshold", color=color.red)
hline(-0.5, "Lower Threshold", color=color.green)