Tags:

This strategy is designed based on the linear regression RSI indicator. It generates buy and sell signals by calculating the crossover between the linear regression RSI and EMA. The strategy also provides two options for the buy logic that can be selected as needed.

The strategy first calculates a 200-period linear regression, then computes a 21-period RSI based on the linear regression result. After that, a 50-period EMA is calculated. When the RSI crosses above the EMA, a buy signal is generated. When the RSI crosses below the EMA, a sell signal is triggered to close the position.

The strategy offers two types of buy logic:

- Buy when RSI crosses above EMA
- Buy when RSI is above EMA and also above the overbought line

The appropriate buy logic can be selected based on market conditions.

This strategy combines the strengths of both linear regression RSI and EMA, which effectively filters out some price noise and generates more reliable trading signals.

The linear regression RSI better captures the trend, and the EMA helps identify turning points. The combination of the two can find mean reversion opportunities within trends.

The strategy provides two optional buy logics for more flexibility to adapt to different market stages. For example, the first logic can be used in strong trends, while the second logic fits better for ranging markets.

The main risk of this strategy lies in the potential change of relationship between the RSI and EMA, which may lead to incorrect trade signals.

In addition, the lagging nature of RSI and EMA as indicators can also cause certain delays in entries and exits, failing to perfectly capture turning points. This introduces some degree of practical risks.

To mitigate the risks, parameters like the lengths of RSI and EMA may be optimized for better coordination between the two. Also, proper position sizing is necessary to limit losses on single trades.

The strategy can be improved from the following aspects:

- Optimize lengths of linear regression RSI and EMA to find best parameter combinations
- Add other indicators like MACD, Bollinger Bands etc. for signal quality enhancement
- Incorporate volatility metrics to adjust position sizing
- Utilize machine learning techniques to automatically optimize parameters

This strategy designs a mean reversion strategy based on linear regression RSI and EMA, identifying reversal opportunities within ranges by looking at RSI-EMA crosses. It also provides two optional buy logics for flexibility to adapt to varying markets. Overall, by combining multiple indicators, the strategy can effectively discover reversal chances. With parameter tuning and additional filters, it has the potential for better performance.

/*backtest start: 2023-01-17 00:00:00 end: 2024-01-23 00:00:00 period: 1d basePeriod: 1h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=4 strategy("Linear RSI") startP = timestamp(input(2017, "Start Year"), input(12, "Month"), input(17, "Day"), 0, 0) end = timestamp(input(9999, "End Year"), input(1, "Month"), input(1, "Day"), 0, 0) _testPeriod() => true //inputs length = input(defval=200, minval=1, title="LR length") length2 = input(defval=21, minval=1, title="RSI length") ema_fast = input(defval=50, minval=1, title="EMA") lag = 0 overBought = input(50) overSold = input(50) //rsi src = close Lr = linreg(src, length, lag) rsi = rsi(Lr, length2) ema = ema(rsi, ema_fast) plot(rsi, color = rsi > overBought ? color.green : rsi < overSold ? color.red : color.silver) plot(overBought, color=color.purple) plot(overSold, color=color.purple) plot(ema, color=color.blue) first_type = input(true, title="Use first logic?") second_type = input(false, title="Use second logic?") long_condition = (first_type ? crossover(rsi, ema) and _testPeriod() : false) or (second_type ? rsi > ema and rsi > overBought and _testPeriod() : false) strategy.entry('BUY', strategy.long, when=long_condition) short_condition = crossunder(rsi, ema) strategy.close('BUY', when=short_condition)

- Dual Moving Average Breakout Strategy
- RSI and Moving Average Breakout Strategy
- EMA Tracking Strategy
- Trend Following Strategy Based on Moving Average
- SMA Crossover Ichimoku Market Depth Volume Based Quantitative Trading Strategy
- Trend Tracking Stop Loss Take Profit Strategy
- Bi-directional Crossing Zero Axis Qstick Indicator Backtest Strategy
- Moving Average Crossover Trading Strategy
- Moving Average Divergence Strategy
- Reversal High Frequency Trading Strategy Based on Shadow Line
- This strategy is a bidirectional adaptive range filtering momentum tracking strategy
- Dual Moving Average Trend Tracking Strategy
- Force Breakthrough Strategy
- RSI CCI Williams%R Quantitative Trading Strategy
- Dynmaic Risk Adjusted Momentum Trading Strategy
- Momentum Moving Average Crossover Trading Strategy
- Bollinger Band Limit Market Maker Strategy
- Long-term Moving Average Crossover Renko Strategy
- 币安新交易对上线监控
- Dual-direction Trend Tracking Renko Trading Strategy