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The Mean Reversion Bollinger Bands strategy uses the Bollinger Bands indicator to gauge market volatility and moving averages to determine the trend, taking trend trades during periods of low volatility to profit from the trend while avoiding excessive randomness.

The strategy calculates the moving average and upper/lower bands representing a certain multiplier of standard deviation above and below the moving average, forming the Bollinger Bands. When price approaches the bands, it indicates increased volatility. When price is within the bands, it signals decreased volatility.

The strategy goes long when price breaks above the lower band on an uptrending moving average, and goes short when price breaks below the upper band on a downtrending moving average. The corresponding band is used as the stop loss to control risk.

The advantage of this approach is participating in the trend during periods of low volatility, avoiding excessive random price fluctuations and increasing the probability of profit.

- Trading the trend on low volatility reduces randomness and increases stability

By only trading the trend when the Bollinger Bands contract and volatility decreases, the strategy avoids uncertain periods of high volatility, reducing randomness and increasing stability.

- Moving average assists trend judgment, improving accuracy

The moving average, in addition to the Bollinger Bands gauging volatility, helps determine the trend direction, with the two validating each other and improving accuracy.

- Built-in stop loss controls risk

The strategy sets stop loss levels at the bands for each trade, allowing quick stops and risk control.

- Trend misjudgment risk

The moving average direction may change during band contraction, leading to incorrect trend judgment and losses.

Adding other indicators to confirm the trend can help minimize this risk.

- Excessive band volatility risk

If bands are too wide due to excessive standard deviation multiplier, ineffective trades will be too frequent.

Optimizing the parameter or adding band width threshold filters can improve this.

- Breakout failure risk

Price may fail to trend after breaking the bands, causing losses.

Using only closing breaks or adding volume confirmation can reduce failed breakouts.

- Add more indicator confirmations

Adding indicators like MACD and KDJ to confirm moving average signals improves accuracy.

- Optimize parameters

Backtesting to find optimal moving average and standard deviation multiplier parameters improves performance.

- Optimize entry timing

Using only closing breaks or adding volume filters improves timing.

- Optimize stop loss strategy

Trailing stops and moving stops can help lock in profits and prevent giving back gains.

The Mean Reversion Bollinger Bands Strategy cleverly uses the bands to identify low volatility periods and the moving average to determine trend direction, participating in trends when volatility decreases. This filters out excessive randomness and increases stability. The strategy has advantages but also risks to watch out for. Further improvements in stability and profitability can come from additional indicator confirmations, parameter optimization, improved entry timing, and advanced stop loss strategies.

/*backtest start: 2022-10-24 00:00:00 end: 2023-10-24 00:00:00 period: 1d basePeriod: 1h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=4 strategy("Trading Public School", overlay=true) source = close length = input(20, minval=1) mult = input(2.0, minval=0.001, maxval=50) basis = sma(source, length) dev = mult * stdev(source, length) upper = basis + dev lower = basis - dev buyEntry = crossover(source, lower) sellEntry = crossunder(source, upper) if (crossover(source, lower)) strategy.entry("BBandLE", strategy.long, stop=lower, oca_name="BollingerBands", comment="BBandLE") else strategy.cancel(id="BBandLE") if (crossunder(source, upper)) strategy.entry("BBandSE", strategy.short, stop=upper, oca_name="BollingerBands", comment="BBandSE") else strategy.cancel(id="BBandSE") //plot(strategy.equity, title="equity", color=color.red, linewidth=2, style=plot.style_areabr)

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