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Momentum Oscillator Bollinger Band RSI Trading Strategy

Author: ChaoZhang, Date: 2023-09-18 14:07:51
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Overview

This strategy combines Bollinger Bands and the Relative Strength Index (RSI) indicator to predict price volatility and determine optimal entry points. The logic is straightforward - we watch for closing prices that touch the Bollinger lower band, after which there are two possible scenarios: either the price bounces back from the lower Bollinger band, or it continues falling. To confirm the price movement, we use a second indicator, RSI, to further investigate the trend. For example, if the price reaches the lower Bollinger band but the RSI value is not in oversold territory, we can conclude the price will continue down. If the RSI value is oversold, we can use this area as our entry point.

A stop loss is necessary to avoid losing too much capital if the RSI lingers too long in oversold territory.

The best take profit area is when the price rebounds back above the Bollinger middle band/upper band or when RSI reaches overbought levels, whichever comes first.

Long entry:

RSI < 30 and close price < Bollinger lower band

Long exit:

RSI > 70

Strategy Logic

The strategy first calculates the RSI indicator and sets upper/lower boundaries to determine overbought/oversold levels. It then calculates the Bollinger middle, upper and lower bands. When the closing price touches the lower band and RSI is below 30, go long. When RSI is above 70, close the position.

Upon entering long, set stop loss and take profit points. The take profit is set at entry price * (1 + fixed percentage), stop loss is set at entry price * (1 - fixed percentage).

This allows us to buy at Bollinger lower band when RSI is low and sell when RSI is high, profiting from the reversal. Stop loss and take profit control risk.

Advantage Analysis

  • Bollinger Bands determine reversal points accurately
  • RSI filters out false breakouts, ensuring reliable entry
  • Stop loss and take profit manage trade risk effectively
  • Extensive backtesting and parameter optimization ensure stable profitability

Risk Analysis

  • Bollinger Bands do not perfectly predict reversals, some failures occur
  • RSI can also give false signals
  • Stop loss too close cannot hold position, too loose increases risk

Risks can be mitigated by adjusting Bollinger parameters, using other indicators, and widening stop loss appropriately.

Optimization Directions

  • Consider combining with other indicators like KD, MACD to filter entries
  • Dynamically adjust stop loss/take profit percentages
  • Optimize Bollinger parameters
  • Test robustness across different products

Conclusion

The overall risk/reward profile of this strategy is balanced and backtest results are good. Further improvements can be made through parameter optimization and indicator enhancements. The reversal trading concept based on Bollinger Bands is simple and reliable, warranting further research and refinement.

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/*backtest
start: 2023-09-10 00:00:00
end: 2023-09-17 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
//strategy(title="Bollinger Band with RSI", shorttitle="BB&RSI", format=format.price, precision=2, pyramiding=50, initial_capital=10000, calc_on_order_fills=false, calc_on_every_tick=true, default_qty_type=strategy.cash, default_qty_value=1000, currency="USD")
len = input(14, minval=1, title="Length")
src = input(close, "Source", type = input.source)
up = rma(max(change(src), 0), len)
down = rma(-min(change(src), 0), len)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
plot(rsi, "RSI", color=#8E1599)
band1 = hline(70, "Upper Band", color=#C0C0C0)
band0 = hline(30, "Lower Band", color=#C0C0C0)
fill(band1, band0, color=#9915FF, transp=90, title="Background")

length_bb = input(20,title="BB Length", minval=1)
mult = input(2.0, minval=0.001, maxval=50, title="BB StdDev")
basis = sma(src, length_bb)
dev = mult * stdev(src, length_bb)
upper = basis + dev
lower = basis - dev
offset = input(0, "BB Offset", type = input.integer, minval = -500, maxval = 500)


Plot_PnL = input(title="Plot Cummulative PnL", type=input.bool, defval=false)
Plot_Pos = input(title="Plot Current Position Size", type=input.bool, defval=false)

long_tp_inp = input(10, title='Long Take Profit %', step=0.1)/100
long_sl_inp = input(25, title='Long Stop Loss %', step=0.1)/100
// Take profit/stop loss
long_take_level = strategy.position_avg_price * (1 + long_tp_inp)
long_stop_level = strategy.position_avg_price * (1 - long_sl_inp)

entry_long = rsi < 30 and src < lower
exit_long = rsi > 70

plotshape(entry_long, style=shape.labelup, color=color.green,  location=location.bottom, text="L", textcolor=color.white, title="LONG_ORDER")
plotshape(exit_long, style=shape.labeldown, color=color.red,  location=location.top, text="S", textcolor=color.white, title="SHORT_ORDER")

strategy.entry("Long",true,when=entry_long)    
strategy.exit("TP/SL","Long", limit=long_take_level, stop=long_stop_level)
strategy.close("Long", when=exit_long, comment="Exit")
plot(Plot_PnL ? strategy.equity-strategy.initial_capital : na, title="PnL", color=color.red)
plot(Plot_Pos ? strategy.position_size : na, title="open_position", color=color.fuchsia)


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