Bollinger Bands Fibonacci Retracement Trading Strategy

This strategy identifies price channels using Bollinger Bands and determines support/resistance levels based on Fibonacci retracement ratios for algorithmic trading. It detects Bollinger Bands breakouts, tracks…

Pine ScriptMachine learning Breakout Trend following SMA Bollinger Bands ATR Fibonacci Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 61 lines
Parameters: 10
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculating middle, upper and lower bands of Bollinger Bands
- Middle band is SMA, upper/lower bands are SMA +/- multiples of ATR
- Bollinger Bands expand and contract based on market volatility
Calculating Fibonacci retracement levels based on ratios
- Retracement ratios are multiples of ATR * Fibonacci ratios
- Multiple Fib levels are calculated based on middle band
Monitoring price breaking out of Bollinger Bands
- Consider going long when price breaks above upper band
- Consider going short when price breaks below lower band
Entering trades and setting SL/TP around Fib retracement zones
- Enter trades when price pulls back to Fib zone
- Set stop loss and take profit on the other side of the zone

Advantage Analysis

Bollinger Bands clearly identify market volatility range and trends
Fibonacci ratios grasp key support and resistance levels
Combining indicators allows algorithmic trading
Pullback entries increase probability of success and avoid chasing
Adjustable parameters adapt to different periods and products

Risk Analysis

Bollinger Bands breakouts may be false signals
Difficult to predict precisely when price will retrace to Fib levels
Improper stop loss placement could increase losses
Insufficient or excessive pullback magnitude affects strategy
Ineffective parameters or persistent trending markets could invalidate strategy
Enhancing Bollinger Bands logic, considering volume, dynamic zone adjustment, etc.

Optimization Directions

Optimize Bollinger Bands parameters for better trend and S/R judgment
Add volume indicators to validate breakout signals
Utilize machine learning for pullback probability prediction
Incorporate more technical indicators for signal validation
Select reasonable parameters based on product characteristics and trading sessions
Timely adjust pullback zone strength for changing volatility

Conclusion

This strategy combines the strengths of Bollinger Bands and Fibonacci retracements to identify trends and enter at high-probability pullback levels. Risks can be reduced and results improved by parameter optimization, additional signal validation, dynamic zone adjustment, etc. There is room for expansion by incorporating volume, machine learning models, etc. The strategy can be further refined through continuous optimization.

Parameters

Name	Default	What it controls
v_input_1	20	Length
v_input_2_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_3	false	Offset
v_input_4	1.618	Fibonacci Ratio 1
v_input_5	2.618	Fibonacci Ratio 2
v_input_6	4.236	Fibonacci Ratio 3
v_input_7	false	Use Reverse Buy?
v_input_8	0	Fibonacci Buy: Fibo 1 \| Fibo 2 \| Fibo 3
v_input_9	false	Use Reverse Sell?
v_input_10	0	Fibonacci Sell: Fibo 1 \| Fibo 2 \| Fibo 3

Source Pine Script

/*backtest
start: 2023-08-27 00:00:00
end: 2023-09-26 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy(shorttitle="BBands Fibo", title="Bollinger Bands Fibonacci Ratios", overlay=true)

length      =   input(20, minval=1, type=input.integer, title="Length")
src         =   input(close, title="Source")
offset      =   input(0, "Offset", type = input.integer, minval = -500, maxval = 500)
fibo1       =   input(defval=1.618, title="Fibonacci Ratio 1")
fibo2       =   input(defval=2.618, title="Fibonacci Ratio 2")
fibo3       =   input(defval=4.236, title="Fibonacci Ratio 3")

fiboBuyReverse = input(false, title = "Use Reverse Buy?")
fiboBuy       =   input(options = ["Fibo 1", "Fibo 2", "Fibo 3"],defval = "Fibo 1", title="Fibonacci Buy")
fiboSellReverse = input(false, title = "Use Reverse Sell?")
fiboSell       =   input(options = ["Fibo 1", "Fibo 2", "Fibo 3"],defval = "Fibo 1", title="Fibonacci Sell")

sma = sma(src, length)
atr = atr(length)

ratio1 = atr * fibo1
ratio2 = atr * fibo2
ratio3 = atr * fibo3

upper3 = sma + ratio3
upper2 = sma + ratio2
upper1 = sma + ratio1

lower1 = sma - ratio1
lower2 = sma - ratio2
lower3 = sma - ratio3

plot(sma, style=0, title="Basis", color=color.orange, linewidth=2, offset = offset)

upp3 = plot(upper3, transp=90, title="Upper 3", color=color.teal, offset = offset)
upp2 = plot(upper2, transp=60, title="Upper 2", color=color.teal, offset = offset)
upp1 = plot(upper1, transp=30, title="Upper 1", color=color.teal, offset = offset)

low1 = plot(lower1, transp=30, title="Lower 1", color=color.teal, offset = offset)
low2 = plot(lower2, transp=60, title="Lower 2", color=color.teal, offset = offset)
low3 = plot(lower3, transp=90, title="Lower 3", color=color.teal, offset = offset)

fill(upp3, low3, title = "Background", color=color.new(color.teal, 95))

targetBuy = fiboBuy == "Fibo 1" ? upper1 : fiboBuy == "Fibo 2" ? upper2 : upper3
targetBuy := fiboBuyReverse == false ? targetBuy : fiboBuy == "Fibo 1" ? lower1 : fiboBuy == "Fibo 2" ? lower2 : lower3
buy = low < targetBuy and high > targetBuy

targetSell = fiboSell == "Fibo 1" ? lower1 : fiboSell == "Fibo 2" ? lower2 : lower3
targetSell := fiboSellReverse == false ? targetSell : fiboSell == "Fibo 1" ? upper1 : fiboSell == "Fibo 2" ? upper2 : upper3
sell = low < targetSell and high > targetSell

strategy.entry("Buy", true, when = buy)
strategy.entry("Sell", false, when = sell)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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