Camarilla Pivot Points Strategy Based on Bollinger Bands

This strategy first calculates the Camarilla pivot points based on the previous trading day's highest price, lowest price and closing price. It then filters the price with Bollinger Bands indicator to generate…

Pine ScriptBreakout Bollinger Bands Fibonacci Pivots / S&R

Author: ChaoZhang
Complexity: Starter · 64 lines
Parameters: 5
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculate highest price, lowest price and closing price of previous trading day
Calculate Camarilla pivot lines including upper rails H4, H3, H2, H1 and lower rails L1, L2, L3, L4 according to formulas
Calculate 20-day Bollinger Bands upper band and lower band
Go long when price breaks above lower band, go short when price breaks below upper band
Set stop loss near Bollinger Bands upper or lower band

Advantage Analysis

Camarilla pivot lines contain multiple key support and resistance levels to enhance reliability of trading signals
Combining with Bollinger Bands effectively filters false breakouts
Multiple parameter combinations make trading flexible

Risk Analysis

Improper Bollinger Bands parameter settings may cause wrong trading signals
Camarilla pivot points rely on previous trading day's price, may be impacted by overnight gaps
Both long and short positions carry loss risks

Optimization Directions

Optimize Bollinger Bands parameters to find best combination
Add other indicators to filter false breakout signals
Increase stop loss strategies to reduce single loss

Summary

This strategy combines Camarilla pivot lines and Bollinger Bands, generating trading signals when price breaks key support and resistance levels. Strategy profitability and stability can be improved through parameter optimization and signal filtering. Overall, this strategy has clear trading logic and high operability, worth live trading verification.

Parameters

Name	Default	What it controls
v_input_1	D	Resolution
v_input_2	true	width
v_input_3	0	Sell from : R1 \| R2 \| R3 \| R4
v_input_4	0	Buu from : S1 \| S2 \| S3 \| S4
v_input_5	false	Trade reverse

Source Pine Script

/*backtest
start: 2024-01-28 00:00:00
end: 2024-02-04 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 12/05/2020
// Camarilla pivot point formula is the refined form of existing classic pivot point formula. 
// The Camarilla method was developed by Nick Stott who was a very successful bond trader. 
// What makes it better is the use of Fibonacci numbers in calculation of levels.
//
// Camarilla equations are used to calculate intraday support and resistance levels using 
// the previous days volatility spread. Camarilla equations take previous day's high, low and 
// close as input and generates 8 levels of intraday support and resistance based on pivot points. 
// There are 4 levels above pivot point and 4 levels below pivot points. The most important levels 
// are L3 L4 and H3 H4. H3 and L3 are the levels to go against the trend with stop loss around H4 or L4 . 
// While L4 and H4 are considered as breakout levels when these levels are breached its time to 
// trade with the trend.
//
// WARNING:
//  - For purpose educate only
//  - This script to change bars colors.
////////////////////////////////////////////////////////////
strategy(title="Camarilla Pivot Points V2 Backtest", shorttitle="CPP V2", overlay = true)
res = input(title="Resolution", type=input.resolution, defval="D")
width = input(1, minval=1)
SellFrom = input(title="Sell from ", defval="R1", options=["R1", "R2", "R3", "R4"])
BuyFrom = input(title="Buu from ", defval="S1", options=["S1", "S2", "S3", "S4"])
reverse = input(false, title="Trade reverse")
xHigh  = security(syminfo.tickerid,res, high)
xLow   = security(syminfo.tickerid,res, low)
xClose = security(syminfo.tickerid,res, close)
H4 = (0.55*(xHigh-xLow)) + xClose
H3 = (0.275*(xHigh-xLow)) + xClose
H2 = (0.183*(xHigh-xLow)) + xClose
H1 = (0.0916*(xHigh-xLow)) + xClose
L1 = xClose - (0.0916*(xHigh-xLow))
L2 = xClose - (0.183*(xHigh-xLow))
L3 = xClose - (0.275*(xHigh-xLow))
L4 = xClose - (0.55*(xHigh-xLow))
pos = 0
S = iff(BuyFrom == "S1", H1, 
      iff(BuyFrom == "S2", H2,
       iff(BuyFrom == "S3", H3,
         iff(BuyFrom == "S4", H4,0))))
B = iff(SellFrom == "R1", L1, 
      iff(SellFrom == "R2", L2,
       iff(SellFrom == "R3", L3,
         iff(SellFrom == "R4", L4,0))))
pos := iff(close > B, 1,
       iff(close < S, -1, nz(pos[1], 0))) 
possig = iff(reverse and pos == 1, -1,
          iff(reverse and pos == -1 , 1, pos))	   
if (possig == 1) 
    strategy.entry("Long", strategy.long)
if (possig == -1)
    strategy.entry("Short", strategy.short)	 
if (possig == 0) 
    strategy.close_all()
barcolor(possig == -1 ? #b50404: possig == 1 ? #079605 : #0536b3 )

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-05. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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