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Strategy vault / Breakout

Golden Ratio Breakout Long Strategy

The Golden Ratio Breakout Long Strategy is a swing trading strategy based on the golden ratio levels of the highest and lowest prices over the past 21 days. It features a backtesting mechanism, long-only setup, and…

Pine ScriptBreakoutMachine learningTrend followingPivots / S&R
Author
ChaoZhang
Complexity
Starter · 42 lines
Parameters
5
Backtest bar
1d · swing
Tested on
BTC/USDT · futures
Jump to codeView in vaultOriginal on FMZ

IMG

Overview

The Golden Ratio Breakout Long Strategy is a swing trading strategy based on the golden ratio levels of the highest and lowest prices over the past 21 days. It features a backtesting mechanism, long-only setup, and long-term holding period.

Strategy Logic

The strategy first calculates the 21-day highest price (high21) and the 21-day lowest price (low21), then computes the difference between them as diff. The trading signal triggers when the current low price breaks above low21 + 0.382 * diff while the previous bar's close is higher than the previous bar's open. The stop loss is set at low21 + 0.236 * diff. In other words, when the price breaks the 38.2% golden ratio line of the recent 21-day price range with upwards elasticity, a long position is initiated. The stop loss line is the 23.6% golden ratio line.

The golden ratio levels are used here as they generally correspond to common market support and resistance areas. 0.382 and 0.236 are watched as retracement and bounce levels, making the golden ratio one of the most intriguing numbers in nature.

Advantage Analysis

The advantages of this strategy are:

  1. Guided by mature technical analysis methodology - golden ratio theory.

  2. Long-only setup lowers system risk.

  3. Trend tracking mechanism identifies accurate entry timing.

  4. Clear stop loss controls risk.

  5. Customizable backtest parameters suit different market environments.

Risk Analysis

There are also some risks:

  1. Reliance on historical data causes insensitivity to market regime shifts.

  2. The tight stop loss may get stopped out by overnight gaps.

  3. False signals can happen if violent price swings occur under improper backtest periods.

  4. Slippage impacts profitability.

These risks can be reduced by adjusting backtest periods, optimizing stop loss placement, considering slippage cost, etc.

Optimization Directions

The strategy can be upgraded in the following aspects:

  1. Auto-optimize parameters with machine learning algorithms to better fit the current market.

  2. Incorporate leverage products like index futures for position amplification.

  3. Improve handling of extreme events like price gaps.

  4. Optimize stop loss rules, e.g. set dynamic stops based on volatility.

Conclusion

In conclusion, this is a long-only strategy that provides clear entry and stop loss logic based on the golden ratio theory. It can be transformed into a robust quantitative trading strategy through parameter tuning, model optimization, portfolio combination, and other enhancement techniques.

Parameters

NameDefaultWhat it controls
v_input_121High Days
v_input_221Low Days
v_input_3trueStart Date
v_input_4trueStart Month
v_input_52019Start Year

Source Pine Script

/*backtest
start: 2022-11-21 00:00:00
end: 2023-11-27 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © omkarkondhekar

//@version=4
strategy("GRBLong", overlay=true)

highInput = input(title = "High Days", type = input.integer, defval = 21, minval = 11)
lowInput = input(title = "Low Days", type = input.integer, defval = 21, minval = 5)

// Configure backtest start date with inputs
startDate = input(title="Start Date", type=input.integer,
     defval=1, minval=1, maxval=31)
startMonth = input(title="Start Month", type=input.integer,
     defval=1, minval=1, maxval=12)
startYear = input(title="Start Year", type=input.integer,
     defval=2019, minval=1800, maxval=2100)

// See if this bar's time happened on/after start date
afterStartDate = (time >= timestamp(syminfo.timezone,
     startYear, startMonth, startDate, 0, 0))

high21 = highest(high, highInput)
low21 = lowest(low, lowInput)

diff = high21 - low21

longEntrySignal = low > low21 + (diff * 0.382) and close[1] > open[1] 

strategy.entry("Long", strategy.long, limit = low, when = longEntrySignal and afterStartDate)
strategy.exit("Long Exit", "Long", stop = low21 + (diff * 0.236))

plot(low21 + (diff * 0.382), color= color.green)
plot(low21 + (diff * 0.236), color = color.red)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-28. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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