Golden Ratio Moving Average Trading Strategy

The golden ratio moving average trading strategy is a quantitative trading strategy that attempts to use the golden cross of short-term and long-term moving averages as trading signals. The strategy also incorporates…

Pine ScriptMachine learning Mean reversion Trend following RSI SMA

Author: ChaoZhang
Complexity: Starter · 52 lines
Parameters: 6
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy is mainly based on two moving averages: the 200-day MA as the long-term MA and the 10-day MA as the short-term MA. A buy signal is generated when the short-term MA crosses over the long-term MA; A sell signal is generated when the short-term MA crosses below the long-term MA. This is the famous "golden cross". The strategy also incorporates the RSI indicator so that the strategy only opens long positions in the oversold area when RSI is less than 30.

Specifically, a long position will be opened if the following conditions are met:

10-day MA crosses above 200-day MA
Currently no position
RSI less than 30

The closing position conditions are as follows:

Stop loss: stop loss when the price falls below a certain percentage (adjustable) of the opening price
Take profit: take profit when the price exceeds a certain percentage (adjustable)

Advantage Analysis

The strategy has the following advantages:

It utilizes the golden cross signal of moving averages, which is a classic and effective technical indicator trading signal
Incorporating RSI avoids buying at the highs, which can control risks to some extent
With stop loss and take profit settings, it can lock in profits and avoid risks

Risk Analysis

The strategy also has some risks:

Moving average strategies are prone to generating wrong signals and whipsaws
RSI can fail in some strong trending markets
If the stop loss is set too small, it may lead to ultra short-term trading and frequent stop loss activation

To reduce these risks, the following optimization measures can be considered:

Adjust the MA parameters, or add more MAs
Incorporate other indicators to confirm RSI signals
Adjust stop loss and take profit parameter settings

Optimization Directions

There is room for further optimization of the strategy:

Increase more indicator filters to avoid wrong signals
Optimize moving average parameters
Incorporate volatility indicators to set dynamic stops
Add machine learning models to judge market conditions
Use algorithms to automatically optimize parameters

Conclusion

In summary, the golden ratio moving average trading strategy is a simple and effective trend following strategy. It generates trading opportunities using classic MA crossover signals and has stops to control risks. The strategy can be further improved through multi-indicator combinations, parameter optimization, machine learning, etc. to obtain better strategy performance.

Parameters

Name	Default	What it controls
v_input_int_1	200	(?パラメータ)Long-term moving averageBASE200
v_input_int_2	10	Long-term moving averageBASE10
v_input_int_3	20	Stop loss ratio％
v_input_int_4	5	Take profit ratio％
v_input_1	timestamp(01 Jan 2018 13:30 +0000)	(?(Period) Start date for backtesting
v_input_2	timestamp(1 Jan 2099 19:30 +0000)	バックテスをFinal day

Source Pine Script

/*backtest
start: 2022-12-29 00:00:00
end: 2024-01-04 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/
// © tsujimoto0403

//@version=5
strategy("Holy Grail", overlay=true,default_qty_type=strategy.percent_of_equity,
     default_qty_value=100)

//ユーザーインプットのPrepare
malongperiod=input.int(200,"Long-term moving averageBASE200",group = "パラメータ")
mashortperiod=input.int(10,"Long-term moving averageBASE10",group = "パラメータ")
stop=input.int(20,title = "Stop loss ratio％",group = "パラメータ")
profit=input.int(5,title = "Take profit ratio％",group = "パラメータ")
startday=input(title="バックテストをThe day to open", defval=timestamp("01 Jan 2018 13:30 +0000"), group="period")
endday=input(title="バックテスをBad day", defval=timestamp("1 Jan 2099 19:30 +0000"), group="period")

//UseうVariable
var float stopprice=0
var float takeprofit=0

//とりあえずUseうインジケーターをプロット
malong=ta.sma(close,malongperiod)
mashort=ta.sma(close,mashortperiod)


plot(malong,color=color.aqua,linewidth = 2)
plot(mashort,color=color.yellow,linewidth = 2)
bgcolor(ta.rsi(close,3)<30?color.rgb(229, 86, 86, 48):na)

//Period conditions
datefilter = true

//エントリーcondition
if close>malong and close<mashort and strategy.position_size == 0 and datefilter and ta.rsi(close,3)<30
    strategy.entry(id="long", direction=strategy.long)

if strategy.position_size>0 
    strategy.exit(id="long",stop=(1-0.01*stop)*strategy.position_avg_price)

//Sellり
if  strategy.position_size > 0 and close>mashort and close<low[1] 
    strategy.close(id ="long")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-05. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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