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This strategy is based on two well-known indicators: MACD and Relative Strength (RS). By coupling them, we obtain powerful buy signals. In fact, the special feature of this strategy is that it creates an indicator from an indicator. Thus, we construct a MACD whose source is the value of the RS. The strategy only takes buy signals, ignoring sell signals as they are mostly losers. There’s also a money management method enabling us to reinvest part of the profits or reduce the size of orders in the event of substantial losses.

RS is an indicator that measures the anomaly between momentum and the assumption of market efficiency. It is used by professionals and is one of the most robust indicators. The idea is to own assets that do better than average, based on their past performance. We calculate RS using this formula:

RS = Current Price / Highest High over RS Length period

We can thus situate the current price in relation to its highest price over this user-defined period.

MACD is one of the best-known indicators, measuring the distance between two exponential moving averages: one fast and one slower. A wide distance indicates fast momentum and vice versa. We’ll plot the value of this distance and call this line macdline. The MACD uses a third moving average with a lower period than the first two. This last moving average will give a signal when it crosses the macdline. It is therefore constructed using the values of the macdline as its source.

It’s important to note that the first two MAs are constructed using RS values as their source. So we’ve just built an indicator of an indicator. This kind of method is very powerful because it is rarely used and brings value to the strategy.

This strategy combines two individually very powerful indicators: MACD and RS. MACD is able to capture short-term trends and momentum shifts while RS reflects the robustness of medium to long term trends. Using them together considers both short-term and long-term factors, making buy signals more reliable.

Additionally, the strategy is very unique by deriving MACD from the RS indicator, creatively enhancing the strategy’s effect. Such innovative design is likely to lead to alpha returns since few use this approach.

Lastly, the strategy has risk management and stop loss mechanisms that effectively control risks and limit losses per trade.

The biggest risk of this strategy is the possibility of RS and MACD indicators giving wrong signals. Even though both indicators are robust, no technical indicator can 100% predict the future and signals may occasionally fail. Also, the RS itself is biased towards medium-long term trends judgment and may produce misleading signals in the short run.

To reduce risks, parameters of RS and MACD can be tuned to better fit specific trading instruments and market environments. Also, more stringent stop loss range can be imposed. In general, using stop loss to control per trade loss is the best method to address risks of this strategy.

Firstly, test which market (e.g. stocks, forex, crypto etc) gives the best effect of this strategy, then focus on that optimal asset.

Secondly, try utilizing machine learning algorithms to auto-optimize RS and MACD parameters instead of fixing them manually. This could greatly improve adaptiveness of the parameters.

Thirdly, consider incorporating other indicators to establish trading signals, forming a multi-factor model to improve signal accuracy. For example, adding volume indicators etc.

This strategy leverages MACD and RS indicators synergistically to supply strong buy signals. Its novelty lies in deriving MACD from RS indicator, realizing coupling between indicators to enhance efficacy. The strategy has clear entry, stop loss and money management mechanisms that effectively control risks. Next steps could be further improving the strategy via parameter optimization, refining signal generation, adding other factors etc.

/*backtest start: 2022-12-14 00:00:00 end: 2023-12-20 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/ // © gsanson66 //This strategy calculates the Relative Strength and plot the MACD of this Relative Strenght //We take only buy signals send by MACD //@version=5 strategy("MACD OF RELATIVE STRENGHT STRATEGY", shorttitle="MACD RS STRATEGY", precision=4, overlay=false, initial_capital=1000, default_qty_type=strategy.cash, default_qty_value=950, commission_type=strategy.commission.percent, commission_value=0.18, slippage=3) //------------------------------TOOL TIPS--------------------------------// t1 = "Relative Strength length i.e. number of candles back to find the highest high and compare the current price with this high." t2 = "Relative Strength fast EMA length used to plot the MACD." t3 = "Relative Strength slow EMA length used to plot the MACD." t4 = "Macdline SMA length used to plot the MACD." t5 = "The maximum loss a trade can incur (in percentage of the trade value)" t6 = "Each gain or losse (relative to the previous reference) in an amount equal to this fixed ratio will change quantity of orders." t7 = "The amount of money to be added to or subtracted from orders once the fixed ratio has been reached." //----------------------------------------FUNCTIONS---------------------------------------// //@function Displays text passed to `txt` when called. debugLabel(txt, color, loc) => label.new(bar_index, loc, text=txt, color=color, style=label.style_label_lower_right, textcolor=color.black, size=size.small) //@function which looks if the close date of the current bar falls inside the date range inBacktestPeriod(start, end) => (time >= start) and (time <= end) //---------------------------------------USER INPUTS--------------------------------------// //Technical parameters rs_lenght = input.int(defval=300, minval=1, title="RS Length", group="Technical parameters", tooltip=t1) fast_length = input(title="MACD Fast Length", defval=14, group="Technical parameters", tooltip=t2) slow_length = input(title="MACD Slow Length", defval=26, group="Technical parameters", tooltip=t3) signal_length = input.int(title="MACD Signal Smoothing", minval=1, maxval=50, defval=10, group="Technical parameters", tooltip=t4) //Risk Management slMax = input.float(8, "Max risk per trade (in %)", minval=0, group="Risk Management", tooltip=t5) //Money Management fixedRatio = input.int(defval=400, minval=1, title="Fixed Ratio Value ($)", group="Money Management", tooltip=t6) increasingOrderAmount = input.int(defval=200, minval=1, title="Increasing Order Amount ($)", group="Money Management", tooltip=t7) //Backtesting period startDate = input(title="Start Date", defval=timestamp("1 Jan 2020 00:00:00"), group="Backtesting Period") endDate = input(title="End Date", defval=timestamp("1 July 2024 00:00:00"), group="Backtesting Period") //----------------------------------VARIABLES INITIALISATION-----------------------------// strategy.initial_capital = 50000 //Relative Strenght Calculation rs = close/ta.highest(high, rs_lenght) //MACD of RS Calculation [macdLine, signalLine, histLine] = ta.macd(rs, fast_length, slow_length, signal_length) //Money management equity = math.abs(strategy.equity - strategy.openprofit) var float capital_ref = strategy.initial_capital var float cashOrder = strategy.initial_capital * 0.95 //Backtesting period bool inRange = na //------------------------------CHECKING SOME CONDITIONS ON EACH SCRIPT EXECUTION-------------------------------// //Checking if the date belong to the range inRange := true //Checking performances of the strategy if equity > capital_ref + fixedRatio spread = (equity - capital_ref)/fixedRatio nb_level = int(spread) increasingOrder = nb_level * increasingOrderAmount cashOrder := cashOrder + increasingOrder capital_ref := capital_ref + nb_level*fixedRatio if equity < capital_ref - fixedRatio spread = (capital_ref - equity)/fixedRatio nb_level = int(spread) decreasingOrder = nb_level * increasingOrderAmount cashOrder := cashOrder - decreasingOrder capital_ref := capital_ref - nb_level*fixedRatio //Checking if we close all trades in case where we exit the backtesting period if strategy.position_size!=0 and not inRange strategy.close_all() debugLabel("END OF BACKTESTING PERIOD : we close the trade", color=color.rgb(116, 116, 116), loc=macdLine) //-----------------------------------EXIT SIGNAL------------------------------// if strategy.position_size>0 and histLine<0 strategy.close("Long") //-------------------------------BUY CONDITION-------------------------------------// if histLine>0 and not (strategy.position_size>0) and inRange qty = cashOrder/close stopLoss = close*(1-slMax/100) strategy.entry("Long", strategy.long, qty) strategy.exit("Exit Long", "Long", stop=stopLoss) //---------------------------------PLOTTING ELEMENT----------------------------------// hline(0, "Zero Line", color=color.new(#787B86, 50)) plot(macdLine, title="MACD", color=color.blue) plot(signalLine, title="Signal", color=color.orange) plot(histLine, title="Histogram", style=plot.style_columns, color=(histLine>=0 ? (histLine[1] < histLine ? #26A69A : #B2DFDB) : (histLine[1] < histLine ? #FFCDD2 : #FF5252))) plotchar(rs, "Relative Strenght", "", location.top, color=color.yellow)

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