Name
相对强度MACD策略MACD-of-Relative-Strength-Strategy
Author
ChaoZhang
Strategy Description
[trans]
本策略基于两个著名指标:MACD和相对强度(RS)。通过把它们结合在一起,我们得到强大的买入信号。事实上,该策略的特别之处在于,它从一个指标中派生出另一个指标。因此,我们构建一个MACD,其来源是RS的值。该策略只考虑买入信号,忽略卖出信号,因为它们大多是亏损的。还有一个资金管理方法,使我们能够重新投资部分利润,或者在出现重大亏损时减少订单规模。
RS是一个测量动量与市场效率假设之间异常的指标。它被专业人士使用,是最稳健的指标之一。其思想是持有表现优于平均水平的资产,根据它们过去的表现。我们使用以下公式计算RS:
RS = 当前价格 / RS长度内最高价
因此,我们可以比较当前价格与在这个用户定义的时段内的最高价格。
MACD是最著名的指标之一,它测量两个指数移动平均线之间的距离:一个快速线和一个较慢的线。距离越宽表示动量越大,反之亦然。我们将绘制这个距离线的值,并称之为macd线。MACD使用一个低于前两个的第三条移动平均线。这最后一条移动平均线将在穿过macd线时发出信号。因此,它是使用macd线的值作为其来源构建的。
需要注意的是,前两个移动平均线是使用RS值作为其来源构建的。所以,我们刚刚从一个指标中构建了另一个指标。这种方法非常强大,因为它很少被使用,为策略带来了价值。
该策略结合了MACD和RS这两个单独就很强大的指标。MACD能够捕捉短期趋势和动量变化,而RS则反映了中长期趋势的坚实性。将它们结合使用,既考虑了短期因素,也考虑了长期因素,使买入信号更加可靠。
另外,该策略非常独特,通过从RS指标派生出MACD指标,创造性地提高了策略的效果。这种创新性设计很可能会带来超额收益,因为很少有人这么做。
最后,策略具有资金管理和止损机制,可以有效控制风险,限制个别交易的损失。
该策略最大的风险在于RS和MACD指标发出错误信号的可能性。尽管这两个指标都很稳健,但任何技术指标都无法100%预测未来,信号可能偶尔会失效。此外,RS指标本身就更偏重于中长期趋势判断,短期内可能出现误导信号。
为减少风险,可以适当调整RS和MACD的参数,使其更符合具体交易品种和市场环境。另外也可以设置更严格的止损幅度。总的来说,利用止损来控制单笔损失是应对该策略风险的最佳方法。
第一,可以测试不同市场(如股票、外汇、加密货币等)哪个品种该策略效果最好,然后专注最佳品种。
第二,可以尝试利用机器学习算法自动优化RS和MACD参数,而不是人工选择固定值。这可能大大提高参数的适应性。
第三,可以考虑增加其他指标参与建立交易信号,形成多因子模型,提高信号的准确性。比如加入成交量指标等。
本策略综合运用MACD和RS两个指标提供强劲的买入信号。其创新之处在于,从RS指标派生出MACD指标,实现指标与指标的耦合,提高效果。该策略有明确的入场、止损和资金管理机制,能够有效控制风险。下一步,可以通过参数优化、改进信号Generation、增加其他因子等方法进一步完善该策略。
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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.
[/trans]
Strategy Arguments
| Argument | Default | Description |
|---|---|---|
| v_input_int_1 | 300 | (?Technical parameters)RS Length |
| v_input_1 | 14 | MACD Fast Length |
| v_input_2 | 26 | MACD Slow Length |
| v_input_int_2 | 10 | MACD Signal Smoothing |
| v_input_float_1 | 8 | (?Risk Management)Max risk per trade (in %) |
| v_input_int_3 | 400 | (?Money Management)Fixed Ratio Value ($) |
| v_input_int_4 | 200 | Increasing Order Amount ($) |
| v_input_3 | timestamp(1 Jan 2020 00:00:00) | (?Backtesting Period)Start Date |
| v_input_4 | timestamp(1 July 2024 00:00:00) | End Date |
Source (PineScript)
/*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)
Detail
https://www.fmz.com/strategy/436108
Last Modified
2023-12-21 12:01:01