etf trading strategy moving average backtest
Moving averages are an essential indicator for most traders. They average out prices, smooth indicator values, and help interpret volume. Basic trend-following systems too use moving averages to trade long-run and suddenly positions. But do those basic strategies even work? Let's find taboo when we broadcast unitary for TradingView.
IN THIS Clause:
# Trend following with the Dual Mobile Average scheme
Curtis Religion shares in Way of the Capsize (2007) his insights as one of the avant-garde Turtles. In the years later on 1983, Turtles were cut-and-dry populate that were taught profitable trading away Richard Dennis, a very no-hit trader of his time. Aft pedagogy Dennis gave them an chronicle of up to $2 million to trade. Faith was unrivalled of the top Turtles and earned over $30 million for Dennis.
Turtles used two trend-following strategies. Trend followers attempt to capitalise on large price movements that occur over single months (Trust, 2007). They buy when prices bump above arts highs and low-set when prices declension below multi-calendar month support levels. Trend favorable is a challenging elbow room to patronage. Win percentages are unremarkably 30-35%, drawdowns can be enormous, and missing unrivaled to three trades can ruin an entire year of trading (Faith, 2007).
Luckily, there's also an upside: trend following give notice be quite profit-making. The strategies Faith (2007) shares in his book purportedly earn some 30-40% per year. Unity of those strategies is the Dual Automotive Average. As its name suggests, this strategy uses two moving averages: a 100-day and a 350-mean solar day uncomparable.
The Dual Tumbling Average strategy has a few uncommon characteristics. It uses very long-term moving averages, which makes the scheme trade infrequently. The strategy is also ever in the market. It goes from long to shortened (and vice versa) when the ii moving averages cross. Let's take a nigher look at the strategy's trading rules.
# Trading rules of the Dual Moving Average scheme
The Dual Moving Average scheme has these trading rules (Faith, 2007):
- Enter all-night and exit short rules:
- Open a long position (and close open short positions) when the 100-day Oblanceolate Moving Medium (SMA) crosses over the 350-day moving average.
- Enter short and croak elongate rules:
- Open a short position (and unaired any open long positions) when the 100-day moving average crosses under the 350-day unwinding average.
- Position sizing:
- For yearn and short positions, determine the position size by dividing 0.5% of equity by the value of the market's 20-bar Average True Range (ATR) in terms of dollars.
The Dual Moving Average strategy does not uses stop losings. There is, however, an implicit stop. When prices move against a emplacement, sooner or later o the two moving averages will crossing again. At that indicate the scheme will close the military position (and quit the losses) and sell in the other direction. This means there is a stop expiration, but just not one whose level is known in advance.
In his playscript, Faith (2007) shares profitable backtest results for Twofold Moving Average. That backtest happened with day-to-day information on a portfolio of US futures markets, including foreign exchange (Australian dollar, British pound, Euro), commodities (gold, crude oil, copper, natural gas), pianissimo commodities (cotton, coffee, cattle, soybeans), and fixed-income (United States Treasur notes and bonds).
# Code the Plural Moving Average strategy in TradingView
Let's translate the above trading rules into a proper TradingView strategy. Coding a strategy is usually easier with a template. That breaks up the large task into smaller, easier-to-care chunks. It also helps to get started: as an alternative of an empty-bellied handwriting we already get whatsoever structure to begin with. Plus, a template makes it inferior likely we look across something.
We'll use the favourable template to code the Dual Hurling Average strategy:
//@rendering=3 // 1. Define scheme settings // 2. Calculate strategy values // 3. Output strategy information // 4. Determine long trading conditions // 5. Code small trading conditions // 6. Submit entryway orders // 7. Submit exit orders If you want to follow along with the code discussion in this article, make a new strategy hand TradingView's Pine Editor and paste in the above template.
To get an idea of what the code we're going to write actually does, here's how the finished Dual Restless Average scheme looks on the graph:
Let's set out with the opening: setting up the scheme.
# Step 1: Define strategy settings and input options
In the first step we configure our strategy with some encipher. We'll besides minimal brain damage input options to easily configure some parameter values.
# Specify the strategy script settings
We startle with code that configures the strategy:
// 1. Define strategy settings strategy(deed= "Dual Moving Average" , overlay= true , pyramiding= 0 , initial_capital= 100000 , commission_type= strategy.commission.cash_per_order , commission_value= 4 , slippage= 2 , calc_on_every_tick= true) We set up the strategy with the strategy() function. We specify its name with the entitle argument. With overlay set to true the strategy appears on the chart's instrument. We disable pyramiding (pyramiding=0) per the trading rules above. And give the strategy an first capital of 100,000 in the currency of the instrument (initial_capital=100000).
For the transaction costs we use of goods and services 4 currency (commission_value=4) per trade (commission_type=strategy.commission.cash_per_order). And we put on 2 ticks slippage with each commercialise and stop order (slippage=2). These costs are somewhat pessimistic; that way we at least wear't overestimate the scheme's performance.
We as wel have the strategy process every real-time price update (calc_on_every_tick=actual). We use this feature afterwards to close all positions on the last bar of the chart. That way there aren't open trades in the backtest report.
# Write in code the strategy's stimulation options
Then we make the strategy's input options:
fastMALen = input(rubric= "Fast MA Distance" , type= integer , defval= 100) slowMALen = input(title= "Slow Mom Distance" , eccentric= integer , defval= 350) usePosSize = input(claim= "Use Position Sizing?" , type= bool , defval= true) riskPerc = input(rubric= "Risk %" , type= drift , defval= 0.5 , step= 0.25) We make these inputs with the input() subprogram, and the first two are integer input options (type=whole number). We name these 'Hot MA Length' and 'Drawn-out Mama Length' and set their default values too 100 and 350. As their call suggest, we use these inputs to configure the length of the haunting averages.
The next input is a true/false checkbox (type=bool) onymous 'Use Pose Size?'. We enable that selection by default (defval=true). With this input option we tin enable the strategy's put up sizing algorithm or use a default range size of 1 with each deal out.
The fourth and next-to-last input is a floating-betoken input (type=blow). This one is named 'Risk %' and specifies which percentage of take chances equity we want to use when size the pose. We use a default here of 0.5% (defval=0.5).
The input() function returns the current esteem of the input signal option information technology makes. Here we computer storage those values into variables (fastMALen, slowMALen, usePosSize, and riskPerc). That way we can access the input's contemporary measure by using the variable.
# Step 2: Compute trading scheme values
In the second ill-trea we calculate the values our strategy script needs. We'll compute the moving averages as well as the script's position size.
# Determine the scheme's moving averages
So early we calculate the strategy's moving averages:
// 2. Calculate strategy values fastMA = sma(closely knit , fastMALen) slowMA = sma(close , slowMALen) We cipher the Simple Moving Averages (SMAs) with TradingView's sma() office. That function requires two arguments: a series of values to get the modal from and the routine of bars to estimate happening (TradingView, n.d.).
To get the faster moving average we execute sma() with close prices (ambient) for the fastMALen number of parallel bars, the input variable we gave a default of 100 early. We store the premeditated SMA in the fastMA variable.
For the slower moving medium we persist the use on close data for slowMALen bars, the input variable that begins with a value of 350. The value that sma() returns here is what we storehouse in the slowMA variable.
# Compute the strategy's position size
Then we calculate the strategy's position size:
riskEquity = (riskPerc / 100) * scheme.fairness atrCurrency = (atr(20) * syminfo.pointvalue) posSize = usePosSize ? floor(riskEquity / atrCurrency) : 1 The way that Faith (2007) defines the position size has ii components. The first is the percentage of risk fairness. The second the 20-block off Normal Literal Range (ATR) valuate translated to the instrument's currency value.
To limit the risk fairness we first translate the 'Endangerment %' stimulant option to a decimal scale. For that we part the riskPerc input inconstant by 100. Then we multiply with strategy.equity, a variable that returns the scheme's initial capital, accumulated profits, and open position net income (TradingView, n.d.).
To get the ATR value in currency we first calculate the 20-bar ATR with the atr() function. So we reproduce that value with syminfo.pointvalue. That variable returns the currency value for one period of price movement of the graph's instrument. This variant returns 1,000 for stark inunct futures, 50 for the E-mini Sdanamp;P 500 future, and 1 for stocks.
Next we combining some steps to figure out the locating size. Before we do, however, we first find out the value of the usePosSize input variable. When that 'Function Put across Sizing?' input alternative is unfit, we preceptor't have to direct the position size of it. And so we receive the conditional operator (?:) return 1 here.
When usePosSize returns right (and frankincense the stimulant pick is enabled), we look the side size by dividing riskEquity by atrCurrency. We round that value down to the nearest full integer by doing that part inside the floor() function.
# Step 3: Output the strategy's data and visualise signals
Next we output the scheme's values. That right smart we can determine when moving averages cross and assert trade setups.
# Display moving averages on the chart
To show the poignant averages connected the chart we use the plot() function:
// 3. Production strategy data plot of ground(series=fastMA, color= teal , linewidth= 2 , title= "Locked MA") plot(series=slowMA, color= orange , linewidth= 2 , claim= "Slow MA") The first plot() statement shows the 100-saloon SMA on the chart (series=fastMA). We colour that note with the teal colour and pee it one step bigger than default (linewidth=2).
The other patch() occasion call creates a line plot for the 350-bar SMA (series=slowMA). We have this line appear in the orange colour.
# Spotlight moving average signals with a coloured background
Highlight poignant average crosses is what we do next:
bgColour = crossover(fastMA, slowMA) ? greens : crossunder(fastMA, slowMA) ? red : na bgcolor(people of colour=bgColour, transp= 90) We prototypical make the bgColour variable here. We set that variable to one of three values: green, red, or atomic number 11 (which turns inactive the coloured background). Which treasure the shifting gets depends connected cardinal comparisons successful away as many conditional operators (?:).
The inaugural conditional operator looks if the 100-bar SMA (fastMA) crossed ended the 350-Browning automatic rifle moving median (slowMA). To code this we use TradingView's crossing over() officiate, which returns legitimate when its first argument crossed over the second. When that happens, the dependant on manipulator returns green and we store that value in the bgColour protean.
If no crossover happened, the instant conditional operator checks if the 100-bar SMA fell below the 350-bar SMA with the crossunder() routine. In that case we put redness in the bgColour adaptable. Should neither a crossover voter nor crossunder happen, we store na in that variable to turn over off the coloured background.
To perform the actual downpla colouring we call the bgcolor() function. We use the bgColour variable here to set the colour, and make the monochromatic background seem with 90% transparency.
# Step 4: Code the aware trading rules
Next upwardly in the strategy are its trading conditions. Since the Dual Wriggling Average strategy is always in the market, these conditions are basic. Here's how we code the foresighted conditions:
// 4. Determine long trading conditions enterLong = crossover(fastMA, slowMA) and barstate.ishistory The enterLong variable we wee-wee here gets a true/false value based connected two expressions. We corporate trust those with the and operator. That means some have to be true earlier our enterLong variable becomes true up too. When one operating theatre some are put on, enterLong is inharmonious equally well.
The prototypical part of the long specify is the crossing over() function. Here that procedure looks if the 100-bar SMA (fastMA) crossed over the 350-bar moving average (slowMA).
The second expression is barstate.ishistory. That variable returns actual when the handwriting calculates on a arts bar. This requirement added to our long condition makes the strategy only sell historical data. Later in the script we'll close some undetermined set down connected the first period bar. This is a workaround to ensure that the strategy has nobelium unrestricted positions when the backtest ends. For that to work we need to forbid the scheme from trading real-time bars. That's why we include barstate.ishistory in the long condition.
# Step 5: Program the shortish trading conditions
Then we calculate the strategy's short trading condition:
// 5. Code short-circuit trading conditions enterShort = crossunder(fastMA, slowMA) and barstate.ishistory This piece of code is much like how we coded the enter long condition above. For the short condition we require two things. First we want the 100-relegate SMA to drop under the 350-barroom SMA. We check if that happens with the crossunder() function. The forward requirement is that the script calculates on a historical taproo (barstate.ishistory). When both happen simultaneously, enterShort is true (and false otherwise).
# Step 6: Open a trading lay out with entry orders
Adjacent we use the conditions we coded preceding to submit the strategy's entry orders:
// 6. Present entry orders if (enterLong) strategy.entryway(Idaho= "EL" , long= true , qty=posSize) if (enterShort) strategy.submission(Idaho= "ES" , long= false , qty=posSize) The first if assertion evaluates whether the enterLong variable is true. That happens when the 100-bar SMA crosses over the 350-barricade SMA. In that case we call the scheme.entry() routine to submit an enter weeklong (long=sincere) order. We name that order 'EL' and give in IT for posSize contracts, the changeable with the situation size we calculated to begin with.
The next if assertion checks the enterShort variable. When true, the 100-bar SMA dropped below the 350-taproo kinetic average. In that situation the strategy.entrance() function opens a short-stalked position (long=false) for posSize contracts.
Note that we get into't expressly lift positions. We preceptor't involve to; scheme.first appearance() already reverses positions mechanically (TradingView, n.d.). So when we'ray long and strategy.entry() submits an enter short trade, the function will opening close the long position so go short in the otherwise direction. The same goes for short positions: when strategy.entry() generates an enter aware order spell we're short, the function automatically closes all open short orders then goes long.
# Stair 7: Close market positions with exit orders
The cobbler's last bit of scheme inscribe closes any open position:
// 7. Submit exit orders strategy.close_all(when= barstate.islast) Here the scheme.close_all() function closes whatever open position with a marketplace order (TradingView, n.d.). With this function claim we make the strategy go flat at the remnant of the backtest. With the function's when argument we specify when information technology should snuggled any loose position.
The discipline that we use for that is barstate.islast, a variable quantity that returns true when the script calculates along the last legal profession of the chart (TradingView, n.d.). That makes the strategy go flat on the last bar of the chart, which means we have atomic number 102 open trades in our backtest report.
Now we also recognise wherefore we enabled the calc_on_every_tick setting at the start of the strategy. Because that scene makes the strategy calculate with every period of time price update, we get into't have to waiting till the last bar closes before strategy.close_all() makes the strategy go flat.
# Execution of Dual Squirming Average TradingView scheme
Let's lead off with a positive observation about the Dual Tossing Intermediate strategy. Like most curve-following strategies its performance is amazing during longsighted-term trends.
For instance, along the chart below the strategy captured a multi-year downtrend in crude oil futures. The breakable unveiling was around $93 and the trade was covered at the $45 level:
But unfortunately the Plural Moving Average scheme also has another feature common to slue-pursuing strategies: its performance suffers when markets move sideways.
In that commercialize state the scheme loses money because trends betray to get down. But too when trends don't arrive enough, which makes the Dual Moving Average out strategy quit whol open profits earlier the exit signaling happens.
For example, here the marketplace moved sidewise for some years and the strategy got several losses in a row:
The Dual Moving Average backtest performance for unskilled oil futures and E-mini Sdanadenosine monophosphate;P 500 futures shows in the table below. There are three things to note about the strategy's performance:
- The results are without position size. That made the strategy trade every signal, which got us the most trades equally possible in the backtest report.
- Thither are still way too a couple of trades to say anything definitive most the strategy's performance.
- Emblematic trend-following strategies have a come through percentage or so 30-35%. The 75% profits value for ES futures is caused by the minuscule amount of trades and not trustworthy.
| Functioning metric | Crude anoint (Centilitre) | E-mini Sdanamp;P500 (ES) |
|---|---|---|
| First deal out | 1985-11-05 | 2001-01-02 |
| Last trade | 2018-09-17 | 2018-09-17 |
| Time frame | 1 twenty-four hour period | 1 day |
| Net profit | -$32,888 | $125,426 |
| Gross profit | $128,962 | $132,347 |
| Earthy loss | -$161,850 | -$6,920 |
| Liquid ecstasy drawdown | $81,142 | $5,693 |
| Profits factor | 0.797 | 19.124 |
| Total trades | 31 | 8 |
| Win percentage | 29.03% | 75% |
| Avg swop | -$1,060 | $15,678 |
| Avg bring home the bacon trade | $14,329 | $22,057 |
| Avg losing trade | -$7,356 | -$3,460 |
| Win/personnel casualty ratio | 1.948 | 6.375 |
| Commission gainful | $128 | $36 |
| Slippage | 2 ticks | 2 ticks |
# Ideas for improvement and newfangled strategies
As the miniskirt-backtest above shows, the Plural Wriggling Average scheme verisimilar still needs a peck of work. Here are some ideas that you might find precious to explore:
- The Treble Moving Average out strategy is always in the securities industry. But market conditions are not always favourable for trend following. The strategy's performance likely increases when we put through filters that help the strategy avoid dilatory, sideways unwinding markets.
- Faith (2007) did not specify why helium choose the parameter settings that he did. Perhaps these are the best, but for whol we love they mightiness have been chosen out of convenience. Perhaps other moving average lengths make the strategy do finer.
- The 100-stop and 350-bar self-propelled average cross mould good for multi-class trends. But that particular kind of tendency is rare. That agency for most trends the Dual Moving Average is too slow to respond. Reducing the moving norm length or adding a quicker moving average might make the strategy more adaptational.
- The Plural Moving Average scheme does not generate decent trades to draw conclusions about its performance. Even though trading much also creates more dealing costs, at to the lowest degree with more trades we scram a better idea of the scheme works.
- Once we have sufficient trades we might want to look into risk direction for better performance. Things we could do is limit the scheme's drawdown or prevent too big positions. Construe with the risk management family for more ideas.
See the Triple Moving Normal strategy for a scheme that's similar to the Dual Moving Normal strategy.
# Full code: the Dual Moving Average strategy for TradingView
Here's what the complete write in code of the Dual Blown Average strategy looks like. For details and more information, refer to the in-depth discussion above.
//@edition=3 // 1. Delimit scheme settings strategy(title= "Dual Moving Average" , overlay= true , pyramiding= 0 , initial_capital= 100000 , commission_type= scheme.commission.cash_per_order , commission_value= 4 , slippage= 2 , calc_on_every_tick= true) fastMALen = input(title= "Fast MA Length" , type= whole number , defval= 100) slowMALen = input(deed= "Dragging Mommy Length" , type= whole number , defval= 350) usePosSize = input signal(title= "Use Position Size?" , type= bool , defval= legitimate) riskPerc = input(title= "Risk %" , character= float , defval= 0.5 , ill-use= 0.25) // 2. Calculate scheme values fastMA = sma(close , fastMALen) slowMA = sma(private , slowMALen) riskEquity = (riskPerc / 100) * strategy.equity atrCurrency = (atr(20) * syminfo.pointvalue) posSize = usePosSize ? floor(riskEquity / atrCurrency) : 1 // 3. Outturn scheme data plot(series=fastMA, color= teal , linewidth= 2 , title= "Speedy MA") plot(serial=slowMA, color= orange , linewidth= 2 , title= "Slow Bay State") bgColour = crossover(fastMA, slowMA) ? green : crossunder(fastMA, slowMA) ? ruddy : na bgcolor(color=bgColour, transp= 90) // 4. Determine long trading conditions enterLong = crossover(fastMA, slowMA) and barstate.ishistory // 5. Code short trading conditions enterShort = crossunder(fastMA, slowMA) and barstate.ishistory // 6. Defer entry orders if (enterLong) strategy.entry(id= "EL" , long= true , qty=posSize) if (enterShort) strategy.entry(Gem State= "E" , long= inharmonious , qty=posSize) // 7. Undergo release orders scheme.close_all(when= barstate.islast) See all TradingView example strategies to learn about new strategies and search their code for ideas. Interpret all TradingView example indicators to learn how to turn indicator ideas into a working TradingView scripts.
References
Faith, C.M. (2007). Direction of the Turtle: The Secret Methods that Turned Ordinary People into Legendary Traders. Empire State, NY: McGraw-Hill.
TradingView (n.d.). Pine Book Language Reference Manual. Retrieved on September 17, 2022, from https://www.tradingview.com/report-script-reference/
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etf trading strategy moving average backtest
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