NinjaTrader Order Methods
Ninjascript, the C# API used for programming strategies in NinjaTrader, attempts to stirke a balance between making the strategy development process as simple as possible while preserving flexibility in the types of strategies that it can support. It handles these competing requirements by splitting strategies into two types of order methods. Many programmers within the NinjaTrader community also refer to these types as approaches.
Managed approach
The first and most common order method is a managed approach. The managed approach is also the default assumption that NinjaTrader assumes you’re using when writing a strategy.
I backtest strategies this way 90%+ of the time. It’s way easier to write them using a managed approach once all of the variables and properties are set up. NinjaScript makes available a number of methods that all inter-relate. The names and functionality of each is obvious. EnterLong(), for example, enters a long trade.
All of these methods have premapped relationships between each other. When the strategy calls ExitLong(), NinjaTrader takes care of the details such as knowing which long it needs to exit. That seems obvious enough, but many trading applications do not connect the dots so easily between trading functionality. MetaTrader 4 & 5 are good examples of this type of simple functionality not being available.
The ease with which trading decisions are handled makes it ideal for developing quick and dirty code to test simple ideas. Most of my strategy ideas follow the KISS principle; keep it simple, stupid. It’s usually a very simple development process (10-20 minutes) to program and test a trading idea from the moment it enters my mind. Most ideas don’t work out, but at least I get the satisfaction of knowing the answer quickly.
I discovered the hard way that the managed approach makes assumptions about order handling. EnterLongLimit(), for example, automatically deletes a pending limit order after one bar. I remember spending several hours with a live strategy trying to figure out why so many of my orders entered correctly on tick charts, only to have them disappear on the next tick. Carefully poring over the documentation led me to discover an overloaded method with the liveUntilCandelled parameter.
It’s examples like this pre-programmed functionality that you need to watch out for. This is especially true if you have a bug that doesn’t make sense in the context of your strategy.
Unmanaged approach
An unmanaged approach takes away all of the assumptions that NinjaTrader makes while a strategy runs in real time. This option leaves it up to the coder to store the position status in memory and make appropriate decisions.
Every strategy that we’ve written with unmanaged orders uses pending stop and limit orders. A recent example involves a market making strategy at Interactive Brokers. The strategy is 100% in the market during the US and Asian sessions. We originally started with a managed approach but ran into problems with the commissions and overfills.
The strategy is only trading 2 mini lots during the testing phase, which is the minimum trade size. Although the standard commission of 0.2 pips per side is quite low, the minimum commission is $2.50. That’s relatively high for such a small position size. I noticed on the first day that my client was getting charged commissions for the entry leg and exit leg, respectively.
The first trade entered with 20k. When it came time to exit, the managed approach set an exit limit for 20k and a separate entry limit for another 20k. IB charged commissions twice for both orders, which cost $5 per round turn trade. The trading report looked like:
BOT 20k $2.50
SLD 20k $2.50
SLD 20k $2.50
BOT 20k $2.5o
BOT 20k $2.50
SLD 20k $2.50
BOT 20k $2.50
SLD 20k $2.50
That’s effectively 4 trades at a cost of $20.
Grouping the trades into a single order more or less cut the commissions in half. I found it easier to do with an unmanaged approach because I didn’t have to worry about how the Entry() and Exit() methods would interact. The trade report changed to:
BOT 20k $2.50
SLD 40k $2.50
BOT 40k $2.50
SLD 40k $2.50
BOT 20k $2.50
The same sequence grouped together drops the cost of the 4 trades down to $12.50. The initial entry and the final exit cost $2.50, but the trades in between only pay $2.50 for the entry and exit combined. When the order size increases to standard lots, the commission savings will dramatically improve the client return.
The other reason that we switched to an unmanaged approach was that NinjaTrader kills a strategy whenever an overfill occurs. An overfill is when a strategy requests cancellation of a pending order, but that pending order gets filled before the cancel request arrives. It then exits the new position at market and turns off the strategy.
I found this nearly intolerable with a market making strategy because an overfill would almost certainly occur at least once daily. Unmanaged orders have the option of disabling an overfill and handling it in whatever manner the trader deems appropriate. In my case, it was to simply increase the amount of position that the strategy was trying to exit.
The main disadvantage to unmanaged orders is that everything is tracked internally. If you need to disable a strategy to change the settings or lose the broker connection, the code is probably not going to interact very well with positions that are already open. Whatever IOrder objects were stored in the memory, which is how the strategy knows what positions are open, disappear whenever the strategy is removed from a chart.
Unmanaged strategies also take a lot more time to test. The amount of code involved does not differ dramatically from managed orders. It’s larger, but not grotesquely so. The reason that it takes more time is that the interaction between the order types is not polished like it is in managed orders. The programmer must write everything from scratch, which invariably means that the strategy will suffer from many more initial bugs, especially in a live environment.
Strategies that rely on market orders do not need to consider this approach in my opinion. It causes a lot more work and I have yet to encounter a situation where it makes sense. More importantly, I can’t think of a hypothetical situation where it would make sense either.
Breakeven Trailing Stop
The trailing stop that we build in most of our custom expert advisors varies somewhat from the generic trailing stops out there. The code uses two inputs. An input is basically a variable that pops up on the screen when you load the expert advisor. You can change the input value without the need for additional programming.
This is a screenshot of typical inputs for a MetaTrader expert advisor
The unique aspect of our trailing stop is that it does not trail until the trade reaches a certain amount of profit. Delaying the adjustment allows the user to treat the stop order as a take profit tool instead of simply exiting at a loss. Most traders feel that once a runner appears, only then does it make sense to adjust the initial rules for exiting at a loss. Acting defensively about the trade only when a decent amount of profit is on the table avoids early stop outs, or at least so the theory goes.
TrailStart is the input which controls when the stop moves from losing to breakeven. It is only at this point of profit that the EA adjusts the exit conditions to avoid a loss.
Say, for example, that the TrailStart is set to activate at 20 pips. That means when your buy trade goes up 20 pips from the entry price, the EA automatically adjusts the stop loss to equal the entry price. The EA cannot lose from that point forward, not counting the effects of slippage.
TrailAmount kicks in only after the stop already adjusted to breakeven. This input controls the distance to increments at which the stop loss should update favorably. If TrailAmount equals 5, it means that the stop loss should adjust in your favor 5 pips for every 5 pips of extra profit beyond the TrailStart at 20 pips.
If the stop loss already moved to breakeven at 20 pips, it means that the stop loss is currently at 0 pips. The trade neither wins nor loses if the market hits the stop. If the price advances another 5 pips (TrailAmount ), the expert advisor determines that it must trail the stop again by 5 pips. The price reaching +25 causes the stop to advance from 0 to +5. When the price moves another 5 pips to +30, the stop advances to +10.
Notice how the stop distance remains a consistent 20 pips in the example. It’s the exact same amount as the TrailStart input.
Random Trailing Limit
I got the idea for a trailing limit from Van Tharpe’s market classic Trade Your Way to Financial Freedom. I made Jon Rackley read through the book as part of his training when I first hired him. He brought my attention to an unusual claim made on page 267. Van Tharpe says that it’s often possible to make money even with random numbers.
Random numbers are a pet theory of mine. I’ve long put effort into figuring out whether I could develop a strategy that trades totally at random and still makes money. As the owner of a programming company for traders, and as part of Jon’s initial training for NinjaTrader in December, I assigned him the task of programming the strategy into code.
The book states that trading with totally random entries and a 3 ATR trailing stop generally leads to making money. Flip a coin. You go long if it lands on heads. Go short if it lands on tails. One important note is that we elected to use pure random numbers in our programming instead of the pseudo-random numbers that computers generate. Doing so allows us to avoid time biases in the seeding process that generally pop up when the seeds used are close together in time.
The first working version that I reviewed displayed everything contrary to Van Tharpe’s claims. Using a trailing stop, regardless of the instrument and time frame tested, inevitably led to devastating losses. The profit factors consistently came in near 0.7, a truly awful number.
We did what most of our clients do when they find abysmal strategies. We flipped the strategy on its head. The new strategy uses only a 3 ATR (50 period) trailing limit.
Trailing Limit Analysis
The early conclusion is that trailing limits seem to offer a great deal of potential. Although Jon sent me the code several months ago, it’s only this evening that I had a chance to properly review and test everything.
The profit factors are very encouraging. It depends on the chart that I tested. The worst that I found was a 1.0 profit factor. Everything else came out with profit factors greater than 1. The small table below contains the initial test results. Before you go off salivating that this is the next hot winner, there are a few considerations to keep in mind:
- The results depend entirely on the sequence of random numbers used. Using different sets of random numbers will cause different outcomes.
- The better results on the higher time frames likely result from sampling error. The number of trades involved was only ~160, which is not enough to make definitive conclusions on the nature of the performance. I prefer to see 400 or more trades before drawing definitive conclusions.
- These results do not include spread costs or commissions.
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 Random trades sometimes produce solid looking equity curves. Remember that this was generated with purely random numbers and a trailing limit |
What made me feel better about the results was reviewing the entry and exit efficiencies of each strategy. The number of trades involved really cluttered the graphic. I ran a backtest on a much shorter time period so that the horizontal, blue line would appear clearly on each graph.
 The entry efficiency of a random entry is... random (45.45% entry efficiency) |
 The exit efficiencies of random entries with trailing limits is off the charts. (77.73% exit efficiency) |
The entry efficiency tells us exactly what we would expect to find. Trades which enter at random do not perform better than random (obviously). What’s interesting is how the trailing limit exit strategy actually skews the entry efficiencies to read slightly worse than random. This is a good example of why it’s dangerous to rely purely on statistics. Keeping the big picture in mind reduces the likelihood of making an erroneous conclusion, in this case that there might be something “wrong” with our perfectly random entries.
The exit strategy, which is what we’re truly testing, looks absolutely stellar. It shows that acting as a conditional probability allows for a great deal of adverse movement while capturing extreme points of the move.
Adding Money Management
The percentage of winners for the tested charts ranges from 60-67% accuracy. High percentages of winners often lead to winning streaks. My cursory glance through the chart led to my easily finding a suitable example to cherry pick. Here, 5 trades in a row reach their near maximum take profits.
Picking trades at random occasionally leads to streaks of lucky winners
Testing that I’ve done in the past tells me that it’s often advantageous to increase the position size based on consecutive winners when a strategy is more than 50% accurate. My first idea after reviewing the initial results was to modify the forex money management strategy to pursue the consecutive winners. We unfortunately do not have time to pursue those changes in the code right now. Let me know if you’re interested in seeing this and we’ll make it a priority if enough readers respond.
While increasing the risk after consecutive winners works out statistically in your favor, it does add to the risk. It’s entirely possible for a “winning” set of trades to start losing based solely on the money management strategy. There’s no way to know whether your set of trades will be the luck beneficiary of the extra risk or whether it will be the unlikely loser.
Conclusion
Everyone talks about stops. You always have to have a stop. Blah blah blah. I know it’s going to be the first question that everyone asks.
My research shows that trading with a stop is for suckers. Larry Connors’ book Short Term Trading Strategies That Work was the first trading book where I actually felt like I read valuable information. One of my favorite sections is on stop losses. His research shows that using a stop loss (even a 50% stop loss) always reduces the performance of a strategy. My own independent analysis bears this out.
Not using stops does not mean never taking losses. On the contrary, refusing to exit the market at a loss makes for a 100% odds of blowing up one day. The point of using a stop or limit is to empirically define, and to never waver from, a specific point or points in the market at which you will exit. A trailing limit accomplishes that goal admirably.
Interestingly, the trailing limit is one feature which FAP Turbo incorporates that I have yet to find in any other expert advisors. Although I’m not a fan of FAP Turbo type of strategies, it certainly does interest me that one of its main features aligns with this research. Also notable is the fact that the trailing limit continues down even to the point where it accepts losses.
Range Trade at High Frequency
Range trading systems make the best candidates for high frequency systems. They are less execution sensitive than trending systems for a simple reason. Range trades “catch the falling knife,” making them suitable for using limit orders.
High frequency prices vary from the normal M30 and H1 charts. The lower the time frame, the better that the chart fits to a normal bell curve. One common theme in systems trading since the 2008 crash has been “tail risk” or “fat tails”, which refer to the edges of a probability distribution like the bell curve. The fatter the tails, the more likely that a range trading system is to crash and burn.
The bell curve shows the tail risk of important events. The tails are colored in red. Fat tails mean that important news happens more frequently
The real world events captured in the tails reflect headline news like Bernanke speaking or Ireland announcing another referendum on all this bailout nonsense. The events only happen once, obviously. If you consider the news events in the context of hourly charts, they happen frequently as a percentage of the overall period. If you look at a one minute chart, that same event is now about 1/60th as important. Dropping down to tick charts nearly makes the events disappear in the statistical profile.
My experience is that the news cycle drives trends on a macro basis. “Macro basis” and high frequency are two topics that don’t belong together. Trending systems should focus on long term trading, while ranging systems are far more suited to high frequency. If your system trend trades, you can throw it in the rubbish bin for high frequency trading ideas.
High frequency considerations
Keep in mind that there are effectively two ways to participate in the forex market: you can either act as a price taker or as a price marker. Price takers range across all market participants. A hedge fund or university endowment is just as likely to take a price as they are to make one. CTAs and retail forex traders are much more likely to make their decisions based on the expected market direction. Timing is critical for them, so they don’t want to leave it to chance whether or not they’ll get to enter a trade.
The trader gets filled right away. That’s the major advantage. The main disadvantage to acting as a price taker is that you pay the spread every single time that you want to enter a position.
I sat with AvaFX in Dublin on my last trip. They charge a 3 pip fixed cost spread. I mentioned my concern about how that spread affects my client’s EA performance. His MetaTrader expert advisor trades 4 times per day on 2 currency pairs. If you do the math on a 3 pip spread, it works out to 8 * 260 = 2,080 trades per year. If you’re paying 3 pips and trading a $10,00 account, you would have to earn $6,240 per year – a 62.4% return, just to cover trading costs. I don’t care how good a system is – it will never cover those kinds of costs. Trading on margin will not do anything to resolve the issue. Spread costs are directly proportional to the amount traded, which impacts the profit. There is no way to trade and make money if the transaction costs are too high.
Designing an expert advisor is difficult enough, but it’s even harder when you factor in the trading costs. Say, for example, that I develop a EA that wins 75% of the time with a payout of 0.5:1 before trading costs. When the EA wins, it earns $0.5. It loses $1 whenever a loss occurs. The profit is 75 wins * $0.5 = $37.5. The loss is 25 * $1 = $25. The expert advisor’s profit factor is 37.5/25 = 1.5.
That should sound great. The problem occurs when the total commission outweighs the total expected profit. This example required 100 trades. Let’s say that we were trading mini lots with an average win of 5 pips and the average loss of 10 pips. That puts the gross profit at $375 and the gross loss at $250. The return is $125 for the 100 trades, excpet that we must now subtract the $100 for trading costs. The total profit plummets to a measly $25.
If the expert advisor’s expectations held true for something like a 10 pip take profit and 20 pip stop loss, the trader might be better off to change the exit points. The reason is that the profitability may actually improve. The goal would be to reduce the number of trading opportunities with an eye towards making them more profitable relative to the costs.
A better approach, in my opinion, would be to switch over to market making. Although you usually still pay to trade, the advantage to market making is that you earn the spread rather than paying it. The spread is overwhelmingly most traders biggest cost. Not paying it opens the possibility of applying the strategy where one normally could not afford it.
Market making only works if your forex broker allows you to post best bid/best offer and have the price reflected on the screen. Most brokers claim that they are ECNs. A real forex ECN allows you to post limit orders. Whenever that order represents the best bid or offer, the price and size of your order shows up on the screen. The only retail trader friendly brokers that I know of are Interactive Brokers and MB Trading.
I ran my NinjaTrader license at MB Trading last week to test the execution and order flow. The test only use traded a microlot (0.01) and posted best bid or best offer on the EURUSD. The orders remained valid for anywhere from 1-10 minutes. Despite the small trade size and lengthy time period as best bid/offer, the orders only filled 75% of the time. That meant that I caught 100% of the losers but only 56% of the potential winners. Not good, in spite of getting paid for the limit orders.
Interactive Brokers is the next test candidate. They have been around much longer and should have far more order flow. I’m hoping that the low fill rate that I experienced making a market at MB Trading will improve substantially when I shift the same strategy to Interactive Brokers.
I expect to find a few other changes as well. The spread that I earn should fall from around 0.9 pips on EURUSD to 0.5 pips, which is indicative of Interactive Brokers’ improved pricing. I also will have to pay a 0.2 pip commission, which reduces the net credit from 1.0 pips at MB Trading (0.9 spread + 0.1 commission) to 0.3. Nonetheless, I expect the improved fill rate on winning trades to work more in my favor.
The thing that most people will hate is that you can only test a market making approach with live money. It’s sufficient to backtest a strategy using market orders with a 0 spread assumption. The goal is to weed out the junk from diamonds in the rough. No method exists, however, to accurately determine whether or not a trade would have gotten filled with a limit order. The only way to find out is to test an idea with live money, then to compare the results to a backtest over the same period. If the live, high frequency performance is similar to a backtest, then you probably have a winning approach.
The real motivation here is to get as many opportunities as possible. Just like the casino does everything to help you pull the slot machine faster, the trader should look for as many favorable setups as possible. High frequency stands out in this area. The inherent advantages of a system are more likely to manifest more quickly. Assuming that you get a handle on the trading cost problem, the profit is often limited only by the number of trades that can be squeezed into a day.
Programming options at high frequency
MetaTrader 4 is not a good candidate unless you expect to post orders once per minute or slower. MetaTrader suffers from the Trade Context is Busy error. Running an expert advisor on more than a single instrument could cause orders to enter too slowly or not at all. MetaTrader is only an option with MB Trading. Interactive Brokers does not support MetaTrader.
NinjaTrader works great and offers a lot of the broker portability that comes with programming in MQL. Programming a high frequency strategy in NinjaTrader works at most human speeds (5 seconds or more). For the brokerages where NinjaTrader submits orders using the broker’s API, I find a speed bump affect at work. NinjaTrader processes the orders lightning fast, but the broker API cannot handle the speed and starts to choke. If you want to test any frequency that’s not ultra high frequency, I recommend programming in NinjaTrader.
The FIX Protocol is the best option for the institutional trader that cares about maximal performance and does not suffer from the usual budget constraints. FIX is a fancy way of controlling communications between a custom platform and the broker. It does not involve software, only rules. The FIX protocol allows the trader to write software 100% from scratch. The trades and orders can go out the door literally as fast the machine can process them. It’s the advantage that comes with building everything from scratch.
Market Depth
Moving from small time retail forex accounts to a serious account size comes with some bumps in the road. Most traders see the prices of forex pairs on the screen and assume that they can buy and sell unlimited quantities at any time. Although the forex market is the largest market of any in the world, making it the most liquid, there is still a limited size to what you can trade at any moment.
Reading Forex Market Depth
NinjaTrader and MB Trading both make market depth information available in their trading screens. It shows where all of the nearby liquidity lies. Say, for example, that you wanted to place a big order for EUR/USD. You can see in the screenshot from MB Trading’s platform that the liquidity gets bigger as you get further from the price. They call these “Level 2″ quotes, which is jargon taken from equities traders.
MB Trading's Navigator shows the market depth of the EUR/USD. Light colors show the best prices, darker prices indicate distance from the best price.
I took this screenshot in the late afternoon when liquidity is at its worst. The best bid shows a depth of 200, which is measured in mini lots of 10,000. You could sell up to 2 million EUR/USD and get filled at that price. Notice, however, that most of the liquidity is further away. 3 million sits at the next best price and another 3 million even further from that. The net available liquidity is 8 million on the short side and 6 million on the long side for a total of 14 million.
The FX Pro screen in NinjaTrader makes it even clearer. I took this screenshot several minutes later, which is why liquidity numbers are different. One thing I really like about this screen is that NinjaTrader converts the liquidity depth into more tangible numbers. The formatting makes more sense to me. Plus, it’s much easier to keep track of the varying spread.
The FX Pro screen in NinjaTrader
What you learned in economics doesn’t apply to trading. Dealing in bulk actually leads to worse pricing instead of improved pricing. The reason is that forex instruments are so standardized that there’s effectively never a lack of customers. It’s really an issue of getting how much much you want at a certain speed.
Only a fool would hit the market with 20M EURUSD instantaneously unless you’re desperate to exit a position. If you push a market order judging solely from the quote on the screen, you may get 2-8 million filled at the displayed price. But, the rest of the order will get filled at progressively worse prices. The traders making a market want their pound of flesh for letting someone into the market so quickly.
Traders cannot see the liquidity depth of most brokers because they elect not to show it. Their platforms encourage the buy this, buy that psychology. The more information that they broadcast, the more bandwidth that’s required, which means that better servers are required.
The EURUSD is the most liquid forex pair in the world. This varies largely by broker, but at any given time you should be able to trade 20-30 million EUR/USD. That doesn’t mean that you’ll get filled at the price on the screen. What that means is that that is the sum quantity available at any given moment.
Some brokers hide their quantity. It’s not like the stock market where if there are 15,000 shares of Microsoft ready to trade at any given moment, you can see 100% of the liquidity available. In forex, as an OTC market, the broker may wish to restrict the viewable book for a few reasons.
If you offer markets from say 5 banks, it’s very rare for the broker to feed the best competitive price and to let the banks fight it out? Why? Because the broker also needs the banks to stick around when nobody wants to trade.
It’s an informal agreement that if I’m retail broker A and UBS is my main liquidity feed, I go out of my way to give UBS the good flow. My customers expect to trade during NFP and other volatile markets (although they really shouldn’t!). If the brokerage simply lets the banks fight it out, then the banks have every reason to let the brokerage rot on the vine when their customers want to trade but it’s bad for the banks. The banks certainly don’t want to take positions in volatile markets. Their only incentive for doing so is if the “good flow” that the forex broker sends during normal markets incentivizes them to accept the risk of losing more than they care to during NFP.
News traders are the most likely to try trading during a thin market. They are also the most likely to complain about not being able to trade. These are the arguments to which I’m least sympathetic. If you’re trading the news, you are overwhelmingly likely to have less than one year of trading experience. The decision has nothing to do with researching systems or evaluating whether or not it’s a good idea. It has everything to do with gambling.
Retail traders are the most likely to trade during volatile events, not just news but really any type of momentum. Almost everyone follows a breakout or momentum strategy. It has everything to do with what traders perceive as the most likely outcome. When the market explodes in one direction, it takes nerves of steel to stand in front of the freight train. Therefore, it’s probably a good idea because it’s counter-intuitive.
Trends happen so slowly that they don’t excite the gambling buzz that most retail forex traders are after. My friend Afshin in Dublin fell victim to this last week. He saw the EUR/USD rising day after day after day. He felt like it was simply overdue for a correction. The urge to participate, rather than coming from a desire for a quick hit, instead came from a desire to be right before there was any clear indication of the opportunity to be right. The point is that what feels natural to do is often precisely the wrong thing to do. It feels natural to every other trader, too.
Market Depth and Direction
One research project that I’d eventually love to do is to study how market depth on any given side of a market affects direction. Some traders run simple liquidity businesses where they receive trading rebates in exchange for accepting the risk of holding a position over the short run. These entities are less likely to concern themselves with picking the direction of the market.
Trading desks that make markets, however, often want the flow so that they can establish a position and earn the spread while doing so. These entities are picking direction – and they are backed by very intelligent math geeks with PhDs and a lot of time on their hands. If those desks make a visibly deep market and it’s sufficiently one sided, then it’s probably safe to assume that they expect to the market to move in the opposite direction.
When you’re buying a forex pair, the bank is selling it to you. So if everyone stacks the liquidity so that you can buy but the liquidity is thin on the short side, it should be telling you that the smart money wants to go short right now.
Trading Time in Programming
The major automated trading platforms such as MetaTrader 4, NinjaTrader and TradeStation all count time in the same way. This makes it quite convenient for ordering trading strategies and expert advisors; you don’t have to do any mental gymnastics to describe the strategy in different platforms. The consistent arrangement of time makes it easy for us to translate trading strategies across multiple platforms.
We tend to think of time as moving in the same direction as when we read. English speakers, who read from left to right, think of time as moving the same direction. If you speak a language like Arabic that reads right to left, you tend to think of time as marching to the left.
All of these charting platforms are written by speakers of left to right languages. The past is anything that’s not on the far right side. The present is the square on the far right. Each square represents an equal time interval. Traders know these as bars.
A visual display of time and how it's segmented
What tends to confuse everyone ordering expert advisors is that even though time marches to the right, trading programmers count the bars to the left. What makes things more confusing is that programmers always start counting from 0 instead of 1.
Even though time moves to the right, we count it from the right and move back left
If you want to trade a moving average cross strategy that waits for the bars to close, what you’re looking at is “bar 2″ and “bar 1″. The way I describe this in the scope of work is the value at two closed bars ago and the value at the last closed bar, respectively.
An expert advisor that uses closed bars ignores bar 0 because it is still open, which causes the moving average values to fluctuate. The only way to know for certain a moving average value at a particular bar is to wait for the bar to close, which means it is no longer bar 0. When a client requests an expert advisor that trades intrabar, they intend to compare the moving averages at “bar 1″ with “bar 0″. In plain language, that means to compare the value at the last closed bar with the currently open bar.
Hopefully, this description makes sense when you open a chart and see the bars already loaded. The final confusing element is when a new bar pops onto the screen. The previous examples showed 5 bars on the chart (bars 0-4). When a sixth bar pops up, the count is reset to the new time period on every update.
Say that we’re looking at an H1 chart in MetaTrader and that the current time is 06:00. When the new hour strikes, the chart loads a new candle to represent 07:00. It’s at this time that the count resets.
When a new bar appears, your charting platform resets the count based on the newest bar.