risk reward ratio Archives - Algorithmic and Mechanical Forex Strategies

43 million real trades reveal the tactic of profitable forex traders

June 20, 2016 by Shaun Overton 4 Comments

Traders that follow one simple rule are 3.118 times more likely to be profitable 12 months later than those that don’t.

The critical feature of profitable traders is their reward to risk ratio. Yes, you’ve probably read that before, but this time it’s backed up with research. FXCM studied 43 million real trades from traders around the world to produce this analysis.

Image credit: DailyFX

Everyone “knows” that 90-95% of traders lose money. The good news is that the real percentage is noticeably lower. 83% of all traders lose money. And, that’s among the worst group. When traders use a reward to risk ratio of 1 or more, 50% of all traders are profitable after 12 months.

Be warned: the phrase “correlation is not causation” very much applies here. I cannot promise you that based on the data that using reward to risk ratios greater than 1 will automatically give you 50-50 odds of being profitable in the long run.

Logic, however, suggests that using good reward to risk ratios is a good idea. The advice to use reward-risk ratios above one appears in every trading book ever written for a good reason.

When traders use a reward to risk ratio of 1 or more, 50% of all traders are profitable after 12 months.

I suspect that it’s not the ratio itself that’s important. Instead, a large ratio discourages the worst mistakes that traders make.

I remember a project when I worked as a broker at FXCM. The systems desk analyzed the trades of the company’s most consistent losing traders. Perhaps taking the opposite signal of the worst traders might lead to profitable trades?

Alas, we found something far more mundane: the worst traders lose because they over-trade.

Trading costs

Think about how trading costs apply to the reward risk ratio. If you earn $2 for every $1 that you lose, it makes scalping an impossible activity

Traders using a 2:1 ratio need to use more patience. Even though FXCM offers low spreads and commissions, a 2:1 reward risk ratio implies further distances to the profit target. Longer pip distances lower the cost of every pip of profit.

Cost examples

FXCM averages a 1.4 pip spread on EURUSD. Let’s see how our reward-risk ratio affects trading costs using the 1.4 pip spread for our 2 examples.

Scalping

Profit target: 10 pips
Spread: 1.4 pips
Spread as a percentage of the profit target: 14%

Intraday Trend Trading

Profit target: 50 pips
Spread: 1.4 pips
Spread as a percentage of the profit target: 2.8%

Your cost as a percentage of profit in these examples are 5x higher when you scalp. That’s not good!

Holding trades with bigger profit targets minimizes the impact of trading costs. Said another way, you get to keep more pips when you win by increasing the distance of your profit target from your entry price.

The advice to use reward-risk ratios above one appears in every trading book ever written for a good reason.

Following a reward risk ratio greater than 1 naturally pushes you towards lower trading costs. Lowering your trading costs logically suggests you have a higher likelihood of long term profitability. If you want to get other critical tips for similar results, then make sure to sign up for the Foundations of Profitable Trading Checklist.

Reward risk ratio explained

The reward risk ratio compares your average profit to your average loss. If your average winning trade is $30 and your average losing trading is $15, then you have a reward risk ratio of 2:1. If your average winning trade is only $8, but your average losing trade is $16, then your reward risk ratio is 0.5:1.

Does the winning percentage matter?

Amazingly, the percentage of winning trades doesn’t seem to matter. The high frequency trading firm Virtu is a great example of this. Virtu wins on 99.999% of trading days even though it only wins on 49% of its trades.

The FXCM data shows that the average trader wins more than 50% of the time. EURUSD trades won 61% of the time, while some pairs were closer to 50%. The percentage of winning trades on all currency pairs is greater than 50%.

Image credit: DailyFX

Despite winning more than 50% of the time, trades with a poor reward risk ratio only had a 17% chance of earning a profit 12 months later.

… you get to keep more pips when you win by increasing the distance of your profit target from your entry price

If you’re currently struggling with your profitability, you’ve probably thought to yourself, “I need to win on more of my trades.” It’s like a business owner saying, “I need more customers.”

Smart business owners know that finding more customers is time consuming and expensive. It’s often much easier to sell more stuff to the customers that you already have.

It works the same way in trading. Instead of worrying about winning more often, you should focus your efforts on squeezing a few extra pips out of your winning trades.

If there’s anything that you should learn from this research, it’s this: the fastest way to improve is to earn more pips on your winning trades. You do not need more winning trades to do better.

Types of strategies with good reward risk ratios

The type of strategy that you select almost automatically dictates your reward risk ratio. Ranging strategies usually have ratios less than 1, which the FXCM data shows have a 17% likelihood of long term profitability. Trending strategies have ratios greater than 1, which have 50% probabilities of long term profitability.

Ranging strategies

If you daytrade EURUSD where the daily range has recently been around 80 pips, then that 80 pip range is the hard ceiling of what you could possibly make in a day. You know from experience that getting the bottom tick or the top tick of the day almost never happens. If you’re lucky, you may enter within 10-20 ticks from the bottom.

Upon entry, you also need to give the trade breathing room. That stop loss probably needs to be something like 25 pips if it’s a tight stop or 40 pips in order to have plenty of breathing room.

The best exits in a ranging market occur in the middle. You don’t know if the market will push back to its ceiling. It has just as much chance as going back to support and it does up to resistance.

The mid point of an 80 pip range is 40 pips, but you’re likely entering 10-20 pips from the true bottom. That only gives you a potential range of profit targets from 20-30 pips.

The most realistic, good ratio is a 30 pip profit target on a 25 pip stop loss, which is 1.2. Most strategies will probably risk 40 pips to make 20, which is a ratio of only 0.67.

Consider what a range trading strategy is. The market is stuck. It’s having a hard time going anywhere. You should only range trade if you have a well researched strategy with a long term edge. Otherwise, the typical trader is 83% likely to walk away with losses after a year.

Trending strategies

Trend trading strategies should last for weeks or months at a time. Looking again at EURUSD on a multi-month time frame, the current long term range is from 1.05 up to 1.16. That’s a range of 900 pips, but it’s not like the market wobbles up and down through that range. Instead, it gets stuck near 108, then briefly pushes down. It comes back to 1.08, then pushes up to 1.12. It might push up again to 1.15, then trade back down to 1.08. It’s hard to guess whether the next move will be up or down.

A 3,498.4 pip move in the EURUSD over a 10 month period.

Better long term plays are to sit on trades and let them pick a direction. The best recent EURUSD example began on May 8, 2014 at 1.39934 and ended March 13, 2015, at 1.04946. That’s a colossal 3,498.4 pip move in just 10 months.

Is there a scenario where you’ll risk almost 4,000 pips on a trade? Of course not. What about 1,000? No! What about 500? No!

The natural risk reward ratio for these types of trends is astronomically high. For a few hundred pips of risk, you can make 10 or more pips for every one risked.

As long as you’re not aggressively trading, trending strategies are far more difficult to mess up. If you can click a button, enter a stop loss and then do nothing for months at a time, then you’re qualified to consider trend trading.

The practical application is of course more difficult than that description, but that’s the idea in a nutshell. If you’re a newbie forex trader and wondering where to start, long term trends are the place where you’re less likely to get hurt.

The problem for newbies, though, is that they’re looking for excitement. It’s not terribly exciting to place on trade and then do nothing for months. It’s one of the paradoxes of the market that less work can often lead to better results.

How to improve your trading

The reward to risk ratio is a critical element for new traders to increase their chances of success, but it’s not the only one. Click here to register for our free Foundations of Profitable Trading Checklist. You’ll learn simple, but useful, tips to improve your trading.

Effective Trendline Navigation

June 24, 2015 by Richard Krivo Leave a Comment

As with virtually any trading scenario, we must first determine the direction that we need to trade the pair for the greatest likelihood of success.

By looking at the historical 4 hour chart of the GBPUSD below, there are several reasons we know that we want to go long (buy) the pair. Price action is above the 200 Simple Moving Average and is pulling away from it. The pair has been making higher highs (green lines) and higher lows which indicates an uptrend. Also, at the time of this chart, the GBP was the strongest currency and the USD was one of the weaker currencies.

All these point to a buying opportunity.

But, the question remains, when do we enter the trade?

Here’s where we bring in the trendline…

Let’s take a look at the historical 4 hour chart of the GBPUSD pair below…

We can see that price action has come in contact with trendline support at several points – note the blue boxes.

Since price has tested and respected the trendline for at least three “touches”, we know that our trendline is valid.

Our entry strategy to buy this pair using trendline support will be to wait for price to trade down to the trendline and into the “Buy Zone”. If price trades into the Buy Zone and stalls and a candle does not close below trendline support, just as in our blue box examples, we can take a long position on the pair with our stop just below the trendline or just below the lowest wick that penetrates the trendline.

The trader could exit the trade if price reaches resistance, the previous high, or by employing a simple 1:2 Risk Reward Ratio.

Now let’s take a look at a historical 4 hour chart of the USDCHF for an example of selling against Trendline Resistance in a downtrend…

This trading scenario will be virtually the opposite of what we did in the previous buy example.

We want to sell the pair a it has been making lower lows (red lines) and lower highs. Price action is below the 200 SMA and pulling away from it. Also, at the time of this chart, the USD was weak and the CHF was strong.

Again, price action has tested our resistance line at several points (the blue boxes) so we know the trendline to be valid. In this example we would wait for price to trade up to trendline resistance in the “Sell Zone”. As long as a candle does not close above the trendline, we would sell the pair with a stop just above the trendline or just above the highest wick to penetrate the trendline.

The trade could be closed should price reach the previous low or we could use a 1:2 Risk Reward Ratio to exit the trade.

RKrivoFX@gmail.com

@RKrivoFX

Retail trader disadvantage

October 28, 2013 by Shaun Overton Leave a Comment

Michael Halls-Moore invited a reply to one of my tweets last week, “Retail traders have an advantage over the pros? Me thinks not.” He wrote a great overview of why the institutional traders look longingly at the retail crowd and all the hoops that they don’t have to jump through.

His points are all valid, but he overlooked the big picture. Pricing is everything to a trader. Retail traders get the short end of the stick when it comes to the cost of doing business.

The cost of trading is massively disproportionate

Let’s say that you’re a would be quantitative trader and that you’re looking for opportunities. Let’s say you trade mini lots in the forex market with 60% accuracy and 1:1 risk reward ratios. If you’re not familiar with what a typical trading system looks like, those numbers means that you have an enormous edge.

Some of the less reputable forex brokers out there charge 3 pip fixed spreads. If you’re trading with a broker offering fixed spreads, I urge you to start price shopping. Fixed spreads are wildly overrated. You pay a huge premium for the certainty of a fixed spread. I can’t think of anything remotely plausible to justify them.

The larger forex brokers charge typical spreads in the neighborhood of 2 pips on the largest majors. Although most seem to find this reasonable, the comparison between a 2 pip average spread and institutional spreads is night and day.

Do you know what the average EURUSD spread looks like on the interbank market? It’s often 0.2-0.5 pips. Retail traders pay an average markup of over 300% on their trades.

Retail traders facing the institutions is a bit like David and Goliath.

Retail forex prices have declined in recent years. A few brokers like MB Trading and Pepperstone offer raw spreads with commissions tied to the dollar volume traded. These are, in my opinion, are about the fairest prices available to low balance traders running an expert advisor.

The best deal available to semi-institutional forex traders (CTAs, large balance retail traders, etc) is Interactive Brokers. The customer support is famously poor; they’re cheap for a reason. IB also offers raw spreads with a commission.

My experience with IB has been excellent, but you need to trade size for the economics to work. A 0.5 pip typical spread is great, but the 2 mini-lot minimum trade size and $2.50 minimum commission really adds up. Trading with IB doesn’t approach institutional type pricing until your average trade size approaches 1 standard lot.

So, how does pricing affect the final outcome with our 1:1 risk reward strategy that wins 60%?

Free trading: After 100 trades, you’ve earned $600 and lost $400. The hypothetical net profit is $200.
Fixed spread: You’ve spent $300 in spread costs to enter 100 trades. The total net profit is -$100 ($200-$300).
Average retail: You’ve spent $200. There is no profit because you breakeven ($200 hypothetical profit – $200 in costs). However, your broker loves you for doing that many trades.
Good retail pricing: Let’s say the average cost of a trade is 1.3 pips after commissions. You’ve spent ~$130 placing 100 trades. The total profit is $70.

Even with good strategies, the profitability of your algorithm is as simple as choosing the cheapest broker.

Equities pricing

Trading stocks is even more expensive than forex. I remember back in the day when I thought Scottrade was cheap for offering $7 commissions. It gets worse and worse when you go through the list of stock brokers. Most of them try to get away with charging $7-10 per trade. If customer service is important to you, then those are the shops to look at.

If your top priority is trading profitably, then again, broker selection is critical. The only way that a small guy can make it is by chipping away at the costs. Interactive brokers is again a great option, charging fractions of a penny per share traded. If you decide to trade 2 shares of Google (GOOG: $1,017 per share) or 1,000 shares of Fannie Mae (FNMA: $2.35 per share), the transaction costs are tiny. Two ticks in your favor is all it takes to cover the trade.

You might be thinking that I said two ticks in forex is expensive. How can I say that two ticks in equities is reasonable?

Volatility. Two ticks in the stock market is a little hiccup. It’s not at all uncommon to see highly liquid stocks move 2-3% in a single day. Forex is only interesting because of the leverage. The currency pairs themselves rarely move more than 1%, and that’s usually on major news.

Risk Management

Every employee knows that they’re only one mistake away from getting fired. That’s the reason that everyone hates having a boss. There’s a single person with unilateral authority to financially murder you. Who’s going to look upon that as a good thing?

Well, the truth is that bosses exist for a reason. It’s someone that calls you out when you do something stupid. More importantly, the boss has the power and influence to ensure that you stop doing stupid things.

The dream of entrepreneurship is not having a boss. You go on vacation when you can, you don’t have to play office politics, you don’t have to waste time selling good ideas. You just go out and do them.

Even with good strategies, the profitability of your algorithm is as simple as choosing the cheapest broker

I can tell you as a small business owner that the negatives stand out strongly in my mind. When you don’t have someone to hold you accountable, even if it’s a mentor, you make many more dumb mistakes than you should. It takes incredible discipline to hold the line consistently. Knowing that I’m not going to look stupid or have to explain myself to anyone probably gives me a lot more false confidence than I really need.

Self-employed traders working at home experience the same thing. Who calls you out when you’re trading just because you’re bored?

The decline in the trading account points out the obvious, but that’s not enough to necessarily stop the bad behavior. We’re social creatures. Most people need to speak with other people to maintain their sanity. When you’re trading at home alone, it takes a lot of effort to ensure that you’re getting enough social contact. A good boss prevents you from indulging in bad behaviors.

Conclusion

Selecting the right broker is enough to determine whether or not a good strategy will wind up making money or not. It’s expensive to trade. The bigger you are, the better your pricing.

Retail trading prices have reached a point where it’s at least possible to trade profitably. Nonetheless, the number of strategy types out there is limited because the lower, shorter term strategies are prohibitively expensive to trade.

The quantitative traders and hedge funds get the more active strategy space to themselves. Their trading costs are so low that they’re really the only people that can afford to trade actively.

SPY Crisis Strategy

October 11, 2013 by Shaun Overton 3 Comments

Yesterday’s musings on an S&P 500 doji strategy led to a general discussion of stocks and market crises. I promised to analyze a price-moving average cross strategy and to analyze the performance in times of exceptional volatility.

The results are in and they’re exactly what I predicted. I’m shamelessly tooting my own horn on this one – it’s so rare where strategies do exactly what I predicts.

The direction of the returns matches any traders definition of crisis and regular trading periods over the past decade

SPY Crisis Strategy Rules

The trading rules only initiate short trades. No long positions are allowed.

Enter short next bar at market when:
The price crossed and closed below the 20 day SMA on the last closed bar
The trader believes that a crisis environment either currently exists or is about to exist

Exit an open short trade when:
The price crosses and closes above the 20 day SMA on the last closed bar

The position size is equal to a fixed dollar value divided by the current share price. As an example, SPY currently trades at $169.24. If you wanted to control a position size worth $1,000, then the number of shares is the floor of $1,000/$169.24 = 5 shares.

This strategy is intended to be timely for the current trading environment. Based on all of my proposed definitions below, most of the crisis alarm bells are ringing at the moment.

Defining a crisis

The most difficult part of this type of strategy comes from defining a “crisis environment” quantitatively. Crises don’t happen very often by definition, so I don’t think it’s a worthwhile endeavor to try to quantity the crisis bit. That said, a few obvious crisis indicators come to mind based on basic market mechanics.

PE Ratio

The morons on Tout TV (CNBC and company) keep on screaming how cheap stocks are. I’m not a fundamental trader, but the PE ratio contains useful information. Even the most hard core technical analysis buff would agree that companies generating huge positive cash flow and earning growth have to appreciate at some point. The argument isn’t about if that type of stock will rise; it’s just a question of when.

I don’t see how anyone could possibly look at the current PE ratio of 19.3 and argue that stocks are cheap. They aren’t. Stocks are currently very expensive based on a historical comparison.

VIX

VIX is a CBOE benchmark index that allows traders to compare the price of front month options traded on the S&P 500. A more detailed explanation of the VIX is available on Wikipedia if the concept is new. There’s nothing magical about the 20 level. It’s my general experience that most traders consider that number the one to watch. They think of VIX < 20 as "normal" and VIX > 20 as a severe market move.

Most traders regard a VIX above 20 as a dangerous level.

Put-Call Ratio

Options are effectively leveraged bets on market movements with fixed downside risk. When traders load up on puts, they’re expecting the market to fall. When traders buy more calls, they’re expecting the market to rise.

The put call ratio is simply the number of put contracts traded / the number of call contracts traded. A number > 1 means that more puts were purchased that day than calls, indicating an expectation of a market drop.

Theory has it that short term traders are wrong, making the put call ratio a contrarian indicator. I see the put call ratio as more of a lagging indicator.

When a move is real and already happened, traders react too late and buy protection that they no longer need. The 2008 financial crisis a great example when the ratio spiked to 1.5, a wild number. Just in the past week the ratio went as high as 1.3 before settling back down. The volatility in the number indicates a panicky crowd in my opinion.

Margin debt

Leverage is a two way sword. The theory is that it’s a way to multiply returns by risking debt in the market.

Most traders, and especially retail traders, wind up using leverage as the rope to hang themselves with. Stocks are most commonly purchased with cash among investors. Unlike forex and futures where almost every trader enters a position with leverage, the average retail stock trader enters a position using only the cash present in his account.

An increased willingness among traders to move from cash to margin debt is typically a sign of froth, bubble fever or whatever you want to call it. The chart of margin debt from Business Insider and Zero Hedge show that stocks are currently trading near historical highs.

Conclusion

The 20 day SMA price cross strategy is a great way to run account protection whenever market warning signs are going off. The warning signs may not predict the precise market turning point, but the strategy can function as an effective form of insurance.

The strategy would roughly break even over time if someone were foolish enough to run it that way. Say that you mistime the crisis. Big deal. This type of strategy can run for months without causing irreparable harm to the account.

The signals can run in the background. If you’re only a little bit right with your crisis predictions, the risk reward ratio is massively in your favor. If you’re wrong, the consequences appear to be slow losses that lose a couple of percentage points per quarter. If you’re feeling edgy, I think it’s a great strategy to run in the background to calm your mind.

Money Management Modeling

April 2, 2012 by Shaun Overton 4 Comments

We recently developed software to model the affects of random chance on money management. Although computers are capable of generating pseudo-random numbers, the pseudo-random process introduces bias into the random distribution.

We opted to source our random numbers from random.org. The web site generates purely random streams of bits taken from atmospheric noise. We then use binary mathematics to change those bits into numbers ranging from 1-10,000. Say, for example, that we want to model a trading system with a winning percentage of 50%. Whenever a number comes out between 1-5,000, we consider that a winner. Anything above 5,000 marks a loser.

Changing the winning percentage to 65% works the same way. Any number less than 6,500 represents a winning trade. Numbers above 6,500 signal a loss. The modeling quality is accurate to the thousandth decimal place. That type of accuracy is way more accurate than the “known” accuracy of your trading system, which can only be known within a few whole percentage points.

Most traders fall into the trap of thinking about their trades as individual outcomes. The more appropriate way to view returns is as the sum of all individual outcomes. Losing on any given trade does not matter. It only matters whether the sum of all your winners is greater than all of the losers.

It gets more complicated, unfortunately. A system with 50% wins and a 1:1 payout will almost never come out at exactly breakeven. The mathematical expectation is that we expect to see a degree of drift in the returns solely due to random chance. I suggest reading more in the random trade outcomes and dollar profits section to get a better understanding of drift.

Lastly, we must define a sampling period for evaluating the final result. I arbitrarily set the default value to 200. That means that the software tells us the range of outcomes after 200 total trades. That may take more than a year for some traders. Daytraders may reach that benchmark in several weeks of trading. The question that we are answering is “what is my account balance likely to be after placing 200 trades?”

Coin Toss Trading Experiment

The first experiment is to analyze how dollar returns vary with a coin toss game and the most basic money management method. A starting balance of $100,000 is used with a risk of 1%. The risk will not change as in the fixed fractional method. Instead, we will leave the lots fixed in order to strictly understand random chance. Wins always earn $1,000. Losses always lose $1,000. The odds of a coin toss are 50% wins with 50% losses and a 1:1 reward risk ratio.

The average trade comes out to $99,868.36, almost exactly $100,000. It’s what we expect for a 50-50 game with a 1:1 reward risk ratio. What I find interesting is the standard deviation of $14,377 and how it changes. I don’t want to cover scary math topics. The layman’s explanation is that the standard deviation is the “normal” range from the average that you might expect. The $100,000 balance, in most cases, would either lose $14,000 or make $14,000.

Everything beyond those standard deviation boundaries represent the less likely wild scenarios. The minimum outcome comes out to $58,o00, a massive 42% loss. This had a 0.54% chance of occurring. The maximum outcome shows as $158,000, a monster 58% return. This had an even smaller chance of occurring, only 0.1% (1 in every 1,000 trials).

Changing the account risk dramatically affects the standard deviation. 1% strikes most traders as sane and reasonable. Yet, there is a small chance of losing half the account to drawdown strictly because of terrible luck. Decreasing the overall risk by a fourth to 0.25% drops the standard deviation by exactly one fourth. The worst case scenario shrinks to a highly tolerable $11,500 drawdown (11.5%). Most traders would find a number between 10%-20% reasonable. The consequence of the reduced risk is that the best case scenario drops correspondingly down to a 14.5% gain.

Stretching the risk out to 2%, a normal industry practice, turns out to be risking accounting suicide with the coin toss game. The worst case scenario drops the final account balance down to $8,000, a staggering 92% loss.

The goal is to help you define risk from a gut feeling matter into something more tangible and calculated. Too many traders enter the market day dreaming about profits. Risk enters the picture, but too few traders actually understand the relationship between risk and reward. Hopefully, the picture of best, worst and average scenarios is starting to become more clear for you.