In life we generally want to be right. This is why you may hear traders framing their trading success by saying they won nine out of the last 10 trades, or that they have a 90% success rate.

However, having lots of winning trades does not necessarily mean that you will be a profitable trader in the long run. This concept is Ray Downer, Senior Trader Coach at Learn to Trade, explores below as he talks Finance Monthly through trade expectancies.

Let’s take two traders: Sarah and Mike are both traders that have placed 100 trades and started with the same amount of money in their trading account:

• Mike wins 75 trades and loses 25 trades
• Sarah wins 30 trades and loses 70 trades

Who is the better trader?

Although we can see Mike is right more often than Sarah is when trading, to determine who is the better overall trader we are missing some key pieces of information.

Firstly, we need to know the amount of profit made when one of our traders is right, as well as the amount lost when wrong. Another way of putting this is that we need to know our traders’ average reward-to-risk over their 100 trades.

So let us look at both of our traders again, but this time take into consideration their reward-to-risk:

• When Mike is right he makes a profit on average of £100 per trade; when wrong he makes an average £300 loss. This means that his reward-to-risk is 1:3.
• When Sarah is right she makes £300 on average per trade and when she’s wrong she loses on average £100. This means she has reward-to-risk of 3:1.

This gives us a bit more insight into the traders. We can see that mike, for example, is willing to risk three times more than he stands to gain in any one trade. Sarah in contrast is looking for a bigger pay-off but not willing to risk as much as Mike per trade.

Neither of those approaches is inherently good or bad as a trading strategy.

To really understand how each of our traders’ strategies stack up against each other, we need to take into consideration the two things we have mentioned here: firstly how frequently our traders have winning trades and secondly how much is gained or lost with each trade.

In trading terms, what we are figuring out is Mike and Sarah’s trade expectancy. Trade expectancy essentially tells us how much we stand to gain or lose as a trader for every pound risked.

Expectancy = (average gain x probability of gain) – (average loss x probability of loss)

We can make this a bit clearer using Mike and Sarah’s results:

• Mike’s expectancy per trade = (£100 win x 0.75) - (£300 loss x 0.25) = £0
• Sarah’s expectancy per trade = (£300 win x 0.3) - (£100 x 0.70 loss) = £20

What this tells us is that over the long run Mike is breaking even with each trade despite winning 75% of the time. As a trader the long term goal is of course to make a profit rather than break-even or lose money. For Mike’s strategy to become profitable he either needs to win more often and/or reduce his risk per trade.

Sarah’s expectancy tells us that she is making an average £20 per trade in the long run, even though she is winning just 30% of her trades. Her reward-to-risk strategy means that she can be wrong much more frequently than Mike, but still make a profit overall.

Both Mike and Sarah’s expectancy can improve or worsen depending on trading conditions and whether they stick to their trading plans. Nevertheless, expectancy is a good benchmark to evaluate a trading strategy. You could also think of expectancy as how much you can theoretically expect to get paid for each trade you take over time.

As we all know, it’s impossible to always be right when trading forex. However, figuring out your expectancy helps shift focus away from being right per trade to instead how right you are overall.

According to ATTOM Data Solutions’ Q1 2017 US Home Sales Report, which shows that homeowners who sold in the first quarter realized an average price gain of $44,000 since purchase, representing an average 24% return on the purchase price — the highest average price gain for home sellers in terms of both dollars and percentage returns since Q3 2007.

Meanwhile, the report also shows that homeowners who sold in the first quarter had owned an average of 7.97 years, down slightly from a record-high average homeownership tenure of 8.00 years in Q4 2016 but still up from 7.68 years in Q1 2016. Homeownership tenure averaged 4.26 years nationwide between Q1 2000 and Q3 2007, prior to the Great Recession.

"The first quarter of 2017 was the most profitable time to be a home seller in nearly a decade, and yet homeowners are continuing to stay put in their homes longer before selling," said Daren Blomquist, senior vice president with ATTOM Data Solutions. "This counterintuitive combination is in part the result of the low inventory of move-up homes available for current homeowners, while also perpetuating the scarcity of starter homes available for first-time homebuyers.

"The average homeownership tenure was down from a year ago in nine of the 66 markets we analyzed, including Memphis, Dallas, Boston, Portland and Tampa," Blomquist added.

Markets with biggest home seller price gains
Among 97 metropolitan statistical areas with at least 1,000 home sales in Q1 2017 (and with previous sales price information available), those with the highest average price gain since purchase realized by home sellers during the quarter were San Jose, California ($356,500 average price gain); San Francisco, California ($276,750 average price gain) and Los Angeles, California ($187,000 average price gain).

"Across our Southern California markets, low listing inventory has continued to drive multiple-offer scenarios," said Michael Mahon, president at First Team Real Estate covering the Southern California market. "We have noticed many buyers now leveraging investment accounts, as well as some leverage of reverse mortgages, to enable their ability to negotiate in competitive multiple-offer scenarios. This level of competition, as well as continued signals of a growth economy, has created momentum particularly in the luxury market of over $1 million in sales price."

Metro areas with the highest% return on the previous purchase price were San Jose, California (71% average ROI); San Francisco, California (65%); and Seattle, Washington (56%);

"Thanks to Seattle's robust economic and job growth, home prices continue to rise at well above average rates and have now surpassed their pre-housing bubble peak. Because of this, it's no surprise that distressed sales continue to fall," said Matthew Gardner, chief economist at Windermere Real Estate, covering the Seattle market. "The increase in all-cash home sales in Seattle is likely not a result of investors, but rather all-cash buyers who are using this tactic to win homes in what it is a hyper-competitive housing market."

Cash sales share down from a year ago, still above pre-recession levels
All-cash sales represented 30.0% of all single family and condo sales in Q1 2017, up from 29.1% in the previous quarter but down from 32.1% in Q1 2016. The 30.0% share in the first quarter was well below the peak of 44.7% in Q1 2011 but was still above the pre-recession average of 20.4% from Q1 2000 to Q3 2007.

"With a stronger market and overall sales increasing, we are seeing a decrease in foreclosure sales across the markets we serve, as well as seeing a decrease in institutional investors purchasing homes," said Matthew Watercutter, senior regional vice president and broker of record for HER Realtors, covering the Dayton, Columbus and Cincinnati markets in Ohio. "With the stronger market and availability of money from institutional lenders such as mortgage companies and credit unions, we are seeing a decrease in cash purchases, as more properties are being sold to owner occupants and fewer to investors."

(Source: ATTOM Data Solutions)