USD/JPY Net-Long Exposure Decrease At Faster Rate Than Net-Short USDJPY: Retail trader data shows 48.4% of traders are net-long with the ratio of traders short to long at 1.06 to 1. The number of traders net-long is 12.4% lower than yesterday and 30.0% lower from last week, while the number of traders net-short is 10.1% lower than yesterday and 18.3% lower from last week.
To gain more insight to how we use sentiment to power our trading,. USD/JPY Trading Sentiment Suggests Bullish Trading Bias We typically take a contrarian view to crowd sentiment, and the fact traders are net-short suggests prices may continue to rise. Traders are further net-short than yesterday and last week, and the combination of current sentiment and recent changes gives us a stronger USDJPY-bullish contrarian trading bias. About your FOREX.com Demo Account A demo account is intended to familiarize you with the tools and features of our trading platforms and to facilitate the testing of trading strategies in a risk-free environment. Results achieved on the demo account are hypothetical and no representation is made that any account will or is likely to achieve actual profits or losses similar to those achieved in the demo account.
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Conditions in the demo account cannot always reasonably reflect all of the market conditions that may affect pricing and execution in a live trading environment. Leveraged trading in foreign currency or off-exchange products on margin carries significant risk and may not be suitable for all investors. We advise you to carefully consider whether trading is appropriate for you based on your personal circumstances. Forex trading involves risk.
Losses can exceed deposits. We recommend that you seek independent advice and ensure you fully understand the risks involved before trading. FX Publications Inc (dba DailyFX) is registered with the Commodities Futures Trading Commission as a Guaranteed Introducing Broker and is a member of the National Futures Association (ID# 0517400). Registered Address: 32 Old Slip, Suite 803; New York, NY 10005.
Many businesses know some portions of their data are extremely valuable, while others have no value. The problem is, they often don’t know how to identify, much less take advantage of, data that can make a difference.
The exacerbates this dilemma by giving organizations even more data to store and sift through. So it makes sense that, increasingly, they are seeking help in developing strategies to mine their data for insights that save and make money. A major amusement park, for example, was collecting plenty of data from concessions, parking, ticketing, memberships and myriad other sources. Each source had a separate storage system, so the park reached out for help in putting all of its data into a central warehouse. The idea was that it could then start looking for interesting patterns. That’s a common approach, but it’s also a flawed one because it puts the cart before the horse. We recommended that, instead, the amusement park start by determining its goals, such as increasing revenue for certain lines of business.
Then it would know where to start looking for patterns instead of trying to analyze every last byte. Start with Objectives for a More Efficient Data Analysis Suppose, for example, that an analysis of the park’s food sales data revealed an increase in purchases the hour leading up to on-stage entertainment performances. That insight would help to inform other business goals, such as optimizing staff levels. Now, the park knows exactly when more employees are needed to provide fast service — and when they aren’t. A goals-driven approach has several benefits: • It gives you something you can achieve and track, which helps to convince finance teams that the project is justifiable. Otherwise, IT and the line of business are just asking for money to go on a fishing expedition, without any way to measure success and show a return on that investment.
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