An expert-driven approach to trading
Follow a stream-lined approach to Contrarian trading where expertise guides you through a graduated learning program. Select pre-screened opportunities that support a complete entry and exit strategy and also track your orders to target with recommended adjustments based on current market sentiment.
If you are interested in finding out more about this successful approach to trading and how to become part of this community, please email me direct at randystradingdesk@gmail.com
Tuesday, June 4, 2019
June 04, 2019- Stocks Rebound on Trade Hopes
Reflecting traders' recent laser-like focus on any trade-related
new, stocks are likely to move to the upside in early trading on Tuesday
after China's Ministry of Commerce said the U.S.-China trade dispute
can only be resolved through further trade talks. Concerns about the economic impact of the trade dispute have recently weighed on the markets, with the recent collapse of U.S.-China trade talks helping to drag the major averages down to multi-month lows.
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Delivering Consistent Returns
Since Nov 2009 this system has delivered an average of 1.5% per week returns . The "Weekly Average" includes taking a loss on a stock 15% of the time following a prescribed stop-loss.
That means the same $10,000 invested at 1.5% per week would turn into $540,693 after 5 years. That's assuming you are only buying 50% of the recommended picks published for investors.
Now you can see why this is only available to a select number of individuals.
I challenge you to find another solution that provides as detailed a service in terms recommendations, pricing and tracking to target. And, I also challenge you to find one as accurate as this trading solution.
Remember, this is my specialty and a full-time focus as we continue to deliver a graduated learning program to an elite trading community.
Successful Trading requires:
1. Following a set of proven disciplines
2. Building confidence that removes emotion
3. Patience know "when to hold em and when to fold em"
4. Continuous Learning
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