Joe Marwood – Trading For Yield – Swing Trading System
A simple mean reversion system based on US treasury yields that that shows a win rate of over 75% and a profit factor over 3 in historical simulations.
US treasury yields are probably the most important cog in the entire global financial system. They affect the level of interest rates within the economy and so impact organisations and businesses at every level.
Because of this, US treasury yields often act as leading indicators and can be utilised to great effect by discretionary and systematic traders.
In this PDF report, we present an interesting mean reversion strategy that looks at US treasury yields (specifically five year and ten year yields) in order to trade a watch list of five purposely selected ETFs.
The result is a system that shows a win rate of over 75% and a profit factor over 3 in historical simulations.
Not only that, but this system made money every year we tested between 2007 and 2017!
Based on a sample of 236 trades over 10 years of historical data this system has two other significant factors in its favour.
First, the system is based on a concept that makes sound economic sense.
Second, the system operates in an area that very few people are looking at.
Both of these two factors lend extra credibility and robustness to the model and increase its chances of success.
Enroll in Trading For Yield today to receive:
A complete trading strategy based on an economic rationale
Full rules, method and back-test results
Fully disclosed trading system source code (Amibroker)
What You’ll Learn In Trading For Yield – Swing Trading System
Trading For Yield
Trading For Yield System – PDF Report
Trading For Yield System – Code
About Joe Marwood
Joe Marwood is an independent trader and investor specialising in financial market analysis and trading systems. He worked as a professional futures trader for a trading firm in London and has a passion for building mechanical trading strategies. He has been in the market since 2008 and working with Amibroker since 2011.
