CFDs - Mean Reversion

Mean reversion (MR) trading is a short term technique that takes advantage of a well-documented market anomaly whereby rapid short term price falls are often followed by a ‘reversion’ to the ‘mean’.

Here’s what some typical mean reversion trades look like.

Please note all results are theoretical backtests, made on real historic stock market data.

Winning Trade	Winning Trade
Break Even Trade	Losing Trade

Of course MR trading is not just as simple as buying any share that falls! If you do that, you will of course lose money!

Our MR systems use an extensive array of filters that narrow the tradeable universe of shares down to those that are most likely to exhibit this momentum behaviour, and combine this with sensible risk management and exit criteria.

Example Mean Reversion Trades

Before looking at these results, you should read about the risks of trading.

Download Mean Revert Trade Database

Properties of Mean Reversion Systems

Please note all results are theoretical backtests, made on real historic stock market data.

This is an equity curve of the Sniper trading system that we published to our members.

The assumptions used in collating this data are:

• Starting capital of $20,000
• Trade size of $10,000
• Maximum face value exposure of $50,000 (2.5 x leverage)
• Interest paid on face value of CFD positions at 10% p.a
• Brokerage of 0.2%
• All trades taken as per Sniper system rules.

Mean Reversion Trading with the Sniper System.

This strategy offers a method of picking off high probability, short term trades that usually present themselves in times of extreme market volatility.

The strategy is aimed at absolute performance; it will not hold your funds in the market for long periods of time. It is focused on short term, targeted, aggressive trades. As the strategy is volatility based it is unaffected by the general trend in the market at that point in time.

This strategy is suitable for traders with a high risk tolerance, as trades are generally taken at times of high market volatility. Frequency of trades also tends to group around these times of market volatility; mean reversion is the type of system whereby you can truly call yourself a ‘trader’.

The strategy works on purchasing stocks that have been heavily oversold. Time and time again we see buyers return with strength to quality stocks that are oversold. Your aim as a mean reversion trader is to time your entry into the market so that you are in the stock and profiting from the rising prices that the returning investors create.

Disclaimer

Past performance is not a guarantee or a reliable indication of future results. Trading involves the risk of loss and may not be suitable for you. All information contained in our website, documentation, software and training materials is general in nature and should not in any way be considered as personal advice. The decision to invest or trade and the method selected is a personal decision and involves an inherent level of risk, and you must undertake your own investigations and obtain your own advice regarding the suitability of our products for your circumstances.

Please ensure you obtain and read any current offer documentation, including the Financial Services Guide, prior to acquiring the products mentioned herein, so you are fully informed regarding the key risks and costs associated with these products.

It is also important to understand the inherent limitations of backtesting. Backtesting does not involve actual trading or investing, nor actual investment performances being achieved. Backtesting is a simulation of what an investor might have achieved in the past (based on a series of theoretical assumptions) using a particular investment methodology. As with any simulation, the theoretical results will be dependent upon the assumptions made in the backtest, including but not limited to the timing of the execution of trades and transaction costs.

Backtesting is only helpful as broadly a “modelling” tool of what may have occurred had a particular strategy been adopted, subject to the assumptions made by the person who has written the backtesting software. In fact, there may be sharp differences between simulated performance results and actual results subsequently achieved by any particular trading programme because of the inherent limitation of being able to model every possible variable into the programme (such as market sentiment). By way of example, if a person sought to analyse the performance of an investment strategy for internet initial public offerings on world markets by backtesting data from the so-called “dotcom boom” years in the mid to late 1990s, the strategy would have been seen to outperform the market. Applying the same backtested investment strategy between 2000 and 2002 would have resulted in significant losses.

Under absolutely no circumstances should backtested data be used as a predictive or forecasting tool as to what may happen in the future using any particular investment methodology. Past performance is not a reliable indicator of future returns.