Powerful core strategies
Build your own trading algos with the same strategies we are using
Linear Reversion Strategy
Customizable strategy for you to configure for the index and timeframe of your choosing as-is or with additional filters/indicators/code. Major parts of the code is hidden.
This strategy is based on a statistical advantage achieved through a combination of both mean reversion and linear regression. Mean reversion in financial terms assumes that price will tend to converge to an average price over time. Using mean reversion as a timing strategy involves both the identification of the trading range as well as the computation of the average price using quantitative methods.
Linear regression on the other hand is one of the most basic and commonly used methods for predictive analysis. Together these methods provide a great statistical tool for identifying high probability mean reversion opportunities. It has also specifically been developed with the idea of keeping things as simple and uncomplicated as possible. For this reason it features only 2 primary variable inputs which dictate the start of the trading hours and the maximum allowed loss.
What are the strengths of this core strategy?
Built-in volatility filter
Simple and flexible
High Win Rate
Consistent Historical Performance
With a BSc in Business Information Technology, Juan traded a 7-year career as a Systems Analyst for full-time trading. Through logic and methodology, Juan's created numerous successful trading systems and indicators. His contributions to ProRealTime are a testament to his expertise, making him a respected figure in the community.
Validating the Strategy: Parallel Testing and True Spread Adjustments
In preparation for the public launch of the strategy I have been running in it in parallel on both the AUDUSD and AUDCHF markets since the 8th of April (as can be seen in the screenshots from the gallery). The advantages this gave me is that not only could I verify the initial back tested results and edge of the strategy in a Live environment but also get a better idea of the true spread involved. As an example adjusting the back test date to match the period for which I ran the strategy live, I was able to conclude that the true average spread on the AUDUSD market was in fact 2 points compared to the IG quoted value of 0.6. I could then use this new spread to run a more accurate back test for the strategy to see how it would have performed under more realistic conditions in the past. The back test attached to the gallery is therefore based on this ‘true spread’ and validates the robust nature of the strategy.
Average gain is 58% bigger than the average loss
According to the performance calendar we see positive results each year for the past 8 years with an expected average annual return of 16.8%, this result is also achieved by maintaining a high winning rate of ~70% and a gain to loss ratio of 1.46. Other noteworthy aspects are that the average gain is 58% bigger than the average loss. And the largest win is 35% bigger than the worst loss.
Which are the settings to optimize?
A total of 6 different variables to optimize
The core strategy uses a number of different indicators and filters in order to make those perfect trades. These indicators and filters have to be optimized for your intended timeframe and market.
Both long and short conditions have a total of 6 variables to optimize. In addition to that you can choose to optimize the stoploss level. It's also recommended that you think about the trading hours of your system and make sure that the spread you use when developing the algo, aligns with the average spread of the index during the hours your algo is trading.