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The Alpha Engine is a trading strategy resulted from nearly three decades of study that began from an effort to enhance economic theory and then apply it to models. It is interesting to note that it is not only a profitable system but it promotes healthy markets as it provides liquidity to financial markets.

As mentioned in an earlier article, this piece of research identifies three hallmarks of profitable trading that are incorporated into the Alpha Engine system. First, the trading strategy should be parsimonious; it should have a limited set of variables and as a result is more adaptive to changes in market regimes. The next important component of the trading system is self-similarity. The strategy behaves in a similar manner across multiple time frames and thus allows for shorter time frames to be a filter of validity for longer ones. The last hallmark of a profitable trading is modularity. The modelling approach for the system should be modular meaning that it can be built in a bottom up fashion. Smaller blocks can be used to build larger components and therefore create a more complex system.

The instrument universe chosen for backtest of this Alpha Engine is 23 Forex pairs. The Foreign Exchange market is an over-the-counter market that is not constrained by specific exchange based rules which is beneficial to the evaluation of the statistical properties of the system. The symmetry and high liquidity also offer an ideal environment for the development of the strategy.

The paper expounds in detail upon the framework and composition of the Alpha Engine, which is a counter-trending algorithm. The model had an unleveraged return of 21% for eight years for an annual Sharpe ration of 3.06. Furthermore, the max drawdown was around 0.7%. Results of the research show that using leverage of 10:1 yielded approximately 10% per year. When a time series was generated on a random walk, the Alpha Engine yielded profitable results as the model dissected Brownian motion into intrinsic time events.

You can read the full research by Anton Golub, James B. Glattfelder, and Richard B. Olson to learn more about the framework and background of the Alpha Engine here.

Remember, losses can exceed deposits.

submitted by JasonRogers to AlgoTradingFXCM [link] [comments]
As mentioned in an earlier article, this piece of research identifies three hallmarks of profitable trading that are incorporated into the Alpha Engine system. First, the trading strategy should be parsimonious; it should have a limited set of variables and as a result is more adaptive to changes in market regimes. The next important component of the trading system is self-similarity. The strategy behaves in a similar manner across multiple time frames and thus allows for shorter time frames to be a filter of validity for longer ones. The last hallmark of a profitable trading is modularity. The modelling approach for the system should be modular meaning that it can be built in a bottom up fashion. Smaller blocks can be used to build larger components and therefore create a more complex system.

The instrument universe chosen for backtest of this Alpha Engine is 23 Forex pairs. The Foreign Exchange market is an over-the-counter market that is not constrained by specific exchange based rules which is beneficial to the evaluation of the statistical properties of the system. The symmetry and high liquidity also offer an ideal environment for the development of the strategy.

The paper expounds in detail upon the framework and composition of the Alpha Engine, which is a counter-trending algorithm. The model had an unleveraged return of 21% for eight years for an annual Sharpe ration of 3.06. Furthermore, the max drawdown was around 0.7%. Results of the research show that using leverage of 10:1 yielded approximately 10% per year. When a time series was generated on a random walk, the Alpha Engine yielded profitable results as the model dissected Brownian motion into intrinsic time events.

You can read the full research by Anton Golub, James B. Glattfelder, and Richard B. Olson to learn more about the framework and background of the Alpha Engine here.

Remember, losses can exceed deposits.

In order to test a few different FOREX stop-loss strategies, does-it make sense to do this:

**Does it make sense to test such things on computer-generated brownian motion, instead of only historical data?**

Do you sometimes use brownian motion to test your strategies?

submitted by thinkingboutthis to algotrading [link] [comments]
- simulate a (geometric) brownian motion
- randomly open a new position each ~ 1 hour
- let the stop-loss strategy choose when to close position
- do this simulation ~ 10000 times, and see how the stop-loss strategy performs.

Do you sometimes use brownian motion to test your strategies?

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