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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.

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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.

From Brownian motion to operational risk: Statistical physics and financial markets Physica A: Statistical Mechanics and its Applications, Vol. 321, No. 1-2 A Reexamination of Diffusion Estimators With Applications to Financial Model Validation At the root of most every trend following trading system is a way to define a trends existence and determine its direction. Using Dekalog’s Brownian Motion idea as the root of a system might be a unique way to identify trends and extract profits from markets through those trends. Brownian motion is an important part of Stochastic Calculus. When you start developing quantitative trading strategies, pretty soon you will hit upon Brownian Motion. If you are interested in designing and developing algorithmic trading strategies than you should know stochastic calculus and Brownian motion. It will take some effort to learn stochastic calculus and Brownian […] Brownian motion is a Gaussian process, where each of can be expressed as a linear combination of independent normal random variables . Gaussian process is very useful in regression and classification problems in the field machine learning, which I will touch in other posts when we discuss artificial intelligence in quantitative trading. Today we will simply apply Gaussian process for price ... If you are an option trader, who are constantly searching opportunities to set up inverse iron condor position or other strategies, you must be familiar in estimating the range induced by Geometric Brownian Motion (GBM), or Lognormal distribution someone may call. The theory behind is adopted in the Black Scholes Option Pricing model, this assumes the asset price follows the GBM, shown below ... Geometric Brownian motion (GBM) is a stochastic process. It is probably the most extensively used model in financial and econometric modelings. After a brief introduction, we will show how to apply GBM to price simulations. A few interesting special topics related to GBM will be discussed. Although a little math background is required, skipping the […] Dekalog presents an interesting way to use Brownian Motion to identify trending and non-trending periods. Algorithmische und mechanische Forex Strategien OneStepRemoved . Articles; Sophisticated Web Sites; Automated Trading; Erfahrungsberichte; Kontakt; Dekalog der Brownschen Molekularbewegung Indikator. November 6, 2013 von Andrew Selby 10 Kommentare. Dekalog-Blog ist eine interessante ...

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