Walk-forward optimization for algorithmic trading strategies on cloud architecture

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Introduction In almost every trading system there are configurable parameters, e.g. indicator periods, directly affecting the system’s behavior and performance. These parameters are often decided by experts. For example, “Would it be better to use Exponential Moving Average (EMA) with a smoothing factor of 20 or a smoothing factor of 10?”. Optimization means running multiple trials using historical data with different sets of parameters aimed at finding optimal values of these parameters giving the highest profit or lowest volatility or some other desired goal. We need to assess the performance of the optimization process itself before using it for live trading. However, there is a big challenge! Parameter optimization is a very time-consuming task, due to the large search space. A single experiment can take days or weeks rather than minutes or hours. Fortunately, the increased prevalence of cloud computing provides easy access to distributed computing resources and scaling up parameter search to more machines. In this post, I will explain how we leveraged the parallel processing capability on a cloud infrastructure to cut down the runtime of the parameter optimization process, here at

## Walk-forward optimization for algorithmic trading strategies on cloud architecture

## Walk-forward optimization for algorithmic…

## Walk-forward optimization for algorithmic trading strategies on cloud architecture

Introduction In almost every trading system there are configurable parameters, e.g. indicator periods, directly affecting the system’s behavior and performance. These parameters are often decided by experts. For example, “Would it be better to use Exponential Moving Average (EMA) with a smoothing factor of 20 or a smoothing factor of 10?”. Optimization means running multiple trials using historical data with different sets of parameters aimed at finding optimal values of these parameters giving the highest profit or lowest volatility or some other desired goal. We need to assess the performance of the optimization process itself before using it for live trading. However, there is a big challenge! Parameter optimization is a very time-consuming task, due to the large search space. A single experiment can take days or weeks rather than minutes or hours. Fortunately, the increased prevalence of cloud computing provides easy access to distributed computing resources and scaling up parameter search to more machines. In this post, I will explain how we leveraged the parallel processing capability on a cloud infrastructure to cut down the runtime of the parameter optimization process, here at