**Algorithmic Trading: Effortless Profit Generation In San Francisco’s Forex Market** – In the world of algorithmic trading, many practitioners are skeptical about the existence of successful strategies based on machine learning (ML). They have no shortage of arguments: “due to the low signal-to-noise ratio inherent in financial data, ML models cannot learn much from it and will easily become overwhelmed”, “financial data are unlikely to be IID (Independent and Identically Distributed ) and most supervised ML models were created with this assumption in mind,” etc.

Are these reasons enough to give up on the idea of building your own profitable trading algorithm based on ML? It depends on the amount of effort you are willing to put in and it most likely won’t be easy. However, it is quite possible.

## Algorithmic Trading: Effortless Profit Generation In San Francisco’s Forex Market

First, the negatives usually refer to the task of predicting the price of an asset in the near future based on market data. This is quite restrictive. A good dose of domain knowledge, a bit of out-of-the-box thinking, and a huge amount of data gathering and wrangling should lead to the formulation of other ML problems for which the solution could be part of a trading strategy. For example, we could monitor and measure social media activity related to a particular asset and try to predict future movements of that asset. Also, the target variable does not have to be price. It can be anything that you can use to decide whether you want to buy or sell a particular asset. While it may be sufficient for other domains, ML for algorithmic trading requires more than simple data wrangling and model fitting.

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Additionally, we can look around and see some of the big players in the field finding success with ML. For example, Renaissance Technologies LLC, one of the most successful hedge funds of all time, is known for hiring only mathematicians and physicists with little financial background and relying mostly on ML. One could argue that these are top professionals who have access to a tremendous amount of resources in terms of data, infrastructure, and computing power, and that they probably don’t leave much alpha behind. It depends on which markets and assets we are talking about.

As it is still a relatively young market and due to its highly speculative nature, it is hard to believe that the cryptocurrency market adheres to the Efficient Market Hypothesis (EMH). Therefore, as of today, there is likely room to generate alpha while trading cryptocurrencies even for small fish.

At AlphaGrow, we have developed some profitable cryptocurrency trading strategies based on mathematics, statistics and machine learning. You can follow our trading by visiting our website: https://alphagrow.io

In this post, we will present an example methodology for developing a successful ML algorithm to be part of a cryptocurrency trading strategy. We will first select a data set and then construct the appropriate target variable. We will reveal a way to optimize your parametric functions. We will then train and evaluate several XGBoost models in a cross-validation fashion before presenting and discussing the results.

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In this work, we use 1 year of market data (with a sampling period of 1 min) from June 1, 2019 to May 31, 2020 to

Couple This is optional and you may choose to follow the approach below with a different period, a different sampling period and a different financial instrument.

Note that if you are looking for cryptocurrency market data, you can find a regularly updated dataset consisting of 250+

Before we can continue, we need to define what we want to predict. The model should be able to predict everything that allows us to decide whether to buy, sell or do nothing about the asset.

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Basically, we get a binary target variable that will tell us whether to buy the asset with the hope of selling it via

Values. We will do this later when we fine-tune the parameters of our functions to increase their predictive power with respect to the target variable.

To calculate the corresponding label. We decide to store in a variable the number of iterations needed to calculate the label of each time point

. We call this number “patience” because it quantifies how long we had to wait before we could evaluate the tick (either because the take profit was the first to be hit, the stop loss was the first, or the end of the time series was reached). Keep this in mind because it will play an important role later on.

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We also need some form of KPI to know whether the model is satisfactory or not. For example, we can aim for a positive expected return.

The number of false positives (predictions that the classifier tells us to buy when we shouldn’t). Thus, we can define the accuracy metric:

Can be approximated as the percentage of buy orders that resulted in a profitable trade with a profit equal to

. We have to consider the fact that we paid commissions for the buy order and for the sell order.

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Is an approximation of the percentage of buy orders incorrectly suggested by the model that lead to exit via

Now that we have defined the target predictor variable, we need the features that will be used as input data for our model.

Because we protect our IP, we won’t reveal all the details of how our features are designed.

It can be said that our qualities are derived from ongoing operations in terms of price and quantity. Several parameters must be selected, including those corresponding to the sizes of the different rotating windows.

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These parameters, as well as the take profit and stop loss, are fine-tuned so that the predictive power of each feature with respect to the target variable is maximized.

You can create your own functions for this part. Even if they are not parametric, this section remains important because it allows you to find optimal values for

Many practitioners use correlation (Pearson, Kendall, or Spearman) to assess the predictive power of one variable relative to another. However, this statistical metric is limited by its inability to detect non-linear and asymmetric relationships. Additionally, Pearson, Kendall, and Spearman correlations are only for relationships between two numerical variables. Here our target variable is binary and even some functions can be binary or categorical.

) will take values between 0 (no relationship between the two variables) and 1 (there is a direct relationship from

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, the metric of interest becomes MAE (mean absolute error), and the dummy model returns the mean

We decided to solve this with pymoo, a Python library that provides a framework for single and multi-objective optimization.

The first step is to create an object that belongs to (or extends) the “Problem” class that it aims to define

The second step is to choose an appropriate optimization algorithm. This choice depends on the type of problem (eg, single-objective or multi-objective). For multi-objective optimization, the library provides a choice between two genetic algorithms: NSGA-II and R-NSGA-II. Genetic algorithms are metaheuristics that are inspired by the processes of natural selection, more precisely mimicking the mechanisms of mutation, crossover and selection. A mutation is a slight change and thus a diversification of parameters from one generation (

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) with the hope that some changes will yield better optima. Crossing is when the parameters between parents of the same generation (

). In selection, the weakest members (those giving the worst optimum) are of the same generation (

) are discarded so that only the “fittest” can reproduce for the next generation (

Termination criteria must also be selected. There are several options, including the number of generations (iterations) of the algorithm, the execution time of the algorithm, the number of generations after which

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As for the implementation, we will let the reader familiarize themselves with the library, which has very clear and easy-to-follow documentation.

After several iterations, we are left with several sets of parameters that are on the Pareto front. This means that neither set is better than the other because there is at least one feature from one to the other for which the PPS score is better. We choose the set that maximizes the harmonic mean among all optimized PPS results. Within this set, we remove all features for which the PPS score is considered too small (below 0.1).

It gives us the following take profit and stop loss values, which will be used to create the target variable:

Ensemble methods are techniques that produce several simple models (“weak learners”: models that are slightly better than random guesses) and combine them in a way that produces a model that is better than any simple model.

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Boosting is one type of sequencing technique in which weak learners are trained iteratively so that each weak learner compensates for the weaknesses of the previous weak learner.

Over the past few years, XGBoost has become one of the most popular algorithms due to its speed, overall performance, and simplicity.

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