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Brexit puts fintech and high-frequency trading firms in FX winners' circle - What happened?
Brexit puts fintech and high-frequency trading firms in FX winners' circle
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FX banks in London traditionally did very well during periods of increased volatility. Today however, with regulations crimping the amount of risk they can run and increased capital costs applied to holding those risks, banks are less likely to garner outsized gains. In an environment of increased volatility, banks tended to eventually widen their spreads and capture more profits. Today, with non-banks providing an ever-increasing amount of liquidity to the market, banks will find it tougher to pass their costs onto their clients.
the source
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MetaTrader 5 – the best solution for HFT traders!
MetaTrader 5 – the best solution for HFT traders!
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- "Speed is a key attribute of High-Frequency Trading. Everything from data delivery and trade execution to the instant analysis based on huge amounts of data using hundreds of analytical tools must be as fast as possible. All this is available in MetaTrader 5!"
- "In MetaTrader 5, quotes are updated dozens of times per second. The quoting speed is an even more important factor for high-frequency trading robots. HFT robots need to receive a quote, analyze it, and perform a trade as fast as possible in order to catch the best of the market situation. Such a high speed is not available in other platforms, including MetaTrader 4, therefore MetaTrader 5 HFT traders are ahead of other platform users in terms of quote receiving speed."
read more here
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Wall Street’s Speed Demons Are Heroes
Cognitive Shift, Trading Costs, Cozy Relationships and Full Disclosure
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Across the wider world, Wall Street’s speed demons are all too often cast as the villains of the stock market.
As computerized trading firms have become the dominant buyers and sellers of equities, they’ve been blamed for exploiting investors and causing bouts of extreme volatility, and were famously portrayed by Michael Lewis as part of a rigged system in “Flash Boys.” The Bundesbank said they can help trigger flash crashes and Hillary Clinton made policing them an election issue.
But in academic circles at least, high-frequency traders are more often treated like heroes.
Since 2013, positive research has outnumbered negative by a 2-1 margin, a database search of the 30 most-cited papers on HFTs showed. Researchers found automated firms reduced trading costs, and contrary to popular opinion, improved market depth and stability. It’s a turnaround from the previous three years, when most studies were inconclusive or negative. The results were compiled with Microsoft’s search engine for academic research.
“As a whole, the literature strongly supports HFTs being a net positive,” said Jonathan Brogaard, a professor at the University of Washington and co-author of “High-Frequency Trading and Price Discovery,” which has been mentioned by other researchers more than 350 times as the most-cited paper.
Soon-to-be published research by VU University Amsterdam’s Albert Menkveld also indicates trading costs have plunged more than 50 percent as electronic markets and HFTs emerged in the past decade.
“Years ago, somebody said ‘high-frequency trading’ and you immediately associated it with detrimental aspects and implications,” said Ryan Larson, the head of U.S. equity trading at RBC Global Asset Management, which oversees $370 billion. “It’s such a naive viewpoint now.”
the source
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High-frequency trading in the foreign exchange market
High-frequency trading in the foreign exchange market
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In March 2011, the Markets Committee established a Study Group to conduct a fact-finding study on high-frequency trading (HFT) in the foreign exchange (FX) market, with a view to identifying areas that may warrant further investigation by the central banking community. This initiative followed from a number of previous discussions by the Committee about factors contributing to changes in the structure of the global FX market.
The Study Group was chaired by Guy Debelle, Assistant Governor of the Reserve Bank of Australia. The Group drafted an interim report for review by the Committee in May 2011. The finalised report was presented to central bank Governors at the Global Economy Meeting in early September 2011, where it received endorsement for publication.
The subject matter of this report is clearly part of the core expertise of the Markets Committee, which has a long-standing interest in the structure and functioning of the FX market. I hope this report will serve as a timely input to the ongoing discussion about the impact of technological changes, including the rise of algorithmic trading in general and HFT in particular, on the functioning and integrity of financial markets. The FX market focus of this report should also be a valuable complement to a discussion that has so far been based mostly on developments in equity markets.
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Artificial Intelligence Experts Join Sinovation Ventures
China's Sinovation Ventures announced that they have several talents joining the company to further enhance distribution and investment in the artificial intelligence sector.
According to the company, Google's senior engineer Wang Yonggang and Microsoft Research's researcher Wang Jiaping formally joined the artificial intelligence institute of Sinovation Ventures.
At the same time, former marketing director of Google China Huang Huiwen announced plans to join Sinovation Ventures as a partner and chief marketing officer of the company. Former investment vice president of Legend Capital Xiong Hao will join Sinovation Ventures as general manager and investment director of South China region of the company.
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Data Science and Machine Learning (Part 07): Polynomial Regression
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Machine Learning has changed our world a lot in many ways, we have different methods to learn the training data for classification and regression problems, such as linear regression, logistic regression, support vector machine, polynomial regression, and many other techniques, Some parametric methods like polynomial regression and support vector machines stand out as being versatile.
more...
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Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5
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Is a Machine learning paradigm for problems where the available data consists of unlabeled examples. Unlike supervised learning techniques such as regression methods, SVM, decision trees, neural networks, and many others discussed in this article series, where we always have labeled datasets that we fit our models upon. In unsupervised learning, the data is unlabeled so, it's up to the algorithm to figure out the relationship and everything else on itself.
Examples of unsupervised learning tasks are clustering, dimension reduction, and density estimation.
more...
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Data Science and Machine Learning (Part 09) : The K-Nearest Neighbors Algorithm (KNN)
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K-Nearest Neighbors Algorithm is a non-parametric supervised learning classifier that uses proximity to make classifications or predictions about the grouping of an individual data point. While this algorithm is mostly used for classification problems, It can be used for solving a regression problem too, It is often used as a classification algorithm due to its assumption that similar points in the dataset can be found near one another; k-nearest neighbors algorithm is one of the simplest algorithms in supervised machine learning. We will build our algorithm in this article as a classifier.
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Data Science and Machine Learning (Part 10): Ridge Regression
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Ridge regression is the method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. The method provides improved efficiency in parameters estimation problems in exchange for a tolerable amount of bias meanwhile Lasso (Least absolute shrinkage and selection operator) is a regression analysis method that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of the resulting statistical model.
more...
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Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?
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If you have been an algorithmic-trader for a while, chances are high you've heard about Neural Networks. It always seems like they are a way forward to make holy grails trading robots, about this I'm not that sure because it takes more than just adding neural networks to a trading bot to end up with a profitable system. Not to mention you need to understand what you are getting yourself into when using neural networks because even smaller details could mean a success or failure, i.e. profits or losses.
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Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)
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Principal Component Analysis (PCA) is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
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Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps
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Kohonen Maps or Self-Organizing maps(SOM) or Self-Organizing Feature Map(SOFM). Is an unsupervised machine learning technique used to produce a low-dimensional(typically tow-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data.
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Data Science and Machine Learning(Part 15): SVM, A Must-Have Tool in Every Trader's Toolbox
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Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for linear or nonlinear classification and regression tasks, and sometimes outlier detection tasks.
Unlike Bayesian classification techniques, and logistic regression which deploy simple mathematical models to classify information, The SVM has some complex mathematical learning functions aimed at finding the optimal hyperplane that separates the data in an N-dimensional space.
Support vector machine is usually used for classification tasks, something we'll also do in this article.
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Data Science and Machine Learning(Part 21): Unlocking Neural Networks, Optimization algorithms demystified
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It seems like everybody nowadays is interested in Artificial Intelligence, it's everywhere, and the big guys in the tech industry such as Google and Microsoft behind openAI are pushing for AI adaptation in different aspects and industries such as entertainment, healthcare industry, arts, creativity, etc.
I see this trend also in the MQL5 community
why not, with the introduction of
matrices and vectors and
ONNX to Metatrader5, It is now possible to make Artificial intelligence trading models of any complexity, you don't even need to be an expert in Linear algebra or a nerd enough to understand everything that goes into the system.
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Using optimization algorithms to configure EA parameters on the fly
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For novice traders, understanding the basic principles of optimization algorithms can be a powerful tool in finding profitable trades and minimizing risks. For seasoned professionals, deep knowledge in this area can open up new horizons and help create sophisticated trading strategies that exceed expectations.
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Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
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Recurrent Neural Networks (RNNs) are artificial neural networks designed to recognize patterns in sequences of data, such as time series, language, or video. Unlike traditional
neural networks, which assume that inputs are independent of each other, RNNs can detect and understand patterns from a sequence of data (information).
A basic understanding of
Python,
ONNX in MQL5, and
Python machine learning is required to understand the contents of this article fully.
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Feature Engineering With Python And MQL5 (Part I): Forecasting Moving Averages For Long-Range AI Models
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When applying AI to any task, we must try our best to give the model as much useful information about the real world as we can. To describe different properties of the market to our AI models, we must manipulate and transform the input data, this process is referred to as feature engineering. This series of articles will teach you how to transform your market data, to reduce the error levels of your models. Today, I will focus on how to use moving averages to increase the forecasting range of your AI models in a fashion that gives complete control and a reasonable understanding of the global effectiveness of the strategy.
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Deconstructing examples of trading strategies in the client terminal
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In this article, we explore a feature selection algorithm introduced in the paper
'Local Feature Selection for Data Classification' by Narges Armanfard, James P. Reilly, and Majid Komeili. This method aims to identify predictive features that are often overlooked by traditional selection techniques due to their limited global utility. We will begin with a general overview of the algorithm, followed by its implementation in Python to create classifier models suitable for export to MetaTrader 5.
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Data Science and ML (Part 38): AI Transfer Learning in Forex Markets
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Transfer learning is a machine learning technique where a model trained on one task is repurposed as the foundation for a second task.
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