algorithmic trading

Posted on Apr 02, 2021 by Robot James
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I've been helping a family friend with his trading. I've given him a simple systematic strategy to trade by hand. First, we set expectations We can plot the distribution of historic trade returns from past trading or a backtest as a histogram.   This is useful because it gives us a hint as to what the "edge" of our strategy might be - if we could ever truly know such a thing. In this case, our strategy had positive mean and negative skew. (As many things that make money tend do, regrettably) Now, when we make a trade, we're really just taking a random sample from a bucket of returns. You might think of it like we're picking observations out of the bucket described by the histogram we just made. BUT.... The histogram doesn't show us the true nature of the distribution of returns in the bucket - just the returns that occurred in the past. The "true distribution" changes with time (it is stochastic) and cannot be observed directly. We can only infer it from the past. So the...

Posted on Nov 10, 2020 by Robot James
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You rarely meet a rich forex trader. I’ve met plenty of rich traders who trade quant factors or stat arb. Plenty of market makers, futures spreaders and volatility traders that do nicely. But I don’t think I’ve ever met a rich forex trader. Jeez man - what a downer! Don't run away, we're gonna turn this around into something positive... bear with us! This post is a BONUS LESSON taken directly from Zero to Robot Master Bootcamp. In this Bootcamp, we teach traders how to research, build and trade a portfolio of 3 strategies including an Intraday FX Strategy, a Risk Premia Strategy and a Volatility Basis Strategy. If you’re interested in adding strategies to your portfolio or are just keen to start on the path to becoming a successful and sustainable systematic trader, you can check out full details of the Bootcamp here. Let’s look at our map of the trading landscape and briefly discuss why that is. This map shows the effects we can take advantage of in the financial markets to make money, and the strategies we...

Posted on Nov 09, 2020 by Robot James
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  Here’s a chart of long-term asset performance…. The blue line shows returns from US stocks from 1900 to today. That’s a 48,000x increase in nominal value. The yellow line shows the returns from US bonds from 1900 to today. That’s a 300x increase in nominal value. If you look at this in isolation things look easy. You just buy all this stuff. And it is both that easy and not quite that easy… We need to ask: Why does this stuff go up? Can we be confident it’s going to go up in the future? This post is a lesson taken directly from Zero to Robot Master Bootcamp. In this Bootcamp, we teach traders how to research, build and trade a portfolio of 3 strategies including a Risk Premia Strategy, an Intraday FX Strategy and a Volatility Basis Strategy. If you're interested in adding strategies to your portfolio or are just keen to start on the path to becoming a successful and sustainable systematic trader, you can check out full details of the Bootcamp here. For more on Risk...

Posted on Jun 01, 2020 by Kris Longmore
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Anyone that's been around the markets knows that the monthly release of the United States Department of Labor's Non-Farm Payrolls (NFP) data can have a tremendous impact, especially in the short term. NFP is a snapshot of the state of the employment situation in the US, representing the total number of paid workers, excluding farm employees and public servants. We know your barn is hiding a giant mining station, Rick The release of the monthly NFP data typically causes large swings in the currency markets, even when the results are in line with estimates. Here, we are interested in exploring potential seasonal effects around the release of this data. For example, does price tend to drift prior to the release? If so, which way?   For this analysis, we'll explore the EUR/USD exchange rate. To set up this research problem, we need to know that NFP is released on the first Friday of the month at 8:30am ET - usually. If the first Friday is a holiday, NFP is released the following Friday. These sorts of details can make seasonal analysis...

Posted on Jan 23, 2018 by Kris Longmore
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This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you haven’t read that article, it is highly recommended that you do so before proceeding, as the context it provides is important. Read Part 1 here. Part 2 provides a walk-through of setting up Keras and Tensorflow for R using either the default CPU-based configuration, or the more complex and involved (but well worth it) GPU-based configuration under the Windows environment. Read Part 2 here. Part 3 is an introduction to the model building, training and evaluation process in Keras. We train a simple feed forward network to predict the direction of a foreign exchange market over a time horizon of hour and assess its performance. [thrive_leads id='4507'] . Now that you can train your deep learning models on a GPU, the fun can really start....

Posted on Sep 06, 2017 by Kris Longmore
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This article is adapted from one of the units of Advanced Algorithmic Trading. If you like what you see, check out the entire curriculum here. Find out what Robot Wealth is all about here. If you're interested in using artificial neural networks (ANNs) for algorithmic trading, but don't know where to start, then this article is for you. Normally if you want to learn about neural networks, you need to be reasonably well versed in matrix and vector operations - the world of linear algebra. This article is different. I've attempted to provide a starting point that doesn't involve any linear algebra and have deliberately left out all references to vectors and matrices. If you're not strong on linear algebra, but are curious about neural networks, then I think you'll enjoy this introduction. In addition, if you decide to take your study of neural networks further, when you do inevitably start using linear algebra, it will probably make a lot more sense as you'll have something of head start. The best place to start learning about neural networks is the...

Posted on Jul 20, 2017 by Kris Longmore
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Earlier this year, I attended the Google Next conference in San Francisco and gained some first-hand perspective into what’s possible with Google's cloud infrastructure. Since then, I’ve been leaning on Google Cloud Platform (GCP) to run my trading algorithms (and much more) and it has quickly become an important tool in my workflow! In this post, I’m going to show you how to set up a Google Cloud Platform compute instance to act as a server for hosting a trading algorithm. We'll also see why such a setup can be a good option and when it might pay to consider alternatives. Cloud compute instances are just a tiny fraction of the whole GCP ecosystem, so before we go any further, let's take a high-level overview of the various components that make up Google Cloud Platform. What is Google Cloud Platform? GCP consists of a suite of cloud storage, compute, analytics and development infrastructure and services. Google says that GCP runs on the very same infrastructure that Google uses for its own products, such as Google Search. This suite of services...

Posted on Apr 12, 2017 by Kris Longmore
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  This is the final post in our 3-part Back to Basics series. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 2: How to Succeed at Algorithmic Trading We've also compiled this series into an eBook which you can download for free here. Nearly all research related to algorithmic trading is empirical in nature. That is, it is based on observations and experience. Contrast this with theoretical research which is based on assumptions, logic and a mathematical framework. Often, we start with a theoretical approach (for example, a time-series model that we assume describes the process generating the market data we are interested in) and then use empirical techniques to test the validity of our assumptions and framework. But we would never commit money to a mathematical model that we assumed described the market without testing it using real observations, and every model is based on assumptions (to my knowledge no one has ever come up with a comprehensive model of the markets based on first principles...

Posted on Mar 20, 2017 by Kris Longmore
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This is the second post in our 3-part Back to Basics series on successful algorithmic trading. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 3: Backtesting in Algorithmic Trading There is a lot of information about algorithmic and quantitative trading in the public domain today. The type of person who is attracted to the field naturally wants to synthesize as much of this information as possible when they are starting out. As a result, newcomers can easily be overwhelmed with “analysis paralysis” and wind up spending a lot of their valuable spare time working on algorithmic trading without making much meaningful progress. This article aims to address that by sharing the way in which I would approach algorithmic trading as a beginner if I were just starting out now, but with the benefit of many years of hindsight. This article is somewhat tinged with personal experience, so please read it with the understanding that I am describing what works for me. I don’t claim to be a guru...

Posted on Feb 04, 2017 by Kris Longmore
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This is the first post in our 3-part Back to Basics series which serve as an introduction to algorithmic trading. You may be interested in checking out the other posts in this series: Part 2: How to Succeed at Algorithmic Trading Part 3: Backtesting in Algorithmic Trading This is the first in a series of posts in which we will change gears slightly and take a look at some of the fundamentals of algorithmic trading. So far, Robot Wealth has focused on machine learning and quantitative trading research, but I had several conversations recently that motivated me to explore some of the fundamental questions around algorithmic trading. In the next few posts, we will investigate questions such as: What is algorithmic trading? What can algorithmic trading do for me? What are the pre-requisites? What should I think about before getting started? What's all this fuss about curve fitting and robust optimisation? Why should I care? So without further ado, let's dive in! What is Algorithmic Trading? At its most basic level, algorithmic trading is simply the automated buying and selling of financial instruments,...