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ALGORITHMIC TRADING
Algorithmic trading strategies, backtesting and implementation with C++, Python and pandas. KALMAN FILTER-BASED PAIRS TRADING STRATEGY IN QSTRADERSEE MORE ONQUANTSTART.COM
ARIMA+GARCH TRADING STRATEGY ON THE S&P500 STOCK MARKETSEE MORE ONQUANTSTART.COM
BAYESIAN STATISTICS: A BEGINNER'S GUIDE C++ VIRTUAL DESTRUCTORS: HOW TO AVOID MEMORY LEAKS Inheritance is a very commonly used paradigm in C++ and quantitative finance. The prevalence of inheritance within quantitative finance is due to the abundance of is-a relationships between entities. A call option is an option. A Monte Carlo option pricer is an option pricer. A double digital pay-off function is a pay-off function.. Inheritance also lends itself to virtual methods, where JOHANSEN TEST FOR COINTEGRATING TIME SERIES ANALYSIS IN R The rank of the matrix A is given by r and the Johansen test sequentially tests whether this rank r is equal to zero, equal to one, through to r = n − 1, where n is the number of time series under test. The null hypothesis of r = 0 means that there is no cointegration at all. DOWNLOADING HISTORICAL FUTURES DATA FROM QUANDL In Mac/Linux (within the terminal/console) this is achieved by the following command: cd ~ mkdir -p quandl/futures/ES. Bash. Copy. Note: You can obviously choose a different directory structure for your data needs, but I've gone with a simple approach of putting it underneath the Linux/Mac "home" directory. BASICS OF STATISTICAL MEAN REVERSION TESTING Testing for Mean Reversion. A continuous mean-reverting time series can be represented by an Ornstein-Uhlenbeck stochastic differential equation: d x t = θ ( μ − x t) d t + σ d W t. Where θ is the rate of reversion to the mean, μ is the mean value of the process, σ is the variance of the process and W t is a Wiener Process or Brownian AUTOREGRESSIVE MOVING AVERAGE ARMA(P, Q) MODELS FOR TIMESEE MORE ONQUANTSTART.COM
MONTE CARLO SIMULATIONS IN CUDA ALGORITHMIC TRADING, QUANTITATIVE TRADING, TRADINGQUANTSTARTQUANTCADEMYSUCCESSFUL ALGORITHMIC TRADINGADVANCEDALGORITHMIC TRADING
Algorithmic trading strategies, backtesting and implementation with C++, Python and pandas. KALMAN FILTER-BASED PAIRS TRADING STRATEGY IN QSTRADERSEE MORE ONQUANTSTART.COM
ARIMA+GARCH TRADING STRATEGY ON THE S&P500 STOCK MARKETSEE MORE ONQUANTSTART.COM
BAYESIAN STATISTICS: A BEGINNER'S GUIDE C++ VIRTUAL DESTRUCTORS: HOW TO AVOID MEMORY LEAKS Inheritance is a very commonly used paradigm in C++ and quantitative finance. The prevalence of inheritance within quantitative finance is due to the abundance of is-a relationships between entities. A call option is an option. A Monte Carlo option pricer is an option pricer. A double digital pay-off function is a pay-off function.. Inheritance also lends itself to virtual methods, where JOHANSEN TEST FOR COINTEGRATING TIME SERIES ANALYSIS IN R The rank of the matrix A is given by r and the Johansen test sequentially tests whether this rank r is equal to zero, equal to one, through to r = n − 1, where n is the number of time series under test. The null hypothesis of r = 0 means that there is no cointegration at all. DOWNLOADING HISTORICAL FUTURES DATA FROM QUANDL In Mac/Linux (within the terminal/console) this is achieved by the following command: cd ~ mkdir -p quandl/futures/ES. Bash. Copy. Note: You can obviously choose a different directory structure for your data needs, but I've gone with a simple approach of putting it underneath the Linux/Mac "home" directory. BASICS OF STATISTICAL MEAN REVERSION TESTING Testing for Mean Reversion. A continuous mean-reverting time series can be represented by an Ornstein-Uhlenbeck stochastic differential equation: d x t = θ ( μ − x t) d t + σ d W t. Where θ is the rate of reversion to the mean, μ is the mean value of the process, σ is the variance of the process and W t is a Wiener Process or Brownian AUTOREGRESSIVE MOVING AVERAGE ARMA(P, Q) MODELS FOR TIMESEE MORE ONQUANTSTART.COM
MONTE CARLO SIMULATIONS IN CUDA KALMAN FILTER-BASED PAIRS TRADING STRATEGY IN QSTRADER Kalman Filter-Based Pairs Trading Strategy In QSTrader | QuantStart. Previously on QuantStart we have considered the mathematical underpinnings of State Space Models and Kalman Filters, as well as the application of the pykalman library to a pair of ETFs to dynamically adjust a hedge ratio as a basis for a mean reverting trading strategy. BASICS OF STATISTICAL MEAN REVERSION TESTING Testing for Mean Reversion. A continuous mean-reverting time series can be represented by an Ornstein-Uhlenbeck stochastic differential equation: d x t = θ ( μ − x t) d t + σ d W t. Where θ is the rate of reversion to the mean, μ is the mean value of the process, σ is the variance of the process and W t is a Wiener Process or Brownian QSTRADER - QUANTSTART QSTrader is an open source backtesting simulation framework written in Python. It is primarily intended for long/short systematic trading strategies utilising cash equities and ETFs. It is highly modular, object-oriented and freely available. QSTrader is currently used by the QuantStart.com team for internal quant strategy research, by the SYSTEMATIC TACTICAL ASSET ALLOCATION: AN INTRODUCTION In this article we are going to introduce tactical asset allocation (TAA). We will define its investment approach—particularly as it relates to systematic methodology. We will discuss how TAA differs from both buy & hold and shorter-term strategies, presenting SERIAL CORRELATION IN TIME SERIES ANALYSIS The serial correlation or autocorrelation of lag k, ρ k, of a second order stationary time series is given by the autocovariance of the series normalised by the product of the spread. That is, ρ k = C k σ 2. Note that ρ 0 = C 0 σ 2 = E σ 2 = σ 2 σ 2 =1.
RESEARCH BACKTESTING ENVIRONMENTS IN PYTHON WITH PANDAS Research Backtesting Environments in Python with pandas | QuantStart. Backtesting is the research process of applying a trading strategy idea to historical data in order to ascertain past performance. In particular, a backtester makes no guarantee about the future performance of the strategy. They are however an essential componentof the
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE ARIMA(P, D, Q In the previous set of articles (Parts 1, 2 and 3) we went into significant detail about the AR(p), MA(q) and ARMA(p,q) linear time series models.We used these models to generate simulated data sets, fitted models to recover parameters and then applied these models to financial equities data. BEST PROGRAMMING LANGUAGE FOR ALGORITHMIC TRADING SYSTEMS Language choice will now be discussed in the context of performance. C++, Java, Python, R and MatLab all contain high-performance libraries (either as part of their standard or externally) for basic data structure and algorithmic work. C++ ships with the Standard TemplateLibrary, while
HOW TO GET A JOB AT A HIGH FREQUENCY TRADING FIRM I often receive emails from individuals who are interested in joining High-Frequency Trading (HFT) firms. They are sometimes confused as to how to go about applying for roles and are unaware of the technical skills necessary to obtain a job. INTERACTIVE BROKERS DEMO ACCOUNT SIGNUP TUTORIAL 1) The front page of the Interactive Brokers website. Once at the site select the "Open An Account" link from the drop-down menu on the top-right and then subsequently select "Individual, Joint, IRA ALGORITHMIC TRADING, QUANTITATIVE TRADING, TRADINGQUANTSTARTQUANTCADEMYSUCCESSFUL ALGORITHMIC TRADINGADVANCEDALGORITHMIC TRADING
Algorithmic trading strategies, backtesting and implementation with C++, Python and pandas. QUANT READING LISTS ARTICLES QuantStart Content Survey 2020. Quant Reading List Derivative Pricing. Quant Reading List C++ Programming. Quant Reading List Numerical Methods. Quant Reading List Python Programming. 5 Important But Not So Common Books A Quant Should Read Before Applying for a Job. 5 Top Books for Acing a Quantitative Analyst Interview. SYSTEMATIC TRADING ARTICLES Forex Trading Diary #3 - Open Sourcing the Forex Trading System. Forex Trading Diary #4 - Adding a Backtesting Capability. Forex Trading Diary #5 - Trading Multiple Currency Pairs. Forex Trading Diary #6 - Multi-Day Trading and Plotting Results. Forex Trading Diary #7 - NewBacktest Interface.
KALMAN FILTER-BASED PAIRS TRADING STRATEGY IN QSTRADERSEE MORE ONQUANTSTART.COM
MACHINE LEARNING ARTICLES Machine Learning Articles. Training the Perceptron with Scikit-Learn and TensorFlow. Introduction to Artificial Neural Networks and the Perceptron. Installing TensorFlow 2.2 on Ubuntu 18.04 with an Nvidia GPU. Rough Path Theory and Signatures Applied To Quantitative Finance- Part 4.
TIME SERIES ANALYSIS ARTICLES Time Series Analysis Articles. Autoregressive Moving Average ARMA (p, q) Models for Time Series Analysis - Part 2. Generalised Autoregressive Conditional Heteroskedasticity GARCH (p, q) Models for Time Series Analysis. Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R. QR DECOMPOSITION WITH PYTHON AND NUMPY This article will discuss QR Decomposition in Python.In previous articles we have looked at LU Decomposition in Python and Cholesky Decomposition in Python as two alternative matrix decomposition methods. QR Decomposition is widely used in quantitative finance as the basis for the solution of the linear least squares problem, which itself is used for statistical regression analysis. ALGORITHMIC TRADING, QUANTITATIVE TRADING, TRADINGQUANTSTARTQUANTCADEMYSUCCESSFUL ALGORITHMIC TRADINGADVANCEDALGORITHMIC TRADING
Algorithmic trading strategies, backtesting and implementation with C++, Python and pandas. QUANT READING LISTS ARTICLES QuantStart Content Survey 2020. Quant Reading List Derivative Pricing. Quant Reading List C++ Programming. Quant Reading List Numerical Methods. Quant Reading List Python Programming. 5 Important But Not So Common Books A Quant Should Read Before Applying for a Job. 5 Top Books for Acing a Quantitative Analyst Interview. SYSTEMATIC TRADING ARTICLES Forex Trading Diary #3 - Open Sourcing the Forex Trading System. Forex Trading Diary #4 - Adding a Backtesting Capability. Forex Trading Diary #5 - Trading Multiple Currency Pairs. Forex Trading Diary #6 - Multi-Day Trading and Plotting Results. Forex Trading Diary #7 - NewBacktest Interface.
KALMAN FILTER-BASED PAIRS TRADING STRATEGY IN QSTRADERSEE MORE ONQUANTSTART.COM
MACHINE LEARNING ARTICLES Machine Learning Articles. Training the Perceptron with Scikit-Learn and TensorFlow. Introduction to Artificial Neural Networks and the Perceptron. Installing TensorFlow 2.2 on Ubuntu 18.04 with an Nvidia GPU. Rough Path Theory and Signatures Applied To Quantitative Finance- Part 4.
TIME SERIES ANALYSIS ARTICLES Time Series Analysis Articles. Autoregressive Moving Average ARMA (p, q) Models for Time Series Analysis - Part 2. Generalised Autoregressive Conditional Heteroskedasticity GARCH (p, q) Models for Time Series Analysis. Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R. QR DECOMPOSITION WITH PYTHON AND NUMPY This article will discuss QR Decomposition in Python.In previous articles we have looked at LU Decomposition in Python and Cholesky Decomposition in Python as two alternative matrix decomposition methods. QR Decomposition is widely used in quantitative finance as the basis for the solution of the linear least squares problem, which itself is used for statistical regression analysis. BASICS OF STATISTICAL MEAN REVERSION TESTING y ( t) = β x ( t) + ϵ ( t) Where y ( t) is the price of AREX stock and x ( t) is the price of WLL stock, both on day t. If we plot the residuals ϵ ( t) = y ( t) − β x ( t) (for a particular value of β that we will determine below) we create a new time series that, at first glance, looks relatively stationary. This is given in Figure 3: TIME SERIES ANALYSIS ARTICLES Time Series Analysis Articles. Autoregressive Moving Average ARMA (p, q) Models for Time Series Analysis - Part 2. Generalised Autoregressive Conditional Heteroskedasticity GARCH (p, q) Models for Time Series Analysis. Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R. SYSTEMATIC TRADING ARTICLES Forex Trading Diary #3 - Open Sourcing the Forex Trading System. Forex Trading Diary #4 - Adding a Backtesting Capability. Forex Trading Diary #5 - Trading Multiple Currency Pairs. Forex Trading Diary #6 - Multi-Day Trading and Plotting Results. Forex Trading Diary #7 - NewBacktest Interface.
ARIMA+GARCH TRADING STRATEGY ON THE S&P500 STOCK MARKET In this article I want to show you how to apply all of the knowledge gained in the previous time series analysis posts to a trading strategy on the S&P500 US stock market index.. We will see that by combining the ARIMA and GARCH models we can significantly outperform a "Buy-and-Hold" approach over the long term.. Strategy Overview MONTE CARLO SIMULATIONS IN CUDA In this article, I will talk about how to write Monte Carlo simulations in CUDA. More specifically, I will explain how to carry it out step-by -step while writing the code for pricing a down-and-out barrier option, as its path dependency will make it a perfect example for us to learn Monte Carlo in CUDA. HOW TO GET A QUANT JOB ONCE YOU HAVE A PHD In this article we are going to discuss an issue that repeatedly crops up via the QuantStart mailbox, namely how to get a quant job once you have a PhD.There's a lot of confusion around this topic because quite a few people who currently work in academia and want to make the shift believe that it is quite straightforward to "walk into" a high-payingfinancial role.
TRAINING THE PERCEPTRON WITH SCIKIT-LEARN AND TENSORFLOW In the previous article on the topic of artificial neural networks we introduced the concept of the perceptron.We demonstrated that the perceptron was capable of classifying input data via a linear decision boundary. However we postponed a discussion on how to calculate the parameters that govern this linear decision boundary. Determining these parameters by means of 'training' the perceptron DOWNLOADING HISTORICAL FUTURES DATA FROM QUANDL In Mac/Linux (within the terminal/console) this is achieved by the following command: cd ~ mkdir -p quandl/futures/ES. Bash. Copy. Note: You can obviously choose a different directory structure for your data needs, but I've gone with a simple approach of putting it underneath the Linux/Mac "home" directory. SYSTEMATIC TACTICAL ASSET ALLOCATION: AN INTRODUCTION In this article we are going to introduce tactical asset allocation (TAA). We will define its investment approach—particularly as it relates to systematic methodology. We will discuss how TAA differs from both buy & hold and shorter-term strategies, presenting HOW TO GET A JOB AT A HIGH FREQUENCY TRADING FIRM I often receive emails from individuals who are interested in joining High-Frequency Trading (HFT) firms. They are sometimes confused as to how to go about applying for roles and are unaware of the technical skills necessary to obtain a job. ALGORITHMIC TRADING, QUANTITATIVE TRADING, TRADINGQUANTSTARTQUANTCADEMYSUCCESSFUL ALGORITHMIC TRADINGADVANCEDALGORITHMIC TRADING
Algorithmic trading strategies, backtesting and implementation with C++, Python and pandas. KALMAN FILTER-BASED PAIRS TRADING STRATEGY IN QSTRADERSEE MORE ONQUANTSTART.COM
BEST UNDERGRADUATE DEGREE COURSE FOR BECOMING A QUANTSEE MORE ONQUANTSTART.COM
SELF-STUDY PLAN FOR BECOMING A QUANTITATIVE ANALYSTSEE MORE ONQUANTSTART.COM
WHICH PROGRAMMING LANGUAGE SHOULD YOU LEARN TO GET A QUANTSEE MORE ONQUANTSTART.COM
QR DECOMPOSITION WITH PYTHON AND NUMPY This article will discuss QR Decomposition in Python.In previous articles we have looked at LU Decomposition in Python and Cholesky Decomposition in Python as two alternative matrix decomposition methods. QR Decomposition is widely used in quantitative finance as the basis for the solution of the linear least squares problem, which itself is used for statistical regression analysis. BACKTESTING SYSTEMATIC TRADING STRATEGIES IN PYTHON In this article Frank Smietana, one of QuantStart's expert guest contributors describes the Python open-source backtesting software landscape, and provides advice on which backtesting framework is suitable for your own project needs. JOHANSEN TEST FOR COINTEGRATING TIME SERIES ANALYSIS IN R The rank of the matrix A is given by r and the Johansen test sequentially tests whether this rank r is equal to zero, equal to one, through to r = n − 1, where n is the number of time series under test. The null hypothesis of r = 0 means that there is no cointegration at all. COINTEGRATED AUGMENTED DICKEY FULLER TEST FOR PAIRSSEE MORE ONQUANTSTART.COM
MONTE CARLO SIMULATIONS IN CUDA ALGORITHMIC TRADING, QUANTITATIVE TRADING, TRADINGQUANTSTARTQUANTCADEMYSUCCESSFUL ALGORITHMIC TRADINGADVANCEDALGORITHMIC TRADING
Algorithmic trading strategies, backtesting and implementation with C++, Python and pandas. KALMAN FILTER-BASED PAIRS TRADING STRATEGY IN QSTRADERSEE MORE ONQUANTSTART.COM
BEST UNDERGRADUATE DEGREE COURSE FOR BECOMING A QUANTSEE MORE ONQUANTSTART.COM
SELF-STUDY PLAN FOR BECOMING A QUANTITATIVE ANALYSTSEE MORE ONQUANTSTART.COM
WHICH PROGRAMMING LANGUAGE SHOULD YOU LEARN TO GET A QUANTSEE MORE ONQUANTSTART.COM
QR DECOMPOSITION WITH PYTHON AND NUMPY This article will discuss QR Decomposition in Python.In previous articles we have looked at LU Decomposition in Python and Cholesky Decomposition in Python as two alternative matrix decomposition methods. QR Decomposition is widely used in quantitative finance as the basis for the solution of the linear least squares problem, which itself is used for statistical regression analysis. BACKTESTING SYSTEMATIC TRADING STRATEGIES IN PYTHON In this article Frank Smietana, one of QuantStart's expert guest contributors describes the Python open-source backtesting software landscape, and provides advice on which backtesting framework is suitable for your own project needs. JOHANSEN TEST FOR COINTEGRATING TIME SERIES ANALYSIS IN R The rank of the matrix A is given by r and the Johansen test sequentially tests whether this rank r is equal to zero, equal to one, through to r = n − 1, where n is the number of time series under test. The null hypothesis of r = 0 means that there is no cointegration at all. COINTEGRATED AUGMENTED DICKEY FULLER TEST FOR PAIRSSEE MORE ONQUANTSTART.COM
MONTE CARLO SIMULATIONS IN CUDA BASICS OF STATISTICAL MEAN REVERSION TESTING y ( t) = β x ( t) + ϵ ( t) Where y ( t) is the price of AREX stock and x ( t) is the price of WLL stock, both on day t. If we plot the residuals ϵ ( t) = y ( t) − β x ( t) (for a particular value of β that we will determine below) we create a new time series that, at first glance, looks relatively stationary. This is given in Figure 3: THE MARKOV AND MARTINGALE PROPERTIES The Markov and Martingale Properties | QuantStart. In order to formally define the concept of Brownian motion and utilise it as a basis for an asset price model, it is necessary to define the Markov and Martingale properties. These provide an intuition as to how an asset price will behave over time. The Markov property states that astochastic
SERIAL CORRELATION IN TIME SERIES ANALYSIS The serial correlation or autocorrelation of lag k, ρ k, of a second order stationary time series is given by the autocovariance of the series normalised by the product of the spread. That is, ρ k = C k σ 2. Note that ρ 0 = C 0 σ 2 = E σ 2 = σ 2 σ 2 =1.
WHICH PROGRAMMING LANGUAGE SHOULD YOU LEARN TO GET A QUANT These prototypes are then coded up in a (perceived) faster language such as C++, by a quant developer. This was part of my duties when I was working as a "quant dev". If you are interested in a more relaxed environment than a bank trading floor then hedge funds are a good answer. Any Python/MATLAB/R scripting skills will be extremelyvaluable.
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE ARIMA(P, D, Q In the previous set of articles (Parts 1, 2 and 3) we went into significant detail about the AR(p), MA(q) and ARMA(p,q) linear time series models.We used these models to generate simulated data sets, fitted models to recover parameters and then applied these models to financial equities data. RESEARCH BACKTESTING ENVIRONMENTS IN PYTHON WITH PANDAS Research Backtesting Environments in Python with pandas | QuantStart. Backtesting is the research process of applying a trading strategy idea to historical data in order to ascertain past performance. In particular, a backtester makes no guarantee about the future performance of the strategy. They are however an essential componentof the
THE BIAS-VARIANCE TRADEOFF IN STATISTICAL MACHINE LEARNING The bias-variance tradeoff is a particular property of all (supervised) machine learning models, that enforces a tradeoff between how "flexible" the model is and how well it performs on unseen data. The latter is known as a models generalisation performance. We will begin by understanding why model selection is important and thendiscuss the
BACKTESTING SYSTEMATIC TRADING STRATEGIES IN PYTHON In this article Frank Smietana, one of QuantStart's expert guest contributors describes the Python open-source backtesting software landscape, and provides advice on which backtesting framework is suitable for your own project needs. INTERACTIVE BROKERS DEMO ACCOUNT SIGNUP TUTORIAL 1) The front page of the Interactive Brokers website. Once at the site select the "Open An Account" link from the drop-down menu on the top-right and then subsequently select "Individual, Joint, IRA HOW TO GET A JOB AT A HIGH FREQUENCY TRADING FIRM I often receive emails from individuals who are interested in joining High-Frequency Trading (HFT) firms. They are sometimes confused as to how to go about applying for roles and are unaware of the technical skills necessary to obtain a job.* QuantStart
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