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QUANT SOFTWARE
Quant Software. Quantitative finance is a field of applied mathematics and statistics used to solve some of the complex problems in finance. Individuals who work in quantitative finance are called quantitative analysts or quants for short. Some of the problems solved by quants include: derivatives pricing, risk management, and quantitativeOPEN MARKET DATA
Open market data is market data which is freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control. This page highlights some of the best sources of open source data which can be used for algorithmic trading, quantitative finance, and machinelearning.
A QUANT'S VIEW OF CFA LEVEL I WHAT DRIVES REAL GDP GROWTH? 3. Real GDP Growth is positively linked to large, employed, healthy workforce (between the age of 15 and 64) which have access to water, sanitation, and excess capital. 4. Real GDP Growth is positively linked to increased productivity and technology. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph Theory for Systemic Risk Models. The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand how reliant banks are on one another and how interconnected the financial ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISKSEE MORE ONTURINGFINANCE.COM
ALGORITHMIC TRADING SYSTEM REQUIREMENTS Non-functional algorithmic trading system requirements include, Scalability - is the ability of a system to cope and perform under an increased or expanding workload. An ATs should be scalable with respect to the number of data feeds in processes, number of exchanges it trades on, and the securities it can trade. STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKS Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. TESTING THE RANDOM WALK HYPOTHESIS WITH R, PART ONE The Random Walk Hypothesis is a theory about the behaviour of security prices which argues that they are well described by random walks, specifically sub-martingale stochastic processes. The Random Walk Hypothesis predates the Efficient Market Hypothesis by 70-years but is actually a consequent and not a precedent of it. SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the followingQUANT SOFTWARE
Quant Software. Quantitative finance is a field of applied mathematics and statistics used to solve some of the complex problems in finance. Individuals who work in quantitative finance are called quantitative analysts or quants for short. Some of the problems solved by quants include: derivatives pricing, risk management, and quantitativeOPEN MARKET DATA
Open market data is market data which is freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control. This page highlights some of the best sources of open source data which can be used for algorithmic trading, quantitative finance, and machinelearning.
A QUANT'S VIEW OF CFA LEVEL I WHAT DRIVES REAL GDP GROWTH? 3. Real GDP Growth is positively linked to large, employed, healthy workforce (between the age of 15 and 64) which have access to water, sanitation, and excess capital. 4. Real GDP Growth is positively linked to increased productivity and technology. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph Theory for Systemic Risk Models. The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand how reliant banks are on one another and how interconnected the financial ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISKSEE MORE ONTURINGFINANCE.COM
ALGORITHMIC TRADING SYSTEM REQUIREMENTS Non-functional algorithmic trading system requirements include, Scalability - is the ability of a system to cope and perform under an increased or expanding workload. An ATs should be scalable with respect to the number of data feeds in processes, number of exchanges it trades on, and the securities it can trade. STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKS Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. TESTING THE RANDOM WALK HYPOTHESIS WITH R, PART ONE The Random Walk Hypothesis is a theory about the behaviour of security prices which argues that they are well described by random walks, specifically sub-martingale stochastic processes. The Random Walk Hypothesis predates the Efficient Market Hypothesis by 70-years but is actually a consequent and not a precedent of it. SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the followingTURING FINANCE
Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and creditrisk modelling.
ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISK All Models are Wrong, 7 Sources of Model Risk. The 2008 financial crisis revealed to the world (in spectacular fashion) the fragility of financial models. Since the financial crisis two words have come up time and time again: model risk. This article defines model risk and discusses some of the contributors to it which people overlook.HOW TO BE A QUANT
How to be a quant ~ Formalize your ideas as models. Then use those models to think more clearly, test the validity of your ideas, and identify hidden patterns. The last part of this article involves thinking with models. A model is a representation of some object or process in the real world. INTELLIGENT ALGORITHMIC TRADING SYSTEMS Intelligent Algorithmic Trading Systems. Algorithmic trading is the use of computer algorithms to automatically make trading decisions, submit orders, and manage those orders after submission. Algorithmic trading systems are best understood using a simple conceptual architecture consisting of three components which handle differentaspects of
AGENT-BASED COMPUTATIONAL ECONOMIC MODELS The pre-requisite to creating successful agent-based computational economic models is the creation of good software defined intelligent agents. These intelligent agents should codify the bounded rationality, satisficing behaviour, and dynamic nature of real-world economic agents. I can, without a doubt, assure you that this is noeasy feat.
STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKS Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. CLUSTERING USING ANT COLONY OPTIMIZATION Clustering is the problem of forming groups of similar items, in this example there is just one type or call of items namely the dead ants. Each living ant helps the colony to form clusters of these dead ants by autonomously following the very simple two-step algorithm described below, Step One ~ While walking around, if you encounter a dead PORTFOLIO OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION Portfolio optimization using the particle swarm optimization algorithm significantly improved the performance of the carry trade portfolio. A detailed analysis of the results revealed that the optimized portfolio generated superior positive returns when compared to the benchmarks. TESTING THE RANDOM WALK HYPOTHESIS WITH R, PART ONE The Random Walk Hypothesis is a theory about the behaviour of security prices which argues that they are well described by random walks, specifically sub-martingale stochastic processes. The Random Walk Hypothesis predates the Efficient Market Hypothesis by 70-years but is actually a consequent and not a precedent of it. SIMULATED ANNEALING FOR PORTFOLIO OPTIMIZATION Simulated Annealing (SA) is a generic probabilistic and meta-heuristic search algorithm which can be used to find acceptable solutions to optimization problems characterized by a large search space with multiple optima. Portfolio optimization involves allocating capital between the assets in order to maximize risk adjusted return. TURING FINANCERANDOMNESS TESTSCORRELATION ANALYSISALGORITHMIC TRADING SYSTEMSMACHINE LEARNING Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and creditrisk modelling.
QUANT SOFTWARE
Quant Software. Quantitative finance is a field of applied mathematics and statistics used to solve some of the complex problems in finance. Individuals who work in quantitative finance are called quantitative analysts or quants for short. Some of the problems solved by quants include: derivatives pricing, risk management, and quantitative REGRESSION ANALYSIS USING PYTHON The Regression Analysis Class. A RegressionAnalysis class was created so that it would be easy to create and store multiple regressions. The RegressionAnalysis class encapsulates the run_ordinary_least_squares () and the get_quandl_data () methods. This STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKS Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph Theory for Systemic Risk Models. The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand how reliant banks are on one another and how interconnected the financial A QUANT'S VIEW OF CFA LEVEL I ALGORITHMIC TRADING SYSTEM REQUIREMENTS Non-functional algorithmic trading system requirements include, Scalability - is the ability of a system to cope and perform under an increased or expanding workload. An ATs should be scalable with respect to the number of data feeds in processes, number of exchanges it trades on, and the securities it can trade. TESTING THE RANDOM WALK HYPOTHESIS WITH R, PART ONE The Random Walk Hypothesis is a theory about the behaviour of security prices which argues that they are well described by random walks, specifically sub-martingale stochastic processes. The Random Walk Hypothesis predates the Efficient Market Hypothesis by 70-years but is actually a consequent and not a precedent of it. INFORMATION RATIO PYTHON Measures of Risk-adjusted Return. This article is a supplement to some of the topics presented in Dr. Tucker Balch's online MOOC, Computational Investing. Financial markets are complex adaptive systems which are almost always indistinguishable from random processes. That said markets do exhibit quantifiable factors such RANDOM WALKS DOWN WALL STREET, STOCHASTIC PROCESSES IN PYTHONSEE MORE ON TURINGFINANCE.COM TURING FINANCERANDOMNESS TESTSCORRELATION ANALYSISALGORITHMIC TRADING SYSTEMSMACHINE LEARNING Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and creditrisk modelling.
QUANT SOFTWARE
Quant Software. Quantitative finance is a field of applied mathematics and statistics used to solve some of the complex problems in finance. Individuals who work in quantitative finance are called quantitative analysts or quants for short. Some of the problems solved by quants include: derivatives pricing, risk management, and quantitative REGRESSION ANALYSIS USING PYTHON The Regression Analysis Class. A RegressionAnalysis class was created so that it would be easy to create and store multiple regressions. The RegressionAnalysis class encapsulates the run_ordinary_least_squares () and the get_quandl_data () methods. This STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKS Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph Theory for Systemic Risk Models. The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand how reliant banks are on one another and how interconnected the financial A QUANT'S VIEW OF CFA LEVEL I ALGORITHMIC TRADING SYSTEM REQUIREMENTS Non-functional algorithmic trading system requirements include, Scalability - is the ability of a system to cope and perform under an increased or expanding workload. An ATs should be scalable with respect to the number of data feeds in processes, number of exchanges it trades on, and the securities it can trade. TESTING THE RANDOM WALK HYPOTHESIS WITH R, PART ONE The Random Walk Hypothesis is a theory about the behaviour of security prices which argues that they are well described by random walks, specifically sub-martingale stochastic processes. The Random Walk Hypothesis predates the Efficient Market Hypothesis by 70-years but is actually a consequent and not a precedent of it. INFORMATION RATIO PYTHON Measures of Risk-adjusted Return. This article is a supplement to some of the topics presented in Dr. Tucker Balch's online MOOC, Computational Investing. Financial markets are complex adaptive systems which are almost always indistinguishable from random processes. That said markets do exhibit quantifiable factors such RANDOM WALKS DOWN WALL STREET, STOCHASTIC PROCESSES IN PYTHONSEE MORE ON TURINGFINANCE.COMQUANT SOFTWARE
Quant Software. Quantitative finance is a field of applied mathematics and statistics used to solve some of the complex problems in finance. Individuals who work in quantitative finance are called quantitative analysts or quants for short. Some of the problems solved by quants include: derivatives pricing, risk management, and quantitativeHOW TO BE A QUANT
How to be a quant ~ Formalize your ideas as models. Then use those models to think more clearly, test the validity of your ideas, and identify hidden patterns. The last part of this article involves thinking with models. A model is a representation of some object or process in the real world. A QUANT'S VIEW OF CFA LEVEL I A Quant's view of CFA Level I. Having just written and, thankfully, passed the CFA Level I exam I wanted to take this opportunity to share my experience writing the CFA Level I exam given that I come from an unconventional academic background and work in the industry as a quantitative analyst. I also want to share some helpful onlineresources
WHAT DRIVES REAL GDP GROWTH? 3. Real GDP Growth is positively linked to large, employed, healthy workforce (between the age of 15 and 64) which have access to water, sanitation, and excess capital. 4. Real GDP Growth is positively linked to increased productivity and technology. INTELLIGENT ALGORITHMIC TRADING SYSTEMS Intelligent Algorithmic Trading Systems. Algorithmic trading is the use of computer algorithms to automatically make trading decisions, submit orders, and manage those orders after submission. Algorithmic trading systems are best understood using a simple conceptual architecture consisting of three components which handle differentaspects of
CLUSTERING USING ANT COLONY OPTIMIZATION Clustering is the problem of forming groups of similar items, in this example there is just one type or call of items namely the dead ants. Each living ant helps the colony to form clusters of these dead ants by autonomously following the very simple two-step algorithm described below, Step One ~ While walking around, if you encounter a dead PERFECT IMPERFECTION, AGENT BASED MODELS Agent Based Models for Artificial Stock Markets. Agent based models are computational models of complex systems, such as the market, which use an individualistic approach (bottom-up) to simulate the systemic (emergent) effects caused by the actions and interactions between autonomous agents. For example, an agent based model consisting of two ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISK All Models are Wrong, 7 Sources of Model Risk. The 2008 financial crisis revealed to the world (in spectacular fashion) the fragility of financial models. Since the financial crisis two words have come up time and time again: model risk. This article defines model risk and discusses some of the contributors to it which people overlook. USING GENETIC PROGRAMMING TO EVOLVE TRADING STRATEGIES A friend and I recently worked together on a research assignment where we successfully used Genetic Programming (GP) to evolve solutions to a real world financial classification problem. This problem, called security analysis, involves determining which securities ought to be bought in order to realize a good return on investment in the future. SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the following TURING FINANCETHE PROMISE OF COMPUTINGALGORITHMIC TRADING SYSTEMSA QUANT'S VIEW OF CFA LEVEL I Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and creditrisk modelling.
QUANT SOFTWARE
Quant Software for Trading. Quantitative trading is similar to counting cards in a game of Blackjack (21).. Even though the order in which pairs of cards are dealt from a shuffled deck is random and the odds are in favour of the house, if we count the number of high and the number of low cards we may identify times when we are at anadvantage.
HOW TO BE A QUANT
ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISKSEE MORE ONTURINGFINANCE.COM
WHAT DRIVES REAL GDP GROWTH? 3. Real GDP Growth is positively linked to large, employed, healthy workforce (between the age of 15 and 64) which have access to water, sanitation, and excess capital. 4. Real GDP Growth is positively linked to increased productivity and technology. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph Theory for Systemic Risk Models. The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand how reliant banks are on one another and how interconnected the financial STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKS Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. A QUANT'S VIEW OF CFA LEVEL I REGRESSION ANALYSIS USING PYTHON The Regression Analysis Class. A RegressionAnalysis class was created so that it would be easy to create and store multiple regressions. The RegressionAnalysis class encapsulates the run_ordinary_least_squares () and the get_quandl_data () methods. This SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the following TURING FINANCETHE PROMISE OF COMPUTINGALGORITHMIC TRADING SYSTEMSA QUANT'S VIEW OF CFA LEVEL I Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and creditrisk modelling.
QUANT SOFTWARE
Quant Software for Trading. Quantitative trading is similar to counting cards in a game of Blackjack (21).. Even though the order in which pairs of cards are dealt from a shuffled deck is random and the odds are in favour of the house, if we count the number of high and the number of low cards we may identify times when we are at anadvantage.
HOW TO BE A QUANT
ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISKSEE MORE ONTURINGFINANCE.COM
WHAT DRIVES REAL GDP GROWTH? 3. Real GDP Growth is positively linked to large, employed, healthy workforce (between the age of 15 and 64) which have access to water, sanitation, and excess capital. 4. Real GDP Growth is positively linked to increased productivity and technology. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph Theory for Systemic Risk Models. The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand how reliant banks are on one another and how interconnected the financial STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKS Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. A QUANT'S VIEW OF CFA LEVEL I REGRESSION ANALYSIS USING PYTHON The Regression Analysis Class. A RegressionAnalysis class was created so that it would be easy to create and store multiple regressions. The RegressionAnalysis class encapsulates the run_ordinary_least_squares () and the get_quandl_data () methods. This SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the followingTURING FINANCE
Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and creditrisk modelling.
QUANT SOFTWARE
Quant Software for Trading. Quantitative trading is similar to counting cards in a game of Blackjack (21).. Even though the order in which pairs of cards are dealt from a shuffled deck is random and the odds are in favour of the house, if we count the number of high and the number of low cards we may identify times when we are at anadvantage.
A QUANT'S VIEW OF CFA LEVEL I A Quant's view of CFA Level I. Having just written and, thankfully, passed the CFA Level I exam I wanted to take this opportunity to share my experience writing the CFA Level I exam given that I come from an unconventional academic background and work in the industry as a quantitative analyst. I also want to share some helpful onlineresources
INTELLIGENT ALGORITHMIC TRADING SYSTEMS Intelligent Algorithmic Trading Systems. Algorithmic trading is the use of computer algorithms to automatically make trading decisions, submit orders, and manage those orders after submission. Algorithmic trading systems are best understood using a simple conceptual architecture consisting of three components which handle differentaspects of
MONTE CARLO K-MEANS CLUSTERING OF COUNTRIES Clustering Theory - The K-Means Clustering Algorithm. The K-Means Clustering algorithm is a centroid-based partitional clustering algorithm which works using the mean-shift heuristic. The K-means clustering algorithm consists of three steps (Initialization, Assignment, and Update). PORTFOLIO OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION Portfolio optimization using the particle swarm optimization algorithm significantly improved the performance of the carry trade portfolio. A detailed analysis of the results revealed that the optimized portfolio generated superior positive returns when compared to the benchmarks. SIMULATED ANNEALING FOR PORTFOLIO OPTIMIZATION Simulated Annealing (SA) is a generic probabilistic and meta-heuristic search algorithm which can be used to find acceptable solutions to optimization problems characterized by a large search space with multiple optima. Portfolio optimization involves allocating capital between the assets in order to maximize risk adjusted return. PERFECT IMPERFECTION, AGENT BASED MODELS Agent Based Models for Artificial Stock Markets. Agent based models are computational models of complex systems, such as the market, which use an individualistic approach (bottom-up) to simulate the systemic (emergent) effects caused by the actions and interactions between autonomous agents. For example, an agent based model consisting of two SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the following TESTING THE RANDOM WALK HYPOTHESIS WITH R, PART ONE The Random Walk Hypothesis is a theory about the behaviour of security prices which argues that they are well described by random walks, specifically sub-martingale stochastic processes. The Random Walk Hypothesis predates the Efficient Market Hypothesis by 70-years but is actually a consequent and not a precedent of it. TURING FINANCETHE PROMISE OF COMPUTINGALGORITHMIC TRADING SYSTEMSA QUANT'S VIEW OF CFA LEVEL I Fitness Landscape Analysis for Computational Finance June 29, 2015 | StuartReid | 11 Comments Some of the most interesting new research coming out of the Computational Intelligence Research Group (CIRG), which is applicable to numerous computational finance and machine learning optimization problems, is the development of fitness landscape analysis techniques.QUANT SOFTWARE
Quant Software for Trading. Quantitative trading is similar to counting cards in a game of Blackjack (21).. Even though the order in which pairs of cards are dealt from a shuffled deck is random and the odds are in favour of the house, if we count the number of high and the number of low cards we may identify times when we are at anadvantage.
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A QUANT'S VIEW OF CFA LEVEL I ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISKSEE MORE ONTURINGFINANCE.COM
WHAT DRIVES REAL GDP GROWTH? Constructing the Data Sets. The first challenge with any empirical research is that it needs to be based on data. As such this research started off with the construction of the following data-sets using Quandl.com.Three data sets containing absolute values were constructed; one from ~2004, another from ~2009, and lastly one from~2014.
REGRESSION ANALYSIS USING PYTHON The case for linear vs. non-linear regression analysis in finance remains open. The issue with linear models is that they often under-fit and may also assert assumptions on the variables and the main issue with non-linear models is that they often over-fit.Training and data-preparation techniques can be used to minimize over-fitting. STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKSRANDOM REDDIT IMAGERANDOM REDDIT IMAGERANDOM REDDIT NAMEREDDIT RANDOM VIDEOSREDDITRANDOM VIDEOS
Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph theory. A graph / network is a collection of nodes and the arcs that connect those nodes to one another. In the context of banking, each node represents a different bank and each arc represents some link between two banks. SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the following TURING FINANCETHE PROMISE OF COMPUTINGALGORITHMIC TRADING SYSTEMSA QUANT'S VIEW OF CFA LEVEL I Fitness Landscape Analysis for Computational Finance June 29, 2015 | StuartReid | 11 Comments Some of the most interesting new research coming out of the Computational Intelligence Research Group (CIRG), which is applicable to numerous computational finance and machine learning optimization problems, is the development of fitness landscape analysis techniques.QUANT SOFTWARE
Quant Software for Trading. Quantitative trading is similar to counting cards in a game of Blackjack (21).. Even though the order in which pairs of cards are dealt from a shuffled deck is random and the odds are in favour of the house, if we count the number of high and the number of low cards we may identify times when we are at anadvantage.
HOW TO BE A QUANT
A QUANT'S VIEW OF CFA LEVEL I ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISKSEE MORE ONTURINGFINANCE.COM
WHAT DRIVES REAL GDP GROWTH? Constructing the Data Sets. The first challenge with any empirical research is that it needs to be based on data. As such this research started off with the construction of the following data-sets using Quandl.com.Three data sets containing absolute values were constructed; one from ~2004, another from ~2009, and lastly one from~2014.
REGRESSION ANALYSIS USING PYTHON The case for linear vs. non-linear regression analysis in finance remains open. The issue with linear models is that they often under-fit and may also assert assumptions on the variables and the main issue with non-linear models is that they often over-fit.Training and data-preparation techniques can be used to minimize over-fitting. STOCK MARKET PRICES DO NOT FOLLOW RANDOM WALKSRANDOM REDDIT IMAGERANDOM REDDIT IMAGERANDOM REDDIT NAMEREDDIT RANDOM VIDEOSREDDITRANDOM VIDEOS
Article Outline. This series will début with Lo and MacKinlay's first paper: Stock Markets Do Not Follow Random Walks: Evidence from a Simple Specification Test.In this paper Lo and MacKinlay exploited the fact that under a Geometric Brownian Motion model with Stochastic Volatility variance estimates are linear in the sampling interval, to devise a statistical test for the random walk hypothesis. GRAPH THEORY FOR SYSTEMIC RISK MODELS Graph theory. A graph / network is a collection of nodes and the arcs that connect those nodes to one another. In the context of banking, each node represents a different bank and each arc represents some link between two banks. SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the followingTURING FINANCE
Fitness Landscape Analysis for Computational Finance June 29, 2015 | StuartReid | 11 Comments Some of the most interesting new research coming out of the Computational Intelligence Research Group (CIRG), which is applicable to numerous computational finance and machine learning optimization problems, is the development of fitness landscape analysis techniques. A QUANT'S VIEW OF CFA LEVEL I Quantitative Finance Blogs. Quantocracy - this website aggregates hundreds of different quant blogs from around the world, including this one, and offers a fantastic source of new quant strategies every day.; QuantStart - as the name suggests, this blog is all about helping people get started with a career in quantitative finance. It covers many of the softer aspects of being a quant as well. INTELLIGENT ALGORITHMIC TRADING SYSTEMS Symbolic and Fuzzy Logic Models. Symbolic logic is a form of reasoning which essentially involves the evaluation of predicates (logical statements constructed from logical operators such as AND, OR, and XOR) to either true or false. Fuzzy logic relaxes the binary true or false constraint and allows any given predicate to belong to the set of true and or false predicates to different degrees. TESTING THE RANDOM WALK HYPOTHESIS WITH R, PART ONE The Efficient Market Hypothesis (EMH) is an economic theory which proposes that financial markets accurately and instantaneously take into account information about any given security into the current price of that security.The Efficient Market Hypothesis was introduced by Professor Eugene Fama from 1965 to 1970.. If true, actively trading securities in the market based on historical ALGORITHMIC TRADING SYSTEM REQUIREMENTS Currently I am taking a class about software architectures. For this class each student chooses a system, defines its architectural requirements, and designs a solution capable of satisfying thoserequirements.
MONTE CARLO K-MEANS CLUSTERING OF COUNTRIES Clustering Theory - The K-Means Clustering Algorithm. The K-Means Clustering algorithm is a centroid-based partitional clustering algorithm which works using the mean-shift heuristic. The K-means clustering algorithm consists of three steps (Initialization, Assignment, and Update). PORTFOLIO OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION My research topic for this year was Currency Carry Trade Portfolio Optimization using Particle Swarm Optimization (PSO).In this article I will introduce portfolio optimization and explain why it is important. Secondly, I will demonstrate how particle swarm optimization SIMULATED ANNEALING FOR PORTFOLIO OPTIMIZATION This article applies the Simulated Annealing (SA) algorithm to the portfolio optimization problem. Simulated Annealing (SA) is a generic probabilistic and meta-heuristic search algorithm which can be used to find acceptable solutions to optimization problems characterized by a PERFECT IMPERFECTION, AGENT BASED MODELS Great write-up! I think agent-based economic models are currently primarily used for simulation-based macroeconomic experimentation. While there is potential (and a need) for their application in policy making, I struggle to wrap my head around how a simulated stock market would look like which incorporates the type of emergent behavior that financial markets often display. SOFTWARE ARCHITECTURE DESIGN DOCUMENT: ALGORITHMIC TRADING 2.3. Non-functional requirements Whether initiating a software engineering project to build a new algorithmic trading system, or initiating a sourcing and selection exercise to buy an algorithmic trading system, the following Please enable javascript to view this site. Turing Finance | March 22, 2020Select a Page:
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FITNESS LANDSCAPE ANALYSIS FOR COMPUTATIONAL FINANCE June 29, 2015 | StuartReid| 11 Comments
Some of the most interesting new research coming out of the Computational Intelligence Research Group (CIRG), which is applicable to numerous computational finance and machine learning optimization problems, is the development of fitness landscape analysis techniques. Fitness landscape analysis aims to characterize high dimensional ... Read More A RECIPE FOR THE 2008 FINANCIAL CRISIS May 5, 2015 | StuartReid| 13 Comments
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In 2008 when the market crashed I was 16-years old and visiting London for the very first time. At that age I was already obsessed with the markets. Feeling confident that I could understand the crash after having read the classic investment books such ... ReadMore
RANDOM WALKS DOWN WALL STREET, STOCHASTIC PROCESSES IN PYTHON April 7, 2015 | StuartReid| 33 Comments
James Bond is not a quant, but many famous quantitative fund managers enjoy playing poker in their spare time. Stochastic processes can be used to model the odds of such games. This article discusses some of the popular ... Read More MONTE CARLO K-MEANS CLUSTERING OF COUNTRIES February 9, 2015 | StuartReid| 20 Comments
In the first part of this three-part series, _What Drives Real GDP Growth?,_ I identified four themes which drive real GDP growth. These themes are based on 19 socioeconomic indicators whose average Spearman and Pearson correlations to real GDP growth were statistically ... Read More WHAT DRIVES REAL GDP GROWTH? January 15, 2015 | StuartReid| 6 Comments
Econometrics is the application of statistical and computational techniques to the study of economic data. It differs from classical economics in that it is based on empirical findings rather than theories. One benefit of this approach is that ... Read More DIMENSIONALITY REDUCTION TECHNIQUES October 27, 2014 | StuartReid| 14 Comments
The curse of dimensionality is the phenomena whereby an increase in the dimensionality of a data set results in exponentially more data being required to produce a representative sample of that data set. To combat the curse of dimensionality, numerous linear and non-linear dimensionality reduction ... Read More ALL MODELS ARE WRONG, 7 SOURCES OF MODEL RISK September 6, 2014 | StuartReid| 10 Comments
The 2008 financial crisis revealed to the world (in spectacular fashion) the fragility of financial models. Since the financial crisis two words have come up time and time again: _model risk_. This article defines model risk and discusses some of the contributors ... Read More COMPUTATIONAL FINANCE AT IEEE WCCI 2014 July 27, 2014 | StuartReid| 3 Comments
I recently had the awesome opportunity to present my honours research at this years IEEE World Congress for Computational Intelligence conference (IEEE-WCCI) in Beijing. My trip was sponsored by the University of Pretoria's Computational Intelligence Research Group (CIRG) so ... Read More REGRESSION ANALYSIS USING PYTHON June 7, 2014 | StuartReid| 18 Comments
This tutorial covers regression analysis using the Python StatsModels package with Quandl integration. For motivational purposes, here is what we are working towards: a regression analysis program which receives multiple data-set names from Quandl.com, automatically downloads the data, analyses it, and plots the results... Read More
10 MISCONCEPTIONS ABOUT NEURAL NETWORKS May 8, 2014 | StuartReid| 58 Comments
Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and credit risk modelling. They ... Read More SIMULATED ANNEALING FOR PORTFOLIO OPTIMIZATION March 15, 2014 | StuartReid| One Comment
This article applies the Simulated Annealing (SA) algorithm to the portfolio optimization problem. Simulated Annealing (SA) is a generic probabilistic and meta-heuristic search algorithm which can be used to find acceptable solutions to optimization problems characterized by alarge ... Read More
COMPUTATIONAL DECISION MAKING METHODS February 13, 2014 | StuartReid| 2 Comments
Artificial intelligence is broadly defined as the ability of an agent or a model to make either optimal or satisficing decisions. Decision-making in this context is a process which culminates in the selection of a particular course of ... Read More GRAPH THEORY FOR SYSTEMIC RISK MODELS January 29, 2014 | StuartReid| 5 Comments
The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand ... Read More AGENT-BASED COMPUTATIONAL ECONOMIC MODELS January 13, 2014 | StuartReid| 3 Comments
Economists subscribe to many often contradictory schools of thought. This results in businesses and governments adopting economic policies whose consequences are neither agreed upon nor understood. Furthermore, because the economy is actually a complex adaptive system most traditional economic ... Read More PORTFOLIO OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION December 22, 2013 | StuartReid| 20 Comments
My research topic for this year was _Currency Carry Trade Portfolio Optimization using Particle Swarm Optimization (PSO)_. In this article I will introduce portfolio optimization and explain why it is important. Secondly, I will demonstrate how particle swarm ... ReadMore
ALGORITHMIC TRADING SYSTEM ARCHITECTURE November 6, 2013 | StuartReid| 18 Comments
Previously on this blog I have written about the conceptual architecture of an intelligent algorithmic trading system as well as the functional and non-functional requirements of a production algorithmic trading system. Since then I have designed a system architecture ... Read More ALGORITHMIC TRADING SYSTEM REQUIREMENTS October 6, 2013 | StuartReid| 6 Comments
Currently I am taking a class about software architectures. For this class each student chooses a system, defines its architectural requirements, and designs a solution capable of satisfying those requirements. I chose an algorithmic trading system because of ...Read More
BRICS ECONOMIC FORECASTING USING NEURAL NETWORKS September 18, 2013 | StuartReid| 6 Comments
This weekend I finished an interesting research assignment in which I used five computational techniques to train artificial neural networks to forecast the 2011 GDP growth rates for Brazil, Russia, India, China, and South Africa (BRICS nations). The ... Read More MEASURES OF RISK-ADJUSTED RETURN September 1, 2013 | StuartReid| 17 Comments
This article is a supplement to some of the topics presented in Dr. Tucker Balch's online MOOC, _Computational Investing_. Financial markets are complex adaptive systems which are almost always indistinguishable from random processes. That said markets do exhibit quantifiable factors such ... Read More PERFECT IMPERFECTION, AGENT BASED MODELS August 16, 2013 | StuartReid| 12 Comments
When I was 17 years old the Boy Scouts of America invited nine international delegates and I to present at a conference and partake in a 7-day 100 kilometer hike through the Rocky Mountains on the Philmont Scout Ranch. In the week prior ... Read More INTELLIGENT ALGORITHMIC TRADING SYSTEMS July 7, 2013 | StuartReid| 8 Comments
Algorithmic trading is the use of computer algorithms to automatically make trading decisions, submit orders, and manage those orders after submission. Algorithmic trading systems are best understood using a simple conceptual architecture consisting of three components which handle different ... Read More USING GENETIC PROGRAMMING TO EVOLVE TRADING STRATEGIES June 3, 2013 | StuartReid| 22 Comments
A friend and I recently worked together on a research assignment where we successfully used Genetic Programming (GP) to evolve solutions to a real world financial classification problem. This problem, called security analysis, involves determining which securities ought to ... Read More CLUSTERING USING ANT COLONY OPTIMIZATION April 15, 2013 | StuartReid| 11 Comments
For many years entomologists have studied the behaviour of ant colonies and marveled at their ability to solve complex problems collectively. An example of this collective intelligence observed by entomologists is that ants leaving their colony will often follow very efficient routes between ... Read MoreMore Headlines
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