This document summarizes research on applying a threshold cointegration pair trading strategy to pairs of assets in the Thai stock and futures markets. The research finds cointegrating relationships between 3 pairs - SET50 spot and futures, KTB spot and futures, and TRUE spot and futures. A threshold vector error correction model is used to formulate pair trading rules. Backtesting shows the threshold cointegration strategy generates higher profits than a traditional pair trading strategy based on 2 standard deviation bands. However, low futures trading volumes currently limit the attractiveness of using pair trading in Thailand.
This document provides guidelines for formatting Java code through conventions for file names, file organization, indentation, comments, declarations, statements, whitespace, naming, programming practices, and examples. It recommends using suffixes like .java and .class for files, and placing package and import statements before class declarations in source files. The document provides formatting recommendations for elements like comments, declarations, statements and more to improve code readability and maintenance.
The document describes a "Counter-Condor" trading strategy that aims to achieve market neutrality through pairing ETFs with their inverses in short-term option spreads. The strategy involves opening spreads on both an ETF and its inverse that are chosen so their values move in opposite directions, offsetting losses. Backtesting showed an 84% win rate with average gains over 50% of risk per trade. A repair strategy is also outlined to address potential losses through buying an ATM condor if the ETF is between strike prices at expiration.
Algorithmic trading uses computer programs to generate and execute large orders in electronic markets. The main objectives are to control execution costs and market risk. Algorithms split large orders into many small orders and determine how to execute them over time. Popular execution strategies include time-weighted average price (TWAP), volume-weighted average price (VWAP), and percentage of volume (POV) which aim to minimize market impact. The Almgren-Chriss model optimizes execution based on both market impact and price risk over time. Various quantitative strategies employ algorithms, such as statistical arbitrage, market making, and index/ETF arbitrage.
1) Banks cannot directly lend out reserves held at the central bank. The level of reserves is determined by central bank asset purchases, public demand for cash, and government deposits at the central bank.
2) Banks create loans by extending credit, which simultaneously creates new deposits. However, aggregate bank lending does not reduce reserves, as reserves only leave one bank to be held by another.
3) While QE aims to spur bank lending, it does so indirectly by lowering interest rates and improving economic conditions, not by allowing banks to directly "lend out" reserves. Excess reserves created by QE could be extinguished by the central bank if needed to control inflation.
The document provides an introduction to quantitative finance concepts including option pricing models. It begins with an outline and terminology. It then covers the Black-Scholes option pricing model, which uses stochastic calculus to derive a partial differential equation for pricing European options. The document also discusses replicating strategies in discrete and continuous time models, as well as extensions like American options and the Greeks.
This document summarizes research on applying a threshold cointegration pair trading strategy to pairs of assets in the Thai stock and futures markets. The research finds cointegrating relationships between 3 pairs - SET50 spot and futures, KTB spot and futures, and TRUE spot and futures. A threshold vector error correction model is used to formulate pair trading rules. Backtesting shows the threshold cointegration strategy generates higher profits than a traditional pair trading strategy based on 2 standard deviation bands. However, low futures trading volumes currently limit the attractiveness of using pair trading in Thailand.
This document provides guidelines for formatting Java code through conventions for file names, file organization, indentation, comments, declarations, statements, whitespace, naming, programming practices, and examples. It recommends using suffixes like .java and .class for files, and placing package and import statements before class declarations in source files. The document provides formatting recommendations for elements like comments, declarations, statements and more to improve code readability and maintenance.
The document describes a "Counter-Condor" trading strategy that aims to achieve market neutrality through pairing ETFs with their inverses in short-term option spreads. The strategy involves opening spreads on both an ETF and its inverse that are chosen so their values move in opposite directions, offsetting losses. Backtesting showed an 84% win rate with average gains over 50% of risk per trade. A repair strategy is also outlined to address potential losses through buying an ATM condor if the ETF is between strike prices at expiration.
Algorithmic trading uses computer programs to generate and execute large orders in electronic markets. The main objectives are to control execution costs and market risk. Algorithms split large orders into many small orders and determine how to execute them over time. Popular execution strategies include time-weighted average price (TWAP), volume-weighted average price (VWAP), and percentage of volume (POV) which aim to minimize market impact. The Almgren-Chriss model optimizes execution based on both market impact and price risk over time. Various quantitative strategies employ algorithms, such as statistical arbitrage, market making, and index/ETF arbitrage.
1) Banks cannot directly lend out reserves held at the central bank. The level of reserves is determined by central bank asset purchases, public demand for cash, and government deposits at the central bank.
2) Banks create loans by extending credit, which simultaneously creates new deposits. However, aggregate bank lending does not reduce reserves, as reserves only leave one bank to be held by another.
3) While QE aims to spur bank lending, it does so indirectly by lowering interest rates and improving economic conditions, not by allowing banks to directly "lend out" reserves. Excess reserves created by QE could be extinguished by the central bank if needed to control inflation.
The document provides an introduction to quantitative finance concepts including option pricing models. It begins with an outline and terminology. It then covers the Black-Scholes option pricing model, which uses stochastic calculus to derive a partial differential equation for pricing European options. The document also discusses replicating strategies in discrete and continuous time models, as well as extensions like American options and the Greeks.
This document discusses pair trading strategies using threshold co-integration models. It aims to improve trading performance by applying a Threshold Vector Error Correction Model (TVECM) pair trading strategy. The strategy examines price relationships between futures contracts and their underlying assets using Thailand stock and futures market data from 2014. It finds co-integrating relationships and estimates TVECM models to generate trading signals. Backtesting shows the TVECM pair trading strategy performs better than traditional pair trading. The document also reviews relevant theories on market efficiency, arbitrage opportunities, and pair trading strategies.
This document defines stream processing and discusses how the Data Distribution Service (DDS) can be used to implement stream processing architectures. It describes how DDS topics represent streams of data, DDS data writers act as sources, and DDS data readers act as sinks. Content filtered topics and history QoS policies allow filtering and windowing of stream data. The document provides an example of a moving average filter implemented using DDS data readers and content filtering.
Paradigms of trading strategies formulationQuantInsti
The webinar aims to look at trading strategies from different perspectives. The aim has been to provide the audience with the metrics to formulate, evaluate the strategy based on the paradigms that suits one's trading style. We have often seen, when a same strategy is been used by two different traders, results have been quite different. What causes this difference has been the theme for this webinar.
This webinar will cover the following topics:
A. Latency - Metrics and Limits
B. Tick to Trade Latencies
C. Cause of Degradation
D. Present Landscape and Foreseeable Future
The webinar was taken by Mr. Gaurav Raizada, he is a Director at iRageCapital Advisory Private Ltd and also Senior faculty of QuantInsti, leads the firm's advisory practice in India on the Systems, Performance and Strategies. He has consulted extensively with core focus on strategy development and execution including trading systems development, latency reduction, optimization and transaction cost analysis. Gaurav is IIT and IIM Alumnus.
Algorithmic Trading in Different LandscapesQuantInsti
Presentation on "Algorithmic Trading in different geographies"
This presentation highlights the trading landscape in different geographies and compares them on four parameters:
i) Technological protocols in various geographies
ii) Regulatory environments
iii) Competitive landscape
iv) Market Volumes
This presentation was presented by senior QI faculty and co-founder Rajib Ranjan Borah at the pre-conference workshop of the "4th Annual Conference: Behavioral Models and Sentiment Analysis Applied to Finance", in London in June 2014.
The two day pre-conference workshop on "Market Microstructure, Liquidity and Automated Trading Workshop" was conducted at Fitch Learnings, London.
To view a video recording of the presentation, please contact contact@quantinsti.com
Pairs trading is a hedge fund strategy that involves buying one security and short selling another security that have historically moved together. When the spread between the two securities widens, the trader will take the opposite position, betting that the prices will converge again. Key aspects of pairs trading include avoiding data snooping to test for higher potential profits, using algorithms to select pairs based on similar historical state prices according to the Law of One Price, and ensuring the component prices are cointegrated with common nonstationary factors to justify the strategy. Bankruptcy risk in one security of a pair can also drive profits if it has a temporarily increasing probability versus the other security with a constant or decreasing probability.
Here output is attached of pairs trading strategy using R. Daily free data of NIFTY and included bank stock are used for analysis. For details on strategy building, statistical analysis and financial model visit www.financemodel.co
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
Indian Exchanges follow the Price-Time Priority principle in Limit Order Books. This allows for quantification of the costs that a trader is willing to pay or receive in order to Trade. In order to gain price priority, the cost is in terms of ticks paid to gain price precedence over others. In terms of Time priority, it is atleast one tick, that allows the trader's order to leapfrog others at the same price level.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/empirical-analysis-of-limit-order-books/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Quantifying News For Automated Trading - Methodology and ProfitabilityQuantInsti
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
News is the prime factor which affects prices of financial assets, everything else is secondary. However, owing to the huge volume of news information continuously released by modern electronic communication, it becomes increasingly difficult to process all the information in a timely manner.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/quantifying-news-for-automated-trading-methodology-and-profitability/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume.
Algorithmic trading and Machine Learning by Michael Kearns, Professor of Comp...Quantopian
Traditional financial markets have undergone rapid technological change due to increased automation and the introduction of new exchanges and mechanisms. Such changes have brought with them challenging new problems in algorithmic trading, many of which invite a machine learning approach. In this talk, Michael will examine several algorithmic trading problems, focusing on their novel ML aspects, including limiting market impact, dealing with censored data, and incorporating risk considerations.
This presentation was part of QuantCon 2015 hosted by Quantopian. Visit us at: www.quantopian.com.
This document discusses financial market microstructure theory and trading algorithms. It provides background on market microstructure, which deals with trading dynamics and how prices adjust to information. It also covers optimal execution algorithms, which obtain the best price for orders, and speculative algorithms, including pairs trading strategies. The paper tests various trading algorithms on high-frequency equity data from the London Stock Exchange to evaluate their performance and excess returns.
With heavyweights like PIMCO entering the managed futures mutual fund space, interest appears to be rising from both investors and asset managers in this investment vehicle. PIMCO recently filed documents with the SEC for a new mutual fund that will pursue a quantitative trading strategy to capture trends in global markets and commodities. While PIMCO declined to comment, analysts from Morningstar indicate the interest from a large firm like PIMCO shows ongoing demand for managed futures despite poor performance in recent years.
This document summarizes a paper about a projection-free first-order optimization method for convex problems. The method generalizes Frank-Wolfe optimization to arbitrary convex domains by solving a linearized problem at each iteration instead of a projection step. The method guarantees an epsilon duality gap within O(1/epsilon) iterations. It provides sparse or low-rank solutions to 1-norm and nuclear norm regularized problems, with sparsity or rank bounds of O(1/epsilon). Applications include support vector machines, compressed sensing, and matrix completion.
The document describes efficient algorithms for projecting a vector onto the l1-ball (sum of absolute values) constraint. It presents two methods: 1) An exact projection algorithm that runs in O(n) expected time, where n is the dimension. 2) A method for vectors with k perturbed elements outside the l1-ball, which projects in O(k log n) time. It demonstrates these algorithms outperform interior point methods on various learning tasks, providing models with high sparsity.
This document describes methods for determining whether a time series reflects linear or nonlinear processes. It compares the forecasting performance of linear versus nonlinear models. The two-step procedure involves using simplex projection to identify the best embedding dimension, and then using that dimension in the S-map procedure to assess nonlinearity. Both methods evaluate out-of-sample forecast skill by dividing the time series in half, using one half to build the model and the other to evaluate forecasts.
The document compares five evolutionary optimization algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, ant colony systems, and shuffled frog leaping. It provides a brief description of each algorithm, including how they are inspired by natural processes and behaviors. It also includes pseudocode to facilitate implementing each algorithm. The document then presents benchmark comparisons of the five algorithms on continuous and discrete optimization problems in terms of processing time, convergence speed, and solution quality. It discusses the performance of evolutionary algorithms and provides guidelines for determining the best parameters for each.
This document discusses pair trading strategies using threshold co-integration models. It aims to improve trading performance by applying a Threshold Vector Error Correction Model (TVECM) pair trading strategy. The strategy examines price relationships between futures contracts and their underlying assets using Thailand stock and futures market data from 2014. It finds co-integrating relationships and estimates TVECM models to generate trading signals. Backtesting shows the TVECM pair trading strategy performs better than traditional pair trading. The document also reviews relevant theories on market efficiency, arbitrage opportunities, and pair trading strategies.
This document defines stream processing and discusses how the Data Distribution Service (DDS) can be used to implement stream processing architectures. It describes how DDS topics represent streams of data, DDS data writers act as sources, and DDS data readers act as sinks. Content filtered topics and history QoS policies allow filtering and windowing of stream data. The document provides an example of a moving average filter implemented using DDS data readers and content filtering.
Paradigms of trading strategies formulationQuantInsti
The webinar aims to look at trading strategies from different perspectives. The aim has been to provide the audience with the metrics to formulate, evaluate the strategy based on the paradigms that suits one's trading style. We have often seen, when a same strategy is been used by two different traders, results have been quite different. What causes this difference has been the theme for this webinar.
This webinar will cover the following topics:
A. Latency - Metrics and Limits
B. Tick to Trade Latencies
C. Cause of Degradation
D. Present Landscape and Foreseeable Future
The webinar was taken by Mr. Gaurav Raizada, he is a Director at iRageCapital Advisory Private Ltd and also Senior faculty of QuantInsti, leads the firm's advisory practice in India on the Systems, Performance and Strategies. He has consulted extensively with core focus on strategy development and execution including trading systems development, latency reduction, optimization and transaction cost analysis. Gaurav is IIT and IIM Alumnus.
Algorithmic Trading in Different LandscapesQuantInsti
Presentation on "Algorithmic Trading in different geographies"
This presentation highlights the trading landscape in different geographies and compares them on four parameters:
i) Technological protocols in various geographies
ii) Regulatory environments
iii) Competitive landscape
iv) Market Volumes
This presentation was presented by senior QI faculty and co-founder Rajib Ranjan Borah at the pre-conference workshop of the "4th Annual Conference: Behavioral Models and Sentiment Analysis Applied to Finance", in London in June 2014.
The two day pre-conference workshop on "Market Microstructure, Liquidity and Automated Trading Workshop" was conducted at Fitch Learnings, London.
To view a video recording of the presentation, please contact contact@quantinsti.com
Pairs trading is a hedge fund strategy that involves buying one security and short selling another security that have historically moved together. When the spread between the two securities widens, the trader will take the opposite position, betting that the prices will converge again. Key aspects of pairs trading include avoiding data snooping to test for higher potential profits, using algorithms to select pairs based on similar historical state prices according to the Law of One Price, and ensuring the component prices are cointegrated with common nonstationary factors to justify the strategy. Bankruptcy risk in one security of a pair can also drive profits if it has a temporarily increasing probability versus the other security with a constant or decreasing probability.
Here output is attached of pairs trading strategy using R. Daily free data of NIFTY and included bank stock are used for analysis. For details on strategy building, statistical analysis and financial model visit www.financemodel.co
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
Indian Exchanges follow the Price-Time Priority principle in Limit Order Books. This allows for quantification of the costs that a trader is willing to pay or receive in order to Trade. In order to gain price priority, the cost is in terms of ticks paid to gain price precedence over others. In terms of Time priority, it is atleast one tick, that allows the trader's order to leapfrog others at the same price level.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/empirical-analysis-of-limit-order-books/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Quantifying News For Automated Trading - Methodology and ProfitabilityQuantInsti
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
News is the prime factor which affects prices of financial assets, everything else is secondary. However, owing to the huge volume of news information continuously released by modern electronic communication, it becomes increasingly difficult to process all the information in a timely manner.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/quantifying-news-for-automated-trading-methodology-and-profitability/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume.
Algorithmic trading and Machine Learning by Michael Kearns, Professor of Comp...Quantopian
Traditional financial markets have undergone rapid technological change due to increased automation and the introduction of new exchanges and mechanisms. Such changes have brought with them challenging new problems in algorithmic trading, many of which invite a machine learning approach. In this talk, Michael will examine several algorithmic trading problems, focusing on their novel ML aspects, including limiting market impact, dealing with censored data, and incorporating risk considerations.
This presentation was part of QuantCon 2015 hosted by Quantopian. Visit us at: www.quantopian.com.
This document discusses financial market microstructure theory and trading algorithms. It provides background on market microstructure, which deals with trading dynamics and how prices adjust to information. It also covers optimal execution algorithms, which obtain the best price for orders, and speculative algorithms, including pairs trading strategies. The paper tests various trading algorithms on high-frequency equity data from the London Stock Exchange to evaluate their performance and excess returns.
With heavyweights like PIMCO entering the managed futures mutual fund space, interest appears to be rising from both investors and asset managers in this investment vehicle. PIMCO recently filed documents with the SEC for a new mutual fund that will pursue a quantitative trading strategy to capture trends in global markets and commodities. While PIMCO declined to comment, analysts from Morningstar indicate the interest from a large firm like PIMCO shows ongoing demand for managed futures despite poor performance in recent years.
This document summarizes a paper about a projection-free first-order optimization method for convex problems. The method generalizes Frank-Wolfe optimization to arbitrary convex domains by solving a linearized problem at each iteration instead of a projection step. The method guarantees an epsilon duality gap within O(1/epsilon) iterations. It provides sparse or low-rank solutions to 1-norm and nuclear norm regularized problems, with sparsity or rank bounds of O(1/epsilon). Applications include support vector machines, compressed sensing, and matrix completion.
The document describes efficient algorithms for projecting a vector onto the l1-ball (sum of absolute values) constraint. It presents two methods: 1) An exact projection algorithm that runs in O(n) expected time, where n is the dimension. 2) A method for vectors with k perturbed elements outside the l1-ball, which projects in O(k log n) time. It demonstrates these algorithms outperform interior point methods on various learning tasks, providing models with high sparsity.
This document describes methods for determining whether a time series reflects linear or nonlinear processes. It compares the forecasting performance of linear versus nonlinear models. The two-step procedure involves using simplex projection to identify the best embedding dimension, and then using that dimension in the S-map procedure to assess nonlinearity. Both methods evaluate out-of-sample forecast skill by dividing the time series in half, using one half to build the model and the other to evaluate forecasts.
The document compares five evolutionary optimization algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, ant colony systems, and shuffled frog leaping. It provides a brief description of each algorithm, including how they are inspired by natural processes and behaviors. It also includes pseudocode to facilitate implementing each algorithm. The document then presents benchmark comparisons of the five algorithms on continuous and discrete optimization problems in terms of processing time, convergence speed, and solution quality. It discusses the performance of evolutionary algorithms and provides guidelines for determining the best parameters for each.
This document summarizes a course on programming in C++ for quantitative finance applications. The course teaches C++ programming from basic examples to object-oriented applications, using examples from quantitative finance. It assumes no prior programming knowledge. Students will learn to model quantitative finance problems algorithmically and solve them through C++ code. By the end of the course, students will have a practical knowledge of C++ and skills in computational methods for pricing derivatives. The course is suitable for anyone working in financial markets or hoping to, and aims to provide skills needed for jobs requiring C++.
This document summarizes a study on trend following algorithms for technical trading in the stock market. It presents two trend following algorithms: 1) Static P&Q, which uses static values for parameters P and Q to determine when to enter and exit trades, and 2) Adaptive P&Q, which uses dynamically adjusted P and Q values. The algorithms were tested in a stock market simulation, and the Static P&Q algorithm achieved average monthly returns of 75.63%. However, performance degraded as market trend fluctuations increased, implying the need to pause trading during periods of high volatility.
This document describes a new genetic algorithm (GA)-based system for predicting the future performance of individual stocks. The system uses GAs for inductive machine learning rather than optimization. It is compared to a neural network system using data from over 1,600 stocks. The study finds that the GA system can predict stock returns 12 weeks in the future and that combining GA and neural network forecasts provides synergistic benefits.
The document describes efficient algorithms for projecting a vector onto the l1-ball (sum of absolute values being less than a threshold). It presents two methods: 1) An exact projection algorithm that runs in expected O(n) time, where n is the dimension. 2) A method for vectors with k perturbed elements outside the l1-ball, which projects in O(k log n) time. It demonstrates these algorithms outperform interior point methods on various learning tasks, providing models with high sparsity.
In tech an-innovative_systematic_approach_to_financial_portfolio_management_v...Tomasz Waszczyk
This document describes a new approach to financial portfolio management using PID (proportional-integral-derivative) control. PID controllers are commonly used in engineering to control processes. The approach models financial assets in a portfolio as a "process plant" controlled by a PID controller. It aims to stabilize portfolio returns and reduce volatility over time compared to a benchmark portfolio. The PID controller recalibrates the asset weights in the experimental portfolio each month based on the error between the actual and target returns. The approach is evaluated over 12 years using 20 diversified assets, with the experimental portfolio outperforming the unmanaged benchmark portfolio in risk-adjusted returns.
Banks cannot lend out reserves directly to customers. When banks make loans, they simultaneously create a new deposit in the borrower's bank account, thereby creating new money. The level of bank reserves is determined by the central bank's asset purchases, the public's demand for cash, and government deposits. While quantitative easing aims to spur bank lending, it works through more indirect channels than the conventional view that excess reserves will be lent out. Understanding the balance sheet mechanics of how banks create credit is important for evaluating policies like quantitative easing.
11. Psychologischer Aspekt
03.11.2012 Münsteraner Börsenparkett e.V. www.boersenparkett.org facebook.com/mbpev
Annahme: Anleger verhalten sich
in der Masse ähnlich
Folge: Massenpsychologie
beeinflusst den Markt
12. Zitate
03.11.2012 Münsteraner Börsenparkett e.V. www.boersenparkett.org facebook.com/mbpev
"Konzentrieren Sie Ihre Investments. Wenn Sie
über einen Harem mit vierzig Frauen verfügen,
lernen Sie keine richtig kennen."
Warren Buffet berühmter amerikanischer
Anleger
"Die Zeit des größten Pessimismus ist die beste
Zeit des Kaufens, die Zeit des größten
Optimismus ist die beste Zeit zu verkaufen!"
John Templeton
"Drei Dinge treiben den Menschen zum
Wahnsinn: Die Liebe, die Eifersucht und das
Studium der Börsenkurse."
John Maynard Keynes