Andrew Palashewsky developed the Advance IQ Capital model beginning in 2011 to create an algorithmic trading strategy based on his decades of experience. The model uses proprietary momentum measurements to determine buy and sell signals across different market conditions. Backtesting of the model on futures, currencies, and ETFs from 2008-2014 shows annual returns ranging from 9.4% to 30% compared to benchmarks. However, past performance is not indicative of future results.
Parametric provides strategies for exploiting increased market volatility, including rebalancing portfolios and using options strategies. Rebalancing reduces concentration risks and volatility over time by selling assets that have increased in value and buying those that have decreased, capturing returns from volatility. Options strategies can also provide downside protection for portfolios while retaining upside potential. Parametric implemented an options overlay for a client in 2008 that protected against a 5-20% market decline while retaining upside to 30%, balancing protection and participation in gains.
This document describes the investment strategy of JMS Partners, a management group. They take a concentrated equity approach investing in 10-15 stocks, and use derivatives to provide downside protection. They have developed proprietary software that analyzes market data and correlates variables to identify statistically anomalous stocks and relationships. The software profiles stocks based on probability and mean reversion theory to select investments with the highest probability of success. Their strategy aims to generate superior risk-adjusted returns through a collaborative, multi-disciplinary approach combining fundamental analysis, derivatives hedging, and quantitative modeling.
The document describes a pairs trading model and software implementation in three parts:
1. It outlines four mathematical methods - normalized differences, cointegration, stochastic spread, and time varying mean reversion - to analyze pair spreads and generate trading signals.
2. It discusses how the accompanying software add-in allows running the computationally intensive methods in EViews and producing summary outputs, charts, and test results.
3. It provides examples of the add-in interface and sample trading signal and statistical output to demonstrate the model's application and usefulness for financial decision making despite some limitations.
Algorithmic Finance Meetup: Starmine Short Interest Talk Quantopian
With the commoditization of such basic quant factors as value and momentum, in recent years systematic investors have turned more and more to sentiment based alpha signals. Aggregated open short interest level provides a profitable, low turnover signal rooted in buy-side sentiment, aka "the smart money." Dr. Stauth will cover the basics of short selling and data availability and will review the research and proprietary formulation of the StarMine short interest model as well as covering a range of sample trading strategies.
Technical analysis, market efficiency, and behavioral financeBabasab Patil
Technical analysis uses patterns in stock prices and trading volume to predict future market movements and identify trading opportunities. The efficient market hypothesis states that stock prices instantly reflect all available information, making technical analysis ineffective. However, behavioral finance suggests psychological factors influence investor decisions and market anomalies exist, challenging the notion of complete market efficiency.
"Enhancing Statistical Significance of Backtests" by Dr. Ernest Chan, Managin...Quantopian
Insufficient historical data is a major hurdle in building a trading model free from data snooping bias. Dr. Chan's talk will discuss several techniques, some borrowed from machine learning, that can alleviate overfitting and enhance the statistical significance of a backtest.
"Portfolio Optimisation When You Don’t Know the Future (or the Past)" by Rob...Quantopian
We generally assume the past is a good guide to the future, but well do we even know the past? What effect does this uncertainty when estimating inputs have on the notoriously unstable algorithms for portfolio optimization?
I explore this issue, look at some commonly used solutions, and also introduce some alternative methods.
How important are the rules used to create smart beta portfoliosRalph Goldsticker
Most Smart Beta presentations are about: “What and Why?”
This presentation addresses: “Do the rules used to construct a Smart Beta portfolio matter?”
Our approach was to use alternative portfolio construction rules to simulate multiple 25-year return histories for Low Volatility, Fundamental Indexing and Momentum strategies, and then compare their average returns, risks, drawdowns and factor exposures.
Parametric provides strategies for exploiting increased market volatility, including rebalancing portfolios and using options strategies. Rebalancing reduces concentration risks and volatility over time by selling assets that have increased in value and buying those that have decreased, capturing returns from volatility. Options strategies can also provide downside protection for portfolios while retaining upside potential. Parametric implemented an options overlay for a client in 2008 that protected against a 5-20% market decline while retaining upside to 30%, balancing protection and participation in gains.
This document describes the investment strategy of JMS Partners, a management group. They take a concentrated equity approach investing in 10-15 stocks, and use derivatives to provide downside protection. They have developed proprietary software that analyzes market data and correlates variables to identify statistically anomalous stocks and relationships. The software profiles stocks based on probability and mean reversion theory to select investments with the highest probability of success. Their strategy aims to generate superior risk-adjusted returns through a collaborative, multi-disciplinary approach combining fundamental analysis, derivatives hedging, and quantitative modeling.
The document describes a pairs trading model and software implementation in three parts:
1. It outlines four mathematical methods - normalized differences, cointegration, stochastic spread, and time varying mean reversion - to analyze pair spreads and generate trading signals.
2. It discusses how the accompanying software add-in allows running the computationally intensive methods in EViews and producing summary outputs, charts, and test results.
3. It provides examples of the add-in interface and sample trading signal and statistical output to demonstrate the model's application and usefulness for financial decision making despite some limitations.
Algorithmic Finance Meetup: Starmine Short Interest Talk Quantopian
With the commoditization of such basic quant factors as value and momentum, in recent years systematic investors have turned more and more to sentiment based alpha signals. Aggregated open short interest level provides a profitable, low turnover signal rooted in buy-side sentiment, aka "the smart money." Dr. Stauth will cover the basics of short selling and data availability and will review the research and proprietary formulation of the StarMine short interest model as well as covering a range of sample trading strategies.
Technical analysis, market efficiency, and behavioral financeBabasab Patil
Technical analysis uses patterns in stock prices and trading volume to predict future market movements and identify trading opportunities. The efficient market hypothesis states that stock prices instantly reflect all available information, making technical analysis ineffective. However, behavioral finance suggests psychological factors influence investor decisions and market anomalies exist, challenging the notion of complete market efficiency.
"Enhancing Statistical Significance of Backtests" by Dr. Ernest Chan, Managin...Quantopian
Insufficient historical data is a major hurdle in building a trading model free from data snooping bias. Dr. Chan's talk will discuss several techniques, some borrowed from machine learning, that can alleviate overfitting and enhance the statistical significance of a backtest.
"Portfolio Optimisation When You Don’t Know the Future (or the Past)" by Rob...Quantopian
We generally assume the past is a good guide to the future, but well do we even know the past? What effect does this uncertainty when estimating inputs have on the notoriously unstable algorithms for portfolio optimization?
I explore this issue, look at some commonly used solutions, and also introduce some alternative methods.
How important are the rules used to create smart beta portfoliosRalph Goldsticker
Most Smart Beta presentations are about: “What and Why?”
This presentation addresses: “Do the rules used to construct a Smart Beta portfolio matter?”
Our approach was to use alternative portfolio construction rules to simulate multiple 25-year return histories for Low Volatility, Fundamental Indexing and Momentum strategies, and then compare their average returns, risks, drawdowns and factor exposures.
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.
The QuantCon Keynote: "Counter Trend Trading – Threat or Complement to Trend ...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Over the past 30 years, trend following has been a remarkably successful futures trading strategy. Once a fringe trading style barely known outside of Chicago, it has grown into a 300 billion dollar global industry. It would be very difficult indeed to claim that trend following doesn’t work in the face of decades of empirical evidence otherwise. But trend following isn’t completely without problems.
It is well known that classic trend following models tend to lose money on a majority of trades. This is not necessarily an issue, since trend following is all about accepting a large number of small losses in exchange for a small number of large gains. As long as the net is positive, all is fine. That is the underlying idea of the strategy and it has historically worked very well.
However, if you dissect trend following models you can find weaknesses which could be exploited. This is what counter trend trading models are about. These counter trend models usually operate on a shorter time frame and with nearly opposite logic.
As counter trend models are gaining popularity in the systematic trading hedge fund field, a few questions arise. Are these models a threat to trend following? Can they be a complement to trend following? Can trend following be adapted to be less susceptible to the counter trend issue?
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...Quantopian
Engineers design stuff. Why do Quants prefer to fit? In this talk, Robert will explain what designing a trading system actually involves, explore why designing might be better than fitting, and introduce some of the tools you could use. He will also take you through the design process for an example trading strategy.
Finally, he will discuss how we can have the best of both worlds: strategies that are well designed and also fitted to the data.
A pair trade is the taking of a long position in one security together with an equal short position in another that is strongly correlated with it. It is sometimes used to refer to multiple long and short positions that are similarly matched.
Trading Strategies Based on Market Impact of Macroeconomic Announcementsby A...Quantopian
1) The document discusses trading strategies based on the market impact of macroeconomic announcements. It analyzes 18 major US macroeconomic indicators from 2009-2013 and their impact on equity ETF returns on announcement days.
2) Key findings include several indicators having statistically significant impact on returns, including ISM Manufacturing Index, Non-Farm Payrolls, International Trade Balance, and Housing Starts. Trading strategies based on announcements of significant indicators achieved higher risk-adjusted returns than buy-and-hold.
3) The study also analyzes the impact of economic announcement surprises, actual changes, and expected changes. It found that strategies based on actual changes generally had the lowest volatility and performed well even before and after the
This presentation gives you a lot of ideas on how to consistently outperform markets and earn a consistent and growing ROI (Return on Investment) on your investments.
Originally published in 2005. Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions. Delisted companies were included to account for survivorship bias. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system.
"Deep Q-Learning for Trading" by Dr. Tucker Balch, Professor of Interactive C...Quantopian
Reinforcement Learning (RL) has been around for a long time, but it has not attracted much attention over the last decade. Until, that is, a group of Google researchers showed how RL can be used to train a computer to play video games at far above human capabilities.
Besides video games, the RL problem is also well aligned to solve trading problems as well (e.g., work by Dr. Michael Kearns). In this talk, Tucker will provide a gentle introduction to Q-Learning, one of the leading RL methods.
He will also show how Q-Learning can be integrated with artificial neural network learners and how such a system can be used to learn and execute a trading strategy. This is joint work with David Byrd at Georgia Tech.
Why Emerging Managers Now? - Infusion Global Partners WhitepaperAndrei Filippov
Traditional asset classes appear to offer uninspiring beta returns at present, and recent years’ hedge fund returns have disappointed both in magnitude and diversification benefits, likely reflecting capacity pressures associated with the concentration of AUM and inflows with larger funds. We argue that, by contrast, Emerging hedge funds offer a rich opportunity set with far fewer capacity issues where skilled managers with concrete competitive advantages in less efficient, smaller capitalization market segments can generate better, more sustainable and less correlated excess returns. Emerging managers do involve more investment and operational risk than larger peers; to that challenge we offer some suggestions on a thoughtful and rigorous approach to constructing an Emerging Managers allocation and balancing effective due diligence with scalability.
"From Alpha Discovery to Portfolio Construction: Pitfalls and Solutions" by D...Quantopian
From QuantCon 2017: Implementation is the efficient translation of alpha research into portfolios. It includes portfolio construction and trading. It is a vital step in the quant equity workflow, as poor implementation can ruin even the best alpha ideas. Two crucial challenges must be solved: how to construct a portfolio that most efficiently captures a given alpha signal; and, in the presence of multiple signals, how to optimally combine them into a single composite alpha factor.
This talk addresses these challenges, examines common pitfalls in the implementation of quantitative strategies and good practices to avoid them. A common theme is striking the right balance between factor signal purity and investability. We look at how factor models and optimisation techniques help professional investors answer three key questions:
· What risks should your risk model be cognisant of?
· What objective function should you use?
· What effect do investability constraints have on your portfolio?
The document provides information on investment analysis, including definitions, methods, and concepts. It discusses two main types of analysis: fundamental analysis and technical analysis. Fundamental analysis examines basic company data like earnings, sales, and financial statements to determine a stock's intrinsic value. Technical analysis uses historical market data like prices and trading volumes to identify patterns that can predict future price movements. The document also covers the efficient market hypothesis, which proposes that stock prices reflect all publicly available information.
"A Framework for Developing Trading Models Based on Machine Learning" by Kris...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Machine learning is improving facets of our lives as diverse as health screening, transportation and even our entertainment choices. It stands to reason that machine learning can also improve trading performance, however the practical application is fraught with pitfalls and obstacles that nullify the benefits and present a high barrier to entry. Building on background information and introductory material, Kris will propose a framework for efficient and robust experimentation with machine learning methods for algorithmic trading. The framework's objective is to arrive at parsimonious models whose positive past performance is unlikely to be due to chance. The framework is demonstrated via practical examples of various machine learning models for algorithmic trading.
An introduction to implementing 5 basic quant strategies on Quantopian. Presented to the Bay Area Algorithmic Trading Group and the Bay Area Trading Signals meetup groups at the Hacker Dojo Feb 6th, 2014 by Jess Stauth
1. The document analyzes value and growth stocks between 1975-2004, comparing their returns and risks. It finds that value stocks generally outperformed growth stocks over this period.
2. A moving average analysis of the value-growth return spread shows it fluctuated between positive and negative returns with no clear pattern, contradicting the theory that value stocks always outperform. The spreads were also small relative to the portfolios' volatility.
3. Regression analyses found the CAPM model did not accurately predict returns. The growth portfolio underperformed predictions by -0.15% annually, while the value portfolio outperformed by 0.14%, contradicting CAPM. The spread portfolio had low correlation to the market, as
Stock Return Forecast - Theory and Empirical EvidenceTai Tran
The document discusses several models for stock return forecasting including CAPM, the Fama-French three-factor model, a four-factor model with momentum, and a five-factor model including asset growth. Empirical evidence is presented analyzing daily returns of Coca-Cola stock in 2005, finding that momentum is highly significant in predicting returns, while beta is less so. Multi-factor models, particularly the four and five-factor models, provide improved forecasting over CAPM alone, though with increasing complexity. Limitations include selection bias and issues with beta estimation.
The document discusses an event study conducted by a financial analyst to test the semi-strong form of market efficiency. The analyst examined 4 companies that announced dividend increases and calculated the characteristic lines for each company based on weekly returns over the prior 6 years. Abnormal returns were then calculated for each company over the 4 weeks before and after the announcement date. The average abnormal returns and cumulative average abnormal returns were close to zero, supporting the semi-strong form hypothesis that the market incorporated the information of the dividend increases prior to the official announcement.
2012 what drives value tilt portfolios overperformanceFrederic Jamet
- Value tilt portfolios that invest in stocks with low valuations like price-to-book ratios have historically outperformed the overall market. There are various methods to construct value tilt indexes and ETFs.
- There are rational explanations for the outperformance like receiving higher returns for bearing additional market risk, as well as behavioral explanations involving investor overreaction. However, some argue the outperformance could be coincidental and may not continue in the future.
- The document discusses several well-known value indexes from providers like MSCI, FTSE, and Russell, and analyzes the characteristics of a hypothetical value tilt portfolio that outperformed with similar risk to the overall market.
This document summarizes a chapter on corporate financing and market efficiency. It discusses five main topics:
1) Whether financing decisions can create value by examining an example of a provincial loan guarantee.
2) How capital markets are described as efficient when stock prices quickly reflect all available information.
3) The different types of market efficiency: weak form reflects past prices/volume, semi-strong reflects public info, strong reflects all info.
4) Evidence for different forms of efficiency from studies on mutual funds, reaction to announcements, and insider trading regulations.
5) Implications of an efficient market that firm financing cannot affect stock prices through accounting and that issues cannot be timed.
"Is Momentum Still Relevant for Today’s Markets?" by Anthony Ng, Senior LecturerQuantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Despite being ‘discovered’ over 20 years ago, there is still confusion on what a momentum strategy entails and people ‘invest in momentum’. There are two generally accepted definitions of momentum in academic literature. In the quantitative equity investment sphere, momentum is frequently referred to as across securities or assets (cross-sectional or relative) and typically traded in a long-short or hedged manner. In futures trading, momentum is often referred to the past return of the security (time-series) and normally traded in a directional fashion.
Following from the above, we conducted an analysis on the performance of a momentum strategy of different asset classes: equity, fixed income, futures, and currencies. The study showed that both types of momentum are prevalent and persistent across all asset classes. Furthermore, as the correlations between the two types of momentum strategies and amongst the asset classes are quite low, substantial diversification benefit can be derived by combining them.
The document discusses the efficient market hypothesis which holds that current stock prices fully reflect all available public information. It describes different levels of market efficiency and the random walk theory that stock prices move randomly. The document notes that technical analysis which tries to predict prices from past trends has failed, while broad market indexes are difficult for professionals to consistently beat. Index funds are recommended as they match market returns over the long run.
This document provides an overview of spread trading strategies in the US Treasury market. It defines spread trading as taking long and short positions in different futures contracts to profit from perceived mispricing. The document discusses why spread trading requires lower margins and forces traders to think in terms of price targets. It provides examples of common spread trading strategies like intermarket, calendar, butterfly, and condor spreads. It also addresses frequently asked questions about spread trading and lists topics covered in the accompanying yield curve trading strategies course.
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.
The QuantCon Keynote: "Counter Trend Trading – Threat or Complement to Trend ...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Over the past 30 years, trend following has been a remarkably successful futures trading strategy. Once a fringe trading style barely known outside of Chicago, it has grown into a 300 billion dollar global industry. It would be very difficult indeed to claim that trend following doesn’t work in the face of decades of empirical evidence otherwise. But trend following isn’t completely without problems.
It is well known that classic trend following models tend to lose money on a majority of trades. This is not necessarily an issue, since trend following is all about accepting a large number of small losses in exchange for a small number of large gains. As long as the net is positive, all is fine. That is the underlying idea of the strategy and it has historically worked very well.
However, if you dissect trend following models you can find weaknesses which could be exploited. This is what counter trend trading models are about. These counter trend models usually operate on a shorter time frame and with nearly opposite logic.
As counter trend models are gaining popularity in the systematic trading hedge fund field, a few questions arise. Are these models a threat to trend following? Can they be a complement to trend following? Can trend following be adapted to be less susceptible to the counter trend issue?
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...Quantopian
Engineers design stuff. Why do Quants prefer to fit? In this talk, Robert will explain what designing a trading system actually involves, explore why designing might be better than fitting, and introduce some of the tools you could use. He will also take you through the design process for an example trading strategy.
Finally, he will discuss how we can have the best of both worlds: strategies that are well designed and also fitted to the data.
A pair trade is the taking of a long position in one security together with an equal short position in another that is strongly correlated with it. It is sometimes used to refer to multiple long and short positions that are similarly matched.
Trading Strategies Based on Market Impact of Macroeconomic Announcementsby A...Quantopian
1) The document discusses trading strategies based on the market impact of macroeconomic announcements. It analyzes 18 major US macroeconomic indicators from 2009-2013 and their impact on equity ETF returns on announcement days.
2) Key findings include several indicators having statistically significant impact on returns, including ISM Manufacturing Index, Non-Farm Payrolls, International Trade Balance, and Housing Starts. Trading strategies based on announcements of significant indicators achieved higher risk-adjusted returns than buy-and-hold.
3) The study also analyzes the impact of economic announcement surprises, actual changes, and expected changes. It found that strategies based on actual changes generally had the lowest volatility and performed well even before and after the
This presentation gives you a lot of ideas on how to consistently outperform markets and earn a consistent and growing ROI (Return on Investment) on your investments.
Originally published in 2005. Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions. Delisted companies were included to account for survivorship bias. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system.
"Deep Q-Learning for Trading" by Dr. Tucker Balch, Professor of Interactive C...Quantopian
Reinforcement Learning (RL) has been around for a long time, but it has not attracted much attention over the last decade. Until, that is, a group of Google researchers showed how RL can be used to train a computer to play video games at far above human capabilities.
Besides video games, the RL problem is also well aligned to solve trading problems as well (e.g., work by Dr. Michael Kearns). In this talk, Tucker will provide a gentle introduction to Q-Learning, one of the leading RL methods.
He will also show how Q-Learning can be integrated with artificial neural network learners and how such a system can be used to learn and execute a trading strategy. This is joint work with David Byrd at Georgia Tech.
Why Emerging Managers Now? - Infusion Global Partners WhitepaperAndrei Filippov
Traditional asset classes appear to offer uninspiring beta returns at present, and recent years’ hedge fund returns have disappointed both in magnitude and diversification benefits, likely reflecting capacity pressures associated with the concentration of AUM and inflows with larger funds. We argue that, by contrast, Emerging hedge funds offer a rich opportunity set with far fewer capacity issues where skilled managers with concrete competitive advantages in less efficient, smaller capitalization market segments can generate better, more sustainable and less correlated excess returns. Emerging managers do involve more investment and operational risk than larger peers; to that challenge we offer some suggestions on a thoughtful and rigorous approach to constructing an Emerging Managers allocation and balancing effective due diligence with scalability.
"From Alpha Discovery to Portfolio Construction: Pitfalls and Solutions" by D...Quantopian
From QuantCon 2017: Implementation is the efficient translation of alpha research into portfolios. It includes portfolio construction and trading. It is a vital step in the quant equity workflow, as poor implementation can ruin even the best alpha ideas. Two crucial challenges must be solved: how to construct a portfolio that most efficiently captures a given alpha signal; and, in the presence of multiple signals, how to optimally combine them into a single composite alpha factor.
This talk addresses these challenges, examines common pitfalls in the implementation of quantitative strategies and good practices to avoid them. A common theme is striking the right balance between factor signal purity and investability. We look at how factor models and optimisation techniques help professional investors answer three key questions:
· What risks should your risk model be cognisant of?
· What objective function should you use?
· What effect do investability constraints have on your portfolio?
The document provides information on investment analysis, including definitions, methods, and concepts. It discusses two main types of analysis: fundamental analysis and technical analysis. Fundamental analysis examines basic company data like earnings, sales, and financial statements to determine a stock's intrinsic value. Technical analysis uses historical market data like prices and trading volumes to identify patterns that can predict future price movements. The document also covers the efficient market hypothesis, which proposes that stock prices reflect all publicly available information.
"A Framework for Developing Trading Models Based on Machine Learning" by Kris...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Machine learning is improving facets of our lives as diverse as health screening, transportation and even our entertainment choices. It stands to reason that machine learning can also improve trading performance, however the practical application is fraught with pitfalls and obstacles that nullify the benefits and present a high barrier to entry. Building on background information and introductory material, Kris will propose a framework for efficient and robust experimentation with machine learning methods for algorithmic trading. The framework's objective is to arrive at parsimonious models whose positive past performance is unlikely to be due to chance. The framework is demonstrated via practical examples of various machine learning models for algorithmic trading.
An introduction to implementing 5 basic quant strategies on Quantopian. Presented to the Bay Area Algorithmic Trading Group and the Bay Area Trading Signals meetup groups at the Hacker Dojo Feb 6th, 2014 by Jess Stauth
1. The document analyzes value and growth stocks between 1975-2004, comparing their returns and risks. It finds that value stocks generally outperformed growth stocks over this period.
2. A moving average analysis of the value-growth return spread shows it fluctuated between positive and negative returns with no clear pattern, contradicting the theory that value stocks always outperform. The spreads were also small relative to the portfolios' volatility.
3. Regression analyses found the CAPM model did not accurately predict returns. The growth portfolio underperformed predictions by -0.15% annually, while the value portfolio outperformed by 0.14%, contradicting CAPM. The spread portfolio had low correlation to the market, as
Stock Return Forecast - Theory and Empirical EvidenceTai Tran
The document discusses several models for stock return forecasting including CAPM, the Fama-French three-factor model, a four-factor model with momentum, and a five-factor model including asset growth. Empirical evidence is presented analyzing daily returns of Coca-Cola stock in 2005, finding that momentum is highly significant in predicting returns, while beta is less so. Multi-factor models, particularly the four and five-factor models, provide improved forecasting over CAPM alone, though with increasing complexity. Limitations include selection bias and issues with beta estimation.
The document discusses an event study conducted by a financial analyst to test the semi-strong form of market efficiency. The analyst examined 4 companies that announced dividend increases and calculated the characteristic lines for each company based on weekly returns over the prior 6 years. Abnormal returns were then calculated for each company over the 4 weeks before and after the announcement date. The average abnormal returns and cumulative average abnormal returns were close to zero, supporting the semi-strong form hypothesis that the market incorporated the information of the dividend increases prior to the official announcement.
2012 what drives value tilt portfolios overperformanceFrederic Jamet
- Value tilt portfolios that invest in stocks with low valuations like price-to-book ratios have historically outperformed the overall market. There are various methods to construct value tilt indexes and ETFs.
- There are rational explanations for the outperformance like receiving higher returns for bearing additional market risk, as well as behavioral explanations involving investor overreaction. However, some argue the outperformance could be coincidental and may not continue in the future.
- The document discusses several well-known value indexes from providers like MSCI, FTSE, and Russell, and analyzes the characteristics of a hypothetical value tilt portfolio that outperformed with similar risk to the overall market.
This document summarizes a chapter on corporate financing and market efficiency. It discusses five main topics:
1) Whether financing decisions can create value by examining an example of a provincial loan guarantee.
2) How capital markets are described as efficient when stock prices quickly reflect all available information.
3) The different types of market efficiency: weak form reflects past prices/volume, semi-strong reflects public info, strong reflects all info.
4) Evidence for different forms of efficiency from studies on mutual funds, reaction to announcements, and insider trading regulations.
5) Implications of an efficient market that firm financing cannot affect stock prices through accounting and that issues cannot be timed.
"Is Momentum Still Relevant for Today’s Markets?" by Anthony Ng, Senior LecturerQuantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Despite being ‘discovered’ over 20 years ago, there is still confusion on what a momentum strategy entails and people ‘invest in momentum’. There are two generally accepted definitions of momentum in academic literature. In the quantitative equity investment sphere, momentum is frequently referred to as across securities or assets (cross-sectional or relative) and typically traded in a long-short or hedged manner. In futures trading, momentum is often referred to the past return of the security (time-series) and normally traded in a directional fashion.
Following from the above, we conducted an analysis on the performance of a momentum strategy of different asset classes: equity, fixed income, futures, and currencies. The study showed that both types of momentum are prevalent and persistent across all asset classes. Furthermore, as the correlations between the two types of momentum strategies and amongst the asset classes are quite low, substantial diversification benefit can be derived by combining them.
The document discusses the efficient market hypothesis which holds that current stock prices fully reflect all available public information. It describes different levels of market efficiency and the random walk theory that stock prices move randomly. The document notes that technical analysis which tries to predict prices from past trends has failed, while broad market indexes are difficult for professionals to consistently beat. Index funds are recommended as they match market returns over the long run.
This document provides an overview of spread trading strategies in the US Treasury market. It defines spread trading as taking long and short positions in different futures contracts to profit from perceived mispricing. The document discusses why spread trading requires lower margins and forces traders to think in terms of price targets. It provides examples of common spread trading strategies like intermarket, calendar, butterfly, and condor spreads. It also addresses frequently asked questions about spread trading and lists topics covered in the accompanying yield curve trading strategies course.
This short course introduces novice traders to spread trading strategies on the US Treasury futures market. . Answers to questions relating to the yield curve, fixed income markets, and economic macro-fundamentals are offered.
The document discusses different approaches to investing, including passive vs active investing, fundamental vs technical analysis, and top-down vs bottom-up strategies. It provides beliefs, methods, advantages and disadvantages for each approach. The key points are that a top-down, technically-focused approach analyzing broad market and sector trends first may provide an edge over focusing solely on individual companies. The "Tortoise strategy" described uses ETFs in a weekly top-down analysis of global markets to identify relatively strong performing regions.
Black Swan Event and How to Prepare for ItSamir Halim
This document discusses preparing for a potential "Black Swan" market event and strategies for market timing. It suggests that while impossible to perfectly predict, active managers can see warnings through indicators on multiple timeframes. The author advocates diversifying across asset classes and holdings, scaling positions, following multiple indicators, and building cash reserves. An example portfolio combines equity and volatility holdings across systems to produce stable returns with minimal drawdowns compared to buy-and-hold. The document also covers relative vs absolute returns and discusses market timing approaches and limitations.
Algorithmic strategy with adoptable trading frequency, effectively works with relatively inefficient markets. To the attention of potential investors/partners.
Pokfulam investments:A Model of Equity Market PricingPeter Rice
Pokfulam Investments has developed a unique investment process based on new research in equity analysis and forecasting. This allows them to reduce costs, increase performance consistency, and gain a competitive advantage. The process is based on doctoral work and has achieved excess returns of up to 20% annually in various markets. Pokfulam plans to expand the process globally over the next 7-10 years. The key aspects of the process are a new way of quantifying capital gain expectations, risk measurement, and understanding the impact of dividends on pricing.
This document summarizes a statistical arbitrage strategy that evaluates mean reversion in stock prices over time. It describes the strategy's assumptions that stock prices temporarily diverge from their equilibrium relative to the market before reverting. The experiment uses S&P 500 stock data to calculate daily returns, correlations, betas and residuals over rolling 60-day windows. When residuals exceed +/-2 standard deviations, positions are taken assuming reversion will occur. While backtested returns are appealing, live trading realities like transaction costs and limited share availability would likely reduce profits versus this theoretical analysis.
The document discusses an investment strategy that utilizes quantitative techniques to generate alpha from multiple uncorrelated signals. It examines factors like valuations, momentum, and reversions across equities to construct a market neutral portfolio. The strategy aims to maximize returns while minimizing risks by optimizing weights between the various alpha signals. It takes a rules-based approach to ranking stocks and implementing the portfolio.
The document provides risk disclosures and information about trading systems called Checkmate, Synergy, Fusion, and Interplay from Strategic Trading Systems, Inc. It discusses the high risks of commodity trading and that past performance results are hypothetical. It also summarizes the concepts and logic behind the Checkmate and Synergy trading systems, provides examples of trades from the systems, and evaluates their historical performance based on backtesting results.
- Arbitrage funds aim to generate returns by exploiting short-term price differences between the cash and futures markets for the same asset. They adopt strategies like stock spot-futures arbitrage and index arbitrage.
- Compared to other short-term debt funds, arbitrage funds offer tax benefits as they are considered equity funds. Returns are generally stable with low risk. However, most schemes levy exit loads if redeemed within 3 months.
- Top performing schemes over the last 1, 3 and 5 years included Reliance Arbitrage Advantage, ICICI Pru Equity Arbitrage and SBI Arbitrage Opportunities. While returns are similar to liquid funds over the long run
The document describes an intraday equities trend system that aims to identify liquid stocks exhibiting volatility and momentum for short-term trading. The system uses a proprietary technique to select stocks in a favorable "volatility and liquidity sweet spot" and takes long positions in strong stocks and short positions in weak stocks. Hypothetical backtested performance from 2007-2008 showed gains of 9% in high volatility periods and losses of 5% in low volatility periods, with the strategy aiming to return 15-20% annually while limiting drawdowns.
This is a Presentation by Mr. Omkar Godbole & Mr. Aditya Dasgupta, for the purpose of financial training.
Please do not replicate without proper consent from the team of Total Package Project Associates.
Total Package Project Associates is into Business Auxiliary Services, and facilitates for the main dimensions for new and existing businesses. The following are our main activities :
- Project Financing / Financial Advisory (Equity / Currency Segment)
- IT Infrastructure Development
- Marketing Solutions
- Recruitment / Training & Development
- Operations Strategy (Start Up, Doc ! Project Blueprints)
Disclaimer : Total Package Project Associates is a proprietary concern registered in accordance with Municipal Laws of Mumbai (Maharashtra, India). For more information please mail us on director@totalpackageprojects.com or aditya@totalpackageprojects.com.
Forex trading may appear to be both difficult and dangerous. Some even believe that one cannot win in trading without the ability to comprehend complex charts. This is not to be the case. There are various profitable simple Forex strategy
Forex trading may appear to be both difficult and dangerous. Some even believe that one cannot win in trading without the ability to comprehend complex charts. This is not to be the case . There are various profitable simple Forex strategy
How AI learnt large-scale Pair-Trading on S&P 500? (Updated)Kamer Ali Yuksel
This presentation demonstrates the fascinating ability of (Deep Reinforcement Learning) AI to evolve market-neutral, risk-sensitive, commission-avoidant and market-agnostic Financial Portfolio Management strategies, which are able to avoid transaction costs and generalize out-of-sample periods on both FX and US-stock markets, by acting very similar to the industry-standard pair-trading and performing that strategy efficiently on a very large-scale via learning and exploiting correlations among complex hierarchical clusters of financial assets.
Managed futures involve professional money managers investing in futures contracts across various markets like energy, agriculture, currencies, and equities using techniques like fundamentals analysis, technical analysis, arbitrage, or algorithms. A study found that including a managed futures index in a portfolio increased returns and reduced risk compared to only including stocks. Managed futures provide diversification benefits and can hedge against various economic risks due to investing across global markets and using different strategies.
This document discusses a study analyzing the historical fair value of foreign exchange (FX) options. It examines daily option premium and payout data for various currency pairs and tenors going back to 1995. The study finds that short-dated FX options tend to be overpriced, while long-dated options offer better value. It presents analysis showing the premium, forward point contribution, and actual spot contribution to returns for carry trades. The document also discusses how to calculate option values using Black-Scholes and the costs to include, and considers what results might indicate options are fairly or unfairly priced.
The document summarizes research on the performance of trend-following investing across global markets from 1903 to 2012. Key findings include:
1) Trend-following strategies have delivered consistently strong positive returns each decade for over a century, with low correlation to traditional assets.
2) Trend-following strategies performed best during large equity market declines, helping diversify traditional portfolios.
3) Backtesting shows that allocating 20% of a 60% stock/40% bond portfolio to trend-following from 1903 to 2012 would have increased returns, lowered volatility, and reduced maximum drawdown.
Forex trading strategies describe how you enter and exit transactions using technical indicators to identify critical price levels. While there are hundreds of techniques to choose from, we’ve produced a list of the most popular forex trading strategies.
Similar to Advance IQ Capital Quantitative Models (20)
In a tight labour market, job-seekers gain bargaining power and leverage it into greater job quality—at least, that’s the conventional wisdom.
Michael, LMIC Economist, presented findings that reveal a weakened relationship between labour market tightness and job quality indicators following the pandemic. Labour market tightness coincided with growth in real wages for only a portion of workers: those in low-wage jobs requiring little education. Several factors—including labour market composition, worker and employer behaviour, and labour market practices—have contributed to the absence of worker benefits. These will be investigated further in future work.
1. Elemental Economics - Introduction to mining.pdfNeal Brewster
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Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
Solution Manual For Financial Accounting, 8th Canadian Edition 2024, by Libby...Donc Test
Solution Manual For Financial Accounting, 8th Canadian Edition 2024, by Libby, Hodge, Verified Chapters 1 - 13, Complete Newest Version Solution Manual For Financial Accounting, 8th Canadian Edition by Libby, Hodge, Verified Chapters 1 - 13, Complete Newest Version Solution Manual For Financial Accounting 8th Canadian Edition Pdf Chapters Download Stuvia Solution Manual For Financial Accounting 8th Canadian Edition Ebook Download Stuvia Solution Manual For Financial Accounting 8th Canadian Edition Pdf Solution Manual For Financial Accounting 8th Canadian Edition Pdf Download Stuvia Financial Accounting 8th Canadian Edition Pdf Chapters Download Stuvia Financial Accounting 8th Canadian Edition Ebook Download Stuvia Financial Accounting 8th Canadian Edition Pdf Financial Accounting 8th Canadian Edition Pdf Download Stuvia
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
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2. The person who says it cannot be done
should not interrupt the person doing it.
Old Chinese Proverb
3. Today, we stand at an extraordinary time in financial markets. Electronic
round-the-clock trading has been a reality in many markets for at least
10 years. Technology, trading costs and the ability to perform complex
quantitative analysis have improved dramatically through this period.
Moreover, during this time, various Equity, Commodity and FX markets
have gone through a few cycles each.
This allows the study of present-day markets to encompass innovations
in technology, expansion of choices and a substantial range of market
conditions. Therefore, we can be more comprehensive than ever in our
search for workable strategies.
4. Introduction to Andrew Palashewsky, CMT and Partners
Primary Concepts of the Advance IQ Capital model
Primary Problems of the model
Phases of the Markets
Application of the Model
Risks
Testing of the Model
Purpose
Advantages
5. 2008 – Present: Independent Trader and Founder of Advance IQ
Capital. Developer of Advance IQ Capital Model
2007 – 2008: Senior Analyst at QAS (Quantitative Analysis
Services)
2000 – 2006: Senior Financial Analyst at Pershing BNY
1995 – 2000: VP and Director Investment Strategy at First Liberty
Investment Group
1993 – 1995: Financial Advisor
1981 – 1993: Account Executive at Janney Montgomery Scott
6. Andrew Palashewsky, CMT began building algorithmic
models in 2011 to quantify an investment philosophy honed
over decades as an investment professional and analyst to
emulate a multi-year period of extraordinary trading
productivity.
Beginning in 2010, electronic trading, 24-hr markets and
catalytic events from different time zones highlighted the
need for a logarithmic strategy to synthesize and objectify
the prior discretionary approach.
The Advance IQ Capital Model is the summation of this
work, using proprietary price momentum measurements
that expand and contract based on market conditions.
7. Excessive leverage is the long-term enemy of performance.
If price direction is understood, sizable returns can be
captured without much leverage.
If not, amplifying small returns with high leverage can
backfire spectacularly.
Low leverage maintains integrity of capital.
Low leverage means that much more capital can
effectively be deployed without disrupting markets.
8. Model envisions using 1 to 3x leverage on commodity
futures.
Currency contracts are less volatile, so leverage of up to
5x can be used while maintaining risk profile.
ETFs can be margined 2x or left unleveraged.
9. Huge macro theme bets may be very profitable if the PM is
correct in his predictions.
Many PMs who got one major theme right have had trouble
with subsequent major theme victories.
Solution
The Model adapts to changing conditions.
Model typically produces 15 changes in direction each year.
There is less room for entrenched ego to hamper results.
10. Selected investment instruments are selected for their
tendency to trend well and make broad swings in price.
Solution
Commodity futures contracts, FX and related ETFs are
prime instruments.
Broad market ETFs and Futures are poor choices because
they aggregate sector rotation and, thus, tend to exhibit
choppier price movements.
11. Momentum based systems produce
Too many sells on the way up
Too many buys on the way down
Image shows our unfiltered sells
Great from peak to through
Poor in advancing markets
Solution
• Suppress sells on the way up and suppress buys on the way down.
• To program Hyper-Redundancy for buys on the way up as well as
Hyper-Redundancy for sells on the way down.
• Image shows Hyper-Redundant sells, but without signal suppression.
12. Traders often employ a variety of tools such as RSI, MACD, ROC
and other common and widely accepted oscillators.
It is difficult to harmonize them when indications conflict. This
becomes a discretionary guessing game.
Solution
We have developed one singularly unified proprietary smoothed
momentum indicator configured to:
Emulate time intervals ranging from shorter to much longer in
time and from rapid to much slower in movement.
These different intervals of time harmonize naturally, allowing
robust and efficient rule design.
13. Lag in indicators
Late entries and late exits
Vertical lines show lateness
in crossovers on Momentum
indicator – Not Accurate
Solution
• Longer curves, as shown above, help to define market condition.
• Best buys register when curves are declining.
• Best sells register as curves are rising.
• Employing curves allows an experienced quantitative analyst to
measure acceleration and deceleration.
14. Choppy, trendless markets:
Generally difficult to
trade
Confuse momentum tools
Erase gains made in prior
trends
Solution
• Our proprietary trending indicator rises when markets trend
and declines when markets enter into trading ranges.
• In trading ranges: underweight position, employ no leverage or
do not trade.
• In trends: overweight position and/or expand leverage.
15. Most momentum-based trading tools fail because of a one-size-fits-all
approach to all market conditions.
Solution
We break market movements down into four basic phases:
1. Rising – accelerating
2. Rising – decelerating
3. Declining - accelerating
4. Declining – decelerating
What works very well in one phase may not work in another. Each
phase requires its own set of rules.
Our proprietary momentum curves allow us to mathematically define
these phases and exhaustively back-test their respective rules.
16. We employ price momentum across a variety of time Intervals.
Hence – Interval-Quant
Momentum readings are smoothed. Their harmonized
relationship to each other is the model’s “secret.”
Longer time frame curves provide context to define how rules
are set.
All rules are objectively and quantitatively tested, then refined
over a 12-year period – sufficient to capture a few market cycles.
These rules produce the buy and sell signals.
17. We have developed models for the following assets as of 12/31/14
Futures Strategy applicable to ETFs
1. AD Australian Dollar
2. JY Japanese Yen
3. C Corn
4. W Wheat
5. S Soybeans
6. KC Coffee
7. OIH Market Vectors Oil Services ETF
8. EEM iShares MSCI Emerging Markets ETF
9. EPI Wisdom Tree India Earnings ETF
10. EWT iShares MSCI Taiwan ETF
11. EWH iShares MSCI Hong Kong ETF
12. RSX Market Vectors Russia ETF
1. CL Light Crude Oil
2. SI Silver
3. GC Gold
4. DX US Dollar
5. EC Euro
6. USDCHF US Dollar vs. Swiss Franc
USO United States Oil Fund LP
SLV iShares Silver Trust
GLD SPDR Gold Trust
UUP Powershares US Dollar Bull and
UUD US Dollar Bear
Candidates identified for further model development:
Futures ETFs
18. Models have been extensively tested across 12 years of data history on
selected Futures and FX contracts.
Multiple bullish and bearish cycles and conditions
Models assume taking both Long and Short positions.
Model overlaid onto appropriate Exchange Traded Funds (ETF).
ETFs have shorter data histories, allowing 7 to 9 year tests
Tests assume commissions and slippage.
Tests assume changing position size only at the beginning of each year.
Position size is Beginning Value/ Share Price
Test results are hypothetical and are not the result of trading in real
money. Recent results, however, are in real-time.
Sharpe Ratios, Max Drawdown and Equity Curves can be shown and
discussed in face to face meetings.
19. Primary risk is that a major move can unfold without being
preceded by an appropriate signal.
mitigated by stop loss techniques
Market volatility can change in a way that is outside the
“harmonic resonance” of the model.
Trading ranges are volatile and open to interpretation, often
producing a series of losses.
possibly mitigated by trending indicator
Major unexpected events such as a terrorist attack or Tsunami
could close the markets for a time – after which, re-opening
dislocations across markets would be anyone’s guess.
mitigated by low to non-existent leverage.
20. Crude oil has moved from $60 per barrel to $150, back to $35, then to $110,
now to $47 per barrel.
Silver has moved from $16 per ounce to $50 and is now back to $16 per
ounce.
Gold has been less volatile than silver, but equally dramatic.
The last eight years, the period covered by the following tests, have
seen swings that are historically extraordinary.
These wide swings amplify the exceptional results in testing in these
three commodities.
However, such vast swings may not be repeated in the coming eight
years. Fortunately, the model appears to work very well with smaller
amplitude swings. The US Dollar model reflects the strategy’s potential
within a lower volatility environment.
21. Shares(adj) Pr./Sh Beg Value Profit Ending Value BchMk Model
5/7/08 4365 $ 22.91 $ 100,002 12/31/08 $ 10,999 $ 111,001 7.8% 11.0%
1/1/09 4496 $ 24.69 $ 111,001 12/31/09 $ 31,967 $ 142,968 -6.5% 28.8%
1/1/10 6194 $ 23.08 $ 142,968 12/31/10 $ 42,924 $ 185,892 -1.6% 30.0%
1/1/11 8185 $ 22.71 $ 185,892 12/31/11 $ 46,654 $ 232,546 -1.9% 25.1%
1/1/12 10442 $ 22.27 $ 232,546 12/31/12 $ 35,085 $ 267,631 -2.1% 15.1%
1/1/13 12271 $ 21.81 $ 267,631 12/31/13 $ 53,624 $ 321,255 -1.3% 20.0%
1/1/14 14928 $ 21.52 $ 321,255 12/31/14 $ 30,304 $ 351,559 11.4% 9.4%
12/31/14 14667 $ 23.97 $ 351,559
Total Change in UUP Yr Ending No of Trades % Profitable
5/7/08 - 12/31/14 4.6% 12/31/08 16 62.5%
12/31/09 14 64.3%
Total Rate of Return in UUP 12/31/10 17 70.6%
For Model 5/7/08 - 12/31/14 251.6% 12/31/11 14 85.7%
12/31/12 19 73.7%
Average Change in UUP 12/31/13 21 66.7%
5/7/08 - 12/31/14 0.8% 12/31/14 17 58.8%
Average Rate of Return in UUP Average 17 68.9%
For Model 5/7/08 - 12/31/14 19.9%
This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
22. This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
23. Shares(adj) Pr./Sh Beg Value Profit Ending Value BchMk Model
7/18/07 1790 $ 55.90 $ 100,061 12/31/07 $ 21,918 $ 121,979 35.5% 21.9%
1/1/08 1610 $ 75.76 $ 121,979 12/31/08 $ 213,550 $ 335,529 -56.3% 175.1%
1/1/09 10137 $ 33.10 $ 335,529 12/31/09 $ 405,885 $ 741,414 18.7% 121.0%
1/1/10 18875 $ 39.28 $ 741,414 12/31/10 $ 370,705 $ 1,112,119 -0.7% 50.0%
1/1/11 28516 $ 39.00 $ 1,112,119 12/31/11 $ 572,601 $ 1,684,720 -2.3% 51.5%
1/1/12 44207 $ 38.11 $ 1,684,720 12/31/12 $ 887,676 $ 2,572,396 -12.4% 52.7%
1/1/13 77087 $ 33.37 $ 2,572,396 12/31/13 $ 990,567 $ 3,562,963 5.8% 38.5%
1/1/14 100877 $ 35.32 $ 3,562,963 12/31/14 $ 2,681,311 $ 6,244,274 -42.4% 75.3%
12/31/14 306693 $ 20.36 $ 6,244,274
Total Change in USO Yr Ending No of Trades % Profitable
7/18/07 -
12/31/14 -63.6% 12/31/07 17 37.5%
12/31/08 9 77.8%
Total Rate of Return in USO 12/31/09 9 88.9%
For Model
7/18/07 -
12/31/14 6140.5% 12/31/10 17 76.5%
12/31/11 13 61.5%
Average Change in USO 12/31/12 13 84.6%
7/18/07 -
12/31/14 -6.8% 12/31/13 20 55.0%
12/31/14 17 76.5%
Average Rate of Return in USO
For Model
7/18/07 -
12/31/14 73.2% Average 14 69.8%
This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
24. This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
25. Shares(adj) Pr./Sh Beg Value Profit Ending Value BchMk Model
7/18/07 7500 $ 13.33 $ 99,975 12/31/07
$
(13,563) $ 86,412 10.3% -13.6%
1/1/08 5878 $ 14.70 $ 86,412 12/31/08 $ 94,669 $ 181,081 -23.8% 109.6%
1/1/09 16168 $ 11.20 $ 181,081 12/31/09 $ 185,835 $ 366,916 47.7% 102.6%
1/1/10 22184 $ 16.54 $ 366,916 12/31/10 $ 473,322 $ 840,238 82.5% 129.0%
1/1/11 27841 $ 30.18 $ 840,238 12/31/11 $ 1,459,909 $ 2,300,147 -10.7% 173.7%
1/1/12 85380 $ 26.94 $ 2,300,147 12/31/12 $ 1,187,545 $ 3,487,692 9.0% 51.6%
1/1/13 118750 $ 29.37 $ 3,487,692 12/31/13 $ 2,526,933 $ 6,014,625 1.9% 72.5%
1/1/14 201024 $ 29.92 $ 6,014,625 12/31/14 $ 2,506,672 $ 8,521,297 -49.7% 41.7%
12/31/14 565823 $ 15.06 $ 8,521,297
Total Change in SLV Yr Ending No of Trades % Profitable
7/18/07 -
12/31/14 13.0% 12/31/07 11 27.3%
12/31/08 15 66.7%
Total Rate of Return in SLV 12/31/09 17 58.8%
For Model
7/18/07 -
12/31/14 8423.4% 12/31/10 15 86.7%
12/31/11 15 73.3%
Average Change in SLV 12/31/12 16 50.0%
7/18/07 -
12/31/14 8.4% 12/31/13 12 91.7%
12/31/14 17 64.7%
Average Rate of Return in SLV
For Model
7/18/07 -
12/31/14 83.4% Average 15 64.9%
This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
26. This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
27. Shares(adj) Pr./Sh Beg Value Profit Ending Value BchMk Model
2/22/06 1807 $ 55.34 $ 99,999 12/31/06 $ 56,577 $ 156,576 14.2% 56.6%
1/1/07 2477 $ 63.21 $ 156,576 12/31/07 $ 39,458 $ 196,034 30.5% 25.2%
1/1/08 2377 $ 82.46 $ 196,034 12/31/08 $ 132,826 $ 328,860 4.7% 67.8%
1/1/09 3808 $ 86.35 $ 328,860 12/31/09 $ 164,010 $ 492,870 23.8% 49.9%
1/1/10 4609 $ 106.93 $ 492,870 12/31/10 $ 210,677 $ 703,547 29.7% 42.7%
1/1/11 5072 $ 138.72 $ 703,547 12/31/11 $ 569,433 $ 1,272,980 9.6% 80.9%
1/1/12 8375 $ 151.99 $ 1,272,980 12/31/12 $ 125,038 $ 1,398,018 6.6% 9.8%
1/1/13 8629 $ 162.02 $ 1,398,018 12/31/13 $ 646,484 $ 2,044,502 -28.8% 46.2%
1/1/14 17718 $ 115.39 $ 2,044,502 12/31/14 $ 463,503 $ 2,508,005 -1.6% 22.7%
12/31/14 22081 $ 113.58 $ 2,508,005
Total Change in GLD Yr Ending No of Trades % Profitable
2/22/06 -
12/31/14 105.2% 12/31/06 9 66.7%
12/31/07 21 52.4%
Total Rate of Return in GLD 12/31/08 18 66.7%
For Model
2/22/06 -
12/31/14 2408.0% 12/31/09 10 90.0%
12/31/10 16 81.3%
Average Change in GLD 12/31/11 9 100.0%
2/22/06 -
12/31/14 9.9% 12/31/12 21 38.1%
12/31/13 20 75.0%
Average Rate of Return in GLD 12/31/14 13 53.9%
For Model
2/22/06 -
12/31/14 44.6%
Average 14 61.9%
This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
28. This is not a solicitation to buy or sell securities. Nor does it constitute advice.
Past performance does not guarantee future results.
29. Signals register at end of the daily session and holding periods
range from days to months, giving us ample time to:
Analyze each signal against prior successful signals of its type to
check for anomalies before making a transaction; and
Enter into and exit positions gradually – allowing for larger position
sizes.
Model exhibits strong consistency across a range of selected
asset classes and market cycles.
Low leverage allows for larger position sizing than would
otherwise be possible. As presently envisioned, given the
variety of Futures markets and ETFs, liquidity is sufficient to
accommodate eventual capacity of between $500 MM to $1B in
net capital without unduly disturbing the market.
30. Model has been developed for a primary partnership with a select
Long/Short, Macro or Multi-Strategy Hedge Fund.
Its primary purpose is capital management – not subscriptions or
advise columns.
Advance IQ Capital is actively searching for the right relationship
partner.
Such a partner would prefer to establish an equity position in an
already developed model rather than invest millions of dollars and
years in time to reverse engineer or to develop something similar.