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吳牧恩教授為數理背景出身、資工博士畢業,其研究領域包括資訊理論與金融資料分析。現為東吳大學數學系助理教授,致力於推廣資金管理的數學與研發金融交易策略,期望破除市場上大眾追求穩賺聖盃的迷思。其亦為知名財金部落格幣圖誌 (Bituzi) 專欄作家 -- 牧清華。
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Philip Zheng
吳牧恩教授為數理背景出身、資工博士畢業,其研究領域包括資訊理論與金融資料分析。現為東吳大學數學系助理教授,致力於推廣資金管理的數學與研發金融交易策略,期望破除市場上大眾追求穩賺聖盃的迷思。其亦為知名財金部落格幣圖誌 (Bituzi) 專欄作家 -- 牧清華。
吳牧恩/一個賭徒的告白 2:交易策略建構與分析,為何你該賭小一點?
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2019 時間序列與量化交易研討會 姜林杰祐教授
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台灣量化交易協會
吳牧恩 (Mu-En Wu) 東吳大學數學系助理教授 從數理專業轉向資訊工程,近年著重於金融資料分析、博弈理論、預測市場等研究。曾就讀於清大數學系、交大應數所,2009 年畢業於清華資工研究所。喜愛探討期權交易知識、熱衷操練鐵人三項。另一身份為幣圖誌專欄〈謀權奪利真英雄〉作家-牧清華。
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Contrary to popular wisdom the difference between a retail quant trader and a professional portfolio manager is not in "having better trade entry and exit rules". Rather it is the difference in how each approaches the concepts of portfolio optimisation and risk management. Both of these topics are synonymous with heavy math, which can be off-putting for beginner retail systematic traders. Hence, it can be extremely daunting for those without institutional experience to know how to turn a set of trading rules into a robust portfolio and risk management system. In this talk, Mike will discuss how to take a typical retail quant strategy and place it in a professional quantitative trading framework, with proper position sizing and risk assessment, without resorting to pages of formulas or the need to have a PhD in statistics!
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Join CMT Level 1, 2 & 3 Program Courses & become a professional Technical Analyst, CMT USA Best COACHING CLASSES. CMT Institute Live Classes by Expert Faculty. Exams are available in India. Best Career in Financial Market. https://www.ptaindia.com/chartered-market-technician/
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RM-CVaR: Regularized Multiple β-CVaR Portfolio(IJCAI Presentation)
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Ch1 教學
Ch1 教學
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"Build Effective Risk Management on Top of Your Trading Strategy" by Danielle...
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Was ist angesagt?
金融交易博大精深,少數人能夠在這市場獲利,大部分人卻總是賠錢。許多有趣的現象也伴隨科技的進步跟著演變。過去股市名嘴喊盤,現在資料科學說話。這就是為何我們要辦這場講座的原因。舉凡交易策略建構、資金管裡的理論與實務,甚至老師最愛說的技術分析,K 線型態,高手最愛強調的盤感、盤感、盤感,說穿了這些都是資料科學的應用。我們希望藉由近幾年資料科學的興起,推廣正確的金融交易知識,教導大家如何分析金融資料。自己的策略自己做,自己的風險自己控,自己的部位自己顧,自己的 $$ 自己賺。在本課程裡,1 就是 1,2 就是 2,40 就是 40 (事實)。沒有怪力亂神,沒有定義不清,一切的一切,統計說話,數據說話!
[DSC 2016] 系列活動:吳牧恩、林佳緯 / 用 R 輕鬆做交易策略分析及自動下單
[DSC 2016] 系列活動:吳牧恩、林佳緯 / 用 R 輕鬆做交易策略分析及自動下單
台灣資料科學年會
我們希望藉由近幾年資料科學的興起,推廣正確的金融交易知識,包括金融資料分析與建立自己的演算法交易事業。自己的策略自己做,自己的風險自己控,自己的部位自己顧。在本課程裡,1 就是 1,2 就是 2,40 就是 40 (事實),沒有怪力亂神,沒有定義不清,一切的一切,統計說話,數據說話!本課程與上一課程 (用 R 輕鬆做交易策略分析及自動下單) 部分重覆,本次除針對理論做更深入講解,亦增加股票、期貨、選擇權開發實務分享。
[系列活動] 使用 R 語言建立自己的演算法交易事業
[系列活動] 使用 R 語言建立自己的演算法交易事業
台灣資料科學年會
Contrary to popular wisdom the difference between a retail quant trader and a professional portfolio manager is not in "having better trade entry and exit rules". Rather it is the difference in how each approaches the concepts of portfolio optimisation and risk management. Both of these topics are synonymous with heavy math, which can be off-putting for beginner retail systematic traders. Hence, it can be extremely daunting for those without institutional experience to know how to turn a set of trading rules into a robust portfolio and risk management system. In this talk, Mike will discuss how to take a typical retail quant strategy and place it in a professional quantitative trading framework, with proper position sizing and risk assessment, without resorting to pages of formulas or the need to have a PhD in statistics!
"A Framework-Based Approach to Building Quantitative Trading Systems" by Dr. ...
"A Framework-Based Approach to Building Quantitative Trading Systems" by Dr. ...
Quantopian
You have created a great trading strategy, backtested, traded it and now you want to take it to the next level. You may find that developing the strategy was just the first of many difficult steps. With the increased availability of low cost, high quality quant modelling platforms, the field is much more open than it once was. The interest for algorithmic trading his higher than ever and anyone has the potential develop a great trading model. But having a great trading model is not enough. The work is not done yet. This presentation will discuss turning your algorithmic trading strategy into a business or a great job, and becoming a professional trader. We’re going to talk about what it takes to move to the next level and where the common pitfalls lay. What kind of strategies are marketable are which are not. The pros and cons of trading your own money and how to go about finding external capital and gaining traction in the business. Are you ready to take the step?
"From Trading Strategy to Becoming an Industry Professional – How to Break in...
"From Trading Strategy to Becoming an Industry Professional – How to Break in...
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.
"Portfolio Optimisation When You Don’t Know the Future (or the Past)" by Rob...
"Portfolio Optimisation When You Don’t Know the Future (or the Past)" by Rob...
Quantopian
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
DIY Quant Strategies on Quantopian
DIY Quant Strategies on Quantopian
Jess Stauth
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th. Quantitative trading is distinguishable from other trading methodologies like technical analysis and analysts’ opinions because it uniquely provides justifications to trading strategies using mathematical reasoning. Put differently, quantitative trading is a science that trading strategies are proven statistically profitable or even optimal under certain assumptions. There are properties about strategies that we can deduce before betting the first $1, such as P&L distribution and risks. There are exact explanations to the success and failure of strategies, such as choice of parameters. There are ways to iteratively improve strategies based on experiences of live trading, such as making more realistic assumptions. These are all made possible only in quantitative trading because we have assumptions, models and rigorous mathematical analysis. Quantitative trading has proved itself to be a significant driver of mathematical innovations, especially in the areas of stochastic analysis and PDE-theory. For instances, we can compute the optimal timings to follow the market by solving a pair of coupled Hamilton–Jacobi–Bellman equations; we can construct sparse mean reverting baskets by solving semi-definite optimization problems with cardinality constraints and can optimally trade these baskets by solving stochastic control problems; we can identify statistical arbitrage opportunities by analyzing the volatility process of a stochastic asset at different frequencies; we can compute the optimal placements of market and limit orders by solving combined singular and impulse control problems which leads to novel and difficult to solve quasi-variational inequalities.
"Quantitative Trading as a Mathematical Science" by Dr. Haksun Li, Founder an...
"Quantitative Trading as a Mathematical Science" by Dr. Haksun Li, Founder an...
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.
Algorithmic trading and Machine Learning by Michael Kearns, Professor of Comp...
Algorithmic trading and Machine Learning by Michael Kearns, Professor of Comp...
Quantopian
It takes hard work, skill and time to develop robust trading models, but that is just the beginning of the journey. The question then is what you can do with it, and how to go about building a career in quant finance. If your plan is to move beyond hobby trading and build a career in in the professional quant trading field, the work is not over once you have a great model. This presentation will discuss how to leverage your trading models into building a successful career in quant trading. We will look at the various options available, and their respective merits and faults. Whether you want to trade your own money for a living, find a job in the industry or build your own business, your model design will have to be adapted to your aim. We will discuss what type of models and results there is a market for, how to go about finding investors for your trading, and how the real economics of the business look.
"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...
"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...
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.
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...
Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th. Even with a wide range of statistical tools available, selection of algorithmic trading strategies can leave the trader with significant out-of-sample variability. In most cases the final decision making is still a manual process. This presentation will show how a combination of statistical methods and machine learning can help to automate strategy selection and boost the robustness of automated trading systems.
Automated Selection and Robustness for Systematic Trading Strategies by Dr. T...
Automated Selection and Robustness for Systematic Trading Strategies by Dr. T...
Quantopian
Return predictability has been a controversial topic in finance for a long time. We show there is substantial predictive power in combining forecasting variables. We apply correlation screening to combine twenty variables that have been proposed in the return predictability literature, and demonstrate forecasting power at a six-month horizon. We illustrate the economic significance of return predictability through a simulation which takes positions in SPY proportional to the model forecast. The simulated strategy yields annual returns more than twice that of the buy-and-hold strategy, with a Sharpe ratio four times as large. This application of big data ideas to return predictability serves to shift the sentiment associated with market timing.
Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016
Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016
Quantopian
Join CMT Level 1, 2 & 3 Program Courses & become a professional Technical Analyst, CMT USA Best COACHING CLASSES. CMT Institute Live Classes by Expert Faculty. Exams are available in India. Best Career in Financial Market. https://www.ptaindia.com/chartered-market-technician/
Asset Relationships - CH 11 - Relative Strength | CMT Level 3 | Chartered Mar...
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Presentation slide for IJCAI2020@2021/1/14
RM-CVaR: Regularized Multiple β-CVaR Portfolio(IJCAI Presentation)
RM-CVaR: Regularized Multiple β-CVaR Portfolio(IJCAI Presentation)
Kei Nakagawa
Ch1 教學
Ch1 教學
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hungchiayang1
From QuantCon 2017: Fundamental and quantitative stock selection research has long focused on creating accurate forecasts of company fundamentals such as earnings and revenues. In this talk we examine why fundamental forecasts are powerful and survey some classic methods for generating these forecasts. Next we explore some newer methodologies which can be effective in generating more accurate fundamental forecasts, including new uses of traditional data as well as novel crowdsourced and online behavior databases. Finally, we present new research examining the temporal variation in efficacy of these forecasts with an eye towards understanding the market conditions in which an accurate fundamental forecast can be more or less profitable.
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlpha
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlpha
Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th. Risk management is an essential but often overlooked prerequisite to success in trading. No one would like to see their substantial profits generated over his lifetime of trading just vanishing over a few bad trades. In this talk, Danielle will discuss a quantitative understanding of risk. She will then share a few techniques in risk management, with a case study to show how a proper risk management system helps improve the overall performance of trading strategies.
"Build Effective Risk Management on Top of Your Trading Strategy" by Danielle...
"Build Effective Risk Management on Top of Your Trading Strategy" by Danielle...
Quantopian
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.
"Is Momentum Still Relevant for Today’s Markets?" by Anthony Ng, Senior Lecturer
"Is Momentum Still Relevant for Today’s Markets?" by Anthony Ng, Senior Lecturer
Quantopian
Section I - CH 1 - System Design and Testing By Professional Training Academy
Section I - CH 1 - System Design and Testing.pdf
Section I - CH 1 - System Design and Testing.pdf
Professional Training Academy
Factor modeling and style premia are historically well documented and extensively researched in generating abnormal returns. Despite the large amount of research around factors, there is less clarity around effectively capturing and extracting this alpha from a given universe. In this presentation, Cheng will demonstrate different techniques for combining multiple factors, and the rationale behind maximizing alpha while maintaining scalability.
"Maximize Alpha with Systematic Factor Testing" by Cheng Peng, Software Engin...
"Maximize Alpha with Systematic Factor Testing" by Cheng Peng, Software Engin...
Quantopian
Was ist angesagt?
(20)
[DSC 2016] 系列活動:吳牧恩、林佳緯 / 用 R 輕鬆做交易策略分析及自動下單
[DSC 2016] 系列活動:吳牧恩、林佳緯 / 用 R 輕鬆做交易策略分析及自動下單
[系列活動] 使用 R 語言建立自己的演算法交易事業
[系列活動] 使用 R 語言建立自己的演算法交易事業
"A Framework-Based Approach to Building Quantitative Trading Systems" by Dr. ...
"A Framework-Based Approach to Building Quantitative Trading Systems" by Dr. ...
"From Trading Strategy to Becoming an Industry Professional – How to Break in...
"From Trading Strategy to Becoming an Industry Professional – How to Break in...
"Portfolio Optimisation When You Don’t Know the Future (or the Past)" by Rob...
"Portfolio Optimisation When You Don’t Know the Future (or the Past)" by Rob...
DIY Quant Strategies on Quantopian
DIY Quant Strategies on Quantopian
"Quantitative Trading as a Mathematical Science" by Dr. Haksun Li, Founder an...
"Quantitative Trading as a Mathematical Science" by Dr. Haksun Li, Founder an...
Algorithmic trading and Machine Learning by Michael Kearns, Professor of Comp...
Algorithmic trading and Machine Learning by Michael Kearns, Professor of Comp...
"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...
"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...
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Automated Selection and Robustness for Systematic Trading Strategies by Dr. T...
Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016
Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016
Asset Relationships - CH 11 - Relative Strength | CMT Level 3 | Chartered Mar...
Asset Relationships - CH 11 - Relative Strength | CMT Level 3 | Chartered Mar...
RM-CVaR: Regularized Multiple β-CVaR Portfolio(IJCAI Presentation)
RM-CVaR: Regularized Multiple β-CVaR Portfolio(IJCAI Presentation)
Ch1 教學
Ch1 教學
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlpha
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlpha
"Build Effective Risk Management on Top of Your Trading Strategy" by Danielle...
"Build Effective Risk Management on Top of Your Trading Strategy" by Danielle...
"Is Momentum Still Relevant for Today’s Markets?" by Anthony Ng, Senior Lecturer
"Is Momentum Still Relevant for Today’s Markets?" by Anthony Ng, Senior Lecturer
Section I - CH 1 - System Design and Testing.pdf
Section I - CH 1 - System Design and Testing.pdf
"Maximize Alpha with Systematic Factor Testing" by Cheng Peng, Software Engin...
"Maximize Alpha with Systematic Factor Testing" by Cheng Peng, Software Engin...
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計量化交易策略的開發與運用 法人版
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Flash Order 的操作方式
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