#finance #algotrading #ml #businessidea
Эрни Чан - бывший сотрудник хедж-фонда, уже много лет как частный трейдер, автор 3 хороших книг и множества лекций. Очень интересно наблюдать эволюцию его взглядов на трейдинг и применимость ML в финансах. Идея об уходе от общих таргетов заслуживает внимания и проверки, тем более что о ней же говорит и ДеПрадо (в его книге эта техника называется "метаразметкой").
https://www.youtube.com/watch?v=nAOjbL5bsjA
Книга Quantitative Trading: How to Build Your Own Algorithmic Trading Business https://www.amazon.com/gp/product/B097QGPVND/
Ещё у него есть активный блог: http://epchan.blogspot.com/
Эрни Чан - бывший сотрудник хедж-фонда, уже много лет как частный трейдер, автор 3 хороших книг и множества лекций. Очень интересно наблюдать эволюцию его взглядов на трейдинг и применимость ML в финансах. Идея об уходе от общих таргетов заслуживает внимания и проверки, тем более что о ней же говорит и ДеПрадо (в его книге эта техника называется "метаразметкой").
https://www.youtube.com/watch?v=nAOjbL5bsjA
Книга Quantitative Trading: How to Build Your Own Algorithmic Trading Business https://www.amazon.com/gp/product/B097QGPVND/
Ещё у него есть активный блог: http://epchan.blogspot.com/
YouTube
What is Corrective AI and how it can improve your investment decisions | Dr Ernest Chan
Dr Ernest Chan introduces us to the concept of corrective artificial intelligence and its applications in the financial markets.
-----------------------------------------
Chapters:
00:00 Introduction
02:27 What is corrective AI?
07:23 ML for risk management…
-----------------------------------------
Chapters:
00:00 Introduction
02:27 What is corrective AI?
07:23 ML for risk management…
#finance #optimalstopping
Вау, сам Альберт Николаевич Ширяев. Применяет теорию оптимальной остановки случайного процесса к задаче buy & hold. Что интересно, в области ООП работал в своё время даже... Березовский )
https://www.youtube.com/watch?v=Hv1iZhYvfoE
Вау, сам Альберт Николаевич Ширяев. Применяет теорию оптимальной остановки случайного процесса к задаче buy & hold. Что интересно, в области ООП работал в своё время даже... Березовский )
https://www.youtube.com/watch?v=Hv1iZhYvfoE
YouTube
#ЦМФ Альберт Николаевич Ширяев, академик (Мехмат МГУ): Стратегия Buy&Hold #Финансовая_математика
Альберт Николаевич Ширяев — ученик А. Н. Колмогорова, заведующий кафедрой Теории вероятностей Мехмата МГУ, создатель российской школы финансовой математики
0:01 Что такое Buy&Hold?
1:55 Практическая постановка задачи в банке: как продать акцию по максимальной…
0:01 Что такое Buy&Hold?
1:55 Практическая постановка задачи в банке: как продать акцию по максимальной…
#trading #zhang #finance
Недавно постил лекцию Жихонг Жанга по DL на MOB, вы только посмотрите, насколько плодовит данный учёный!
Мама мия, 37 работ, и многие очень интересны уже по заголовкам. А, или то был Жихао. Все молодцы, в общем )
С нетерпением начту прочтение их совместной работы с Хуйлинь Юанем, не побоюсь этого имени, "Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatility".
https://arxiv.org/search/q-fin?searchtype=author&query=Zhang%2C+Z
Недавно постил лекцию Жихонг Жанга по DL на MOB, вы только посмотрите, насколько плодовит данный учёный!
Мама мия, 37 работ, и многие очень интересны уже по заголовкам. А, или то был Жихао. Все молодцы, в общем )
С нетерпением начту прочтение их совместной работы с Хуйлинь Юанем, не побоюсь этого имени, "Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatility".
https://arxiv.org/search/q-fin?searchtype=author&query=Zhang%2C+Z
#trading #finance #lob #dl
Очень интересные открытия: нет следов большой нестационарности (даже для 1 года OOS), 1 модель для всех активов лучше отдельных моделей для каждого актива. Использование моделек с памятью типа LSTM улучшает точность (от 100 до 5000 тиков, к примеру, на 1% - path dependence/long memory effects).
https://www.youtube.com/watch?v=diLtyRg6cl4
Очень интересные открытия: нет следов большой нестационарности (даже для 1 года OOS), 1 модель для всех активов лучше отдельных моделей для каждого актива. Использование моделек с памятью типа LSTM улучшает точность (от 100 до 5000 тиков, к примеру, на 1% - path dependence/long memory effects).
https://www.youtube.com/watch?v=diLtyRg6cl4
YouTube
Universal features of intraday price formation: an exploration via Deep Learning
Live from QuantMinds International, Professor Rama Cont, Professor Of Mathematics And Chair In Mathematical Finance at Imperial College London presents on universal features of intraday price formation, including looking at a Deep Learning approach to analysis…
#entropy #mutualinformation #prediction #finance #trading #causality
https://www.youtube.com/watch?v=tdWh7agQfTk
https://www.youtube.com/watch?v=tdWh7agQfTk
YouTube
Entropy, Mutual Information & Prediction
University of Illinois at Springfield Mathematics Colloquium page: https://www.uis.edu/math/colloquium
Title : Entropy, Mutual Information & Prediction
Speaker: Doug Hamilton
Date: April 13th, 2023
Location: University of Illinois Springfield (Mathematics…
Title : Entropy, Mutual Information & Prediction
Speaker: Doug Hamilton
Date: April 13th, 2023
Location: University of Illinois Springfield (Mathematics…
#finance
Это просто бриллиант какой-то. Абсолютно бесполезная по содержанию лекция, но докладчик посетил курсы ораторского мастерства. Как будто в театр сходил! На 20й минуте заснул. Советую против бессонницы.
https://www.youtube.com/watch?v=z-HHsUfc1C8
Это просто бриллиант какой-то. Абсолютно бесполезная по содержанию лекция, но докладчик посетил курсы ораторского мастерства. Как будто в театр сходил! На 20й минуте заснул. Советую против бессонницы.
https://www.youtube.com/watch?v=z-HHsUfc1C8
YouTube
Frontiers of Factor Investing with Andrew Ang | ESMT Berlin
In the ESMT Open Lecture "Frontiers of Factor Investing" with Andrew Ang, Head of Factor Investing Strategies, BlackRock, he addresses four main topics:
• Are we at capacity in factor investing?
• Should we time factor exposures?
• Factors in illiquid…
• Are we at capacity in factor investing?
• Should we time factor exposures?
• Factors in illiquid…
#jobs #finance #trading
Нашёл классный сайт efinancialcareers.com Скандалы, интриги, расследования в мире финансов.
https://www.efinancialcareers.com/news/ken-griffin-citadel
Нашёл классный сайт efinancialcareers.com Скандалы, интриги, расследования в мире финансов.
https://www.efinancialcareers.com/news/ken-griffin-citadel
eFinancialCareers
Ken Griffin, Citadel's blue-eyed boss, and his talent machine
Ken Griffin has mechanized the production of winners.
#finance #trading #timeseries #lstm #rv
Интересная мысль, что для рекуррентных сетей input dimension начинает играть роль гиперпараметра (причём важного).
https://www.youtube.com/watch?v=lc8qKP8yH3E
Интересная мысль, что для рекуррентных сетей input dimension начинает играть роль гиперпараметра (причём важного).
https://www.youtube.com/watch?v=lc8qKP8yH3E
YouTube
Герман Родиков | Predicting specific time series by blending deep learning techniques
Спикер: Герман Родиков, University of Bologna
Тема доклада: Predicting specific time series by blending deep learning techniques with domain-specific knowledge
Data Fest 2024: https://ods.ai/events/datafest2024
Презентацию к докладу Вы можете скачать в…
Тема доклада: Predicting specific time series by blending deep learning techniques with domain-specific knowledge
Data Fest 2024: https://ods.ai/events/datafest2024
Презентацию к докладу Вы можете скачать в…
#emh #finance #physics
Внезапно увидел аналогию в финансах, машинном обучении и физике. Есть такая EMH, гипотеза эффективного рынка. А кто угадает, о какой аналогии я подумал в машинном обучении и даже, если разобраться, в астрофизике? Какие там существуют похожие концепции?
Внезапно увидел аналогию в финансах, машинном обучении и физике. Есть такая EMH, гипотеза эффективного рынка. А кто угадает, о какой аналогии я подумал в машинном обучении и даже, если разобраться, в астрофизике? Какие там существуют похожие концепции?
Wikipedia
Efficient-market hypothesis
economic theory that asset prices fully reflect all available information, so that it is impossible to "beat the market" consistently on a risk-adjusted basis
#emh #finance #physics #nflt #ccc
Ну ладно, раз уж практически всё отгадали, публикую ответ. Конечно же, это ML-теорема об отсутствии бесплатных завтраков (No Free Lunch theorem) и Принцип космической цензуры (Cosmic Censorship Conjecture) Пенроуза.
Вы только подумайте, просматривается связь физики, машинного обучения и поведенческих финансов. Возможно, физику я несколько притягиваю за уши, судите сами. Я попросил сами знаете кого обозначить параллели.
"The Cosmic Censorship Conjecture (CCC) in physics, which suggests that singularities (places where gravity becomes infinite) are hidden behind event horizons, has intriguing parallels with the Efficient Market Hypothesis (EMH) and the No Free Lunch Theorem (NFLT) in machine learning. All three concepts imply that exploitable extremes or shortcuts are fundamentally "censored" by the natural order of the system, and they share some core ideas:
1. Hidden Extremes and Limited Access
Cosmic Censorship Conjecture: Singularities (places with potentially infinite gravitational forces) are hidden behind an event horizon, so they can’t affect or be exploited by an external observer. This keeps the universe predictable and prevents runaway effects from unbounded gravitational fields.
Efficient Market Hypothesis: Similarly, in EMH, any “infinite gain” opportunity is concealed by the rapid incorporation of information into asset prices. Just as a singularity is hidden from view, a perfect arbitrage opportunity (extreme profit) is obscured by market efficiency, making it virtually unreachable.
No Free Lunch Theorem: In machine learning, NFLT states that no model is universally better across all datasets, implying that there are no "shortcuts" that work everywhere. Every dataset poses new challenges, and there’s no access to a "singularity" of knowledge or performance that guarantees success universally.
2. Equilibrium and Stability
Cosmic Censorship Conjecture: By hiding singularities, CCC maintains stability in spacetime and keeps the equations of general relativity well-behaved, creating a predictable universe.
Efficient Market Hypothesis: EMH maintains market stability by ensuring that all known information is reflected in prices, which keeps the market in a kind of equilibrium. When information is immediately priced in, it prevents drastic, destabilizing price swings due to secret information exploitation.
No Free Lunch Theorem: NFLT implies an equilibrium of algorithmic performance: without tailoring models to specific problems, performance can’t exceed random guessing across all tasks. This prevents the dominance of any one algorithm and promotes stability in ML development.
3. Limits on Observability and Exploitability
Cosmic Censorship Conjecture: In CCC, the "naked" singularities (unshielded by an event horizon) are prohibited, meaning that singularities can't be directly observed or interacted with. This places fundamental limits on what can be known about the interior of black holes and ensures that any infinite values are hidden from observers.
Efficient Market Hypothesis: In EMH, since all information is reflected in prices, potential inefficiencies that traders might exploit are hidden or fleeting. This places a limit on how much information a trader can “observe” to gain a consistent edge.
No Free Lunch Theorem: NFLT in ML implies that there's no one-size-fits-all model, so knowledge about one dataset does not translate to others. The unpredictability of the optimal approach for new data is a kind of “observability limit,” preventing universal exploitability of one algorithm.
Ну ладно, раз уж практически всё отгадали, публикую ответ. Конечно же, это ML-теорема об отсутствии бесплатных завтраков (No Free Lunch theorem) и Принцип космической цензуры (Cosmic Censorship Conjecture) Пенроуза.
Вы только подумайте, просматривается связь физики, машинного обучения и поведенческих финансов. Возможно, физику я несколько притягиваю за уши, судите сами. Я попросил сами знаете кого обозначить параллели.
"The Cosmic Censorship Conjecture (CCC) in physics, which suggests that singularities (places where gravity becomes infinite) are hidden behind event horizons, has intriguing parallels with the Efficient Market Hypothesis (EMH) and the No Free Lunch Theorem (NFLT) in machine learning. All three concepts imply that exploitable extremes or shortcuts are fundamentally "censored" by the natural order of the system, and they share some core ideas:
1. Hidden Extremes and Limited Access
Cosmic Censorship Conjecture: Singularities (places with potentially infinite gravitational forces) are hidden behind an event horizon, so they can’t affect or be exploited by an external observer. This keeps the universe predictable and prevents runaway effects from unbounded gravitational fields.
Efficient Market Hypothesis: Similarly, in EMH, any “infinite gain” opportunity is concealed by the rapid incorporation of information into asset prices. Just as a singularity is hidden from view, a perfect arbitrage opportunity (extreme profit) is obscured by market efficiency, making it virtually unreachable.
No Free Lunch Theorem: In machine learning, NFLT states that no model is universally better across all datasets, implying that there are no "shortcuts" that work everywhere. Every dataset poses new challenges, and there’s no access to a "singularity" of knowledge or performance that guarantees success universally.
2. Equilibrium and Stability
Cosmic Censorship Conjecture: By hiding singularities, CCC maintains stability in spacetime and keeps the equations of general relativity well-behaved, creating a predictable universe.
Efficient Market Hypothesis: EMH maintains market stability by ensuring that all known information is reflected in prices, which keeps the market in a kind of equilibrium. When information is immediately priced in, it prevents drastic, destabilizing price swings due to secret information exploitation.
No Free Lunch Theorem: NFLT implies an equilibrium of algorithmic performance: without tailoring models to specific problems, performance can’t exceed random guessing across all tasks. This prevents the dominance of any one algorithm and promotes stability in ML development.
3. Limits on Observability and Exploitability
Cosmic Censorship Conjecture: In CCC, the "naked" singularities (unshielded by an event horizon) are prohibited, meaning that singularities can't be directly observed or interacted with. This places fundamental limits on what can be known about the interior of black holes and ensures that any infinite values are hidden from observers.
Efficient Market Hypothesis: In EMH, since all information is reflected in prices, potential inefficiencies that traders might exploit are hidden or fleeting. This places a limit on how much information a trader can “observe” to gain a consistent edge.
No Free Lunch Theorem: NFLT in ML implies that there's no one-size-fits-all model, so knowledge about one dataset does not translate to others. The unpredictability of the optimal approach for new data is a kind of “observability limit,” preventing universal exploitability of one algorithm.
#emh #finance #physics #nflt #ccc
4. Self-Correcting Systems
Cosmic Censorship Conjecture: If any unshielded singularity exists, it would theoretically destabilize the fabric of spacetime, causing unpredictable consequences. By “shielding” them, CCC ensures that the universe operates as a self-correcting system.
Efficient Market Hypothesis: EMH similarly implies that markets are self-correcting. If someone finds a profit opportunity, the market rapidly adjusts as participants act on it, neutralizing the opportunity—effectively “censoring” it through price adjustments.
No Free Lunch Theorem: NFLT also implies a self-correcting mechanism: the variability of datasets corrects for any over-reliance on a single model. In essence, no shortcut applies universally, so the diversity of data prevents overfitting to any one approach.
5. Conservation of Predictability
Cosmic Censorship Conjecture: By hiding singularities, CCC ensures that the universe remains predictable on a macroscopic level, with singularities hidden in a way that preserves the general structure of spacetime.
Efficient Market Hypothesis: EMH enforces predictability in financial markets by ensuring that prices reflect all known information, preventing “wild” deviations or exploitable patterns that would otherwise exist in an inefficient market.
No Free Lunch Theorem: NFLT reinforces the conservation of predictability by implying that machine learning performance is fundamentally limited by the variability of data. This lack of universal shortcuts keeps machine learning practice tethered to specific, data-driven methods without general predictability.
In essence, Cosmic Censorship Conjecture, Efficient Market Hypothesis, and No Free Lunch Theorem all maintain system stability, equilibrium, and self-correction by “shielding” the unbounded extremes that would otherwise lead to unpredictability and exploitation. They serve as guardrails in complex systems, placing inherent limits on predictability, observability, and exploitability."
4. Self-Correcting Systems
Cosmic Censorship Conjecture: If any unshielded singularity exists, it would theoretically destabilize the fabric of spacetime, causing unpredictable consequences. By “shielding” them, CCC ensures that the universe operates as a self-correcting system.
Efficient Market Hypothesis: EMH similarly implies that markets are self-correcting. If someone finds a profit opportunity, the market rapidly adjusts as participants act on it, neutralizing the opportunity—effectively “censoring” it through price adjustments.
No Free Lunch Theorem: NFLT also implies a self-correcting mechanism: the variability of datasets corrects for any over-reliance on a single model. In essence, no shortcut applies universally, so the diversity of data prevents overfitting to any one approach.
5. Conservation of Predictability
Cosmic Censorship Conjecture: By hiding singularities, CCC ensures that the universe remains predictable on a macroscopic level, with singularities hidden in a way that preserves the general structure of spacetime.
Efficient Market Hypothesis: EMH enforces predictability in financial markets by ensuring that prices reflect all known information, preventing “wild” deviations or exploitable patterns that would otherwise exist in an inefficient market.
No Free Lunch Theorem: NFLT reinforces the conservation of predictability by implying that machine learning performance is fundamentally limited by the variability of data. This lack of universal shortcuts keeps machine learning practice tethered to specific, data-driven methods without general predictability.
In essence, Cosmic Censorship Conjecture, Efficient Market Hypothesis, and No Free Lunch Theorem all maintain system stability, equilibrium, and self-correction by “shielding” the unbounded extremes that would otherwise lead to unpredictability and exploitation. They serve as guardrails in complex systems, placing inherent limits on predictability, observability, and exploitability."
Wikipedia
No free lunch theorem
the theorem that, if a machine-learning algorithm does well on some problems, then it pays for that on all other problems