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.
"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 Framework-Based Approach to Building Quantitative Trading Systems" by Dr. ...Quantopian
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!
"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?
"From Trading Strategy to Becoming an Industry Professional – How to Break in...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?
"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...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.
Should You Build Your Own Backtester? by Michael Halls-Moore at QuantCon 2016Quantopian
The huge uptake of Python and R as first-class programming languages within quantitative trading has lead to an abundance of backtesting libraries becoming widely available. It can take months, if not years, to develop a robust backtesting and trading infrastructure from scratch and many of the vendors (both commercial and open source) have a huge head start. Given such prevalence and maturity of the available software, as well as the time investment needed for development, is there any benefit to building your own?
In this talk, Mike will argue the advantages and disadvantages of building your own infrastructure, how to develop and improve your first backtesting system and how to make it robust to internal and external risk events. The talk will be of interest whether you are a retail quant trader managing your own capital or are forming a start-up quant fund with initial seed funding.
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
"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 Framework-Based Approach to Building Quantitative Trading Systems" by Dr. ...Quantopian
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!
"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?
"From Trading Strategy to Becoming an Industry Professional – How to Break in...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?
"Quant Trading for a Living – Lessons from a Life in the Trenches" by Andreas...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.
Should You Build Your Own Backtester? by Michael Halls-Moore at QuantCon 2016Quantopian
The huge uptake of Python and R as first-class programming languages within quantitative trading has lead to an abundance of backtesting libraries becoming widely available. It can take months, if not years, to develop a robust backtesting and trading infrastructure from scratch and many of the vendors (both commercial and open source) have a huge head start. Given such prevalence and maturity of the available software, as well as the time investment needed for development, is there any benefit to building your own?
In this talk, Mike will argue the advantages and disadvantages of building your own infrastructure, how to develop and improve your first backtesting system and how to make it robust to internal and external risk events. The talk will be of interest whether you are a retail quant trader managing your own capital or are forming a start-up quant fund with initial seed funding.
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
"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.
Classification of quantitative trading strategies webinar pptQuantInsti
There exist thousands of academic research papers written on trading strategies. Learn what these academics found out and how we can use their knowledge in the trading world.
The webinar covers:
- Overview of research in a field of quantitative trading
- Taxonomy of quantitative trading strategies
- Where to look for unique alpha
- Examples of lesser-known trading strategies
- Common issues in quant research
Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/
Most Useful links
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
Like us on Facebook: https://www.facebook.com/quantinsti/
Follow us on Twitter: https://twitter.com/QuantInsti
Access the webinar recording here: http://ow.ly/1YwO30dz5FD
Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/
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?
Beware of Low Frequency Data by Ernie Chan, Managing Member, QTS Capital Mana...Quantopian
It is commonly believed that low frequency strategies require only low frequency data for backtesting. We will show that using low frequency data can lead to dangerously inflated backtest results even for low frequency strategies. Examples will be drawn from a closed end fund strategy, a long-short stock strategy, and a futures strategy.
This presentation was part of the QuantCon 2015 Conference hosted by Quantopian. Visit us at: www.quantopian.com.
"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.
"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.
Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016Quantopian
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.
COMPLETE WEBINAR RECORDING: https://blog.quantinsti.com/introduction-price-action-trading-webinar-18-october-2022/
---------------------------------------------
This session introduces you to the skill of trading without using technical indicators by understanding the price behaviour.
It covers several important price action trading tools such as supply and demand analysis, patterns, pivot points, etc.
---------------------------------------------
Overview:
- Need for price action trading
- Fundamentals of price action trading
- Tools of price action trading
- Backtesting and evaluating price action trading strategies
- Automating price action trading
- Interactive Q&A
---------------------------------------------About the Speaker:
Varun Kumar Pothula (Quantitative Analyst at QuantInsti)
Varun holds a Masters degree in Financial Engineering. He has experience working as a trader, a global macro analyst, and also an algo trading strategist.
Currently, working in the Content & Research Team at QuantInsti as a Quantitative Analyst, his contributions help in creating offerings for learners in the domain of algorithmic & quantitative trading.
---------------------------------------------
Link to our Blog: https://blog.quantinsti.com/
Like us on Facebook @ https://www.facebook.com/quantinsti/
Follow us on Twitter @ https://twitter.com/QuantInsti
Follow us on LinkedIn @ https://www.linkedin.com/school/quantinsti/
Follow us on Instagram @ https://www.instagram.com/quantinstian/
E-mail us @ sales@quantinsti.com
-----------------------------------------
#priceaction #priceactiontrading #technicalanalysis #chart #chartpatterns #pivotpoints #technicalindicators
QuantConnect - Introduction to Pairs TradingQuantConnect
Introduction to pairs trading on the QuantConnect platform. Webinar provided by Interactive Brokers. Learn the fundamentals of pairs trading in a non-technical manner. Using the research environment we'll investigate XOM and CVX for cointegration; and then backtest them in QuantConnect.
"Lessons Learned from running a quant crypto fund" presented by Michael Feng, CEO and Co-founder of hummingbot
1. Crypto enables new quant strategies
2. Build a chain of production
3. Preventing overfitting is job #1
4. Establish a disaster response plan
5. Every model has an expiration date
Learn more about algo crypto trading: https://www.hummingbot.io
A Guided Tour of Machine Learning for Traders by Tucker Balch at QuantCon 2016Quantopian
You’ve probably heard about Machine Learning and you likely know it is of emerging importance for trading and investing. Unfortunately it is a deeply technical field and the complexity and jargon get in the way of broader use and understanding. There are literally hundreds of learning algorithms that each solve a slightly different problem. Which algorithms really matter for investing? In this presentation, Professor Balch will help declutter the ML jungle. He’ll introduce a few of the most important ML algorithms and show how they can be applied to the challenges of trading.
"The 6 Stages of a Quant Equity Workflow" by Dr. Jessica Stauth, Vice Preside...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
This talk will provide a deconstruction of the algorithm development process for a popular and deep area of the quantitative investment world: systematic cross-sectional equity investing, also known as statistical arbitrage or equity market neutral investing.
Dr. Jess Stauth will break this workflow into 6 distinct stages, each of which presents its own challenges and opportunities for differentiation to the algorithm developer. During this talk, she will give you an insider's look at how legions of quants at the biggest hedge funds in the world spend their days.
She will also briefly explore how innovations in the fintech space have the potential to reshape this workflow and throw open the doors wide open to a new global pool of talent.
Combining the Best Stock Selection Factors by Patrick O'Shaughnessy at QuantC...Quantopian
Patrick will explore how to combine the value factor with other stock selection factors to build a superior stock selection strategy. He will discuss unique ways of using momentum, share buybacks, and quality factors to improve on a simple value screen. He will discuss portfolio concentration, rebalancing, and risk management. He will also explain why the best versions of these strategies are only possible for smaller firms and investors.
"How to Run a Quantitative Trading Business in China with Python" by Xiaoyou ...Quantopian
From QuantCon 2017: Running a quantitative trading business in China used to be very difficult and require strong IT skills, however it's getting much easier nowadays, when traders with no professional IT training can also do all the tasks in quantitative trading using Python.
In this sharing session, Xiaoyou will share his experience in using Python for data collection, strategy development and automated trading. He will also introduce some related open source projects including TuShare, quantOS, vn.py and so on.
Statistics - The Missing Link Between Technical Analysis and Algorithmic Trad...Quantopian
Trading leveraged derivatives using only technical analysis or speculative analysis can lead to windfall losses for even the most disciplined trader and investor. Statistics are often an ignored area of work when it comes to derivatives trading. Our talk shall focus upon how volatility can be used for dynamically adjusting the stop losses. It will talk about how correlation is an essential method to diversify the class of derivatives being traded or hedged. It will focus on co-integration as a key method to distinguish a mean reverting time series to a non-mean reverting time series. It will touch upon other essential time series econometrics like OU process, VRT as well as statistical tools like PCA, ARCH, GARCH etc. which are essential for derivatives pricing and forecasting the volatility.
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlphaQuantopian
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.
"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.
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.
Scalping futures is a technique which can provide a steady revenue stream to talented traders. This course explains the basics of the techniques involved in short term trading of index futures and what is involved in becoming a successful day trader.
"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.
Classification of quantitative trading strategies webinar pptQuantInsti
There exist thousands of academic research papers written on trading strategies. Learn what these academics found out and how we can use their knowledge in the trading world.
The webinar covers:
- Overview of research in a field of quantitative trading
- Taxonomy of quantitative trading strategies
- Where to look for unique alpha
- Examples of lesser-known trading strategies
- Common issues in quant research
Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/
Most Useful links
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
Like us on Facebook: https://www.facebook.com/quantinsti/
Follow us on Twitter: https://twitter.com/QuantInsti
Access the webinar recording here: http://ow.ly/1YwO30dz5FD
Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/
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?
Beware of Low Frequency Data by Ernie Chan, Managing Member, QTS Capital Mana...Quantopian
It is commonly believed that low frequency strategies require only low frequency data for backtesting. We will show that using low frequency data can lead to dangerously inflated backtest results even for low frequency strategies. Examples will be drawn from a closed end fund strategy, a long-short stock strategy, and a futures strategy.
This presentation was part of the QuantCon 2015 Conference hosted by Quantopian. Visit us at: www.quantopian.com.
"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.
"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.
Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016Quantopian
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.
COMPLETE WEBINAR RECORDING: https://blog.quantinsti.com/introduction-price-action-trading-webinar-18-october-2022/
---------------------------------------------
This session introduces you to the skill of trading without using technical indicators by understanding the price behaviour.
It covers several important price action trading tools such as supply and demand analysis, patterns, pivot points, etc.
---------------------------------------------
Overview:
- Need for price action trading
- Fundamentals of price action trading
- Tools of price action trading
- Backtesting and evaluating price action trading strategies
- Automating price action trading
- Interactive Q&A
---------------------------------------------About the Speaker:
Varun Kumar Pothula (Quantitative Analyst at QuantInsti)
Varun holds a Masters degree in Financial Engineering. He has experience working as a trader, a global macro analyst, and also an algo trading strategist.
Currently, working in the Content & Research Team at QuantInsti as a Quantitative Analyst, his contributions help in creating offerings for learners in the domain of algorithmic & quantitative trading.
---------------------------------------------
Link to our Blog: https://blog.quantinsti.com/
Like us on Facebook @ https://www.facebook.com/quantinsti/
Follow us on Twitter @ https://twitter.com/QuantInsti
Follow us on LinkedIn @ https://www.linkedin.com/school/quantinsti/
Follow us on Instagram @ https://www.instagram.com/quantinstian/
E-mail us @ sales@quantinsti.com
-----------------------------------------
#priceaction #priceactiontrading #technicalanalysis #chart #chartpatterns #pivotpoints #technicalindicators
QuantConnect - Introduction to Pairs TradingQuantConnect
Introduction to pairs trading on the QuantConnect platform. Webinar provided by Interactive Brokers. Learn the fundamentals of pairs trading in a non-technical manner. Using the research environment we'll investigate XOM and CVX for cointegration; and then backtest them in QuantConnect.
"Lessons Learned from running a quant crypto fund" presented by Michael Feng, CEO and Co-founder of hummingbot
1. Crypto enables new quant strategies
2. Build a chain of production
3. Preventing overfitting is job #1
4. Establish a disaster response plan
5. Every model has an expiration date
Learn more about algo crypto trading: https://www.hummingbot.io
A Guided Tour of Machine Learning for Traders by Tucker Balch at QuantCon 2016Quantopian
You’ve probably heard about Machine Learning and you likely know it is of emerging importance for trading and investing. Unfortunately it is a deeply technical field and the complexity and jargon get in the way of broader use and understanding. There are literally hundreds of learning algorithms that each solve a slightly different problem. Which algorithms really matter for investing? In this presentation, Professor Balch will help declutter the ML jungle. He’ll introduce a few of the most important ML algorithms and show how they can be applied to the challenges of trading.
"The 6 Stages of a Quant Equity Workflow" by Dr. Jessica Stauth, Vice Preside...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
This talk will provide a deconstruction of the algorithm development process for a popular and deep area of the quantitative investment world: systematic cross-sectional equity investing, also known as statistical arbitrage or equity market neutral investing.
Dr. Jess Stauth will break this workflow into 6 distinct stages, each of which presents its own challenges and opportunities for differentiation to the algorithm developer. During this talk, she will give you an insider's look at how legions of quants at the biggest hedge funds in the world spend their days.
She will also briefly explore how innovations in the fintech space have the potential to reshape this workflow and throw open the doors wide open to a new global pool of talent.
Combining the Best Stock Selection Factors by Patrick O'Shaughnessy at QuantC...Quantopian
Patrick will explore how to combine the value factor with other stock selection factors to build a superior stock selection strategy. He will discuss unique ways of using momentum, share buybacks, and quality factors to improve on a simple value screen. He will discuss portfolio concentration, rebalancing, and risk management. He will also explain why the best versions of these strategies are only possible for smaller firms and investors.
"How to Run a Quantitative Trading Business in China with Python" by Xiaoyou ...Quantopian
From QuantCon 2017: Running a quantitative trading business in China used to be very difficult and require strong IT skills, however it's getting much easier nowadays, when traders with no professional IT training can also do all the tasks in quantitative trading using Python.
In this sharing session, Xiaoyou will share his experience in using Python for data collection, strategy development and automated trading. He will also introduce some related open source projects including TuShare, quantOS, vn.py and so on.
Statistics - The Missing Link Between Technical Analysis and Algorithmic Trad...Quantopian
Trading leveraged derivatives using only technical analysis or speculative analysis can lead to windfall losses for even the most disciplined trader and investor. Statistics are often an ignored area of work when it comes to derivatives trading. Our talk shall focus upon how volatility can be used for dynamically adjusting the stop losses. It will talk about how correlation is an essential method to diversify the class of derivatives being traded or hedged. It will focus on co-integration as a key method to distinguish a mean reverting time series to a non-mean reverting time series. It will touch upon other essential time series econometrics like OU process, VRT as well as statistical tools like PCA, ARCH, GARCH etc. which are essential for derivatives pricing and forecasting the volatility.
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlphaQuantopian
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.
"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.
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.
Scalping futures is a technique which can provide a steady revenue stream to talented traders. This course explains the basics of the techniques involved in short term trading of index futures and what is involved in becoming a successful day trader.
Unifying Technical Analysis and Statistical ArbitrageZakMaymin
Technical Analysis and Statistical Arbitrage employ different tools and speak different languages. But they are trying to solve the same problem: create a good trading strategy. Is it possible to combine them?
Using Java & Genetic Algorithms to Beat the MarketMatthew Ring
I presented this at JavaOne 2011 along with a demo of software that I've written. Since you won't see the demo, I added a few more slides to explain it.
A.I. for Dynamic Decisioning under Uncertainty (for real-world problems in Re...Ashwin Rao
Slides from the Research Seminar talk I gave at Nvidia. The topic was: A.I. for Dynamic Decisioning under Uncertainty (for Real-World problems in Retail and in Financial Trading)
Financial Trading as a Game: A Deep Reinforcement Learning Approach謙益 黃
An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used ε-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
Investigation of Frequent Batch Auctions using Agent Based ModelTakanobu Mizuta
Recently, the speed of order matching systems on financial exchanges increased due to competition between markets and due to large investor demands. There is an opinion that this increase is good for liquidity by increasing providing liquidity of market maker strategies (MM), on the other hand, there is also the opposite opinion that this speed causes socially wasteful arms race for speed and these costs are passed to other investors as execution costs.
A frequent batch auction (FBA) which reduces the value of speed advantages proposed, however, is also criticized that MM providing liquidity are exposed to more risks, and then they can continue to provide liquidity, then many MM retire, and finally liquidity will be reduced.
In this study we implemented a price mechanism that is changeable between a comparable continuance double auction (CDA) and FBA continuously, and analyzing profits/losses and risks of MM, we investigated whether MM can continue to provide liquidity even on FBA by using an artificial market model.
Our simulation results showed that on FBA execution rates of MM becomes smaller and this causes to reduce liquidity supply by MM. They also suggested that on FBA MM cannot avoid both an overnight risk and a price variation risk intraday, furthermore, it is very difficult that MM is rewarded for risks and continues to provide liquidity. Only on CDA MM is rewarded for risks and continue to provide liquidity.
This suggestion implies that MM that can provide liquidity on CDA cannot continue to provide liquidity on FBA and then many MM retire, finally liquidity will be reduced.
Statistical Arbitrage
Pairs Trading, Long-Short Strategy
Cyrille BEN LEMRID

1 Pairs Trading Model 5
1.1 Generaldiscussion ................................ 5 1.2 Cointegration ................................... 6 1.3 Spreaddynamics ................................. 7
2 State of the art and model overview 9
2.1 StochasticDependenciesinFinancialTimeSeries . . . . . . . . . . . . . . . 9 2.2 Cointegration-basedtradingstrategies ..................... 10 2.3 FormulationasaStochasticControlProblem. . . . . . . . . . . . . . . . . . 13 2.4 Fundamentalanalysis............................... 16
3 Strategies Analysis 19
3.1 Roadmapforstrategydesign .......................... 19 3.2 Identificationofpotentialpairs ......................... 19 3.3 Testingcointegration ............................... 20 3.4 Riskcontrolandfeasibility............................ 20
4 Results
22
2
Contents

Introduction
This report presents my research work carried out at Credit Suisse from May to September 2012. This study has been pursued in collaboration with the Global Arbitrage Strategies team.
Quantitative analysis strategy developers use sophisticated statistical and optimization techniques to discover and construct new algorithms. These algorithms take advantage of the short term deviation from the ”fair” securities’ prices. Pairs trading is one such quantitative strategy - it is a process of identifying securities that generally move together but are currently ”drifting away”.
Pairs trading is a common strategy among many hedge funds and banks. However, there is not a significant amount of academic literature devoted to it due to its proprietary nature. For a review of some of the existing academic models, see [6], [8], [11] .
Our focus for this analysis is the study of two quantitative approaches to the problem of pairs trading, the first one uses the properties of co-integrated financial time series as a basis for trading strategy, in the second one we model the log-relationship between a pair of stock prices as an Ornstein-Uhlenbeck process and use this to formulate a portfolio optimization based stochastic control problem.
This study was performed to show that under certain assumptions the two approaches are equivalent.
Practitioners most often use a fundamentally driven approach, analyzing the performance of stocks around a market event and implement strategies using back-tested trading levels.
We also study an example of a fundamentally driven strategy, using market reaction to a stock being dropped or added to the MSCI World Standard, as a signal for a pair trading strategy on those stocks once their inclusion/exclusion has been made effective.
This report is organized as follows. Section 1 provides some background on pairs trading strategy. The theoretical results are described in Section 2. Section 3
Being open (source) in the traditionally secretive field of quant finance.Quantopian
The field of quantitative finance is intensely competitive and maniacally secretive as a rule. The tendency toward secrecy is perhaps unsurprising given that the smallest of competitive advantages can translate to substantial profits. Indeed, over the past decade a growing list of legal prosecutions for alleged code theft or misuse have underscored how high the stakes can be for developers looking to leverage and contribute to open source projects. Notable exceptions to this approach include work from Wes McKinney and Travis Oliphant, whose work on open source projects like pandas and numpy, which have gained widespread adoption. In this talk we will review some of the costs and benefits of engaging with open source as a “two way street” and frame the modern quant workflow as a mosaic of open sourced, third party, and proprietary components.
Stauth common pitfalls_stock_market_modeling_pqtc_fall2018Quantopian
Data Modeling the Stock Market Today - Common Pitfalls to Avoid
The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be deceptively tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, under-estimation of real-world costs, and as many more as we have time to cover.
"Three Dimensional Time: Working with Alternative Data" by Kathryn Glowinski,...Quantopian
From QuantCon 2017: Lookahead bias and stale data when used in an algorithm are generally categorized as "incorrect data". In fact, the issue does not lie with the data itself, but instead is an issue of perspective. This talk will examine how data is typically viewed through the lens of time, and why, on the whole, that approach is wrong.
At Quantopian, we've tried several ways of handling data with regards to time, and we'll talk about lessons learned along the way. We'll also discuss what multidimensionality means for financial data specifically, and how we can apply this to get better results in backtesting.
Additionally, we'll touch on how to apply multidimensionality to more general data, and why it's important for anyone working with applied data to take this approach.
"Alpha from Alternative Data" by Emmett Kilduff, Founder and CEO of Eagle AlphaQuantopian
From QuantCon 2017: At J.P. Morgan's annual quantitative conference 93% of investors said alternative data will change the investment landscape.
In this presentation, Emmett will discuss the rapidly increasing adoption of alternative data, give a detailed overview of the 24 different types of alternative data, outline the applications of alternative data for quantitative funds, discuss interesting datasets that are available (including Asian datasets) and present case studies that evidence value in alternative datasets.
"Supply Chain Earnings Diffusion" by Josh Holcroft, Head of Quantitative Rese...Quantopian
Supply chains and network effects are becoming increasingly important and increasingly transparent in the global economy. However, conventional techniques are poorly equipped to handle relational data, and new techniques are required to decode the meaning of supply chain effects. We explore a novel technique for modelling and forecasting the diffusion of earnings revisions, known as a diffusion graph kernel support vector machine.
“Real Time Machine Learning Architecture and Sentiment Analysis Applied to Fi...Quantopian
From QuantCon Singapore 2017: The vast proliferation of data related to the financial industry introduces both new opportunities and challenges to quantitative investors. These challenges are often due to the nature of big data and include: volume, variety, and velocity.
In this talk, Dr. Cheng will take the audience on a tour of the “big-data production line” in InfoTrie and show how the financial news collected from various and customizable sources are transformed into quantitative signals in a real-time manner. The talk will touch on various kind of topics like sentiment analysis, entity detection, topic classification, and big-data tools.
“Market Insights Through the Lens of a Risk Model” by Olivier d'Assier, Head ...Quantopian
From QuantCon Singapore 2017: In this presentation, Olivier d’Assier, Managing Director of APAC Applied Research, will discuss the major drivers of the change in risk year-to-date and how the risk environment is affecting investor’s portfolios. This talk will look at global markets with a focus on the Asian region and how it compares to others with regards to its risk footprint.
"Maximize Alpha with Systematic Factor Testing" by Cheng Peng, Software Engin...Quantopian
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.
"Deep Reinforcement Learning for Optimal Order Placement in a Limit Order Boo...Quantopian
From QuantCon 2017: Financial trading is essentially a search problem. The buy-side agent needs to find a counterpart sell-side agent willing to trade the financial asset at the set quantity and price.
Ilija will present a deep reinforcement learning algorithm for optimizing the execution of limit-order actions to find an optimal order placement. The reinforcement learning agent utilizes historical limit-order data to learn an optimal compromise between fast order completion but with higher costs and slow, riskier order completion but with lower costs.
The talk will continue with the challenges of applying reinforcement learning to optimal trading and their potential solutions. Finally, Ilija will share the system architecture and discuss future work.
"Building Diversified Portfolios that Outperform Out-of-Sample" by Dr. Marcos...Quantopian
Hierarchical Risk Parity (HRP) portfolios address three major concerns of quadratic optimizers in general and Markowitz’s CLA in particular: Instability, concentration and underperformance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-of-sample variance than CLA, even though minimum-variance is CLA’s optimization objective. HRP also produces less risky portfolios out-of-sample compared to traditional risk parity methods.
Read the corresponding white paper here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2713516
"From Insufficient Economic data to Economic Big Data – How Trade Data is red...Quantopian
Over the last 10 years, the world of economics has been playing a catching up game and many economists have been struggling to explain their theories. The world has adopted technology in nearly every aspect of life, from phones to cars; however, good, reliable and quality data in economics is still elusive.
There is over reliance on macroeconomic principles in comparison to the quality of data available. Macro-economic figures move markets, only to get revised one, two or three times in the following months. Some fields of economic study are exceptions, such as analysing trade data. Trade data, with the support of technology, has become readily available and can now be analysed in depth, providing actual numbers indicating the health and state of economies.
Trade data, which is export and import information of all the goods and services from one country to another, can be seen as an inseparable marker of real economic activity. It can be used to predict various market indicators exhibiting high correlations, from currencies to commodities to equities to macroeconomic data, with varying degree of certainties. Trade data, at an in-depth level, acts like a compilation of millions of real life mathematical functions.
This presentation explores this new economic area of trade data as a quantitative tool, its intense big data analysis and its applications in trading markets.
"Quantum Hierarchical Risk Parity - A Quantum-Inspired Approach to Portfolio ...Quantopian
Maxwell will present the methodologies and results behind the algorithm that has been developed by 1QBit, named Quantum Hierarchical Risk Parity, or QHRP.
This is an extension of the work done by Marcos Lopez de Prado on
Hierarchical Risk Parity in his paper "Building Diversified Portfolios that Outperform Out-of-Sample."
QHRP tackles the problem of minimizing the risk of a portfolio of assets using a quantum-inspired approach. Although the ideas surrounding this go back to Markowitz’s mean-variance portfolio optimization of 1952’s Portfolio Selection, we have applied recent quantum-ready machine learning tools to the problem to demonstrate strong performance in terms of a variety of risk measures and lower susceptibility to inaccuracies in the input data.
The quantum-ready approach to portfolio optimization is based on
an optimization problem that can be solved using a quantum annealer. The algorithm utilizes a hierarchical clustering tree that is based on the covariance matrix of the asset returns. The results of real market data used to benchmark this approach against other common portfolio optimization methods will be shared in this presentation.
View the White Paper: https://bit.ly/2k5xTxW.
"Snake Oil, Swamp Land, and Factor-Based Investing" by Gary Antonacci, author...Quantopian
BlackRock forecasts smart beta investing oriented toward size, value, quality, momentum, and low volatility to reach $1 trillion by 2020 and $2.4 trillion by 2025. Gary’s talk will show that this growth may not be justified due to these factors' lack of robustness, consistency, persistence, intuitiveness, and investability. Gary will also show that the success attributed to these factors would be better directed toward macro momentum and the short interest ratio.
"Bayesian Deep Learning: Dealing with Uncertainty and Non-Stationarity" by Dr...Quantopian
Deep Learning continues to build out its dominance over other machine learning approaches on several challenging tasks including image, hand-writing, and speech recognition, image synthesis, as well as playing board and computer games exceeding human expert abilities.
This has generated a lot of interest in the quant finance community to try and mirror Deep Learning's success in the domain of algorithmic trading. Unfortunately, algorithmic trading poses a unique set of challenges. Specifically, the risk (i.e. uncertainty) of certain trading decisions as well as the fact that market behavior changes over time (i.e. non-stationarity) is not handled well by deep learning.
In this talk, I will show how we can embed Deep Learning in the Probabilistic Programming framework PyMC3 and elegantly solve these issues. Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. This talk is focused on practitioners and will be introductory and hands-on with many code examples.
"On the Bayesian Interpretation of Black–Litterman" by Dr. Gordon Ritter, Sen...Quantopian
We will present the most general model of the type considered by Black and Litterman (1991) after fully clarifying the duality between Black–Litterman optimization and Bayesian regression.
Our generalization is itself a special case of a Bayesian network or graphical model. As an example, we will work out in full detail the treatment of views on factor risk premia in the context of APT.
We will also consider a more speculative example in which the portfolio manager specifies a view on realized volatility by trading a variance swap.
"Correlated Volatility Shocks" by Dr. Xiao Qiao, Researcher at SummerHaven In...Quantopian
Commonality in idiosyncratic volatility cannot be completely explained by time-varying volatility. After removing the effects of time-varying volatility, idiosyncratic volatility innovations are still positively correlated. This result suggests correlated volatility shocks contribute to the comovement in idiosyncratic volatility.
Motivated by this fact, we propose the Dynamic Factor Correlation (DFC) model, which fits the data well and captures the cross-sectional correlations in idiosyncratic volatility innovations. We decompose the common factor in idiosyncratic volatility (CIV) of Herskovic et al. (2016) into the volatility innovation factor (VIN) and time-varying volatility factor (TVV). Whereas VIN is associated with strong variation in average returns, TVV is only weakly priced in the cross section
A strategy that takes a long position in the portfolio with the lowest VIN and TVV betas, and a short position in the portfolio with the highest VIN and TVV betas earns average returns of 8.0% per year.
What price will pi network be listed on exchangesDOT TECH
The rate at which pi will be listed is practically unknown. But due to speculations surrounding it the predicted rate is tends to be from 30$ — 50$.
So if you are interested in selling your pi network coins at a high rate tho. Or you can't wait till the mainnet launch in 2026. You can easily trade your pi coins with a merchant.
A merchant is someone who buys pi coins from miners and resell them to Investors looking forward to hold massive quantities till mainnet launch.
I will leave the telegram contact of my personal pi vendor to trade with.
@Pi_vendor_247
Even tho Pi network is not listed on any exchange yet.
Buying/Selling or investing in pi network coins is highly possible through the help of vendors. You can buy from vendors[ buy directly from the pi network miners and resell it]. I will leave the telegram contact of my personal vendor.
@Pi_vendor_247
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...beulahfernandes8
The financial landscape in India has witnessed a significant development with the recent collaboration between Poonawalla Fincorp and IndusInd Bank.
The launch of the co-branded credit card, the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card, marks a major milestone for both entities.
This strategic move aims to redefine and elevate the banking experience for customers.
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.
how can i use my minded pi coins I need some funds.DOT TECH
If you are interested in selling your pi coins, i have a verified pi merchant, who buys pi coins and resell them to exchanges looking forward to hold till mainnet launch.
Because the core team has announced that pi network will not be doing any pre-sale. The only way exchanges like huobi, bitmart and hotbit can get pi is by buying from miners.
Now a merchant stands in between these exchanges and the miners. As a link to make transactions smooth. Because right now in the enclosed mainnet you can't sell pi coins your self. You need the help of a merchant,
i will leave the telegram contact of my personal pi merchant below. 👇 I and my friends has traded more than 3000pi coins with him successfully.
@Pi_vendor_247
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just telegram this number below. I sold about 3000 pi coins to him and he paid me immediately.
Telegram: @Pi_vendor_247
how can I sell pi coins after successfully completing KYCDOT TECH
Pi coins is not launched yet in any exchange 💱 this means it's not swappable, the current pi displaying on coin market cap is the iou version of pi. And you can learn all about that on my previous post.
RIGHT NOW THE ONLY WAY you can sell pi coins is through verified pi merchants. A pi merchant is someone who buys pi coins and resell them to exchanges and crypto whales. Looking forward to hold massive quantities of pi coins before the mainnet launch.
This is because pi network is not doing any pre-sale or ico offerings, the only way to get my coins is from buying from miners. So a merchant facilitates the transactions between the miners and these exchanges holding pi.
I and my friends has sold more than 6000 pi coins successfully with this method. I will be happy to share the contact of my personal pi merchant. The one i trade with, if you have your own merchant you can trade with them. For those who are new.
Message: @Pi_vendor_247 on telegram.
I wouldn't advise you selling all percentage of the pi coins. Leave at least a before so its a win win during open mainnet. Have a nice day pioneers ♥️
#kyc #mainnet #picoins #pi #sellpi #piwallet
#pinetwork
what is the future of Pi Network currency.DOT TECH
The future of the Pi cryptocurrency is uncertain, and its success will depend on several factors. Pi is a relatively new cryptocurrency that aims to be user-friendly and accessible to a wide audience. Here are a few key considerations for its future:
Message: @Pi_vendor_247 on telegram if u want to sell PI COINS.
1. Mainnet Launch: As of my last knowledge update in January 2022, Pi was still in the testnet phase. Its success will depend on a successful transition to a mainnet, where actual transactions can take place.
2. User Adoption: Pi's success will be closely tied to user adoption. The more users who join the network and actively participate, the stronger the ecosystem can become.
3. Utility and Use Cases: For a cryptocurrency to thrive, it must offer utility and practical use cases. The Pi team has talked about various applications, including peer-to-peer transactions, smart contracts, and more. The development and implementation of these features will be essential.
4. Regulatory Environment: The regulatory environment for cryptocurrencies is evolving globally. How Pi navigates and complies with regulations in various jurisdictions will significantly impact its future.
5. Technology Development: The Pi network must continue to develop and improve its technology, security, and scalability to compete with established cryptocurrencies.
6. Community Engagement: The Pi community plays a critical role in its future. Engaged users can help build trust and grow the network.
7. Monetization and Sustainability: The Pi team's monetization strategy, such as fees, partnerships, or other revenue sources, will affect its long-term sustainability.
It's essential to approach Pi or any new cryptocurrency with caution and conduct due diligence. Cryptocurrency investments involve risks, and potential rewards can be uncertain. The success and future of Pi will depend on the collective efforts of its team, community, and the broader cryptocurrency market dynamics. It's advisable to stay updated on Pi's development and follow any updates from the official Pi Network website or announcements from the team.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
how to sell pi coins at high rate quickly.DOT TECH
Where can I sell my pi coins at a high rate.
Pi is not launched yet on any exchange. But one can easily sell his or her pi coins to investors who want to hold pi till mainnet launch.
This means crypto whales want to hold pi. And you can get a good rate for selling pi to them. I will leave the telegram contact of my personal pi vendor below.
A vendor is someone who buys from a miner and resell it to a holder or crypto whale.
Here is the telegram contact of my vendor:
@Pi_vendor_247
"Quantitative Trading as a Mathematical Science" by Dr. Haksun Li, Founder and CEO, Numerical Method Inc.
1. Quantitative Trading as a Mathematical Science
QuantCon Singapore 2016
Haksun Li
haksun.li@numericalmethod.com
www.numericalmethod.com
2. Abstract
2
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.
3. Speaker Profile
Dr. Haksun Li
CEO, NM LTD.
(Ex-)Adjunct Professors, Industry Fellow, Advisor,
Consultant with the National University of Singapore,
Nanyang Technological University, Fudan University,
the Hong Kong University of Science and Technology.
Quantitative Trader/Analyst, BNPP, UBS
Ph.D., Computer Science, University of Michigan Ann
Arbor
M.S., Financial Mathematics, University of Chicago
B.S., Mathematics, University of Chicago
3
5. Quantitative Trading?
5
Quantitative trading is the buying and selling of assets
following the instructions computed from a set of
proven mathematical models.
The differentiation from other trading methodologies
or the emphasis is on how a strategy is proven and
not on what strategy is created.
It applies (rigorous) mathematical reasoning in all
steps during trading strategy construction from the
start to the end.
6. Moving Average Crossover as a TA
6
A popular TA signal: Moving Average Crossover.
A crossover occurs when a faster moving average (i.e. a
shorter period moving average) crosses above/below a
slower moving average (i.e. a longer period moving
average); then you buy/sell.
In most TA book, it is never proven only illustrated
with an example of applying the strategy to a stock for
a period of time to show profits.
7. Technical Analysis is Not Quantitative Trading
7
TA books merely describes the mechanics of strategies
but never prove them.
Appealing to common sense is not a mathematical
proof.
Conditional probabilities of outcomes are seldom
computed. (Lo, Mamaysky, & Wang, 2000)
Application of TA is more of an art (is it?) than a
science.
How do you choose the parameters?
For any TA rule, you almost surely can find an asset
and a period that the rule “works”, given the large
number of assets and many periods to choose from.
8. Fake Quantitative Models
Data snooping
Misuse of mathematics
Assumptions cannot be quantified
No model validation against the current regime
Ad-hoc take profit and stop-loss
why 2?
How do you know when a model is invalidated?
Cannot explain winning and losing trades
Cannot be analyzed (systematically)
8
10. NM Quantitative Trading Research Process
10
1. Translate a vague trading intuition (hypothesis) into
a concrete mathematical model.
2. Translate the mathematical symbols and equations
into a computer program.
3. Strategy evaluation.
4. Live execution for making money.
11. Step 1 - Modeling
11
Where does a trading idea come from?
Ex-colleagues
Hearsays
Newspapers, books
TV, e.g., Moving Average Crossover (MA)
A quantitative trading strategy is a math function, f,
that at any given time, t, takes as inputs any
information that the strategy cares and that is
available, Ft, and gives as output the position to take,
f(t,Ft).
12. Step 2 - Coding
12
The computer code enables analysis of the strategy.
Most study of a strategy cannot be done analytically.
We must resort to simulation.
The same piece of code used for research and
investigation should go straight into the production
for live trading.
Eliminate the possibility of research-to-IT translation
errors.
13. Step 3 – Evaluation/Justification
13
Compute the properties of a trading strategy.
the P&L distribution
the holding time distribution
the stop-loss
the maximal drawdown
http://redmine.numericalmethod.com/projects/publi
c/repository/svn-
algoquant/show/core/src/main/java/com/numericalm
ethod/algoquant/execution/performance
14. Step 4 - Trading
14
Put in capitals incrementally.
Install safety measures.
Monitor the performance.
Regime change detection.
16. Moving Average Crossover as a Quantitative
Trading Strategy
16
There are many mathematical justifications to Moving
Average Crossover.
weighted Sum of lags of a time series
Kuo, 2002
Whether a strategy is quantitative or not depends not
on the strategy itself but
entirely on the process to construct it;
or, whether there is a scientific justification to prove it.
17. Step 1 - Modeling
Two moving averages: slower (𝑛) and faster (𝑚).
Monitor the crossovers.
𝐵𝑡 =
1
𝑚
∑ 𝑃𝑡−𝑗
𝑚−1
𝑗=0 −
1
𝑛
∑ 𝑃𝑡−𝑗
𝑛−1
𝑗=0 , 𝑛 > 𝑚
Long when 𝐵𝑡 ≥ 0.
Short when 𝐵𝑡 < 0.
18. How to Choose 𝑛 and 𝑚?
It is an art, not a science (so far).
They should be related to the length of market cycles.
Different assets have different 𝑛 and 𝑚.
Popular choices:
(250, 5)
(250 , 20)
(20 , 5)
(20 , 1)
(250 , 1)
19. Two Simplifications
19
Reduce the two dimensional problem to a one
dimensional problem.
Choose 𝑚 = 1. We know that m should be small.
Replace arithmetic averages with geometric averages.
This is so that we can work with log returns rather than
prices.
22. Acar Framework
Acar (1993): to investigate the probability distribution
of realized returns from a trading rule, we need
the explicit specification of the trading rule
the underlying stochastic process for asset returns
the particular return concept involved
23. Knight-Satchell-Tran Intuition
Stock returns staying going up (down) depends on
the realizations of positive (negative) shocks
the persistence of these shocks
Shocks are modeled by gamma processes.
Asymmetry
Fat tails
Persistence is modeled by a Markov switching process.
25. Knight-Satchell-Tran Process
𝑅𝑡 = 𝜇𝑙 + 𝑍𝑡 𝜀𝑡 − 1 − 𝑍𝑡 𝛿𝑡
𝜇𝑙: long term mean of returns, e.g., 0
𝜀𝑡, 𝛿𝑡: positive and negative shocks, non-negative, i.i.d
𝑓𝜀 𝑥 =
𝜆1
𝛼1 𝑥 𝛼1−1
Γ 𝛼1
𝑒−𝜆1 𝑥
𝑓𝛿 𝑥 =
𝜆2
𝛼2 𝑥 𝛼2−1
Γ 𝛼2
𝑒−𝜆2 𝑥
26. Step 3 – Evaluation/Justification
Assume the long term mean is 0, 𝜇𝑙 = 0.
When 𝑛 = 2,
𝐵𝑡 ≥ 0 ≡ 𝑅𝑡 ≥ 0 ≡ 𝑍𝑡 = 1
𝐵𝑡 < 0 ≡ 𝑅𝑡 < 0 ≡ 𝑍𝑡 = 0
27. GMA(2, 1) – Naïve MA Trading Rule
Buy when the asset return in the present period is
positive.
Sell when the asset return in the present period is
negative.
28. Naïve MA Conditions
The expected value of the positive shocks to asset
return >> the expected value of negative shocks.
The positive shocks persistency >> that of negative
shocks.
29. 𝑇 Period Returns
𝑅𝑅 𝑇 = ∑ 𝑅𝑡 × 𝐼 𝐵𝑡−1≥0
𝑇
𝑡=1
Sell at this time point
𝑇
𝐵 𝑇 < 0
0 1
hold
36. MA Using the Whole History
An investor will always expect to lose money using
GMA(∞,1)!
An investor loses the least amount of money when the
return process is a random walk.
42. Unique Guiding Principle
What Others Do:
Start with a trading strategy.
Find the data that the
strategy works.
Result:
Paper P&L looks good.
Live P&L depends on luck.
Trading strategies are
results of a non-scientific,
a pure data snooping
process.
What We Do:
Start with simple assumptions
about the market.
Compute the optimal trading
strategy given the
assumptions.
Result:
Can mathematically prove
that no other strategy will
work better in the same
market conditions.
Trading strategies are results
of a scientific process.
42
43. Optimal Trend Following (TREND)
We make assumptions that the market is a two (or
three) state model. The market state is either up,
down, (or sideway).
In each state, we assume a random walk with positive,
negative, or zero drift.
We use math to compute what the best thing to do is
in each of the states.
We estimate the conditional probability, 𝑝, of that the
market is going up given all the available information.
When 𝑝 is big enough, i.e., most certainly that the
market is going up, we buy.
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
140.00%
160.00%
trading period 2015/1/2 - 2016/5/2
assets traded
Hang Seng china enterprises
index futures
annualized
return 107.00%
max
drawdown 6.61%
Sharpe ratio 4.79
Result:
44. Optimal Trend Following (Math)
Two state Markov model for a stock’s prices: BULL and
BEAR.
𝑑𝑆𝑟 = 𝑆𝑟 𝜇 𝛼 𝑟
𝑑𝑑 + 𝜎𝜎𝐵𝑟 , 𝑡 ≤ 𝑟 ≤ 𝑇 < ∞
The trading period is between time 𝑡, 𝑇 .
𝛼 𝑟 = 1,2 are the two Markov states that indicates
the BULL and BEAR markets.
𝜇1 > 0
𝜇2 < 0
𝑄 =
−𝜆1 𝜆1
𝜆2 −𝜆2
, the generator matrix for the Markov
chain.
When 𝑖 = 0, expected return is
E0,𝑡 𝑅𝑡 =
E 𝑡 𝑒 𝜌 𝜏1−𝑡 ∏
𝑆 𝜈 𝑛
𝑆 𝜏 𝑛
1−𝐾𝑠
1+𝐾 𝑏
𝐼 𝜏 𝑛<𝑇
𝑒 𝜌 𝜏 𝑛+1−𝜐 𝑛∞
𝑛=1
We are long between 𝜏 𝑛 and 𝜈 𝑛 and the return is
determined by the price change discounted by the
commissions.
We are flat between 𝜈 𝑛 and 𝜏 𝑛+1 and the money
grows at the risk free rate.
Value function:
J0 𝑆, 𝛼, 𝑡, Λ0 =
E 𝑡
𝜌 𝜏1 − 𝑡 +
∑ log
𝑆 𝜈 𝑛
𝑆 𝜏 𝑛
+ 𝐼 𝜏 𝑛<𝑇 log
1−𝐾𝑠
1+𝐾 𝑏
+ 𝜌 𝜏 𝑛+1 − 𝜐 𝑛
∞
𝑛=1
J1 𝑆, 𝛼, 𝑡, Λ1 =
E 𝑡
log
𝑆 𝜈1
𝑆
+ 𝜌 𝜏2 − 𝜈1 + log 1 − 𝐾𝑠 +
∑ log
𝑆 𝜈 𝑛
𝑆 𝜏 𝑛
+ 𝐼 𝜏 𝑛<𝑇 log
1−𝐾𝑠
1+𝐾 𝑏
+ 𝜌 𝜏 𝑛+1 − 𝜐 𝑛
∞
𝑛=2
Find an optimal trading sequence (the stopping times) so that
the value functions are maximized.
𝑉𝑖 𝑝, 𝑡 = sup
Λ 𝑖
𝐽𝑖 𝑆, 𝑝, 𝑡, Λ 𝑖
𝑉𝑖: the maximum amount of expected returns
�
𝑉0 𝑝, 𝑡 = sup
𝜏1
𝐸𝑡 𝜌 𝜏1 − 𝑡 − log 1 + 𝐾𝑏 + 𝑉1 𝑝𝜏1
, 𝜏1
𝑉1 𝑝, 𝑡 = sup
𝜈1
𝐸𝑡 log
𝑆 𝜈1
𝑆𝑡
+ log 1 − 𝐾𝑠 + 𝑉0 𝑝 𝜈1
, 𝜈1
Hamilton-Jacobi-Bellman Equations
�
min −ℒ𝑉0 − 𝜌, 𝑉0 − 𝑉1 + log 1 + 𝐾𝑏 = 0
min −ℒ𝑉1 − 𝑓 𝜌 , 𝑉1 − 𝑉0 − log 1 − 𝐾𝑠 = 0
with terminal conditions: �
𝑉0 𝑝, 𝑇 = 0
𝑉1 𝑝, 𝑇 = log 1 − 𝐾𝑠
ℒ = 𝜕𝑡 +
1
2
𝜇1−𝜇2 𝑝 1−𝑝
𝜎
2
𝜕 𝑝𝑝 + − 𝜆1 + 𝜆2 𝑝 + 𝜆2 𝜕 𝑝
Based on: M Dai, Q Zhang, QJ Zhu, "Trend following trading under a regime switching
model," SIAM Journal on Financial Mathematics, 2010.
45. Optimal Mean Reversion (MR)
45
Basket construction problem:
Select the right financial instruments.
Obtain the optimal weights for the
selected financial instruments.
Basket trading problem:
Given the portfolio can be modelled
as a mean reverting OU process,
dynamic spread trading is a stochastic
optimal control problem.
Given a fixed amount of capital,
dynamically allocate capital over a
risky mean reverting portfolio and a
risk-free asset over a finite time
horizon to maximize a general
constant relative risk aversion (CRRA)
utility function of the terminal
wealth .
Allocate capital amongst several
mean reverting portfolios.
Based on: Mudchanatongsuk, S., Primbs, J.A., Wong, " Optimal Pairs Trading: A
Stochastic Control Approach," Dept. of Manage. Sci. & Eng., Stanford Univ., CA.
47. Intraday Volatility Trading (VOL)
In mid or high frequency trading, or
within a medium or short time
interval, prices tend to oscillate.
If there are enough oscillations
before prices move in a direction,
arbitrage exists.
47
profit region
loss region
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
140.00%
trading period 2014/2/27 - 2015/2/27
assets traded
Hang Seng china enterprises
index futures
annualized
return 122.32%
max
drawdown 10.24%
Sharpe ratio 18.45
Live Result:
48. Intraday Volatility Trading (Math)
For a continuous price process 𝑋𝑡, we
define H-variation
𝑉𝑇 𝐻, 𝑋 = sup
𝑇
∑ 𝑋 𝑡𝑙 − 𝑋 𝑡𝑙−1
𝐿
𝑙=1
It can be shown that for any H, there
exists a sequence 𝜏 𝑛
∗
, 𝜏 𝑛 𝑛=0,1,…,𝑁 such
that 𝜏 𝑛 𝑛=0,1,…,𝑁 are Markovian and 𝜏 𝑛
∗
are defined by 𝑋𝑡 on intervals 𝜏 𝑛−1, 𝜏 𝑛 .
And they satisfy the equality:
𝑉𝑇 𝐻, 𝑋 = ∑ 𝑋 𝜏 𝑛
∗ − 𝑋 𝜏 𝑛−1
∗𝑁
𝑙=1
𝑁 𝑇 𝐻, 𝑋 is the number of KAGI-
inversion in the T-interval.
H-volatility:
𝜂 𝑇 𝐻, 𝑋 =
𝑉 𝑇 𝐻,𝑋
𝑁 𝑇 𝐻,𝑋
For an no-arbitrage Wiener process, we
have
lim
𝑇→∞
𝜂 𝑇 𝐻, 𝜎𝜎 = 𝐾𝐾 = 2𝐻
Define a trading strategy such that the
position of X is:
𝛾�𝑡
𝐾
𝐻, 𝑋 =
∑ sign 𝜒 𝜏 𝑛−1 − 𝜒 𝜏 𝑛−1
∗
𝜒 𝜏 𝑛−1,𝜏 𝑛
𝑡
𝑁 𝑇 𝐻,𝑋
𝑛=1
The trend following P&L is:
𝑌�𝑡
𝐾
𝐻, 𝑋 = ∫ 𝛾� 𝑢
𝐾 𝐻, 𝑋 𝑑𝑑 𝑢
𝑡
0
= 𝜂 𝑇 𝐻, 𝑋 − 2𝐻 𝑁 𝑇 𝐻, 𝑋 + 𝜀
The expected income per trade is:
𝑌𝑡
𝐾
𝐻, 𝑋 = ∫ 𝛾𝑢
𝐾 𝐻, 𝑋 𝑑𝑑 𝑢
𝑡
0
𝑦𝑡
𝐾
𝐻, 𝑋 =
𝑌𝑡
𝐾 𝐻,𝑋
𝑁𝑡
𝐾 𝐻,𝑋
lim
𝑇→∞
E 𝑦𝑡
𝐾
𝐻, 𝑋 = 𝐾 − 2 𝐻
48
Based on: SV Pastukhov, "On some probabilistic-statistical methods in technical
analysis," Theory of Probability & Its Applications, SIAM, 2005.
49. Optimal Market Making (MM)
We optimally place limit and market
orders depending on the current
inventory and spread.
49
the best market making strategy:
trading period 2015/7/16 - 2016/3/1
assets traded
rebar + iron ore commodity
futures
annualized
return 65%
max
drawdown 0.90%
Sharpe ratio 16.71
Live Result:
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
50. Optimal Market Making (Math)
State variable:
𝑋, 𝑌, 𝑃, 𝑆
cash, inventory, mid price, spread
Objective:
max
𝛼
E 𝑈 𝑋 𝑇 − 𝛾 ∫ 𝑔 𝑌𝑡 𝑑𝑑
𝑇
0
𝑌𝑇 = 0, e.g., don’t hold position overnight
𝑈: utility function
𝑋 𝑇: terminal wealth
𝛾: penalty for holding inventory
Liquidation function (how much we get by selling
everything):
𝐿 𝑥, 𝑦, 𝑝, 𝑠 = 𝑥 − 𝑐 −𝑦, 𝑝, 𝑠 = 𝑥 + 𝑦𝑦 − 𝑦
𝑠
2
− 𝜀
Equivalent problem (get rid of 𝑌𝑇 = 0):
max
𝛼
E 𝑈 𝐿 𝑋 𝑇, 𝑌𝑇, 𝑃 𝑇, 𝑆 𝑇 − 𝛾 ∫ 𝑔 𝑌𝑡 𝑑𝑑
𝑇
0
Value function:
𝑣 𝑡, 𝑧, 𝑠 = sup 𝛼 E
𝑡,𝑧,𝑠
𝑈 𝐿 𝑍 𝑇, 𝑆 𝑇 − 𝛾 ∫ 𝑔 𝑌𝑢 𝑑𝑑
𝑇
𝑡
𝑧 = 𝑥, 𝑦, 𝑝
This is a mixed regular/impulse control problem in a
regime switching jump-diffusion model.
Quasi-Variational Inequality
min −
𝜕𝜕
𝜕𝜕
− sup ℒ 𝑞,𝑙
𝑣 + 𝛾𝛾, 𝑣 − ℳ𝑣 = 0
Terminal condition:
𝑣 𝑇, 𝑥, 𝑦, 𝑝, 𝑠 = 𝑈 𝐿 𝑥, 𝑦, 𝑝, 𝑠
For each state 𝑖, we have
min
−
𝜕𝑣 𝑖
𝜕𝜕
− 𝒫𝑣𝑖 − ∑ 𝑟𝑖𝑖 𝑡 𝑣𝑗 𝑡, 𝑥, 𝑦, 𝑝 − 𝑣𝑖 𝑡, 𝑥, 𝑦, 𝑝𝑚
𝑗=1
− sup 𝜆𝑖
𝑏
𝑞 𝑏
𝑣𝑖 𝑡, 𝑥 − 𝜋𝑖
𝑏
𝑞 𝑏
, 𝑝 𝑙 𝑏
, 𝑦 + 𝑙 𝑏
, 𝑝 − 𝑣𝑖 𝑡, 𝑥, 𝑦, 𝑝
− sup 𝜆𝑖
𝑎
𝑞 𝑎
𝑣𝑖 𝑡, 𝑥 + 𝜋𝑖
𝑎
𝑞 𝑎
, 𝑝 𝑙 𝑎
, 𝑦 − 𝑙 𝑎
, 𝑝 − 𝑣𝑖 𝑡, 𝑥, 𝑦, 𝑝
+𝛾𝛾,
𝑣𝑖 𝑡, 𝑥, 𝑦, 𝑝 − sup 𝑣𝑖 𝑡, 𝑥 − 𝑐𝑖 𝑒, 𝑝 , 𝑦 + 𝑒, 𝑝
= 0
𝑣𝑖 𝑇, 𝑥, 𝑦, 𝑝 = 𝑈 𝐿𝑖 𝑥, 𝑦, 𝑝
Assumptions:
𝑈 𝑥 = 𝑥; we care about only how much money made.
𝑃𝑡 𝑡 is a martingale; we know nothing about where the market will move.
Solution:
𝑣𝑖 𝑡, 𝑥, 𝑦, 𝑝 = 𝑥 + 𝑦𝑦 + 𝜙𝑖 𝑡, 𝑦
𝜙𝑖 𝑡, 𝑦 is the solution to the system of integro-differential equations
(IDE):
min
−
𝜕𝜙 𝑖
𝜕𝜕
− ∑ 𝑟𝑖𝑖 𝑡 𝜙𝑗 𝑡, 𝑦 − 𝜙𝑖 𝑡, 𝑦𝑚
𝑗=1
− sup 𝜆𝑖
𝑏
𝑞 𝑏
𝜙𝑖 𝑡, 𝑦 + 𝑙 𝑏
− 𝜙𝑖 𝑡, 𝑦 +
𝑖𝑖
2
− 𝛿1 𝑞 𝑏=𝑃𝑡
𝑏+ 𝑙 𝑏
− sup 𝜆𝑖
𝑎
𝑞 𝑎
𝜙𝑖 𝑡, 𝑦 − 𝑙 𝑎
− 𝜙𝑖 𝑡, 𝑦 +
𝑖𝑖
2
− 𝛿1 𝑞 𝑎=𝑃𝑡
𝑎− 𝑙 𝑎
+𝛾𝛾 𝑦 ,
𝜙𝑖 𝑡, 𝑦 − sup 𝜙𝑖 𝑡, 𝑦 + 𝑒 −
𝑖𝑖
2
𝑒 − 𝜀
= 0
𝜙𝑖 𝑇, 𝑦 = − 𝑦
𝑖𝑖
2
− 𝜀
50
Based on: F Guilbaud, H Pham, "Optimal high-frequency trading with limit and market
orders," Quantitative Finance, 2013.
52. FinMath Infrastructure Support
All these mathematics and simulations are possible only with a
finmath technology that serves as the modeling infrastructure.
52
• Linear algebra • Calculus
� 𝑓 𝑥
𝑏
𝑎
= 𝐹 𝑏 − 𝐹 𝑎
• Unconstrained
optimization
• Statistics • Differential
Equations
local minimumglobal minimum
Financial
Mathematics
Advanced
Mathematics
Foundation
Mathematics
• Parallelization
• Cointegration
• Optimization (LP, QP,
SQP, SDP, SOCP, IP,
GA)
• Digital Signal Processing• Time Series Analysis
Applications
• Optimal Trading Strategies • Portfolio Optimization • Extreme Value Theory
• Trading Signals • Asset Allocation • Risk Management
54. An Emerging Field
54
It is a financial industry where mathematics and
computer science meet.
It is an arms race to build
more reliable and faster execution platforms (computer
science);
more comprehensive and accurate prediction models
(mathematics).