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
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.
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.
"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.
"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.
"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.
"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.
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.
"Quantitative Trading as a Mathematical Science" by Dr. Haksun Li, Founder an...Quantopian
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.
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.
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.
"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.
"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.
"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.
"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.
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.
"Quantitative Trading as a Mathematical Science" by Dr. Haksun Li, Founder an...Quantopian
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.
"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.
"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?
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.
"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?
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.
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?
"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.
"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
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.
Backtesting And Live Trading With Interactive Brokers Using Python With Dr. H...QuantInsti
For the Webinar video, you can also visit: https://blog.quantinsti.com/ibridgepy-webinar-14-november-2019/
-----------------------------------------
Session Outline:
- IBridgePy installation
- A simple algorithmic trading strategy, daily close reverse
- Go through the code and basic functions used in this strategy
- Backtest strategy using historical data from IB in IBridgePy
- Backtest strategy using historical data from local csv file
- How to live trade a strategy
- Place orders to multiple accounts
- Analyze trading results from a strategy
Speaker Profile:
Dr. Hui Liu - Faculty, Executive Programme in Algorithmic Trading by QuantInsti
He is the author of IBridgePy (open-sourced software to trade with Interactive Brokers) and founder of Running River Investment LLC. His major trading interests are US equities and Forex market. Running River Investment LLC is a private hedge fund specialized in the development of automated trading strategies using Python.
He obtained his bachelor degree and master degree in materials science and engineering from Tsinghua University, China and Ph.D from University of Virginia, U.S.A. His MBA was from Indiana University, U.S.A and his study interest at Indiana was quantitative analysis.
-----------------------------------------
For the Webinar video, you can also visit: https://blog.quantinsti.com/ibridgepy-webinar-14-november-2019/
-----------------------------------------
Learn more about our EPAT® course here: https://www.quantinsti.com/epat/
OR Visit us at: https://www.quantinsti.com/
Like and Follow us on:
Facebook: https://www.facebook.com/quantinsti/
LinkedIn: https://www.linkedin.com/company/quantinsti
Twitter: https://twitter.com/QuantInsti
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 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.
Automated Selection and Robustness for Systematic Trading Strategies by Dr. T...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Even with a wide range of statistical tools available, selection of algorithmic trading strategies can
leave the trader with significant out-of-sample variability. In most cases the final decision making
is still a manual process.
This presentation will show how a combination of statistical methods and machine learning can help to automate strategy selection and boost the robustness of automated trading systems.
Order book dynamics in high frequency tradingQuantInsti
An important task of high-frequency trading is to successfully capture the dynamics in the Data. Empirical Data on Indian Exchanges show that 95% of all NEW orders are placed within 5 ticks of best-bid and best-ask. The Quantinsti® Replacement Matrix shows that most of the orders that are being replaced are among the top 3 levels and these replacements allow us to visualize and generalize about market behaviour. This matrix gives a visual representation of the cost metrics and replacement behaviour.
Execution Algorithms provide a price which is between Limit Order Execution and Market Order Execution. Market Orders guarantee execution within a certain time but the price that it may get the trader remains uncertain. Limit Order guarantees the price but it may remain un-executed if price moves away. Most Execution Algorithms balance between these two order types.
The speaker, Mr. Gaurav Raizada, discusses Quantinsti® Replacement Matrix in the webinar along with basics on order book management theory for high frequency traders.
Peculiarities of Volatilities by Ernest Chan at QuantCon 2016Quantopian
Ernie will explore some interesting features of both realized and implied volatilities that are useful to traders. These include the term structure of volatility, simple methods of volatility prediction, and what volatility and its siblings can tell us about future returns.
"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.
"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?
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.
"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?
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.
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?
"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.
"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
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.
Backtesting And Live Trading With Interactive Brokers Using Python With Dr. H...QuantInsti
For the Webinar video, you can also visit: https://blog.quantinsti.com/ibridgepy-webinar-14-november-2019/
-----------------------------------------
Session Outline:
- IBridgePy installation
- A simple algorithmic trading strategy, daily close reverse
- Go through the code and basic functions used in this strategy
- Backtest strategy using historical data from IB in IBridgePy
- Backtest strategy using historical data from local csv file
- How to live trade a strategy
- Place orders to multiple accounts
- Analyze trading results from a strategy
Speaker Profile:
Dr. Hui Liu - Faculty, Executive Programme in Algorithmic Trading by QuantInsti
He is the author of IBridgePy (open-sourced software to trade with Interactive Brokers) and founder of Running River Investment LLC. His major trading interests are US equities and Forex market. Running River Investment LLC is a private hedge fund specialized in the development of automated trading strategies using Python.
He obtained his bachelor degree and master degree in materials science and engineering from Tsinghua University, China and Ph.D from University of Virginia, U.S.A. His MBA was from Indiana University, U.S.A and his study interest at Indiana was quantitative analysis.
-----------------------------------------
For the Webinar video, you can also visit: https://blog.quantinsti.com/ibridgepy-webinar-14-november-2019/
-----------------------------------------
Learn more about our EPAT® course here: https://www.quantinsti.com/epat/
OR Visit us at: https://www.quantinsti.com/
Like and Follow us on:
Facebook: https://www.facebook.com/quantinsti/
LinkedIn: https://www.linkedin.com/company/quantinsti
Twitter: https://twitter.com/QuantInsti
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 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.
Automated Selection and Robustness for Systematic Trading Strategies by Dr. T...Quantopian
Presented at QuantCon Singapore 2016, Quantopian's quantitative finance and algorithmic trading conference, November 11th.
Even with a wide range of statistical tools available, selection of algorithmic trading strategies can
leave the trader with significant out-of-sample variability. In most cases the final decision making
is still a manual process.
This presentation will show how a combination of statistical methods and machine learning can help to automate strategy selection and boost the robustness of automated trading systems.
Order book dynamics in high frequency tradingQuantInsti
An important task of high-frequency trading is to successfully capture the dynamics in the Data. Empirical Data on Indian Exchanges show that 95% of all NEW orders are placed within 5 ticks of best-bid and best-ask. The Quantinsti® Replacement Matrix shows that most of the orders that are being replaced are among the top 3 levels and these replacements allow us to visualize and generalize about market behaviour. This matrix gives a visual representation of the cost metrics and replacement behaviour.
Execution Algorithms provide a price which is between Limit Order Execution and Market Order Execution. Market Orders guarantee execution within a certain time but the price that it may get the trader remains uncertain. Limit Order guarantees the price but it may remain un-executed if price moves away. Most Execution Algorithms balance between these two order types.
The speaker, Mr. Gaurav Raizada, discusses Quantinsti® Replacement Matrix in the webinar along with basics on order book management theory for high frequency traders.
Peculiarities of Volatilities by Ernest Chan at QuantCon 2016Quantopian
Ernie will explore some interesting features of both realized and implied volatilities that are useful to traders. These include the term structure of volatility, simple methods of volatility prediction, and what volatility and its siblings can tell us about future returns.
How AI learnt large-scale Pair-Trading on S&P 500? (Updated)Kamer Ali Yuksel
This presentation demonstrates the fascinating ability of (Deep Reinforcement Learning) AI to evolve market-neutral, risk-sensitive, commission-avoidant and market-agnostic Financial Portfolio Management strategies, which are able to avoid transaction costs and generalize out-of-sample periods on both FX and US-stock markets, by acting very similar to the industry-standard pair-trading and performing that strategy efficiently on a very large-scale via learning and exploiting correlations among complex hierarchical clusters of financial assets.
This is a Presentation by Mr. Omkar Godbole & Mr. Aditya Dasgupta, for the purpose of financial training.
Please do not replicate without proper consent from the team of Total Package Project Associates.
Total Package Project Associates is into Business Auxiliary Services, and facilitates for the main dimensions for new and existing businesses. The following are our main activities :
- Project Financing / Financial Advisory (Equity / Currency Segment)
- IT Infrastructure Development
- Marketing Solutions
- Recruitment / Training & Development
- Operations Strategy (Start Up, Doc ! Project Blueprints)
Disclaimer : Total Package Project Associates is a proprietary concern registered in accordance with Municipal Laws of Mumbai (Maharashtra, India). For more information please mail us on director@totalpackageprojects.com or aditya@totalpackageprojects.com.
Originally published in 2005. Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions. Delisted companies were included to account for survivorship bias. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system.
Through examining their nature and mechanisms, identifying their spin-offs and analyzing their performance, this presentation is designed to discuss what to look out for when conduct due diligence on different hedge fund strategies.
See how I successfully trade stocks by looking back at some past trades.
These are NOT hindsight trades and were posted real real time.
You can see all my setups posted to TraderPlanet here: http://www.traderplanet.com/newsletter-issues/articles/1/Steven+Place/
And you can get more awesome stuff by going to my site at investingwithoptions.com
Introduction to Indian Financial System ()Avanish Goel
The financial system of a country is an important tool for economic development of the country, as it helps in creation of wealth by linking savings with investments.
It facilitates the flow of funds form the households (savers) to business firms (investors) to aid in wealth creation and development of both the parties
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
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
Empowering the Unbanked: The Vital Role of NBFCs in Promoting Financial Inclu...Vighnesh Shashtri
In India, financial inclusion remains a critical challenge, with a significant portion of the population still unbanked. Non-Banking Financial Companies (NBFCs) have emerged as key players in bridging this gap by providing financial services to those often overlooked by traditional banking institutions. This article delves into how NBFCs are fostering financial inclusion and empowering the unbanked.
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
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
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
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
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 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
2. DIY Quant strategies:
Is it possible to roll your own?
Jess Stauth, PhD
VP Quant Strategy
Bay Area Algorithmic Trading Meetup
Hacker Dojo * February 6, 2014
3. What makes a good equity quant strategy?
Intuition. If you can‟t explain why it works, it doesn‟t
work.
Reproducibility. If you can‟t backtest it, it doesn‟t work
(note the inverse does not necessarily hold).
Access to data. If you can‟t get the signal (or get it in
time) you can‟t trade it. ($$$)
Capacity/Execution You can‟t push a camel through the
eye of a needle. (1/$$$)
4. 5 Basic Quant Strategies
1. Mean Reversion – What goes up… (special case: Pairs Trade)
2. Momentum – The trend is your friend.
3. Valuation – Buy low, sell high.
4. Sentiment – Buy the rumor, sell the news.
5. Seasonality – Sell in May and go away.
Out of scope for today‟s talk:
Acronym soup (e.g. ML, OLMAR, PCA, ICA, OLS, etc.)
Portfolio construction, risk optimization, etc.
Asset clases
5. Pairs Trading
Intuition: Find two assets linked to a single underlying „value‟
and exploit transient mispricing between them.
Reproducibility: The phenomenon is well documented1,2.
Data: For basic strategies all you need is pricing.
Capacity: Can be quite small depending on the instruments.
Common pitfalls:
Ignore the intuition requirement at your own peril! Cointegration works great, until it doesn‟t.
Market neutral or „hedged‟ strategy, so you are forgoing any upward drift in the longer term.
1. Pairs Trading, Vidyamurthy 2004
2. Quantitative Trading, Chan 2009
6. Pairs Trading
Simplistic Intuition (cont‟d): If you assume the spread between stock 1 and stock 2 is
„stationary‟ and „normally distributed‟, then statistically you should be able to make money
by „buying‟ or „selling‟ the spread when it takes on extreme tail values.
Zx = (Price Stock1 – Price Stock2)/ Price Stock1
8. Momentum Trading
Intuition: Comes in many flavors (stock level, sector level, asset
class level) but comes back to the behavioral bias of „herding‟.
Reproducibility: The phenomenon is well documented1.
Data: For basic strategies all you need is pricing.
Capacity: Can be quite small depending on the instruments.
Common pitfalls:
The trend is your friend, until it isn‟t. Reversals can be devastating, especially when using
leverage.
1. Jegadeesh and Titman, Returns to Buying Winners and Selling Losers: Implications for Stock
Market Efficiency. Journal of Finance March 1993
2. Faber, A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management 2013
9. Momentum Trading
Simple rules based approach
Rank 1 > N stocks (sectors) by : [r20 – r200]
Buy top K stocks (sectors) where absolute
momentum (20 vs. 200 day MA) > some
threshold.
Else, hold cash.
10. Momentum Trading – Meb Faber RS Strategy
Backtest range: 11/04 – 2/13
John Chia Posted Feb 2013
“Mebane Faber Relative Strength Strategy with MA Rule”
https://www.quantopian.com/posts/mebane-faber-relative-strength-strategy-with-ma-rule
11. Valuation
Intuition: In a nutshell, bargain shopping. Use fundamental ratio
analysis to identify stocks trading at a discount (or premium) and
buy (or sell) them accordingly.
Reproducibility: The phenomenon is well documented.
Data: Requires good coverage (breadth and depth) of
normalized corporate fundamental data.
Capacity: Small cap stocks can be riskier, and higher friction to
trade.
Common pitfalls:
Some cheap stocks are cheap for a reason. “Catch a falling knife” adage.
12. Valuation
Simple example: use price to earnings ratio as a proxy for „value‟
where low P/E looks „cheap‟ and high P/E looks „expensive‟.
Rank universe 1-100 (or sector universe) on P/E
Long only: buy the bottom (lowest P/E) decile
Market neutral: buy the bottom decile, sell the top decile
In practice, a quant model would typically blend a number of
backward looking ratios an forward looking estimates along with
making sector specific adjustments and other bells, whistles.
13. Valuation: Screen on corporate fundamentals
Backtest range 11/25/2009 – 10/10/2013
Sam Lunt (11/4/2013) “Using Fetcher with Quandl”
https://www.quantopian.com/posts/using-the-fetcher-with-quandl
14. Sentiment: Short sellers
Intuition: Follow the (short) money. Short sellers are the „smart
money‟, their trades are $ for $ higher conviction (to balance
risk).
Reproducibility: The phenomenon is well documented.
Data: Bi-monthly (delayed) short interest can be scraped from
NASDAQ. Borrow rates, real-time daily short interest data
aggregated from brokers is available for $$$.
Capacity: Can be quite small depending on the instruments.
Common pitfalls:
Beware the Short Squeeze! Crowded short trades can lead to a squeeze as short sellers rush to close
positions.
15. Sentiment: Short sellers
Rank stocks 1 > N on Days To Cover ratio*
Buy top 10%, short bottom 10%
Rebalance periodically
*Days to cover =
Shares Held Short
Avg Daily Trade Share volume
The number of days of „average‟ trading it would take to
unwind the existing short positions.
16. Sentiment: Short sellers – Rank on Days to Cover
Backtest range: 3/15/12 – 3/15/13
Fawce (April 2013)
“Ranking and Trading on Days to Cover”
https://www.quantopian.com/posts/ranking-and-trading-on-days-to-cover
17. Seasonality
Intuition: Sometimes (calendar driven fund flows
e.g. month end).
Reproducibility: There‟s healthy debate on this
one.
Data: end of day pricing and a calendar.
Capacity: Depends on the instruments.
Common pitfalls:
Overfitting / data mining is rampant in this type of analysis.
18. Seasonality
Simplest example is a simple 100% stock/bond annual
rotation model.
Buy and hold equities (SPY) October thru April
Buy and hold bonds (BSV) May thru Sept.
19. Seasonality: Sell in May
Backtest range: 10/1/09 – 12/31/12
Jess(May 2013)
“Sell in May and go away”
https://www.quantopian.com/posts/time-to-sell-in-may-and-go-away
20. Which of these strategies are most popular among
the „retail‟ or individual quants using Quantopian?
Mean Reversion
Momentum
Valuation
Sentiment
Seasonality
Other
21. 25 Top Shared Algorithms of All Time
Combo Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Post Title
Replies
Google Search Terms predict market movements
OLMAR implementation
Easy Volatility Investing by Tony Cooper @ Double-Digit Numerics
Global Minimum Variance Portfolio
discuss the sample algorithm
ML - Stochastic Gradient Descent Using Hinge Loss Function
Mebane Faber Relative Strength Strategy with MA Rule
OLMAR w/ NASDAQ 100 & dollar-volume
Bollinger Bands With Trading
Brent/WTI Spread Fetcher Example
Ernie Chan's Pairs Trade
Ranking and Trading on Days to Cover
Using the CNN Fear & Greed Index as a trading signal
Determining price direction using exponential and log-normal distributions
Time to sell in may and go away?
Simple Mean Reversion Strategy
Neural Network that tests for mean-reversion or momentum trending
Using weather as a trading signal
Momentum Trade
Trading Strategy: Mean-reversion
Global market rotation strategy
trading earnings surprises with Estimize data
Turtle Trading Strategy
SPY & SH algorithm - please review
New Feature: Fetcher!
TOTALS:
Views
Clones
64
64
57
28
12
10
22
31
18
17
15
4
18
9
27
6
4
6
5
13
53
34
11
21
27
31913
26039
15117
10222
18348
20400
11104
7760
8363
10821
10387
24906
9212
9539
8192
11794
10062
11940
8800
8228
7621
7496
7815
7443
7507
809
697
839
700
2882
972
617
697
560
327
328
379
318
606
261
270
402
199
455
213
94
129
299
194
108
576
311,029
13,355
22. 25 Top Shared Algorithms of All Time
Combo Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Post Title
Google Search Terms predict market movements
OLMAR implementation
Easy Volatility Investing by Tony Cooper @ Double-Digit Numerics
Global Minimum Variance Portfolio
discuss the sample algorithm
ML - Stochastic Gradient Descent Using Hinge Loss Function
Mebane Faber Relative Strength Strategy with MA Rule
OLMAR w/ NASDAQ 100 & dollar-volume
Bollinger Bands With Trading
Brent/WTI Spread Fetcher Example
Ernie Chan's Pairs Trade
Ranking and Trading on Days to Cover
Using the CNN Fear & Greed Index as a trading signal
Determining price direction using exponential and log-normal distributions
Time to sell in may and go away?
Simple Mean Reversion Strategy
Neural Network that tests for mean-reversion or momentum trending
Using weather as a trading signal
Momentum Trade
Trading Strategy: Mean-reversion
Global market rotation strategy
trading earnings surprises with Estimize data
Turtle Trading Strategy
SPY & SH algorithm - please review
New Feature: Fetcher!
Replies
Views
Clones
64
64
57
28
12
10
22
31
18
17
15
4
18
9
27
6
4
6
5
13
53
34
11
21
27
31913
26039
15117
10222
18348
20400
11104
7760
8363
10821
10387
24906
9212
9539
8192
11794
10062
11940
8800
8228
7621
7496
7815
7443
7507
809
697
839
700
2882
972
617
697
560
327
328
379
318
606
261
270
402
199
455
213
94
129
299
194
108
23. 25 Top Shared Algorithms of All Time
Categorized
Volatility
5%
Technical
3%
Seasonality
3%
Portfolio Risk
6%
Momentum
18%
Mean
Reversion
37%
Area ~ page views
Sentiment
28%
What‟s missing from this picture??
*Not a Mutually Exclusive CollectivelyExhaustive list.
avg 30 day return = 0.93%
select po.title, po.replies_count, po.views_count, al.clone_countfrom posts as po left join backtests as ba on ba.id=po.backtest_idjoin algorithms as al on ba.algorithm_id=al.idorder by views_countdesclimit 31