The trouble with vpin

The trouble with vpinThe Trouble with VPIN

New economic indicator fails to live up to its promises

Based on the research of Torben Andersen and Oleg Bondarenko

Stock markets in the United States on May 6, 2010, were not having a good day. By early afternoon, concerns over the European debt crisis and an upcoming jobs report had driven most major indices solidly into negative territory. But as bad as things looked, they were about to get a lot worse.

At 2:41 PM, prices for E-mini SP 500 futures—the worlds most liquid equity index contract—started plunging. By 2:44 PM, algorithms used by high-frequency traders to buy and sell that contract were going crazy, selling more than they were buying and threatening to vaporize liquidity. Mere seconds later, those same algorithms bought and sold over 27,000 contracts in just 14 seconds, yet netted only 200 additional contracts. The market was going haywire. One second after that frenzy, trading in E-mini SP 500 futures was halted for five seconds, forcing the computers to take a breather. By 3:00 PM, markets recovered from the crash. In just 20 minutes, the Dow Jones Industrial Average had lost and then regained nearly 1,000 points.

The incident had taken a psychological toll. Investors were spooked. And worse, no one seemed to know how it had happened. It would take the Securities and Exchange Commission and Commodity Futures Trading Commission five months to release a report that drew only tentative conclusions. Since then, some preventative measures have been put in place but many experts are not convinced they are enough.

Imagine peoples relief when a new method was announced that could predict imminent flash crashes. Devised by two well-respected economists and the research head of a hedge fund, the measure, called VPIN, or volume-synchronized probability of informed trading, monitors imbalances in trading—when sellers outnumber buyers or vice versa—and purports to peak before problems arise. Its inventors hail it as superior to existing market indicators like VIX, the widely watched volatility index.

The trio behind VPIN—David Easley and Maureen OHara, both economists at Cornell University, and Marcos López de Prado, head of high-frequency trading at Tudor Investment—believe the measure has the potential to become a critical financial indicator and have filed for a patent. Furthermore, they are urging regulators to use VPIN as a watchdog signal. There is every reason to think that might happen—in addition to her position at Cornell, OHara also serves on a panel convened by the SEC and the CFTC to investigate the flash crash. She and her co-authors strongly believe that VPIN could alert market regulators to an impending crash like that which occurred on May 6, 2010.

The problem is, not everyone agrees.

Dissecting VPIN

Torben Andersen is one of those people. His research focuses on market volatility and asset pricing, two factors that are central to understanding the flash crash. His interest in peculiar market events is what led him to pick up the VPIN paper.

“It has policy relevance,” says Andersen, a professor of finance at the Kellogg School of Management, of the working paper. What also piqued his interest was the authors claim that VPIN could predict market imbalances and short-term volatility better than VIX. “I have other papers where I criticize VIX,” he says, “but I still think its fairly good. Maybe you could make it a little bit better, but its good.”

“So I started reading it,” Andersen says of the VPIN paper. “Its just—I cant get my hands on this thing. Its such a complicated beast. Not in its construction, but in its mixing of all these different concepts. And when we started looking at it systematically, in terms of forecasting it performs much worse than VIX.”

In saying “we,” Andersen is referring to himself and his co-author, Oleg Bondarenko, a professor at the University of Illinois at Chicago. Together, the two took VPIN apart to mathematically analyze each component. When they were finished, they concluded that VPIN was not bad, per se . but it could not do what its creators had claimed. “We cant completely get the results they got,” Andersen says.

To calculate VPIN in its simplest form, you group consecutively traded contracts—say 50,000 in a row—into bins, regardless of time or date. Grouping sequential trades in that way is called trading time, and depending on market volume it can vary substantially with respect to clock and calendar time. In the next step you analyze how many minutes those 50,000 trades spanned, down to one-minute increments, also known as time bars. If trading is happening at a furious pace, it is possible all 50,000 trades in a bin could be squeezed into one minute, or time bar. After identifying the individual time bars, you assign each bar a “buy” label if there were more contracts bought than sold in that span or a “sell” label if more were sold than bought. Time bars labeled as “buy” are valued +1, while time bars labeled “sell” are valued 1. You then construct a volume-weighted average of the buy-sell indicators for the time bars and take the absolute value of that number. Finally, you merge that bin with the 50 bins preceding it, perform some more mathematical wizardry, and presto—you have calculated VPIN for that minute.

Removing Bias

According to Andersen, the problems with VPIN are numerous. One issue is the way in which it mixes trading volume and time. “How many contracts that are traded in a minute is very highly correlated with volatility. When volatility is high, people trade more, and so these minutes will contain many more contracts. As a result, there will be many less minutes in the volume bucket, and that will bias the measure towards the extreme in a completely mechanical way,” he says.

The conflation of volume and time also caused another problem. Because trades are first grouped sequentially and then next by regular clock time, the separation between two days trading sessions is obscured. For example, if there are not enough trades from one day to complete a group, then trades from the next day are used until the proper number, say 50,000, is reached. As a result, VPIN is highly dependent on when exactly you start counting trades. If you start counting one day later than someone else, your groups will contain different trades and your VPIN will be different. It is also vitally important that you have all the trades for that time period. Any missing trades also will shift the contents of the groups, potentially leading to very different results.

At first, Andersen and Bondarenko could not reproduce Easley, OHara, and López de Prados findings. “We had to start in ten or fifteen different places in the past in order to replicate their results,” he recounts. Andersen and Bondarenko also realized they were using a different data source than Easley, OHara, and López de Prado did. “Upon inspection, it was evident that our trading volume was on average a little bit bigger than theirs,” Andersen says.

The data both groups of researchers used are trades of E-mini SP 500 contracts on the Chicago Mercantile Exchange—the same instrument that precipitated the flash crash. Easley, OHara, and López de Prado obtained their data from the real-time data feed of a hedge fund, while Andersen and Bondarenko received historical data directly from the CME Group. “Ive subsequently spoken to the guys at the CME Group. This particular contract is only traded electronically, and all the trades are recorded in their system,” Andersen recounts. “Thats the only complete historical record for this data.”

Figure 1. Minute-by-minute data for the E-mini SP 500 futures index level, the VPIN measure constructed from one-minute data, the SP 500 volatility index, VIX, and the volume of traded contracts of the E-mini SP 500 futures on the CME for May 6, 2010. Vertical green lines indicate the timing of the “flash crash.”

When Andersen and Bondarenko were finally able to find the proper starting point, they ran into other problems. The most alarming was that VPIN spiked after the crash, not before, hinting that it may be a reactive metric rather than a predictive one (Figure 1). Also troublesome was that VPIN did not hit an all-time high around the time of the flash crash. In fact, it crossed the same threshold as during the flash crash on at least two other occasions, neither of which corresponded with an errant market.

Revamping VPIN

Despite his reservations, Andersen does not think VPIN is a fatally flawed measure. With some changes, he says, “there is some encouragement that you might be able to say something useful” with VPIN. “If you measured more sensibly, it lines up much more with what actually happened” in the flash crash, he adds.

The first thing Andersen and Bondarenko suggest is using signed measurements, both within and across the time bars. By taking the absolute value of trading imbalance each minute, VPIN ignores information about the direction in which the market is moving. It may also prevent alternating periods of buying and selling from canceling each other out. For example, if the selling indicator outpaces the buying indicator over one volume bucket by a margin of 0.5 but reverses by the same amount over the next, both buckets are scored 0.5 by VPIN. The result is an average imbalance of 0.5, despite the overall perfect balance of buying and selling. In contrast, an unsigned VPIN measure would report a change of zero (0.5 0.5), providing observers with better information on cumulative imbalances or lack thereof.

Andersen and Bondarenko would also do away with the switch between trading time and calendar time. Starting with trading volume and sticking with trading volume—as opposed to starting with trading volume and then studying trades per minute—a modified VPIN would not be artificially biased toward extreme values during periods of stress.

Preparing for the Next Flash Crash

On September 20, 2010, the SEC and CFTC released a joint report of their investigations. They laid blame for the May 6 flash crash at the feet of a mutual fund, later identified by the Wall Street Journal as Waddell Reed Financial of Overland Park, Kansas. At around 2:32 PM that day, a trader from Waddell Reed had started a sell program to unload 75,000 E-mini SP 500 contracts worth about $4.1 billion, an enormous sale for that instrument. The rate of selling was pegged to 9 percent of trading volume in the previous minute, which meant as volume ramped up the program dumped even more contracts onto the market.

High-frequency trading firms initially picked up the contracts with the intention of quickly turning them around. But the subsequent glut of contracts caused the price to drop further. The high-frequency traders algorithms panicked, sparking the 14 seconds of fevered trading in which 27,000 trades were made but only 200 positions were added—what regulators called a “hot potato.” CME computers then stepped in, halting trading for five seconds, enough for everyone to catch their digital breaths. The market started to recover from there, though major indices still ended the day with substantial losses.

It has been nearly two years since the flash crash. The SEC has implemented “circuit breakers” that halt trading when prices start flailing, along with “limit-up, limit-down” controls to prevent individual stock prices from trading outside specified bands. Still, fears linger that despite these defenses another such flash crash could sweep the market.

Easley, OHara, and López de Prado are urging regulators to use VPIN as an early warning tool. Andersen, as you might imagine, is less sanguine about VPINs utility. For now, though, his hands are tied. “Whats holding us back is that regulators have collected important data about individual firms trading activity that American scholars may access, but I cant as Im only a resident,” he says. But that does not mean the inquiry is over. “The regulators have this data,” he says, adding, “People are actively working on it.”

“I think theres hope that you could come up with some useful measures related to VPIN. Exactly how successful they will be, I dont know.”

Related reading on Kellogg Insight

Richardlogan3s blog

Richardlogan3s blog07/06/2012

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Python Developer – Algo Trading Systems - Sydney ( Speedy_Snake_AB ) Location. locations globally and a reputation for creating robust trading strategies.

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Are you a Python Guru with experience in Numpy and Scipy and a strong. in multiple locations globally and a reputation for creating robust trading strategies sat.

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ultra-finance - Python project for real-time financial data.

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Python and its extensions are open source and so allow you to see under the hood. Trading Strategies and Portfolio Constructions based on Cross Sectional Regression?

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Forums - Building an automated trading system in Python

be based around my loess filter algorithm, which is implemented in the Python. is a free platform that allows automated execution of predefined trading strategies.

Switching from Matlab to Python for Quant Trading and Research.

Building Your Own Trading System - Part 5 | Stock Trading: Futures.

Python project for real-time financial data collection, analyzing backtesting trading strategies. Welcome Ultra-finance is a pure Python library utility for real.

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Vpin trading strategy

Vpin trading strategyForex Pin Bar Trading Strategy

April 9, 2014

The Forex pin bar trading strategy is by far my favorite price action pattern. In this lesson were going to cover what makes a pin bar a pin bar, how to know if a pin bar is worth trading as well as entry and exit strategies. As always, the term bar is interchangeable with candlestick, however the common term has always been pin bar, not pin candlestick ??

Before getting into the actual Forex pin bar trading strategy, we need to know the parts that make up a pin bar so we can easily identify them.

What is a Pin Bar?

Lets start with the tail of the pin bar, which is its defining characteristic and also sometimes called the wick or shadow. The tail of a pin bar should be at least 2/3 the length of the entire bar. The longer the better, but it must make up at least 2/3 of the bar from end to end. Notice in the image to the right, the tail is about 3/4 of the entire bar, so this qualifies.

The body of a pin bar is also important as it represents the open and close of the pin bar. The open and close should be close together; the closer the better. The body should also be close to the end of the pin bar. Notice how close the open and close are to the nose of the pin bar in the image.

Last but not least, the nose of the pin bar. While not as important as the tail or body, the nose is important only as it relates to the tail and body. This is because if the tail is at least 2/3 of the entire bar and the body is small, then the nose should also be relatively small. Also know that a pin bar doesnt need a nose to be a pin bar. Sometimes its non-existent if the open or close occur at the extreme end of the pin bar.

Two Types of Pin Bars

There are two main types of pin bars as it relates to price action patterns that are taught in my price action course. Most traders assume the pin bar is simply a reversal pattern, and it is, but theres another way to trade pin bars that Ill explain shortly. First, lets look at the more common way to trade pin bars as a reversal pattern.

The reversal pin bar (above) is best played in a ranging market or on a pullback within a larger trend. Lets look at both in action.

Below is a great example of a pin bar that formed after price broke through support and then retested it from the other side as resistance. This is actually a pattern thats still taking shape as I type this.

Now for the other type of reversal pin bar, which can be found in a ranging market

Justin Bennett is a full-time Forex trader and Owner of Daily Price Action. His Forex trading career began 6 years ago and has followed a path similar to many traders. For the first 3 years he tried nearly every indicator and strategy known to man, but each time the journey ended where it began, frustrated and in search of the next holy grail that would bring consistent profits. It wasnt until he cleared every indicator from his chart that he had his ah ha moment. For the past 3 years, Justin has worked to perfect that moment into something that can be easily duplicated by other traders in search of consistent profits.

Trading strategy developer(java,c,python,mathematics,com in codublin,ireland

Trading strategy developer(java,c,python,mathematics,com in codublin,irelandTrading Strategy Developer (Java, C++, Python, Mathematics, Com in Co. Dublin, Ireland

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Quantum blog

Quantum blogMonday, February 4, 2013

Trading With Python online course starting in April

A couple of observant readers already figured out that in spite of the url of this blog, past posts have been based on Python code. The gradual transfer of all of my research code from Matlab to Python is now complete. After working with Python for more than two years, I can state that it an excellent tool for research and data crunching. When it comes to collecting data from the web, cleaning and aligning datasets with missing data or building GUIs, it is the best tool I have ever come across. And don't forget that Python is open source and free.

Tuesday, January 1, 2013

Intraday mean reversion

In my previous post I came to a conclusion that close-to-close pairs trading is not as profitable today as it used to be before 2010. A reader pointed out that it could be that mean-reverting nature of spreads just shifted towards shorter timescales. I happen to share the same idea, so I decided to test this hypothesis.

This time only one pair is tested: 100$ SPY vs -80$ IWM. Backtest is performed on 30-second bar data from 11.2011 to 12.2012.

The rules are simple and similar to strategy I tested in the last post:

if bar return of the pair exceeds 1 on z-score, trade the next bar .

The result looks very pretty:

Algo trading with python

Algo trading with pythonAlgo Trading with python

One of the practical goals in my education plan is:

Develop programming skills that will aid in my trading

I believe trading to be more art then science. Still back testing rule based strategies is necessary, but it is a lot of work if you do this manually. And that's where programming comes in handy. Also monitoring markets and signal creation is something that I would want to automate.

So last Friday I registered for Jev Kuznetsov's Trading with python course. I have been keeping an eye on Jev's blog for some time know, as he seems to be doing exactly what I intend to be doing. And that is: back testing trading strategies algorithmically using python. Be sure to check out his blog if you are interested. You can register for his course here. In below video Jev explains what the course is about.

In the course Jev will be using a book that was already on my wish list:

So I have gone ahead and bought the ebook version of the book. Googling for the author Wes McKinney, I came across this blog post:

And from there I found out about Quantopian. a community driven online algorithmic trading platform. Check out below video to learn more.

Quantopian uses the Zipline python library for back testing. The library is available from github: github/quantopian/zipline

I installed zipline and made an account on Quantopian. So now I can start playing around with some algos on my machine and in the browser. Quantiopian seems to be a great place for quickly testing new ideas and for practicing. In the future they will also be offering the ability to paper trade and to trade live on selected brokers right from their platform.

Then finally I watched this presentation: Intro to algorithmic trading models by Prof. Ahmed Namini. Below video and slides go hand in hand. I embedded them both below so it is easy to watch the presentation.

If you found this post helpful, please be sure to share.

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Vectorized vs event driven backtesting

Vectorized vs event driven backtestingVectorized vs Event Driven Backtesting

Vectorized vs Event Driven Backtesting

One is vectorized, one is event driven Obviously?

I am not sure there is a question of realism here - it is quote directly about technological approaches only. Not everything has a clear better/worse.

Realism is not about what fundamental programming approach you take, but how good you program (saying as someone just rewriting his exchange simulator into I think version 6 no to handle some issues I have with timing).

Thank you for your answer.

The following quotation is from quantsart blog:

We've spent the last couple of months on QuantStart backtesting various trading strategies utilising Python and pandas (pandas. pydata/ ). The vectorised nature of pandas ensures that certain operations on large datasets are extremely rapid. However the forms of vectorised backtester that we have studied to date suffer from some drawbacks in the way that trade execution is simulated. In this series of articles we are going to discuss a more realistic approach to historical strategy simulation by constructing an event-driven backtesting environment using Python .

The reason I was asking the differences between them was that I do not know R, MATLAB or Python. I wanted to start learning the most realistic one.

So what you are saying is, if I do the coding with slippage, commissions and other cost included, the realism will be the same in R or MATLAB or Python. Or did I misunderstand you?

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Quantitative finance e-booksQUANTITATIVE FINANCE E-BOOKS

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Pythoneye develop python more brightly

Pythoneye develop python more brightlyScripting language for trading strategy development

I'm currently working on a component of a trading product that will allow a quant or strategy developer to write their own custom strategies. I obviously can't have them write these strategies in natively compiled languages (or even a language that compiles to a bytecode to run on a vm) since their dev/test cycles have to be on the order of minutes.

Best Answer

Mark-Jason Dominus, the author of Perl's Text::Template module, has some insights that might be relevant:

When people make a template module like this one, they almost always start by inventing a special syntax for substitutions. For example, they build it so that a string like %%VAR%% is replaced with the value of $VAR. Then they realize the need extra formatting, so they put in some special syntax for formatting. Then they need a loop, so they invent a loop syntax. Pretty soon they have a new little template language.

This approach has two problems: First, their little language is crippled. If you need to do something the author hasn't thought of, you lose. Second: Who wants to learn another language?

If you write your own mini-language, you could end up in the same predicament -- maintaining a grammar and a parser for a tool that's crippled by design.

If a real programming language seems a bit too low-level, the solution may not be to abandon the language but instead to provide your end users with higher-level utility functions, so that they can operate with familiar concepts without getting bogged down in the weeds of the underlying language.

That allows beginning users to operate at a high level; however, you and any end users with a knack for it -- your super-users -- can still leverage the full power of Ruby or Python or whatever.

Trading algo using python for interactive broker tws

Trading algo using python for interactive broker twsTRADING ALGO USING PYTHON FOR INTERACTIVE BROKER TWS

Всего заявок

Описание проекта

We'd need some help in writing in Python a simple Forex/Futures trading strategy (which is simple and already well defined) so that the IB API of Interactive Brokers could automatically send to market buy and sell orders on various currency pairs following a basic set of trading rules to be written as mentioned on Python.

This algorithm takes about 5-6 key inputs, including futures tickers, number of seconds to track (S), number of std dev to trigger or stop a trade, position size (ideally, should have the last inputs as default)

- Needs to connect to an Interactive Brokers (IB) account through API (can use IB Gateway or IB TWS)

- starts tracking real-time data for the futures when turned on or at assume 8am NY time and downloads prices real time keeping track of the few data points,

- next step is to compute moving averages over different horizons (1 week, 1 month, 100day, 200day, 12mth etc) using the 4pm closing data points

- every second it will make a couple of calculations using the MAs or the last few days's pricings on the two pricing arrays including simple math and standard deviations and with the most current data point, a simple calculation will trigger a Short/Long trade

- when there is a Short/Long new position triggered, it needs to place that order for IB to execute immediately. There should be a switch using which user can decide if trades are meant to be automatically executed or just sent as limit orders.

- Strong checks need to be built to ensure in case of software or market data errors multiple orders are not sent causing unwanted trades

- once trades are done there should be a mechanism to track the trade wise PL and portfolio PL for a take profit or stop loss level

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Python backtesting libraries for quant trading strategies

Python backtesting libraries for quant trading strategiesPython Backtesting Libraries For Quant Trading Strategies

( 3 votes, average: 5.00 out of 5)

Written by Khang Nguyen Vo, khangvo88gmail. for the RobustTechHouse blog. Khang is a graduate from the Masters of Quantitative and Computational Finance Program, John Von Neumann Institute 2014. He is passionate about research in machine learning, predictive modeling and backtesting of trading strategies.

Frequently Mentioned Python Backtesting Libraries

It is essential to backtest quant trading strategies before trading them with real money. Here, we review frequently used Python backtesting libraries. We examine them in terms of flexibility (can be used for backtesting, paper-trading as well as live-trading), ease of use (good documentation, good structure) and scalability (speed, simplicity, and compatibility with other libraries).

Zipline . This is an event-driven backtesting framework used by Quantopian.

Zipline has a great community, good documentation, great support for Interactive Broker (IB) and Pandas integration. The syntax is clear and easy to learn.

It has a lot of examples. If your main goal for trading is US equity, then this framework might be the best candidate. Quantopian allows one to backtest, share, and discuss trading strategies in its community.

However, in our experiment, Zipline is extremely slow. This is the biggest disadvantage of this library. Quantopian has some work-around such as running the Zipline library in parallel in the cloud. You can take a look at this post if this interests you.

Zipline also seems to work poorly with local file and non-US data.

It is difficult to use this framework for different financial asset classes.

PyAlgoTrade . This is another event-driven library which is active and supports backtesting, paper-trading and live-trading. It is well-documented and also supports TA-Lib integration (Technical Analysis library). It outperforms Zipline in terms of speed and flexibility. However, one big drawback of PyAlgoTrade is that it does not support Pandas-object and Pandas modules.

pybacktest . Vectorized backtesting framework in Python that is very simple and light-weight. This project seemed to be revived again recently on May 21 st ,2015.

TradingWithPython . Jev Kuznetsov extended the pybacktest library and build his own backtester. This library seems to updated recently in Feb 2015. However, the documentation and course for this library costs $395.

Some other projects: ultra-finance

Python Backtesting Libraries For Quant Trading Strategies

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We are fundamentally different than most other trading forums:

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Python backtesting framework

Python backtesting frameworkPython Backtesting framework

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Python Backtesting framework

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Python Backtesting framework

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veterans and active duty military, and other adults. Their disabilities include Autism, Traumatic Brain Injury,

Cerebral Palsy, Spinal Cord Injuries, Muscular Dystrophy, Multiple Sclerosis, and Post Traumatic Stress Disorder (PTSD).

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Algo trading backtestingAlgo Trading BackTesting /Optimization Report (MT4, Python, MatLab)

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Trading strategy in python

Trading strategy in pythonHow to Backtest a Strategy in Python

I need to be able to determine whether a particular "trade" (indicated by "signal") resulted in a profit or loss by indicating a win or loss for each.

I need Python to check the next location ( the signal or entry point or date + 1 ) in the High and Low lists ( the lists: close . highs . and lows will have the same number of values ) for an increase in value equal to or greater than 2.5% at some point beyond the entry signal.

However, I also want Python to determine if the value drops 3% or more prior to appreciating 2.5% or more.

This must occur for each entry in signal .

In essence, I need a limit to sell at 102.5% and a stop at 97%.

Unfortunately, the code I developed so far doesn't seem to be working.

What am I missing?

A VIX ETP Strategy from Trading with Python

This is a follow up to a strategy from the excellent blog Trading with Python (TWP). The strategy uses the relationship between the VIX and VXV indices to trade VIX ETPs like XIV.

It is similar to this previous test from our blog, but rather that just directly comparing VXV to VIX, it compares VXV to the expected value of VXV based on a quadratic regression of the two indices.

Strategy results trading XIV (inverse volatility) are in blue, compared to buying and holding XIV in grey, from mid-2004 to present:

A modified version of TWP’s rules:

Near the close, perform a quadratic regression, approximating VXV = f(VIX) . Then calculate delta . or the deviation from our regression, as delta = VXV – f(VIX) . Note: I’ve assumed that each day we could only use the data available at that moment in time.

Go long XIV at the close when both delta > 0, and VXV is greater than the VIX index.

Read about test assumptions . Get help following this strategy .

When taken together, the two criteria for entry (VXV > VIX and delta > 0) would be signs of particularly strong contango, favoring XIV.

I’ve made a number of changes to TWP’s original strategy:

I’ve assumed we traded long XIV rather than short VXX, as this is generally more reliable in the real world.

Rather than base our regression on all data (which would introduce look-ahead bias), I’ve assumed we could only use the data available up to that moment in time. Note that VXV data begins 01/2002.

I’ve added in the extra requirement that VXV be greater than VIX to signal a trade. This improved performance, but more importantly, made better sense given the strategy’s stated purpose.

We have already shown that directly comparing the VIX and VXV indices has historically been an effective strategy, so the important question is whether TWP’s additional rule (delta > 0) improved performance. To answer that, below I’ve shown the current strategy in blue, and compared it to when VXV > VIX, but delta < 0, in orange.

Does skipping the “orange” trades represent an improvement?

It has been an improvement since late-2012, but it provided no additional information before early-2007, and was a no show in the years between. Could that be because we had so little VXV data to consider in our regression in the early part of the test? I cant say for sure, but in the meantime, we’ll continue to keep this strategy on our radar.

When the strategies that we cover on our blog (including this one) signal new trades, we include an alert on the daily report sent to subscribers. This is completely unrelated to our own strategy’s signal; it just serves to add a little color to the daily report and allows subscribers to see what other quantitative strategies are saying about the market.

Click to see Volatility Made Simple’s own elegant solution to the VIX ETP puzzle.

a Flexible and Easy-to-use python tool to trade with Interactive Brokers

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Part 1 Explore trading strategies: You will learn statistical modeling and data processing, for example, time series analysis and regression. Then, two classic and effective strategies will be analyzed: momentum strategy and mean reversion strategy.

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Price and volume trend(pvt)

Price and volume trend(pvt)Price and Volume Trend (PVT)

The Price and Volume Trend is an indicator close to On Balance Volume using a cumulative volume total suffered adjustment. The On Balance Volume works through taking a sum of all volume of the positively closing days and subtracting the compound volume of all the lower closing days, whether the PVT carries out these operations only with a part of the volume of the day.

PVT is thought to show the money flow going into and out of security better than OBV does because it calculates the volume to add through the prices increase or decline in accordance with closing price of the day before. The principle according to which OBV works is summing an equal volume with the indicator in case the closing price of the security is a fraction higher or double.

Otherwise, PVT is supposed to add a larger part of volume to the indicator in case of significant price changes and a smaller part if the changes are less considerable.

The Price and Volume Trend is calculated: