Off your game?

Read our free Dr K report on how to optimize your mind and body so you can boost your focus when trading the markets.
Your email will always remain private.

Dr K VIX Volatility Timing Model (VVM)

VIX Volatility Model is down -6.5% in 2017 as of 4-24-17.
Its high-water mark for the year was +54.3% as of 3-3-17 in Real-Time Trading.
Please read the update here.

Getting started is as easy as 1, 2, 3:
  1. You will receive an email whenever the model switches signals.
  2. Choose from the list of suggested ETFs. Buy the ETF.
  3. Await the next change in signal.

Overview of VVM:

The VIX Volatility Model (VVM) seeks to turn human emotions into profits by capitalizing on emotion-driven events. These events carry predictive value. Price/volume charts are used as a guide, after all, charts are human emotions on parade.

The VIX Volatility Model uses price/volume action of leading stocks, a dynamic list that is updated as needed, and major averages to capitalize on changes in volatility in the market. It uses regression to the mean as well as trending strategies depending on market conditions. That said, it does not require an uptrending or downtrending market to be profitable.

  • Not A Black Box

Developing a seasoned, contextual "chart eye" over my 25+ year career has been essential. It allows me, and thus the VVM, to remain fluid with changing market conditions. Indeed, there is no such thing as a static "black box" strategy. Any system of value should in principle be self-learning thus self-evolving. Such a system learns from new data each day. This is the way I have always operated my strategies since the 1990s. It was an early, manual incarnation of Google's cutting edge "deep learning/machine learning" approach.

  • Excels in Up, Down, and Sideways Markets

The VVM incorporates a number of strategies which can profit from a broad spectrum of market behaviors. For example, the VVM excels in trendless or downtrending markets where volatility is amplified. It also knows how to ride uptrending markets which tend to be less volatile. Its returns have well outperformed the major averages in backtests and in real-time trading.

That said, I would like each member to read this FAQ on potential weaknesses of the model.

The model's focus is to analyze in real-time whether there is sufficient buying or selling pressure to warrant a change in signal. Live signals shown in the table start on 6/27/16. Backtested results in the table are shown from 12/23/15 to 6/24/16.

  • Evolutions

The model has gone through a series of evolutions or "growing pains" similar to software that updates, making improvements and removing old bugs. Indeed, the challenging markets that have become ever-more challenging with each passing year have been a gift. The model is that much stronger for it.

All signals now incorporate the following:

  1. 1. The UVXY profit taking rules activate should profits exceed a certain threshold. This acts as a trigger to let profits run but stops are kept very tight. The threshold is contextual to market conditions but seeks at least a 12-18% profit. The numerous boldfaced entries in the results table show when the profit taking rules were activated.
  2. No override or "gaming" of the model's signals.
  3. No override or "gaming" of the fail-safes. Note, as we have stated, the model is self-learning so adjustments have been and will continue to be made along the way. One such adjustment was removal of some fail-safes with new fail-safes starting with the sell signal on 4-3-17. This adjustment showed that over the 8+ year backtests, reward was increased while overall risk was reduced. The downside is larger potential losses for a single signal. Note, the loss of -26.82% for the 4-18-17 buy signal was due to a black swan type of gap down which fail-safes cannot guard against. Same as the -18.4% loss due to the Brexit event in June 2016. That said, UVXY is highly volatile so in very rare cases, it can lose more than -20% even without a gap down though losses should be contained to -12% or less. For XIV used in sell signals, losses should be contained to -6% or less but can exceed more than -10%. Please read the update here.
  4. Integration of the VIX Spiking strategy discussed here.

NO DOWN YEARS in backtests and numerous triple digit percentage (>100%) years:


CAUTION: I believe the risk/reward of this strategy is by far the best I've achieved so far in my 25+ year trading career. But that is no reason to oversize your positions. Make sure you understand the risks involved in trading this model before risking any capital. Know that big gains can be reversed under unusual circumstances, such as when the VIX had one of its worst one-day losses on April 24, 2017 in decades which resulted in a signal loss of 26.82%, a single signal record loss for the VVM. Any strategy with big potential upside will also have big potential downside though various fail-safe strategies within the model have been fine-tuned to strike a balance between reducing the number of whipsaw losses while keeping maximum losses, notwithstanding gap-downs, also manageable. The rare black swan gap-down occurred on April 24, 2017 but was still not outside the performance metrics of the model as drawdowns were still roughly equivalent to prior maximum drawdowns.

  • Results

Not shown: Backtests using VXX start 2/2/09. Prior to 2/2/09, backtests start 3/10/99 using QQQ.

Results Table:

When the model issues a sell signal, it is selling volatility thus anticipating rising markets. When the model issues a buy signal, it is buying volatility thus anticipating falling markets.

You will receive an email whenever the model switches signals. Choose from the list of suggested ETFs. Buy the ETF. Await the next change in signal.

All results starting on 11-8-16 are in real-time. Prior to the 11-8-16 buy signal, results shown do not reflect debugging which improved the algorithm's profit/loss across the entire run which dates back to early 2009. Drawdowns would thus be contained to -27.9% in 2016 (not the larger amount shown in the table) and net profits for 2016 would be +61.8%.

The boldfaced entries in the table show that the profit taking strategies have made a material difference to profits. Also notice that the fail-safes built into the model can cause strings of small losses, multiple times a day in some cases. This serves to keep risk under control. One must lose to win. Profits have always well outweighed smaller losses in all backtests in any given year.

Note that July - October 2016 was the toughest period to make progress both in stocks and in market timing for both models. The S&P 500 traded in the shallowest band in its entire 59-year trading history. Fortunately, such rare periods always come to an end. Indeed, the Trump victory seems to have dislodged the markets out of their almost non-tradeable doldrums.

UVXY is used for buy signals, XIV is used for sell signals. UVXY moves up or down twice that of XIV. If XIV is up +5%, UVXY would be down roughly -10%. More conservative investors may opt to use VXX or VIXY.

All data shown below are delayed by up to two months for non-members.
DateSignal% gain / loss$10,000 becomes
03-30-2017 Sell-1.5521,026
03-27-2017 Cash 21,358
01-31-2017 Sell11.1821,358
01-30-2017 Cash 19,210
01-17-2017 Sell9.2219,210
01-12-2017 Cash 17,588
12-29-2016 Sell14.7417,588
12-14-2016 Cash 15,329
12-14-2016 Sell-1.3815,329
12-12-2016 Cash 15,544
12-09-2016 Sell-1.3815,544
12-08-2016 Cash 15,762
12-08-2016 Sell-1.7215,762
12-07-2016 Cash 16,039
12-07-2016 Sell-0.2216,039
12-06-2016 Cash 16,074
12-06-2016 Buy-2.9416,074
12-01-2016 Cash 16,561
11-21-2016 Sell1.0816,561
11-17-2016 Cash 16,384
11-17-2016 Buy-2.7516,384
11-16-2016 Cash 16,848
11-11-2016 Sell6.7116,848
11-10-2016 Cash 15,788
11-09-2016 Sell0.115,788
11-08-2016 Cash 15,772
11-08-2016 Buy-315,772
10-25-2016 Cash 16,260
10-25-2016 Sell-1.2116,260
10-21-2016 Cash 16,459
10-21-2016 Buy-316,459
09-16-2016 Cash 16,968
09-15-2016 Sell0.3116,968
09-14-2016 Cash 16,915
09-14-2016 Buy-0.4216,915
09-14-2016 Cash 16,986
09-14-2016 Buy-2.4616,986
09-14-2016 Cash 17,415
09-14-2016 Buy-2.3717,415
09-12-2016 Sell-11.2917,837
09-09-2016 Buy-13.6220,107
07-15-2016 Sell17.1623,278
07-15-2016 Cash 19,868
07-14-2016 Sell-2.319,868
07-12-2016 Cash 20,335
07-12-2016 Sell-1.8720,335
07-11-2016 Buy-3.3220,722
07-08-2016 Cash 21,435
07-08-2016 Buy-4.0721,435
07-06-2016 Sell9.7822,345
07-06-2016 Cash 20,354
07-06-2016 Sell-1.520,354
06-29-2016 Cash 20,664
06-29-2016 Buy-2.9320,664
06-27-2016 Cash 21,288
06-24-2016 Buy-0.421,288
06-24-2016 Cash 21,374
06-23-2016 Sell-18.4021,374
06-22-2016 Cash 26,194
06-22-2016 Sell-1.5026,194
06-20-2016 Cash 26,593
06-20-2016 Sell-0.2026,593
06-14-2016 Cash 26,646
06-10-2016 Buy24.1526,646
05-23-2016 Sell-1.3021,463
05-20-2016 Buy-3.0021,746
05-11-2016 Cash 22,419
05-11-2016 Sell-1.5022,419
05-09-2016 Cash 22,760
05-09-2016 Buy-3.0022,760
05-04-2016 Cash 23,464
05-04-2016 Sell-0.2023,464
05-03-2016 Cash 23,511
05-03-2016 Buy-0.4023,511
03-15-2016 Sell23.2023,601
03-15-2016 Cash 19,157
03-14-2016 Sell-0.8719,157
03-10-2016 Cash 19,325
03-10-2016 Buy-0.4019,325
03-10-2016 Cash 19,403
03-08-2016 Buy-2.0619,403
03-08-2016 Cash 19,811
03-08-2016 Buy-0.4019,811
03-07-2016 Sell-0.2019,891
03-02-2016 Cash 19,931
03-01-2016 Buy-3.0019,931
02-29-2016 Cash 20,547
02-29-2016 Sell-0.2020,547
02-24-2016 Cash 20,588
02-23-2016 Buy15.0020,588
02-23-2016 Cash 17,903
02-23-2016 Buy-0.4017,903
02-22-2016 Cash 17,975
02-22-2016 Buy-3.0017,975
02-19-2016 Sell7.3918,531
02-16-2016 Cash 17,255
02-16-2016 Buy-0.4017,255
02-11-2016 Cash 17,325
02-11-2016 Sell8.4917,325
02-11-2016 Cash 15,968
02-11-2016 Sell-1.5015,968
02-11-2016 Cash 16,211
02-11-2016 Sell-0.2016,211
02-10-2016 Cash 16,244
02-10-2016 Sell-3.0016,244
02-09-2016 Cash 16,746
02-09-2016 Buy-3.0016,746
02-05-2016 Cash 17,264
02-05-2016 Sell-1.5017,264
02-03-2016 Cash 17,527
02-02-2016 Buy15.0017,527
01-29-2016 Cash 15,241
01-29-2016 Buy-3.0015,241
01-28-2016 Sell1.2515,712
01-28-2016 Cash 15,518
01-28-2016 Sell-1.5015,518
01-21-2016 Cash 15,755
01-21-2016 Buy-0.4015,755
01-14-2016 Cash 15,818
01-14-2016 Sell-0.2015,818
01-14-2016 Cash 15,849
01-14-2016 Sell-1.5015,849
01-13-2016 Cash 16,091
01-13-2016 Buy15.0016,091
01-11-2016 Cash 13,992
01-07-2016 Buy21.7013,992
01-07-2016 Cash 11,497
01-07-2016 Buy-3.0011,497
01-06-2016 Cash 11,853
01-06-2016 Buy-3.0011,853
01-04-2016 Sell-1.6412,220
01-04-2016 Cash 12,424
01-04-2016 Sell-1.5012,424
12-30-2015 Buy24.5712,613
12-28-2015 Sell3.2010,125
12-28-2015 Cash 9,811
12-28-2015 Sell-1.509,811
12-23-2015 Cash 9,960
12-23-2015 Buy-0.409,960
Fewer resultsspinner
BOLD: Profit taking strategies were activated.