DUST Daily Trading Strategy

This trading strategy is for stock symbol DUST the Direxion Daily Gold Miners Bear 3X ETF. It is the complementary stock to NUGT which I analyzed last week. Its not hard to find strategies which would have exploited the intense volatility of this kind of security, if you have access to an optimizing backtester. The strategy presented here gave good returns and reward-risk. Most of the returns were in the period Feb 2013 to Nov 2013, in which period $10K would have turned into $750,000. By Nov 2014, the investment compounded would have been worth $5m and since then until now (Mar 2015) it would have turned into $13m (list of trades). It will be interesting to see if the growth continues. I wouldn't expect it to, given the inconsistent performance, but you never know. In contrast, buy and hold lost 20% over the same period.

The strategy itself can be read off from the table below, its a daily trading strategy which means that you would have had to attend to it on a daily basis. It would have taken a few minutes a day to work out the signals and enter the orders. Note that you would have to short the stock if you were long and a sell signal showed up. Using NUGT as the shorting vehicle would have given different results.

Just a reminder; don't expect to implement this kind of algorithm and get similar results. I'll be tracking it on paper and give updates, so watch this space.

This strategy was corrected 1/1/16 to account for an error in the short-side returns.



Update 1/1/16

This strategy did OK after publication. For the period 3/24/15 to 12/29/15 the equity and stats are shown below:


DUST-D Equity 1-1-16

DUST-D Table 1-1-16

The optimum buy and sell points for this period would have been 4.13% and 8.45% (returning $66,246).

Update 6/23/2016


Since the original posting in March 2016, this strategy has returned 183% annual return, but in an extremely choppy fashion:

DUST-D Update 6-23-16 TimeDUST-D Update 6-23-16 Table

The optimum buy point over this period was 4.12%, with a sell point of 8.32%. The results for this optimization are shown in the table below. Its odd how the optimum buy point was almost exactly twice that of the original optimization, not the first time I have seen this happen.

DUST-D Update 6-23-16 Table Opt

DUST-D Update 6-23-16 Time Opt

Update 10/20/2016


Performance has deteriorated somewhat:

DUST Trading Strategy (Daily) Update 10-20-2016

DUST Trading Strategy (Daily) Update 10-20-2016





IBM Trading Strategy (Weekly)

This IBM trading strategy made 11x the total returns (4x the annualized return) of the underlying stock with half the drawdown. The backtest period was 10years. The strategy itself (see table below) was straightforward with both buy and sell signals triggered by falling prices. The buy signal keyed off the 52 week high while the sell signal keyed off the closing price of the previous week. All buying and selling was done at the weekly open. It turns out that 225 out of 528 weeks were dual signal days, this often led to periods where a buy weeks alternated with sell weeks (see list of trades in excel format). Ignoring dual signal days would have led to very low gains, so the alternating strategy must have worked somehow.

The strategy had a buy bias, there were 141 days with just a buy signal and 55 days with just a sell signal. Since the underlying stock seems to be downwardly trending, this bias may work negatively if the downtrend continues. We shall see.

The backtest assumes a commission of $7 per trade.


Update 1-3-16

This this strategy turned out to be a dramatic failure. Here are the updated table and equity curves:

IBM-W Equity1-3-16

IBM-W Table 1-3-16

Update 1-3-16

Still a dramatic failure, but less so:

IBM Trading Strategy (Weekly) Update 10-20-2016

IBM Trading Strategy (Weekly) Update 10-20-2016



NUGT Trading Strategy (Daily)

Original Post March 12th 2015

Please note, this post was corrected 1-3-16 to account for a short-side error in the original calculations. Apologies for this.

This NUGT trading strategy (Direxion Daily Gold Miners Bull 3X ETF) gave a theoretical $1.8 million profit for a $10K initial investment over 2.1 years. The trading strategy required once a day attention. Signals were triggered by rising prices on both the buy and sell side. The buy side reference was 1.0629 times the average of the previous day high, the previous day low and the current day open. You would need to calculate it every day after the open and then put in the buy and cover orders, if you were short. On a few occasions this meant buying at the open price, but usually there was time to get the stop orders in.

The sell side target was a bit simpler, 1.0536 times the previous days low. You could enter the sell and short limit orders after each close, if you were long.

There is a Bear version of this fund, the Direxion Daily Gold Miners Bear 3X ETF (DUST), however the results would have been different if you had used it for the short side.

Here is the list of trades.


Update 1-3-16

The algorithm peaked at the end of Aug 2015:

NUGT.D Equity 1-3-16

Update 10-20-2016

Since it peaked at the end of Aug 2015 the algorithm has not recovered:

NUGT Trading Strategy (Daily) Update 10-20-2016

NUGT Trading Strategy (Daily) Update 10-20-2016

AAPL Trading System (Monthly)

With $94 billion in profits (for $10K initial investment) this has one of the highest total returns I have seen for any algorithm.

It is an interesting study, but I wouldn't recommend it as a good system to trade moving forward. It is somewhat over-tuned, and the parameters are close to regions which would have failed. At one time the equity went negative and you would have required extra capital to satisfy margin calls. The algorithm uses the "last sell price" as a reference. This can lock up an algorithm if the stock price is trending, in this case starving it of buy signals if the buy and sell percentages were chosen wrongly.

That said, I've been watching it for a year or so and it has behaved itself. The one take-away that you might find valuable--if you are holding AAPL and you see that portentous 19.44% or more price drop in a month, maybe you should take heed and sell at the next monthly open. The 29 times this happened over the last 34 years, you could buy at a better price later on.

Notes: returns are compounded, commission was $7 per trade. Here is a list of trades: AAPL.M Trades

Note that the short side of the algorithm has a drawdown of 123%. If you look at the trades, you will see that this corresponds to a peak of $833,000 in Oct 90 and a trough of -$196,000 in May 91. This can happen on the short side of an algorithm, and would have led to expensive and quite possibly disastrous margin calls. The algorithm, however, recovered the following month when the stock price fell dramatically.  The long side of the algorithm made $1,066,052,956 with only $10K at risk.

The scan shows how the return changes with choice of buy and sell point. We have chosen to show the peak, but better choices would have been in the center of the profitable regions.

These scans show how returns change with buy/sell point for different timeframes.

The scan shows returns for the entire buy/sell point space. High points are good, low points are, in this case, losses.

Please note that this post was edited 12/29/2105 to correct a calculation error on the short side of the algorithm.

AAPL Apple Trading Strategy (Monthly) Update 10/20/2016

Strategy returned 11%, buy-hold -5%

AAPL Trading Strategy (M) Update 10/20/2016

AAPL Trading Strategy (M) Update 10/20/2016

AAPL Trading Strategy: Equity curve as of 10-20-2016

AAPL Trading Strategy: Equity curve as of 10-20-2016

GILD Trading Strategy (Weekly intervention)

This is a GILD trading strategy with weekly intervention required. I found algorithms with better returns, but they were less consistent over time and had more parameter sensitivity. I chose this one because of many favorable characteristics:-

  • Buy low, sell high type of algorithm, biased towards buying as befitting an upward trending stock like GILD.
  • Buy and sell at the weekly open was easy and convenient to implement
  • Buy and sell signals were well differentiated (only 4 dual signals) with good reinforcement on the buy side.
  • Good lifetime characteristics with each 2.5 year  period beating buy-hold.
  • 87% successful trades and a Sharpe Ratio of 2.03 vs 1.05 for buy-hold.
  • Gentle slopes on the parameter surface.

Note that charts were corrected 1-2-16 for an error in short-side return


Update 1-2-2016

GILD-W1 1-2-16 Update Table

Update 10/20/2016

GILD Weekly Trading Strategy Update Oct 20th 2016

GILD Weekly Trading Strategy Update Oct 20th 2016

PANW Trading Strategy (Daily Intervention)

This Palo Alto Networks PANW trading strategy would have given a 119% annualized return. Here, I chose to show only the long side of the algorithm because its more impressive than the short side (which only gave 27% annualized return). To appreciate the algorithm more, notice that the efficiency of the algorithm was close to 200%. Efficiency (as we define it) is the annualized return divided by the percentage of time you were in the market. Its the return you would have received if you had realized the same return all the time as you had got while you were in the market. The theory is that since you are not in the market all the time, you could have invested the money elsewhere.

Efficiency only applies to long or short style algorithms, since if you were running both long and short (L&S) you are in the market 100% and efficiency equals annualized return.

Updated 1-2-2016 to correct an error in the short-hold returns.

Update Aug 28th 2015:

PANW.D Update 8-28-15

Update 2-1-2016

Algorithm has had better return, efficiency and drawdown than Buy/Hold, but not consistently. Optimum buy point dropped to 1.25% (which would have given a return of $4,316)

PANW-D Update 1-2-16




Update 10-10-2016

Algorithm continues to have better return, efficiency and drawdown than Buy/Hold. Buy-hold lost 12% annualized, this strategy made 28.7% annualized. Efficiency (55%) is what the strategy would have made annualized, had the money been invested at the same rate while the strategy was out of the market.

PANW Trading Strategy Performance since publication as of Oct 10th 2016

PANW Trading Strategy Performance since publication as of Oct 10th 2016

Here is a view of the performance since original publication in March 2015:

PANW Trading Strategy, performance since initial publication, 60% gain vs 12.3% for buy-hold

PANW Trading Strategy, performance since initial publication, 60% gain vs 12.3% for buy-hold.

GOOGL Trading Strategy (Weekly Setup)

Algorithm for GOOGL based on monthly OHLC data and requiring monthly setup.


Buy signals around 3 times more frequent than sell signals, some overlap (dual signal months shown as white lines). 80% long, 20% short. Algorithm showing flattening out of performance in recent months.


GOOGL.M Performance table and strategy description. Showed about 3 times the reward-risk of buy-hold. Strong bias to buy side (as is appropriate for upward trending stocks), clearly shown in signal count and %days long vs. short.


Return vs. parameter for buy and sell parameters. Buy side is reasonably insensitive. Sell side seems more highly tuned, however algorithms of the "misses target" type often have this characteristic of being tightly clustered around zero.


The life chart shows parameter sensitivity for different time periods. All timeframes beat buy-hold by a decent margin.

GOOGL.M Surface

GOOGL Surface chart showing how return changed with buy and sell parameters.


Update 10/20/2016

GOOGL Trading Strategy Weekly Update Oct 19th 2016

GOOGL Trading Strategy Weekly Update Oct 19th 2016