Not many trades, but performed well. TSLA Daily Trading System List of Trades Spreadsheet
Not many trades, but performed well. TSLA Daily Trading System List of Trades Spreadsheet
Here is a Walt Disney Company (DIS) trading strategy with daily maintenance which had quite nice characteristics; 59% annualized return over the last 2 years.
The performance was better than long buy-hold with lower drawdown and about three times the reward-risk. Signal reinforcement was good, and not many dual signal days.
Below I show the return for the DIS trading strategy for each 6 month period going back 2 years. Comparing with long buy-hold you can see better consistency (higher minimum and lower StdDev).
Please note that this is simply a measurement, not an opinion or financial advice.
This post was corrected 1/8/2016 to correct a miscalculation in the short-side returns.
Strategy peaked 11/23/15, shortly after the stock price peaked.
GLD is the much traded SPDR Gold Trust ETF. I find these two GLD trading strategies interesting because they gave reasonable results (32.6% and 48% annualized return) for each of the four 6 month periods of the analysis. The strategies require daily intervention.
This is a buy on fall, sell on rise strategy using the close price as the buy reference and the high price as the sell reference. As you can see from the life chart and the longevity analysis, the lowest return for the last four 6 month periods was close to 30% annualized. You can easily find algorithms with over 50% annualized return for GLD, but they are not as consistent, with lowest quartus returns of around 14%.
I would prefer to see more reinforcement on the signals, but there it is. As of Sat Sept 5th, this strategy is Short with no transactions pending.
You can view the trades in spreadsheet format here: GLD.D Trades
In many ways this strategy shows better results than the BCS AHC strategy, for example there was lower drawdown, higher return, better signal reinforcement and good consistency (minimum 6 month quartus return of 38.43%). On the other hand, signals were cluttered, with 67 dual signal days, 50 buy signal days and 75 sell signal days. Also, its not an trading strategy that makes intuitive sense; buy on rise, sell on rise. Maybe it is one of those serendipitous occurrences, we shall see. Notice the sell strategy is almost the same as for BCS AHC but the sell signal percentage is quite different.
Algorithms were discovered by SignalSolver.
Please note, the above analysis was corrected on 12/28/2015 to reflect a bug fix in SignalSolver. Original returns were $9530 and $12753 respectively.
Both algorithms peaked 12/30/2015.
This TSLA trading strategy would have given a 1062% return over 2.1 years vs. a buy-hold return of 86% for the same period. The strategy is based on buying and selling when the stock price rises above specific thresholds. The buy side keyed off the day’s open price; the buy and cover signal appeared when the price rose 4.27% above the open price of the day, and the buy is at the signal price, so you would have to set up stop orders for the buy and the cover.
The sell and short signals came along when the price rose 2.82% above the previous day’s high, and the sell actions occured at the subsequent open using market orders. Every day you would have to recalculate the buy or sell point to find the new buy or sell price. SignalSolver will re-calculate the prices each time you update the prices from the web or enter the latest prices manually. Here is the list of trades.
The equity curve shows the return of $10,000 over time for the algorithm (yell0w) and buy-hold (gray):
This algorithm spent 64% of its time short. Looking at the signals at the bottom of the chart, you may notice that they are fairly thin, and there are 21 dual signal days, 28 buy signal days and 36 sell signal days. There is occasional reinforcement of signals, OK but not great.
From the performance table you can see that the long side of the algorithm worked much harder than the short side (the leftmost two columns), but the combination (always being long or short) gave annualized return of 223%.
The dotted line is buy-hold annualized return at 34.45%. The colored lines are the return for different values of buy and sell percentage in different time periods. The blue line is the overall performance for the whole 2.1 year period (528 daily data points), the other green, red, yellow and white lines each represent one quarter of the data (which I call a quartus). As you move the buy or sell point out of the region, you can see that some quartus’s would have been lossy. The worst performing quartus for the chosen buy and sell points was the most recent one, 02/19/15 to 08/26/15, and the annualized return was 161%. The algorithm was found by instructing SignalSolver to find strategies with the best minimum quartus return.
If you map the return for a large range of buy and sell points you notice that the overall surface is a little peaky.
While there is a fair amount of space under the peaks, there are also steep cliffs in the vicinity, so if the buy and sell points were to move around over time you would be in trouble. For that reason I would not expect such high gains in the future.
I will be paper trading this strategy for a while and will post the results from time to time.
The above analysis has been corrected 12/29/15 for a bug in the short side return.
This OMER trading strategy is signal rich; there were 174 dual signal days out of the 528 days in the analysis. Added to that 151 buy signal only days and 39 sell signal only days and you get 364 signal days, of which 250 were actionable signals leading to trades, of which only 146 were good. Still, all that activity led to a theoretical $311,341 profit from $10K invested over the 2 year period from 7/12/13 to 8/14/15.
The algorithm itself is a bit of an odd one with buys triggering off price changes from the open price and sells triggering off price changes from the previous day’s open price. Is it just a fluke that it has worked so consistently? In its worst quartus (132 trading days in this case) this algorithm returned 250% annualized or 93% actual return.
You can see from the list of trades and the equity chart that there were several periods of daily reversals from long to short and back again. You might think that ignoring dual signals would work better, but it doesn’t–it leads to an 80% reduction in returns.
This strategy was found by optimizing for minimum quartus returns and then doing some minor tweaking by hand, which is quite easy to do since the Scan charts in Signalsolver are interactive. I just moved the buy and sell points to areas away where there were lower returns. I’m just going to post the charts, if you need help interpreting them I would refer you to yesterday’s AAPL post where I discuss the methodology in detail.
By the way, OMER took off today gapping up 70% or so. I was working on it before that so this change doesn’t show on the data, but the proceeds would have jumped to $929,212. It would be cheating to track this strategy knowing it had already added 70%, so I’m not planning on doing so.
Follow up 12/29/2015
This algorithm peaked and then failed dramatically immediately after the 70% gap up on Aug 18th, very shortly after publication. Here is the 528 day equity curve:
Here are the stats:
Buy-hold would have been a much better option. For a profit, one solution was a buy point of 2.0% and a sell point of 4.4%, which would have given a return of $13,964 over the 94 days. Unfortunately, these parameter changes don’t appear to be predictable.
This UWTI trading strategy would have returned $676,147 for $10K outlay over a 2 year period. It was a very straightforward strategy with simple maintainence, once a day you would have put in either market orders to cover and buy or stop orders to sell and short before the open. This is another result discovered by optimizing minimum quartus returns, a SignalSolver backest optimization technique. A quartus is one-fourth of the data and we look for algorithms which give the best minimum return of all four quartus results. In this case it was 206% annualized for the period June 2013 to Jan 2014. It happened to be the fourth best result found for this particular scan of 500 algorithms but I chose it because the drawdown was significantly better than for the other 3 (30.7% vs 55% for short-hold).
Results for this strategy were not consistent, most of the gains were made in the period June 2014 to Feb 2015.
A list of trades in spreadsheet format: UWTI-D Trades
The above analysis has been corrected for a miscalculation of the short-side return of this algorithm.
This algorithm turned a complete loss:
In contrast to yesterday’s TNA trading strategy optimized for low drawdown, this one is optimized for minimum quartus annual return, a new feature of SignalSolver. A quartus is one quarter of the data, 132 days in this instance, and the minimum return was 99% annualized for the most recent quartus Feb 3 to Aug 11th 2015.
You can see how quartus annualized returns fluctuate with buy/sell parameters from the lifetime graphs. Note that this algorithm took both long and short positions–when you were not long, you were short. I have no idea how or why this kind of algorithm works–you would think the buy and sell signals are so similar that it would give more random returns, but you can see from the surface plot that the results are positive for most of the parameter space.
The “user defined price” was found by averaging the previous day’s high, the previous day’s low and the current day’s open price. Buying and selling was done at the daily close or next day’s open.
List of trades in .xlsx format: TNA-D Trades
Trades since the original backtest endpoint on 8/11/2015:
The above analyses have been corrected for an error in the short-side returns. Overall gain of the original erroneous post was $104,971
Here are the updates for 12/30/15. The algorithm peaked around Oct 13th and now appears to be failing:
A bit of an improvement:
In the same vein as yesterday's FAS analysis, here is a low drawdown trading strategy for TNA, with daily intervention. Prasad had asked me to search for low drawdown strategies for a few of the triple leveraged ETFs, this being one of them. As with FAS, the algorithm was found by optimizing the SignalSolver backtest engine for drawdown (100% weighting), with a min QA return weight of 50% thrown in to get rid of all the zero trades-zero return hits. Period of the analysis was 1.9 years.
Again, the result had a low time in the market (18%), low drawdown (4.9%) and an annualized return which, while modest (38%), was better than the underlying ETF.
This is one of the triple leveraged funds which Prasad had asked me to come up with a low drawdown trading strategy for, I'll be looking at the others soon. You can set up SignalSolver to optimize for low drawdown. The way you do that is by setting up the Figure of Merit parameters to look for low drawdown, however you need to fold return into the picture, otherwise it simply comes up with algorithms that don't trade and have zero drawdown. In this instance I set the FOM parameters to be Total Return (25%), drawdown (100%) and Min Quartus Return of (25%). The data is divided into four quartus's, so including Min Quartus Return gives more consistent returns than just including Total Return.
A few things to note about this result. The drawdown (the worst case loss you would have suffered if you had entered and exited the strategy once) was 4.5% compared with the underlying stock's drawdown of 24%, and the total return beat the return of the underlying stock (56.7% annualized vs. 41.7%). Its a long-only strategy. If you had shorted instead of exiting, the drawdown was close to 30%. The trading strategy was also very efficient, that is, the returns were good given the small amount of time (20%) you were in the market. If you could have invested at the same rate of return during the 'out' periods, you would be talking about a 288% annualized return (a figure I call Efficiency).
The trading strategy didn't trade frequently, nor was it particularly consistent in its results, as you can see from the last two graphs below. Also, its not particularly stable with respect to the buy and sell point percentages as you can see from the surface plot. None of these factors suggest a strategy worth pursuing--its really just a curiosity, a demonstration that you can find low drawdown strategies that worked if you look for them.
One last point of clarification, the buy signal is a negative percentage (-3.74%) with respect to the reference (the 50 day simple MA). If you were waiting for a buy signal and the 50 day SMA at the previous daily close was at $100, then the buy signal would occur if the stock price failed to rise above $96.26, assuming no sell signal. Buying was done at the subsequent open, selling at the close on the day of the sell signal.
Post has been corrected 1-2-15 for an error in short-hold calculation.
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.
Since the original posting in March 2016, this strategy has returned 183% annual return, but in an extremely choppy fashion:
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.
Performance has deteriorated somewhat: