NFLX Signals Daily Equity

Netflix (NFLX) Signals-Daily

These Netflix (NFLX) signals traded as directed would have performed around 4.6 times better than buy-hold with an ROI of 1291% for the period 15-Aug-16 to 18-Sep-18

NFLX Signals Daily

These trading signals for NFLX were selected from over a million backtest results for their reward/risk and parameter sensitivity characteristics. Backtests don't always generate reliable signals which can be counted on moving forward but many traders find value in knowing what buy and sell signals would have worked well in the past.

Returns

For the 528 day (2.1 year) period from Aug 15 2016 to Sep 18 2018, these signals for Netflix (NFLX) traded both long and short would have yielded $129,107 in profits from a $10,000 initial investment, an annualized return of 252.1%. Traded long only (no short selling) the signals would have returned $64,042, an annualized return of 160.4%. 79.4% of time was spent holding stock long. The return would have been $27,969 (an annualized return of 89.2%) if you had bought and held the stock for the same period.

Signals and Trades

Not all signals are acted upon and signals are often reinforced in this type of strategy. If you are long in the security, buy signals are not acted on, for example. Similarly if you are short you must ignore sell signals. There were 65 buy signals and 36 sell signals for this particular NFLX strategy .These led to 22 round trip long trades of which 15 were profitable, and 21 short trades of which 18 were profitable. This is a daily strategy; daily OHLC data is used to derive all signals and there is at most one buy and sell signal and one trade per day.

Drawdown and Reward/Risk

Drawdown (the worst case loss for an single entry and exit into the strategy) was 15% for long-short and 14% for long only. This compares to 25% for buy-hold. The reward/risk for the trading long and short was 12.36 vs. 2.94 for buy-hold, an improvement factor of around 4.2 If traded long only, the reward/risk was 8.44. We use drawdown plus 5% as our risk metric, and annualized return as the reward metric.

The backtests assume a commission per trade of $7.

DUST Signals Equity

Direxion Daily Gold Miners Bear 3X ETF (DUST) Signals-Daily

These Direxion Daily Gold Miners Bear 3X ETF (DUST) signals traded as directed would have performed around 117.8 times better than buy-hold with an ROI of 9504% for the period 11-Aug-16 to 14-Sep-18

DUST Signals Daily

These trading signals for DUST were selected from over a million backtest results for their reward/risk and parameter sensitivity characteristics. Backtests don't always generate reliable signals which can be counted on moving forward but many traders find value in knowing what buy and sell signals would have worked well in the past.

Returns

For the 528 day (2.1 year) period from Aug 11 2016 to Sep 14 2018, these signals for Direxion Daily Gold Miners Bear 3X ETF (DUST) traded both long and short would have yielded $950,426 in profits from a $10,000 initial investment, an annualized return of 786.7%. Traded long only (no short selling) the signals would have returned $151,215, an annualized return of 277.8%. 39.4% of time was spent holding stock long. The return would have been $8,065 (an annualized return of 32.7%) if you had bought and held the stock for the same period.

Signals and Trades

Not all signals are acted upon and signals are often reinforced in this type of strategy. If you are long in the security, buy signals are not acted on, for example. Similarly if you are short you must ignore sell signals. There were 111 buy signals and 46 sell signals for this particular DUST strategy .These led to 26 round trip long trades of which 22 were profitable, and 26 short trades of which 19 were profitable. This is a daily strategy; daily OHLC data is used to derive all signals and there is at most one buy and sell signal and one trade per day.

Drawdown and Reward/Risk

Drawdown (the worst case loss for an single entry and exit into the strategy) was 55% for long-short and 34% for long only. This compares to 73% for buy-hold. The reward/risk for the trading long and short was 13.03 vs. 0.42 for buy-hold, an improvement factor of around 31.0 If traded long only, the reward/risk was 7.21. We use drawdown plus 5% as our risk metric, and annualized return as the reward metric.

The backtests assume a commission per trade of $7.

NUGT Signals Equity

Direxion Daily Gold Miners (NUGT) Signals-Daily

These Direxion Daily Gold Miners (NUGT) signals traded as directed would have performed around 86.8 times better than short-hold with an ROI of 7930% for the period 11-Aug-16 to 14-Sep-18.

NUGT Signals Daily

These trading signals for NUGT were selected from over a million backtest results for their reward/risk and parameter sensitivity characteristics. Backtests don't always generate reliable signals which can be counted on moving forward but many traders find value in knowing what buy and sell signals would have worked well in the past.

Returns

For the 528 day (2.1 year) period from Aug 11 2016 to Sep 14 2018, these signals for Direxion Daily Gold Miners (NUGT) traded both long and short would have yielded $793,045 in profits from a $10,000 initial investment, an annualized return of 714.0%. Traded long only (no short selling) the signals would have returned $30,185, an annualized return of 94.4%. 37.2% of time was spent holding stock long. The return would have been -$9,133 (an annualized return of -68.9%) if you had bought and held the stock for the same period. The signals would have returned $174,580 (303% annualized return) if traded short only.

Signals and Trades

Not all signals are acted upon and signals are often reinforced in this type of strategy. If you are long in the security, buy signals are not acted on, for example. Similarly if you are short you must ignore sell signals. There were 292 buy signals and 453 sell signals for this particular NUGT strategy .These led to 162 round trip long trades of which 91 were profitable, and 161 short trades of which 109 were profitable. This is a daily strategy; daily OHLC data is used to derive all signals and there is at most one buy and sell signal and one trade per day.

Drawdown and Reward/Risk

Drawdown (the worst case loss for an single entry and exit into the strategy) was 46% for long-short and 41% for long only. This compares to 92% for buy-hold. The reward/risk for the trading long and short was 13.99 vs. 1.88 for short-hold, an improvement factor of around 7.4. If traded long only, the reward/risk was 2.06, short only was 5.12 (54% drawdown). We use drawdown plus 5% as our risk metric, and annualized return as the reward metric.

The backtests assume a commission per trade of $7.

Apple (AAPL) Signals Daily Equity

Apple (AAPL) Signals-Daily

These Apple (AAPL) signals traded as directed would have performed around 2.4 times better than buy-hold with an ROI of 266% for the period 11-Aug-16 to 14-Sep-18

Apple AAPL Signals Daily

These trading signals for AAPL were selected from over a million backtest results for their reward/risk and parameter sensitivity characteristics. Backtests don't always generate reliable signals which can be counted on moving forward but many traders find value in knowing what buy and sell signals would have worked well in the past.

Returns

For the 528 day (2.1 year) period from Aug 11 2016 to Sep 14 2018, these signals for Apple (AAPL) traded both long and short would have yielded $26,635 in profits from a $10,000 initial investment, an annualized return of 86.0%. Traded long only (no short selling) the signals would have returned $18,379, an annualized return of 64.7%. 75.6% of time was spent holding stock long. The return would have been $11,309 (an annualized return of 43.6%) if you had bought and held the stock for the same period.

Signals and Trades

Not all signals are acted upon and signals are often reinforced in this type of strategy. If you are long in the security, buy signals are not acted on, for example. Similarly if you are short you must ignore sell signals. There were 162 buy signals and 43 sell signals for this particular AAPL strategy .These led to 23 round trip long trades of which 14 were profitable, and 24 short trades of which 18 were profitable. This is a daily strategy; daily OHLC data is used to derive all signals and there is at most one buy and sell signal and one trade per day.

Drawdown and Reward/Risk

Drawdown (the worst case loss for an single entry and exit into the strategy) was 9% for long-short and 11% for long only. This compares to 14% for buy-hold. The reward/risk for the trading long and short was 6.09 vs. 2.33 for buy-hold, an improvement factor of around 2.6 If traded long only, the reward/risk was 4.14. We use drawdown plus 5% as our risk metric, and annualized return as the reward metric.

The backtests assume a commission per trade of $7.

Alibaba Signals Equity Growth

Alibaba (BABA) Signals – Daily

From 11-Aug-16 to 14-Sep-18, these trading signals for Alibaba (BABA) used as directed would have performed around 5.2 times better than buy-hold.

Alibaba (BABA) Daily Signals

These trading signals for BABA were selected from over a million backtest results for their reward/risk and parameter sensitivity characteristics.While backtests don't always provide reliable signals which can be counted on moving forward, many traders find value in knowing what buy and sell signals would have worked well in the past.

For the 528 day (2.1 year) period from Aug 11 2016 to Sep 14 2018, these signals for Alibaba (BABA) traded both long and short would have yielded $41,203 in profits from a $10,000 initial investment, an annualized return of 118.3%. Traded long only (no short selling) the signals would have returned $19,507, an annualized return of 67.8%. 78.0% of time was spent holding stock long. If you had bought and held the stock for the same period the profit would have been $7,879 (an annualized return of 32.0%).

For this type of strategy, not every signal is acted upon and signals are often reinforced. If you are long in the security, buy signals can be ignored, for example. Similarly if you are short you can ignore sell signals. For this particular BABA strategy there were 519 buy buy signals and 214 sell sell signals.These led to 114 round trip long trades of which 69 were profitable, and 113 short trades of which 66 were profitable. This is a daily strategy; daily OHLC data is used to derive all signals and there is at most one buy and sell signal and one trade per day.

Drawdown (the worst case loss for an single entry and exit into the strategy) was 12% for long-short and 13% for long only, compared to 27% for buy-hold. Using drawdown plus 5% as our risk metric, and annualized return as the reward metric, the reward/risk for the trading long and short was 6.93 vs. 1.00 for buy-hold, an improvement factor of around 6.9 If traded long only, the reward/risk was 3.71

The backtests assume a commission per trade of $7.

Facebook (FB) Signals – Daily

From 09-Aug-16 to 12-Sep-18, these trading signals for Facebook (FB) used as directed would have performed around 10.6 times better than buy-hold.

Facebook (FB) Signals-Daily

These trading signals for FB were selected from over a million backtest results for their reward/risk and parameter sensitivity characteristics. While backtests don't always provide reliable signals which can be counted on moving forward, many traders find value in knowing what buy and sell signals would have worked well in the past.

For the 528 day (2.1 year) period from Aug 9 2016 to Sep 12 2018, these signals for Facebook, Inc. (FB) traded both long and short would have yielded $30,973 in profits from a $10,000 initial investment, an annualized return of 96.3%. Traded long only (no short selling) the signals would have returned $13,330, an annualized return of 49.9%. 83.5% of time was spent holding stock long. If you had bought and held the stock for the same period the profit would have been $2,925 (an annualized return of 13.1%).

For this type of strategy, not every signal is acted upon and signals are often reinforced. If you are long in the security, buy signals can be ignored, for example. Similarly if you are short you can ignore sell signals. For this particular FB strategy there were 79 buy signals and 16 sell signals.These led to 16 round trip long trades of which 13 were profitable, and 15 short trades of which 14 were profitable. This is a daily strategy; daily OHLC data is used to derive all signals and there is at most one buy and sell signal and one trade per day.

Drawdown (the worst case loss for an single entry and exit into the strategy) was 14% for long-short and 14% for long only, compared to 26% for buy-hold. Using drawdown plus 5% as our risk metric, and annualized return as the reward metric, the reward/risk for the trading long and short was 5.00 vs. 0.43 for buy-hold, an improvement factor of around 11.8. If traded long only, the reward/risk was 2.59.

The backtests assume a commission per trade of $7.

The MASC BAO Trading Strategy

Finding MASC BAO strategies on randomly selected stocks

The MASC BAO trading strategy frequently shows up in the top ten backtests. It has the distinction of being the strategy used in the most profitable backtest SignalSolver has ever found--AAPL Monthly MASC BAO which would have generated over $400 Billion from $10,000 invested.

Here we look for MASC BAO strategies which beat Buy/Hold for stock symbols A through F (i.e. an arbitrary selection) using backtests run against daily, weekly and monthly data for each stock. The point of the exercise is simply to show that you can usually find a MASC BAO algorithm that beats buy or short-hold. Here are the results:

MASC BAO backtest results

Each table shows the difference between the buy-hold returns and the returns from the MASC BAO strategy. Strategy returns are shown in three parts, those for the long only, short only and combined long&short (L&S). Also of interest are the Reward/Risk numbers (higher is better).

You can see that for all 18 data sets, SignalSolver was able to find MASC BAO trading algorithms which beat buy-hold or short-hold. Many were significantly better, such as the 83% annualized return for E daily (vs 0% for buy-hold), however a few were only marginally better such as the 15.3% for D weekly (vs. 12.8% for buy-hold).

More about the MASC BAO strategy

MASC BAO roughly follows a "buy the dips but sell at the first sign of trouble" methodology. The MASC BAO strategy for the Percentage Band is this:

  1. A BUY signal occurs if, in a given period, the price fails to rise a fixed percentage above the last sale price.
  2. A SELL signal occurs if the price goes below a fixed percentage below the median price (i.e. half H+L) of the previous period.
  3. A sell signal turns a bullish position to bearish at the open of the subsequent period.
  4. A buy signal switches a bearish position bullish at the close of the period.
  5. You only respond to one signal per time period.

For example, if the last sale price was 100, a buy signal for [MASC 10% PB] would occur if the price in a given day, week or month failed to go above 110. This could be dangerous if you were trading short or long and short. If you were short and the price broke out to new highs, there would be no buy (cover) signals and you would lose all if the price went to double your short sale price. If you were just trading long, you would be simply be locked out of a bull run.

For other bands, the percentage value is calculated on the band in use and added into the reference (in this case last sale price on buys, median price on sells), as described here. Interestingly, only 4 bands emerged from this study: SMA (Simple moving average), TMTR (Trimmed mean of the true range), Bol1 (Bollinger band 1) and the PB (percentage band). Other bands often gave good results, but these four bands gave the best results.

As always, good backtest results don't necessarily translate into future profits for any trading strategy.

Settings

If you care to reproduce these results, here are the settings:

AMZN top 4 algorithms day by day

The out of sample multi-algorithm approach was quite successful for AMZN (see results) so lets do it live and see how it ends up. On Dec 21st, I did the optimization using the EMA band and Figure of merit optimization I have been talking about in the last few posts. For the next 25 trading days and maybe beyond, I will update the following trading guidelines for the top 4 algorithms. All these strategies were found using SignalSolver.

Update Feb 23rd 2017: Summary and Wrap Up: 10, 25 and 42 day

I'd like to wrap up this study by publishing the 25 day results as promised. Since yesterday was day 42, I've also added in the 42 day results although the algorithms were never expected to have a 42 day lifetime.  I'm happy to report that the algorithms were profitable. As predicted, the 10 day results gave the best average annualized return (121%) for the algorithms, with a noticeable fall-off in the 25 day (56.6%) and subsequent 42 day (39.3%) results:

This study was done over a time period of unusual gains in the stock market and AMZN, the algorithms for 25 day and 42 day failed to keep up with buy/hold. But if you focus on that, you would be missing the most important point--that following the algorithms eliminates all the buy/sell/short/cover decision making.

The Strategies

The following sections were updated daily during the course of the live study, so that readers could follow along with the four algorithms as they progressed day to day. The last day published this way was day 20 which is shown below.


 

Strategy 1: AOS 0.67% EMA AOC 1.37% EMA--Day 20

 


 

Strategy 2: AOS 0.16% EMA MAYC 0.12% EMA--Day 20


 

Strategy 3: ACS 0.91% EMA AYO 2.0% EMA--Day 20


 

Strategy 4: MBOC 0.31% EMA AUO 2.17% EMA--Day 20


 

The Strategies

Strategy 1:

Strategy 2

Strategy 3:

Strategy 4:


 

AMZN Top 4 Algorithms: performance update at day 10 (Jan 5th 2017)

Happy new year. If you had been following along for the last 10 trading days you would have seen me publish a daily description of each the trade before and after it was made, including the type of order to be placed and when, so there was no cheating--this was a live, real-time test.

At trading day 10 (yesterday Jan 5th 2017) I am pleased to report that all 4 algorithms are showing a profit: For $10K invested in each, alg 1 profit was $258, alg 2 $142, alg 3 $558, alg 4 $147. This includes all trading costs ($7 per trade) and was for Long and Short sides combined (see L&S columns below). Just the long side did almost as well (Long column below). Of 15 total trades, 13 were profitable. Average profit was $276 or around 95% annualized return. For the same period, buy-hold return was $161 or 50.6% annualized.

The point to note here is that the combined algorithms showed a profit. Not only that, they did a much better job of exploiting the price movements than either buy-hold or short-hold. The SignalSolver algorithms make buy-sell decisions based purely on numerical analysis of  past price movements. As a technical analysis methodology I must say it did a good job in this case.

I will continue to give daily trade instructions just after each NYSE open. I will report performance out again on day 25, the end of the test period, although I expect it to decline as the algorithms age. After that we need to re-optimize to new algorithms as I expect performance of these 4 to fall off.

 

Multi-algorithm test results for NUGT (4 algorithms)

Multi-algorithm study results for NUGT DUST and X

Multi-algorithm study results for NUGT DUST and X

In the interests of scientific method, I'd like to continue the multi-algorithm study by discussing the worst results, those for X (United States Steel Corp. ), NUGT(Direxion Daily Gold Miners Bull 3X ETF) and DUST(Direxion Daily Gold Miners Bear 3X ETF). There was plenty of potential for disaster in these stocks, very large swings indeed. In fact, as you shall see, these often led to total loss of capital because the algorithms were run both Long and Short--if the stock price doubles and you are short, you are wiped out. All the results for the study can be found in the spreadsheets.

Multi-algorithm test results for X (United States Steel Corp.)

     Multi-algorithm test results for X (United States Steel Corp.)

Multi-algorithm test results for X (United States Steel Corp.)

While the September and Jan optimizations were almost all lossy, the Jun optimizations led to profits for the 50 and 99 day horizons, so overall, losses were fairly neutral. But the technique clearly failed to exploit the price movements of X. Look at the January buy-hold figures, there was plenty of room for profit on the long side, but harshly negative profits for the algorithms.  Similarly for Sept optimizations on the short side.

Multi-algorithm test results for NUGT

Multi-algorithm test results for NUGT (4 algorithms)

Multi-algorithm test results for NUGT (4 algorithms)

The January optimization results are the worst. 25 days into the test, 3 out of the 4 algorithms had gone belly up with a complete loss. Before the 99 days had elapsed, the fourth algorithm had also died off. Not that difficult to understand, though. For the Jan 15 to Jan 16 optimization period the stock price had dropped from $42 down to $3.50, consequently, all the algorithms had a strong short bias. Just at the start of the out-of-sample runs, the stock price started rising, from $3.50 up to $21 at the end of the 99 days. Oh well. On the positive side, most of these losses were offset by the June optimization results, so it  ends up being pretty flat returns overall, but sadly leaving these huge swings unexploited.

Multi-algorithm test results for DUST

Four algorithm result for DUST

Four algorithm result for DUST

While the June optimizations led to some decent exploits, the Jan results were again pitiful, stock price dropped from $1000 to $50, and the algorithms had a buy bias, so not a good result.

 

 

Multi-algorithm results for UWTI

Oil had a rocky year, lets look at how our multi-algorithm approach worked on an oil related stock, UWTI (VelocityShares 3x Long Crude Oil ETN). In this post, I'm just going to summarize the results:

Briefly, the method used is to run SignalSolver to find trading systems which worked for a 250 day period, then run these trading system against the data immediately following the 250 day period to see how they performed. This is known as out-of-sample testing as none of this following data is included in the optimizations.

The methodology is identical to my previous posts. If you need more details you can download the results spreadsheets, or download SignalSolver and run the optimizations yourself. I have omitted the result for the Return optimization on the PB band this time because I think we have established that the Figure of Merit optimizations usually work better. They do here.

The idea is behind multi-algorithms is that using a single algorithm is risky, but running multiple algorithms at once averages out your risk and leads to more consistent and more efficient (since you are sometimes out of the market) results. Here, we averaged over the top 4 algorithms for each period.

You can see that the multi-algorithmic approach gave very good results for the 25 day (5 weeks of trading) and 50 day runs. If you look at the buy-hold profile you can see that you would have had a rough time guessing long and short entry and exits to get the same results. Not saying it couldn't be done, but using the multi-algorithmic approach would have given nice returns for UWTI with absolutely zero guesswork. And its based on sound numerical analysis, not hunches and tweets. Is anybody listening? 🙂