AAPL Trading System using SignalSolver Sentiment

AAPL Trading using SignalSolver Sentiment

AAPL trading using SignalSolver Sentiment

Using multiple algorithms to drive trading strategy

Original Post July 23 2021

AAPL Trading System using SignalSolver Sentiment

AAPL trading system using SignalSolver Sentiment

Methodology

Shown above is the simulated result of trading AAPL using SignalSolver Sentiment. The sentiment is shown as a blue area chart in the background. The equity curve for the strategy is shown in yellow, buy-hold equity in white. Sentiment is calculated each day after the close of business by assessing what percentage of the top 50 SignalSolver backtest algorithms are bullish. The buy and sell thresholds are fixed at 50% (red line) with bullish being above the threshold. A trade is executed at the next open whenever a change in sentiment is indicated, so the trade price is always out-of-sample from the backtest period which is fixed at 250 trading days. The simulation then walks forward to the next day, repetitively. Algorithms are re-parametrized every 10 trading days and flushed and refreshed every 50 trading days. For more information on methodology, please see here.

Performance of simulated AAPL trading system using SignalSolver Sentiment

Performance of AAPL trading system using SignalSolver Sentiment

Performance

Trading on sentiment (L&S column above) performed around four times better in this simulation than buy-hold in terms of reward/risk, with final equity being around 3 times better for Long/Short trading of the signals and trading long only being about twice as good. In all cases, drawdown was lower for the sentiment trading than for buy-hold.

Below is shown the threshold surface for the equal buy/sell thresholds showing that annualized return (CAGR) is insensitive to threshold changes. The peak return is $26,863 at a threshold value of 83%.

Click here to see the SignalSolver settings for this strategy: AAPL Sentiment Settings

We now move into the paper-trading phase for this project. Updates will be shown below.

Updates

Updates to this strategy and current sentiment can be found here.

AAPL Update 29 Aug 21

AAPL Update 29 Aug 21

AAPL $31,000,000 trading strategy, equity curve

AAPL $31,000,000 Algorithm

This AAPL trading strategy would have given a return of $31,712,009 for every dollar invested in December 1980. That's 123,000 times better than buy-hold, with nearly four times the annualized return (61.4% vs 16.7% for buy-hold) and roughly half the drawdown which  amounts to over six times better reward/risk than buy-hold.

AAPL $31,000,000 trading strategy: Table of results

AAPL $31,000,000 trading strategy: Table of results

This is a variation of the MASC BAO algorithm which I have published before,  except this time the buy point is related to a Bollinger Band instead of just the sell price. I found it using SignalSolver.

Here is the strategy:

Description of the AAPL $31,000,000 trading strategy

Description of the AAPL $31,000,000 trading strategy

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In this case the Bollinger band is the 7 period StdDev of median prices (monthly (H+L)/2), and the band is built around the last sell price, not the usual method of building it around a moving average. A little unconventional but it worked wonderfully. Algorithms with buy signals based on sell price often get stuck, but this one didn't. Sell signals were based on a raw Percentage Band built around the monthly median (H+L)/2 prices, just like the original version of this.

Here is the equity curve:

AAPL $31,000,000 trading strategy, equity curve

We are looking at quite a bit of reinforcement on buy signals, but sell signals tend to come in ones or twos. There are some dual signal months but not too many. Dual signals will trigger a trade for this algorithm.

Moving onto the scans, you can see that there is a single area of profitability:

Annualized return vs. buy and sell point for the AAPL $31,000,000 trading strategy

Getting the buy or especially the sell point wrong would have led to a complete loss, as this algorithm was run Long and Short (you were either long or short at all times). Here's the entire parameter surface, for completeness:

AAPL $31,000,000 trading strategy, parameter surface

A bit of a steep sided mesa, but a reasonably large plateau.

Since I found it in June, the algorithm has added about 3%, but it hasn't traded and is still long.

AAPL MASC BAO Update Aug 4th 2017

The algorithm has added around 40% since publication in Dec 2016, return on $10K would now be $432,155,847,777 . There have been no signals or trades and the position remains long.

AAPL Monthly Trading Algorithm MASC BAO $432,155,847,777 return for $10K initial investment, update 8/4/2017

AAPL Monthly Trading Algorithm MASC BAO $432,155,847,777 return for $10K initial investment, update 8/4/2017

 

AAPL MASC BAO Update 5/4/2021

Just to round this out, the algorithm finally hit the dust in Aug 2020 making a total loss. Here's how it performed since its original publication in 2016. A mixed bag really, It kept you on the right side and did around 30% better than the underlying until about 3 years after publication, then broke down.  Like all algorithms based on backtests, they work until they don't. And you can't predict when the end will come.

In case you are wondering, re-parameterizing would have helped. A sell point of 27.28% or greater would have given you the same result as buy-hold, but without trading, so not terribly meaningful. But that's the best possible outcome for this algorithm over the last 5 years, a 45% annual return.