SX Wealth Proprietary Analytical Approach

In This Guide

  • Overview of SX Wealth’s objective, tested, and proven algorithms
  • SX Wealth’s approach to adjusting algorithms for volatility

We are objective traders and analysts relying on testable and proven mathematical approaches to speculative trading.  We’re not specialists in subjective and untestable methods.  We’re not looking for triple diamond chart formations with an inverted dragonfly.  Nor are we looking to pick bottoms based on the positions of planetary movements, or the shape and sequence of price “waves.”

Have you ever noticed that there is a lot of subjectivity in defining a price “wave,” and that those who pursue wave “counts” are always revising those counts? 

In our many decades of market experience, we’ve witnessed the largest and most spectacular losses coming from those who relied on subjective analysis.

Yes, we’ve seen subjective traders obtain success. From our perspective, they were simply good traders who had solid trading instincts regardless of their analytical approach—they had a knack for maximizing opportunity when things happened to go their way and for minimizing losses when things weren’t working out. 

Good trading habits, namely maximizing opportunities and limiting losses, and optimally allocating trading capital and sizing positions, are fundamental reasons for success. These traders don’t follow the latest esoteric fad in subjective analysis, nor do they plunge in with all their capital on the latest hot pick, nor do they hold positions with substantial losses hoping that those positions will return to profitability. 

Another important reason we eschew subjective approaches is that they cannot be objectively tested. And without objective testing, traders tend to end up with haphazard and inconsistent approaches to market analysis and trading. Consistency is a key to long-term, sustained trading success.

Objective, tested, and proven algorithms.

We strongly prefer to pursue objective approaches that can be tested on real data, to develop and reinforce good trading habits.  Past performance is not entirely predictive of future results, but the majority of successful trading professionals rely on objective, tested, and proven algorithms rather than subjective divinations.

We designed our proprietary algorithms to profitably track market trends while reducing the frequency and number of the inevitable false signals that are an unavoidable consequence of a trend-following approach. 

We use a variety of techniques to adjust algorithms by the amount of volatility in the market or by the degree of trending “energy” that’s present.  An excellent example of the benefits of this approach is our volatility-adjusted moving averages (VaMA). 

What is a volatility-adjusted moving average? 

Market prices, unfortunately, contain a lot of noise.  The majority of price movements are meaningless noise, and the objective of a trend-following analyst is to isolate the actual signal from all of that noise.  Moving averages are a standard approach to teasing-out the signal from the noise. 

Average values exhibit less “wiggle” compared to their underlying price series. Yet, anyone who has experience with moving averages knows that the trade-off for the benefit of the smoothness of the averaging process is the penalty of the delay of those averages. Greater accuracy is terrific, but it doesn’t add value if those signals come too late or are out of synch with the market. 

The longer the sample size for the average (e.g., the larger the number of days in an average), the smoother the average will be.  But this comes at the cost of increased delay.  The larger the sample size, the smoother the signal, and the later the signal. 

When markets are trending, we don’t need as much smoothness, and in that circumstance, shorter-term averages work better. When markets aren’t trending, we need more smoothness as longer-term averages are less apt to generate false signals in trend-less environments.

To meet these twin goals, we use moving averages adjusted by volatility to reduce delay when prices are trending and to accentuate smoothness when trends are absent.  Our algorithms effectively shorten the length of moving averages when markets exhibit greater trending energy and extend the effective length when trending energy is on the wane.  The adjustment reduces the “noise” in the average.  Less noise results in less false moves by the average when a market is not trending. Which, in turn, reduces the frequency of false signals.

For an example of the benefits of this approach, while conducting extensive research to develop our proprietary models, we tested some conventional moving averages alongside our volatility-adjusted averages. The tests included a 50-day simple moving average and a classic 10-day / 40-day moving average crossover system. Using the same historical price data set, the traditional approaches using those simple moving averages generated more than 4 times as many false signals as our proprietary volatility-adjusted two moving average crossover system.