Aether Analytics Strategy Deck

8/18/2019 Aether Analytics Strategy Deck

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Long-Short · Quantitative · Machine Learning

Æther Analytics

Available Quantitative Strategies



MESA AMG

Adaptive Cycle Trend

Futures Trading System



MESA AMG Futures Strategy Mechanics

Comprised of two equity (ES & YM) and two US treasury futures (ZN & ZB) contracts

Multi strategy system that monitors for specific market regimes then applies the highest quality signal

Signals aim at ANTICIPATING market turns rather than reacting to market prices

Positions are held for profit according to the evolving market state. If the market begins to cycle it will take

profits at a cycle swing. If the market begins to trend it will hold for the maximum duration of that trend.

Signals generated at end of day for entry on the next day at the market open

There are no resting stop orders for this system but there are emergency stop loss orders that will cover all

positions if the market has a extreme price movement.

Strategy Description

Our algorithmic market model uses digital signal processing (DSP) techniques to separate futures contract

prices into three distinct components of cycle, trend, and noise. Proprietary DSP filters and signal processing

algorithms are used to extract, de-trend, amplify, and isolate each component using MESA techniques origi-

nally developed for the radar and aerospace industries by John F. Ehlers.The dominant market cycle waveform is prepared from scientific measurements to determine the cyclic am-

plitude, frequency, and phase. From this, the waveform is phase-shifted into the future to anticipate the next

cyclic peak or trough. Cycle peaks and troughs are further analyzed and anticipated based on a compo-

nent that incorporates market vigor as a precursor to short-term cyclic reversals.



MESA AMG Futures Strategy Mechanics

Market cycles vary in amplitude and duration, and are ephemeral. In short-term trading, it is essential to

quickly determine whether a tradable cycle is present and its’ characteristics to exploit any market inefficien-

cies that may be present. Traditional techniques such as Fourier analysis are inadequate in this environment,

primarily because they lack resolution. Proprietary MESA algorithms are used as they offer superior perfor-mance over classical technical analysis techniques. In a low signal to noise environment such as that pro-

duced by market data, MESA typically provides better accuracy with reduced lag resulting in a higher per-

centage of winning trades.

There are also periods of time when the trend or noise components effectively swamp the cyclical compo-

nent. Thus our algorithm includes a cycle mode versus trend mode switch to a take different approach in

cases where the cycle is deemed to be ineffective. Cyclic entries are taken counter to the trend while trend

entries are based on momentum in the direction of the trend. In cases where the noise measurement is ex-

cessive, the system remains out of the market.

Many if not most algorithmic trading systems are trend-followers. In the past, trend-following systems have

been effective but due to their widespread usage, trend-followers have been rendered less effective in re-

cent years. Trend-following algorithms typically use breakout detection in their approach. With breakout de-tection, the trading system samples a trade each time a presumed trend begins. The idea is to be willing to

take a larger number of small losses and that are made up for by catching and riding a big trend every once

in a while. Our trend detection algorithm is unique in that we use the cyclic component to determine a trend

to cycle ratio. Whenever this ratio is above a threshold, the system is in trend mode. The trading system thus

provides a unique strategy for trend detection coupled with a cycle detection algorithm and a switch to de-

termine when to trade the cycle and when to trade the trend.



Hypothetical equity curve



Hypothetical Backtest & Walk Forward Out of Sample Returns

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Return Max DD

2016 12.63% 6.33% 20%** 6.8%

2015 0.00% -5.84% 5.62% 6.88% -2.76% 7.19% 3.03% -0.17% 7.34% 1.93% -2.93% 1.65% 23.02%** 11.24%

2014 4.36% 4.92% 0.33% 5.05% 1.27% 0.15% -1.91% 5.94% -0.31% 5.74% 4.41% 4.29% 39.60%** 10.88%

2013 -0.46% 0.10% 5.72% 0.53% 0.81% -2.58% 6.53% 4.76% 7.19% 3.27% -0.79% -0.35% 27.04% 13.10%

2012 4.80% 1.28% -3.36% -1.93% 0.95% 2.55% 3.37% -2.41% 7.18% 10.37% -9.36% 3.40% 16.48% 8.54%

2011 -0.24% 4.80% 7.82% 3.23% 1.50% 2.82% 7.52% 11.21% 5.65% 2.43% -2.20% 7.36% 64.99% 16.24%

2010 -1.58% 8.99% -0.19% 1.01% -2.07% 2.83% 3.19% -0.87% 11.34% -0.04% 1.91% -1.34% 24.66% 9.81%

2009 -1.33% 0.21% 8.88% 7.20% 3.70% -7.08% 6.02% 0.51% 4.13% -0.30% 5.82% -6.01% 22.36% 12.67%

2008 3.27% -2.63% 5.69% 4.79% 0.91% 9.34% 4.59% 3.16% -0.21% 13.51% 10.56% 2.04% 69.42% 18.12%

2007 5.13% 1.65% -0.55% 0.73% 0.45% -3.76% 5.25% 2.00% 5.58% 5.53% 3.43% 2.82% 31.66% 11.53%

2006 0.00% 0.36% 2.75% -2.56% 0.82% 0.40% 5.38% 2.39% -0.55% -0.57% 1.33% -2.02% 7.73% 7.85%

average expected monthly re- 2.38% Aver- 32% 11.12%

average up month 4.24% Max 69.99% 18.12%

average down month -2.07% Min 7.73% 6.8%

** 2014 — 2015 return are 100% out of sample



Risk Management

Assumptions of Risk Calculation

Through Monte Carlo Analysis the most likely profit is divided by five times the two sigma drawdown (worstcase scenario max loss), to produce expected percent gain. The reason for five times the drawdown is that

5 * 20 = 100 and we don't want to exceed a 20% drawdown at any point. Note our method of calculating

risk is EXTRODINARILY conservative and does not even include compounding in the returns. Essentially the

system is designed to maximize return without ever breaching the 20-25% max drawdown limit.

50,000 Monte Carlo Run Summary

Probable Annual Drawdown : $12,100 / 12.1%

Reward to Risk : 2.8 : 1

1 Sigma Drawdown : $18,260 / 18.26%

2 Sigma Drawdown : $27,000 / 27.5%



Profit Expectancy

Assumptions of Profit Expectancy

Through Monte Carlo Analysis the most likely profit is divided by 5 times the 2 sigma drawdown (worst casescenario max loss), to produce expected percent gain. The reason for 5 times the drawdown is that 5 * 20 =

100 and we don't want to exceed a 20% drawdown at any point. Note our method of calculating risk is

EXTRODINARILY conservative and does not even include compounding in the returns. Essentially the system is

designed to maximize return without ever breaching the 20-25% max drawdown limit. Position sizing is

assumed to be 1 contract each : ES, TY, US, YM Per $100,000 starting capital

50,000 Monte Carlo Run Summary

Probable Annual Profit : $33,670 / 33.67%%

Reward to Risk : 2.8 : 1

1 Sigma Profit : $52,300 / 52.3%

2 Sigma Drawdown : $73,000 / 73%



Sector RhythmSmart Sector Rotation

Long — Short SPDR ETFs



Sector Rhythm - Strategy Mechanics

Quantitative model that predicts cyclical turning points in the 9 Sector SPDR Etfs

Most market models are formed from linear system theory. Our market model is one of a deterministic signalwaveform with additive white noise. The technical analysis problem using this model is simple and straightfor-

ward, removing the noise by filtering or smoothing. What was left is free of noise and therefore must be thetrue signal. Then, having the true signal, all one has to do is to properly interpret it to create algorithmic rulesfor a profitable trading system.

A casual examination of daily and weekly charts shows they appear the same if the labels are removed. Theresulting implication is that the amplitude of the cyclic swings in the markets are inversely proportional to fre-quency. The generalized “1/F” , or Pink Noise is ubiquitous across all physical phenomena. This observation in

market data is hardly novel.; Mandelbrot described it as self-replicating fractals Fibonaccians describe thegrowth rate of the logarithmic spiral as 1.618.; the Hurst coefficient attempts to measure the growth rate.Most generally, Pink Noise amplitude double every time the wavelength is doubled. The shorthand notationfor this growth rate is that the noise spectral power grows 6 dB per octave.

While the core of the strategy relies on a directional model to determine the short term price movement ofsecurities, we employ additional models to optimize returns while managing risk.

Three distinctly different models are employed Each direction model is optimized for performance on each sector ETF Directional Models are dynamically selected, based on the slope of there recent equity curve divided by

there drawdown Sectors are traded independently, but net long and net short positions are limited.



Sector Rhythm - Strategy Mechanics

Noise With Memory

1/F noise is often called Noise With Memory. This characterization certainly fits market data because intradaytraders remember the opening price, yesterday’s daily range, etc. It works on every scale. Most traders re-

member what happened to prices in 2008. Noise with memory perspective applies to other fields as well.Stealth aircraft are derived from the work of Peter Swerling who described classes of “fluctuating target” scat-

tering models to characterize the performance of the pulsed radar systems. The noise was explained as inde-pendent reflections from different parts of the airplane that added together in a noise-like way as the air-plane changed aspect angle relative to the radar transmitter. Ehlers has built a deception radar jammer thatcould look like any aircraft from an F-117 to a B-52 using a white noise source followed by a filter that is the an-alog equivalent of an Exponential Moving Average (EMA).

Application to Trading Strategies

Two key design requirements arise from the Pink Noise structure of market data: First, a filter must be em-ployed to equalize the 6 dB per octave growth of the cyclic component amplitudes so that all cycles can beprocessed equally. Second, the DSP signal processing of the data must be conducted in the frequency do-main to avoid distortions that are introduced by common technical indicators. Trading signals are statisticalin nature, providing the optimum timing for predicting a reversion to the mean.



Hypothetical Backtest Equity Curve



Hypothetical Backtest Statistics

* Includes 1.5% annual Management fee and 15% performance (High Water Mark)



Incubation — Live Account Performance



Aether EquitiesLong Only Mean Reversion

US and EU Stocks



Aether Equities - Strategy Mechanics

Aether Equities is an algorithmic portfolio of quantitative strategies for short-term trading of best of breedlarge-cap equities, ETFS or Equity Options. The strategy utilizes proprietary digital signal processing (DSP) tech-niques to measure each stock’s dominant market cycle and generate analytical waveforms to forecast short-term turning points on a ticker-by-ticker basis. The strategy produces end-of-day long position buy/sell trading

signals for the next market-on-open. The maximum number of open positions and new positions per day areconfigurable. During times of heightened volatility trades can be hedged to mitigate risk. In addition the strat-egy has strict risk management controls that monitor over all macro market conditions, and when conditionsare deemed to risk to trade it automatically reduces risk or intermittently goes into a 100% cash position. Ae-ther Equities is also scalable to European and Asian Equites.

Holds a Maximum of 32 open positions at any 1 time, but can be scaled up to unlimited positions

Delays the entry of those positions over multiple days to prevent clumping

Attempts to utilize capital at 80% or higher at all times

Trades are entered at the open of the day, to ensure maximum liquidity and minimum slippage

Strategy was built 100% on OUT OF SAMPLE / WALKFORWARD methodology.

This prevents the possibility of “Curve Fitting” or “Lookback Bias”

Performance results over a ten year time frame shows robust performance of the MESA Strat93 trading sys-

tem under various market conditions. The trading system is identical for all ticker symbols. The system madevery few trades during the market collapse of late 2008 through early 2009.

System cannot predict “Black Swan” events with brief unexpected volatility but it has internal RISK aversion

components that will shut off signals if it has data showing possible high volatility down moves (Prolonged

Bear Markets)



Hypothetical Backtest Equity Curve



Hypothetical Backtest Statistics

Reflects Reg T 2:1 leverage

Performance includes 1.5% management fee and 15 % performance fee ( high water mark)

With 2:1 leverage max drawdown is still less than 25%



For more information contact

Address : Montecito, California

Telephone: +1.252.805.7161

Email: [email protected]

Website : Gaviota Capital Management

Aether Analytics Strategy Deck

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