Technical Analysis Mean Reversion Basic

Historical trades (4/20/2009 - 5/17/2024)
Model Summary
Model CAGR
SPX CAGR13.06%
Winning Trades444
Losing Trades53
Win %89.34%
Max Drawdown-19.92%
Max Drawdown duration464 days
Final Value $1,155,052
YTD Return
SPX YTD Return10.70%
Since GB Launch
SPX Since GB Launch11.89%
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Model Description

Overview and Implementation

For maximum responsiveness with 23/5 trading, this model exclusively trades the S&P 500 Futures Contract. However, it can be implemented fairly accurately through any ETF or other instrument that tracks the S&P 500 such as SPY or VOO. The model is always either 100% long SPX or 100% cash. Each trade alternates between these two positions. In practice, many investors implement our models through a hedging system instead. For example, on a buy signal, you would hold your regular diversified portfolio of ETFs and stocks as you do now. During a sell signal, you would sell S&P 500 Futures contracts against the value of your holdings for a net market neutral overall position. This method has the added benefit of lower overall tax liability.

Trade Frequency

TA-MR Basic is our most frequently trading model with 497 total trades over the 15.07 year period. On average, the model made one trade per 7.64 trading days. However, the trades are not uniformly distributed; during periods of higher market volatility, more trades were made compared to relatively calm periods.

TA-MR Basic is best for traders who want a high win-rate scalping system. At 89.34% trade win rate, traders following this model will rarely suffer losses. On the flip side, the vast majority of trades in this model are not homeruns. Think of this model as building a long term advantage against the benchmark through many small wins over time.

Key Indicators

This model focuses on searching for mean reversion opportunities, a theory used in finance that suggests that asset price volatility and historical returns eventually will revert to the long-run mean or average level of the entire dataset.

Indicator Overview

A few of the indicators this model uses behind the scenes are:

  1. Bollinger Bands
  2. Chande Momentum Oscillator (CMO)
  3. Relative Strength Index (RSI)

The model uses these and other minor indicators under varying and adaptive time parameters to identify overbought and oversold entry points for high win-rate scalp trades. As with all our models, we run the backtest through our proprietary anti-overfitting machine learning system to ensure we are identifying real and repeatable patterns.

Indicator Analysis

Technical analysis (TA) is a trading discipline used to identify trading opportunities by analyzing statistical trends gathered from trading activity, such as price movement and volume. In aggregate, TA is about analyzing the psychology of market participants through their transactions. Some examples of TA used in this model:

  • Bollinger Bands with RSI and SMA synchronicity on multiple time frames. Bollinger Bands (BB) draw two lines around a simple moving average (SMA) based on a positive and negative standard deviation. During times of volatility, the bands widen and then contract when volatility subsides. When price moves well outside the bands in either direction, it can be a a warning sign that the price action has been unsustainable and that price is likely to mean-revert back inside the bands soon. Combining these extreme movements with the relative strength index (RSI) as well as where the current price rests in relation to important SMAs (such as 20 day, 50 day and 200 day) can be a tip to the model that we are likely near a local minima or maxima.
  • Chande Momentum Oscillator (CMO) uses momentum to identify relative strength or weakness, classifying such periods by degree of overbought / oversold. By itself, CMO usually does not generate fantastic signals, but when combined with RSI, SMA, BB width and other TA indicators and analyzed across multiple time frames for synchronicity, it can be a valuable tool in guiding the model to certain signals.

When building the model, we took these TA tools and many others into consideration and carefully tested the patterns using machine learning and heuristics to distinguish those with predictive power against those that merely seemed like they had predictive power. This is what we call our proprietary anti-overfitting engine, and although no model can ever perfectly predict market movements, with this engine you can rest easy knowing the model is backed by the latest science and engineering. We leave human emotion at the door; the signals are generated 100% mechanically.


TA-MR Basic achieved an annual CAGR of 17.62% vs. 13.06% for SPX which we use as the benchmark. This represents a substantial outperformance of 4.56% per year. $100,000 invested in the model at inception would have grown to $1,155,052. Following the rules of all investment professionals, we must of course disclaim that past performance does not guarantee similar results in the future. However, our testing period covers many different market conditions, from the uber turbulent COVID-19 drawdown in Feb-Mar 2020 to the incredibly complacent 2013 and 2017 years. This lengthy period of analysis, combined with our proprietary anti-overfitting system, gives us confidence that our model has alpha.


Historically, the model had a maximum drawdown of -19.92% vs -36.01% for the SPX. This represents a substantial reduction in drawdown vs. the benchmark of 44.68%. When looking for a great model or market timing system, it's very important to not only consider absolute performance but also drawdown because investors should value risk-adjusted returns above absolute. The model's combination of absolute outperformance and smaller drawdown is exactly what savvy investors should be looking for in an actionable market timing system.

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