S&P 500 Index Timing System Statistics
January 1996 - December 2006

The back-tested figures 1996-2006 on this page are based on the S&P 500 index-timing model. Calculations assume all trades are placed at market close on the day a trade signal was generated. The trade results listed here do not take into consideration slippage, brokers commissions, fees, taxes, or dividends and interest earned on cash positions. (The returns would be greater using a money market fund instead of cash and by reinvesting all dividends.) 

The independently verified historical performance since our first live signal in January 2006 through December 31, 2008 can be found here.

Equity Curve Graph:

Account Equity (trading system profit) is portrayed as the solid green area of the graph. Equity changes over time as the trading system positions gain or lose value. Profitable areas of the graph are filled in green, while red areas signify losses. The Buy-and-Hold profit curve is overlaid as a blue line. The Linear Regression Line, which is magenta in color, is calculated by performing a linear regression of the Account Equity.

Notice how Trend-Chart traders (green line) were able to avoid losses and grow capital during the bear market years between 2000-2002, while Buy-and-Hold investors (blue line) lost a lot of money. (SPY lost 49%.)

Annual Return Graph

Unlike most trading system designers, we optimized for consistent performance during all types of market conditions, not just for optimal profit. This trading strategy does bring consistent profit in nearly all types of markets (up, down, and sideways) with very low risk and small drawdowns. Trading a low-risk system with small drawdowns makes trading on margin (which would double your gains) or SPDRS options more attractive, and even higher profits are possible.

S&P 500 Buy-and-Hold (no timing) returns versus S&P 500 timing system returns:

During the 11-year period from 1996 to 2006, there was not a single losing year even though there was a ferocious bear market from 2000-2002. That's an amazing 100% win rate! The returns vary from year to year, depending on the market conditions, but the market timing signals consistently outperform the general market and the buy-and-hold approach. Since January 1996, our market timing approach has produced an exceptional average annual return of 33.77%, which is more than four times the 7.74% annualized return of the S&P 500.

Annual returns are calculated as the equity on the last day of the period minus the equity on the last day of the previous period. Consequently, the return value is calculated by the change in the equity curve, not by the sum of exits in the specified period.

The simulation assumes a starting equity (investment) of $10.000.

Profit Distribution Graph:

The Profit Distribution histogram displays the profit distribution of all the trades that were generated by the system as a percentage of net profit. Net profit (percentages) of the trades is divided into a number of evenly distributed bins. You can get a good idea of the system's dynamics by looking at the Profit Distribution graph. Our S&P 500 timing strategy aims at consistently accumulating small monthly gains, ultimately making money by taking a disciplined, low-risk trading approach

MAE/MFE Graph

MFE stands for Maximum Favorable Excursion and is the largest intra-day profit that a trade achieved during its lifetime. MAE is Maximum Adverse Excursion and represents the largest intra-day loss that a trade suffered within its lifetime. The graphs show MFE/MAE breakdowns for all of the closed trades generated by the simulation.

Drawdown Graph:

The Underwater Equity Curve displays equity drawdown on a walk-forward basis. (This means that the percentage of drawdown is with respect to maximum equity achieved up to that point in time.) The depth of the current drawdown is displayed on a bar-by-bar basis, covering the complete period of the historical simulation. You can quickly identify the periods of deepest and longest drawdown.

Maximum drawdown (the largest peak-to-valley percentage decline in the system's Equity Curve on a closing price basis) is 10%. The drawdown for SPY in the Buy-and-Hold approach for the same period was 49%.

Ulcer Index Risk Score:

The Ulcer Index is a mathematical measure of risk of an investment asset, or a trading strategy. It's designed as a measure of volatility, but only volatility in the downward direction. The index is based on a given past period of N days. Working from oldest to newest a highest price (highest closing price) seen so-far is maintained, and any close below that is a retracement, expressed as a percentage:

 

The measurement includes every drop in performance in the period being studied. Funds or trading systems with high ulcer-index readings should be avoided. Traders are advised to pick investments with low Ulcer index values.

Third-Party Verification/Historical trades 2006-2008

Trades and returns are independently verified and tracked by TimerTrac.com, since the first live signal in January 2006. Click on the TimerTrac logo to see a verified chart:

The following table is a summary of the S&P 500 timing performance on TimerTrac.com, since our first live signal in January 2006 through December 31, 2009: 

The stats above is provided by courtesy of TimerTrac.com.

Past results are not necessarily indicative of future performance

All computer-simulated and back-tested trading programs are subject to the fact that they are designed with the benefit of hindsight. The past performance of any trading system or methodology is not necessarily indicative of future performance.

One of the problems with back-testing is curve fitting/over optimizing. It's not too difficult to construct a timing system that works great on a small segment of historical data. Then you can tweak it a little here and a little there, and the next thing you know you're thinking you have unearthed the Holy Grail of trading systems. But then you discover it doesn't work on many other segments of data or for different time periods.

We seek to avoid this problem in three ways.

1. Unlike most trading system designers, we optimized for consistent performance during all types of stock market conditions, not just for optimal profit. This is where many traders go wrong when backtesting a trading system. Traders often focus only on profits when looking at a system. The key, however, is robustness. System parameters that work over a range of values and adapt to changing market conditions are robust.

2. No neural networks or genetic algorithms were used. A neural network might perform well on the training set, but it often performs poorly later during actual trading. A robust system is not an overly complex system with many rules that merely captures nuances within the test data, which may never repeat.

3. A robust system can handle a variety of market conditions. Thus it should continue to perform well on data that it has never processed before. Optimized trading systems must be tested against out-of-sample data (data not used during development and optimization of the trading system).

Initially, we used 10 years, which is a very long time. The fact that we used such a long back-testing period means curve fitting the system to a specific type of market behavior is not an option. The 10-year test period is a good choice, because it shows how the system handled over 100 trades in a variety of market conditions - including, for example, advances, declines, chop, and drift. During the initial test period from 1996 to 2006, we had a little of everything, including strong bull markets, prolonged sideways markets, a three-year-long bear market/crash, war, terrorist activity, changes in interest-rates, mixed economic reports, and presidential elections.

The system handily beat the S&P 500 every year, and with a 33.77% annualized gain the results were nothing short of spectacular.