Quantitative Analysis Technology Summary

Risk Management / Diversification Goals Achieved

· Smoothes out portfolio equity curves by contributing non-correlated equity curves which improves the co-variance of the portfolio.

· Is altogether different from and thereby complementary to traditional fundamental-, cycle-, or technical analysis- based technologies.

· Is non-trend-following.

· Is non-discretionary, requiring no trader insight.

· Diversifies at all design levels (input data, trading frequency, indicator, model, market instrument, and portfolio).

· Is risk averse.

· Is statistically valid.

· Trades consistently over time.

· Functions across diverse instruments: Futures; Equities; Mutual Funds; Options; Fixed Income; FOREX; Precious Metals

Correct Market Assumptions Maintained

· Markets are non-stationary (structure underpinning the markets is evolving over time).

· Market forces are non-linear.

· Parameter set usefulness decays over time requiring an adaptive solution.

· Efficient market theory & random walk hypotheses have been discredited and rejected.

Indicator Research Domain Well-Defined

· Robust, self-adaptive, non-linear, statistically valid, predictive indicators.

· Non-price-based (cannot be trend-following) indicator inputs.

· Buy/Sell indicators are a function of volatility, basis, option, inter-market, sentiment, volume, open interest, monetary, and other data which is exogenous to price.

Statistical Principles Observed

· Proper handling of time series relative to the number of degrees of freedom involved.

· Balance maintained between the demands of complexity (the "law" of requisite variety) and the demands of statistical validity (the central limit theorem).

· Cross-validation is achieved to further ensure statistical validity.

· Over training, curve fitting, and other known trading system abuses are avoided.

· Sharpe Ratio used as a risk-adjusted measure of return.

Appropriate Exploratory Tools Employed

· Heuristic (exploratory) tool kit used (over 250 different commands).

· Statistical Pattern Recognition (not chart patterns) finds weak signals in noisy data.

· Sophisticated mathematical and statistical modeling language.

· Tracking of parameter sets as they migrate in parameter space.

· Indicator parameter sets are constructed to adapt over time or to market impulses.

· Comprehensive tool kit promotes statistical validity.

The Importance of this Website to Your Business – As the markets become more volatile, you would do well to train your quants to protect your portfolio against the ill effects of nonstationarity.

Exogenous Data Based Models – The good and bad characteristics of Exogenous Data. (Don’t miss the interesting visualization of some SPX Index Option data.)

Visualization of Exogenous Data – In case you missed it above.

Quantitative Analysis Platform – A user-friendly modeling platform for improving the productivity of quantitative analysts.

Overview – Advanced Automated trading Systems.

Consulting Services – Helping your quantitative analysts deliver a better product for your clients.

Trading Model Building Services – Continuous and Discrete Models, using Price or Exogenous Data.

Quantitative Analysis Training Seminars – Topics covered in typical training seminars.

Model Validation – A Catch-22 in the struggle between the Central Limit Theorem and the “Law” of Requisite Variety.

Non Trend-Following, Non Technical Analysis Methods – The difference that a non-price market view can make in your portfolio’s success.

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