Indices DJIA S&P 500 DAX 30 Nasdaq 100 Nasdaq Comp Nikkei 225 FTSE 100 SMI DJ Stoxx 50 CAC 40 Hang Seng Kospi Nyse Comp AMEX   -     -     -     -     -     -     -     -     -     -     -     -     -     -   10261.1 1085.4 6087 1829.5 2187.7 9062.8 5371.4 6328.8 2715.7 6087 20868.9 1775.7 6926.2 1926.8 Commodites Light Crude Oil Brent Crude Copper Gold Pallad Platinum Silver Dow Diamonds QQQQ   -     -     -     -     -     -     -     -     -   73.63 74.85 258.96 1251.85 520.5 1543 19.57 102.7 45.02 Last update: 02-09-2010 15:29:00 (GMT - Live)

Optimization & Curve-Fitting Neural network technology and trading systems Profit Target Exits Gunning Slippage Contrarian Trading Testing speed Linear Programming Optimization & Curve-Fitting What if the Markets Change? Monte Carlo Simulations Walk-Forward Testing What are genetic algorithms? A Plan for Identifying and Trading Pullbacks Automated Trading Systems for Financial Markets and Recommendations for Their Usage Another issue found in trading system development is optimization, i.e., improving the performance of a system by adjusting its parameters until the system performs its best on what the developer hopes is a representative sample. When the system fails to hold up in the future (or on out-of-sample data), the optimization process is pejoratively called curve-fitting. However, there is good curve-fitting and bad curve-fitting. Good curve-fitting is when a model can be fit to the entire relevant population (or, at least, to a sufficiently large sample thereof), suggesting that valid characteristics of the entire population have been captured in the model. Bad curve-fitting occurs when the system only fits chance characteristics, those that are not necessarily representative of the population from which the sample was drawn. source: T H E E N C Y C L O P E D I A O F T R A D I N G S T R A T E G I E S