Walk forward optimization
Walk forward optimization is a method used in finance to determine the optimal parameters for a trading strategy. The trading strategy is optimized with in-sample data for a time window in a data series. The remaining data is reserved for out of sample testing. A small portion of the reserved data following the in-sample data is tested and the results are recorded. The in-sample time window is shifted forward by the period covered by the out of sample test, and the process repeated. Lastly, all of the recorded results are used to assess the  trading strategy. After the most suitable parameters are found, the system is run using another segment of data. The two segments of data do not overlap each other. It is the culmination of the following methods and aids in creation of robust systems. Past data is used for Backtesting of a trading system. It refers to applying a trading system to historical data to verify how a system would have performed during the specified time period and is useful if a system was not profitable in the past. Forward testing (also known as Walk forward testing) is the simulation of  the real markets' data on paper only. One moves along the markets live and is not using real money, but virtually trading in the markets to understand their movements better. Hence, it is also called Paper Trading. Forward performance testing is a simulation of actual trading and involves following the system's logic in a live market. Overview. One of the biggest issues with system development is that many systems do not hold up into the future. There are several reasons for this. The first is that the system is not based on a valid premise. Another is that the  testing is not sound for reasons such as: Walk Forward Analysis does optimization on a training set; test on a period after the set and then rolls it all forward and repeats the process. We have multiple out-of-sample periods and look at these results combined. Walk forward analysis was originally discussed by Robert E. Pardo. Walking forward can keep a trading model a step ahead. Walk forward is so called, as we have multiple walk training and testing periods is less likely to suffer from overfitting. "Walk forward testing allows us to develop a trading system while maintaining  a reasonable 'degree of freedom"'. Walk-forward testing carries the idea of 'out-of-sample' testing to the next level. It is a specific application of a technique known as Cross-validation. It means to take a segment of your data to optimize a system, and another segment of data to validate. Hence, here you optimize a window of data say past 1000 bars, and then test it on next 200 bars. Then roll the whole thing forward 200 bars and repeat the process. This gives you a large out of sample period and allows you to see how stable  the system is over time. Suppose you consider a strategy around a moving average. You take the first 3 months of data, and find that for that period a 20-minute moving average was optimal (using tick data). You then validate this rule by assessing its performance for the 4th month (i.e. profit, reward/risk or any other statistic of interest). Next, you repeat the optimization using data from month 2–4, and validate using month 5, and keep repeating this until you've reached the end of the data. The performance you get for the validation months (4-13) are  your out-of-sample performance. The basics behind the data used. Before doing the backtesting or optimization, one needs to set up the data required which is the historical data of a specific time period. This historical data segment is divided into the following two types: The process is to first develop a trading system using in-sample data and then apply the out-of-sample data to the system. The results of both cases can then be compared and tested. Conclusion. For a better understanding, please see the example here. In order to evaluate any system, one should check  out its performance when using the "Out-of-Sample Data" (test data) and not the "In-Sample Data" (data used for optimization of the system). Thus, walk forward test determines the optimized system performance as follows: Hence, the out-of-sample data plays a crucial role in determining the validity and reliability of the system and is a realistic estimate of how a system should work in real markets.