Here is a simple Video which displays what Walk forward Testing is. Could be useful for beginners with Backtesters & Trading Strategy Optimization Techniques.
For those uninitiated Backtesting is nothing but applying your own trading strategies on the Historical Data and to test the performace of the trading system( Profit/Loss, Winning ratio, Profit %, Max Drawdown, CAR/MDD, Consecutive profit/Loss…etc).
And once you had identified the right trading system using backtesting with better results. The next step is to optimize your trading system for better performance against any tough trading condtions.
But What Doest a Walk Forward Testing Mean? How it is related to Backtesting and Optimization?
In case of backtesting and optimization we genrally apply our trading rule over a specific time period alone and compute the results. But in case of Walk Forward testing we generally do multiple backtest and optimization in different overlapping time periods. i.e
1)Divide the timeframe into timeframe sets
2)In each timeframe sets again divide the data into two. The first half is called as In Sample Data and the later half as Out of Sample Data.
3)In Sample Data is used for optimization and Out Sample Data for Backtesting
4)For the First set of timeperiod optimize it using In Sample Data and backtest it with out-sample data with the best parameters. Similarly this procedure is repeated for all the overlapping timeperiod sets.
So what does all the three Does ( Backtesting, Optimization, Walk Forward Testing)
Backtesting – Backtesting Computes the Performace of the Trading system.
Optimization – Optimization helps you in identifying the best parameters that improves the performance of your trading system
Walk Forward Testing – Walk forward Testing make your trading system more robust. What makes walk-forward testing different from other optimization methods is the unique multi-step approach to strategy testing. Its Invention is mostly credited to Robert Pardo
Extracted from Amibroker
The purpose of walk-forward test is to determine whenever the performance of optimized trading system is the realistic or the result of curve-fitting. The performance of the system can be considered realistic if it has predicitive value and performs good on unseen (out-of-sample) market data. When the system is properly designed, the real-time trading performance should be in relation to that uncovered during optimization. If the system is going to work in real trading, it must first pass a walk-forward test. In other words, we don’t really care about in-sample results as they are (or should be) always good. What matters is out-of-sample system performance. It is the realistic estimate of how the system would work in real trading and will quickly reveal any curve-fitting issues. If out-of-sample performance is poor then you should not trade such a system.
WALK-FORWARD PROCESS: HOW IT WORKS
Walk-Forward testing is an on-going and dynamic process to determine whether parameters optimisation just curve fits the price and noise or produces statistically valid out-of-sample results. Here is how it works:
Let’s say we have 10 years of data from 1999 to 2009. Optimisation period is three years (in-sample data) and Verification period is one year (out-of-sample data). To begin, you start by optimising your system using only the first three years of data – in this example, 1999-2001. When the system is optimised, record the optimal parameter values and use them in the test with new data (out-of-sample) starting with 2002.
Slide the three-year window of data forward (2000-2002) and perform the same process. Once you have processed all the data available, you can collate the performance of all out-of-sample tests and compare those to in-sample optimisation runs. If the comparison shows that the system is sufficiently robust to be traded live, you simply continue the walk-forward process in real time by re-optimising every year.