Walk Forward Optimization

NumWFOCycles

Strategy parameters and trade rules are generated separately for every cycle in [Train] mode, and are separately loaded for every test segment in [Test] mode. This way, the strategy test is guaranteed to be out of sample - it uses only parameters and rules that were generated in the preceding training cycle from data that does not occur in the test. This simulates the behavior of real trading where parameters and rules are generated from past price data. A Walk Forward Test gives the best prediction possible of the strategy performance over time.

WFOPeriod

SelectWFO

Type:

WFOCycle

WFOBar

Type:

Remarks:

• WFO theory is explained in the Training chapter and in more detail in the Black Book. In training, every WFO cycle is a separate simulation run including INITRUN and EXITRUN. In backtesting all WFO cycles are tested in a single simulation run. In live trading, only the data trained in the last WFO cycle is used.
• Due to the receding training window, the overall test period is reduced by WFO (see diagrams above). For a longer test period, increase the number of WFO cycles and/or reduce DataSplit. The length of a test and training cycle can be found in the performance report under "WFO Test Cycles" and "WFO Train Cycles".
• When changing the number of WFO cycles or the backtest period, the system must be trained again. Otherwise the WFO test and training cycles will be out of sync. For avoiding unintended changes of the test period when new price data becomes available, set a fixed StartDate and EndDate.
• If DataSplit is not set otherwise, WFO uses a default training period of 85% and a default test period of 15%.
• DataSlope can improve the parameter quality with anchored WFO cycles.
• Use DataHorizon to prevent test bias when training is based on future prices or future trade returns, as for some machine learning algorithms.
• WFO training is much faster than normal training when using multiple CPU cores.
• For executing code at the begin of every WFO cycle, check if(WFOBar == 0) ... .
• The optimal number of WFO cycles depends on the strategy, the backtest period, and the time frame. Normally a WFO cycle should cover about six to twelve months. Large time frames or infrequent trading require a small number of long WFO cycles for getting enough trades per cycle. Very market-dependent strategies with fast expiring parameters require a high number of short WFO cycles. If the strategy performance highly varies with small changes of NumWFOCycles, a periodic seasonal effect can be the reason. Try to determine and eliminate seasonal effects before optimizing a strategy.
• Parameters and rules by WFO are stored in files in the Data folder. The number of the cycle is added to the file name, except for the last WFO cycle. F.i. a parameter WFO of a script "Trade.c" and 4 cycles will generate the following parameter files in the Data folder: Trade_1.par, Trade_2.par, Trade_3.par, Trade.par, Trade.fac. [Test] mode reloads the parameters and rules for every segment during the test cycle. [Trade] mode uses the parameters and rules from the last WFO cycle, without an attached number.
• Normally the LookBack period precedes the first WFO cycle in [Test] mode. If the RECALCULATE flag is set, a dedicated lookback period precedes every WFO cycle. The content of all series is discarded and recalculated from the parameters and rules of the new cycle. This increases the test time, but produces a slightly more realistic test by simulating the start of a new trade session at every WFO cycle.
• WhWhile live trading a walk-forward optimized strategy, it is recommended to re-train the last cycle perodically for making the strategy independent on parameter settings. The best time period between retraining is the time of a WFO test cycle.
• The last WFO cycle (cycle 5 in the figure above) has no test period. It can be a few bars longer than the other cycles to ensure training until the very EndDate. If SelectWFO is set to the last cycle in [Test] mode, the out-of-sample period before the training is tested instead (OOS Test in the figure; rolling WFO only). The OOS test often generates low profit, but can inform about the long-term performance of the strategy when it is not re-trained.
• AAnchored WFO can be used to test the lifetime of parameters or rules. If anchored WFO generates a better result than rolling WFO, the strategy is longlived and does not need to be retrained often. Normally, rolling WFO produces better results, meaning that the market changes and that parameters / rules must be adapted from time to time.
• Use the plotWFOCycle and plotWFOProfit functions for analyzing the profit curve over one or several WFO cycles.

Examples:

// anchored WFO, 10 cycles 
function run()
{
  NumWFOCycles = -10;
  DataSplit = 80; // 20% test cycle
  DataSlope = 2;  // more weight to more recent data
  set(PARAMETERS+TESTNOW); // run a test immediately after WFO
  ...
}

// WFO with 120 bars training and 30 bars test
function run()
{
  WFOPeriod = 120+30;
  DataSplit = 100*120/WFOPeriod;
  ..
}

// calculate NumWFOCycles from the test period in days
// DataSplit and LookBack must be set before
function setWFO2(int daysTest)
{
  asset(Asset); // requires NumBars, so asset must be set
  int barsTest = daysTest * 1440/BarPeriod;
  int barsTrain = barsTest * DataSplit/(100-DataSplit);
  int barsTotal = NumBars-LookBack;
  NumWFOCycles = (barsTotal-barsTrain)/barsTest + 1;
}

See also: