feat: train forecasts on trade outcomes

tools/run_torch_retrain.ps1

@@ -18,6 +18,7 @@ param(
     [double]$WeightDecay = 0,
     [int]$Epochs = 0,
     [int]$Patience = 0,
+    [int]$ValidationWindow = 0,
     [int]$HoldoutWindow = 0,
     [string]$Interval = "",
     [string]$EnvFile = "",
@@ -155,6 +156,7 @@ if ($LearningRate -le 0) { $LearningRate = if ($env:TORCH_RETRAIN_LEARNING_RATE)
 if ($WeightDecay -le 0) { $WeightDecay = if ($env:TORCH_RETRAIN_WEIGHT_DECAY) { [double]$env:TORCH_RETRAIN_WEIGHT_DECAY } else { 0.0005 } }
 if ($Epochs -le 0) { $Epochs = if ($env:TORCH_RETRAIN_EPOCHS) { [int]$env:TORCH_RETRAIN_EPOCHS } else { 70 } }
 if ($Patience -le 0) { $Patience = if ($env:TORCH_RETRAIN_PATIENCE) { [int]$env:TORCH_RETRAIN_PATIENCE } else { 8 } }
+if ($ValidationWindow -le 0) { $ValidationWindow = if ($env:TORCH_RETRAIN_VALIDATION_WINDOW) { [int]$env:TORCH_RETRAIN_VALIDATION_WINDOW } else { 720 } }
 if ($HoldoutWindow -le 0) { $HoldoutWindow = if ($env:TORCH_RETRAIN_HOLDOUT_WINDOW) { [int]$env:TORCH_RETRAIN_HOLDOUT_WINDOW } else { 1000 } }
 if (-not $Interval -and $env:TORCH_RETRAIN_INTERVAL) { $Interval = $env:TORCH_RETRAIN_INTERVAL }
 if (-not $EnvFile -and $env:TORCH_RETRAIN_ENV) { $EnvFile = $env:TORCH_RETRAIN_ENV }
@@ -190,6 +192,7 @@ try {
         "--dropouts", $Dropouts,
         "--epochs", $Epochs.ToString(),
         "--patience", $Patience.ToString(),
+        "--validation-window", $ValidationWindow.ToString(),
         "--holdout-window", $HoldoutWindow.ToString(),
         "--ensemble-seeds", $EnsembleSeeds,
         "--selection-folds", $SelectionFolds.ToString(),
@@ -242,6 +245,7 @@ try {
         "-u",
         "tools\calibrate_torch_thresholds.py",
         "--limit", $Limit.ToString(),
+        "--horizon", $Horizon.ToString(),
         "--calibration-window", ([Math]::Min(2400, [Math]::Max(1200, [int]($Limit / 2)))).ToString(),
         "--min-trades", "24",
         "--walk-forward-folds", "8",