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Quants & Researchers

For quantitative analysts and researchers who prioritize statistical rigor and want systematic optimization and walk-forward validation.

Quantitative Evaluation Features

Feature Details
Walk-Forward Validation Automatic IS/OOS splits to detect overfitting
Optuna Optimization Bayesian search over parameter space (200–1000 trials)
Multiple Objectives Choose from Sharpe ratio, max drawdown, Calmar ratio, etc.
Reproducibility Lock seeds and config in JSON for fully reproducible experiments
Journal Logging All experiment results automatically recorded as JSON/CSV

Typical Research Workflow

# 1. Declare hypothesis in JSON
alpha-forge strategy create --template hmm_bb_pipeline_v1 --out regime_test.json

# 2. Grid search over the parameters declared in the strategy JSON
# (the grid is defined by optimizer_config.param_ranges in regime_test's JSON)
alpha-forge optimize grid QQQ --strategy regime_test --top-k 10

# 3. Walk-forward validation (5 folds)
alpha-forge optimize walk-forward QQQ --strategy regime_test --windows 5

# 4. Save experiment to journal
alpha-forge journal note regime_test "HMM period sensitivity analysis"

Evaluating Overfitting Risk

AlphaForge provides walk-forward testing (WFT) as a standard feature. The output is a table listing each window's IS Score and OOS Score; a large drop from IS to OOS suggests overfitting (compute the degradation yourself from IS/OOS).

Window     IS Score   OOS Score  Best Params
1            1.8000     1.4000    {...}   ← small drop, acceptable
2            2.5000     0.3000    {...}   ← large drop, likely overfit