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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
forge strategy create regime_test --template hmm_bb_rsi

# 2. Grid search over multiple parameters
forge optimize grid QQQ --strategy regime_test \
  --param rsi_period 10 14 20 \
  --param bb_period 15 20 25

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

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

Evaluating Overfitting Risk

AlphaForge provides walk-forward testing (WFT) as a standard feature. A large IS/OOS performance degradation suggests overfitting.

IS Period  OOS Period  Sharpe(IS)  Sharpe(OOS)  Degradation
2020-22    2023        1.8         1.4          22%  ← acceptable
2020-22    2023        2.5         0.3          88%  ← likely overfit