--- /dev/null
+schema: spec-driven
+
+context: |
+ Tech stack: Python 3.11+, FreqAI (Freqtrade), Docker
+ ML libraries: XGBoost, LightGBM, CatBoost, NGBoost, scikit-learn, NumPy, pandas
+ Reinforcement learning: Stable-Baselines3 (ReforceXY strategy)
+ Testing: pytest
+ Domain: Algorithmic crypto trading strategies with machine learning
+
+ Key strategies:
+ - QuickAdapter: Regression-based strategy with zigzag labeling and adaptive thresholds
+ - ReforceXY: Reinforcement learning strategy with customizable reward functions
+
+ Conventions from .github/copilot-instructions.md:
+ - Prefer established design patterns (factory, singleton, strategy)
+ - DRY: avoid duplication of logic, data, and naming
+ - Single source of truth: maintain canonical defaults for configuration tunables
+ - Naming coherence: semantically accurate names, avoid ambiguous synonyms
+ - English-only: code, tests, logs, comments, documentation
+ - Tests-first mindset: add or update tests before refactoring or feature changes
+ - Conventional Commits format for PR titles and commit messages
+
+ Configuration guidelines:
+ - Dynamic generation: derive CLI/config options from canonical defaults
+ - Merge precedence: defaults < user options < explicit overrides
+ - Validation: enforce constraints (choices, ranges, types) at the option layer
+
+ Statistical conventions:
+ - Report effect sizes alongside test statistics and p-values
+ - Document divergence metric direction explicitly
+ - Use confidence intervals when feasible
+
+# Per-artifact rules (optional)
+# Add custom rules for specific artifacts.
+# Example:
+# rules:
+# proposal:
+# - Keep proposals under 500 words
+# - Always include a "Non-goals" section
+# tasks:
+# - Break tasks into chunks of max 2 hours