strategy-workflow

What This Skill Does

The strategy-workflow skill is a professional-grade framework designed for quantitative traders to manage the entire lifecycle of algorithmic strategy development. It acts as an orchestration layer between raw market data and high-performance validation environments. This skill streamlines hypothesis generation, feature engineering, and massive parallelization of backtests across GPU-accelerated clusters like Vast.ai. By integrating Bayesian hyperparameter optimization (Optuna) and rigorous walk-forward validation protocols, it removes manual bottlenecking in the research process. It supports complex setups, including regime-dependent position sizing, multi-timeframe feature alignment, and automated tearsheet generation, ensuring that every strategy undergoes strict statistical sanity checks—such as verifying data integrity and testing for look-ahead bias—before it ever touches live markets.

Installation

To integrate this skill into your OpenClaw environment, use the internal package manager provided by the platform:

clawhub install openclaw/skills/skills/ahuserious/strategy-workflow

Once installed, ensure your local or remote environment has the required shell access to run the orchestration scripts. For distributed runs, verify your SSH keys and environment variables (VENV_PATH, RESULTS_ROOT) are configured in your shell profile.

Use Cases

• Quantitative Research: Rapidly iterating through hundreds of signal variations using multi-GPU parallelization to find edges in volatile markets.
• Risk Management: Developing and testing position sizing rules, such as Kelly Criterion or volatility-adjusted scaling, to protect capital during backtesting.
• Institutional Validation: Conducting robust out-of-sample testing and walk-forward analysis to ensure strategies do not succumb to overfitting or curve-fitting.
• Infrastructure Management: Orchestrating always-on swarm watchdogs that maintain data integrity and monitor the state of large-scale, multi-symbol optimization runs.

Example Prompts

1. "Run a single-symbol optimization for BTC/USDT using the native engine with 50,000 pre-screened samples and regime-based filtering."
2. "Execute the strategy-workflow swarm watchdog in my local environment using the project directory at /data/quant/strategy-alpha."
3. "Analyze the current feature set for my mean-reversion strategy and prepare the environment for a multi-GPU backtest on Vast.ai."

Tips & Limitations

• Pre-Flight is Critical: Never skip validation.py. Statistical significance is impossible with fewer than 30 trades or flawed MTF resampling.
• Data Quality: Ensure your data path is clean and free of NaN values before initiating large-scale optimization to prevent GPU compute wastage.
• Hardware Requirements: While single-symbol testing is light, full swarm orchestration requires dedicated compute nodes. Avoid running large-scale sweeps on non-optimized local hardware to prevent system lockups.
• Look-Ahead Bias: Always review your feature generation logic; even the best GPU optimization cannot fix a flawed, biased hypothesis.