A comprehensive framework for quantitative trading strategy development, from initial hypothesis to validated production deployment using GPU-accelerated tools.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install strategy-workflow
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install strategy-workflow using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Strategy Workflow?
The Strategy Workflow is a robust, end-to-end framework designed for quantitative traders using Openclaw Skills. It bridges the gap between raw trading ideas and production-ready strategies by providing a structured path through hypothesis formulation, high-performance backtesting, and rigorous validation. This skill is engineered to handle intensive computational tasks, leveraging both local resources and cloud-based GPU instances like Vast.ai for large-scale parameter sweeps.
By integrating this tool into your Openclaw Skills library, you gain access to a professional-grade research environment. It supports sophisticated workflows like Bayesian hyperparameter optimization with Optuna and event-driven backtesting via NautilusTrader, ensuring that every trading signal is backed by statistically significant data and realistic execution modeling.
Strategy Workflow Use Cases
- Developing new quantitative trading hypotheses using price-based and technical indicators.
- Executing massive parallel parameter sweeps across thousands of symbol-combination pairs.
- Validating strategy robustness through walk-forward analysis and out-of-sample testing.
- Generating professional performance tearsheets and risk metrics for portfolio analysis.
How Strategy Workflow Works
- Formulate a measurable trading hypothesis and select relevant market features for signal generation.
- Perform mandatory pre-flight validation to ensure data integrity, sufficient trade counts, and no look-ahead bias.
- Execute Phase 0 prescreening using GPU-accelerated vectorized backtests for rapid parameter filtering.
- Conduct Phase 1 event-driven backtesting for high-fidelity simulation of top candidates including slippage and commissions.
- Utilize Bayesian optimization via Optuna for intelligent hyperparameter tuning and exploitation of the search space.
- Apply walk-forward validation (rolling or anchored) to ensure the strategy remains effective across different market regimes.
Strategy Workflow Setup
To get started with this component of your Openclaw Skills, initialize the control plane to manage hardware utilization:
bash scripts/start_swarm_watchdogs.sh
For local environments requiring explicit path definitions, use the following configuration:
VENV_PATH=/path/to/.venv/bin/activate \
RESULTS_ROOT=/path/to/backtests \
STATE_ROOT=/path/to/backtests/state \
bash scripts/start_swarm_watchdogs.sh
Launch parallel optimization runs using the unified execution wrapper:
scripts/backtest-optimize --parallel
Strategy Workflow Data Schema & Taxonomy
The skill organizes results within a structured directory hierarchy designed for clear metadata taxonomy under the Backtests/optimizations/{SYMBOL}/{MODE}/ path:
| Artifact | Type | Description |
|---|---|---|
best_sharpe/ |
Directory | Contains the config.json and metrics.json for the top Sharpe ratio trial. |
all_trials.json |
JSON File | A complete record of every Optuna trial performed during the optimization phase. |
phase0_top500.json |
JSON File | Results of the initial GPU-accelerated screening phase. |
metrics.json |
JSON File | Specific performance metrics including Drawdown, Win Rate, and Kelly Criterion. |
Strategy Workflow Advanced Features
- Multi-GPU support for massive parallel execution on cloud infrastructure like Vast.ai.
- Bayesian optimization with warm-start capabilities from initial prescreening results.
- Hardware watchdog hooks that enforce CPU/GPU utilization targets and prevent silent fallbacks.
- Distributed optimization using JournalStorage or RDBStorage for multi-process and cluster-based runs.
- Integration with PAL MCP for multi-model research consensus and strategic advisory.
SKILL.md
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