# Send Strategy Workflow to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "strategy-workflow", "name": "Strategy Workflow", "source": "tencent", "type": "skill", "category": "效率提升", "sourceUrl": "https://clawhub.ai/ahuserious/strategy-workflow", "canonicalUrl": "https://clawhub.ai/ahuserious/strategy-workflow", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/strategy-workflow", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=strategy-workflow", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "backtest-optimize.md", "references/strategy_generation.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "strategy-workflow", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-01T08:59:00.079Z", "expiresAt": "2026-05-08T08:59:00.079Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=strategy-workflow", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=strategy-workflow", "contentDisposition": "attachment; filename=\"strategy-workflow-0.1.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "strategy-workflow" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/strategy-workflow" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/strategy-workflow", "downloadUrl": "https://openagent3.xyz/downloads/strategy-workflow", "agentUrl": "https://openagent3.xyz/skills/strategy-workflow/agent", "manifestUrl": "https://openagent3.xyz/skills/strategy-workflow/agent.json", "briefUrl": "https://openagent3.xyz/skills/strategy-workflow/agent.md" } } ``` ## Documentation ### Strategy Workflow Comprehensive strategy development workflow for quantitative trading, from hypothesis to validated production deployment. ### Overview This skill provides a complete framework for developing, testing, and validating trading strategies. It supports: Hypothesis-driven strategy development Multi-GPU backtesting on Vast.ai Bayesian hyperparameter optimization with Optuna Walk-forward validation and out-of-sample testing Automated tearsheet generation ### Control Plane (Swarm Orchestration) Always-on watchdog loops that manage hardware utilization and self-healing: bash scripts/start_swarm_watchdogs.sh For local environments, set explicit paths: VENV_PATH=/path/to/.venv/bin/activate \\ RESULTS_ROOT=/path/to/backtests \\ STATE_ROOT=/path/to/backtests/state \\ LOGS_ROOT=/path/to/backtests/logs \\ bash scripts/start_swarm_watchdogs.sh ### Work Plane (Parallel Execution) Unified wrapper that starts control plane and launches parallel work: scripts/backtest-optimize --parallel Multi-GPU, multi-symbol execution: cd WORKFLOW && ./launch_parallel.sh ### Single-Symbol Pipeline For focused optimization on a single asset: scripts/backtest-optimize --single --symbol SYMBOL --engine native --prescreen 50000 --paths 1000 --by-regime ### 1. Hypothesis Formulation Define your strategy hypothesis in measurable terms: What market inefficiency are you exploiting? What is the expected holding period? What are the entry/exit conditions? What is the target risk-adjusted return? ### 2. Feature Selection Identify relevant features for signal generation: Price-based (OHLCV, returns, volatility) Technical indicators (EMA, RSI, Bollinger Bands) Multi-timeframe features (MTF resampling) Volume analysis (PVSRA, VWAP) Market microstructure (order flow, spread) ### 3. Signal Generation Convert features into actionable signals: Directional bias (trend following, mean reversion) Entry conditions (threshold crossings, pattern recognition) Exit conditions (take-profit, stop-loss, trailing stops) Position sizing rules ### 4. Position Sizing Implement risk-aware position sizing: Fixed fractional Kelly criterion Volatility-adjusted Regime-dependent scaling ### Pre-Flight Validation MANDATORY before every optimization run: python validation.py --check-all --data-path DATA_PATH --symbol SYMBOL Validation checks: Data >= 90 days with no gaps/NaN Min trades >= 30 for statistical significance MTF resampling implemented correctly No look-ahead bias ### Multi-GPU Execution on Vast.ai Deploy to cloud GPU instances for large-scale parameter sweeps: # Copy workflow files scp -P PORT workflow_files root@HOST:/root/WORKFLOW/ # Run optimization ssh -p PORT root@HOST "cd /root/WORKFLOW && python optimize_strategy.py \\ --data-path /root/data --symbol SYMBOL --mode aggressive \\ --prescreen 5000 --paths 200 --engine gpu" ### Prescreening with Vectorized Backtests Phase 0: GPU-accelerated parameter screening: Generate N random parameter combinations Batch evaluate on GPU Filter by minimum trades (30+) Return top K by Sharpe ratio Performance baseline (RTX 5090, 730d lookback, 250k combos): ~4s per mode. ### Full Backtests with NautilusTrader Phase 1: Event-driven backtesting for top candidates: High-fidelity simulation with realistic execution Slippage and commission modeling Multi-asset portfolio backtests ### Optuna for Hyperparameter Search Phase 2: Bayesian optimization with warm-start from prescreening: import optuna study = optuna.create_study( direction="maximize", sampler=optuna.samplers.TPESampler(seed=42), pruner=optuna.pruners.MedianPruner() ) study.optimize(objective, n_trials=1000) ### Grid Search vs Bayesian Optimization MethodUse CaseGrid SearchSmall parameter space, exhaustive coverage neededRandom SearchLarge space, quick explorationBayesian (TPE)Efficient optimization, exploitation/exploration balanceCMA-ESContinuous parameters, smooth objective ### Pruning Strategies MedianPruner: Prune if worse than median of completed trials PercentilePruner: Prune bottom X% of trials HyperbandPruner: Multi-fidelity optimization SuccessiveHalvingPruner: Aggressive early stopping ### Distributed Optimization For large-scale runs, use persistent storage: # JournalStorage for multi-process storage = optuna.storages.JournalStorage( optuna.storages.JournalFileStorage("journal.log") ) # RDBStorage for distributed clusters storage = optuna.storages.RDBStorage("postgresql://...") ### Rolling Window Validation Slide the training/test window through time: [Train 1][Test 1] [Train 2][Test 2] [Train 3][Test 3] Parameters: train_window: Training period length test_window: Out-of-sample test length step_size: Window advancement increment ### Anchored Walk-Forward Expand training window while sliding test window: [Train 1 ][Test 1] [Train 1 + 2 ][Test 2] [Train 1 + 2 + 3 ][Test 3] Use when historical regime diversity improves model robustness. ### Epoch Selection Criteria Intelligent selection of training periods: Regime-aware: Match training regimes to expected deployment conditions Volatility-adjusted: Include both high and low volatility periods Event-inclusive: Ensure major market events are represented Recency-weighted: Emphasize recent data while maintaining diversity ### Out-of-Sample Testing Final validation phase: Hold out 20-30% of data for final OOS test No parameter tuning on OOS data Monte Carlo stress testing Regime-conditional performance analysis ### Utilization Targets CPU utilization target: >= 70% GPU utilization target: >= 70% No silent GPU fallback for GPU sweeps ### Hardware Watchdog Hooks Enforced by: hooks/hardware_capacity_watchdog.py scripts/process_auditor.py ### Capacity Monitoring Control plane loops monitor: Worker health and liveness Progress artifact freshness Resource utilization Job queue depth Self-healing actions: Automatic worker restart on crash Fill lanes for underutilized resources Cooldown guardrails to prevent thrashing ### Tearsheet Generation Generate QuantStats-style performance reports: scripts/generate-tearsheet STRATEGY_NAME \\ --trades /path/to/trades.csv \\ --capital 10000 \\ --output ./tearsheets See tearsheet-generator skill for detailed visualization options. ### PAL MCP Integration Attach PAL as an MCP server for research/consensus across multiple model providers: Config template: config/mcp/pal.mcp.json.example Docs: docs/reference/PAL_MCP_INTEGRATION.md Providers: OpenRouter, OpenAI, Anthropic, xAI, local models ### Documentation VectorBT Documentation NautilusTrader Docs Optuna Documentation QuantStats ### Project References config/workflow_defaults.yaml - Default configuration config/model_policy.yaml - Model policy (advisory) docs/guides/SWARM_OPTIMIZATION_RUNBOOK.md - Detailed runbook hooks/pipeline-hooks.md - Hook contracts docs/reference/VECTORBT_GRAPH_INGEST.md - VectorBT PRO integration ### Results Structure Backtests/optimizations/{SYMBOL}/{MODE}/ best_sharpe/ config.json # Best Sharpe configuration metrics.json # Performance metrics best_returns/ lowest_drawdown/ best_winrate/ all_trials.json # All Optuna trials phase0_top500.json # Prescreening results ## Trust - Source: tencent - Verification: Indexed source record - Publisher: ahuserious - Version: 0.1.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-01T08:59:00.079Z - Expires at: 2026-05-08T08:59:00.079Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/strategy-workflow) - [Send to Agent page](https://openagent3.xyz/skills/strategy-workflow/agent) - [JSON manifest](https://openagent3.xyz/skills/strategy-workflow/agent.json) - [Markdown brief](https://openagent3.xyz/skills/strategy-workflow/agent.md) - [Download page](https://openagent3.xyz/downloads/strategy-workflow)