Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Productivity
Strategy Workflow
Comprehensive strategy development workflow from ideation to validation. Use when creating trading strategies, running backtests, parameter optimization, or...
skill openclawclawhub Free
Comprehensive strategy development workflow from ideation to validation. Use when creating trading strategies, running backtests, parameter optimization, or...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, backtest-optimize.md, references/strategy_generation.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- ahuserious
- Source page
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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
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs
- SKILL.md
- backtest-optimize.md
- references/strategy_generation.md