Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
ML Pipeline
Complete machine learning pipeline for trading: feature engineering, AutoML, deep learning, and financial RL. Use for automated parameter sweeps, feature cre...
skill openclawclawhub Free
Complete machine learning pipeline for trading: feature engineering, AutoML, deep learning, and financial RL. Use for automated parameter sweeps, feature cre...
β¬ 0 downloads β
0 stars Unverified but indexed
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, assets/README.md, references/README.md, scripts/README.md, scripts/data_validation.py, scripts/data_visualizer.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
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
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- ahuserious
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
ML Pipeline
Unified skill for the complete ML pipeline within a quant trading research system. Consolidates eight prior skills into a single authoritative reference covering the full lifecycle: data validation, feature creation, selection, transformation, anti-leakage checks, pipeline automation, deep learning optimization, and deployment.
1. When to Use
Activate this skill when the task involves any of the following: Creating, selecting, or transforming features for an ML-driven strategy. Auditing an existing feature pipeline for data leakage or overfitting risk. Automating an end-to-end ML pipeline (data prep through model export). Evaluating feature importance, scaling, encoding, or interaction effects. Integrating features with a feature store (Feast, Tecton, custom Parquet store). Explaining core ML concepts (bias-variance, cross-validation, regularisation) in the context of feature engineering decisions.
2. Inputs to Gather
Before starting work, collect or confirm: InputDetailsObjectiveTarget metric (Sharpe, accuracy, RMSE ...), constraints, time horizon.DataSymbols / instruments, timeframe, bar type, sampling frequency, data sources.Leakage risksPoint-in-time concerns, survivorship bias, look-ahead in labels or features.Compute budgetCPU/GPU limits, wall-clock budget for AutoML search.LatencyOnline vs. offline inference, acceptable prediction latency.InterpretabilityRegulatory or research need for explainable features / models.Deployment targetWhere the model will run (notebook, backtest harness, live engine).
3.1 Numerical Features
Interaction terms: price * volume, high / low, close - open. Rolling statistics: mean, std, skew, kurtosis over configurable windows. Polynomial / log transforms: log(volume + 1), spread^2. Binning / discretisation: equal-width, quantile-based, or domain-driven bins.
3.2 Categorical Features
One-hot encoding: for low-cardinality categoricals (sector, exchange). Target encoding: mean-target per category with smoothing (careful of leakage -- use only in-fold means). Ordinal encoding: when categories have a natural order (credit rating).
3.3 Time-Series Specific
Lag features: return_{t-1}, return_{t-5}, etc. Calendar features: day-of-week, month, quarter, options-expiry flag. Rolling z-score: (x - rolling_mean) / rolling_std for stationarity. Fractional differentiation: preserve memory while achieving stationarity (Lopez de Prado).
3.4 Feature Selection Techniques
Filter methods: mutual information, variance threshold, correlation pruning. Wrapper methods: recursive feature elimination (RFE), forward/backward selection. Embedded methods: L1 regularisation, tree-based importance, SHAP values. Permutation importance: model-agnostic; run on out-of-fold predictions.
4. Anti-Leakage Checks
Data leakage is the single most common cause of inflated backtest results. Apply these checks at every pipeline stage:
4.1 Label Leakage
Labels must be computed from future returns relative to the feature timestamp. Verify that the label window does not overlap the feature window. Use purging and embargo when labels span multiple bars.
4.2 Feature Leakage
No feature may use information from time t+1 or later at prediction time t. Rolling statistics must use a closed left window: df['feat'].rolling(20).mean().shift(1). Target-encoded categoricals must be computed on the training fold only.
4.3 Cross-Validation Leakage
Use purged k-fold or walk-forward CV for time-series. Never use random k-fold on ordered data. Insert an embargo gap between train and test folds to prevent bleed-through from autocorrelation.
4.4 Survivorship & Selection Bias
Ensure the universe of instruments at time t reflects what was actually tradable at that time (delisted stocks, halted symbols removed later). Backfill from point-in-time databases where available.
4.5 Validation Checklist
Run before every backtest: [ ] Labels computed strictly from future returns (no overlap with features) [ ] All rolling features shifted by at least 1 bar [ ] Target encoding uses in-fold means only [ ] Walk-forward or purged CV used (no random shuffle on time-series) [ ] Embargo gap >= max(label_horizon, autocorrelation_lag) [ ] Universe is point-in-time (no survivorship bias) [ ] No global scaling fitted on full dataset (fit on train, transform test)
5.1 Prerequisites
Python environment with one or more AutoML libraries: Auto-sklearn, TPOT, H2O AutoML, PyCaret, Optuna, or custom Optuna pipelines. Training data in CSV / Parquet / database. Problem type identified: classification, regression, or time-series forecasting.
5.2 Pipeline Steps
StepAction1. Define requirementsProblem type, evaluation metric, time/resource budget, interpretability needs.2. Data infrastructureLoad data, quality assessment, train/val/test split strategy, define feature transforms.3. Configure AutoMLSelect framework, define algorithm search space, set preprocessing steps, choose tuning strategy (Bayesian, random, Hyperband).4. Execute trainingRun automated feature engineering, model selection, hyperparameter optimisation, cross-validation.5. Analyse & exportCompare models, extract best config, feature importance, visualisations, export for deployment.
5.3 Pipeline Configuration Template
pipeline_config = { "task_type": "classification", # or "regression", "time_series" "time_budget_seconds": 3600, "algorithms": ["rf", "xgboost", "catboost", "lightgbm"], "preprocessing": ["scaling", "encoding", "imputation"], "tuning_strategy": "bayesian", # or "random", "hyperband" "cv_folds": 5, "cv_type": "purged_kfold", # or "walk_forward" "embargo_bars": 10, "early_stopping_rounds": 50, "metric": "sharpe_ratio", # domain-specific metric }
5.4 Output Artifacts
automl_config.py -- pipeline configuration. best_model.pkl / .joblib / .onnx -- serialised model. feature_pipeline.pkl -- fitted preprocessing + feature transforms. evaluation_report.json -- metrics, confusion matrix / residuals, feature rankings. deployment/ -- prediction API code, input validation, requirements.txt.
6.1 Bias-Variance Trade-off
More features increase model capacity (lower bias) but risk overfitting (higher variance). Use regularisation (L1/L2), feature selection, or dimensionality reduction to manage.
6.2 Evaluation Strategy
Walk-forward validation: the gold standard for time-series strategies. Roll a fixed-width training window forward; test on the next out-of-sample period. Monte Carlo permutation tests: shuffle labels and re-evaluate to estimate the probability that observed performance is due to chance. Combinatorial purged CV (CPCV): generate many train/test combinations with purging for more robust performance estimates.
6.3 Feature Scaling
Fit scalers (StandardScaler, MinMaxScaler, RobustScaler) on the training set only. Apply the same fitted scaler to validation and test sets. RobustScaler is often preferred for financial data due to heavy tails.
6.4 Handling Missing Data
Forward-fill then backward-fill for price data (be aware of leakage on backfill). Indicator column for missingness can itself be informative. Tree-based models can handle NaN natively; linear models cannot.
7. Workflow
For any feature engineering task, follow this sequence: Restate the task in measurable terms (metric, constraints, deadline). Enumerate required artifacts: datasets, feature definitions, configs, scripts, reports. Propose a default approach and 1-2 alternatives with trade-offs. Implement feature pipeline with anti-leakage checks built in. Validate with walk-forward CV, Monte Carlo, and the leakage checklist above. Deliver repo-ready code, documentation, and a run command.
8.1 Optimizer Selection
OptimizerBest ForLearning RateAdamMost cases, adaptive1e-3 to 1e-4AdamWTransformers, weight decay1e-4 to 1e-5SGD + MomentumLarge batches, fine-tuning1e-2 to 1e-3RAdamStability without warmup1e-3
8.2 Learning Rate Scheduling
OneCycleLR: Best for short training, fast convergence CosineAnnealing: Smooth decay, good generalization ReduceOnPlateau: Adaptive when validation loss plateaus Warmup + Decay: Standard for transformers
8.3 Regularization Techniques
Dropout: 0.1-0.5 for fully connected layers L2 (Weight Decay): 1e-4 to 1e-2 Batch Normalization: Stabilizes training Early Stopping: Monitor validation loss, patience 5-10 epochs
8.4 PyTorch Lightning Integration
import pytorch_lightning as pl class TradingModel(pl.LightningModule): def configure_optimizers(self): optimizer = torch.optim.AdamW(self.parameters(), lr=1e-4) scheduler = torch.optim.lr_scheduler.OneCycleLR( optimizer, max_lr=1e-3, total_steps=self.trainer.estimated_stepping_batches ) return [optimizer], [scheduler]
8.5 Financial Reinforcement Learning
State: Market features, portfolio state, position Action: Buy/Sell/Hold, position sizing Reward: Risk-adjusted returns (Sharpe, Sortino) Frameworks: Stable-Baselines3, RLlib, FinRL
9. Error Handling
ProblemCauseFixAutoML search finds no good modelInsufficient time budget or poor featuresIncrease budget, engineer better features, expand algorithm search space.Out of memory during trainingDataset too large for available RAMDownsample, use incremental learning, simplify feature engineering.Model accuracy below thresholdWeak signal or overfittingCollect more data, add domain-driven features, regularise, adjust metric.Feature transforms produce NaN/InfDivision by zero, log of negativeAdd guards: np.where(denom != 0, ...), np.log1p(np.abs(x)).Optimiser fails to convergeBad hyperparameter rangesTighten search bounds, increase iterations, exclude unstable algorithms.
10. Bundled Scripts
All scripts live in scripts/ within this skill directory. ScriptPurposedata_validation.pyValidate input data quality before pipeline execution.model_evaluation.pyEvaluate trained model performance and generate reports.pipeline_deployment.pyDeploy a trained pipeline to a target environment with rollback support.feature_engineering_pipeline.pyEnd-to-end feature engineering: load, clean, transform, select, train.feature_importance_analyzer.pyAnalyse feature importance (permutation, SHAP, tree-based).data_visualizer.pyVisualise feature distributions and relationships to target.feature_store_integration.pyIntegrate with feature stores (Feast, Tecton) for online/offline serving.
Frameworks
scikit-learn -- preprocessing, feature selection, pipelines. Auto-sklearn / TPOT / H2O AutoML / PyCaret -- automated pipeline search. Optuna -- flexible hyperparameter optimisation. SHAP -- model-agnostic feature importance. Feast / Tecton -- feature store management. PyTorch Lightning -- https://lightning.ai/docs/pytorch/stable/ Stable-Baselines3 -- https://stable-baselines3.readthedocs.io/ FinRL -- https://github.com/AI4Finance-Foundation/FinRL
Key References
Lopez de Prado, Advances in Financial Machine Learning (2018) -- purged CV, fractional differentiation, meta-labelling. Hastie, Tibshirani & Friedman, The Elements of Statistical Learning -- bias-variance, regularisation, model selection. scikit-learn user guide: feature extraction, preprocessing, model selection.
Best Practices
Always start with a simple baseline before running AutoML. Balance automation with domain knowledge -- blind search rarely beats informed priors. Monitor resource consumption; set hard timeouts. Validate on true out-of-sample holdout data, not just cross-validation. Document every pipeline decision for reproducibility.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- assets/README.md
- references/README.md
- scripts/README.md
- scripts/data_validation.py
- scripts/data_visualizer.py