ml4t
- 61 skills
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- 9 hours ago last updated
- ▌ Ml4t Build Bars · ml4tAggregate tick data into time, volume, and dollar bars. Use when resampling raw tick data into regular or information-driven bars.
- ▌ Ml4t Fetch Data · ml4tReliable data acquisition with provider abstraction and schema validation. Use when ingesting market data from APIs, databases, or files.
- ▌ Ml4t Cpcv · ml4tCombinatorial Purged CV generates a distribution of backtest paths instead of a single estimate. Use when quantifying strategy robustness and overfitting probability.
- ▌ Ml4t Data Export · ml4tExport financial data in efficient columnar formats with schema enforcement. Use when persisting datasets for reproducible research or cross-pipeline sharing.
- ▌ Ml4t Calendar Ops · ml4tTrading calendar awareness for correct date alignment and rolling windows. Use when aligning data across markets or computing holiday-aware windows.
- ▌ Ml4t Tearsheet · ml4tGenerate comprehensive performance reports from backtest returns. Use when summarizing backtest results for review or comparison.
- ▌ Ml4t Validate Data · ml4tSystematic data quality validation before modeling. Use when checking for gaps, outliers, stale prices, or schema violations in datasets.
- ▌ Ml4t Cost Model · ml4tCommission, slippage, and market-impact cost models for realistic strategy simulation. Use when backtesting to ensure P&L accounts for transaction costs.
- ▌ Ml4t Define Universe · ml4tDefine point-in-time tradeable universes with liquidity filters. Use when constructing the investable asset set that avoids survivorship and liquidity bias.
- ▌ Ml4t Meta Labels · ml4tSecondary model predicts whether a primary signal will be profitable. Use when sizing positions or filtering low-conviction trades from a base alpha model.
- ▌ Ml4t Rl Execution · ml4tReinforcement learning for trade execution and hedging. Use when optimizing execution algorithms or dynamic hedging policies.
- ▌ Ml4t Run Backtest · ml4tEvent-driven backtesting with realistic order execution, position tracking, and performance measurement. Use when simulating a trading strategy on historical data.
- ▌ Ml4t Data Leakage · ml4tPrevent train-test contamination, target leakage, and temporal leakage. Use when splitting data, fitting preprocessors, or engineering features for time-series ML.
- ▌ Ml4t Kill Switch · ml4tAutomated risk limits that halt trading when thresholds are breached. Use when deploying live strategies that need drawdown or loss-limit protection.
- ▌ Ml4t Stress Test · ml4tTest portfolios against historical crises and hypothetical shocks. Use when quantifying tail risk before deployment or during risk reviews.
- ▌ Ml4t Point In Time · ml4tEnsure data reflects what was known at each decision point, not revised or restated values. Use when joining fundamental, macro, or alternative data to price series.
- ▌ Ml4t Feature Store · ml4tOrganize computed features in versioned storage with schema enforcement and point-in-time retrieval. Use when persisting features for reproducible ML experiments or sharing across pipelines.
- ▌ Ml4t Risk Metrics · ml4tCompute portfolio risk measures including drawdown, VaR, CVaR, and tail metrics. Use when assessing portfolio risk beyond simple return statistics.
- ▌ Ml4t Lookahead Bias · ml4tDetect future information leaking into features, labels, or evaluation. Use when any pipeline step might expose data not yet available at prediction time.
- ▌ Ml4t Continuous Futures · ml4tBuild roll-adjusted continuous futures series without artificial price jumps. Use when backtesting futures strategies that span contract rollovers.
- ▌ Ml4t Horizon Design · ml4tChoose prediction horizon by analyzing IC decay, turnover cost, and feature-horizon alignment. Use when determining the optimal lookahead window for labels.
- ▌ Ml4t Latent Factors · ml4tExtract latent factors from return panels using PCA, IPCA, or autoencoders with proper noise diagnostics. Use when reducing dimensionality or discovering risk structure.
- ▌ Ml4t Triple Barrier · ml4tLabel trades using profit-target, stop-loss, and time barriers with volatility-adaptive thresholds. Use when creating supervised labels for financial time series.
- ▌ Ml4t Live Trading · ml4tTransition from backtest to live trading with zero code changes. Use when deploying a validated strategy to paper or live trading via broker APIs.
- ▌ Ml4t Regime Features · ml4tFeatures capturing market regime - volatility state, trend strength, and liquidity conditions. Use when building regime-aware models or conditioning on changing market environments.
- ▌ Ml4t Shap Analysis · ml4tExplain model predictions with SHAP values instead of biased built-in feature importance. Use when interpreting which features drive model decisions.
- ▌ Ml4t Non Stationarity · ml4tHandle changing statistical properties in financial time series. Use when features or model performance degrade over time.
- ▌ Ml4t Regime Awareness · ml4tMarket regimes as conditioning features for risk scaling, not timing signals. Use when incorporating regime detection into strategy logic.
- ▌ Ml4t Compute Features · ml4tSystematic feature computation across multiple assets with group-aware operations. Use when computing technical or fundamental features for a panel of securities.
- ▌ Ml4t Feature Families · ml4tFive families of financial features - momentum, mean-reversion, volatility, carry, and value. Use when designing a feature set to ensure coverage across complementary market dynamics.
- ▌ Ml4t Position Sizing · ml4tConvert signals to position sizes using volatility targeting and risk budgets. Use when scaling trade size relative to conviction and portfolio risk.
- ▌ Ml4t Factor Research · ml4tSystematic factor research from hypothesis through IC analysis, decay profiling, and capacity assessment. Use when developing a new alpha factor end-to-end.
- ▌ Ml4t Survivorship Bias · ml4tAccount for delisted and removed securities in historical analysis. Use when constructing universes or computing cross-sectional features to avoid survivor-only inflation.
- ▌ Ml4t Transaction Costs · ml4tEstimate whether a strategy can survive spread, slippage, and market impact before full simulation. Use when screening strategy feasibility early.
- ▌ Ml4t Feature Selection · ml4tSelect informative features using IC ranking, mutual information, or RFE - always within CV folds. Use when reducing feature dimensionality before training.
- ▌ Ml4t Deflated Sharpe · ml4tAdjust the Sharpe ratio for multiple testing bias when selecting from many trials. Use when reporting strategy performance after parameter or model search.
- ▌ Ml4t Drift Detection · ml4tDetect when model inputs or predictions shift from the training distribution using PSI and statistical tests. Use when monitoring deployed models or diagnosing performance degradation.
- ▌ Ml4t Evaluate Factor · ml4tEvaluate alpha factor quality with IC analysis, quantile spreads, turnover, and decay. Use when deciding whether a signal has enough predictive power to trade.
- ▌ Ml4t Purging Embargo · ml4tRemove training samples whose labels overlap the test period and add an embargo buffer. Use when performing time-series CV to prevent leakage between folds.
- ▌ Ml4t Walk Forward Cv · ml4tRolling or expanding window CV that preserves temporal order. Use when evaluating ML models on time-series data where standard k-fold causes temporal leakage.
- ▌ Ml4t Model Validation · ml4tMulti-gate model validation from cross-validation through stress testing to deployment sign-off. Use when qualifying a model for production use.
- ▌ Ml4t Feature Validation · ml4tValidate features before training - IC significance, stability, redundancy, and contamination checks. Use when auditing feature quality before model fitting.
- ▌ Ml4t Exposure Analysis · ml4tDecompose portfolio into factor, sector, and concentration exposures. Use when checking for unintended bets or risk concentrations.
- ▌ Ml4t Strategy Workflow · ml4tEnd-to-end strategy development lifecycle from hypothesis to live trading. Use when starting a new strategy project or onboarding to the ML4T workflow.
- ▌ Ml4t Agent Governance · ml4tSecurity and governance controls for autonomous financial agents. Use when agents can call tools, read untrusted content, or affect research or trading decisions.
- ▌ Ml4t Research Operator · ml4tThin autonomous research operator pattern for ML4T experiments. Use when an agent should inspect artifacts, read skills, run scripts, and produce an auditable recommendation.
- ▌ Ml4t Sensitivity Analysis · ml4tTest strategy robustness to parameter variation and detect overfitting cliffs. Use when validating that performance is stable across parameter perturbations.
- ▌ Ml4t Backtest Overfitting · ml4tDetect and prevent overfitting to historical data via multiple testing corrections and pre-registration. Use when evaluating strategy variants to ensure performance is not a data-mining artifact.
- ▌ Ml4t Stationarity Tests · ml4tTest whether time-series features are stationary using ADF and KPSS tests. Use when selecting or transforming features to verify stationarity assumptions for predictive models.
- ▌ Ml4t Agent State Memory · ml4tDurable agent state, memory, and replay for autonomous research workflows. Use when an agent must resume, audit, or compare multi-step runs.
- ▌ Ml4t Causal Identification · ml4tValidate causal claims using DAG adjustment sets, bad-control detection, and refutation tests. Use when distinguishing genuine factor effects from confounded associations.
- ▌ Ml4t Polars Patterns · ml4tPolars-first data processing patterns for financial data. Use when writing efficient grouped, windowed, or lazy-evaluated data transformations.
- ▌ Ml4t Registry System · ml4tContent-addressed experiment tracking for ML trading models. Use when versioning models, features, or experiment artifacts for reproducibility.
- ▌ Ml4t Monitoring Alerting · ml4tReal-time monitoring and alerting for live trading systems. Use when building observability for data pipelines, model drift, or execution quality.
- ▌ Ml4t Production Readiness · ml4tPre-deployment checklist covering data pipelines, risk limits, monitoring, and governance. Use when preparing to go live with a new strategy or model.
- ▌ Ml4t Canonical Schema · ml4tStandardized data schema across all financial datasets. Use when defining or enforcing column names, types, and index conventions.
- ▌ Ml4t Agent Tool Contracts · ml4tTyped tool contracts for autonomous research agents. Use when exposing files, search, databases, or execution tools to an LLM agent.
- ▌ Ml4t Information Coefficient · ml4tMeasure predictive signal quality with IC, Rank IC, and IC_IR. Use when evaluating whether a feature has predictive power for returns.
- ▌ Ml4t Case Study Development · ml4tStage-gated research workflow from hypothesis through data prep, feature engineering, modeling, and backtest. Use when developing a trading strategy end-to-end with disciplined gate checks.
- ▌ Ml4t Case Study Pipeline · ml4tFilesystem and artifact-contract pattern for reproducible case studies. Use when organizing a research project for reproducibility and collaboration.
- ▌ Ml4t Multi Agent Forecasting · ml4tMulti-agent probability forecasting with diversity, aggregation, and debate controls. Use when combining several agent forecasts or evaluating forecast ensembles.