aster-timesfm-pipeline

Connect Aster DEX klines to TimesFM for zero-shot crypto price forecasts.

6|1|Updated Apr 7, 2026
One-click install
npx skills add https://github.com/kmshihab7878/claude-code-setup --skill aster-timesfm-pipeline
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aster-timesfm-pipeline
Source: https://github.com/kmshihab7878/claude-code-setup/tree/main/skills/aster-timesfm-pipeline
Command: npx skills add https://github.com/kmshihab7878/claude-code-setup --skill aster-timesfm-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides an end-to-end pipeline that pulls historical klines from the Aster DEX, transforms market data into numeric series, and produces zero-shot point forecasts with quantile prediction intervals via TimesFM to support trading signal generation and risk assessment.

Core Features & Use Cases

  • Aster MCP ingestion: retrieve historical klines and optional funding rates at multiple intervals for any supported trading pair.
  • TimesFM forecasting: run TimesFM 2.5 to generate point forecasts and full quantile prediction intervals for uncertainty-aware decisions.
  • Batch multi-pair processing: forecast many pairs in a single batch for efficient signal generation and portfolio-level risk analysis.
  • Risk-aware rules: interpret quantile spreads for position sizing, backtest forecast accuracy, and reduce exposure when uncertainty is high.
  • Use Case: produce 24-hour hourly forecasts for BTCUSDT to inform trade entry/exit decisions and assess downside/upside risk via the 10th–90th percentiles.

Quick Start

Request a 24-hour hourly forecast for BTCUSDT with quantile intervals and return the point forecast plus the 10th and 90th percentile bounds.

Frequently Asked Questions about aster-timesfm-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is zero-shot crypto forecasting with quantiles?

Zero-shot crypto forecasting with quantiles uses pre-trained models like TimesFM to predict future prices without task-specific training, returning point forecasts alongside percentile intervals for uncertainty-aware trading decisions.

How do I get hourly crypto price forecasts with prediction intervals?

To get hourly crypto price forecasts with prediction intervals, you pull historical klines from Aster DEX, format the time series into numpy arrays, and run TimesFM to generate 24-hour point forecasts with 10th–90th percentile bounds.

Can I run multi-pair crypto forecasting in a single batch?

Yes, you can run multi-pair crypto forecasting in a single batch to efficiently generate trading signals for multiple pairs like BTCUSDT, ETHUSDT, and SOLUSDT simultaneously for portfolio-level risk analysis.

Do I need Aster MCP klines access for TimesFM forecasting?

Yes, you need Aster MCP klines access to retrieve historical market data, along with numpy-formatted time series, TimesFM 2.5 model availability, sufficient compute, and network access to download the model.

How are quantile spreads used for position sizing in crypto?

Quantile spreads from TimesFM forecasts are used for position sizing by adjusting exposure based on uncertainty; wider spreads signal higher risk, allowing you to reduce position size and backtest forecast accuracy for risk-aware decisions.