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6 quant & statistical indicators, from the AlgoBrain wiki. Statistical and systematic building blocks: cointegration, ML features, execution and robustness checks. Each lists what it measures, the strategies that use it, the Crypto Data API endpoints that serve it or its inputs, and a prompt for an AI agent to compute it. All of them are in the API: GET /api/v1/indicators/catalog?group=quant-statistical.
Compute the Cointegration for BTC, ETH and SOL using the CryptoDataAPI (X-API-Key header).
1. Definition and parameters: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/concepts/indicators/cointegration.md
2. Inputs:
- GET https://cryptodataapi.com/api/v1/backtesting/klines
- GET https://cryptodataapi.com/api/v1/backtesting/funding
- GET https://cryptodataapi.com/api/v1/quant/coins
3. Pinned: 4h bars, 500-bar lookback, the playbook's default parameters. Return the latest value, its 30-day percentile, and a one-line read of what it says now. Research only.
The Laguerre Filter is a smoothing filter built from a four-stage cascade of Laguerre polynomial sections, introduced by John Ehlers in the 2004 article "Time Warp – Without Space Travel" and in Cybernetic Analysis for Stocks and Futures.
Used byA building block; no catalogue strategy declares it directly.
Compute the Laguerre Filter for BTC, ETH and SOL using the CryptoDataAPI (X-API-Key header).
1. Definition and parameters: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/concepts/indicators/laguerre-filter.md
2. Inputs:
- GET https://cryptodataapi.com/api/v1/backtesting/klines
- GET https://cryptodataapi.com/api/v1/backtesting/funding
- GET https://cryptodataapi.com/api/v1/quant/coins
3. Pinned: 4h bars, 500-bar lookback, the playbook's default parameters. Return the latest value, its 30-day percentile, and a one-line read of what it says now. Research only.
Quadratic regression fits a second-order polynomial y = a + b·x + c·x² to the last n bars by least squares and reads the fitted value at the most recent bar.
Used byA building block; no catalogue strategy declares it directly.
Via API/api/v1/indicators/catalog/quadratic-regression
AI-agent prompt
Compute it with an AI agent
Compute the Quadratic Regression (Polynomial Baseline) for BTC, ETH and SOL using the CryptoDataAPI (X-API-Key header).
1. Definition and parameters: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/concepts/indicators/quadratic-regression.md
2. Inputs:
- GET https://cryptodataapi.com/api/v1/backtesting/klines
- GET https://cryptodataapi.com/api/v1/backtesting/funding
- GET https://cryptodataapi.com/api/v1/quant/coins
3. Pinned: 4h bars, 500-bar lookback, the playbook's default parameters. Return the latest value, its 30-day percentile, and a one-line read of what it says now. Research only.
Via API/api/v1/indicators/catalog/supersmoother-filter
AI-agent prompt
Compute it with an AI agent
Compute the SuperSmoother Filter for BTC, ETH and SOL using the CryptoDataAPI (X-API-Key header).
1. Definition and parameters: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/concepts/indicators/supersmoother-filter.md
2. Inputs:
- GET https://cryptodataapi.com/api/v1/backtesting/klines
- GET https://cryptodataapi.com/api/v1/backtesting/funding
- GET https://cryptodataapi.com/api/v1/quant/coins
3. Pinned: 4h bars, 500-bar lookback, the playbook's default parameters. Return the latest value, its 30-day percentile, and a one-line read of what it says now. Research only.
Theil-Sen is a non-parametric robust regression: instead of minimising squared error, it takes the median of the slopes of every pairwise combination of points in the window.
Used byA building block; no catalogue strategy declares it directly.
Via API/api/v1/indicators/catalog/theil-sen-regression
AI-agent prompt
Compute it with an AI agent
Compute the Theil-Sen Regression for BTC, ETH and SOL using the CryptoDataAPI (X-API-Key header).
1. Definition and parameters: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/concepts/indicators/theil-sen-regression.md
2. Inputs:
- GET https://cryptodataapi.com/api/v1/backtesting/klines
- GET https://cryptodataapi.com/api/v1/backtesting/funding
- GET https://cryptodataapi.com/api/v1/quant/coins
3. Pinned: 4h bars, 500-bar lookback, the playbook's default parameters. Return the latest value, its 30-day percentile, and a one-line read of what it says now. Research only.
A whipsaw is the repeated in-and-out losing churn that happens when a trading signal triggers an entry, reverses against the position almost immediately, stops it out, and then often reverses again.
Used byA building block; no catalogue strategy declares it directly.
Compute the Whipsaw for BTC, ETH and SOL using the CryptoDataAPI (X-API-Key header).
1. Definition and parameters: GET https://cryptodataapi.com/api/v1/algobrain/page?path=wiki/concepts/indicators/whipsaw.md
2. Inputs:
- GET https://cryptodataapi.com/api/v1/backtesting/klines
- GET https://cryptodataapi.com/api/v1/backtesting/funding
- GET https://cryptodataapi.com/api/v1/quant/coins
3. Pinned: 4h bars, 500-bar lookback, the playbook's default parameters. Return the latest value, its 30-day percentile, and a one-line read of what it says now. Research only.
Any key works, Free included — mint one in a single call. The list endpoint returns summaries; the per-slug endpoint adds the prompts. Full playbooks come from /api/v1/algobrain/page?path=… using each entry's wiki_path. Or use the MCP server.
What are quant & statistical indicators?
Statistical and systematic building blocks: cointegration, ML features, execution and robustness checks.