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Institutional Order Flow Skill

Analyze institutional order flow, whale movements, and COT data with Python.

5|1|Updated Feb 9, 2026
Search Tags:#dark pool#institutional#cot#order flow#whale tracking#smart money
One-click install
npx skills add https://github.com/kayzaa/k.i.t.-bot --skill institutional-order-flow-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Institutional Order Flow Skill
Source: https://github.com/kayzaa/k.i.t.-bot/tree/main/skills/order-flow
Command: npx skills add https://github.com/kayzaa/k.i.t.-bot --skill institutional-order-flow-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python>=3.8, and includes scripts (resource) components.

What problem does it solve?

This skill helps traders and analysts identify and track the movements of large institutional players, often referred to as "smart money," to gain an edge in the market.

Core Features & Use Cases

  • Real-time Order Flow Analysis: Monitor buy/sell imbalances, volume profiles, and footprint charts.
  • Whale & Dark Pool Detection: Identify large whale orders, hidden iceberg orders, and dark pool transactions.
  • Institutional Positioning: Analyze Commitment of Traders (COT) data and options flow for sentiment.
  • Use Case: A trader can use this skill to detect significant buy-side pressure building in BTC/USDT through whale accumulation and dark pool activity, informing a potential long entry.

Quick Start

Analyze the real-time order flow for BTC/USDT on Binance, looking at the last 20 data points for the 15-minute period.

Frequently Asked Questions about Institutional Order Flow Skill

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

FAQPage Schema
How do I track smart money movements using order flow data?▼

To track smart money movements, you analyze institutional order flow data to monitor buy and sell imbalances, volume profiles, and footprint charts. This reveals where large players are positioning themselves in the market.

How do I detect dark pool activity and hidden iceberg orders?▼

Dark pool activity and hidden iceberg orders are detected by analyzing large whale transactions and institutional order flow data. This identifies significant buy or sell pressure from large institutional players.

Can I use Python to analyze Commitment of Traders data for market sentiment?▼

Yes, you can use Python to analyze Commitment of Traders (COT) data and options flow for institutional positioning. This provides interpretation of market sentiment based on large trader positions.

Does this tool work with real-time volume profiles for crypto pairs like BTC/USDT?▼

Yes, this tool analyzes real-time order flow for crypto pairs like BTC/USDT. It processes recent data points over specified periods to inform potential long or short entries based on whale accumulation.

What are the limitations of footprint charts for whale tracking?▼

Footprint charts rely on simulated or processed order flow data, meaning real-time accuracy depends on the data feed quality. Limitations include potential delays in detecting hidden iceberg orders and dark pool transactions.

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