CFTC Commitment of Traders (COT) Reports & Orderflow Analytics 📈

An automated, serverless Apify Actor that scrapes, cleans, and analyzes weekly Commitment of Traders (COT) reports from the official U.S. Commodity Futures Trading Commission (CFTC).

Designed for Forex traders, Commodity analysts, Crypto investors, and Algorithmic trading systems looking for Smart Money Orderflow data.

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🚀 Features

• Multi-Asset Coverage (20+ Assets):
  • Forex: EUR, JPY, GBP, AUD, NZD, CAD, CHF, USD, MXN, BRL, ZAR.
  • Cryptocurrencies: Bitcoin (BTC), Ethereum (ETH).
  • Metals & Energy: Gold (GOLD), Silver (SILVER), Copper (COPPER), Crude Oil (OIL), Natural Gas (GAS).
  • Indices: S&P 500, NASDAQ-100, Dow Jones.
• Smart Money Metrics:
  • long_positions & short_positions (Non-Commercial positions).
  • change_long & change_short (Weekly position injection/unwinding).
  • net_position ($Long - Short$).
• Smart Money Orderflow Rankings: Automatically computes the weekly institutional buying and selling pressure ranking against historical data.
• AI & MCP Ready: Fully compatible with Claude, Cursor, and AI agents via the Model Context Protocol (MCP).
• Export Formats: JSON, CSV, Excel, XML.

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📥 Input Configuration

{
  "assets": ["GOLD", "EUR", "BTC", "OIL"],
  "categories": ["ALL"],
  "years": [2025, 2026],
  "includeRankings": true,
  "outputFormat": "flat_records"
}

Input Parameters:

Field	Type	Default	Description
assets	Array	["ALL"]	List of assets to scrape or ["ALL"].
categories	Array	["ALL"]	Filter by forex, crypto, metals, index, other.
years	Array	[2025, 2026]	Years of historical reports to fetch from CFTC.
includeRankings	Boolean	true	Computes Smart Money Orderflow injection rankings.
outputFormat	String	flat_records	flat_records or aggregated_by_asset.

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📤 Output Example (Dataset)

{
  "date": "24/09/25",
  "iso_date": "2025-09-24",
  "asset": "GOLD",
  "category": "metals",
  "contract_code": "088691",
  "long_positions": 284520,
  "short_positions": 41200,
  "change_long": 14200,
  "change_short": -3100,
  "net_position": 243320,
  "report_id": "deacmxsf"
}

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🤖 Using with Apify Client in Python (e.g. Streamlit)

from apify_client import ApifyClient
import pandas as pd

client = ApifyClient("YOUR_APIFY_TOKEN")

# Run the actor
run = client.actor("your-username/cftc-cot-reports-analytics").call(
    run_input={"assets": ["GOLD", "EUR", "BTC"], "years": [2025, 2026]}
)

# Fetch results as DataFrame
items = client.dataset(run["defaultDatasetId"]).list_items().items
df = pd.DataFrame(items)
print(df.head())

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🛠️ Local Development & Testing

# 1. Install dependencies
pip install -r requirements.txt

# 2. Run with Apify CLI
apify run

# Or run directly with Python
python -m src.main