edge-sdk 0.3.0

Python SDK for accessing Edge's agricultural commodity data including CME timeseries, USDA, CFTC, and market data

Edge SDK

Python SDK for accessing Edge's agricultural commodity data API. Get real-time and historical CME futures data, USDA market reports, and more.

Installation

pip install edge-sdk

Quick Start

from edge import Edge

# Initialize the client with your API key
edge = Edge(api_key="your-api-key")

# Get CME futures data
corn_data = edge.cme.get_futures_prices(
    symbol="ZCH5",
    start_date="2025-01-01",
    end_date="2025-01-31"
)

print(corn_data.head())

Authentication

API Key Required: All API endpoints require a valid API key. Get your API key from the Edge platform.

Configuration Options

Option 1: Pass API key directly

from edge import Edge

edge = Edge(api_key="edge_your_api_key_here")

Option 2: Use environment variables

export EDGE_API_KEY="edge_your_api_key_here"

from edge import Edge

edge = Edge()  # Automatically uses EDGE_API_KEY from environment

Option 3: Use .env file

# .env file
EDGE_API_KEY=edge_your_api_key_here

from edge import Edge

edge = Edge()  # Automatically loads from .env

API Reference

Core Client Methods

health_check()

Check API health status.

status = edge.health_check()
print(status)  # {'status': 'healthy', 'timestamp': '...'}

list_sources()

List all available data sources.

sources = edge.list_sources()
print(sources)

list_commodities(category=None, is_active=True)

List available commodities.

# All commodities
all_commodities = edge.list_commodities()

# Filter by category
livestock = edge.list_commodities(category="LIVESTOCK")

list_regions(region_type=None, country_code=None)

List geographic regions.

# All regions
regions = edge.list_regions()

# Filter by type
countries = edge.list_regions(region_type="COUNTRY")

# Filter by country
us_states = edge.list_regions(country_code="US")

list_themes()

List time series themes.

themes = edge.list_themes()

search_series(search_term, source_name=None, limit=50)

Search for time series by keyword.

# Search all sources
results = edge.search_series("corn")

# Search specific source
usda_corn = edge.search_series("corn", source_name="USDA", limit=10)

get_series(series_code, source_name="USDA", start_date=None, end_date=None, limit=None)

Get time series data (convenience method).

data = edge.get_series(
    series_code="SOME_SERIES_CODE",
    source_name="USDA",
    start_date="2024-01-01",
    end_date="2024-12-31"
)

CME Service (edge.cme)

Access CME futures and calendar spread data.

list_symbols(curve)

List all available CME symbols for a specific commodity curve. Returns live contract listings including individual contracts, calendar spreads, and continuous contracts.

# Get all Corn futures contracts
corn_symbols = edge.cme.list_symbols(curve="ZC")
print(corn_symbols)
# ['ZC.c.0', 'ZC.c.1', 'ZCH5', 'ZCK5', 'ZCN5', 'ZCU5', 'ZCZ5',
#  'ZCH5-ZCK5', 'ZCH5-ZCN5', ...]

# Get Lean Hogs contracts
hogs_symbols = edge.cme.list_symbols(curve="HE")

# Get Live Cattle contracts
cattle_symbols = edge.cme.list_symbols(curve="LE")

get_futures_prices(symbol, start_date=None, end_date=None, limit=None)

Get OHLCV data for a specific futures contract. No default limit - returns all available historical data.

# Get corn December 2025 futures
corn_data = edge.cme.get_futures_prices(
    symbol="ZCZ5",
    start_date="2025-01-01",
    end_date="2025-01-31"
)

# Get continuous front-month contract (recommended for long-term analysis)
front_month = edge.cme.get_futures_prices(
    symbol="ZC.c.0",  # Front month continuous
    start_date="2020-01-01"
)

# Get 3 years of historical data
three_years = edge.cme.get_futures_prices(
    symbol="ZC.c.0",
    start_date="2022-01-01",
    end_date="2025-01-01"
)

# Returns DataFrame with columns:
# observation_datetime, open, high, low, close, volume,
# open_interest, settlement_price, bid_price, ask_price, etc.

Symbol Types:

• Individual contracts: ZCH5 (Corn March 2025), HEG5 (Lean Hogs February 2025)
• Calendar spreads: ZCH5-ZCK5, HEG5-HEJ5
• Continuous contracts: ZC.c.0 (front month), ZC.c.1 (second month), up to ZC.c.5

get_spread_data(spread_symbol, start_date=None, end_date=None, limit=None)

Get calendar spread data between two contract months.

# Get Dec 2025 - Mar 2026 corn spread
spread = edge.cme.get_spread_data(
    spread_symbol="ZCZ5-ZCH6",
    start_date="2025-01-01",
    end_date="2025-01-31"
)

# Returns DataFrame with OHLCV for the spread

Supported CME Curves:

• Grains: ZC (Corn), ZS (Soybeans), ZW (Wheat), ZL (Soybean Oil), ZM (Soybean Meal)
• Livestock: LE (Live Cattle), GF (Feeder Cattle), HE (Lean Hogs)
• Energy: CL (Crude Oil), NG (Natural Gas), RB (Gasoline), HO (Heating Oil)
• Softs: SB (Sugar), KC (Coffee), CT (Cotton), CC (Cocoa)
• Metals: GC (Gold), SI (Silver), HG (Copper)
• Financials: ES (S&P 500), NQ (Nasdaq), YM (Dow)

Time Series Service (edge.time_series)

Generic time series data access.

get_data(series_code, source_name, start_date=None, end_date=None, limit=None)

Get time series data for any source.

data = edge.time_series.get_data(
    series_code="BEEF_CUTOUT_SERIES",
    source_name="USDA",
    start_date="2024-01-01"
)

get_metadata(series_code=None, source_name=None, theme_name=None, commodity_code=None, region_code=None, limit=50)

Get metadata about available time series.

# Get all USDA series metadata
usda_meta = edge.time_series.get_metadata(source_name="USDA", limit=100)

# Filter by commodity
corn_series = edge.time_series.get_metadata(commodity_code="CORN")

search(search_term, source_name=None, limit=50)

Search time series by description or code.

results = edge.time_series.search("beef prices", limit=20)

get_multiple(series_configs, start_date=None, end_date=None, limit=None)

Get multiple time series in one request.

configs = [
    {"series_code": "ZCH5", "source_name": "CME"},
    {"series_code": "ZCN5", "source_name": "CME"},
]
data = edge.time_series.get_multiple(
    series_configs=configs,
    start_date="2025-01-01"
)

Usage Examples

Example 1: Listing Available Contracts

from edge import Edge

# Initialize client
edge = Edge(api_key="your-api-key")

# List all Corn futures contracts
corn_symbols = edge.cme.list_symbols(curve="ZC")
print(f"Found {len(corn_symbols)} Corn contracts")

# Separate individual contracts from spreads and continuous
raw_contracts = [s for s in corn_symbols if '.' not in s]
continuous = [s for s in corn_symbols if '.' in s]
individual = [s for s in raw_contracts if '-' not in s]
spreads = [s for s in raw_contracts if '-' in s]

print(f"Individual contracts: {individual}")
print(f"Calendar spreads: {spreads[:5]}")  # First 5 spreads
print(f"Continuous contracts: {continuous}")

edge.close()

Example 2: Getting Historical Data

from edge import Edge
from datetime import datetime, timedelta

edge = Edge(api_key="your-api-key")

# Get 3 years of continuous front-month data
end_date = datetime.now()
start_date = end_date - timedelta(days=3*365)

corn_data = edge.cme.get_futures_prices(
    symbol="ZC.c.0",  # Front month continuous
    start_date=start_date.strftime("%Y-%m-%d"),
    end_date=end_date.strftime("%Y-%m-%d")
)

print(f"Retrieved {len(corn_data)} records")
print(f"Date range: {corn_data['observation_datetime'].min()} to {corn_data['observation_datetime'].max()}")
print(f"Price range: ${corn_data['close'].min():.2f} - ${corn_data['close'].max():.2f}")

# Calculate statistics
print(f"Average Close: ${corn_data['close'].mean():.2f}")
print(f"High: ${corn_data['high'].max():.2f}")
print(f"Low: ${corn_data['low'].min():.2f}")

edge.close()

Example 3: Multiple Commodities Analysis

from edge import Edge
import pandas as pd

edge = Edge(api_key="your-api-key")

commodities = {
    "ZC": "Corn",
    "ZS": "Soybeans",
    "HE": "Lean Hogs",
    "LE": "Live Cattle",
    "CL": "Crude Oil"
}

# Fetch data for all commodities
all_data = {}
for symbol, name in commodities.items():
    print(f"Fetching {name} ({symbol})...")

    df = edge.cme.get_futures_prices(
        symbol=f"{symbol}.c.0",  # Front month continuous
        start_date="2024-01-01"
    )

    if not df.empty:
        all_data[symbol] = df
        print(f"  ✓ Retrieved {len(df)} records")

# Create combined price matrix
price_data = {}
for symbol, df in all_data.items():
    df_clean = df[['observation_datetime', 'close']].copy()
    df_clean['observation_datetime'] = pd.to_datetime(df_clean['observation_datetime'])
    df_clean = df_clean.set_index('observation_datetime')
    df_clean.columns = [symbol]
    price_data[symbol] = df_clean

combined_df = pd.concat(price_data.values(), axis=1)
combined_df = combined_df.sort_index()

# Calculate correlation matrix
print("\nCorrelation Matrix:")
print(combined_df.corr().round(2))

# Save to CSV
combined_df.to_csv("combined_futures_prices.csv")

edge.close()

Example 4: Spread Analysis

from edge import Edge

edge = Edge(api_key="your-api-key")

# Get calendar spread data
spread = edge.cme.get_spread_data(
    spread_symbol="ZCZ5-ZCH6",
    start_date="2025-01-01"
)

print(f"Current Spread: ${spread.iloc[-1]['close']:.2f}")
print(f"Average Spread: ${spread['close'].mean():.2f}")
print(f"Spread Range: ${spread['close'].min():.2f} to ${spread['close'].max():.2f}")

edge.close()

Working with DataFrames

All data is returned as pandas DataFrames for easy analysis:

import matplotlib.pyplot as plt

# Get CME data
data = edge.cme.get_futures_prices("ZCH5", start_date="2025-01-01")

# Plot OHLC
data.plot(x="observation_datetime", y=["open", "high", "low", "close"], figsize=(12, 6))
plt.title("Corn Futures - March 2025")
plt.show()

# Calculate statistics
print(data[["open", "high", "low", "close"]].describe())

# Resample to weekly
weekly = data.set_index("observation_datetime")["close"].resample("W").mean()

Error Handling

from edge import Edge
from edge.exceptions import EdgeAPIError, DataNotFoundError, AuthenticationError

try:
    edge = Edge(api_key="your-api-key")
    data = edge.cme.get_futures_prices("INVALID_SYMBOL")
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except DataNotFoundError as e:
    print(f"Data not found: {e}")
except EdgeAPIError as e:
    print(f"API error: {e}")

Context Manager

Use the client as a context manager for automatic cleanup:

with Edge(api_key="your-key") as edge:
    data = edge.cme.get_futures_prices("ZCH5")
    print(data.head())
    # Client automatically closes when done

Advanced Configuration

# Custom timeout for large data requests
edge = Edge(
    api_key="your-api-key",
    timeout=120  # 120 second timeout for large historical datasets
)

# Get limited results for testing
data = edge.cme.get_futures_prices(
    symbol="ZCH5",
    limit=100  # Only get 100 most recent records
)

Data Returned

CME Futures Data

CME futures data includes the following fields:

Field	Type	Description
observation_datetime	datetime	Timestamp of the observation
open	float	Opening price
high	float	High price
low	float	Low price
close	float	Closing price
volume	float	Trading volume
open_interest	float	Open interest
settlement_price	float	Settlement price
bid_price	float	Bid price
ask_price	float	Ask price
bid_size	float	Bid size
ask_size	float	Ask size

Time Series Data

Generic time series data includes:

Field	Type	Description
observation_date	date	Date of observation
value	float	Primary value
value_high	float	High value (if applicable)
value_low	float	Low value (if applicable)
volume	float	Volume (if applicable)
quality_flag	string	Data quality indicator

Exception Types

The SDK provides specific exception types for better error handling:

• EdgeAPIError: Base exception for all API errors
• AuthenticationError: Raised when API key is invalid or missing
• DataNotFoundError: Raised when requested data is not found (404)

from edge.exceptions import EdgeAPIError, AuthenticationError, DataNotFoundError

Requirements

• Python 3.9+
• pandas>=2.0
• httpx>=0.25
• python-dotenv>=1.0

Support

For issues or questions, please contact [e-mail]

License

MIT License - see LICENSE file for details.