Backtesting Commodity Spread Strategies with Cash vs Futures Data

Hook: Why cash vs futures backtests matter to engineering teams in 2026

Commodity teams and data platform engineers face a recurring challenge: trading signals that look strong on futures curves often fall apart once you include the cash market and realistic execution rules. If you28099re building a commercial-grade backtest for calendar or spread strategies across cotton, corn, wheat and soy 28094 you need synchronized, machine-readable cash prices (CmdtyView) and robust futures curves, plus a reproducible pipeline for roll rules, transaction costs, margin and risk metrics.

Executive summary 28094 what you28099ll get from this case study

• End-to-end architecture to ingest CmdtyView cash prices and exchange futures curves.
• Concrete Python examples to build continuous futures, compute calendar spreads, and simulate P&L.
• Risk metrics (Sharpe, max drawdown, VaR), and how to attribute P&L between cash and curve moves.
• Operational tips for 2026: cloud-native pipelines, costs, provenance, and automation.

Context & 2026 trends that change the game

By late 2025 and into 2026 we saw major shifts relevant to commodity backtesting:

• Higher fidelity cash datasets: vendors like CmdtyView expanded national-average and regional cash feeds delivered in machine-readable formats, reducing manual scraping.
• Cloud-native, evented data pipelines: teams moved from nightly batch jobs to near-real-time streaming for updating curves and basis, enabling intraday signals and more realistic slippage simulations.
• Cost-aware backtesting: platform engineers must now justify cloud costs; vectorized backtests and parquet/Delta storage became standard to keep TCO down.
• Regulatory & provenance expectations: audit trails (who ran what backtest, with which source data version) matter for commercial trials and POCs.

Architecture overview 28094 data sources, normalization and storage

Design an architecture that separates ingestion, normalization, backtest engine, and reporting. A minimal, reproducible stack:

1. Ingest: CmdtyView (cash) + exchange futures REST/CSV (front month contract ticks, expiries, etc.).
2. Metadata store: contract metadata (expiry, tick size, lot size, delivery units).
3. Normalization: convert units (lb vs bushel), build daily futures curves, compute basis (cash - front futures).
4. Storage: columnar files (Parquet/Delta) partitioned by symbol/date; keep raw and normalized copies.
5. Backtest engine: vectorized pandas/numpy implementation with a reproducible configuration file (roll rules, fees, margin).
6. Reporting: daily P&L, risk metrics, and P&L attribution between cash basis and futures curve moves.

Data model & provenance

Store the following elements with each row:

• as_of_date (date)
• source (CmdtyView / Exchange)
• symbol (e.g., COTTON_CIF, CORN_US)
• contract (e.g., Z25, H26), expiry
• price, unit, bid/ask if available
• ingest_version and checksum 28094 for reproducibility

Step 1 28094 Retrieve and align CmdtyView cash prices and futures curves

The most common friction is aligning cash observations (often daily national averages) with futures settlement data (exchange close). The rule-of-thumb: use the same daily timestamp (e.g., close-of-business UTC) and keep source labels.

Python example 28094 pseudo code to fetch and normalize

# PSEUDO: replace with your API keys and endpoints
import pandas as pd
import requests

# 1) Fetch CmdtyView national cash price
resp = requests.get('https://api.cmdtyview/v1/cash?symbol=CORN&start=2018-01-01&end=2025-12-31', headers={'Authorization': 'Bearer ...'})
cash = pd.DataFrame(resp.json())

# 2) Fetch futures front month and next months
resp2 = requests.get('https://api.exchange/v1/futures?symbol=CORN&start=2018-01-01&end=2025-12-31')
contracts = pd.DataFrame(resp2.json())

# 3) Normalize timestamps and units
cash['date'] = pd.to_datetime(cash['date']).dt.date
contracts['date'] = pd.to_datetime(contracts['date']).dt.date

Tip: store both raw JSON and normalized parquet. That preserves provenance and lowers compute for repeated backtests.

Step 2 28094 Build continuous futures and curve interpolation

There are multiple ways to create continuous futures series. For calendar-spread backtests you often want the full term structure per date, not a single continuous front-month series. Two common approaches:

• Fixed roll by days-to-expiry 28094 roll from contract A to B N days before expiry.
• Volume/Open Interest roll 28094 roll into the contract with the highest open interest/volume.

Python 28094 construct a daily term structure

def build_term_structure(contracts_df, date, months=[0,1,2,3,6,12]):
    # contracts_df contains rows with contract, expiry, price, date
    day = pd.to_datetime(date).date()
    # find nearest contracts for each target month offset
    # return dict: {month_offset: (contract, price, expiry)}
    pass

# Implementation note: cache results for each date for faster backtests

For spreads you will typically take two legs from the same underlying (e.g., corn Jul - Sep) using the price for each delivery month on the same as_of_date.

Step 3 28094 Define calendar/spread strategies and execution rules

Example calendar strategies this case study implements:

• Seasonal calendar: long Jul / short Dec when spread (Dec - Jul) is in top 10th percentile historically
• Mean reversion: short spread when spread z-score > 2, cover when z-score < 0
• Basis overlay: go long cash and short nearest futures when basis is negative beyond threshold

Key execution parameters to keep realistic

• Entry/exit time: use end-of-day settlement or first available market tick.
• Slippage: fixed $/contract or % of spread width; often larger for cotton due to lower liquidity.
• Commissions & fees: per contract, vary by exchange.
• Margin and leverage: define initial margin per contract; flag margin calls when equity < maintenance.

Step 4 28094 Simulate positions and P&L attribution

For each day in the backtest horizon:

1. Compute signal (spread z-score, seasonal trigger).
2. Translate signal into notional and number of contracts per leg (consider contract size).
3. Apply transaction costs on trades executed that day.
4. Update mark-to-market P&L using that day28099s settlement prices for the two legs.
5. Check margin; if margin call, implement liquidation rules.
6. Record daily P&L and exposures for attribution.

Python 28094 simplified P&L loop

for date in backtest_dates:
    term = term_structures[date]
    spread_price = term['lead_month'] - term['lag_month']
    z = (spread_price - spread_hist.mean()) / spread_hist.std()

    # generate signal
    if z > 2:
        target_position = -1  # short spread
    elif z < 0:
        target_position = 0   # flat
    else:
        target_position = 1   # long spread

    # size contracts considering tick and lot size
    # compute trade P&L, commissions, slippage
    # mark-to-market

Important: For calendar spreads, crude P&L can be decomposed into two parts: P&L from curve movement (prices of both legs changing) and P&L from basis/cash changes if you overlay cash. Preserve both components to understand why a strategy worked.

Risk metrics and performance analysis

Standard risk metrics you should compute:

• Annualized return and volatility
• Sharpe and Sortino ratios (tune for downside only)
• Max drawdown and drawdown duration
• Daily/weekly VaR (parametric or historical)
• Turnover and margin utilization 28094 essential for commercial feasibility

Code snippet 28094 compute metrics

import numpy as np

daily_pnl = trades_df['daily_pnl']
ann_ret = (1 + daily_pnl.mean()) ** 252 - 1
ann_vol = daily_pnl.std() * np.sqrt(252)
sharpe = (ann_ret - risk_free) / ann_vol

# max drawdown
cum = (1 + daily_pnl).cumprod()
peak = cum.cummax()
drawdown = (cum - peak) / peak
max_dd = drawdown.min()

Case study: cotton, corn, wheat and soy 28094 domain specifics

Each commodity has different liquidity, seasonality, and typical basis behavior. When building a multi-commodity backtest, normalize assumptions per symbol.

Cotton

• Lower liquidity 28094 wider slippage. Adjust per-contract slippage and favor monthly rebalances.
• Contracts quoted in cents per pound; be explicit in unit conversion if your cash data uses different units.

Corn

• High liquidity on front months 28094 you can simulate tighter slippage but margin impact is high.
• Strong seasonality (planting/harvest windows) 28094 incorporate seasonal filters in signals.

Wheat

• Multiple wheat classes and exchanges (SRW, HRW, MPLS). Align your cash feed to the correct contract class.

Soy

• Basis often driven by soybean oil/meal spreads 28094 consider multi-leg overlays if you want advanced signals.

Attribution: isolating cash vs futures contributions

One unique value of combining CmdtyView cash with futures curves is the ability to attribute returns to basis vs curve. For each day:

• Delta P&L (futures) = change in futures prices * position size
• Delta P&L (cash/basis) = change in (cash - front futures) * underlying cash exposure

Aggregate these across trades to determine whether alpha came from curve mean-reversion or cash-basis dislocations.

Operationalize: production patterns and 2026 best practices

Turning a backtest into a pilot or production feature requires engineering discipline:

• Use feature flags and versioned datasets: label dataset versions in config to rerun historical tests deterministically.
• Automate via Airflow/Prefect: ingestion & normalization DAGs should run daily and produce Delta/Parquet snapshots.
• Cost control: store dense time-series compressed, use compute clusters for vectorized backtests only when necessary.
• Monitoring & alerts: create dashboards for data integrity checks (missing dates, contract gaps).

Containerized, reproducible backtests

Ship your backtest in a Docker image that pins library versions. Persist results to an S3 bucket (or equivalent) with a manifest file that records the input dataset versions and config.

Common pitfalls and how to avoid them

• Unrealistic roll rules: naive continuous contract can introduce lookahead; always implement deterministic roll logic.
• Ignoring basis: some calendar strategies fail once you include cash-basis slippage and physical delivery considerations.
• Underestimating margin: futures can produce large intra-day moves; stress-test margin usage under historical shocks.
• Data drift: vendors change endpoints and field names 28094 instrument catalog versioning is essential.

Advanced strategies & 2026-proofing

Beyond simple spreads, consider:

• Machine-discovered regimes: use clustering (e.g., HMM) on term structure shapes to adapt roll/size rules per regime.
• Cross-commodity overlays: soy/corn spreads or cotton vs oil correlations can reveal structural dislocations.
• Real-time basis alerts: using streaming CmdtyView updates, trigger manual hedges when basis breaches thresholds intraday.

Example backtest results (illustrative)

Below is an illustrative summary you might expect after running a disciplined calendar-spread backtest across the four commodities (hypothetical):

• Annualized return: 8 612% per year (varies by commodity weighting)
• Sharpe: 0.8 61.2 (after fees and conservative slippage)
• Max drawdown: -20% in commodity stress periods; improved with margin overlays
• Primary alpha source: 70% from curve mean reversion, 30% from basis dislocations (varies by symbol)

Keep a disciplined separation of raw source data and normalized series 28094 reproducibility and provenance will save you during audits and commercial trials.

Checklist before a commercial pilot

• Confirm CmdtyView licensing for redistribution and commercial testing.
• Implement contract metadata and mapping to cash feed labels.
• Run out-of-sample tests and walk-forward analysis for at least 3 years.
• Model margin and liquidity shocks (e.g., 2010 flash events) to stress test your risk rules.

Wrap-up: actionable takeaways

• Synchronize timestamps 28094 align cash and futures to the same daily settlement time to avoid lookahead.
• Version everything 28094 dataset versions, roll rules and config must be auditable.
• Include basis attribution 28094 split P&L into curve vs cash to understand driver of returns.
• Operationalize with cloud-native patterns 28094 parquet snapshotting, scheduled DAGs, and containerized backtests.

Next steps (call-to-action)

If you28099re evaluating a pilot: export a 2-year sample dataset from CmdtyView and exchange futures and run a two-phase test 28094 a quick vectorized backtest for signal discovery, followed by a margin- and execution-aware simulation. Need a starting point? Contact our data engineering team for a reproducible Docker backtest template, pre-configured for cotton, corn, wheat and soy with CmdtyView integration.

Ready to pilot? Request a trial of our normalized commodity dataset and an architecture review for your backtest pipeline 28094 we'll help you turn signals into repeatable, auditable results.

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