fintechpythontutorial

Backtesting an Agricultural Futures Strategy Using Python and Vectorized Data

UUnknown

2026-02-17

10 min read

Build fast, realistic backtests for soy, wheat and corn futures with pandas, NumPy and vectorbt—using open interest for rolls and signal conviction.

Fast, repeatable futures backtests for commodity traders and quant engineers

Struggling with slow backtests, messy contract rolls, and noisy commodity signals? This guide shows how to build production-ready, vectorized backtests for soybean, wheat and corn futures using pandas, NumPy and vectorbt — with OpenInterest as a first-class filter. Follow the step-by-step code examples to get reproducible results in minutes instead of hours.

Why vectorized backtesting for ag futures matters in 2026

Commodity markets have changed a lot through 2024–2026: higher-frequency data access, more retail and algorithmic participation, and larger supply shocks from climate and geopolitical events. That makes commodity strategies both more opportunity-rich and more brittle. The traditional Python loop-based backtest doesn't scale when you want to test hundreds of parameter combinations, multi-commodity portfolios, or realistic roll rules. Vectorized libraries like pandas, NumPy and vectorbt let you express strategy logic as array operations, run full Monte Carlo parameter sweeps, and produce performance metrics quickly — unlocking practical research workflows for developers and quant teams.

What you'll build

Data pipeline: build continuous front-month series for soybeans, wheat and corn using daily CSVs and open interest to determine roll logic.
Two strategies: a mean-reversion intraday-style z-score based on returns, and a momentum ranking across the three crops.
Vectorized signal generation: boolean entry/exit arrays for vectorbt.
Backtest: realistic fees, slippage, and position sizing; then evaluate risk metrics.

Data and inputs — what you need

Download daily historic futures data (front months and subsequent months) including Open, High, Low, Close, Volume and OpenInterest. Sources include exchange data (CME), commercial vendors (Quandl / Nasdaq Data Link), or in-house feeds. For reproducibility, keep raw CSVs per contract with a column identifying the contract month (e.g., 2026-03).

File format (example)

Date,Contract,Open,High,Low,Close,Volume,OpenInterest
2025-11-01,ZS202601,10.45,10.55,10.40,10.52,12345,23456
2025-11-01,ZS202603,10.50,10.65,10.48,10.60,9876,34567
...

Step 1 — Build a continuous front-month using open interest

Rolling contracts by calendar or fixed days-to-expiry creates artifacts. A more market-aware rule: keep the contract with the highest open interest (or volume) each day — it tends to represent the most liquid front-month. We'll vectorize this selection across dates.

Key idea

Use daily open interest to pick the most liquid contract and map its Close into a continuous series. This avoids hard-coded calendar rolls and is robust across regime changes.

Example code (data loading + roll)

import pandas as pd
import numpy as np

# Example: load many contract CSVs into a single frame
# Assumes each CSV has Date, Contract, Close, OpenInterest
frames = []
for fn in ["ZS202601.csv", "ZS202603.csv", ...]:
    df = pd.read_csv(fn, parse_dates=["Date"]) 
    frames.append(df)
raw = pd.concat(frames, ignore_index=True)

# Pivot so rows = dates, cols = contracts
pivot_close = raw.pivot(index='Date', columns='Contract', values='Close')
pivot_oi = raw.pivot(index='Date', columns='Contract', values='OpenInterest')

# Fill forward missing values for each contract (non-trading days)
pivot_close = pivot_close.sort_index().ffill()
pivot_oi = pivot_oi.sort_index().ffill()

# Choose contract with max open interest each day
best_contract = pivot_oi.idxmax(axis=1)

# Build continuous series by selecting per-day contract close
continuous_close = pivot_close.reindex(columns=best_contract.unique()).lookup(pivot_close.index, best_contract)
continuous_close = pd.Series(continuous_close, index=pivot_close.index, name='Close')

Notes: pandas.lookup is deprecated in some versions — you can instead use advanced indexing with NumPy arrays or stack/unstack patterns. The core idea is per-row argmax on open interest, then pick corresponding close.

Step 2 — Feature engineering: returns, z-score, momentum

Create signals using vectorized operations: rolling mean/std for z-score, momentum as past N-day returns, and open interest filters.

Mean-reversion (z-score) — vectorized

close = continuous_close
returns = close.pct_change()
# Use log returns if you prefer: np.log(close).diff()

window = 20
mean = returns.rolling(window).mean()
std = returns.rolling(window).std()
zscore = (returns - mean) / std

# Entry when zscore < -threshold -> long; > threshold -> short
threshold = 1.5
long_signal = zscore < -threshold
short_signal = zscore > threshold

# Shift signals to avoid lookahead (use yesterday's zscore to trade today)
entries_long = long_signal.shift(1).fillna(False)
entries_short = short_signal.shift(1).fillna(False)

Momentum ranking across crops

We will compute N-day returns for each crop and rank them daily. Long the top crop(s) and short the bottom crop(s) using equal risk weights. Using vectorized ranking makes it trivial to test different holding windows.

# Suppose we have a DataFrame close_df with columns ['soy','wheat','corn']
close_df = pd.concat([soy_close, wheat_close, corn_close], axis=1)
lookback = 63  # 3-month momentum
mom = close_df.pct_change(lookback)

# Rank (1 = highest momentum)
ranks = mom.rank(axis=1, method='first', ascending=False)

# Long top (rank == 1), short bottom (rank == 3)
longs = ranks == 1
shorts = ranks == 3

# Shift to avoid lookahead
entries_long = longs.shift(1).fillna(False)
entries_short = shorts.shift(1).fillna(False)

Step 3 — Use open interest as a trade filter and sizing multiplier

Open interest is not only useful for rolling — it also helps avoid illiquid or noise-driven days. Here are pragmatic filters:

Require that trade-day open interest is above its 90th percentile of the last N days.
If open interest is rising (say +5% vs previous day), prefer taking the signal.
Scale position size proportional to open interest (normalized), capped to reduce concentration.

# Example filter: require OI > 25th percentile of last 60 days
oi_threshold = pivot_oi.max(axis=1).rolling(60).quantile(0.25)
valid_oi = pivot_oi.max(axis=1) > oi_threshold

# Apply to entries
entries_long = entries_long & valid_oi.shift(1).fillna(False)
entries_short = entries_short & valid_oi.shift(1).fillna(False)

# For sizing (risk parity across contracts), compute weight = normalized OI
oi_norm = (pivot_oi / pivot_oi.sum(axis=1, keepdims=True)).fillna(0)
weights = oi_norm.reindex(columns=close_df.columns)

Step 4 — Backtest with vectorbt (vectorized portfolio)

vectorbt lets you pass boolean entry/exit arrays or target weights directly. We'll show both approaches. vectorbt also computes full stats and supports parameter sweeps fast — ideal for 2026-style research workflows.

Install notes

Use a modern Python environment (Python 3.10+). Install vectorbt and typical stack:

pip install pandas numpy vectorbt==1.*  # or use the latest stable release

Portfolio from signals (single-contract example)

import vectorbt as vbt

# close is a pd.Series of the continuous front-month close
entries = entries_long  # boolean Series
exits = entries_short   # example: take opposite signal to exit

pf = vbt.Portfolio.from_signals(
    close,
    entries=entries,
    exits=exits,
    init_cash=100_000,
    fees=0.0005,      # 5 bps
    slippage=0.0005,
    freq='1D'
)

print(pf.total_return())
print(pf.stats())

Multi-asset portfolio with weights (soy, wheat, corn)

When trading multiple instruments, compute a target weight DataFrame and pass it to vectorbt. We recommend rebalancing at a fixed frequency (daily or weekly) and using weight targets to express equal-risk or OI-weighted allocations.

# close_df: DataFrame with columns ['soy','wheat','corn']
# weights_df: DataFrame same shape with target allocation each day

actions = weights_df.shift(1).fillna(0)  # avoid lookahead

pf_multi = vbt.Portfolio.from_orders(
    close_df,
    size=actions,               # vectorbt will treat 'size' as notional orders by default
    init_cash=500_000,
    fees=0.0005,
    slippage=0.0005,
    freq='1D'
)

print(pf_multi.stats())

Important implementation details and realistic modeling

Avoid lookahead: always shift indicators by one trading day before placing trades. Use the close that would have been visible at decision time.
Roll costs: real rolls incur bid/ask spreads and sometimes adverse price jumps. Add an explicit roll cost or simulate rolling orders executed at mid-market — see best practices in practical backtest guides.
Margins & leverage: futures trade on margin. When modeling PnL, either simulate notional exposure or model leverage/margin rules explicitly; consider platform constraints such as those discussed for compliance-first trading platforms.
Survivorship bias: include historical contracts that expired; don't only rely on survived front months.
Overnight and intraday gaps: daily close-based backtests miss intraday slippage. If you're sensitive to gap risk, use intraday data or widen slippage assumptions.

Advanced strategy ideas — using spreads and cross-commodity signals

Vectorized code makes it cheap to test cross-commodity ideas:

Soybean-Corn spread mean reversion: compute spread = soy_close - corn_close (or log-price spread), z-score it, and trade the spread pair (long cheap, short rich) with matched notionals.
Open interest divergence: if OI in one crop surges while price stalls, that may indicate new speculative flow — trade the price movement where OI confirms direction.
Momentum with OI conviction: only take momentum signals when OI is trending upward for the instrument (market participation confirming trend).

Example: spread z-score

spread = close_df['soy'] - close_df['corn']
spread_ma = spread.rolling(63).mean()
spread_std = spread.rolling(63).std()
spread_z = (spread - spread_ma) / spread_std

# long spread when z < -2 (soy cheap vs corn), short when z > 2
entries_long_spread = (spread_z < -2).shift(1).fillna(False)
entries_short_spread = (spread_z > 2).shift(1).fillna(False)

# Then create two-leg orders: long soy, short corn with matched notional

you would convert these to orders in vectorbt using from_orders with positive size for soy and negative for corn.

Validation and walk-forward testing

Vectorized sweeps are great, but you must avoid overfitting. Use a walk-forward framework:

Split data into in-sample windows (train) and out-of-sample windows (test).
Run parameter grid search using vectorbt's fast param-sweep capability on each training window.
Fix best parameters and evaluate on the next test window, then roll forward.

This produces a time-ordered view of performance that better reflects live trading.

Performance and scale: 2026 trends to keep in mind

By 2026, several trends make these workflows more effective:

Vectorized backtest libraries (like vectorbt) have matured to support larger multi-asset experiments and integration with GPU-accelerated arrays (NumPy + CuPy) for parameter sweeps.
Cloud compute is cheap: run many vectorized experiments in parallel on ephemeral instances to accelerate research cycles.
Data vendors improved access to clean contract histories and per-contract open interest/volume time series, reducing pre-processing overhead.

Common pitfalls and troubleshooting

NaNs in rolling stats: handle warm-up periods explicitly, drop initial rows or mark them as non-tradable.
Lookahead via weight normalization: if you normalize weights across instruments by same-day values, ensure you use only prior-day info.
Vectorbt API differences: vectorbt evolves fast. Check your installed version and API docs; method names like from_signals/from_orders exist across 1.x releases but argument details can vary.

Example research checklist (reproducible)

Store raw contract CSVs, and a script that builds continuous series using open interest — commit to version control.
Define your signals with explicit shift(1) to avoid future leaks.
Simulate realistic costs: fees, slippage, and roll costs.
Perform walk-forward validation and report out-of-sample metrics (annualized return, Sharpe, max drawdown, win rate).
Log the random seeds and environment (library versions) so results are reproducible.

Practical takeaways

Open interest works: Use OI to select front months and to filter/scale trades — it's a cheap signal of liquidity and participation (see related ag research).
Vectorized wins: Replace Python loops with vectorized boolean signals and vectorbt portfolios to test large parameter grids faster.
Model realism: Include roll costs, slippage, and margin considerations when moving from research to execution.
Cross-commodity strategies: Spread trades (soy-corn, wheat-corn) are natural in ag futures and are easy to express in vectorized arithmetic.

Appendix — Minimal runnable example

import pandas as pd
import numpy as np
import vectorbt as vbt

# Assume soy_close, wheat_close, corn_close are pd.Series aligned on index
close_df = pd.concat([soy_close, wheat_close, corn_close], axis=1)
close_df.columns = ['soy','wheat','corn']

# Momentum rule: top 1 long, bottom 1 short (63-day)
mom = close_df.pct_change(63)
ranks = mom.rank(axis=1, ascending=False)
longs = ranks == 1
shorts = ranks == 3

entries = longs.shift(1).fillna(False)
exits = (~longs).shift(1).fillna(True)  # exit when no longer top

# Build weights: +1 for long, -1 for short, normalized by positions count
weights = pd.DataFrame(0, index=close_df.index, columns=close_df.columns)
weights[longs.shift(1).fillna(False)] = 1
weights[shorts.shift(1).fillna(False)] = -1
weights = weights.div(weights.abs().sum(axis=1).replace(0,1), axis=0)

pf = vbt.Portfolio.from_orders(close_df, size=weights, init_cash=100000, fees=0.0005)
print(pf.stats())

Final thoughts

Backtesting commodity futures requires care: contract rolls, liquidity and realistic costs matter. In 2026, vectorized research workflows are the practical way to iterate quickly and validate ideas that survive realistic market frictions. Use open interest as a primary liquidity signal, pair it with robust signal engineering (shifted indicators, walk-forward validation), and run your experiments with vectorbt for speed.

Want the full notebook?

If you want the complete Jupyter notebook with sample CSVs, walk-forward framework and parameter sweep code tuned for soy, wheat and corn, grab the reproducible repo linked from our community resources at the repo. Start with the data pipeline: once you have reliable continuous series, strategy testing becomes straightforward and repeatable.

Call to action: Try the minimal example with your historical files, then scale to cross-commodity spreads and walk-forward validation. Share your results or questions on our developer channel — we’ll help tune roll rules and vectorized performance for production deployment.

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