Benchmark Futures Trading Datasets: The Foundation of Reproducible Trading Models

The Foundation of Reproducible Modeling

Ask any experienced data scientist where most of their time is spent, and the answer is rarely model design or parameter tuning. It is data preparation.

In futures markets, this reality is amplified. Futures trading datasets are inherently noisy, expensive, and structurally complex. Unlike curated datasets in computer vision or natural language processing, they contain gaps, discontinuities, and regime-dependent artifacts that must be handled explicitly before any modeling can begin.

This article introduces a benchmark futures dataset designed to support reproducible experimentation and model evaluation within this series, building directly on the conceptual foundations outlined in our introduction to AI in futures trading. The objective is not to present a perfect representation of markets, but to establish a standardized foundation that allows results to be compared, verified, and meaningfully interpreted.

Why Clean Futures Data Is Difficult

Before discussing the dataset itself, it is necessary to understand why futures data presents unique challenges that don't exist in equities or other asset classes.

Contract Expiration and Rollover

Futures contracts expire. There is no single, continuous instrument analogous to an equity ticker. A time series must be constructed by stitching together successive contracts, typically rolling from the front month to the next before expiration to avoid delivery or illiquidity.

If this is done naively, artificial price jumps are introduced at rollover points. These discontinuities are not market events but structural artifacts. The December contract might trade at a different price than the March contract due to cost of carry, storage, or seasonal factors. When spliced together without adjustment, this creates a fake gap that appears in the data as a sudden move.

Models trained on such data frequently learn to exploit these artifacts in backtests, producing misleading results. A momentum strategy might appear profitable simply because it's capturing the predictable calendar effect of rolling from one contract to another, not because it's identifying real price trends. This phantom edge disappears instantly in live trading. These structural complexities reinforce a central point discussed in our introduction to AI in futures trading: sophisticated models are meaningless without a rigorous understanding of market structure.

Data Sparsity and Irregularity

While highly liquid contracts like ES or CL trade continuously with tight spreads, futures trading datasets also include instruments that experience long periods of low activity or no trades at all. Agricultural futures outside of crop report windows, certain currency crosses during off hours, or back month energy contracts can go minutes or even hours without a single print, creating uneven informational density across the dataset.

Time series models depend on consistent temporal spacing, a property that many futures trading datasets naturally violate. Missing timestamps or irregular intervals can disrupt sequence-based learning unless they are handled explicitly. An LSTM expecting data every minute may misinterpret a five-minute gap as a continuation of the prior state rather than as missing information. Features computed on sparse data, such as volume-weighted averages or tick counts, become unreliable or undefined, introducing distortions that propagate downstream into model behavior.

Cost and Accessibility

High-quality tick-level futures data is expensive and often inaccessible to independent researchers. Commercial vendors charge thousands of dollars per year per exchange. Even when available, the engineering overhead required to make it usable can be prohibitive.

Raw tick data arrives in exchange-specific formats with varying conventions for timestamps, trade conditions, and message types. Cleaning, normalizing, and storing this data at scale requires infrastructure that many researchers lack. The result is that most independent work relies on lower-quality free sources or pre-aggregated bar data that obscures important microstructure information.

This creates a barrier to entry that concentrates sophisticated modeling in well-funded institutions, limiting the diversity of approaches and slowing progress in the field.

Overview of the Benchmark Dataset

The benchmark dataset introduced here is designed to reduce these barriers. It provides a controlled environment that reflects real market structure while minimizing unnecessary preprocessing work.

Dataset Scope

Asset Classes

• Equity index futures

• Energy futures

Instruments

• E-mini S&P 500 (ES)

• Crude Oil (CL)

These instruments were selected because they exhibit distinct behavioral characteristics. ES is highly liquid and relatively efficient, while CL often displays stronger trends and volatility clustering.

Time Resolution

• One-minute aggregated bars (OHLCV)

While tick data is essential for certain execution-focused strategies, one-minute bars represent a practical balance between granularity and noise for most modeling tasks.

Time Span

• Twenty-four months of continuous data, covering multiple market regimes

Formats

• CSV

• Parquet for efficient loading and storage

Data Structure

Each row represents one minute of trading activity. The dataset is structured to be immediately compatible with standard analytical workflows.

Preprocessing Decisions

Raw futures data is not suitable for modeling without adjustment. Several preprocessing steps were applied to ensure temporal consistency and numerical stability.

Continuous Contract Construction

To address contract expiration, a ratio-adjusted back-stitching method was used.

At rollover points, prices are adjusted backward using the ratio between the outgoing and incoming contracts. This preserves relative price changes while eliminating artificial gaps. If the front month contract closes at 100 and the next contract opens at 102, all historical prices before that rollover are multiplied by 100/102. This maintains the proportional relationship between price levels without creating a false two-point jump in the data.

Since most models operate on returns rather than absolute prices, maintaining proportional continuity is more important than preserving nominal levels. A 5% move remains a 5% move after adjustment, which is what matters for learning price dynamics. The alternative, simple back-adjustment by subtraction, distorts percentage returns and can even produce negative prices in long historical series.

Missing Data Handling

Periods with no trading activity are an unavoidable characteristic of real-world futures markets, and any robust futures trading datasets must account for them explicitly. Less liquid contracts and overnight sessions routinely produce stretches where no trades occur, creating gaps that reflect market reality rather than data errors. Instead of removing these intervals or fabricating price movements through interpolation, missing prices are forward-filled and volumes set to zero.

Handled this way, futures trading datasets preserve the true structure of time without introducing synthetic behavior. Forward-filling recognizes that, absent new transactions, the last traded price remains the most meaningful reference. Zero volume accurately signals inactivity. This allows models trained on futures trading datasets to learn the distinction between quiet and active market states, rather than mistaking artificial data for genuine price dynamics.

Normalization and Derived Features

To facilitate model convergence and reduce sensitivity to scale, derived features are included:

• Log returns computed from closing prices

• Standardized volatility measures using rolling windows

These transformations convert raw prices into stationary, scale-invariant representations that better reflect underlying market dynamics. Log returns are approximately normal and symmetric, making them suitable for statistical modeling. Standardized volatility, computed as the rolling standard deviation of returns divided by its own moving average, produces a dimensionless measure that can be compared across instruments and time periods.

This preprocessing layer sits between raw market data and the model, translating messy reality into structured inputs that preserve informational content while removing pathologies that interfere with learning.

Why This Matters for Model Evaluation

Model performance is inseparable from data quality, a reality that becomes especially clear when working with futures trading datasets. This connects directly to the broader argument made in our introduction to AI in futures trading, where robustness and validation were shown to matter more than architectural novelty. A sophisticated architecture trained on flawed inputs will not learn market structure. It will learn the flaws embedded in the data itself.

Back-tests built on improperly constructed futures trading datasets often produce inflated metrics. Apparent accuracy can emerge from structural artifacts such as rollover gaps or persistent price level bias rather than genuine predictive power. A model may appear to forecast direction with 60 percent accuracy simply because it has learned that prices tend to jump around expiration months due to poor contract stitching. That apparent edge disappears immediately in live trading.

The same failure mode appears when futures trading datasets contain look-ahead bias. If future information leaks into past observations through incorrect alignment or preprocessing, performance becomes impossible to reproduce. A volatility feature computed using the full trading day and then applied to intraday predictions creates the illusion of foresight where none exists. The model is not anticipating the market. It is reading the future.

Standardized futures trading datasets allow practitioners to separate signal from artifact. When improvements persist under controlled, consistent data conditions, they are more likely to reflect real market structure rather than noise. Comparisons between architectures, such as transformers and LSTMs, only become meaningful when both are trained on identical, properly processed futures trading datasets. Otherwise, observed differences may say more about data interactions than about model capability.

Clean data does not guarantee success. But poorly constructed futures trading datasets guarantee failure. The only uncertainty is how long it takes for that failure to surface.

What Comes Next

With data preparation addressed, attention can shift to model construction.

The next article, From Theory to Code: Building Your First LSTM Model for Futures Price Forecasting, will focus on implementing predictive and decision-making models using this benchmark dataset. Topics will include sequence construction, training workflows, loss functions, and evaluation techniques.

The aim is not automation for its own sake, but the establishment of a reproducible, disciplined framework for systematic market analysis.

Models do not eliminate uncertainty. They provide structure when complexity exceeds manual interpretation.