xpertsystems/oil029-sample

OIL-029 — Synthetic Crude Oil Price Dataset (Sample)

SKU: OIL029-SAMPLE · Vertical: Oil & Gas / Commodity Markets License: CC-BY-NC-4.0 (sample) · Schema version: oil029.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise crude oil price dataset for quantitative trading, futures curve analytics, options volatility surface ML, regime classification, commodity risk management, trading signal generation, and macro-coupled price forecasting. The sample covers 2,200 business days (~8.5 years) of WTI + Brent + futures + options + macro data, with 195,842 rows linked across 12 tables.

OIL-029 has the deepest quantitative-finance physics in the catalog — GARCH(1,1) volatility clustering, Schwartz (1997) mean reversion, Working (1949) convenience-yield curve state, cost-of-carry futures pricing, Black-Scholes implied vol surface with smile and skew, and Merton (1976) jump diffusion via 10-class rare events.

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What's in the box

File	Rows	Cols	Description
crude_spot_prices.csv	2,200	8	Daily WTI + Brent spot with regime label + realized vol + log returns
futures_curves.csv	26,400	10	Cost-of-carry pricing F = S × exp((r + storage − conv_yield) × T) per Hull; 12 monthly tenors × 2200 days
volatility_surfaces.csv	138,600	10	Black-Scholes IV surface with smile (m−1)² + skew (1−m); 7 expiries × 9 moneyness × 2200 days
calendar_spreads.csv	2,200	6	M1-M2 / M1-M6 / M1-M12 spreads + curve_state classifier (contango/backwardation/flat)
inventory_levels.csv	2,200	4	Daily US commercial crude inventory per EIA Weekly Petroleum Status Report + rare event flag
opec_events.csv	24	5	OPEC production decisions: cut / increase / no_change_guidance / emergency_meeting + surprise score
refinery_demand.csv	2,200	7	Refinery run rate + 3-product crack spreads (gasoline / diesel / jet) + seasonality + outage
tanker_disruptions.csv	7	6	Region + disruption type (weather/sanctions/port/security/mechanical/labor) + duration + affected volume
macro_factors.csv	2,200	6	DXY + Fed rate + inflation + US rig count per Baker Hughes
intraday_trading.csv	17,600	10	8 intraday bars per day × 2200 days: mid + bid + ask + spread + volume + liquidity state
rare_events.csv	11	7	10-class rare event taxonomy: negative_price_stress / opec_cut / opec_supply_surge / shipping_disruption / sanctions_embargo / refinery_outage / spr_release / flash_crash / hurricane_gulf_disruption / global_demand_collapse
trading_labels.csv	2,200	7	5-day forward direction + vol state + crisis + trading signal — feature-coupled to future returns and curve state

Total: 195,842 rows across 12 CSVs, ~13.3 MB on disk.

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Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named quantitative finance references: Working (1949) "The Theory of Price of Storage" (Journal of Farm Economics — canonical convenience-yield foundation), Schwartz (1997) "The Stochastic Behavior of Commodity Prices" (Journal of Finance — Ornstein-Uhlenbeck commodity mean reversion), Black & Scholes (1973) "The Pricing of Options" (Journal of Political Economy), Merton (1976) "Option Pricing when Underlying Stock Returns are Discontinuous" (Journal of Financial Economics), Engle (1982) ARCH (Econometrica), Bollerslev (1986) GARCH (Journal of Econometrics), Hull "Options, Futures, and Other Derivatives" (cost-of-carry standard), CME WTI Crude Oil Futures specification, ICE Brent Crude Futures specification, EIA Weekly Petroleum Status Report (US commercial crude inventory baselines), EIA Short-Term Energy Outlook (refinery utilization seasonals), OPEC Monthly Oil Market Report (production cut history), CFTC Commitments of Traders, Baker Hughes North American Rig Count.

Sample run (seed 42, n_days=2200, futures_tenors=12, intraday_bars=8):

#	Metric	Observed	Target	Tolerance	Status	Source
1	avg wti price usd	94.7328	80.0	±20.0	✓ PASS	EIA Short-Term Energy Outlook + CME WTI historical averages — long-run WTI crude price for 2015-2024 portfolio (~$60-80 mean; $20-130 range; mean-reverting toward $75 cost-of-production anchor per Schwartz 1997)
2	avg brent wti spread usd	4.1478	4.0	±2.5	✓ PASS	EIA + ICE Brent / CME WTI quality differential — typical Brent-WTI spread (Brent premium of $2-7 reflects light sweet quality difference + transatlantic shipping; narrows to ~$1 in oversupply, widens to $8+ in shortage)
3	avg realized vol annualized	0.4861	0.45	±0.15	✓ PASS	CME WTI historical realized volatility 2015-2024 — mean realized annualized vol for crude oil (~30-50% in normal regimes; spikes to 80-150% during March 2020 COVID / negative price stress)
4	avg atm implied vol	0.4735	0.48	±0.18	✓ PASS	Black-Scholes implied vol surface ATM level — typical 30-day ATM IV for crude options (~35-55% normal; vol risk premium of ~5-10% above realized per CME Group options analytics)
5	avg inventory million bbl	414.1062	420.0	±80.0	✓ PASS	EIA Weekly Petroleum Status Report — typical US commercial crude inventory (380-470M bbl normal range; 350M tight / 500M oversupply per EIA 2015-2024 history)
6	avg refinery run rate pct	86.1454	86.0	±5.0	✓ PASS	EIA Short-Term Energy Outlook + EIA Weekly Refinery Utilization Survey — typical mean refinery run rate (82-90% normal; summer driving season peaks 92-95%; winter maintenance turnarounds 78-82%)
7	inventory convenience yield correlation	-0.8964	-0.55	±0.3	✓ PASS	Working (1949) 'The Theory of Price of Storage' + Schwartz (1997) commodity convenience yield model — expected inverse correlation between inventory levels and convenience yield (high stocks reduce scarcity premium, depress conv_yield, drive contango). Coupling is path-dependent because the regime indicator contributes ±0.025 to conv_yield vs ±0.020 × inv_z; long-horizon paths where regime correlates with inventory show strong coupling (r ≈ -0.9), while paths where regime is decoupled show weaker signal (r ≈ -0.3 to 0).
8	realized atm iv correlation	0.9990	0.95	±0.08	✓ PASS	Black-Scholes (1973) implied vol surface + CME options analytics — expected near-deterministic positive correlation between realized volatility and 30-day ATM implied volatility (real markets show r ≈ 0.85-0.95 with vol risk premium offset)
9	wti brent correlation	0.9984	0.99	±0.05	✓ PASS	ICE Brent / CME WTI cointegration analysis — expected near-perfect positive correlation between WTI and Brent spot prices (r > 0.98 typical; both benchmarks track global crude supply-demand with quality differential as offset)
10	regime diversity entropy	0.8399	0.78	±0.1	✓ PASS	6-class regime taxonomy (balanced / contango / backwardation / supply_shock / demand_collapse / high_volatility) per CFTC Commitments of Traders + EIA Short-Term Energy Outlook regime classification, normalized Shannon entropy. Lower than uniform 1.0 because balanced regime dominates (~30-40%) in long-horizon paths.

Overall: 100.0/100 — Grade A+ (10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

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Schema highlights

crude_spot_prices.csv — full quant finance physics stack:

GARCH(1,1): var_t = 2.0e-5 + 0.08·ε²_{t-1} + 0.80·var_{t-1} (Bollerslev 1986) OU mean reversion (Schwartz 1997): −κ·ln(S/μ), κ=0.005/day, μ=$75 Jump diffusion (Merton 1976): Bernoulli(0.5%) × Beta(2,5) × direction DXY drag: −0.18 × (DXY−102) / 100 / 252 (BIS oil-dollar) 6-regime drift: ±(0.00003 to 0.00045) per day with stochastic switching

The sample's realized vol is ~48% annualized (above the long-run real- market average of 35-45%) reflecting the simulated period's regime mix.

futures_curves.csv — Hull cost-of-carry pricing:

F = S × exp((r + storage_cost − convenience_yield) × T) storage_cost = 0.018 + 0.010 × max(inv_z, 0) convenience_yield = 0.035 − 0.020 × inv_z + 0.025 × (regime ∈ {backwardation, supply_shock})

The sample's inventory ↔ convenience yield Pearson correlation is r ≈ −0.90 — near-deterministic Working (1949) coupling validates the storage theory implementation.

volatility_surfaces.csv — Black-Scholes IV surface with term structure + smile + skew:

IV(K, T) = ATM(realized_vol) + 0.02 × log(1+T)/log(366) (term structure) + 0.16 × (m − 1)² (smile, m = K/S) + 0.10 × max(0, 1 − m) (put skew) + noise

The sample's realized vol ↔ 30d ATM IV Pearson correlation is r ≈ +0.999 — near-deterministic Black-Scholes coupling with vol smile shape (IV at extreme strikes > ATM IV) preserved.

trading_labels.csv — feature-coupled labels keyed to future returns and curve state:

target_5d_direction = up if ret_5d > 0.015 else down if < -0.015 else flat volatility_state = low(<0.25) / medium(0.25-0.45) / high(>0.45) (realized vol bins) crisis_label = (rare_event_flag == 1) OR (realized_vol > 0.70) trading_signal_label = long_bias if (ret_5d > 0.02 AND curve = backwardation) = short_bias if (ret_5d < -0.02 AND curve = contango) = neutral otherwise

This is the first OIL SKU with future-return-coupled supervised learning labels — unlike most catalog SKUs where labels are derived from current features, OIL-029's labels reflect actual 5-day forward price evolution, making this dataset directly trainable for predictive ML.

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Suggested use cases

1. 5-day directional classification — 3-class predictor on target_5d_direction from regime + curve state + vol state features. Strong feature coupling to actual forward returns.
2. Realized volatility regression — predict next-day realized_vol_annualized from GARCH(1,1) features + regime + macro.
3. Implied vol surface regression — predict implied_vol at any moneyness × expiry from realized vol + regime per Black-Scholes. Strong physics: realized↔ATM IV r ≈ +0.999.
4. Curve state classification — 3-class classifier on curve_state from inventory + storage + convenience yield features. Strong physics: inventory↔conv_yield r ≈ −0.90 per Working 1949.
5. Crisis label binary classification — predict rare-event + high-vol crisis state from macro + vol features.
6. OPEC market surprise prediction — predict OPEC market_surprise_score from prior price + inventory + regime features.
7. Crack spread regression — predict gasoline/diesel/jet crack_spread from WTI + seasonality + refinery utilization features.
8. Intraday liquidity classification — binary classifier on liquidity_state (normal vs stressed) from spread + volume + regime.
9. 6-regime classification — predict regime from realized vol + curve state + macro features. (Note: regime is a generator-internal latent variable; in real markets must be inferred.)
10. Multi-table relational ML — entity-resolution across the 12 tables via market_date. Macro + curve + vol + events form rich feature matrices for any predictive task.

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Loading

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil029-sample", data_files="crude_spot_prices.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
spot   = pd.read_csv("hf://datasets/xpertsystems/oil029-sample/crude_spot_prices.csv")
fut    = pd.read_csv("hf://datasets/xpertsystems/oil029-sample/futures_curves.csv")
vol_s  = pd.read_csv("hf://datasets/xpertsystems/oil029-sample/volatility_surfaces.csv")
spreads = pd.read_csv("hf://datasets/xpertsystems/oil029-sample/calendar_spreads.csv")
macro  = pd.read_csv("hf://datasets/xpertsystems/oil029-sample/macro_factors.csv")
labels = pd.read_csv("hf://datasets/xpertsystems/oil029-sample/trading_labels.csv")

# Quant feature engineering for 5-day directional ML:
joined = (spot
    .merge(spreads, on="market_date")
    .merge(macro, on="market_date", suffixes=("", "_macro"))
    .merge(labels, on="market_date"))
# Predict target_5d_direction from WTI + curve_state + vol + macro features

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Reproducibility

All generation is deterministic via the integer seed parameter (driving np.random.default_rng). A seed sweep across [42, 7, 123, 2024, 99, 1] confirms Grade A+ on every seed in this sample.

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Honest disclosure of sample-scale limitations + generator patch

This wrapper applies a documented patch to the underlying generator that adds Schwartz (1997) Ornstein-Uhlenbeck mean reversion toward a $75 long-run anchor (κ=0.005/day, half-life ≈ 140 days) and reduces GARCH persistence (α=0.08, β=0.80 vs original 0.10, 0.86). The patch was needed because the original generator's pure regime-drift GBM exhibited path- dependent runaway in multi-year horizons — WTI mean would walk to $200- $400+ across 8-year paths depending on regime sequence. Real crude oil mean-reverts toward cost-of-production, so this patch makes the sample behave like real markets per Schwartz (1997) canonical OU commodity model.

Several other limitations should be understood before use:

1. Log return kurtosis is too low (~1.2 vs real ~5-10). Mean reversion dampens extreme moves. For tail-risk ML (e.g., VaR backtesting), use the full product (which has uncapped kurtosis at production horizons) or augment with explicit fat-tailed jump samples.

2. DXY ↔ WTI correlation is positive (r ≈ +0.53) instead of negative. In real markets, a stronger dollar usually depresses oil prices. The generator's DXY evolves as an independent random walk modified only slightly by regime (+0.08 × demand_collapse_flag), so long paths produce spurious co-drift rather than the expected negative correlation. For oil-USD ML, treat DXY as a noisy macro feature rather than a primary driver. The full product v1.1 will add proper oil-dollar mean-reverting cointegration.

3. Rare event ↔ realized vol correlation is near zero. Rare events spike vol transiently (1 day), but realized_vol is computed continuously and only ~11 rare events occur in 2200 days. The signal is dominated by GARCH baseline. For rare-event ML, train on the rare_event_flag directly + acute vol responses rather than expecting a strong day-of correlation.

4. Volatility is ~48% annualized vs real ~35-45%. The sample is slightly more volatile than empirical 2015-2024 WTI history because regime parameters bias toward more supply_shock / high_volatility regimes (30%+ supply_shock at sample scale). For models calibrated to real-market volatility, scale vol features by ~0.85x or use the full product which has tighter regime-balance enforcement.

5. Regime is a generator-internal latent variable. In real markets, regime is inferred from observed price/inventory/macro features (e.g., via Hamilton 1989 regime-switching models or modern HMMs). The regime_label field is the ground-truth label and would NOT be available in production trading. For realistic regime ML, treat regime as a hidden state to be classified, not as an input feature.

6. Tanker disruption events are sparse (~7 events over 2200 days). For 6-class disruption-type classification at sample scale, this is insufficient. Use the full product (50,000+ tanker events) for class- balanced disruption ML.

7. Intraday data uses 8 bars/day, not realistic 1-minute or 5-minute bars. CME WTI futures trade ~24 hours with peak liquidity in NYMEX pit hours. For HFT/microstructure ML, use full product (78 bars/day = 5-minute pit hours) or augment with continuous-time simulations.

8. OPEC events are random per-day, not coupled to actual OPEC meeting calendar (typically quarterly + ad-hoc emergency meetings). For event-study ML, derive your own event windows from the dates in opec_events.csv rather than expecting them to align to real OPEC meeting dates (Dec 1-2, Jun 6-7, etc.).

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Where physics IS strong (use these for ML)

Five coupling signals in this sample are physically valid and ML-useful:

Signal	r	Source
Realized vol ↔ 30d ATM IV	+0.999	Black-Scholes implied vol surface
WTI ↔ Brent	+0.998	ICE / CME cointegration
Inventory ↔ convenience yield	−0.896	Working (1949) storage theory
Inventory ↔ curve state	−0.755	Schwartz (1997) commodity model
WTI ↔ crack spread	−0.475	EIA refining margin compression

Plus GARCH(1,1) vol clustering confirmed via |return| autocorrelation (ACF lag 1 ≈ +0.21, lag 5 ≈ +0.14) per Bollerslev (1986).

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Cross-references to other XpertSystems OIL SKUs

This SKU opens a new sub-vertical: commodity markets — complementing the physical operations SKUs with price discovery + financial market physics:

SKU	Layer	Focus
OIL-013, OIL-016, OIL-018	Upstream	Production + decline curves + multiphase
OIL-015, OIL-024, OIL-025, OIL-027	Midstream	Pipeline operations + leak detection + corrosion
OIL-028	Storage	Tank storage + inventory mass balance
OIL-019, OIL-020, OIL-022, OIL-023	Downstream	Refining + catalyst + turnaround
OIL-021	Cross-stream	Equipment performance + PHM
OIL-029	Commodity markets	WTI + Brent + futures + options + GARCH + Schwartz + Working (new sub-vertical)

OIL-029 is the catalog's first quant-finance SKU. All previous OIL SKUs focus on physical operations (drilling, production, refining, transport). OIL-029 captures the price-discovery layer that ties operational decisions to commodity market signals. Use OIL-029 for quant trading / risk management ML, other OIL SKUs for operational ML.

Natural integrations with other OIL SKUs:

• OIL-029 + OIL-028 (storage) → join inventory_levels on market_date for CFTC inventory-trade modeling
• OIL-029 + OIL-020 (yields) → join crack spreads + WTI for refinery margin optimization
• OIL-029 + OIL-016 (decline) → tie production decisions to forward WTI curves for capital allocation

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Full product

The full OIL-029 dataset ships at 25,000 business days (~100-year synthetic history) × 36 monthly futures tenors × 78 intraday bars (5-minute pit hours) producing hundreds of millions of rows with proper oil-dollar mean-reverting cointegration, uncapped jump kurtosis for realistic tail-risk modeling, calendar-aligned OPEC meeting events, realistic regime-balance enforcement (Hamilton-style transitions), multi-asset cross-commodity coupling (natural gas + heating oil + gasoline), and 5-minute intraday microstructure — licensed commercially. Contact XpertSystems.ai for licensing terms.

📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai

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Citation

@dataset{xpertsystems_oil029_sample_2026,
  title  = {OIL-029: Synthetic Crude Oil Price Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil029-sample}
}

Generation details

• Sample version : 1.0.0
• Random seed : 42
• Generated : 2026-05-23 00:44:25 UTC
• Business days : 2200 (~8.5 years)
• Start date : 2015-01-02
• Futures tenors : 12 monthly contracts (M1-M12)
• Vol surface : 7 expiries × 9 moneyness levels
• Intraday bars : 8 per business day
• Regimes : 6 (balanced, contango, backwardation, supply_shock, demand_collapse, high_volatility)
• Rare event types : 10 (negative_price_stress, opec_cut, opec_supply_surge, shipping_disruption, sanctions_embargo, refinery_outage, spr_release, flash_crash, hurricane_gulf_disruption, global_demand_collapse)
• OPEC event types : 4 (cut, increase, no_change_guidance, emergency_meeting)
• Patch applied : Schwartz (1997) OU mean reversion κ=0.005/day, μ=$75; GARCH α=0.08, β=0.80; jump cap 8%
• Calibration basis : Working (1949), Schwartz (1997), Black-Scholes (1973), Merton (1976), Engle (1982), Bollerslev (1986), Hull, CME WTI, ICE Brent, EIA Weekly Petroleum Status, EIA Short-Term Energy Outlook, OPEC MOMR, CFTC COT, Baker Hughes Rig Count
• Overall validation: 100.0/100 — Grade A+