wangdx25/oil029_sample

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

Apache TsFile version of xpertsystems/oil029-sample.

Overview

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.

• Tables: 12 TsFile table(s), 195,842 rows in total.
  • calendar_spreads: 2,200 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00 (UTC)
  • crude_spot_prices: 2,200 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00 (UTC)
  • futures_curves: 26,400 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00.011000 (UTC)
  • intraday_trading: 17,600 rows, 2015-01-02 00:00:00 → 2023-06-08 05:41:00 (UTC)
  • inventory_levels: 2,200 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00 (UTC)
  • macro_factors: 2,200 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00 (UTC)
  • opec_events: 24 rows, 2015-04-30 00:00:00 → 2023-05-23 00:00:00 (UTC)
  • rare_events: 11 rows, 2015-03-09 00:00:00 → 2022-11-22 00:00:00 (UTC)
  • refinery_demand: 2,200 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00 (UTC)
  • tanker_disruptions: 7 rows, 2016-12-29 00:00:00 → 2022-10-24 00:00:00 (UTC)
  • trading_labels: 2,200 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00 (UTC)
  • volatility_surfaces: 138,600 rows, 2015-01-02 00:00:00 → 2023-06-08 00:00:00.062000 (UTC)

Schema (TsFile structure)

calendar_spreads.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (5): front_second_spread (double), front_sixth_spread (double), front_twelfth_spread (double), curve_slope (double), curve_state (string)

crude_spot_prices.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (7): regime (string), wti_price (double), brent_price (double), wti_brent_spread (double), log_return_wti (double), log_return_brent (double), realized_vol_annualized (double)

futures_curves.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (9): benchmark (string), contract_month (int64), time_to_expiry_years (double), spot_price (double), futures_price (double), storage_cost (double), convenience_yield (double), risk_free_rate (double), curve_state_score (double)

intraday_trading.tsfile

• Time (INT64, milliseconds) — the source timestamp column.
• TAG: none — the table is stored as a single device.
• FIELD (9): market_date (string), bar_index (int64), benchmark (string), mid_price (double), bid (double), ask (double), bid_ask_spread (double), trade_volume (int64), liquidity_state (string)

inventory_levels.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (3): inventory_bbl (int64), regime (string), rare_event_flag (int64)

macro_factors.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (5): dxy (double), fed_rate (double), inflation_rate (double), us_rig_count (int64), regime (string)

opec_events.tsfile

• Time (INT64, milliseconds) — the source event_date column.
• TAG: none — the table is stored as a single device.
• FIELD (4): event_type (string), production_change_bpd (int64), market_surprise_score (double), expected_price_impact_pct (double)

rare_events.tsfile

• Time (INT64, milliseconds) — the source event_date column.
• TAG: none — the table is stored as a single device.
• FIELD (6): event_type (string), severity_score (double), wti_price (double), brent_price (double), realized_vol_annualized (double), regime (string)

refinery_demand.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (6): refinery_run_rate_pct (double), gasoline_crack_spread (double), diesel_crack_spread (double), jet_crack_spread (double), seasonality_index (double), outage_flag (int64)

tanker_disruptions.tsfile

• Time (INT64, milliseconds) — the source event_date column.
• TAG: none — the table is stored as a single device.
• FIELD (5): region (string), disruption_type (string), duration_hours (int64), affected_volume_bpd (int64), freight_rate_impact_pct (double)

trading_labels.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (6): regime_label (string), curve_state (string), volatility_state (string), crisis_label (string), target_5d_direction (string), trading_signal_label (string)

volatility_surfaces.tsfile

• Time (INT64, milliseconds) — the source market_date column.
• TAG: none — the table is stored as a single device.
• FIELD (9): benchmark (string), expiry_days (int64), strike_moneyness (double), spot_price (double), strike (double), implied_vol (double), vol_skew_component (double), vol_smile_component (double), regime (string)

Conversion notes

• One TsFile per source CSV (12 tables); each table is stored as a single device, so the rig / well / tank / equipment / sensor identifiers stay ordinary FIELDs.
• Time = the table's own clock column as INT64 milliseconds (event timestamps, sample or inspection dates, and daily record dates). The clock columns used here are event_date, market_date, timestamp.
• No column is dropped. Rows that share a timestamp are ordered by a 1 ms step in stable source order, so daily tables keep their exact calendar day.

Usage

Read this dataset with the Java SDK. It is written with the zstd compressor. The Apache TsFile Java SDK reads every compressor, but the Python SDK (pip install tsfile) implements only UNCOMPRESSED, SNAPPY, GZIP and LZ4; opening this file with it yields no rows.

Read a converted file with the Apache TsFile Java SDK (org.apache.tsfile:tsfile):

import org.apache.tsfile.file.metadata.IDeviceID;
import org.apache.tsfile.read.TsFileReader;
import org.apache.tsfile.read.TsFileSequenceReader;
import org.apache.tsfile.read.common.Path;
import org.apache.tsfile.read.expression.QueryExpression;
import org.apache.tsfile.read.query.dataset.QueryDataSet;
import org.apache.tsfile.write.schema.MeasurementSchema;

import java.util.ArrayList;
import java.util.List;

String path = "calendar_spreads.tsfile";
try (TsFileSequenceReader sequenceReader = new TsFileSequenceReader(path);
     TsFileReader reader = new TsFileReader(sequenceReader)) {
    // Devices are enumerated as IDeviceID objects. Tables with TAG columns
    // have multi-segment device ids that cannot be rebuilt from their string
    // form, so always use getAllDevices() rather than getAllDeviceIds().
    for (IDeviceID device : sequenceReader.getAllDevices()) {
        List<Path> paths = new ArrayList<>();
        for (MeasurementSchema schema : reader.getMeasurement(device)) {
            String name = schema.getMeasurementName();
            if (name != null && !name.isEmpty() && !name.equalsIgnoreCase("Time")) {
                paths.add(new Path(device, name, true));
            }
        }
        QueryDataSet dataSet = reader.query(QueryExpression.create(paths, null));
        while (dataSet.hasNext()) {
            System.out.println(dataSet.next());
        }
    }
}

Source & license

• Original dataset: xpertsystems/oil029-sample
• Source card title: OIL-029 — Synthetic Crude Oil Price Dataset (Sample)
• License: cc-by-nc-4.0 (as declared by the original dataset).