SAMPLE Synthetic Market Prices (Worldwide) — Zero-PII, OHLCV & Sector for Backtesting, Risk & ML

## What this is A privacy-safe **synthetic financial time-series** dataset with realistic OHLCV and sector labels. It mirrors market regimes, volatility clustering, and cross-asset correlations without using licensed exchange data. ## What you get - Instruments: synthetic equities/ETFs with `instrument_id` + `sector` - Fields: date, open/high/low/close, volume - Optional add-ons: splits/dividends (adjustments), factors (momentum, value, size), realized volatility labels, drawdown flags - Formats: CSV and Parquet; sample (2,000 rows) included - Delivery: S3/Compressed file; optional REST endpoints for rolling windows ## Why synthetic? - No exchange licensing; safe for sharing across teams - Start backtesting and ML **immediately** - Stable, debuggable distributions with configurable regimes (bull/bear/shock) ## Known limits - Not tick-by-tick; not a substitute for licensed real-time feeds - Aggregate patterns approximate markets; individual series are synthetic ## Support & customizations Need GCC/US sector mixes, specific regimes, or factor labels? We can tailor generators and refresh cadence.

搜集汇总

背景与挑战

背景概述

该数据集提供隐私安全的合成金融时间序列，包含OHLCV和行业标签，模拟市场制度与波动聚类，适合回测、风险管理和机器学习。它避免使用许可交易所数据，但不可替代实时行情，并支持定制化生成。