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Elliott Wave Market Data - Complete (Quality Validated) ✅
Production-ready, quality-validated OHLCV market data for training Elliott Wave pattern recognition neural networks.
🎯 Key Features
- Quality Validated: Rigorous data quality checks applied
- Complete Coverage: 1,403 unique instruments
- Multi-Timeframe: 1h, 4h, 1d, 1wk data
- 22,546,189 Total Data Points
Dataset Statistics
| Timeframe | Rows | Tickers |
|---|---|---|
| 1h | 9,267,368 | 1,358 |
| 4h | 2,849,583 | 1,358 |
| 1d | 8,648,514 | 1,399 |
| 1wk | 1,780,724 | 1,396 |
Asset Classes
This dataset includes:
- Commodities
- Commodities Extended
- Crypto
- Crypto Extended
- Emerging Markets
- Etfs
- Fixed Income
- Forex
- Forex Extended
- Indices
- Indices Extended
- International
- Leveraged
- Nasdaq
- Reits
- Small Cap
- Stocks
- Thematic Etfs
Quality Validation
All data has passed these quality checks:
- ✅ OHLC consistency (High ≥ Low, etc.)
- ✅ No missing values in required fields
- ✅ No duplicate rows
- ✅ No zero/negative prices
- ✅ No negative volume
- ✅ Extreme outliers removed (>1000% single-bar moves)
- ✅ Minimum 50 bars per ticker/timeframe
- ✅ Valid datetime formatting
- ✅ No future dates
Data Schema
| Column | Type | Description |
|---|---|---|
| datetime | datetime64 | Bar timestamp |
| open | float64 | Opening price |
| high | float64 | Highest price |
| low | float64 | Lowest price |
| close | float64 | Closing price |
| volume | float64 | Trading volume |
| ticker | string | Instrument symbol |
| interval | string | Timeframe (1h/4h/1d/1wk) |
| source_category | string | Asset category |
Usage
import pandas as pd
# Load all daily data
df = pd.read_parquet("hf://datasets/usamaahmedsh/elliott-wave-market-data-complete/market_data_1d.parquet")
# Filter by category
stocks = df[df['source_category'] == 'stocks']
crypto = df[df['source_category'] == 'crypto']
# Filter by ticker
aapl = df[df['ticker'] == 'AAPL']
btc = df[df['ticker'] == 'BTC-USD']
Data Sources
- Primary: Yahoo Finance (via yfinance)
- All data adjusted for splits/dividends
License
MIT License - Free for academic and commercial use.
Generated
2026-02-27 16:03 UTC
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