File size: 3,272 Bytes
8481f3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import os
import pandas as pd
from datetime import datetime
from datasets import Dataset, DatasetDict, Features, Value, GeneratorBasedBuilder, Split
_DESCRIPTION = """\
Qubit Historical Data - Comprehensive cryptocurrency OHLCV data from Binance,
including both spot and futures markets with multiple timeframes.
"""
_HOMEPAGE = "https://huggingface.co/datasets/Yllvar/qubit-historical-data"
_LICENSE = "MIT"
_FEATURES = Features({
"timestamp": Value("timestamp[ms]"),
"open": Value("float64"),
"high": Value("float64"),
"low": Value("float64"),
"close": Value("float64"),
"volume": Value("float64"),
"symbol": Value("string"),
"market_type": Value("string"),
"timeframe": Value("string"),
"exchange": Value("string")
})
class QubitHistoricalData(GeneratorBasedBuilder):
"""Binance historical OHLCV data for cryptocurrencies."""
VERSION = "1.0.0"
DEFAULT_CONFIG_NAME = "all"
BUILDER_CONFIGS = [
{"name": "spot", "description": "Spot market data only"},
{"name": "futures", "description": "Futures market data only"},
{"name": "all", "description": "All market data (spot and futures)"},
]
def _info(self):
return DatasetInfo(
description=_DESCRIPTION,
features=_FEATURES,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
return [
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"data_dir": dl_manager.download(os.getcwd()) if dl_manager.is_streaming else os.getcwd()
},
)
]
def _generate_examples(self, data_dir):
"""Generator with timestamp sorting and warning logging"""
timestamp_warnings = {}
for root, _, files in os.walk(data_dir):
for filename in files:
if filename.endswith('.csv'):
# ... (previous metadata extraction code) ...
df = pd.read_csv(filepath)
df['timestamp'] = pd.to_datetime(df['timestamp'])
# Check for and log timestamp issues
time_diff = df['timestamp'].diff().dt.total_seconds()
if (time_diff < 0).any():
warning_count = sum(time_diff < 0)
timestamp_warnings[filename] = warning_count
df = df.sort_values('timestamp') # Ensure sorted output
# Yield examples
for idx, row in df.iterrows():
yield idx, {
# ... (your field mappings) ...
}
# Log warnings at the end
if timestamp_warnings:
print("\nTimestamp ordering warnings:")
for file, count in timestamp_warnings.items():
print(f"- {file}: {count} timestamp decreases found (data was auto-sorted)")
if __name__ == "__main__":
# For local testing
from datasets import load_dataset
dataset = load_dataset(os.path.abspath(__file__), "all")
print(dataset["train"][0])
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