File size: 4,024 Bytes
f37d0e9 | 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 | import datasets
import os
import pandas as pd
class FeatureCache(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features({
"timestamp": datasets.Timestamp("us"),
"symbol": datasets.Value("string"),
"isin": datasets.Value("string"),
"series": datasets.Value("string"),
"open": datasets.Value("float64"),
"high": datasets.Value("float64"),
"low": datasets.Value("float64"),
"close": datasets.Value("float64"),
"volume": datasets.Value("int64"),
"interval_minutes": datasets.Value("int32"),
"segment": datasets.Value("string"),
"exchange": datasets.Value("string"),
"year": datasets.Value("int64"),
"month": datasets.Value("string"),
# === ALL TA FIELDS ===
**{col: datasets.Value("float64") for col in [
"HT_DCPERIOD","HT_DCPHASE","ADD","DIV","MAX","MIN","MULT","SUB","SUM",
"ACOS","ASIN","ATAN","CEIL","COS","COSH","EXP","FLOOR","LN","LOG10",
"SIN","SINH","SQRT","TAN","TANH","ADX","ADXR","APO","AROONOSC","BOP",
"CCI","CMO","DX","MFI","MINUS_DI","MINUS_DM","MOM","PLUS_DI","PLUS_DM",
"PPO","ROC","ROCP","ROCR","ROCR100","RSI","TRIX","ULTOSC","WILLR",
"DEMA","EMA","HT_TRENDLINE","KAMA","MA","MIDPOINT","MIDPRICE","SAR",
"SAREXT","SMA","T3","TEMA","TRIMA","WMA","AVGPRICE","MEDPRICE",
"TYPPRICE","WCLPRICE","BETA","CORREL","LINEARREG",
"LINEARREG_ANGLE","LINEARREG_INTERCEPT","LINEARREG_SLOPE",
"STDDEV","TSF","VAR","ATR","NATR","TRANGE","AD","ADOSC","OBV"
]},
**{col: datasets.Value("int32") for col in [
"HT_TRENDMODE","MAXINDEX","MININDEX",
"CDL2CROWS","CDL3BLACKCROWS","CDL3INSIDE","CDL3LINESTRIKE",
"CDL3OUTSIDE","CDL3STARSINSOUTH","CDL3WHITESOLDIERS",
"CDLABANDONEDBABY","CDLADVANCEBLOCK","CDLBELTHOLD",
"CDLBREAKAWAY","CDLCLOSINGMARUBOZU","CDLCONCEALBABYSWALL",
"CDLCOUNTERATTACK","CDLDARKCLOUDCOVER","CDLDOJI","CDLDOJISTAR",
"CDLDRAGONFLYDOJI","CDLENGULFING","CDLEVENINGDOJISTAR",
"CDLEVENINGSTAR","CDLGAPSIDESIDEWHITE","CDLGRAVESTONEDOJI",
"CDLHAMMER","CDLHANGINGMAN","CDLHARAMI","CDLHARAMICROSS",
"CDLHIGHWAVE","CDLHIKKAKE","CDLHIKKAKEMOD","CDLHOMINGPIGEON",
"CDLIDENTICAL3CROWS","CDLINNECK","CDLINVERTEDHAMMER",
"CDLKICKING","CDLKICKINGBYLENGTH","CDLLADDERBOTTOM",
"CDLLONGLEGGEDDOJI","CDLLONGLINE","CDLMARUBOZU",
"CDLMATCHINGLOW","CDLMATHOLD","CDLMORNINGDOJISTAR",
"CDLMORNINGSTAR","CDLONNECK","CDLPIERCING","CDLRICKSHAWMAN",
"CDLRISEFALL3METHODS","CDLSEPARATINGLINES","CDLSHOOTINGSTAR",
"CDLSHORTLINE","CDLSPINNINGTOP","CDLSTALLEDPATTERN",
"CDLSTICKSANDWICH","CDLTAKURI","CDLTASUKIGAP",
"CDLTHRUSTING","CDLTRISTAR","CDLUNIQUE3RIVER",
"CDLUPSIDEGAP2CROWS","CDLXSIDEGAP3METHODS"
]},
})
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_dir": "feature_cache"}
)
]
def _generate_examples(self, data_dir):
for file in os.listdir(data_dir):
if file.endswith(".parquet"):
df = pd.read_parquet(os.path.join(data_dir, file))
for i, row in df.iterrows():
yield f"{file}_{i}", row.to_dict() |