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()