diff --git a/pyproject.toml b/pyproject.toml
index 8e1cb1f..2999036 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,19 @@ dependencies = [
     "numpy>=1.26",
     "matplotlib>=3.8",
     "jupyter>=1.0",
+    "polars>=1.40.1",
+    "datetime>=6.0",
+    "asyncio>=4.0.0",
+    "torch>=2.12.0",
+    "polars-ta>=0.5.17",
+    "ccxt>=4.5.50",
+    "tqdn>=0.2.1",
+    "ipykernel>=7.2.0",
+    "plotly>=6.7.0",
+    "tqdm>=4.67.3",
+    "scipy>=1.17.1",
+    "plotly-resampler>=0.11.0",
+    "anywidget>=0.11.0",
 ]
 
 [tool.uv]
diff --git a/uv.lock b/uv.lock
index 97126ba..782efb1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -9,37 +9,37 @@ resolution-markers = [
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 name = "aiodns"
 version = "4.0.0"
-source = { registry = "https://pypi.org/simple" }
+source = { registry = "https://mirrors.cernet.edu.cn/pypi/web/simple/" }
 dependencies = [
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+source = { registry = "https://mirrors.cernet.edu.cn/pypi/web/simple/" }
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+    { url = "https://mirror.sjtu.edu.cn/pypi-packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8" },
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 [[package]]
 name = "aiohttp"
 version = "3.13.5"
-source = { registry = "https://pypi.org/simple" }
+source = { registry = "https://mirrors.cernet.edu.cn/pypi/web/simple/" }
 dependencies = [
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     { name = "aiosignal" },
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     { name = "yarl" },
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+    { name = "asyncio" },
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     { name = "freqtrade" },
+    { name = "ipykernel" },
     { name = "jupyter" },
     { name = "matplotlib" },
     { name = "numpy" },
     { name = "pandas" },
+    { name = "plotly" },
+    { name = "plotly-resampler" },
+    { name = "polars" },
+    { name = "polars-ta" },
+    { name = "scipy" },
+    { name = "torch" },
+    { name = "tqdm" },
+    { name = "tqdn" },
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diff --git a/versions/0.4.2_draft/enhanced_ccxt_processor.py b/versions/0.4.2_draft/enhanced_ccxt_processor.py
new file mode 100644
index 0000000..20e917f
--- /dev/null
+++ b/versions/0.4.2_draft/enhanced_ccxt_processor.py
@@ -0,0 +1,745 @@
+from __future__ import annotations
+
+import asyncio
+from cmath import polar
+import enum
+import math
+import os
+import shutil
+from datetime import datetime
+from typing import Any, List, Literal, Tuple
+
+import ccxt
+import ccxt.async_support
+import numpy as np
+import polars as pl
+from torch import Tensor
+import torch.utils
+import torch.utils.data
+from tqdm.asyncio import tqdm as tqdm_as
+from tqdm.notebook import tqdm as tqdm_nb
+from polars.ml.torch import PolarsDataset
+from gaussian_normalizer import GaussianScaler
+
+
+class TimeStampUnit(enum.Enum):
+    Sec1 = ("1s", 1)
+    Min1 = ("1m", 60)
+    Hour1 = ("1h", 60 * 60)
+    Day1 = ("1d", 60 * 60 * 24)
+
+    def suffix(self) -> str:
+        return self.name[1]
+
+
+class RoundRobinWorkerPool:
+    def __init__(self, creator, worker: int):
+        self.pool = [creator() for _ in range(worker)]
+        self.workers = worker
+        self.idx = 0
+
+    def get(self) -> ccxt.async_support.Exchange:
+        idx = self.idx
+        self.idx = (self.idx + 1) % self.workers
+        return self.pool[idx]
+
+    async def closeall(self):
+        for w in self.pool:
+            await w.close()
+
+
+class CCXTEngineerEnhanced:
+    def __init__(
+        self,
+        exchanger=lambda: ccxt.async_support.binance(),
+        num_worker: int = 8,
+        wait_interval=10,
+        max_concurrent: int = 20,
+        req_per_sec: int = 10,
+        shards_per_type: int = 5,
+        cache_dir="datasets",
+        backup=False,
+        notebook=False,
+    ):
+        self.exchanger_creator = exchanger
+        self.num_worker = num_worker
+        self.wait_interval = wait_interval
+        self.semaphore = asyncio.Semaphore(max_concurrent)
+        self.req_interval = 1.0 / req_per_sec
+        self.shards_per_type = shards_per_type
+        self.tqdm_bar_idx = 0
+        self.cache_dir = cache_dir
+        self.download_sem = 0
+        self.backup = backup
+        if notebook:
+            self.tqdm = tqdm_nb
+        else:
+            self.tqdm = tqdm_as
+
+    async def _fetch(
+        self,
+        worker: ccxt.async_support.Exchange,
+        ktype: str,
+        pair: str,
+        after: int,
+        limit=None,
+    ) -> List[List[Any]]:
+        async with self.semaphore:
+            await asyncio.sleep(self.req_interval)
+            future = worker.fetch_ohlcv(
+                symbol=pair, timeframe=ktype, since=after, limit=limit
+            )
+            return await future  # type:ignore
+
+    def _generate_shards(
+        self, start_time: datetime, end_time: datetime
+    ) -> Tuple[List[Tuple[int, int]], int]:
+        total_span = int((end_time - start_time).total_seconds() * 1000)
+        shard_span = total_span // self.shards_per_type
+        shards = [
+            (
+                int(start_time.timestamp() * 1000) + i * shard_span,
+                min(
+                    int(start_time.timestamp() * 1000) + (i + 1) * shard_span,
+                    int(end_time.timestamp() * 1000),
+                ),
+            )
+            for i in range(self.shards_per_type)
+        ]
+        return (shards, total_span)
+
+    def take_tqdm_position(self) -> int:
+        idx = self.tqdm_bar_idx
+        self.tqdm_bar_idx += 1
+        return idx
+
+    async def _work(
+        self,
+        workers: RoundRobinWorkerPool,
+        pair: str,
+        period_ktype: TimeStampUnit,
+        shard: Tuple[int, int],
+        buffer,
+    ):
+        start_ts, end_ts = shard
+        current_ts = start_ts
+
+        with self.tqdm(
+            range(end_ts - start_ts),
+            desc=f"{pair}-{period_ktype.name}:{start_ts}->{end_ts}",
+            position=self.take_tqdm_position(),
+        ) as pbar:
+            while current_ts < end_ts:
+                try:
+                    data = await self._fetch(
+                        workers.get(),
+                        ktype=period_ktype.value[0],
+                        pair=pair,
+                        after=current_ts,
+                    )
+                    if not data:
+                        continue
+
+                    valid_data = [
+                        row for row in data if start_ts <= int(row[0]) <= end_ts
+                    ]
+                    if not valid_data:
+                        break
+
+                    lts = int(valid_data[-1][0])
+                    pbar.update(lts - current_ts)
+                    buffer.extend(valid_data)
+                    current_ts = lts
+                except Exception as e:
+                    print(f"\n重试分片 {period_ktype}@{current_ts}: {str(e)}\n")
+            pbar.close()
+        self.download_sem -= 1
+
+    async def recover_data_fetch(
+        self,
+        start: datetime,
+        end: datetime,
+        pair_list=["BTC/USDT"],
+        periods=[TimeStampUnit.Min1],
+    ):
+        rec = self.retrieve_download_cache_ohlcv(periods, pair_list, savemem=True)
+        remain: list[tuple[str, TimeStampUnit, datetime]] = []
+
+        for (period, pair), df in rec.items():
+            (_, secs) = period.value
+            df: pl.LazyFrame = df
+            last: datetime = (
+                df.select(pl.col("ts").max()).collect(engine="streaming").item(0, 0)
+            )
+            del df
+            if last is None:
+                remain.append((pair, period, start))
+            elif (end - last).total_seconds() >= int(secs):
+                remain.append((pair, period, last))
+
+        if len(remain) == 0:
+            print("no download process need to be recover!")
+            return
+
+        data_buffers = {
+            (pair, period): [[] for slice in range(self.shards_per_type)]
+            for pair, period, _ in remain
+        }
+
+        raw_schema = {
+            "ts": pl.Datetime("ms"),
+            "open": pl.Float64,
+            "high": pl.Float64,
+            "low": pl.Float64,
+            "close": pl.Float64,
+            "volume": pl.Float64,
+        }
+        actual_schema = {
+            "ts": pl.Datetime("ms"),
+            "open": pl.Float64,
+            "high": pl.Float64,
+            "low": pl.Float64,
+            "close": pl.Float64,
+            "volume": pl.Float64,
+            "pair": pl.String,
+        }
+
+        async def _auto_save(self):
+            while self.download_sem > 0:
+                await asyncio.sleep(60)
+                await _save_all(self)
+
+        async def _save_all(self, sort=False):
+            for pair, period, _ in remain:
+                path = f"{self.cache_dir}/cache/download/{period.name}"
+                df = pl.DataFrame(schema=raw_schema)
+                for shard in data_buffers[(pair, period)]:
+                    df = df.vstack(
+                        pl.DataFrame(
+                            shard,
+                            schema=raw_schema,
+                            orient="row",
+                        )
+                    )
+                    # check if folder exists
+                    os.makedirs(path, exist_ok=True)
+                    file = f"{path}/{pair.replace('/', '_').lower()}_raw.parquet"
+                    print(f"\n新保存{df.height}条数据到{file}\n")
+
+                    if os.path.exists(file):
+                        df = pl.concat([pl.read_parquet(file), df]).unique("ts")
+                        if sort:
+                            df = df.sort(by="ts")
+                        df.write_parquet(file)
+                    else:
+                        df = df.unique("ts")
+                        if sort:
+                            df = df.sort("ts")
+                        df.write_parquet(file)
+                    shard.clear()
+
+        tasks = []
+        workers = RoundRobinWorkerPool(self.exchanger_creator, self.num_worker)
+        for pair, period, begin in remain:
+            shards, _ = self._generate_shards(begin, end)
+            for shard_idx, shard in enumerate(shards):  # 使用enumerate获取索引
+                # 获取对应的缓冲区位置
+                buffer = data_buffers[(pair, period)][shard_idx]
+                # 创建任务并添加到列表
+                task = self._work(workers, pair, period, shard, buffer)
+                tasks.append(task)
+                self.download_sem += 1
+        if self.backup:
+            tasks.append(_auto_save(self))
+        await asyncio.gather(*tasks)
+        await workers.closeall()
+        print("download completed!")
+        await _save_all(self, sort=True)
+
+    async def data_fetch_ohlvc(
+        self,
+        start: datetime,
+        end: datetime,
+        pair_list=["BTC/USDT"],
+        periods=[TimeStampUnit.Min1],
+    ) -> dict[tuple[TimeStampUnit, str], pl.LazyFrame]:
+        data_buffers = {
+            period: {
+                pair: [[] for slice in range(self.shards_per_type)]
+                for pair in pair_list
+            }
+            for period in periods
+        }
+        raw_schema = {
+            "ts": pl.Datetime("ms"),
+            "open": pl.Float64,
+            "high": pl.Float64,
+            "low": pl.Float64,
+            "close": pl.Float64,
+            "volume": pl.Float64,
+        }
+        actual_schema = {
+            "ts": pl.Datetime("ms"),
+            "open": pl.Float64,
+            "high": pl.Float64,
+            "low": pl.Float64,
+            "close": pl.Float64,
+            "volume": pl.Float64,
+            "pair": pl.String,
+        }
+
+        async def _auto_save(self):
+            while self.download_sem > 0:
+                await asyncio.sleep(60)
+                await _save_all(self)
+
+        async def _save_all(self, sort=False):
+            for period in periods:
+                path = f"{self.cache_dir}/cache/download/{period.name}"
+                for pair in pair_list:
+                    pair: str = pair
+                    df = pl.DataFrame(schema=raw_schema)
+                    for shard in range(self.shards_per_type):
+                        df = df.vstack(
+                            pl.DataFrame(
+                                data_buffers[period][pair][shard],
+                                schema=raw_schema,
+                                orient="row",
+                            )
+                        )
+                    # check if folder exists
+                    os.makedirs(path, exist_ok=True)
+
+                    file = f"{path}/{pair.replace('/', '_').lower()}_raw.parquet"
+                    print(f"\n新保存{df.height}条数据到{file}\n")
+
+                    if os.path.exists(file):
+                        df = pl.concat([pl.read_parquet(file), df]).unique("ts")
+                        if sort:
+                            df = df.sort(by="ts")
+                        df.write_parquet(file)
+                    else:
+                        df = df.unique("ts")
+                        if sort:
+                            df = df.sort("ts")
+                        df.write_parquet(file)
+
+                    for shard in data_buffers[period][pair]:
+                        shard.clear()
+
+        tasks = []
+        workers = RoundRobinWorkerPool(self.exchanger_creator, self.num_worker)
+        for period in periods:
+            for pair in pair_list:
+                shards, _ = self._generate_shards(start, end)
+                for shard_idx, shard in enumerate(shards):  # 使用enumerate获取索引
+                    # 获取对应的缓冲区位置
+                    buffer = data_buffers[period][pair][shard_idx]
+                    # 创建任务并添加到列表
+                    task = self._work(workers, pair, period, shard, buffer)
+                    tasks.append(task)
+                    self.download_sem += 1
+        if self.backup:
+            tasks.append(_auto_save(self))
+        await asyncio.gather(*tasks)
+        await workers.closeall()
+        print("download completed!")
+        await _save_all(self, sort=True)
+
+        datasets = {
+            (period, pair): pl.scan_parquet(
+                f"{self.cache_dir}/cache/download/{period.name}/{pair.replace('/', '_').lower()}_raw.parquet",
+                schema=actual_schema,
+            )
+            for period in periods
+            for pair in pair_list
+        }
+        return datasets
+
+    def take_pairs_with_unit_only(
+        self, df_s: dict[tuple[TimeStampUnit, str], pl.LazyFrame], unit: TimeStampUnit
+    ) -> dict[str, pl.LazyFrame]:
+        dfs = {}
+        for (period, pair), df in df_s.items():
+            if period == unit:
+                dfs[pair] = df
+        return dfs
+
+    def retrieve_download_cache_ohlcv(
+        self,
+        ts: list[TimeStampUnit],
+        pairs: list[str],
+        savemem=False,
+        uniquelize_inplace=False,
+        sort=False,
+    ) -> dict[tuple[TimeStampUnit, str], pl.LazyFrame]:
+        outer_schema = {
+            "ts": pl.Datetime("ms"),
+            "open": pl.Float64,
+            "high": pl.Float64,
+            "low": pl.Float64,
+            "close": pl.Float64,
+            "volume": pl.Float64,
+        }
+        datasets = {}
+        for period in ts:
+            for pair in pairs:
+                path = f"{self.cache_dir}/cache/download/{period.name}/{pair.replace('/', '_').lower()}_raw.parquet"
+                df = pl.scan_parquet(
+                    path,
+                    schema=outer_schema,
+                    low_memory=savemem,
+                )
+                dirty = False
+                if uniquelize_inplace:
+                    df = df.unique("ts")
+                    dirty = True
+                if sort:
+                    df = df.sort("ts")
+                    dirty = True
+                if dirty:
+                    df.collect(engine="streaming").write_parquet(path)
+                    df = pl.scan_parquet(path, schema=outer_schema, low_memory=savemem)
+                datasets[(period, pair)] = df
+
+        return datasets
+
+    def retrieve_indicated_cache_ohlcv(
+        self, ts: list[TimeStampUnit], pairs: list[str], savemem=False
+    ) -> dict[tuple[TimeStampUnit, str], pl.LazyFrame]:
+        datasets = {
+            (period, pair): pl.scan_parquet(
+                f"{self.cache_dir}/cache/indicated/{period.name}/{pair.replace('/', '_').lower()}.parquet",
+                low_memory=savemem,
+            )
+            for period in ts
+            for pair in pairs
+        }
+        return datasets
+
+    def add_technical_indicators_to_ohlcv_v2(
+        self,
+        df_s: dict[str, pl.LazyFrame],
+        axises_indicators: dict[str, list[tuple[str, int, list[pl.Expr]]]],
+        agg_func: dict[tuple[str, str, int], list[pl.Expr]] = {},
+    ) -> list[tuple[str, str, int, pl.LazyFrame]]:
+        ret = []
+        for pair, mappers in axises_indicators.items():
+            df_outer = df_s[pair]
+            for axis_col, axis_split, indicator in mappers:
+                df_inner = self.reaxis(
+                    df_outer,
+                    axis_col,
+                    axis_split,
+                    agg_func.get((pair, axis_col, axis_split), []),
+                )
+                ret.append(
+                    (
+                        pair,
+                        axis_col,
+                        axis_split,
+                        df_inner.with_columns(indicator).fill_nan(0).drop_nulls(),
+                    )
+                )
+
+        return ret
+
+    def extract_normalizer_from_v2_dataframes(
+        self, df_s: list[tuple[str, str, int, pl.LazyFrame]]
+    ) -> list[tuple[str, str, int, pl.LazyFrame, GaussianScaler]]:
+        return [
+            (
+                pair,
+                axis_col,
+                axis_split,
+                df,
+                GaussianScaler(pair, axis_col, axis_split, df),
+            )
+            for pair, axis_col, axis_split, df in df_s
+        ]
+
+    def retrieve_normalizer_from_v2_dataframes(
+        self, df_s: list[tuple[str, str, int, pl.LazyFrame]]
+    ) -> list[tuple[str, str, int, pl.LazyFrame, GaussianScaler]]:
+        return [
+            (
+                pair,
+                axis_col,
+                axis_split,
+                df,
+                GaussianScaler(pair, axis_col, axis_split),
+            )
+            for pair, axis_col, axis_split, df in df_s
+        ]
+
+    def add_technical_indicators_to_ohlcv(
+        self,
+        df_s: dict[tuple[TimeStampUnit, str], pl.LazyFrame],
+        tech_indicator_list: dict[tuple[TimeStampUnit, str], list[pl.Expr]]
+        | list[pl.Expr] = [],
+        extra_indicators: list[pl.Expr] = [],
+        stream=False,
+        use_gpu=False,
+    ) -> dict[tuple[TimeStampUnit, str], pl.LazyFrame]:
+        datasets = {}
+        cache_path = f"{self.cache_dir}/cache/indicated"
+        if tech_indicator_list is list:
+            for (period, pair), df in df_s.items():
+                df = (
+                    df.unique("ts")
+                    .sort("ts")
+                    .with_columns(tech_indicator_list)
+                    .with_columns(extra_indicators)
+                )
+                if self.backup:
+                    os.makedirs(f"{cache_path}/{period.name}", exist_ok=True)
+                    file_name = f"{cache_path}/{period.name}/{pair.replace('/', '_').lower()}.parquet"
+                    df.collect(
+                        streaming=stream, engine="gpu" if use_gpu else "cpu"
+                    ).write_parquet(file_name)
+                    df = pl.scan_parquet(file_name)
+                datasets[(period, pair)] = df
+            print("Succesfully add technical indicators")
+        elif tech_indicator_list is dict:
+            for (period, pair), df in df_s.items():
+                df = (
+                    df.unique("ts")
+                    .sort("ts")
+                    .with_columns(tech_indicator_list[(period, pair)])
+                    .with_columns(extra_indicators)
+                )
+                if self.backup:
+                    os.makedirs(f"{cache_path}/{period.name}", exist_ok=True)
+                    file_name = f"{cache_path}/{period.name}/{pair.replace('/', '_').lower()}.parquet"
+                    df.collect(
+                        streaming=stream, engine="gpu" if use_gpu else "cpu"
+                    ).write_parquet(file_name)
+                    df = pl.scan_parquet(file_name)
+                datasets[(period, pair)] = df
+                print(f"Succesfully add technical indicators for ({period},{pair})")
+        return datasets
+
+    def construct_and_save_predict_dataset_v2(
+        self,
+        df_s: list[pl.LazyFrame],
+        index_col: str = "group_id",
+        rolling_window: int = 12,
+        size_limit: int | None = None,
+    ) -> list[PolarsDataset]:
+        ds = []
+        for df in df_s:
+            schema = df.collect_schema()
+            label_len = (
+                schema.len() - 1
+            )  # self predicted endo variable series, expect groupid, all is endo variable
+            feature_count = label_len
+            df = df.head(size_limit + rolling_window) if size_limit is not None else df
+            dataset = (
+                (
+                    df.select(
+                        [
+                            pl.col(index_col),
+                            ##
+                            pl.concat_list(
+                                pl.exclude(index_col).cast(dtype=pl.Float32)
+                            ).alias("unioned_x"),
+                            ##
+                            pl.concat_list(pl.exclude(index_col).cast(dtype=pl.Float32))
+                            .shift(-1)
+                            .list.to_array(feature_count)
+                            .alias("grouped_y"),
+                        ]
+                    )
+                    .drop_nulls()
+                    .filter(pl.col("unioned_x").list.len() == feature_count)
+                    .rolling(index_column=index_col, period=f"{rolling_window}i")
+                    .agg(
+                        [
+                            pl.col("unioned_x")
+                            .head(rolling_window)
+                            .flatten()
+                            .alias("grouped_x"),
+                            pl.col("grouped_y").last().alias("grouped_y"),
+                        ]
+                    )
+                    .filter(
+                        pl.col("grouped_x").list.len() == rolling_window * feature_count
+                    )
+                    .with_columns(
+                        pl.col("grouped_x")
+                        .list.to_array(rolling_window * feature_count)
+                        .alias("grouped_x")
+                    )
+                )
+                .collect(engine="streaming")
+                .to_torch(
+                    return_type="dataset", label="grouped_y", features="grouped_x"
+                )
+            )
+            ds.append(dataset)
+        return ds
+
+    def construct_and_save_predict_dataset_ohlcv_v1(
+        self,
+        df_s: dict[tuple[TimeStampUnit, str], pl.LazyFrame],
+        rolling_windows: dict[TimeStampUnit, int] = {TimeStampUnit.Sec1: 120},
+        labels: list[str] = [f"ROCP_{i:03d}" for i in (1, 3, 5, 10, 20, 60, 120)],
+        ts_col: str = "ts",
+        take_head: int | None = None,
+        offset: int = 0,
+        hint: bool = False,
+    ) -> dict[tuple[TimeStampUnit, str], PredictorNormalizedDatasetV1]:
+        ds = {}
+        for (period, pair), df in df_s.items():
+            schema = df.collect_schema()
+            rolling_window = rolling_windows[period]
+            label_len = len(labels)
+            feature_count = schema.len() - 1 - label_len  # additional -1 for ts col
+            if offset != 0:
+                df = df.slice(offset=offset)
+            if take_head is not None:
+                df = df.head(take_head + 2 * rolling_window)
+            dataset = (
+                df.drop_nulls()
+                .select(
+                    [
+                        pl.col(ts_col),
+                        pl.concat_list(
+                            pl.exclude(
+                                [
+                                    *labels,
+                                    "ts",
+                                ]
+                            ).cast(dtype=pl.Float32)
+                        )
+                        .list.to_array(feature_count)
+                        .alias("unioned_x"),
+                        pl.concat_list(pl.col(labels).cast(dtype=pl.Float32))
+                        .list.to_array(label_len)
+                        .alias("unioned_y"),
+                    ]
+                )
+                .rolling(index_column=ts_col, period=f"{rolling_window}s")
+                .agg(
+                    [
+                        pl.col("unioned_x").head(rolling_window).alias("grouped_x"),
+                        pl.col("unioned_y").head(1).alias("grouped_y"),
+                    ]
+                )
+                .slice(rolling_window)
+                .select(
+                    [
+                        pl.col("grouped_x").list.to_array(rolling_window),
+                        pl.col("grouped_y").list.to_array(1),
+                    ]
+                )
+                .collect(engine="streaming")
+            )
+            if hint:
+                print(dataset.head(5))
+            ds[(period, pair)] = PredictorNormalizedDatasetV1(
+                dataset.to_torch(
+                    "dataset",
+                    label="grouped_y",
+                    features="grouped_x",
+                )
+            )
+        return ds
+
+    def drop_cache(self, nonexist_okay=True):
+        shutil.rmtree(f"{self.cache_dir}/cache", ignore_errors=nonexist_okay)
+
+    def reaxis(
+        self,
+        df: pl.LazyFrame,
+        axis: str,
+        part_range: float,
+        group_agg_func: list[pl.Expr] = [],
+    ) -> pl.LazyFrame:
+        """
+        按照指定轴重新切片金融数据
+
+        参数:
+        df: 包含OHLCV数据的DataFrame
+        axis: 重新采样的轴，如"volume"
+        part_key: 每个间隔的大小，如50000（表示每50000单位volume为一个间隔）
+        tolerance: 严格容忍误差，表示为百分比（0-1之间）
+
+        返回:
+        按指定轴重新采样后的LazyFrame，包含切片内样本的累计信息
+        """
+
+        return (
+            df.with_columns(
+                # 计算累积轴值
+                pl.col(axis).cum_sum().alias(f"cum_{axis}")
+            )
+            .with_columns(
+                # 计算理想的划分点
+                (pl.col(f"cum_{axis}") / part_range).floor().alias("group_id")
+            )
+            .group_by("group_id")
+            .agg(
+                [
+                    # 保留第一个时间戳作为组的时间戳
+                    pl.col("ts").first().alias("first_ts"),
+                    pl.col("ts").last().alias("last_ts"),
+                    # 保留第一个价格作为开盘价
+                    pl.col("open").first().alias("open"),
+                    # 记录区间内的极值
+                    pl.col("high").max().alias("high"),
+                    pl.col("low").min().alias("low"),
+                    # 最后一个价格作为收盘价
+                    pl.col("close").last().alias("close"),
+                    # 计算累积成交量
+                    pl.col("volume").sum().alias("volume"),
+                    # 计算区间内实际累积的轴值（用于验证是否满足tolerance要求）
+                    pl.col(axis).sum().alias("actual_part"),
+                    *group_agg_func,
+                ]
+            )
+            .with_columns(
+                ((pl.col("actual_part") - part_range).abs() / part_range).alias(
+                    "part_error"
+                ),
+                (pl.col("last_ts") - pl.col("first_ts")).alias("period_duration"),
+            )
+            .with_columns(
+                [
+                    pl.col("period_duration")
+                    .dt.total_seconds()
+                    .alias("period_duration_sec"),
+                    pl.col("group_id").cast(pl.UInt64),
+                ]
+            )
+            .drop(["actual_part"])
+            .sort("group_id")
+        )
+
+
+class PredictorNormalizedDatasetV1(torch.utils.data.Dataset):
+    def __init__(self, original_data):
+        self.data = original_data  # 假设原始数据已加载为Tensor或可索引对象
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        # 假设每个样本返回 (feature, label)，feature形状为[120,40]，label形状为[1,7]
+        feature, label = self.data[idx]
+
+        # 特征归一化（逐列处理）
+        mean = torch.mean(feature, dim=0, keepdim=True)  # 保持维度以支持广播
+        std = torch.std(feature, dim=0, keepdim=True, unbiased=False)  # 使用总体标准差
+        epsilon = 1e-8
+        normalized_feature: Tensor = (feature - mean) / (std + epsilon)  # 防止除零
+
+        # 标签二值化（保留符号并处理零值）
+        processed_label = torch.sign(label)
+        processed_label = torch.where(
+            processed_label == 0, torch.ones_like(processed_label) * -1, processed_label
+        )
+
+        return normalized_feature.reshape(
+            [1, normalized_feature.shape.numel()]
+        ), processed_label
diff --git a/versions/0.4.2_draft/example.py b/versions/0.4.2_draft/example.py
new file mode 100644
index 0000000..918c23e
--- /dev/null
+++ b/versions/0.4.2_draft/example.py
@@ -0,0 +1,238 @@
+# -*- coding: utf-8 -*-
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: title,-all
+#     custom_cell_magics: kql
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.11.2
+#   kernelspec:
+#     display_name: auto-quant (3.11.15)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+# pythonscript
+
+# %% initialize
+import ccxt
+import ccxt.async_support
+from enhanced_ccxt_processor import TimeStampUnit
+from enhanced_ccxt_processor import CCXTEngineerEnhanced
+from datetime import datetime
+import asyncio
+
+learn_rate = 1e-3
+batch_size = 32
+train_start_date = datetime(2021, 1, 1, 0, 0, 0)
+train_end_date = datetime(2021, 1, 2, 0, 0, 0)
+pairs = ["BTC/USDT"]
+periods = [TimeStampUnit.Sec1]
+
+
+# %% random shit
+async def gather_data():
+    exchanger = CCXTEngineerEnhanced(
+        notebook=True, num_worker=1, backup=True,shards_per_type=1, cache_dir="datasets"
+    )
+    datasets = await exchanger.data_fetch_ohlvc(
+        start=train_start_date,
+        end=train_end_date,
+        pair_list=pairs,
+        periods=periods,
+    )
+
+
+# %%
+asyncio.run(gather_data())
+#await gather_data()
+
+# %% how to use
+# label data...
+import polars as pl
+import polars.config
+from frac_diff import frac_diff_df
+
+polars.Config.set_tbl_cols(-1).set_tbl_width_chars(-1)
+
+pairs = ["BTC/USDT"]
+periods = [TimeStampUnit.Sec1]
+exp_index = (periods[0], pairs[0])
+exchanger = CCXTEngineerEnhanced(notebook=True, backup=True)
+df = exchanger.retrieve_download_cache_ohlcv(ts=periods, pairs=pairs)[exp_index]
+print(df.collect_schema())
+df = exchanger.reaxis(df, axis="volume", part_range=128)
+df.collect().describe()
+
+n_endo_indicate_col_set = {
+    "group_id",
+    "first_ts",
+    "last_ts",
+    "part_error",
+    "period_duration",
+    "period_duration_sec",
+    "open",
+    "close",
+    "high",
+    "low",
+    "volume",
+}
+
+fraced = frac_diff_cols = (
+    frac_diff_df(
+        df,
+        ["open", "high", "low", "close"],
+        dd=[
+            *[i / 10.0 for i in range(1, 15)],
+        ],
+    )
+    .with_columns(
+        [
+            (pl.col(gcol) - pl.col(lcol).shift(-i)).alias(
+                f"{gcol}-{lcol}_diff_tier_{i}"
+            )
+            for gcol in ["high", "close", "open"]
+            for lcol in ["low", "open", "close"]
+            if gcol != lcol
+            for i in range(2, 9)
+        ],
+    )
+    .collect()
+)
+
+fraced.describe()
+
+# %% and more data shit...
+from math import pi
+
+
+to_be_cleaned_fraced_data = fraced.lazy()
+to_be_cleaned_fraced_data = to_be_cleaned_fraced_data.select(
+    [
+        "volume",
+        "part_error",
+        "period_duration_sec",
+        "group_id",
+        pl.selectors.contains("diff"),
+    ]
+)
+(
+    mean,
+    std,
+) = (
+    to_be_cleaned_fraced_data.mean(),
+    to_be_cleaned_fraced_data.std(),
+)
+normalized_data = (
+    to_be_cleaned_fraced_data.with_columns(
+        [
+            *[
+                ((pl.col(col) - pl.col(col).mean()) / pl.col(col).std()).alias(col)
+                for col in to_be_cleaned_fraced_data.columns
+                if col
+                not in ["part_error", "period_duration_sec", "volume", "group_id"]
+            ],
+        ]
+    )
+    .with_columns(
+        *[
+            pl.col(col).arctan() / pi + 0.5
+            for col in to_be_cleaned_fraced_data.columns
+            if col != "group_id"
+        ],
+    )
+    .with_columns(
+        exchange_rate=(pl.col("part_error") / (pl.col("period_duration_sec") + 1)),
+        group_id=pl.col("group_id").cast(pl.Int64),
+    )
+    .rename(
+        {
+            "part_error": "part-error",
+            "period_duration_sec": "period-duration-sec",
+            "volume": "volume",
+            "exchange_rate": "exchange-rate",
+            "group_id": "group-id",
+        }
+    )
+    .collect()
+)
+normalized_data.describe()
+
+# %% figure out whether the data follows gaussian dist
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from plotly_resampler import register_plotly_resampler
+
+register_plotly_resampler()
+draw_df = normalized_data
+
+# features = ["open", "high", "low", "close", "volume", "exchange_rate", "duration"]
+cols = draw_df.columns
+features = {str(name).split("_")[0] for name in cols}
+
+# 创建子图
+fig = make_subplots(
+    rows=len(features), cols=1, shared_xaxes=True, vertical_spacing=0.02
+)
+
+for i, feature in enumerate(features):
+    # 获取原始数据和对应的分数阶微分列
+    grouped_columns = [col for col in cols if feature in col]
+
+    # if feature in cols:
+    #     # 添加原始数据曲线
+    #     fig.add_trace(
+    #         go.Scattergl(
+    #             x=fraced["first_ts"],
+    #             y=draw_df[feature],
+    #             mode="lines",
+    #             name=f"Original {feature}",
+    #             line=dict(color="blue"),
+    #             # legendgroup=f"group_{i}",
+    #             hovertext=f"{feature}",
+    #         ),
+    #         row=i + 1,
+    #         col=1,
+    #     )
+
+    # 添加分数阶微分后的数据曲线
+    for col in grouped_columns:
+        fig.add_trace(
+            go.Scattergl(
+                x=fraced["first_ts"],
+                y=draw_df[col],
+                mode="lines",
+                name=col,
+                line=dict(dash="dot"),
+                # legendgroup=f"group_{i}",
+                showlegend=True,
+                hovertext=f"{col}",
+                # make legend has auto fit width
+            ),
+            row=i + 1,
+            col=1,
+        )
+
+    # 设置每个子图的标题和Y轴标签
+    fig.update_yaxes(title_text="Value", row=i + 1, col=1)
+    fig.update_xaxes(title_text="", row=i + 1, col=1)
+    fig.update_xaxes(matches="x")
+
+# 设置整体标题和布局
+fig.update_layout(
+    height=2000,
+    width=1400,
+    title_text="Original vs Fractional Differences (Interactive View)",
+    showlegend=True,
+    # make legend has auto fit width
+    legend=dict(
+        traceorder="normal",
+    ),
+)
+
+# 显示图表
+fig.show()
diff --git a/versions/0.4.2_draft/frac_diff.py b/versions/0.4.2_draft/frac_diff.py
new file mode 100644
index 0000000..964833b
--- /dev/null
+++ b/versions/0.4.2_draft/frac_diff.py
@@ -0,0 +1,160 @@
+import polars as pl
+from typing import List, Union
+
+
+def get_weights_ffd(d: float, threshold: float) -> List[float]:
+    """
+    计算分数差分的权重系数(出处为AFML)
+
+    Args:
+        d: 差分阶数
+        threshold: 权重阈值，当权重小于此值时停止计算
+
+    Returns:
+        权重列表
+    """
+    w = [1.0]
+    k = 1.0
+    while True:
+        w_ = -w[-1] / k * (d - k + 1.0)
+        if abs(w_) < threshold:
+            break
+        w.append(w_)
+        k += 1.0
+    return list(reversed(w))
+
+
+def frac_diff(series: pl.Series, d: float, threshold: float = 1e-5) -> pl.Series:
+    """
+    对时间序列进行分数差分，使用polars原生功能
+
+    Args:
+        series: 输入的时间序列
+        d: 差分阶数
+        threshold: 权重阈值，当权重小于此值时停止计算
+
+    Returns:
+        分数差分后的时间序列
+    """
+    # 获取权重
+    weights = get_weights_ffd(d, threshold)
+    n_weights = len(weights)
+
+    # 创建临时DataFrame用于计算
+    df = pl.DataFrame({series.name: series})
+
+    # 使用shift和表达式计算分数差分
+    expr_list = []
+    for i, w in enumerate(weights):
+        expr_list.append(pl.col(series.name).shift(i) * w)
+
+    result_df = df.with_columns(pl.sum_horizontal(expr_list).alias(f"frac_diff_{d}"))
+
+    # 前n_weights-1个值设为null
+    result_df = result_df.with_columns(
+        pl.col(f"frac_diff_{d}").fill_null(None).alias(f"frac_diff_{d}")
+    )
+
+    return result_df[f"frac_diff_{d}"]
+
+
+def frac_diff_expr(
+    expr: pl.Expr, d: float, threshold: float = 1e-5, name: str = None
+) -> pl.Expr:
+    """
+    创建分数差分的表达式，用于LazyFrame计算
+
+    Args:
+        expr: 输入列的表达式
+        d: 差分阶数
+        threshold: 权重阈值，当权重小于此值时停止计算
+        name: 列名，用于生成结果列名
+
+    Returns:
+        分数差分表达式
+    """
+    # 获取权重
+    weights = get_weights_ffd(d, threshold)
+
+    # 使用shift和表达式创建分数差分表达式
+    expr_list = []
+    for i, w in enumerate(weights):
+        expr_list.append(expr.shift(i) * w)
+
+    # 生成结果列名
+    result_name = f"frac_diff_{d}"
+    if name:
+        result_name = f"frac_diff_{d}_{name}"
+
+    return pl.sum_horizontal(expr_list).alias(result_name)
+
+
+def frac_diff_exprs(cols: List[str], d: float, threshold: float = 1e-5) -> pl.Expr:
+    """
+    创建分数差分的表达式，用于LazyFrame计算
+
+    Args:
+        expr: 输入列的表达式
+        d: 差分阶数
+        threshold: 权重阈值，当权重小于此值时停止计算
+        name: 列名，用于生成结果列名
+
+    Returns:
+        分数差分表达式
+    """
+    # 获取权重
+    weights = get_weights_ffd(d, threshold)
+    return (
+        pl.col(cols)
+        .rolling_sum(window_size=len(weights), weights=weights)
+        .name.suffix(f"_frac_diff_{d}")
+    )
+
+    # # 使用shift和表达式创建分数差分表达式
+    # expr_list = []
+    # for i, w in enumerate(weights):
+    #     expr_list.append(expr.shift(i) * w)
+
+    # # 生成结果列名
+    # result_name = f"frac_diff_{d}"
+    # if name:
+    #     result_name = f"frac_diff_{d}_{name}"
+
+    # return pl.sum_horizontal(expr_list).alias(result_name)
+
+
+def frac_diff_df(
+    df: Union[pl.DataFrame, pl.LazyFrame],
+    columns: List[str],
+    dd: list[float],
+    threshold: float = 1e-5,
+) -> pl.LazyFrame:
+    """
+    对DataFrame或LazyFrame中的多个列进行分数差分
+
+    Args:
+        df: 输入的DataFrame或LazyFrame
+        columns: 需要进行分数差分的列名列表
+        d: 差分阶数
+        threshold: 权重阈值，当权重小于此值时停止计算
+
+    Returns:
+        包含原始列和分数差分列的DataFrame或LazyFrame
+    """
+    # 检查输入是DataFrame还是LazyFrame
+    is_lazy = isinstance(df, pl.LazyFrame)
+
+    # 如果是DataFrame，转换为LazyFrame进行处理
+    if not is_lazy:
+        lf = df.lazy()
+    else:
+        lf = df
+
+    # 创建所有分数差分列的表达式
+    expr_list = [frac_diff_exprs(cols=columns, d=dv, threshold=threshold) for dv in dd]
+
+    # 一次性添加所有列，提高性能
+    lf = lf.with_columns(expr_list)
+
+    # 根据输入类型返回相应的结果
+    return lf
diff --git a/versions/0.4.2_draft/gaussian_normalizer.py b/versions/0.4.2_draft/gaussian_normalizer.py
new file mode 100644
index 0000000..ffe1118
--- /dev/null
+++ b/versions/0.4.2_draft/gaussian_normalizer.py
@@ -0,0 +1,84 @@
+import json
+import math
+import os
+import polars as pl
+from polars_ta.wq.preprocess import cs_mad
+from scipy.stats import norm
+
+
+non_endo_indicate_col_set = {
+    "group_id",
+    "first_ts",
+    "last_ts",
+    "part_error",
+    "period_duration",
+    "open",
+    "close",
+    "high",
+    "low",
+    "volume",
+}
+
+
+def construct_indicator_normalizer_params_path(
+    pair: str, split_col: str, split_vol: int
+):
+    path = f"datasets/indicators/normalizer_params"
+    os.makedirs(path, exist_ok=True)
+    return f"{path}/{pair.replace('/', '_').lower()}-{split_col}-{split_vol}.json"
+
+
+class GaussianScaler:
+    def __init__(
+        self,
+        pair: str,
+        split_col: str,
+        split_vol: int,
+        state_dict: dict[str, dict[str, float]] | None | pl.LazyFrame = None,  # type:ignore
+    ):
+        if state_dict.__class__ is pl.LazyFrame:
+            stats_df: pl.DataFrame = (
+                state_dict.collect(engine="streaming")  # type:ignore
+                .describe()
+                .drop(["statistic", *non_endo_indicate_col_set])
+            )
+            state_dict = {
+                "means": stats_df.row(2, named=True),
+                "stds": stats_df.row(3, named=True),
+            }
+            del stats_df
+
+        if state_dict.__class__ is dict:
+            with open(
+                construct_indicator_normalizer_params_path(pair, split_col, split_vol),
+                "w",
+            ) as f:
+                json.dump(state_dict, f, indent=2)
+
+        with open(
+            construct_indicator_normalizer_params_path(pair, split_col, split_vol)
+        ) as f:
+            params = json.load(f)
+        self.mean_map: dict[str, float] = params["means"]
+        self.std_map: dict[str, float] = params["stds"]
+
+    def __create_expr(self, col: str) -> pl.Expr:
+        """生成包含CDF转换的表达式链"""
+        return cs_mad(  # celling掉奇异值
+            (pl.col(col) - self.mean_map[col]) / self.std_map[col]
+        ).map_batches(
+            norm.cdf,
+            return_dtype=pl.Float32,
+        )
+
+    def process_stream(self, preserve: bool = False) -> list[pl.Expr]:
+        # 生成所有列的表达式
+        return [
+            self.__create_expr(col).alias(f"{col}_normalized" if preserve else f"{col}")
+            for col in self.mean_map.keys()
+        ]
+
+    def apply_to_df(
+        self, df: pl.DataFrame | pl.LazyFrame, preserve: bool = False
+    ) -> pl.DataFrame | pl.LazyFrame:
+        return df.with_columns(self.process_stream())
diff --git a/versions/0.4.2_draft/requirements.txt b/versions/0.4.2_draft/requirements.txt
new file mode 100644
index 0000000..070122c
--- /dev/null
+++ b/versions/0.4.2_draft/requirements.txt
@@ -0,0 +1,6 @@
+ccxt
+numpy
+polars
+polars_ta
+torch
+tqdm