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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0xab in position 3: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 609, in wrapped
                  for item in generator(*args, **kwargs):
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
                  batch = f.read(self.config.chunksize)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                        ^^^^^^^^^^^^^^^^^^^^^
                File "<frozen codecs>", line 322, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xab in position 3: invalid start byte
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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1990-12-19
1990-12-20
1990-12-21
1990-12-24
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1991-01-02
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1991-02-19
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1991-03-19
1991-03-20
1991-03-21
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1991-03-25
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1991-03-27
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1991-04-19
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1991-04-24
1991-04-25
1991-04-26
1991-04-29
1991-04-30
1991-05-02
1991-05-03
1991-05-06
1991-05-07
1991-05-08
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1991-05-13
End of preview.

financial-analyst-data-full

A-share historical price + valuation data packaged for financial-analyst — the 14-agent single-stock deep-dive research workstation.

Published: 2026-05-24 Preset: full — 全 A 股完整包 (含历史退市股). 量化研究员 / 重度用户. lite 全 + TDX 历年财报原始 zip (用户跑 import_tdx_financial.py 解) + F10 原始文本 (公司大事/龙虎榜/主力追踪/最新提示 .txt). Size: ~14.1 GB

What's included

  • 5450 stocks daily OHLCV + 7 valuation fields (PE/PB/PS/DV/MV/CIRC_MV/turnover_rate)
  • Date range (daily): 1990-12-19 → 2026-12-31 (8797 trading days)
  • 5min OHLCV for the same stocks (rolling ~7-100 days)
  • 24 parquet files (748.6 MB): industry / concept / index_constituents / TDX F10 / northbound / financials
  • TDX 历年财务原始 zip (117 files, 256.6 MB) — 用 import_tdx_financial.py
  • F10 原始文本 (22090 files, 294.1 MB) — 公司大事/龙虎榜单/主力追踪/最新提示 .txt, agent 读详情用
  • Two binary formats:
    • Qlib .bin (time-series, [4-byte float32 start_idx] + [float32 array]) for OHLCV + factors
    • Apache Parquet (columnar) for financials / events / institutional / industry mappings
  • Compatible with Microsoft Qlib and financial-analyst loaders

Directory layout

cn_data/                          # daily
  calendars/day.txt
  instruments/all.txt             # 5450 codes
  features/{code}/              # e.g. sh600519/
    open.day.bin
    high.day.bin
    low.day.bin
    close.day.bin
    volume.day.bin
    amount.day.bin
    pe_ttm.day.bin
    pb.day.bin
    ps_ttm.day.bin (may be NaN if Tushare-free source)
    dv_ttm.day.bin (may be NaN)
    total_mv.day.bin (单位: 万元)
    circ_mv.day.bin (单位: 万元)
    turnover_rate.day.bin (单位: %)
cn_data_5min/                     # 5-minute, similar layout
parquet/                          # 非时序结构化数据 (列存)
  industry_boards.parquet         # 同花顺一级行业板块
  index_constituents.parquet      # CSI300/CSI500 等指数成份
  tdx_f10_index.parquet           # F10 事件索引 (公司大事/龙虎榜/研报)
  tdx_f10_warnings_latest.parquet # 最新负向预警 (utility 警示)
  northbound_holding.parquet      # 北向资金持仓
  tushare_stock_basic.parquet     # 股票基本信息
  concept_ths_*.parquet           # 同花顺概念
  events/                         # 公司事件 (公告)
  institutional/                  # 机构持仓
  blocks/                         # 板块映射
  xdxr/                           # 分红除权
  financial/                      # 完整财务报表 (~735MB)
tdx_finance/                      # TDX 历年财报原始 zip (用户自解)

Usage

Option 1 — via financial-analyst CLI (recommended)

pip install financial-analyst
fa init  # interactive wizard, picks up this dataset
fa report SH600519

Option 2 — direct download + Qlib

from huggingface_hub import snapshot_download
local_dir = snapshot_download(
    repo_id="jesson-hh/financial-analyst-data-full",
    repo_type="dataset",
    local_dir="~/.financial-analyst/data",
)

import qlib
qlib.init(provider_uri="~/.financial-analyst/data/cn_data", region="cn")
from qlib.data import D
df = D.features(["SH600519"], ["$close", "$volume"],
                start_time="2024-01-01", end_time="2026-05-30", freq="day")

Units & conventions

Field Unit Notes
open / high / low / close 元 (CNY) not adjusted (前复权) — adjustment factor not included
volume 手 (= 100 股) Tushare convention; NOT pytdx convention (股)
amount 成交额
pe_ttm / pb / ps_ttm (无单位) TTM ratios
dv_ttm % dividend yield TTM
total_mv / circ_mv 万元 total / circulating market cap
turnover_rate % daily turnover ratio

Data sources & lineage

  • OHLCV + financials: Tushare Pro (pro.daily + daily_basic), HTTP endpoint
  • 5min OHLCV: TDX main sites via pytdx
  • For daily updates (post-download): users run fa data update which pulls from pytdx main sites (free, no token) + Tencent qt.gtimg.cn realtime (free, no cookie). See docs/research/2026-05-23-direct-data-stability.md.

Disclaimer

This dataset is for research and educational purposes only. Data accuracy not guaranteed; do not use for trading without independent verification. See Tushare ToS for redistribution terms.

License

Apache 2.0 (code) — refer to original sources (Tushare / TDX) for data licensing.


Generated by scripts/publish_hf_dataset.py v1.

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