القائمة
Use when tasks need PandaData/PandaAI market data, reference data, adjustment factors, futures tick downloads, or symbol conversion.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | ingest |
| description | Use when tasks need PandaData/PandaAI market data, reference data, adjustment factors, futures tick downloads, or symbol conversion. |
Use this skill when a task needs market bars, reference data, adjustment factors, or futures tick data from the PandaData SDK.
uv sync --extra panda_data.PANDA_DATA_USERNAME and PANDA_DATA_PASSWORD in the environment.PandaDataClient fetches data only. Persist normalized OHLCV with
skills.store.data_manager.DataManager.from skills.ingest import PandaDataClient
from skills.ingest import to_panda_data_symbol, to_quantspace_symbol
PandaDataClient accepts QuantSpace symbols such as SHSE.510300 and
panda_data native symbols such as 510300.SH. Returned symbol columns are
converted back to QuantSpace format by default.
Bars
fetch_market_data(symbol, start_date, end_date, type="stock")fetch_market_min_data(symbol, start_date, end_date, symbol_type="stock", frequency="1m")fetch_hk_daily(symbol, start_date, end_date)fetch_us_daily(symbol, start_date, end_date)Reference
get_stock_detailget_index_detailget_index_indicatorget_index_weightsget_industry_detailget_industry_constituentsget_stock_industryget_concept_listget_concept_constituentsget_adj_factorFutures Tick Utility
skills.ingest.panda_future_tick contains offline-testable helpers and CLI
building blocks for PandaData futures tick downloads.
Detailed PandaAI docs are split by task under references/. Open only the
specific file needed for the endpoint you are using.
| Reference | Open when you need | Main methods |
|---|---|---|
pandaai-01-overview-setup.md | setup and auth | init_token |
pandaai-02-market-daily.md | A-share/index/futures daily bars | get_market_data |
pandaai-03-market-minute.md | A-share/index/futures intraday bars | get_market_min_data |
pandaai-04-market-hk-us.md | HK/US daily bars | get_hk_daily, get_us_daily |
pandaai-05-reference-securities.md | stock/index metadata | get_stock_detail, get_index_detail |
pandaai-06-reference-classification-index.md | classifications and index weights | get_index_weights |
pandaai-07-equity-market-events.md | market events | get_lhb_list, get_margin |
pandaai-08-equity-corporate-info.md | holders and corporate info | get_top_holders |
pandaai-09-financial-reports.md | financial reports | get_fina_reports |
pandaai-10-factors-adjustment.md | factors and adjustment events | get_factor, get_adj_factor |
pandaai-11-trading-tools.md | calendars and trade lists | get_trade_cal, get_trade_list |
pandaai-12-futures.md | futures metadata and dominant contracts | get_future_detail |
Daily A-share bars
from skills.ingest import PandaDataClient
client = PandaDataClient()
df = client.fetch_market_data("SHSE.600000", "20230101", "20231231", type="stock")
Normalize and save bars
import pandas as pd
from skills.ingest import PandaDataClient
from skills.store.data_manager import DataManager
client = PandaDataClient()
raw = client.fetch_market_data("SHSE.600000", "20230101", "20231231", type="stock")
bars = raw.copy()
bars["eob"] = pd.to_datetime(bars["date"])
bars = bars.set_index("eob")[["open", "high", "low", "close", "volume"]].sort_index()
DataManager().save_symbol("SHSE.600000", bars, frequency="1d", source="panda_data")
Symbol conversion
from skills.ingest import to_panda_data_symbol, to_quantspace_symbol
assert to_panda_data_symbol("SHSE.510300") == "510300.SH"
assert to_quantspace_symbol("RB_DOMINANT.SHF") == "SHFE.RB99"
Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, or time-series distribution and stationarity checks.
Use when tasks need vectorized strategy execution, portfolio weighting, portfolio-level filters, transaction cost helpers, exit A/B analysis, overlay metrics, or multi-strategy return blending.
Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers.
Use when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.
Use when tasks need HTML reports, Markdown strategy reports, PNG chart helpers, or report files under the research reports directory.
Use when tasks need reusable research pipeline templates, factor screening, parameter sensitivity sweeps, or strategy comparison.