Datasets:
File size: 2,291 Bytes
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license: other
task_categories:
- time-series-forecasting
tags:
- finance
- a-share
- qlib
- investment-data
pretty_name: A-share qlib context data for 600809.SH
---
# A-share qlib Context Data for 600809.SH
This dataset is prepared from qlib-compatible data, especially the community data source `chenditc/investment_data` recommended by qlib while the official CN dataset is unavailable.
Source reference: https://github.com/chenditc/investment_data
## Coverage
- Stock: `600809.SH`
- Benchmark: `000300.SH`
- Date range exported: `20250102` to `20260424`
## Tables
- `data/qlib_daily`: raw daily qlib fields available for the stock, such as open, high, low, close, volume, amount, factor.
- `data/qlib_factors_daily`: derived daily context features computed from historical data only.
- `data/benchmark_daily`: raw daily qlib fields for the benchmark, when provided.
- `data/outcome_targets`: automatically computed future return and risk targets. These are outcome targets, not labels for main-force orders.
## Price Adjustment and MA Check
For the `chenditc/investment_data` qlib provider, qlib `open/high/low/close` are modeling-oriented normalized feature prices. They should be kept with `factor`, and they should not be described as standard broker front-adjusted or back-adjusted prices without validation. This exporter keeps both concepts explicit:
- `feature_close = close`
- `display_close = close / factor`
- `ma5_display_close = rolling_mean(display_close, 5)`
Sanity check from the local qlib data: for 山西汾酒 `600809.SH` on `2026-04-22`, the five display closes ending that day are approximately `141.16, 139.50, 138.87, 136.86, 136.91`, so `ma5_display_close = 138.66`. This matches the broker-style MA5 quoted by the user. Use qlib `close`/`feature_close` for modeling features, and use `display_*` fields when comparing to broker screens or business-facing price displays.
## Leakage Policy
`qlib_factors_daily` only uses same-day and historical data. `outcome_targets` uses future prices and must only be used as supervised targets or evaluation fields.
There are no ground-truth labels for "main force orders" here. TWAP/VWAP/iceberg/protection patterns should be engineered from Level-2 data separately and joined by `ts_code` + `trade_date`.
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