Technical Factors Pro

Professional-grade daily technical factors, a superset of stk_factor with 260+ columns, one row per stock per trading day. It spans a rich indicator set (MA/EMA, RSI, MACD, KDJ, BOLL, BBI, DMI, OBV, MFI, and more) and ships three parallel price series: unadjusted, forward-adjusted, and back-adjusted. It suits large-scale factor mining and multi-horizon signal backtesting in quant research, sparing you a custom indicator pipeline. Note the very high column count, so select only the fields you need to control transfer and compute cost, and forward-adjusted columns are recomputed retroactively after corporate actions.

GET /v2/market/technical-factors-pro

Parameters

Name Type Required Description
symbol string No Stock code, e.g. 000001.SZ
start_date string No Start date (YYYYMMDD)
end_date string No End date (YYYYMMDD)
trade_date string No Trading date YYYYMMDD (single day)

Key Response Fields

Field Description
close_qfq / close_hfq Forward / backward adjusted close price
ma_qfq_5 / ma_qfq_10 / ma_qfq_20 Simple moving averages (forward-adjusted)
ema_qfq_5 / ema_qfq_12 / ema_qfq_26 Exponential moving averages (forward-adjusted)
macd_dif_qfq, macd_dea_qfq, macd_qfq MACD (forward-adjusted)
rsi_qfq_6 / rsi_qfq_12 / rsi_qfq_24 RSI (forward-adjusted)
_bfq / _qfq / _hfq Each indicator in 3 variants — unadjusted / forward / backward

API Example

cURL
curl -H "X-API-Key: YOUR_KEY" \
  "https://asharehub.com/v2/market/technical-factors-pro?symbol=000001.SZ"
Python SDK
from asharehub import AShareHub

client = AShareHub(api_key="YOUR_KEY")
df = client.technical_factors_pro(symbol="000001.SZ")
print(df.head())

Sample Data

returns a pandas.DataFrame
symboltrade_dateopenopen_hfqopen_qfqhighhigh_hfqhigh_qfqlowlow_hfq… +251
000001.SZ2026062610.421448.463410.4210.471455.413810.4710.191416.4915
000001.SZ2026062510.471455.413810.4710.591472.094710.5910.411447.0733
000001.SZ2026062410.681484.605410.6810.781498.506210.7810.511460.9741