> ## Documentation Index > Fetch the complete documentation index at: https://docs.tqx.ai/llms.txt > Use this file to discover all available pages before exploring further. # Consensus estimates > Retrieve consensus estimates and analyst recommendations. ## Obtain consensus expectations of non-cyclical indicators ### Method name: get\_consensus\_nonperiod ### Enter parameters | Field | Type | Description | Is it required | | :----- | :--------------------------------- | :------------------------------------------- | :------------- | | symbol | Optional\[Union\[str, List\[str]]] | Stock code | Optional | | fields | Optional\[Union\[str, List\[str]]] | Return fields | Optional | | market | str | Market, supports `hk`, `us`, default is `hk` | required | ### Response parameters Note: For parameters other than symbol, currency, and indicator, the default is the prediction of the latest date, and there are historical dimension derived fields. The derived suffix is ​​week, and 1month\~12month constitutes a total of thirteen derived fields for each field, indicating the predicted value of the corresponding field one week, one month to twelve months ago. Example: mean\_week represents the predicted mean one week ago, std\_6month represents the predicted standard deviation six months ago. | Field | Type | Description | | :----------------------- | :----- | :------------------------------------------------------------------------------------------------------------------- | | symbol | str | stock code | | currency | str | currency unit | | indicator | str | Indicator name: LTGROWTH is the long-term growth in the next 3 to 5 years, TP is the target price in the next 1 year | | mean | double | predicted mean | | median | double | predicted median | | high | double | predicted highest value | | low | double | predicted lowest value | | std | double | forecast standard deviation | | estimates\_num | double | forecast number | | included\_estimates\_num | double | Number of forecasts included in statistics | ### Usage examples #### Obtain some consensus expectations of all Hong Kong stock non-cyclical indicators ```python theme={null} import tqx_data result = tqx_data.get_consensus_nonperiod( market='hk', symbol=[""], fields=["indicator","mean","std_6month","high_week"] ) print(result) ``` **Response Example** ```text theme={null} symbol indicator mean std_6month high_week 0 0001.HK LTGROWTH 4.60000 NaN 4.60 1 0001.HK TP 69.43333 1.50000 78.00 2 0002.HK LTGROWTH 2.70000 0.00000 2.70 3 0002.HK TP 77.02000 4.06262 86.64 4 0003.HK LTGROWTH 6.38000 0.00000 7.30 .. ... ... ... ... ... 972 9996.HK TP 9.43200 0.55741 10.66 973 9997.HK LTGROWTH NaN 0.00000 NaN 974 9997.HK TP NaN 0.00000 NaN 975 9999.HK LTGROWTH 4.80000 0.00000 4.80 976 9999.HK TP 235.88571 46.77894 289.00 ``` ## Obtain consensus expectations for buying and selling recommendations ### Method name: get\_consensus\_recommend ### Enter parameters | Field | Type | Description | Is it required | | :----- | :--------------------------------- | :------------------------------------------- | :------------- | | symbol | Optional\[Union\[str, List\[str]]] | Stock code | Optional | | fields | Optional\[Union\[str, List\[str]]] | Return fields | Optional | | market | str | Market, supports `hk`, `us`, default is `hk` | required | ### Response parameters Note 1: For parameters other than symbol and currency, the default is the prediction of the latest date, and there are historical dimension derived fields. The derived suffix is ​​week, and 1month\~12month constitutes a total of thirteen derived fields for each field, indicating the predicted value of the corresponding field one week, one month to twelve months ago. Example: mean\_week represents the expected mean one week ago, and low\_6month represents the expected lowest value 6 months ago. Note 2: Strong sell, sell, hold, buy, and strong buy are scored as 5, 4, 3, 2, and 1 in sequence. | Field | Type | Description | | :------------------- | :----- | :------------------------------------ | | symbol | str | stock code | | currency | str | currency unit | | mean | double | expected mean | | median | double | expected median | | high | double | expected highest value | | low | double | expected lowest value | | strong\_buy\_num | double | Number of strong buy recommendations | | buy\_num | double | Number of buy recommendations | | hold | double | recommended number of positions | | sell\_num | double | Sell recommendation number | | strong\_sell\_num | double | Number of strong sell recommendations | | no\_opinion\_num | double | No opinion number | | recommendations\_num | double | total number of recommendations | ### Usage examples #### Obtain some consensus expectations for all Hong Kong stock trading recommendations ```python theme={null} import tqx_data result = tqx_data.get_consensus_recommend( market='hk', symbol=[""], fields=["strong_buy_num","buy_num_week"] ) print(result) ``` **Response Example** ```text theme={null} symbol strong_buy_num buy_num_week 0 0001.HK 2.0 5.0 1 0002.HK 1.0 2.0 2 0003.HK 1.0 2.0 3 0004.HK 0.0 2.0 4 0005.HK 3.0 6.0 .. ... ... ... 816 9992.HK 12.0 14.0 817 9995.HK 6.0 6.0 818 9996.HK 1.0 3.0 819 9997.HK NaN NaN 820 9999.HK 7.0 12.0 ``` **3. Financial and market factors** **1. Obtain Hong Kong and US stock backtest factors**