## 14 Monetary policy > Interfaces implemented in this chapter are listed below; uncovered indicators are noted under “To be added” at the end. #### LPR tenors Interface: macro_china_lpr Source URL: https://data.eastmoney.com/cjsj/globalRateLPR.html Description: China LPR tenor data; coverage from 19910421 to present Limits: Returns all historical data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |------------|---------|----------------------------------------------------| | TRADE_DATE | object | Date | | LPR1Y | float64 | LPR 1Y rate (%) | | LPR5Y | float64 | LPR 5Y rate (%) | | RATE_1 | float64 | Short-term loan rate: 6 months to 1 year incl. (%) | | RATE_2 | float64 | Medium/long-term loan rate: over 5 years (%) | Example ```python import meshare as ms macro_china_lpr_df = ms.macro_china_lpr() print(macro_china_lpr_df) ``` Sample data ``` TRADE_DATE LPR1Y LPR5Y RATE_1 RATE_2 0 1991-04-21 NaN NaN 8.64 9.72 1 1993-05-15 NaN NaN 9.36 12.24 2 1993-07-11 NaN NaN 10.98 14.04 3 1995-01-01 NaN NaN 10.98 14.76 4 1995-07-01 NaN NaN 12.06 15.30 ... ... ... ... ... 1533 2023-03-20 3.65 4.3 4.35 4.90 1534 2023-04-20 3.65 4.3 4.35 4.90 1535 2023-05-22 3.65 4.3 4.35 4.90 1536 2023-06-20 3.55 4.2 4.35 4.90 1537 2023-07-20 3.55 4.2 4.35 4.90 [1538 rows x 5 columns] ``` #### SHIBOR report Interface: macro_china_shibor_all Source URL: https://datacenter.jin10.com/reportType/dc_shibor Description: Shanghai interbank offered rate (SHIBOR) report; coverage from 20170317 to present Limits: Returns all historical data in a single call Input | Name | Type | Description | |----|----|----| | - | - | - | Output | Name | Type | Description | |------------|---------|-------------| | Date | object | - | | O/N-Fixing | float64 | - | | O/N-Change | float64 | Unit: bp | | 1W-Fixing | float64 | - | | 1W-Change | float64 | Unit: bp | | 2W-Fixing | float64 | - | | 2W-Change | float64 | Unit: bp | | 1M-Fixing | float64 | - | | 1M-Change | float64 | Unit: bp | | 3M-Fixing | float64 | - | | 3M-Change | float64 | Unit: bp | | 6M-Fixing | float64 | - | | 6M-Change | float64 | Unit: bp | | 9M-Fixing | float64 | - | | 9M-Change | float64 | Unit: bp | | 1Y-Fixing | float64 | - | | 1Y-Change | float | Unit: bp | | ON-Fixing | float64 | - | | ON-Change | float64 | Unit: bp | | 2M-Fixing | float64 | - | | 2M-Change | float64 | Unit: bp | Example ```python import meshare as ms macro_china_shibor_all_df = ms.macro_china_shibor_all() print(macro_china_shibor_all_df) ``` Sample data ``` Date O/N-Fixing O/N-Change 1W-Fixing ... 9M-Fixing 9M-Change 1Y-Fixing 1Y-Change 0 2015-05-08 1.4430 NaN 2.3340 ... 4.3960 NaN 4.5000 NaN 1 2017-03-17 2.6330 119.00 2.7250 ... 4.1340 -26.20 4.1246 -37.54 2 2017-03-20 2.6325 -0.05 2.7471 ... 4.1504 1.64 4.1435 1.89 3 2017-03-21 2.6477 1.52 2.7680 ... 4.1621 1.17 4.1559 1.24 4 2017-03-22 2.6507 0.30 2.7910 ... 4.1713 0.92 4.1649 0.90 ... ... ... ... ... ... ... ... ... 1753 2024-03-28 1.7340 -3.60 1.9480 ... 2.2380 0.00 2.2670 0.10 1754 2024-03-29 1.7180 -1.60 1.9670 ... 2.2380 0.00 2.2680 0.10 1755 2024-04-01 1.7280 1.00 1.8570 ... 2.2350 -0.30 2.2650 -0.30 1756 2024-04-02 1.7040 -2.40 1.8330 ... 2.2330 -0.20 2.2620 -0.30 1757 2024-04-03 1.6850 -1.90 1.8210 ... 2.2270 -0.60 2.2560 -0.60 [1758 rows x 17 columns] ``` #### Reserve requirement ratio Interface: macro_china_reserve_requirement_ratio Source URL: https://data.eastmoney.com/cjsj/ckzbj.html Description: China reserve requirement ratio (RRR) Limits: Returns all historical data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |----------------------------------|---------|-------------| | Announcement date | object | e.g. YYYY-M | | Effective date | object | e.g. YYYY-M | | Large institutions-Before | float64 | Unit: % | | Large institutions-After | float64 | Unit: % | | Large institutions-Change | float64 | Unit: % | | Small/medium institutions-Before | float64 | Unit: % | | Small/medium institutions-After | float64 | Unit: % | | Small/medium institutions-Change | float64 | Unit: % | | Next-day index move-SSE | float64 | Unit: % | | Next-day index move-SZSE | float64 | Unit: % | | Remarks | object | - | Example ```python import meshare as ms macro_china_reserve_requirement_ratio_df = ms.macro_china_reserve_requirement_ratio() print(macro_china_reserve_requirement_ratio_df) ``` Sample data ``` Announcement date ... Remarks 0 2022-11-25 ... To keep liquidity reasonably ample, promote a... 1 2022-04-15 ... To support the real economy, promote a stea... 2 2021-12-06 ... To support the real economy, promote a stea... 3 2021-07-09 ... To support the real economy, promote a stea... 4 2020-04-03 ... To support the real economy and MSMEs, lowe... 5 2020-04-03 ... To support the real economy and MSMEs, lowe... 6 2020-01-01 ... To support the real economy and lower financ... 7 2019-09-06 ... To support the real economy and lower financ... 8 2019-01-04 ... To further support the real economy, optimiz... 9 2019-01-04 ... To further support the real economy, optimiz... 10 2018-10-07 ... From 2018-10-15, cut RRR for large/joint-sto... 11 2018-06-24 ... From 2018-07-05, cut RRR for large/joint-sto... 12 2018-04-17 ... From 2018-04-25, cut RRR for large/joint-sto... 13 2016-02-29 ... From 2016-03-01, broadly cut RMB RRR by 0.5... 14 2015-10-23 ... From 2015-10-24, cut RMB RRR by 0.5 pp to ke... 15 2015-08-25 ... From 2015-09-06, extra cut for rural banks t... 16 2015-04-19 ... From Apr 20, extra 1 pp cut for rural FI; un... 17 2015-02-04 ... Also enhance support for MSMEs, agriculture,... 18 2012-05-12 ... None 19 2012-02-18 ... None 20 2011-11-30 ... None 21 2011-06-14 ... None 22 2011-05-12 ... None 23 2011-04-17 ... None 24 2011-03-18 ... None 25 2011-02-18 ... None 26 2011-01-14 ... None 27 2010-12-10 ... None 28 2010-11-19 ... None 29 2010-11-10 ... None 30 2010-05-02 ... From 2010-05-10, raise RRR by 0.5 pp; rural ... 31 2010-02-12 ... From 2010-02-25, raise RRR by 0.5 pp; boost ... 32 2010-01-12 ... From 2010-01-18, raise RRR by 0.5 pp; streng... 33 2008-12-22 ... None 34 2008-11-26 ... None 35 2008-10-08 ... None 36 2008-09-15 ... From 2008-09-25, except ICBC/ABC/BOC/CCB/BoC... 37 2008-06-07 ... None 38 2008-06-07 ... PBOC to raise RRR by 1 pp on 2008-06-15 and ... 39 2008-05-12 ... None 40 2008-04-16 ... None 41 2008-03-18 ... None 42 2008-01-16 ... None 43 2007-12-08 ... None 44 2007-11-10 ... None 45 2007-10-13 ... None 46 2007-09-06 ... None 47 2007-07-30 ... None 48 2007-05-18 ... None 49 2007-04-29 ... None 50 2007-04-05 ... None 51 2007-02-16 ... None 52 2007-01-05 ... None ``` #### Central bank balance sheet Interface: macro_china_central_bank_balance Source URL: http://finance.sina.com.cn/mac/#fininfo-8-0-31-2 Description: Sina Finance — China macro data — central-bank (monetary authority) balance sheet Limits: Returns all historical data in a single call Input | Name | Type | Description | |----|----|----| | - | - | - | Output | Name | Type | Description | |-----------------------------------------------|---------|------------------------| | Period | object | Year-month | | Foreign assets | float64 | Unit: 100 million yuan | | Foreign exchange | float64 | Unit: 100 million yuan | | Monetary gold | float64 | Unit: 100 million yuan | | Other foreign assets | float64 | Unit: 100 million yuan | | Claims on government | float64 | Unit: 100 million yuan | | Of which: central government | float64 | Unit: 100 million yuan | | Claims on other depository corporations | float64 | Unit: 100 million yuan | | Claims on other financial corporations | float64 | Unit: 100 million yuan | | Claims on non-monetary financial institutions | float64 | Unit: 100 million yuan | | Claims on non-financial corporations | float64 | Unit: 100 million yuan | | Other assets | float64 | Unit: 100 million yuan | | Total assets | float64 | Unit: 100 million yuan | | Reserve money | float64 | Unit: 100 million yuan | | Currency issue | float64 | Unit: 100 million yuan | | Deposits of financial corporations | float64 | Unit: 100 million yuan | | Other depository corporations | float64 | Unit: 100 million yuan | | Other financial corporations | float64 | Unit: 100 million yuan | | Liabilities to financial institutions | float64 | Unit: 100 million yuan | | Reserve deposits | float64 | Unit: 100 million yuan | | Deposits of non-financial corporations | float64 | Unit: 100 million yuan | | Demand deposits | float64 | Unit: 100 million yuan | | Bonds | float64 | Unit: 100 million yuan | | Foreign liabilities | float64 | Unit: 100 million yuan | | Government deposits | float64 | Unit: 100 million yuan | | Own capital | float64 | Unit: 100 million yuan | | Other liabilities | float64 | Unit: 100 million yuan | | Total liabilities | float64 | Unit: 100 million yuan | Example ```python import meshare as ms macro_china_central_bank_balance_df = ms.macro_china_central_bank_balance() print(macro_china_central_bank_balance_df) ``` Sample data ``` Period Foreign assets Foreign exchange ... Own capital Other liabilities Total liabilities 0 2023.7 231736.57 217824.72 ... 219.75 6064.21 408091.68 1 2023.6 231574.38 217733.71 ... 219.75 6544.37 418062.84 2 2023.5 231469.31 217822.22 ... 219.75 6674.58 406420.84 3 2023.4 231338.37 217895.06 ... 219.75 6784.49 412654.20 4 2023.3 230941.18 217893.63 ... 219.75 6690.57 421007.27 .. ... ... ... ... ... ... ... 314 1994.3 2437.60 2253.80 ... 280.50 -298.60 NaN 315 1993.12 1549.50 1431.80 ... 310.30 -255.00 NaN 316 1993.9 1451.00 1332.20 ... 310.30 -286.50 NaN 317 1993.6 1286.50 1183.00 ... 310.30 -411.60 NaN 318 1993.3 1324.00 1222.00 ... 310.30 -384.10 NaN [319 rows x 28 columns] ``` ### Interbank offered rates Interface: rate_interbank Source URL: https://data.eastmoney.com/shibor/shibor.aspx?m=sg&t=88&d=99333&cu=sgd&type=009065&p=79 Description: Eastmoney — interbank offered rates by market, tenor, and indicator Limits: Returns all historical data Input | Name | Type | Description | |-----------|------|--------------------------------------------------------------------------------------| | market | str | market="上海银行同业拆借市场"; see **Market–symbol–indicator table** (Chinese API labels) | | symbol | str | symbol="Shibor人民币"; see **Market–symbol–indicator table** | | indicator | str | indicator="隔夜"; see **Market–symbol–indicator table** | Market–symbol–indicator table | market | symbol | indicator | |-------------|-----------|-----------| | 上海银行同业拆借市场 | Shibor人民币 | 隔夜 | | 上海银行同业拆借市场 | Shibor人民币 | 1周 | | 上海银行同业拆借市场 | Shibor人民币 | 2周 | | 上海银行同业拆借市场 | Shibor人民币 | 1月 | | 上海银行同业拆借市场 | Shibor人民币 | 3月 | | 上海银行同业拆借市场 | Shibor人民币 | 6月 | | 上海银行同业拆借市场 | Shibor人民币 | 9月 | | 上海银行同业拆借市场 | Shibor人民币 | 1年 | | 中国银行同业拆借市场 | Chibor人民币 | 隔夜 | | 中国银行同业拆借市场 | Chibor人民币 | 1周 | | 中国银行同业拆借市场 | Chibor人民币 | 2周 | | 中国银行同业拆借市场 | Chibor人民币 | 3周 | | 中国银行同业拆借市场 | Chibor人民币 | 1月 | | 中国银行同业拆借市场 | Chibor人民币 | 2月 | | 中国银行同业拆借市场 | Chibor人民币 | 3月 | | 中国银行同业拆借市场 | Chibor人民币 | 4月 | | 中国银行同业拆借市场 | Chibor人民币 | 6月 | | 中国银行同业拆借市场 | Chibor人民币 | 9月 | | 中国银行同业拆借市场 | Chibor人民币 | 1年 | | 伦敦银行同业拆借市场 | Libor英镑 | 隔夜 | | 伦敦银行同业拆借市场 | Libor英镑 | 1周 | | 伦敦银行同业拆借市场 | Libor英镑 | 1月 | | 伦敦银行同业拆借市场 | Libor英镑 | 2月 | | 伦敦银行同业拆借市场 | Libor英镑 | 3月 | | 伦敦银行同业拆借市场 | Libor英镑 | 8月 | | 伦敦银行同业拆借市场 | Libor美元 | 隔夜 | | 伦敦银行同业拆借市场 | Libor美元 | 1周 | | 伦敦银行同业拆借市场 | Libor美元 | 1月 | | 伦敦银行同业拆借市场 | Libor美元 | 2月 | | 伦敦银行同业拆借市场 | Libor美元 | 3月 | | 伦敦银行同业拆借市场 | Libor美元 | 8月 | | 伦敦银行同业拆借市场 | Libor欧元 | 隔夜 | | 伦敦银行同业拆借市场 | Libor欧元 | 1周 | | 伦敦银行同业拆借市场 | Libor欧元 | 1月 | | 伦敦银行同业拆借市场 | Libor欧元 | 2月 | | 伦敦银行同业拆借市场 | Libor欧元 | 3月 | | 伦敦银行同业拆借市场 | Libor欧元 | 8月 | | 伦敦银行同业拆借市场 | Libor日元 | 隔夜 | | 伦敦银行同业拆借市场 | Libor日元 | 1周 | | 伦敦银行同业拆借市场 | Libor日元 | 1月 | | 伦敦银行同业拆借市场 | Libor日元 | 2月 | | 伦敦银行同业拆借市场 | Libor日元 | 3月 | | 伦敦银行同业拆借市场 | Libor日元 | 8月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 1周 | | 欧洲银行同业拆借市场 | Euribor欧元 | 2周 | | 欧洲银行同业拆借市场 | Euribor欧元 | 3周 | | 欧洲银行同业拆借市场 | Euribor欧元 | 1月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 2月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 3月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 4月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 5月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 6月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 7月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 8月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 9月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 10月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 11月 | | 欧洲银行同业拆借市场 | Euribor欧元 | 1年 | | 香港银行同业拆借市场 | Hibor港币 | 隔夜 | | 香港银行同业拆借市场 | Hibor港币 | 1周 | | 香港银行同业拆借市场 | Hibor港币 | 2周 | | 香港银行同业拆借市场 | Hibor港币 | 1月 | | 香港银行同业拆借市场 | Hibor港币 | 2月 | | 香港银行同业拆借市场 | Hibor港币 | 3月 | | 香港银行同业拆借市场 | Hibor港币 | 4月 | | 香港银行同业拆借市场 | Hibor港币 | 5月 | | 香港银行同业拆借市场 | Hibor港币 | 6月 | | 香港银行同业拆借市场 | Hibor港币 | 7月 | | 香港银行同业拆借市场 | Hibor港币 | 8月 | | 香港银行同业拆借市场 | Hibor港币 | 9月 | | 香港银行同业拆借市场 | Hibor港币 | 10月 | | 香港银行同业拆借市场 | Hibor港币 | 11月 | | 香港银行同业拆借市场 | Hibor港币 | 1年 | | 香港银行同业拆借市场 | Hibor美元 | 隔夜 | | 香港银行同业拆借市场 | Hibor美元 | 1周 | | 香港银行同业拆借市场 | Hibor美元 | 2周 | | 香港银行同业拆借市场 | Hibor美元 | 1月 | | 香港银行同业拆借市场 | Hibor美元 | 2月 | | 香港银行同业拆借市场 | Hibor美元 | 3月 | | 香港银行同业拆借市场 | Hibor美元 | 4月 | | 香港银行同业拆借市场 | Hibor美元 | 5月 | | 香港银行同业拆借市场 | Hibor美元 | 6月 | | 香港银行同业拆借市场 | Hibor美元 | 7月 | | 香港银行同业拆借市场 | Hibor美元 | 8月 | | 香港银行同业拆借市场 | Hibor美元 | 9月 | | 香港银行同业拆借市场 | Hibor美元 | 10月 | | 香港银行同业拆借市场 | Hibor美元 | 11月 | | 香港银行同业拆借市场 | Hibor美元 | 1年 | | 香港银行同业拆借市场 | Hibor人民币 | 隔夜 | | 香港银行同业拆借市场 | Hibor人民币 | 1周 | | 香港银行同业拆借市场 | Hibor人民币 | 2周 | | 香港银行同业拆借市场 | Hibor人民币 | 1月 | | 香港银行同业拆借市场 | Hibor人民币 | 2月 | | 香港银行同业拆借市场 | Hibor人民币 | 3月 | | 香港银行同业拆借市场 | Hibor人民币 | 6月 | | 香港银行同业拆借市场 | Hibor人民币 | 1年 | | 新加坡银行同业拆借市场 | Sibor星元 | 1月 | | 新加坡银行同业拆借市场 | Sibor星元 | 2月 | | 新加坡银行同业拆借市场 | Sibor星元 | 3月 | | 新加坡银行同业拆借市场 | Sibor星元 | 6月 | | 新加坡银行同业拆借市场 | Sibor星元 | 9月 | | 新加坡银行同业拆借市场 | Sibor星元 | 1年 | | 新加坡银行同业拆借市场 | Sibor美元 | 1月 | | 新加坡银行同业拆借市场 | Sibor美元 | 2月 | | 新加坡银行同业拆借市场 | Sibor美元 | 3月 | | 新加坡银行同业拆借市场 | Sibor美元 | 6月 | | 新加坡银行同业拆借市场 | Sibor美元 | 9月 | | 新加坡银行同业拆借市场 | Sibor美元 | 1年 | Output | Name | Type | Description | |--------|---------|-------------| | Date | object | - | | Rate | float64 | Unit: % | | Change | float64 | Unit: bp | Example ```python import meshare as ms rate_interbank_df = ms.rate_interbank(market="上海银行同业拆借市场", symbol="Shibor人民币", indicator="3月") print(rate_interbank_df) ``` Sample data ``` Report date Rate Change 0 2006-10-08 2.6110 0.00 1 2006-10-09 2.6248 1.38 2 2006-10-10 2.6325 0.77 3 2006-10-11 2.6338 0.13 4 2006-10-12 2.6380 0.42 ... ... ... ... 4391 2024-05-07 1.9960 -0.50 4392 2024-05-08 1.9900 -0.60 4393 2024-05-09 1.9910 0.10 4394 2024-05-10 1.9890 -0.20 4395 2024-05-11 1.9880 -0.10 [4396 rows x 3 columns] ``` ### Repo fixing rate — history Interface: repo_rate_hist Source URL: https://www.chinamoney.com.cn/chinese/bkfrr/ Description: Repo fixing rate history Limits: Returns all historical data for the specified date range (must be within one year) in a single call Input | Name | Type | Description | |------------|------|---------------------------------------------------------------------| | start_date | str | start_date="20200930"; start and end must fall within the same year | | end_date | str | end_date="20201029"; start and end must fall within the same year | Output | Name | Type | Description | |--------|---------|-------------| | date | object | - | | FR001 | float64 | Unit: % | | FR007 | float64 | Unit: % | | FR014 | float64 | Unit: % | | FDR001 | float64 | Unit: % | | FDR007 | float64 | Unit: % | | FDR014 | float64 | Unit: % | Example ```python import meshare as ms repo_rate_hist_df = ms.repo_rate_hist(start_date="20231001", end_date="20240101") print(repo_rate_hist_df) ``` Sample data ``` date FR001 FR007 FR014 FDR001 FDR007 FDR014 0 2023-10-07 1.7000 1.8500 1.82 1.7000 1.8224 1.82 1 2023-10-08 1.5600 1.8000 1.78 1.5300 1.7500 1.75 2 2023-10-09 1.8100 2.0100 1.85 1.7400 1.8000 1.80 3 2023-10-10 1.9300 2.0500 2.22 1.8400 1.8600 1.90 4 2023-10-11 1.9600 2.1400 2.20 1.8700 1.9200 2.00 .. ... ... ... ... ... ... ... 57 2023-12-25 1.6210 1.8117 3.90 1.5900 1.8000 3.15 58 2023-12-26 1.5500 4.3500 3.80 1.4969 1.8305 3.10 59 2023-12-27 1.5100 4.0000 3.10 1.4400 1.8728 3.05 60 2023-12-28 1.5000 3.2000 3.00 1.3900 1.9572 2.95 61 2023-12-29 1.9149 2.4000 2.45 1.7400 2.3000 2.40 [62 rows x 7 columns] ``` ### Repo fixing rate — recent Interface: repo_rate_query Source URL: https://www.chinamoney.com.cn/chinese/bkfrr/ Description: Repo fixing rate (recent) Limits: Returns recent data for the specified symbol in a single call Input | Name | Type | Description | |--------|------|--------------------------------------------------------------------------------------| | symbol | str | symbol="回购定盘利率"; choice of {"回购定盘利率", "银银间回购定盘利率"} (Chinese API labels) | Output | Name | Type | Description | |-------|---------|-------------| | date | object | - | | FR001 | float64 | Unit: % | | FR007 | float64 | Unit: % | | FR014 | float64 | Unit: % | Example ```python import meshare as ms repo_rate_query_df = ms.repo_rate_query(symbol="回购定盘利率") print(repo_rate_query_df) ``` Sample data ``` date FR001 FR007 FR014 0 2021-01-19 2.3000 2.46 2.55 1 2021-01-20 2.5000 2.65 2.80 2 2021-01-21 2.6500 2.60 2.70 3 2021-01-22 2.5500 2.40 2.70 4 2021-01-25 2.5000 2.50 2.78 .. ... ... ... ... 746 2024-01-15 1.8300 2.25 2.20 747 2024-01-16 1.9528 2.25 2.38 748 2024-01-17 2.0107 2.30 2.55 749 2024-01-18 1.8800 2.20 2.30 750 2024-01-19 1.8600 2.15 2.40 [751 rows x 4 columns] ``` #### ChinaBond yield curves Interface: bond_china_yield Source URL: https://yield.chinabond.com.cn/cbweb-pbc-web/pbc/historyQuery?startDate=2019-02-07&endDate=2020-02-04&gjqx=0&qxId=ycqx&locale=cn_ZH Description: ChinaBond — Treasury and other bond yield curves Limits: Returns all data for start_date to end_date in a single call; the span must be less than one year Input | Name | Type | Description | |------------|------|------------------------------------------------------------------------------| | start_date | str | start_date="20190204"; start date; span to end_date must be less than 1 year | | end_date | str | end_date="20200204"; end date; span from start_date must be less than 1 year | Output | Name | Type | Description | |------------|---------|-------------| | Curve name | object | | | Date | object | | | 3M | float64 | | | 6M | float64 | | | 1Y | float64 | | | 3Y | float64 | | | 5Y | float64 | | | 7Y | float64 | | | 10Y | float64 | | | 30Y | float64 | | Example ```python import meshare as ms bond_china_yield_df = ms.bond_china_yield(start_date="20210201", end_date="20220201") print(bond_china_yield_df) ``` Sample data ``` Curve name Date 3M ... 7Y 10Y 30Y 0 ChinaBond MTN yield (AAA) 2021-02-01 3.2000 ... 3.9929 4.1465 NaN 1 ChinaBond bank senior (AAA) 2021-02-01 3.1085 ... 3.8172 3.9554 4.5813 2 ChinaBond Treasury 2021-02-01 2.4501 ... 3.1725 3.1712 3.7342 3 ChinaBond Treasury 2021-02-02 2.4001 ... 3.1829 3.1862 3.7415 4 ChinaBond MTN yield (AAA) 2021-02-02 3.0242 ... 3.9707 4.1243 NaN .. ... ... ... ... ... ... ... 748 ChinaBond bank senior (AAA) 2022-01-29 2.3008 ... 3.3258 3.4677 3.8345 749 ChinaBond Treasury 2022-01-29 1.7876 ... 2.6521 2.7013 3.2750 750 ChinaBond MTN yield (AAA) 2022-01-30 2.4486 ... 3.4477 3.6091 NaN 751 ChinaBond bank senior (AAA) 2022-01-30 2.2908 ... 3.3122 3.4541 3.8209 752 ChinaBond Treasury 2022-01-30 1.7756 ... 2.6555 2.6997 3.2718 [753 rows x 10 columns] ``` #### FR007 interest-rate swap curve history Interface: macro_china_swap_rate Source URL: https://www.chinamoney.com.cn/chinese/bkcurvfxhis/?cfgItemType=72&curveType=FR007 Description: FR007 interest-rate swap curve history (ChinaMoney) Limits: Returns historical data in a single call; only about the latest one year is available, and each call covers at most one month Input | Name | Type | Description | |------------|------|------------------------------------------| | start_date | str | start_date="20231128"; mind the interval | | end_date | str | end_date="20231130" | Output | Name | Type | Description | |------------|---------|-------------| | Date | object | - | | Curve name | object | - | | Time | object | - | | Price type | object | - | | 1M | float64 | - | | 3M | float64 | - | | 6M | float64 | - | | 9M | float64 | - | | 1Y | float64 | - | | 2Y | float64 | - | | 3Y | float64 | - | | 4Y | float64 | - | | 5Y | float64 | - | | 7Y | float64 | - | | 10Y | float64 | - | Example ```python import meshare as ms macro_china_swap_rate_df = ms.macro_china_swap_rate(start_date="20240501", end_date="20240531") print(macro_china_swap_rate_df) ``` Sample data ``` Date Curve name Time Price type ... 4Y 5Y 7Y 10Y 0 2024-05-06 FR007 IRS market curve 9:30 Offer ... 2.1375 2.1652 2.5000 2.5000 1 2024-05-06 FR007 IRS market curve 13:30 Bid ... 2.0750 2.1350 2.1650 2.2650 2 2024-05-06 FR007 IRS market curve 14:00 Offer ... 2.0944 2.1543 2.2512 2.3263 3 2024-05-06 FR007 IRS market curve 14:00 Mean ... 2.0864 2.1530 2.2369 2.3106 4 2024-05-06 FR007 IRS market curve 14:00 Bid ... 2.0784 2.1518 2.2225 2.2950 .. ... ... ... ... ... ... ... ... ... 940 2024-05-31 FR007 IRS market curve 12:30 Offer ... 2.0000 2.0650 2.2450 2.3350 941 2024-05-31 FR007 IRS market curve 12:30 Mean ... 1.9800 2.0450 2.1450 2.2350 942 2024-05-31 FR007 IRS market curve 12:30 Bid ... 1.9600 2.0250 2.0450 2.1350 943 2024-05-31 FR007 IRS market curve 11:00 Mean ... 1.9775 2.0476 2.1721 2.2687 944 2024-05-31 FR007 IRS close curve 16:30 Bid ... 1.9853 2.0569 2.1525 2.2425 [945 rows x 15 columns] ``` #### New bond issuance Interface: macro_china_bond_public Source URL: https://www.chinamoney.com.cn/chinese/xzjfx/ Description: CFETS / National Interbank Funding Center — bond disclosure — new bond issuance; recent issuance data Limits: Returns all available data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |--------------------|---------|------------------------| | Bond full name | object | - | | Bond type | object | - | | Issue date | object | - | | Interest method | object | - | | Price | float64 | Unit: yuan | | Tenor | object | - | | Planned issue size | float64 | Unit: 100 million yuan | | Bond rating | object | - | Example ```python import meshare as ms macro_china_bond_public_df = ms.macro_china_bond_public() print(macro_china_bond_public_df) ``` Sample data ``` Bond full name Bond type Issue date ... Tenor Planned issue size Rating 0 Jiangsu Financial Leasing 2024 green financial bond (1st) Financial 01-16 ... 3Y 15.0 AAA 1 Hangyin Consumer Finance 2024 financial bond (1st) Financial 01-12 ... 3Y 10.0 AAA 2 2024 book-entry coupon Treasury (1st) Treasury 01-12 ... 5Y 990.0 None 3 Hunan Bank 2024 No.006 NCD NCD 01-12 ... 6M 11.0 None 4 Beijing Rural Commercial Bank 2024 No.012 NCD NCD 01-12 ... 6M 19.0 None .. ... ... ... ... ... ... ... 376 Jiangxi State Capital 2024 1st SCP SCP 01-10 ... 180D 30.0 None 377 CSCEC 8th Engineering 2024 2nd SCP (tech note) SCP 01-10 ... 44D 30.0 None 378 Xiamen ITG 2024 2nd SCP SCP 01-10 ... 120D 20.0 None 379 Guangdong Environmental Protection 2024 1st SCP SCP 01-10 ... 260D 5.0 None 380 Taizhou Luqiao Public Assets 2024 1st SCP SCP 01-10 ... 252D 6.3 None [381 rows x 8 columns] ``` #### PBOC policy-rate decisions Interface: macro_bank_china_interest_rate Source URL: https://datacenter.jin10.com/reportType/dc_china_interest_rate_decision Description: China central-bank interest-rate decision report; coverage from 19910105 to present Limits: Returns all historical data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |----------|---------|-------------| | Item | object | - | | Date | object | - | | Actual | float64 | Unit: % | | Forecast | float64 | Unit: % | | Previous | float64 | Unit: % | Example ```python import meshare as ms macro_bank_china_interest_rate_df = ms.macro_bank_china_interest_rate() print(macro_bank_china_interest_rate_df) ``` Sample data ``` Item Date Actual Forecast Previous 0 China PBOC decision 1991-05-01 8.64 NaN NaN 1 China PBOC decision 1991-06-01 8.64 NaN 8.64 2 China PBOC decision 1991-07-01 8.64 NaN 8.64 3 China PBOC decision 1991-08-01 8.64 NaN 8.64 4 China PBOC decision 1991-09-01 8.64 NaN 8.64 .. ... ... ... ... ... 213 China PBOC decision 2015-08-25 4.60 NaN 4.85 214 China PBOC decision 2015-10-23 4.35 NaN 4.60 215 China PBOC decision 2019-09-20 4.20 NaN 4.25 216 China PBOC decision 2019-10-21 4.15 NaN 4.20 217 China PBOC decision 2019-11-20 4.15 4.2 4.20 [218 rows x 5 columns] ``` #### Spot bond market-maker quotes Interface: bond_spot_quote Source URL: https://www.chinamoney.com.cn/chinese/mkdatabond/ Description: CFETS / National Interbank Funding Center — market data — bond market — spot market-maker quotes Limits: Returns all available data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |-----------------|---------|-------------| | Quoting firm | object | | | Bond short name | object | | | Bid clean | float64 | Unit: yuan | | Ask clean | float64 | Unit: yuan | | Bid yield | float64 | Unit: % | | Ask yield | float64 | Unit: % | Example ```python import meshare as ms bond_spot_quote_df = ms.bond_spot_quote() print(bond_spot_quote_df) ``` Sample data ``` Quoting firm Bond short name Bid clean Ask clean Bid yield Ask yield 0 DBS Bank (China) 21EXIM10 100.34 102.44 3.335 3.0750 1 DBS Bank (China) 20ADBC08 101.88 102.87 2.882 2.5901 2 DBS Bank (China) 20ADBC07 100.57 101.26 2.640 2.1401 3 DBS Bank (China) 20ADBC02 99.79 100.03 2.400 2.1701 4 DBS Bank (China) 20EXIM13 101.00 101.71 2.641 2.1605 5 DBS Bank (China) 20EXIM05 100.40 101.02 2.790 2.5700 6 Ping An Securities 22EXIM05 98.38 99.40 2.970 2.7409 7 Ping An Securities 22CDB01 99.85 99.95 2.265 2.1401 8 Ping An Securities 20ADBC08 101.88 102.90 2.882 2.5820 9 Ping An Securities 20ADBC02 99.82 100.00 2.370 2.2000 10 Ping An Securities 20EXIM05 100.48 101.02 2.760 2.5700 11 Ping An Securities 20CDB12 101.63 102.27 2.820 2.6200 12 Ping An Securities 20CDB08 100.21 100.85 2.820 2.6150 13 Ping An Securities 20CDB07 100.71 100.95 2.479 2.3074 14 Ping An Securities 18EXIM03 102.35 102.52 2.295 2.1000 ``` ### China–US Treasury yields Interface: bond_zh_us_rate Source URL: https://data.eastmoney.com/cjsj/zmgzsyl.html Description: Eastmoney — economic data — China–US Treasury yield history Limits: Returns all trading-day data from start_date onward; series starts 19901219 Input | Name | Type | Description | |------------|------|-----------------------| | start_date | str | start_date="19901219" | Output | Name | Type | Description | |-------------------------|---------|-------------| | Date | object | - | | China Treasury 2Y | float64 | - | | China Treasury 5Y | float64 | - | | China Treasury 10Y | float64 | - | | China Treasury 30Y | float64 | - | | China Treasury 10Y–2Y | float64 | - | | China GDP annual growth | float64 | - | | US Treasury 2Y | float64 | - | | US Treasury 5Y | float64 | - | | US Treasury 10Y | float64 | - | | US Treasury 30Y | float64 | - | | US Treasury 10Y–2Y | float64 | - | | US GDP annual growth | float64 | - | Example ```python import meshare as ms bond_zh_us_rate_df = ms.bond_zh_us_rate(start_date="19901219") print(bond_zh_us_rate_df) ``` Sample data ``` Date China Treasury 2Y China Treasury 5Y ... US Treasury 30Y US Treasury 10Y–2Y US GDP annual growth 0 1990-12-19 NaN NaN ... 8.19 0.79 NaN 1 1990-12-20 NaN NaN ... 8.22 0.80 NaN 2 1990-12-21 NaN NaN ... 8.28 0.81 NaN 3 1990-12-24 NaN NaN ... 8.36 0.85 NaN 4 1990-12-26 NaN NaN ... 8.30 0.88 NaN ... ... ... ... ... ... ... 8595 2023-11-27 2.4719 2.6012 ... 4.53 -0.45 NaN 8596 2023-11-28 2.4369 2.5762 ... 4.52 -0.39 NaN 8597 2023-11-29 2.4460 2.5840 ... 4.44 -0.37 NaN 8598 2023-11-30 2.4259 2.5691 ... 4.54 -0.36 NaN 8599 2023-12-01 2.4169 2.5665 ... 4.40 -0.34 NaN [8600 rows x 13 columns] ```