10 Financial Data Analysis
Interfaces implemented in this chapter are listed below; uncovered indicators are noted under “To be added” at the end.
Aggregate financing to the real economy (AFRE) incremental statistics
Interface: macro_china_shrzgm
Source URL: http://data.mofcom.gov.cn/gnmy/shrzgm.shtml
Description: MOFCOM Data Center — domestic trade — AFRE incremental statistics; from 201501 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | Year-month |
| AFRE increment | float64 | Unit: 100 million yuan |
| Of which-RMB loans | float64 | Unit: 100 million yuan |
| Of which-entrusted & FX loans | float64 | Unit: RMB equivalent, 100 million yuan |
| Of which-entrusted loans | float64 | Unit: 100 million yuan |
| Of which-trust loans | float64 | Unit: 100 million yuan |
| Of which-undiscounted bankers’ acceptances | float64 | Unit: 100 million yuan |
| Of which-corporate bonds | float64 | Unit: 100 million yuan |
| Of which-onshore equity financing by non-financial enterprises | float64 | Unit: 100 million yuan |
Example
import meshare as ms
macro_china_shrzgm_df = ms.macro_china_shrzgm()
print(macro_china_shrzgm_df)
Sample data
Month AFRE flow Of which-RMB loans ... Of which-Undiscounted bankers’ acceptances Of which-Corporate bonds Of which-Domestic equity financing by non-financial enterprises
0 201501 20516 14708 ... 1946 1868 526.0
1 201502 13609 11437 ... -592 716 542.0
2 201503 12433 9920 ... -910 1344 639.0
3 201504 10582 8045 ... -74 1616 597.0
4 201505 12397 8510 ... 961 1710 584.0
.. ... ... ... ... ... ... ...
82 202111 25983 13021 ... -383 4006 1294.0
83 202112 23682 10350 ... -1419 2167 2075.0
84 202201 61750 41988 ... 4733 5829 1439.0
85 202202 11928 9084 ... -4228 3377 585.0
86 202203 46531 32291 ... 287 3573 958.0
New credit data
Interface: macro_china_new_financial_credit
Source URL: http://data.eastmoney.com/cjsj/xzxd.html
Description: China new credit data; monthly from 200801 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | - |
| Current month | float64 | Unit: 100 million yuan |
| Current month-YoY | float64 | Unit: % |
| Current month-MoM | float64 | Unit: % |
| Cumulative | float64 | Unit: 100 million yuan |
| Cumulative-YoY | float64 | Unit: % |
Example
import meshare as ms
macro_china_new_financial_credit_df = ms.macro_china_new_financial_credit()
print(macro_china_new_financial_credit_df)
Sample data
Month Current month Current month-YoY Current month-MoM Cumulative Cumulative-YoY
0 Oct 2022 4431.0 -42.840557 -82.749358 183298.0 4.127067
1 Sep 2022 25686.0 44.669107 92.491007 178867.0 6.290669
2 Aug 2022 13344.0 4.963423 226.418787 153181.0 1.763815
3 Jul 2022 4088.0 -51.281135 -86.614276 139837.0 1.468657
4 Jun 2022 30540.0 31.740143 67.526056 135749.0 4.888659
.. ... ... ... ... ... ...
173 May 2008 3185.0 28.790942 -32.089552 21201.0 1.386830
174 Apr 2008 4690.0 11.137441 65.490473 18016.0 -2.288751
175 Mar 2008 2834.0 -35.838805 16.433854 13326.0 -6.273738
176 Feb 2008 2434.0 -41.179314 -69.793994 10492.0 7.050301
177 Jan 2008 8058.0 42.292071 1561.443299 8058.0 42.292071
China money supply
Interface: macro_china_money_supply
Source URL: http://data.eastmoney.com/cjsj/hbgyl.html
Description: East Money — economic data — China macro — China money supply; monthly from 200801 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | Year and month |
| Money & quasi-money (M2)-Amount (100 million yuan) | float64 | - |
| Money & quasi-money (M2)-YoY | float64 | - |
| Money & quasi-money (M2)-MoM | float64 | - |
| Money (M1)-Amount (100 million yuan) | float64 | - |
| Money (M1)-YoY | float64 | - |
| Money (M1)-MoM | float64 | - |
| Currency in circulation (M0)-Amount (100 million yuan) | float64 | - |
| Currency in circulation (M0)-YoY | float64 | - |
| Currency in circulation (M0)-MoM | float64 | - |
Example
import meshare as ms
macro_china_money_supply_df = ms.macro_china_money_supply()
print(macro_china_money_supply_df)
Sample data
Month Money & quasi-money (M2)-Amount (100 million yuan) ... Currency in circulation (M0)-YoY Currency in circulation (M0)-MoM
0 Oct 2022 2612914.57 ... 14.30 -0.258787
1 Sep 2022 2626600.92 ... 13.60 1.482068
2 Aug 2022 2595068.27 ... 14.30 0.747950
3 Jul 2022 2578078.57 ... 13.90 0.518710
4 Jun 2022 2581451.20 ... 13.80 0.485950
.. ... ... ... ... ...
173 May 2008 436221.60 ... 12.88 -2.014673
174 Apr 2008 429313.72 ... 10.70 1.171555
175 Mar 2008 423054.53 ... 11.12 -6.228418
176 Feb 2008 421037.84 ... 5.96 -11.503457
177 Jan 2008 417846.17 ... 31.21 20.896562
M2 money supply YoY
Interface: macro_china_m2_yearly
Source URL: https://datacenter.jin10.com/reportType/dc_chinese_m2_money_supply_yoy
Description: China annual M2 data; from 19980201 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
import meshare as ms
macro_china_m2_yearly_df = ms.macro_china_m2_yearly()
print(macro_china_m2_yearly_df)
Sample data
Item Date Actual Forecast Previous
0 China M2 money supply YoY report 1998-02-01 17.4 NaN NaN
1 China M2 money supply YoY report 1998-03-01 16.7 NaN 17.4
2 China M2 money supply YoY report 1998-04-01 15.4 NaN 16.7
3 China M2 money supply YoY report 1998-05-01 14.6 NaN 15.4
4 China M2 money supply YoY report 1998-06-01 15.5 NaN 14.6
.. ... ... ... ... ...
349 China M2 money supply YoY report 2024-03-12 NaN 8.8 8.7
350 China M2 money supply YoY report 2024-03-13 NaN 8.8 8.7
351 China M2 money supply YoY report 2024-03-14 NaN 8.8 8.7
352 China M2 money supply YoY report 2024-03-15 8.7 8.8 8.7
353 China M2 money supply YoY report 2024-04-11 NaN NaN 8.7
[354 rows x 5 columns]
Money supply
Interface: macro_china_supply_of_money
Source URL: http://finance.sina.com.cn/mac/#fininfo-1-0-31-1
Description: Sina Finance — China macro data — money supply
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Statistical period | object | Year-month |
| Money & quasi-money (broad money M2) | float64 | Unit: 100 million yuan |
| Money & quasi-money (broad money M2) YoY | float64 | Unit: % |
| Money (narrow money M1) | float64 | Unit: 100 million yuan |
| Money (narrow money M1) YoY | float64 | Unit: % |
| Currency in circulation (M0) | float64 | Unit: 100 million yuan |
| Currency in circulation (M0) YoY | float64 | Unit: % |
| Demand deposits | float64 | Unit: 100 million yuan |
| Demand deposits YoY | float64 | Unit: % |
| Quasi-money | float64 | Unit: 100 million yuan |
| Quasi-money YoY | float64 | Unit: % |
| Time deposits | float64 | Unit: 100 million yuan |
| Time deposits YoY | float64 | Unit: % |
| Savings deposits | float64 | Unit: 100 million yuan |
| Savings deposits YoY | float64 | Unit: % |
| Other deposits | float64 | Unit: 100 million yuan |
| Other deposits YoY | float64 | Unit: % |
Example
import meshare as ms
macro_china_supply_of_money_df = ms.macro_china_supply_of_money()
print(macro_china_supply_of_money_df)
Sample data
Period Money & quasi-money (M2) Money & quasi-money (M2)-YoY ... Household deposits-YoY Other deposits Other deposits-YoY
0 2020.8 2136800.00 10.40 ... None 235344.24 None
1 2020.7 2125458.46 10.70 ... None 240538.49 None
2 2020.6 2134948.66 11.10 ... None 228402.91 None
3 2020.5 2100183.74 11.10 ... None 233222.73 None
4 2020.4 2093533.83 11.10 ... None 241313.38 None
.. ... ... ... ... ... ... ...
507 1978.5 None None ... None None None
508 1978.4 None None ... None None None
509 1978.3 None None ... None None None
510 1978.2 None None ... None None None
511 1978.1 None None ... None None None
New RMB loans
Interface: macro_rmb_loan
Source URL: https://data.10jqka.com.cn/macro/loan/
Description: 10jqka — data center — macro data — new RMB loans
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | - |
| New RMB loans-Total | float64 | - |
| New RMB loans-YoY | object | - |
| New RMB loans-MoM | object | - |
| Cumulative RMB loans-Total | float64 | - |
| Cumulative RMB loans-YoY | object | - |
Example
import meshare as ms
macro_rmb_loan_df = ms.macro_rmb_loan()
print(macro_rmb_loan_df)
Sample data
Month New RMB loans-Total New RMB loans-YoY New RMB loans-MoM Cumulative RMB loans-Total Cumulative RMB loans-YoY
0 2023-04 7188.0 11.37% -81.52% 2261643.47 12.15%
1 2023-05 13600.0 -28.04% 89.20% 2275271.41 11.78%
2 2023-06 30500.0 8.54% 124.26% 2305766.69 11.74%
3 2023-07 3459.0 -49.06% -88.66% 2309226.18 11.54%
4 2023-08 13600.0 8.80% 293.18% 2322806.64 11.52%
5 2023-09 23100.0 -6.48% 69.85% 2345924.92 11.31%
6 2023-10 7384.0 20.03% -68.03% 2353309.12 11.33%
7 2023-11 10900.0 -9.92% 47.62% 2364196.44 11.21%
8 2023-12 11700.0 -16.43% 7.34% 2375905.37 11.03%
9 2024-01 49200.0 0.41% 320.51% 2425047.89 10.36%
10 2024-02 14500.0 -19.89% -70.53% 2439604.04 10.11%
11 2024-03 30900.0 -20.57% 113.10% 2470492.81 9.58%
12 2024-04 7300.0 1.56% -76.38% 2477817.72 9.56%
13 2024-05 9500.0 -30.15% 30.14% 2487269.61 9.32%
14 2024-06 21300.0 -30.16% 124.21% 2508526.52 8.79%
15 2024-07 2600.0 -24.83% -87.79% 2511136.90 8.74%
16 2024-08 9000.0 -33.82% 246.15% 2520166.54 8.50%
17 2024-09 15900.0 -31.17% 76.67% 2536108.31 8.11%
18 2024-10 5000.0 -32.29% -68.55% 2541043.57 7.98%
19 2024-11 5800.0 -46.79% 16.00% 2546826.79 7.72%
20 2024-12 9900.0 -15.38% 70.69% 2556778.21 7.61%
21 2025-01 51300.0 4.27% 418.18% 2607690.85 7.53%
22 2025-02 10100.0 -30.34% -80.31% 2617778.05 7.30%
23 2025-03 36400.0 17.80% 260.40% 2654136.68 7.43%
24 2025-04 2800.0 -61.64% -92.31% 2656987.42 7.23%
25 2025-05 6200.0 -34.74% 121.43% 2663212.50 7.07%
26 2025-06 22400.0 5.16% 261.29% 2685590.16 7.06%
27 2025-07 -500.0 -119.23% -102.23% 2685099.78 6.93%
28 2025-08 5900.0 -34.44% 1280.00% 2690987.15 6.78%
29 2025-09 12900.0 -18.87% 118.64% 2703902.22 6.62%
RMB deposit balance
Interface: macro_rmb_deposit
Source URL: https://data.10jqka.com.cn/macro/rmb/
Description: 10jqka — data center — macro data — RMB deposit balance
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | - |
| New deposits-Amount | float64 | - |
| New deposits-YoY | object | - |
| New deposits-MoM | object | - |
| New corporate deposits-Amount | float64 | - |
| New corporate deposits-YoY | object | - |
| New corporate deposits-MoM | object | - |
| New savings deposits-Amount | float64 | - |
| New savings deposits-YoY | object | - |
| New savings deposits-MoM | object | - |
| New other deposits-Amount | float64 | - |
| New other deposits-YoY | object | - |
| New other deposits-MoM | object | - |
Example
import meshare as ms
macro_rmb_deposit_df = ms.macro_rmb_deposit()
print(macro_rmb_deposit_df)
Sample data
Month New deposits-Amount New deposits-YoY ... New other deposits-Amount New other deposits-YoY New other deposits-MoM
0 2023-04 2734467.18 12.44% ... 58014.72 6.58% 9.49%
1 2023-05 2749085.32 11.65% ... 60384.07 0.59% 4.08%
2 2023-06 2786204.53 10.98% ... 49888.25 -10.37% -17.38%
3 2023-07 2774993.33 10.51% ... 58965.86 -2.57% 18.20%
4 2023-08 2787610.76 10.45% ... 58878.32 1.60% -0.15%
5 2023-09 2810037.37 10.20% ... 56750.59 6.77% -3.61%
6 2023-10 2816483.21 10.53% ... 70411.12 9.02% 24.07%
7 2023-11 2841754.85 10.24% ... 67118.35 10.21% -4.68%
8 2023-12 2842623.30 9.97% ... 57937.13 15.84% -13.68%
9 2024-01 2897428.50 9.18% ... 66540.81 17.07% 14.85%
10 2024-02 2906999.38 8.39% ... 62743.55 2.19% -5.71%
11 2024-03 2955054.17 7.89% ... 55082.19 3.95% -12.21%
12 2024-04 2915852.23 6.63% ... 56062.80 -3.36% 1.78%
13 2024-05 2932599.27 6.68% ... 63696.00 5.48% 13.62%
14 2024-06 2957172.45 6.14% ... 55502.91 11.25% -12.86%
15 2024-07 2949199.49 6.28% ... 61956.34 5.07% 11.63%
16 2024-08 2971425.35 6.59% ... 67543.21 14.72% 9.02%
17 2024-09 3008822.65 7.07% ... 65185.15 14.86% -3.49%
18 2024-10 3014771.89 7.04% ... 71149.19 1.05% 9.15%
19 2024-11 3036481.15 6.85% ... 72557.14 8.10% 1.98%
20 2024-12 3022537.95 6.33% ... 55812.16 -3.67% -23.08%
21 2025-01 3065512.47 5.80% ... 59562.94 -10.49% 6.72%
22 2025-02 3109697.90 6.97% ... 72116.15 14.94% 21.08%
23 2025-03 3152232.44 6.67% ... 64428.49 16.97% -10.66%
24 2025-04 3147793.38 7.95% ... 68100.96 21.47% 5.70%
25 2025-05 3169624.01 8.08% ... 76942.86 20.80% 12.98%
26 2025-06 3201739.92 8.27% ... 68776.60 23.92% -10.61%
27 2025-07 3206702.75 8.73% ... 76452.86 23.40% 11.16%
28 2025-08 3227265.78 8.61% ... 78348.39 16.00% 2.48%
29 2025-09 3249387.56 8.00% ... 69906.51 7.24% -10.77%
[30 rows x 13 columns]
Foreign-currency loan data
Interface: macro_china_whxd
Source URL: http://data.eastmoney.com/cjsj/whxd.html
Description: Foreign-currency loan data; monthly from 200802 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | - |
| Current month | float64 | Unit: USD 100 million |
| YoY | float64 | Unit: % |
| MoM | float64 | Unit: % |
| Cumulative | float64 | Unit: USD 100 million |
Example
import meshare as ms
macro_china_whxd_df = ms.macro_china_whxd()
print(macro_china_whxd_df)
Sample data
Month Current month YoY growth MoM growth Cumulative
0 Jan 2008 169.28 4508.33 1789.29 2367.31
1 Feb 2008 217.02 754.75 28.20 2584.33
2 Mar 2008 103.62 279.28 -52.25 2687.95
3 Apr 2008 21.35 39.82 -79.40 2709.30
4 May 2008 30.04 -13.15 40.70 2739.34
.. ... ... ... ... ...
208 May 2025 61.00 191.04 354.17 5394.00
209 Jun 2025 215.00 226.47 252.46 5609.00
210 Jul 2025 -51.00 77.33 -123.72 5558.00
211 Aug 2025 -41.00 61.68 19.61 5517.00
212 Sep 2025 27.00 131.76 165.85 5544.00
[213 rows x 5 columns]
Domestic- and foreign-currency deposits
Interface: macro_china_wbck
Source URL: http://data.eastmoney.com/cjsj/wbck.html
Description: Domestic- and foreign-currency deposits; monthly from 200802 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | - |
| Current month | float64 | Unit: 100 million yuan |
| YoY | float64 | Unit: % |
| MoM | float64 | Unit: % |
| Cumulative | float64 | Unit: 100 million yuan |
Example
import meshare as ms
macro_china_wbck_df = ms.macro_china_wbck()
print(macro_china_wbck_df)
Sample data
Month Current month YoY growth MoM growth Cumulative
0 Oct 2022 -2650.78 -132.917535 -110.068013 2610227.88
1 Sep 2022 26328.73 14.546846 134.206364 2612878.66
2 Aug 2022 11241.68 -19.737487 871.473473 2586549.93
3 Jul 2022 -1457.17 88.621966 -102.976736 2575308.25
4 Jun 2022 48951.94 22.914621 63.520287 2576765.42
.. ... ... ... ... ...
173 May 2008 8763.12 370.965835 22.035258 442540.86
174 Apr 2008 7180.81 69.117415 -33.125126 433777.74
175 Mar 2008 10737.68 25.845804 -19.689339 426596.93
176 Feb 2008 13370.18 152.665137 829.976560 415859.25
177 Jan 2008 1437.69 -72.142704 -59.893939 402489.07
China macro leverage ratio
Interface: macro_cnbs
Source URL: http://114.115.232.154:8080/
Description: National Institution for Finance & Development — China macro leverage ratio
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Year | object | Date, year-month |
| Household sector | float64 | - |
| Non-financial corporate sector | float64 | - |
| Government sector | float64 | - |
| Central government | float64 | - |
| Local government | float64 | - |
| Real economy sector | float64 | - |
| Financial sector-assets | float64 | - |
| Financial sector-liabilities | float64 | - |
Example
import meshare as ms
macro_cnbs_df = ms.macro_cnbs()
print(macro_cnbs_df)
Sample data
Year Household sector Non-financial corporate sector Government sector Central government Local government Real economy sector Financial sector-Assets Financial sector-Liabilities
0 1992-12 7.5 90.0 8.3 4.4 3.9 105.8 7.8 7.2
1 1993-03 7.5 91.1 8.1 4.2 3.9 106.7 7.8 7.3
2 1993-06 7.4 91.1 8.2 4.4 3.8 106.7 7.7 7.3
3 1993-09 7.3 90.2 8.3 4.6 3.7 105.8 7.7 7.3
4 1993-12 7.0 87.8 7.8 4.2 3.6 102.6 8.9 7.1
.. ... ... ... ... ... ... ... ... ...
121 2023-03 63.6 167.7 51.7 21.5 30.2 283.0 52.9 65.9
122 2023-06 63.6 168.4 52.1 21.5 30.6 284.1 53.8 66.8
123 2023-09 63.9 169.2 53.9 22.7 31.2 287.0 52.1 65.5
124 2023-12 63.5 168.4 56.1 23.8 32.3 288.0 52.5 67.2
125 2024-03 64.0 174.1 56.7 23.9 32.8 294.8 53.6 68.8
[126 rows x 9 columns]
Number of bank wealth-management products issued
Interface: macro_china_bank_financing
Source URL: https://data.eastmoney.com/cjsj/hyzs_list_EMI01516267.html
Description: Number of bank wealth-management products issued; from January 2000 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Date | object | - |
| Latest | int64 | - |
| Change % | float64 | Unit: % |
| 3M change % | float64 | Unit: % |
| 6M change % | float64 | Unit: % |
| 1Y change % | float64 | Unit: % |
| 2Y change % | float64 | Unit: % |
| 3Y change % | float64 | Unit: % |
Example
import meshare as ms
macro_china_bank_financing_df = ms.macro_china_bank_financing()
print(macro_china_bank_financing_df)
Sample data
Date Latest Change % ... 1Y change % 2Y change % 3Y change %
0 2000-03-01 4 NaN ... NaN NaN NaN
1 2004-02-01 2 -50.000000 ... -50.000000 -50.000000 -50.000000
2 2004-03-01 8 300.000000 ... 100.000000 100.000000 100.000000
3 2004-04-01 3 -62.500000 ... -25.000000 -25.000000 -25.000000
4 2004-05-01 6 100.000000 ... 50.000000 50.000000 50.000000
.. ... ... ... ... ... ... ...
213 2021-10-01 2640 -27.829415 ... -51.710262 -70.054446 -74.346516
214 2021-11-01 3424 29.696970 ... -45.347167 -59.541534 -70.644719
215 2021-12-01 3876 13.200935 ... -44.422139 -44.117647 -64.904020
216 2022-01-01 2793 -27.941176 ... -45.406568 -50.478723 -77.035027
217 2022-02-01 1779 -36.305048 ... -54.893509 -66.204407 -78.496313