从 API 获取 python 项目的库存历史数据
Getting Stock Historical Data from API for a python project
我正在制作股票分析程序，但为此，我需要 NYSE 股票历史数据 API。
我到处搜索，但找不到有用的解决方案（即 Yahoo/Google 财务 API 已关闭）。
我尝试了 Alpha Vantage API，但它似乎没有输出每只股票的 close/open 值。
有没有其他方法可以让大众访问历史数据，而无需直接从雅虎财经下载每只股票的 .csv？
您可以从准备好的数据集中获取股票数据或从其他网站抓取。
您可以在此处找到它：
https://github.com/eliangcs/pystock-data
或
https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs
https://www.kaggle.com/camnugent/sandp500
尝试Quandl。它非常简单易用，但您必须注册并获得某些库的 API 密钥。
In [11]: mydata = quandl.get('WFE/INDEXES_NYSE')
In [12]: mydata.head(5)
Out[12]:
Value
Date
2016-01-31 9632.70
2016-02-29 9559.53
2016-03-31 10207.38
2016-04-30 10436.92
2016-05-31 10441.00
如果要指定日期范围，请使用以下内容
In [37]: quandl.get("WIKI/AMZN", start_date="2017-7-10", end_date="2018-7-10")
Out[37]:
Open High Low ... Adj. Low Adj. Close Adj. Volume
Date ...
2017-07-10 985.000 999.4392 983.50 ... 983.50 996.470 3462884.0
2017-07-11 993.000 995.9900 983.72 ... 983.72 994.130 2947479.0
2017-07-12 1000.650 1008.5500 998.10 ... 998.10 1006.510 3491988.0
2017-07-13 1004.620 1006.8800 995.90 ... 995.90 999.855 2864533.0
2017-07-14 1002.400 1004.4500 996.89 ... 996.89 1001.810 2066362.0
2017-07-17 1004.690 1014.7500 1003.81 ... 1003.81 1010.040 3636801.0
2017-07-18 1006.000 1026.0300 1004.00 ... 1004.00 1024.380 3957892.0
2017-07-19 1025.000 1031.5900 1022.50 ... 1022.50 1026.870 2936902.0
2017-07-20 1031.590 1034.9700 1022.52 ... 1022.52 1028.700 2964341.0
2017-07-21 1021.280 1026.1000 1011.00 ... 1011.00 1025.670 2677517.0
2017-07-24 1028.340 1043.0100 1027.43 ... 1027.43 1038.950 3212499.0
2017-07-25 1038.050 1043.3300 1032.48 ... 1032.48 1039.870 2432328.0
2017-07-26 1043.200 1053.2000 1043.20 ... 1043.20 1052.800 2828980.0
2017-07-27 1069.550 1083.3100 1040.18 ... 1040.18 1046.000 9905158.0
2017-07-28 1012.140 1032.8500 1001.00 ... 1001.00 1020.040 7624498.0
2017-07-31 1019.050 1019.0500 987.02 ... 987.02 987.780 7246638.0
2017-08-01 996.110 1006.4000 991.58 ... 991.58 996.190 4421395.0
2017-08-02 1001.770 1003.2100 981.73 ... 981.73 995.890 4017780.0
2017-08-03 999.470 999.5000 984.59 ... 984.59 986.920 3203134.0
2017-08-04 989.680 991.6720 982.00 ... 982.00 987.580 2704026.0
2017-08-08 994.350 996.2800 985.79 ... 985.79 989.840 2847527.0
2017-08-09 982.600 988.0000 975.27 ... 975.27 982.010 3434077.0
2017-08-10 976.295 979.8600 954.68 ... 954.68 956.920 5560388.0
2017-08-11 960.000 970.3900 951.38 ... 951.38 967.990 3431423.0
2017-08-14 978.410 985.5000 976.19 ... 976.19 983.300 3008007.0
2017-08-15 988.900 991.7400 982.00 ... 982.00 982.740 2513139.0
2017-08-16 981.650 986.4605 973.22 ... 973.22 978.180 3096051.0
2017-08-17 977.840 977.8400 960.32 ... 960.32 960.570 3396045.0
2017-08-18 961.400 965.4300 954.65 ... 954.65 958.470 3248677.0
2017-08-21 957.570 961.2000 945.46 ... 945.46 953.290 3008450.0
... ... ... ... ... ... ... ...
2018-02-13 1385.930 1419.7200 1383.53 ... 1383.53 1414.510 5858860.0
2018-02-14 1406.250 1452.0600 1403.36 ... 1403.36 1451.050 5881238.0
2018-02-15 1466.890 1468.9400 1436.84 ... 1436.84 1461.760 5598111.0
2018-02-16 1457.370 1465.8000 1446.56 ... 1446.56 1448.690 4410879.0
2018-02-20 1446.490 1488.7700 1446.49 ... 1446.49 1468.350 6388374.0
2018-02-21 1485.000 1503.4900 1478.92 ... 1478.92 1482.920 6216694.0
2018-02-22 1495.360 1502.5400 1475.76 ... 1475.76 1484.760 4732555.0
2018-02-23 1495.340 1500.0000 1486.50 ... 1486.50 1500.000 4327008.0
2018-02-26 1509.200 1522.8400 1507.00 ... 1507.00 1521.950 4909053.0
2018-02-27 1524.500 1526.7800 1507.21 ... 1507.21 1511.980 4708378.0
2018-02-28 1519.510 1528.7000 1512.00 ... 1512.00 1512.450 4426580.0
2018-03-01 1513.600 1518.4900 1465.00 ... 1465.00 1493.450 6835230.0
2018-03-02 1469.100 1501.0500 1455.01 ... 1455.01 1500.250 6587564.0
2018-03-05 1494.240 1525.3800 1481.00 ... 1481.00 1523.610 5233934.0
2018-03-06 1533.200 1542.1300 1528.00 ... 1528.00 1537.640 4561718.0
2018-03-07 1526.520 1545.9000 1522.51 ... 1522.51 1545.000 4174123.0
2018-03-08 1550.000 1554.8800 1545.25 ... 1545.25 1551.860 3512528.0
2018-03-09 1563.500 1578.9400 1559.08 ... 1559.08 1578.890 4417059.0
2018-03-12 1592.600 1605.3300 1586.70 ... 1586.70 1598.390 5115886.0
2018-03-13 1615.960 1617.5400 1578.01 ... 1578.01 1588.180 6427066.0
2018-03-14 1597.000 1606.4400 1590.89 ... 1590.89 1591.000 4164395.0
2018-03-15 1595.000 1596.9100 1578.11 ... 1578.11 1582.320 4026744.0
2018-03-16 1583.450 1589.4400 1567.50 ... 1567.50 1571.680 5145054.0
2018-03-19 1554.530 1561.6600 1525.35 ... 1525.35 1544.930 6376619.0
2018-03-20 1550.340 1587.0000 1545.41 ... 1545.41 1586.510 4507049.0
2018-03-21 1586.450 1590.0000 1563.17 ... 1563.17 1581.860 4667291.0
2018-03-22 1565.470 1573.8500 1542.40 ... 1542.40 1544.100 6177737.0
2018-03-23 1539.010 1549.0200 1495.36 ... 1495.36 1495.560 7843966.0
2018-03-26 1530.000 1556.9900 1499.25 ... 1499.25 1555.860 5547618.0
2018-03-27 1572.400 1575.9600 1482.32 ... 1482.32 1497.050 6793279.0
[179 rows x 12 columns]
有很多服务。
例如，进行 Google 搜索：
已支付
免费
也看看这个 list
python yahoofinancials 模块可以轻松地为您处理这个问题。除了每月、每日等历史股票数据外，它还可以为您提供雅虎财经上任何一家公司的所有基本财务数据（余额sheet、损益表、现金流量、比率等）
您可以使用 pip install yahoofinancials 安装它。它不依赖于封闭的 Yahoo API，也不需要配置网络驱动程序即可工作。此外，无需注册或 API 密钥。它通过散列在每个雅虎金融网页中找到的数据存储对象来工作。
您可以将单个 stock/index 代码作为字符串传递，也可以传递代码字符串列表以一次提取多个 stocks/indexes。数据也很容易放入 Pandas 数据框进行分析。
$ pip install yahoofinancials
多代码拉取示例：
from yahoofinancials import YahooFinancials
tech_stocks = ['AAPL', 'MSFT', 'INTC']
bank_stocks = ['WFC', 'BAC', 'C']
yahoo_financials_tech = YahooFinancials(tech_stocks)
yahoo_financials_banks = YahooFinancials(bank_stocks)
tech_cash_flow_data_an = yahoo_financials_tech.get_financial_stmts('annual', 'cash')
bank_cash_flow_data_an = yahoo_financials_banks.get_financial_stmts('annual', 'cash')
banks_net_ebit = yahoo_financials_banks.get_ebit()
tech_stock_price_data = tech_cash_flow_data.get_stock_price_data()
daily_bank_stock_prices = yahoo_financials_banks.get_historical_stock_data('2008-09-15', '2017-09-15', 'daily')
用法示例：
from yahoofinancials import YahooFinancials
yahoo_financials = YahooFinancials('WFC')
print(yahoo_financials.get_historical_stock_data("2017-09-10", "2017-10-10", "monthly"))
JSON 输出：
{
"WFC": {
"prices": [
{
"volume": 260271600,
"formatted_date": "2017-09-30",
"high": 55.77000045776367,
"adjclose": 54.91999816894531,
"low": 52.84000015258789,
"date": 1506830400,
"close": 54.91999816894531,
"open": 55.15999984741211
}
],
"eventsData": [],
"firstTradeDate": {
"date": 76233600,
"formatted_date": "1972-06-01"
},
"isPending": false,
"timeZone": {
"gmtOffset": -14400
},
"id": "1mo15050196001507611600"
}
}
我正在制作股票分析程序，但为此，我需要 NYSE 股票历史数据 API。
我到处搜索，但找不到有用的解决方案（即 Yahoo/Google 财务 API 已关闭）。
我尝试了 Alpha Vantage API，但它似乎没有输出每只股票的 close/open 值。
有没有其他方法可以让大众访问历史数据，而无需直接从雅虎财经下载每只股票的 .csv？
您可以从准备好的数据集中获取股票数据或从其他网站抓取。
您可以在此处找到它： https://github.com/eliangcs/pystock-data
或 https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs https://www.kaggle.com/camnugent/sandp500
尝试Quandl。它非常简单易用，但您必须注册并获得某些库的 API 密钥。
In [11]: mydata = quandl.get('WFE/INDEXES_NYSE')
In [12]: mydata.head(5)
Out[12]:
Value
Date
2016-01-31 9632.70
2016-02-29 9559.53
2016-03-31 10207.38
2016-04-30 10436.92
2016-05-31 10441.00
如果要指定日期范围，请使用以下内容
In [37]: quandl.get("WIKI/AMZN", start_date="2017-7-10", end_date="2018-7-10")
Out[37]:
Open High Low ... Adj. Low Adj. Close Adj. Volume
Date ...
2017-07-10 985.000 999.4392 983.50 ... 983.50 996.470 3462884.0
2017-07-11 993.000 995.9900 983.72 ... 983.72 994.130 2947479.0
2017-07-12 1000.650 1008.5500 998.10 ... 998.10 1006.510 3491988.0
2017-07-13 1004.620 1006.8800 995.90 ... 995.90 999.855 2864533.0
2017-07-14 1002.400 1004.4500 996.89 ... 996.89 1001.810 2066362.0
2017-07-17 1004.690 1014.7500 1003.81 ... 1003.81 1010.040 3636801.0
2017-07-18 1006.000 1026.0300 1004.00 ... 1004.00 1024.380 3957892.0
2017-07-19 1025.000 1031.5900 1022.50 ... 1022.50 1026.870 2936902.0
2017-07-20 1031.590 1034.9700 1022.52 ... 1022.52 1028.700 2964341.0
2017-07-21 1021.280 1026.1000 1011.00 ... 1011.00 1025.670 2677517.0
2017-07-24 1028.340 1043.0100 1027.43 ... 1027.43 1038.950 3212499.0
2017-07-25 1038.050 1043.3300 1032.48 ... 1032.48 1039.870 2432328.0
2017-07-26 1043.200 1053.2000 1043.20 ... 1043.20 1052.800 2828980.0
2017-07-27 1069.550 1083.3100 1040.18 ... 1040.18 1046.000 9905158.0
2017-07-28 1012.140 1032.8500 1001.00 ... 1001.00 1020.040 7624498.0
2017-07-31 1019.050 1019.0500 987.02 ... 987.02 987.780 7246638.0
2017-08-01 996.110 1006.4000 991.58 ... 991.58 996.190 4421395.0
2017-08-02 1001.770 1003.2100 981.73 ... 981.73 995.890 4017780.0
2017-08-03 999.470 999.5000 984.59 ... 984.59 986.920 3203134.0
2017-08-04 989.680 991.6720 982.00 ... 982.00 987.580 2704026.0
2017-08-08 994.350 996.2800 985.79 ... 985.79 989.840 2847527.0
2017-08-09 982.600 988.0000 975.27 ... 975.27 982.010 3434077.0
2017-08-10 976.295 979.8600 954.68 ... 954.68 956.920 5560388.0
2017-08-11 960.000 970.3900 951.38 ... 951.38 967.990 3431423.0
2017-08-14 978.410 985.5000 976.19 ... 976.19 983.300 3008007.0
2017-08-15 988.900 991.7400 982.00 ... 982.00 982.740 2513139.0
2017-08-16 981.650 986.4605 973.22 ... 973.22 978.180 3096051.0
2017-08-17 977.840 977.8400 960.32 ... 960.32 960.570 3396045.0
2017-08-18 961.400 965.4300 954.65 ... 954.65 958.470 3248677.0
2017-08-21 957.570 961.2000 945.46 ... 945.46 953.290 3008450.0
... ... ... ... ... ... ... ...
2018-02-13 1385.930 1419.7200 1383.53 ... 1383.53 1414.510 5858860.0
2018-02-14 1406.250 1452.0600 1403.36 ... 1403.36 1451.050 5881238.0
2018-02-15 1466.890 1468.9400 1436.84 ... 1436.84 1461.760 5598111.0
2018-02-16 1457.370 1465.8000 1446.56 ... 1446.56 1448.690 4410879.0
2018-02-20 1446.490 1488.7700 1446.49 ... 1446.49 1468.350 6388374.0
2018-02-21 1485.000 1503.4900 1478.92 ... 1478.92 1482.920 6216694.0
2018-02-22 1495.360 1502.5400 1475.76 ... 1475.76 1484.760 4732555.0
2018-02-23 1495.340 1500.0000 1486.50 ... 1486.50 1500.000 4327008.0
2018-02-26 1509.200 1522.8400 1507.00 ... 1507.00 1521.950 4909053.0
2018-02-27 1524.500 1526.7800 1507.21 ... 1507.21 1511.980 4708378.0
2018-02-28 1519.510 1528.7000 1512.00 ... 1512.00 1512.450 4426580.0
2018-03-01 1513.600 1518.4900 1465.00 ... 1465.00 1493.450 6835230.0
2018-03-02 1469.100 1501.0500 1455.01 ... 1455.01 1500.250 6587564.0
2018-03-05 1494.240 1525.3800 1481.00 ... 1481.00 1523.610 5233934.0
2018-03-06 1533.200 1542.1300 1528.00 ... 1528.00 1537.640 4561718.0
2018-03-07 1526.520 1545.9000 1522.51 ... 1522.51 1545.000 4174123.0
2018-03-08 1550.000 1554.8800 1545.25 ... 1545.25 1551.860 3512528.0
2018-03-09 1563.500 1578.9400 1559.08 ... 1559.08 1578.890 4417059.0
2018-03-12 1592.600 1605.3300 1586.70 ... 1586.70 1598.390 5115886.0
2018-03-13 1615.960 1617.5400 1578.01 ... 1578.01 1588.180 6427066.0
2018-03-14 1597.000 1606.4400 1590.89 ... 1590.89 1591.000 4164395.0
2018-03-15 1595.000 1596.9100 1578.11 ... 1578.11 1582.320 4026744.0
2018-03-16 1583.450 1589.4400 1567.50 ... 1567.50 1571.680 5145054.0
2018-03-19 1554.530 1561.6600 1525.35 ... 1525.35 1544.930 6376619.0
2018-03-20 1550.340 1587.0000 1545.41 ... 1545.41 1586.510 4507049.0
2018-03-21 1586.450 1590.0000 1563.17 ... 1563.17 1581.860 4667291.0
2018-03-22 1565.470 1573.8500 1542.40 ... 1542.40 1544.100 6177737.0
2018-03-23 1539.010 1549.0200 1495.36 ... 1495.36 1495.560 7843966.0
2018-03-26 1530.000 1556.9900 1499.25 ... 1499.25 1555.860 5547618.0
2018-03-27 1572.400 1575.9600 1482.32 ... 1482.32 1497.050 6793279.0
[179 rows x 12 columns]
有很多服务。 例如，进行 Google 搜索：
已支付
免费
也看看这个 list
python yahoofinancials 模块可以轻松地为您处理这个问题。除了每月、每日等历史股票数据外，它还可以为您提供雅虎财经上任何一家公司的所有基本财务数据（余额sheet、损益表、现金流量、比率等）
您可以使用 pip install yahoofinancials 安装它。它不依赖于封闭的 Yahoo API，也不需要配置网络驱动程序即可工作。此外，无需注册或 API 密钥。它通过散列在每个雅虎金融网页中找到的数据存储对象来工作。
您可以将单个 stock/index 代码作为字符串传递，也可以传递代码字符串列表以一次提取多个 stocks/indexes。数据也很容易放入 Pandas 数据框进行分析。
$ pip install yahoofinancials
多代码拉取示例：
from yahoofinancials import YahooFinancials
tech_stocks = ['AAPL', 'MSFT', 'INTC']
bank_stocks = ['WFC', 'BAC', 'C']
yahoo_financials_tech = YahooFinancials(tech_stocks)
yahoo_financials_banks = YahooFinancials(bank_stocks)
tech_cash_flow_data_an = yahoo_financials_tech.get_financial_stmts('annual', 'cash')
bank_cash_flow_data_an = yahoo_financials_banks.get_financial_stmts('annual', 'cash')
banks_net_ebit = yahoo_financials_banks.get_ebit()
tech_stock_price_data = tech_cash_flow_data.get_stock_price_data()
daily_bank_stock_prices = yahoo_financials_banks.get_historical_stock_data('2008-09-15', '2017-09-15', 'daily')
用法示例：
from yahoofinancials import YahooFinancials
yahoo_financials = YahooFinancials('WFC')
print(yahoo_financials.get_historical_stock_data("2017-09-10", "2017-10-10", "monthly"))
JSON 输出：
{
"WFC": {
"prices": [
{
"volume": 260271600,
"formatted_date": "2017-09-30",
"high": 55.77000045776367,
"adjclose": 54.91999816894531,
"low": 52.84000015258789,
"date": 1506830400,
"close": 54.91999816894531,
"open": 55.15999984741211
}
],
"eventsData": [],
"firstTradeDate": {
"date": 76233600,
"formatted_date": "1972-06-01"
},
"isPending": false,
"timeZone": {
"gmtOffset": -14400
},
"id": "1mo15050196001507611600"
}
}