介绍#
项目地址 https://github.com/zouxlin3/StockDataAnalysis
使用python分析csv格式的股票数据
使用方法如下

from StockData import StockData # 从源代码下载StockData.py和666666.SZ.csv虚拟数据
1 读取csv#
虚拟数据的股票代码为000001
每个股票配资平台代码应对应一个csv文件
read(symbols: List[str])

stockdata = StockData(‘data’) # 虚拟数据文件所在目录
symbols = [‘000001’] # symbols为股票代码列表
stockdata.read(symbols)
stockdata.dataframes[‘000001’] # 查看内容
Unnamed: 0 OBJECT_ID S_INFO_WINDCODE TRADE_DT CRNCY_CODE S_DQ_PRECLOSE S_DQ_OPEN S_DQ_HIGH S_DQ_LOW S_DQ_CLOSE … S_DQ_ADJPRECLOSE S_DQ_ADJOPEN S_DQ_ADJHIGH S_DQ_ADJLOW S_DQ_ADJCLOSE S_DQ_ADJFACTOR S_DQ_AVGPRICE S_DQ_TRADESTATUS OPDATE OPMODE
0 336601 -488126 000001.SZ 1991-04-03 CNY 61.49 49.00 49.00 49.00 49.00 … 61.49 49.00 49.00 49.00 49.00 1.000000 50.0000 ?? 2016-12-27 10:24:33 0
1 336602 -488127 000001.SZ 1991-04-04 CNY 49.00 48.76 48.76 48.76 48.76 … 49.00 48.76 48.76 48.76 48.76 1.000000 50.0000 ?? 2016-12-27 10:24:33 0
2 336603 -488128 000001.SZ 1991-04-05 CNY 48.76 48.52 48.52 48.52 48.52 … 48.76 48.52 48.52 48.52 48.52 1.000000 50.0000 ?? 2016-12-27 10:24:33 0
3 336605 -488130 000001.SZ 1991-04-08 CNY 48.52 48.04 48.04 48.04 48.04 … 48.52 48.04 48.04 48.04 48.04 1.000000 50.0000 ?? 2016-12-27 10:24:33 0
4 336606 -488131 000001.SZ 1991-04-09 CNY 48.04 47.80 47.80 47.80 47.80 … 48.04 47.80 47.80 47.80 47.80 1.000000 47.5000 ?? 2016-12-27 10:24:33 0
… … … … … … … … … … … … … … … … … … … … … …
6298 6864894 {D1A958FC-E2CC-11E6-9220-6C0B84A6895D} 000001.SZ 2017-01-25 CNY 9.27 9.27 9.28 9.25 9.26 … 971.11 971.11 972.16 969.01 970.06 104.758253 9.2633 ?? 2017-01-25 15:10:52 0
6299 1717817 {1930026C-E396-11E6-9CA9-4437E6DAC6D1} 000001.SZ 2017-01-26 CNY 9.26 9.27 9.34 9.26 9.33 … 970.06 971.11 978.44 970.06 977.39 104.758253 9.3138 ?? 2017-01-26 15:26:14 0
6300 3288514 {518B8FC9-E9DF-11E6-84F4-6C0B84A6895D} 000001.SZ 2017-02-03 CNY 9.33 9.34 9.36 9.23 9.26 … 977.39 978.44 980.54 966.92 970.06 104.758253 9.2756 ?? 2017-02-03 15:09:52 0
6301 6878988 {D21CCA60-EC3A-11E6-85F7-6C0B84A6895D} 000001.SZ 2017-02-06 CNY 9.26 9.26 9.32 9.26 9.31 … 970.06 970.06 976.35 970.06 975.30 104.758253 9.2967 ?? 2017-02-06 15:20:12 0
6302 1204979 {0700EF99-ED04-11E6-A728-6C0B84A6895D} 000001.SZ 2017-02-07 CNY 9.31 9.31 9.32 9.27 9.30 … 975.30 975.30 976.35 971.11 974.25 104.758253 9.2912 ?? 2017-02-07 15:12:19 0
6303 rows × 24 columns

2 根据条件查看数据#
2.1 根据时间段查看#
查看一个股票在时间段的open high low close四个标签的数据
get_data_by_symbol(symbol: str, start_date: str, end_date: str)

stockdata.get_data_by_symbol(‘000001’, ‘19910409’, ‘19910419’)
date open high low close
4 1991-04-09 47.80 47.80 47.80 47.80
5 1991-04-10 47.56 47.56 47.56 47.56
6 1991-04-11 47.56 47.56 47.56 47.56
7 1991-04-12 47.08 47.08 47.08 47.08
8 1991-04-16 46.38 46.38 46.38 46.38
9 1991-04-17 46.15 46.15 46.15 46.15
10 1991-04-18 45.92 45.92 45.92 45.92
11 1991-04-19 45.69 45.69 45.69 45.69
2.2 根据时间段查看#
查看指定日期一些股票的open high low close四个标签的数据
get_data_by_date( adate: str, symbols: List[str])

stockdata.get_data_by_date(‘20170207’, symbols)
symbols open high low close
0 000001 9.31 9.32 9.27 9.3
2.3 根据时间段查看#
查看一些股票指定标签的数据
get_data_by_field(field: str, symbols: List[str])

stockdata.get_data_by_field(‘open’, symbols)
date 000001
0 1991-04-03 49.00
1 1991-04-04 48.76
2 1991-04-05 48.52
3 1991-04-08 48.04
4 1991-04-09 47.80
… … …
6298 2017-01-25 9.27
6299 2017-01-26 9.27
6300 2017-02-03 9.34
6301 2017-02-06 9.26
6302 2017-02-07 9.31
6303 rows × 2 columns

3 绘制走势图#
需要指定一个标签，volume和turnover标签时绘制柱状图，其余为折线图
plot(symbol: str, field: str)

stockdata.plot(‘000001’, ‘open’)
image

4 数据处理#
4.1 价格复权计算#
对open high low close四类价格从后往前复权
adjust_data(symbol: str)

stockdata.adjust_data(‘000001’)
stockdata.dataframes[‘000001’]
Unnamed: 0 OBJECT_ID S_INFO_WINDCODE TRADE_DT CRNCY_CODE S_DQ_PRECLOSE S_DQ_OPEN S_DQ_HIGH S_DQ_LOW S_DQ_CLOSE … S_DQ_ADJFACTOR S_DQ_AVGPRICE S_DQ_TRADESTATUS OPDATE OPMODE forward_af forward_adjust_open forward_adjust_high forward_adjust_low forward_adjust_close
0 336601 -488126 000001.SZ 1991-04-03 CNY 61.49 49 49.00 49.00 49.00 … 1.000000 50.0000 ?? 2016-12-27 10:24:33 0 0.01051 49.0 49.00 49.00 49.00
1 336602 -488127 000001.SZ 1991-04-04 CNY 49.00 48 48.76 48.76 48.76 … 1.000000 50.0000 ?? 2016-12-27 10:24:33 0 0.01051 48.0 48.76 48.76 48.76
2 336603 -488128 000001.SZ 1991-04-05 CNY 48.76 48 48.52 48.52 48.52 … 1.000000 50.0000 ?? 2016-12-27 10:24:33 0 0.01051 48.0 48.52 48.52 48.52
3 336605 -488130 000001.SZ 1991-04-08 CNY 48.52 48 48.04 48.04 48.04 … 1.000000 50.0000 ?? 2016-12-27 10:24:33 0 0.01051 48.0 48.04 48.04 48.04
4 336606 -488131 000001.SZ 1991-04-09 CNY 48.04 47 47.80 47.80 47.80 … 1.000000 47.5000 ?? 2016-12-27 10:24:33 0 0.01051 47.0 47.80 47.80 47.80
… … … … … … … … … … … … … … … … … … … … … …
6298 6864894 {D1A958FC-E2CC-11E6-9220-6C0B84A6895D} 000001.SZ 2017-01-25 CNY 9.27 9 9.28 9.25 9.26 … 104.758253 9.2633 ?? 2017-01-25 15:10:52 0 1.00000 9.0 9.28 9.25 9.26
6299 1717817 {1930026C-E396-11E6-9CA9-4437E6DAC6D1} 000001.SZ 2017-01-26 CNY 9.26 9 9.34 9.26 9.33 … 104.758253 9.3138 ?? 2017-01-26 15:26:14 0 1.00000 9.0 9.34 9.26 9.33
6300 3288514 {518B8FC9-E9DF-11E6-84F4-6C0B84A6895D} 000001.SZ 2017-02-03 CNY 9.33 9 9.36 9.23 9.26 … 104.758253 9.2756 ?? 2017-02-03 15:09:52 0 1.00000 9.0 9.36 9.23 9.26
6301 6878988 {D21CCA60-EC3A-11E6-85F7-6C0B84A6895D} 000001.SZ 2017-02-06 CNY 9.26 9 9.32 9.26 9.31 … 104.758253 9.2967 ?? 2017-02-06 15:20:12 0 1.00000 9.0 9.32 9.26 9.31
6302 1204979 {0700EF99-ED04-11E6-A728-6C0B84A6895D} 000001.SZ 2017-02-07 CNY 9.31 9 9.32 9.27 9.30 … 104.758253 9.2912 ?? 2017-02-07 15:12:19 0 1.00000 9.0 9.32 9.27 9.30
6303 rows × 29 columns

4.2 日频数据重采样#
按照时间窗口进行重采样，采样的时间取区间左端
resample(symbol: str, freq: int)

stockdata.resample(‘000001’, 5)
date open close high low volume turnover vwap
0 1991-04-09 49 47.80 49.00 47.80 12.0 59.000 4.9167
1 1991-04-17 47 46.15 47.56 46.15 26.0 123.000 4.7308
2 1991-04-24 45 44.78 45.92 44.78 50.0 225.000 4.5000
3 1991-05-02 44 43.46 44.56 43.46 31.0 137.000 4.4194
4 1991-05-09 43 42.17 43.24 42.17 129.0 546.000 4.2326
… … … … … … … … …
1255 2016-12-28 9 9.06 9.16 9.02 1631209.0 1483308.481 0.9093
1256 2017-01-05 9 9.17 9.18 9.05 1894909.0 1729934.514 0.9129
1257 2017-01-12 9 9.15 9.17 9.11 1691727.0 1547168.777 0.9145
1258 2017-01-19 9 9.18 9.24 9.07 2675002.0 2447679.663 0.9150
1259 2017-01-26 9 9.33 9.34 9.17 2008986.0 1858492.536 0.9251
1260 rows × 8 columns

4.3 计算移动平均#
计算滑动窗口内数据的平均值
moving_average(symbol: str, field: str, window: int)

stockdata.moving_average(‘000001’, ‘open’, 5)
TRADE_DT
1991-04-03 NaN
1991-04-04 NaN
1991-04-05 NaN
1991-04-08 NaN
1991-04-09 48.0
…
2017-01-25 9.0
2017-01-26 9.0
2017-02-03 9.0
2017-02-06 9.0
2017-02-07 9.0
Name: forward_adjust_open, Length: 6303, dtype: float64
5 相关指标计算#
四类指标
ema(symbol: str, periods: int)
atr(symbol: str, periods: int)
rsi(symbol: str, periods: int)
macd(symbol: str, long: int, short: int, dea_periods: int)

stockdata.ema(‘000001’, 5)
TRADE_DT
1991-04-03 NaN
1991-04-04 NaN
1991-04-05 NaN
1991-04-08 NaN
1991-04-09 49.500000
…
2017-01-25 9.233294
2017-01-26 9.265529
2017-02-03 9.263686
2017-02-06 9.279124
2017-02-07 9.286083
Name: ema_5, Length: 6303, dtype: float64
stockdata.atr(‘000001’, 5)
TRADE_DT
1991-04-03 NaN
1991-04-04 NaN
1991-04-05 NaN
1991-04-08 NaN
1991-04-09 2.738
…
2017-01-25 0.064
2017-01-26 0.062
2017-02-03 0.076
2017-02-06 0.076
2017-02-07 0.070
Name: atr_5, Length: 6303, dtype: float64
stockdata.rsi(‘000001’, 5)
TRADE_DT
1991-04-03 NaN
1991-04-04 NaN
1991-04-05 NaN
1991-04-08 NaN
1991-04-09 NaN
…
2017-01-25 75.156853
2017-01-26 81.025235
2017-02-03 57.391103
2017-02-06 58.812990
2017-02-07 65.561024
Name: rsi_5, Length: 6303, dtype: float64
stockdata.macd(‘000001’, 25, 9, 5)
TRADE_DT
1991-04-03 NaN
1991-04-04 NaN
1991-04-05 NaN
1991-04-08 NaN
1991-04-09 NaN
…
2017-01-25 0.017994
2017-01-26 0.021877
2017-02-03 0.014808
2017-02-06 0.013298
2017-02-07 0.010009
Name: macd, Length: 6303, dtype: float64
6 回报计算#
freq可选m、q、h、y，分别代表月、季度、半年、年的时间跨频率

6.1 回报率#
calc_return(symbol: str, freq: str)

stockdata.calc_return(‘000001’, ‘q’)
date return_q return
0 1991-06-28 NaT -30.6327
1 1991-09-30 NaT -57.0462
2 1991-12-31 NaT 101.0274
3 1992-03-31 NaT -11.2436
4 1992-06-30 NaT 61.2284
… … … …
99 2016-03-31 NaT -11.2594
100 2016-06-30 NaT -18.2331
101 2016-09-30 NaT 4.2529
102 2016-12-30 NaT 0.3308
103 2017-02-07 NaT 2.1978
104 rows × 3 columns

6.2 夏普比率#
calc_sharpe_ratio(symbol: str, freq: str)

stockdata.calc_sharpe_ratio(‘000001’, ‘q’)
0.04988574991791399
6.3 最大回撤率#
calc_max_drawdown_ratio(symbol: str)

stockdata.calc_max_drawdown_ratio(‘000001’)
91.63934426229508

原文链接：https://blog.csdn.net/weixin_45271607/article/details/117744932?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522166281127916781432956941%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=166281127916781432956941&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-11-117744932-null-null.nonecase&utm_term=%E8%82%A1%E7%A5%A8%E9%85%8D%E8%B5%84