学习记录655@python双均线突破量化回测实战-以比亚迪为例
策略逻辑是:如果5日均线大于10日均线,那么就持有一手仓位,否则不持有仓位,就这么简单明了,然后换算成净值,看净值图情况,查看回测的效果。ps：现实情况肯定没这么简单，比如核心要解决的问题就是选股先选到比亚迪，其次是坚定不宜的持有这么久，才能得到这么高的收益。以下代码对比亚迪2020年1月1日至2022年10月5日进行均线策略回测。根据净值图，最高净值可达到最初净值的20倍。如上图所示，最高净值涨
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以下代码对比亚迪2020年1月1日至2022年10月5日进行均线策略回测
策略逻辑是:如果5日均线大于10日均线,那么就持有一手仓位,否则不持有仓位,就这么简单明了,然后换算成净值,看净值图情况,查看回测的效果。
根据净值图，最高净值可达到最初净值的20倍。
ps：现实情况肯定没这么简单，比如核心要解决的问题就是选股先选到比亚迪，其次是坚定不宜的持有这么久，才能得到这么高的收益。
import tushare as ts
ts.set_token('你的token')
# 以比亚迪前复权日线数据为例进行回测
df = ts.pro_bar(ts_code='002594.SZ', adj='qfq', start_date='20200101', end_date='20221004')
df.sort_values(by="trade_date",inplace=True)
df
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 667 | 002594.SZ | 20200102 | 47.6292 | 48.4082 | 47.4495 | 48.1086 | 47.6093 | 0.4993 | 1.0487 | 159345.70 | 765516.493 |
| 666 | 002594.SZ | 20200103 | 48.1386 | 48.9276 | 47.6192 | 47.9788 | 48.1086 | -0.1298 | -0.2698 | 129936.07 | 628361.961 |
| 665 | 002594.SZ | 20200106 | 47.3296 | 49.1273 | 47.1299 | 48.2185 | 47.9788 | 0.2397 | 0.4996 | 169871.38 | 822172.472 |
| 664 | 002594.SZ | 20200107 | 48.2484 | 48.4382 | 47.7091 | 47.9888 | 48.2185 | -0.2297 | -0.4764 | 93400.58 | 449013.435 |
| 663 | 002594.SZ | 20200108 | 47.4894 | 48.2884 | 47.0899 | 47.2198 | 47.9888 | -0.7690 | -1.6025 | 110974.45 | 529249.404 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 002594.SZ | 20220926 | 266.4000 | 276.0000 | 263.5800 | 272.9500 | 266.4000 | 6.5500 | 2.4587 | 143606.40 | 3911414.127 |
| 3 | 002594.SZ | 20220927 | 272.9600 | 274.8300 | 270.0600 | 273.9600 | 272.9500 | 1.0100 | 0.3700 | 83095.93 | 2262822.665 |
| 2 | 002594.SZ | 20220928 | 273.0000 | 273.0000 | 265.5000 | 265.6200 | 273.9600 | -8.3400 | -3.0442 | 85620.18 | 2293531.516 |
| 1 | 002594.SZ | 20220929 | 270.0000 | 271.8000 | 264.0000 | 264.5700 | 265.6200 | -1.0500 | -0.3953 | 88356.68 | 2355802.796 |
| 0 | 002594.SZ | 20220930 | 263.0000 | 263.5500 | 249.0000 | 252.0100 | 264.5700 | -12.5600 | -4.7473 | 214035.88 | 5421385.039 |
668 rows × 11 columns
import talib as ta
# 两条均线的参数
L1=5
L2=10
# 使用talib计算移动均线
df['ma1']=ta.SMA(df.close.values,timeperiod=L1)
df['ma2']=ta.SMA(df.close.values,timeperiod=L2)
df
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | amount | ma1 | ma2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 667 | 002594.SZ | 20200102 | 47.6292 | 48.4082 | 47.4495 | 48.1086 | 47.6093 | 0.4993 | 1.0487 | 159345.70 | 765516.493 | NaN | NaN |
| 666 | 002594.SZ | 20200103 | 48.1386 | 48.9276 | 47.6192 | 47.9788 | 48.1086 | -0.1298 | -0.2698 | 129936.07 | 628361.961 | NaN | NaN |
| 665 | 002594.SZ | 20200106 | 47.3296 | 49.1273 | 47.1299 | 48.2185 | 47.9788 | 0.2397 | 0.4996 | 169871.38 | 822172.472 | NaN | NaN |
| 664 | 002594.SZ | 20200107 | 48.2484 | 48.4382 | 47.7091 | 47.9888 | 48.2185 | -0.2297 | -0.4764 | 93400.58 | 449013.435 | NaN | NaN |
| 663 | 002594.SZ | 20200108 | 47.4894 | 48.2884 | 47.0899 | 47.2198 | 47.9888 | -0.7690 | -1.6025 | 110974.45 | 529249.404 | 47.9029 | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 002594.SZ | 20220926 | 266.4000 | 276.0000 | 263.5800 | 272.9500 | 266.4000 | 6.5500 | 2.4587 | 143606.40 | 3911414.127 | 270.4220 | 272.107 |
| 3 | 002594.SZ | 20220927 | 272.9600 | 274.8300 | 270.0600 | 273.9600 | 272.9500 | 1.0100 | 0.3700 | 83095.93 | 2262822.665 | 270.3720 | 271.023 |
| 2 | 002594.SZ | 20220928 | 273.0000 | 273.0000 | 265.5000 | 265.6200 | 273.9600 | -8.3400 | -3.0442 | 85620.18 | 2293531.516 | 269.4960 | 269.752 |
| 1 | 002594.SZ | 20220929 | 270.0000 | 271.8000 | 264.0000 | 264.5700 | 265.6200 | -1.0500 | -0.3953 | 88356.68 | 2355802.796 | 268.7000 | 269.259 |
| 0 | 002594.SZ | 20220930 | 263.0000 | 263.5500 | 249.0000 | 252.0100 | 264.5700 | -12.5600 | -4.7473 | 214035.88 | 5421385.039 | 265.8220 | 267.862 |
668 rows × 13 columns
# 计算趋势，0代表没有趋势不做操作，空仓，1代表多头趋势，第二日买入
df['trend']=0
con_long=df['ma1']>df['ma2']
con_short=df['ma1']<=df['ma2']
df.loc[con_long,'trend']=1
df.loc[con_short,'trend']=0
df
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | amount | ma1 | ma2 | trend | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 667 | 002594.SZ | 20200102 | 47.6292 | 48.4082 | 47.4495 | 48.1086 | 47.6093 | 0.4993 | 1.0487 | 159345.70 | 765516.493 | NaN | NaN | 0 |
| 666 | 002594.SZ | 20200103 | 48.1386 | 48.9276 | 47.6192 | 47.9788 | 48.1086 | -0.1298 | -0.2698 | 129936.07 | 628361.961 | NaN | NaN | 0 |
| 665 | 002594.SZ | 20200106 | 47.3296 | 49.1273 | 47.1299 | 48.2185 | 47.9788 | 0.2397 | 0.4996 | 169871.38 | 822172.472 | NaN | NaN | 0 |
| 664 | 002594.SZ | 20200107 | 48.2484 | 48.4382 | 47.7091 | 47.9888 | 48.2185 | -0.2297 | -0.4764 | 93400.58 | 449013.435 | NaN | NaN | 0 |
| 663 | 002594.SZ | 20200108 | 47.4894 | 48.2884 | 47.0899 | 47.2198 | 47.9888 | -0.7690 | -1.6025 | 110974.45 | 529249.404 | 47.9029 | NaN | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 002594.SZ | 20220926 | 266.4000 | 276.0000 | 263.5800 | 272.9500 | 266.4000 | 6.5500 | 2.4587 | 143606.40 | 3911414.127 | 270.4220 | 272.107 | 0 |
| 3 | 002594.SZ | 20220927 | 272.9600 | 274.8300 | 270.0600 | 273.9600 | 272.9500 | 1.0100 | 0.3700 | 83095.93 | 2262822.665 | 270.3720 | 271.023 | 0 |
| 2 | 002594.SZ | 20220928 | 273.0000 | 273.0000 | 265.5000 | 265.6200 | 273.9600 | -8.3400 | -3.0442 | 85620.18 | 2293531.516 | 269.4960 | 269.752 | 0 |
| 1 | 002594.SZ | 20220929 | 270.0000 | 271.8000 | 264.0000 | 264.5700 | 265.6200 | -1.0500 | -0.3953 | 88356.68 | 2355802.796 | 268.7000 | 269.259 | 0 |
| 0 | 002594.SZ | 20220930 | 263.0000 | 263.5500 | 249.0000 | 252.0100 | 264.5700 | -12.5600 | -4.7473 | 214035.88 | 5421385.039 | 265.8220 | 267.862 | 0 |
668 rows × 14 columns
# 假设出现信号后，第二天开盘进行交易，每次开仓1手(即100股)
# 注意pos代表目标仓位,如果trend>1,也就是当5日均线大于10日均线,就持有一手仓位,否则补充持有仓位
# new_pos为当日新增仓位,old_pos为老仓位
# 新旧仓位这里不好理解,可按照表格自己计算梳理下
df['pos']=100*df['trend'].shift(1)
df['pos_shift']=df['pos'].shift(1)
df['new_pos']=df['pos']-df['pos'].shift(1)
df['old_pos']=df['pos']-df['new_pos']
df
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | amount | ma1 | ma2 | trend | pos | pos_shift | new_pos | old_pos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 667 | 002594.SZ | 20200102 | 47.6292 | 48.4082 | 47.4495 | 48.1086 | 47.6093 | 0.4993 | 1.0487 | 159345.70 | 765516.493 | NaN | NaN | 0 | NaN | NaN | NaN | NaN |
| 666 | 002594.SZ | 20200103 | 48.1386 | 48.9276 | 47.6192 | 47.9788 | 48.1086 | -0.1298 | -0.2698 | 129936.07 | 628361.961 | NaN | NaN | 0 | 0.0 | NaN | NaN | NaN |
| 665 | 002594.SZ | 20200106 | 47.3296 | 49.1273 | 47.1299 | 48.2185 | 47.9788 | 0.2397 | 0.4996 | 169871.38 | 822172.472 | NaN | NaN | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 664 | 002594.SZ | 20200107 | 48.2484 | 48.4382 | 47.7091 | 47.9888 | 48.2185 | -0.2297 | -0.4764 | 93400.58 | 449013.435 | NaN | NaN | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 663 | 002594.SZ | 20200108 | 47.4894 | 48.2884 | 47.0899 | 47.2198 | 47.9888 | -0.7690 | -1.6025 | 110974.45 | 529249.404 | 47.9029 | NaN | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 002594.SZ | 20220926 | 266.4000 | 276.0000 | 263.5800 | 272.9500 | 266.4000 | 6.5500 | 2.4587 | 143606.40 | 3911414.127 | 270.4220 | 272.107 | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 002594.SZ | 20220927 | 272.9600 | 274.8300 | 270.0600 | 273.9600 | 272.9500 | 1.0100 | 0.3700 | 83095.93 | 2262822.665 | 270.3720 | 271.023 | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 002594.SZ | 20220928 | 273.0000 | 273.0000 | 265.5000 | 265.6200 | 273.9600 | -8.3400 | -3.0442 | 85620.18 | 2293531.516 | 269.4960 | 269.752 | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 002594.SZ | 20220929 | 270.0000 | 271.8000 | 264.0000 | 264.5700 | 265.6200 | -1.0500 | -0.3953 | 88356.68 | 2355802.796 | 268.7000 | 269.259 | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0 | 002594.SZ | 20220930 | 263.0000 | 263.5500 | 249.0000 | 252.0100 | 264.5700 | -12.5600 | -4.7473 | 214035.88 | 5421385.039 | 265.8220 | 267.862 | 0 | 0.0 | 0.0 | 0.0 | 0.0 |
668 rows × 18 columns
# 把开盘价作为交易价格
df['entry_p']=df['open']
df
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | amount | ma1 | ma2 | trend | pos | pos_shift | new_pos | old_pos | entry_p | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 667 | 002594.SZ | 20200102 | 47.6292 | 48.4082 | 47.4495 | 48.1086 | 47.6093 | 0.4993 | 1.0487 | 159345.70 | 765516.493 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 47.6292 |
| 666 | 002594.SZ | 20200103 | 48.1386 | 48.9276 | 47.6192 | 47.9788 | 48.1086 | -0.1298 | -0.2698 | 129936.07 | 628361.961 | NaN | NaN | 0 | 0.0 | NaN | NaN | NaN | 48.1386 |
| 665 | 002594.SZ | 20200106 | 47.3296 | 49.1273 | 47.1299 | 48.2185 | 47.9788 | 0.2397 | 0.4996 | 169871.38 | 822172.472 | NaN | NaN | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 47.3296 |
| 664 | 002594.SZ | 20200107 | 48.2484 | 48.4382 | 47.7091 | 47.9888 | 48.2185 | -0.2297 | -0.4764 | 93400.58 | 449013.435 | NaN | NaN | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 48.2484 |
| 663 | 002594.SZ | 20200108 | 47.4894 | 48.2884 | 47.0899 | 47.2198 | 47.9888 | -0.7690 | -1.6025 | 110974.45 | 529249.404 | 47.9029 | NaN | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 47.4894 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 002594.SZ | 20220926 | 266.4000 | 276.0000 | 263.5800 | 272.9500 | 266.4000 | 6.5500 | 2.4587 | 143606.40 | 3911414.127 | 270.4220 | 272.107 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 266.4000 |
| 3 | 002594.SZ | 20220927 | 272.9600 | 274.8300 | 270.0600 | 273.9600 | 272.9500 | 1.0100 | 0.3700 | 83095.93 | 2262822.665 | 270.3720 | 271.023 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 272.9600 |
| 2 | 002594.SZ | 20220928 | 273.0000 | 273.0000 | 265.5000 | 265.6200 | 273.9600 | -8.3400 | -3.0442 | 85620.18 | 2293531.516 | 269.4960 | 269.752 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 273.0000 |
| 1 | 002594.SZ | 20220929 | 270.0000 | 271.8000 | 264.0000 | 264.5700 | 265.6200 | -1.0500 | -0.3953 | 88356.68 | 2355802.796 | 268.7000 | 269.259 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 270.0000 |
| 0 | 002594.SZ | 20220930 | 263.0000 | 263.5500 | 249.0000 | 252.0100 | 264.5700 | -12.5600 | -4.7473 | 214035.88 | 5421385.039 | 265.8220 | 267.862 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 263.0000 |
668 rows × 19 columns
# 计算旧仓位的盈利和新仓位的盈利
# 新仓位当日盈亏为当日收盘价与开盘价之差乘以新仓位
# 旧仓位当日盈亏为当日收盘价与昨日收盘价之差乘以旧仓位
df['p&l_new']=(df['close']-df['entry_p'])*df['new_pos']
df['p&l_old']=(df['close']-df['close'].shift(1))*df['old_pos']
# 交易费用,new_pos为100或者-100表示有交易,交易费用统一为5元
df['fee']=0
df.loc[(df['new_pos']==100) | (df['new_pos']==-100),'fee']=5
# 每日盈亏由三部分组成
df['p&l']=df['p&l_new']+df['p&l_old']-df['fee']
df
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | ... | trend | pos | pos_shift | new_pos | old_pos | entry_p | p&l_new | p&l_old | fee | p&l | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 667 | 002594.SZ | 20200102 | 47.6292 | 48.4082 | 47.4495 | 48.1086 | 47.6093 | 0.4993 | 1.0487 | 159345.70 | ... | 0 | NaN | NaN | NaN | NaN | 47.6292 | NaN | NaN | 0 | NaN |
| 666 | 002594.SZ | 20200103 | 48.1386 | 48.9276 | 47.6192 | 47.9788 | 48.1086 | -0.1298 | -0.2698 | 129936.07 | ... | 0 | 0.0 | NaN | NaN | NaN | 48.1386 | NaN | NaN | 0 | NaN |
| 665 | 002594.SZ | 20200106 | 47.3296 | 49.1273 | 47.1299 | 48.2185 | 47.9788 | 0.2397 | 0.4996 | 169871.38 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 47.3296 | 0.0 | 0.0 | 0 | 0.0 |
| 664 | 002594.SZ | 20200107 | 48.2484 | 48.4382 | 47.7091 | 47.9888 | 48.2185 | -0.2297 | -0.4764 | 93400.58 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 48.2484 | -0.0 | -0.0 | 0 | 0.0 |
| 663 | 002594.SZ | 20200108 | 47.4894 | 48.2884 | 47.0899 | 47.2198 | 47.9888 | -0.7690 | -1.6025 | 110974.45 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 47.4894 | -0.0 | -0.0 | 0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 002594.SZ | 20220926 | 266.4000 | 276.0000 | 263.5800 | 272.9500 | 266.4000 | 6.5500 | 2.4587 | 143606.40 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 266.4000 | 0.0 | 0.0 | 0 | 0.0 |
| 3 | 002594.SZ | 20220927 | 272.9600 | 274.8300 | 270.0600 | 273.9600 | 272.9500 | 1.0100 | 0.3700 | 83095.93 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 272.9600 | 0.0 | 0.0 | 0 | 0.0 |
| 2 | 002594.SZ | 20220928 | 273.0000 | 273.0000 | 265.5000 | 265.6200 | 273.9600 | -8.3400 | -3.0442 | 85620.18 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 273.0000 | -0.0 | -0.0 | 0 | 0.0 |
| 1 | 002594.SZ | 20220929 | 270.0000 | 271.8000 | 264.0000 | 264.5700 | 265.6200 | -1.0500 | -0.3953 | 88356.68 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 270.0000 | -0.0 | -0.0 | 0 | 0.0 |
| 0 | 002594.SZ | 20220930 | 263.0000 | 263.5500 | 249.0000 | 252.0100 | 264.5700 | -12.5600 | -4.7473 | 214035.88 | ... | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 263.0000 | -0.0 | -0.0 | 0 | 0.0 |
668 rows × 23 columns
# 计算累计盈亏
df['p&l_cum']=df['p&l'].cumsum()
# 计算净值曲线，假设初始资金是1000
ini_cap=1000
df['capital']=df['p&l_cum']+ini_cap
df['net_value']=df['capital']/ini_cap
df['net_value']=df.net_value.apply(lambda x:x if x>=0 else 0)
df
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | ... | new_pos | old_pos | entry_p | p&l_new | p&l_old | fee | p&l | p&l_cum | capital | net_value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 667 | 002594.SZ | 20200102 | 47.6292 | 48.4082 | 47.4495 | 48.1086 | 47.6093 | 0.4993 | 1.0487 | 159345.70 | ... | NaN | NaN | 47.6292 | NaN | NaN | 0 | NaN | NaN | NaN | 0.00000 |
| 666 | 002594.SZ | 20200103 | 48.1386 | 48.9276 | 47.6192 | 47.9788 | 48.1086 | -0.1298 | -0.2698 | 129936.07 | ... | NaN | NaN | 48.1386 | NaN | NaN | 0 | NaN | NaN | NaN | 0.00000 |
| 665 | 002594.SZ | 20200106 | 47.3296 | 49.1273 | 47.1299 | 48.2185 | 47.9788 | 0.2397 | 0.4996 | 169871.38 | ... | 0.0 | 0.0 | 47.3296 | 0.0 | 0.0 | 0 | 0.0 | 0.00 | 1000.00 | 1.00000 |
| 664 | 002594.SZ | 20200107 | 48.2484 | 48.4382 | 47.7091 | 47.9888 | 48.2185 | -0.2297 | -0.4764 | 93400.58 | ... | 0.0 | 0.0 | 48.2484 | -0.0 | -0.0 | 0 | 0.0 | 0.00 | 1000.00 | 1.00000 |
| 663 | 002594.SZ | 20200108 | 47.4894 | 48.2884 | 47.0899 | 47.2198 | 47.9888 | -0.7690 | -1.6025 | 110974.45 | ... | 0.0 | 0.0 | 47.4894 | -0.0 | -0.0 | 0 | 0.0 | 0.00 | 1000.00 | 1.00000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4 | 002594.SZ | 20220926 | 266.4000 | 276.0000 | 263.5800 | 272.9500 | 266.4000 | 6.5500 | 2.4587 | 143606.40 | ... | 0.0 | 0.0 | 266.4000 | 0.0 | 0.0 | 0 | 0.0 | 14668.52 | 15668.52 | 15.66852 |
| 3 | 002594.SZ | 20220927 | 272.9600 | 274.8300 | 270.0600 | 273.9600 | 272.9500 | 1.0100 | 0.3700 | 83095.93 | ... | 0.0 | 0.0 | 272.9600 | 0.0 | 0.0 | 0 | 0.0 | 14668.52 | 15668.52 | 15.66852 |
| 2 | 002594.SZ | 20220928 | 273.0000 | 273.0000 | 265.5000 | 265.6200 | 273.9600 | -8.3400 | -3.0442 | 85620.18 | ... | 0.0 | 0.0 | 273.0000 | -0.0 | -0.0 | 0 | 0.0 | 14668.52 | 15668.52 | 15.66852 |
| 1 | 002594.SZ | 20220929 | 270.0000 | 271.8000 | 264.0000 | 264.5700 | 265.6200 | -1.0500 | -0.3953 | 88356.68 | ... | 0.0 | 0.0 | 270.0000 | -0.0 | -0.0 | 0 | 0.0 | 14668.52 | 15668.52 | 15.66852 |
| 0 | 002594.SZ | 20220930 | 263.0000 | 263.5500 | 249.0000 | 252.0100 | 264.5700 | -12.5600 | -4.7473 | 214035.88 | ... | 0.0 | 0.0 | 263.0000 | -0.0 | -0.0 | 0 | 0.0 | 14668.52 | 15668.52 | 15.66852 |
668 rows × 26 columns
# 绘制净值曲线
df=df.set_index('trade_date')
df.plot(figsize=(12,6),y=['net_value'])
<AxesSubplot:xlabel='trade_date'>
![[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-RxLeXcoi-1664949829950)(output_8_1.png)]](https://i-blog.csdnimg.cn/blog_migrate/9657e5e7172112831d331d8110d27613.png)
如上图所示，最高净值涨幅近20倍。
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