量化投资02---手绘K线

import akshare as ak
import pandas as pd
import mplfinance as mpf

#从网上获取某只股票的历史行情代码
df = ak.stock_zh_index_daily(symbol="sh000688")

#将本地存储的数据读到DataFrame中
#df = pd.read_csv('./sh000688.csv')
df.head()

Out[274]:

	date	open	high	low	close	volume
0	2019-12-31	1000.000	1000.000	1000.000	1000.000	151620533
1	2020-01-02	1005.617	1019.910	1005.617	1019.723	219385276
2	2020-01-03	1021.669	1029.071	1018.681	1024.482	216058348
3	2020-01-06	1019.999	1040.140	1019.999	1027.277	266301181
4	2020-01-07	1025.663	1043.083	1019.858	1043.083	267592167

In [275]:

# 设置索引，否则无法正确绘图
df.index = pd.to_datetime(df.date)
df.sort_index()
df.head()

Out[275]:

	date	open	high	low	close	volume
date
2019-12-31	2019-12-31	1000.000	1000.000	1000.000	1000.000	151620533
2020-01-02	2020-01-02	1005.617	1019.910	1005.617	1019.723	219385276
2020-01-03	2020-01-03	1021.669	1029.071	1018.681	1024.482	216058348
2020-01-06	2020-01-06	1019.999	1040.140	1019.999	1027.277	266301181
2020-01-07	2020-01-07	1025.663	1043.083	1019.858	1043.083	267592167

In [276]:

#计算10日均线和50日均线（收盘价）
df['ma10'] = df['close'].rolling(10).mean()
df['ma50'] = df['close'].rolling(50).mean()
df.head(60)

Out[276]:

	date	open	high	low	close	volume	ma10	ma50
date
2019-12-31	2019-12-31	1000.000	1000.000	1000.000	1000.000	151620533	NaN	NaN
2020-01-02	2020-01-02	1005.617	1019.910	1005.617	1019.723	219385276	NaN	NaN
2020-01-03	2020-01-03	1021.669	1029.071	1018.681	1024.482	216058348	NaN	NaN
2020-01-06	2020-01-06	1019.999	1040.140	1019.999	1027.277	266301181	NaN	NaN
2020-01-07	2020-01-07	1025.663	1043.083	1019.858	1043.083	267592167	NaN	NaN
2020-01-08	2020-01-08	1035.836	1059.436	1034.513	1051.160	320762650	NaN	NaN
2020-01-09	2020-01-09	1060.580	1081.404	1060.580	1078.312	284342095	NaN	NaN
2020-01-10	2020-01-10	1082.310	1088.543	1076.438	1076.438	251547487	NaN	NaN
2020-01-13	2020-01-13	1076.762	1105.848	1069.779	1105.848	246625919	NaN	NaN
2020-01-14	2020-01-14	1107.776	1133.899	1107.776	1118.306	326246382	1054.4629	NaN
2020-01-15	2020-01-15	1113.252	1120.346	1105.575	1114.766	242046421	1065.9395	NaN
2020-01-16	2020-01-16	1116.508	1161.477	1116.508	1154.303	308528799	1079.3975	NaN
2020-01-17	2020-01-17	1159.372	1252.556	1159.372	1238.664	587359671	1100.8157	NaN
2020-01-20	2020-01-20	1239.439	1270.359	1208.690	1240.509	520259197	1122.1389	NaN
2020-01-21	2020-01-21	1233.646	1247.841	1194.797	1195.014	420678873	1137.3320	NaN
2020-01-22	2020-01-22	1178.784	1247.610	1157.290	1244.525	479474004	1156.6685	NaN
2020-01-23	2020-01-23	1219.233	1262.436	1177.094	1202.102	442593437	1169.0475	NaN
2020-02-03	2020-02-03	1018.276	1082.789	1015.597	1030.550	546654316	1164.4587	NaN
2020-02-04	2020-02-04	1049.956	1153.232	1049.956	1153.232	528620605	1169.1971	NaN
2020-02-05	2020-02-05	1166.973	1240.368	1166.973	1203.613	586021339	1177.7278	NaN
2020-02-06	2020-02-06	1191.731	1251.843	1191.731	1238.757	558998166	1190.1269	NaN
2020-02-07	2020-02-07	1240.038	1307.757	1234.234	1307.757	566478458	1205.4723	NaN
2020-02-10	2020-02-10	1310.526	1336.526	1293.430	1322.250	593290694	1213.8309	NaN
2020-02-11	2020-02-11	1318.420	1318.420	1266.214	1283.572	503753086	1218.1372	NaN
2020-02-12	2020-02-12	1274.796	1360.549	1274.796	1348.702	476390579	1233.5060	NaN
2020-02-13	2020-02-13	1360.129	1409.684	1353.045	1360.709	614405501	1245.1244	NaN
2020-02-14	2020-02-14	1359.373	1382.955	1305.974	1327.224	531016642	1257.6366	NaN
2020-02-17	2020-02-17	1332.965	1381.553	1332.965	1381.553	496988892	1292.7369	NaN
2020-02-18	2020-02-18	1381.583	1404.712	1372.526	1399.384	490560871	1317.3521	NaN
2020-02-19	2020-02-19	1398.781	1412.169	1373.444	1373.444	478319039	1334.3352	NaN
2020-02-20	2020-02-20	1377.756	1390.019	1321.734	1348.338	422226619	1345.2933	NaN
2020-02-21	2020-02-21	1337.557	1373.667	1337.557	1346.863	447545498	1349.2039	NaN
2020-02-24	2020-02-24	1341.988	1359.227	1328.747	1351.710	419998820	1352.1499	NaN
2020-02-25	2020-02-25	1312.479	1371.576	1312.479	1367.368	461159876	1360.5295	NaN
2020-02-26	2020-02-26	1348.075	1361.006	1286.345	1288.242	501581402	1354.4835	NaN
2020-02-27	2020-02-27	1293.187	1293.187	1251.451	1264.941	503282338	1344.9067	NaN
2020-02-28	2020-02-28	1206.858	1231.094	1187.096	1191.055	513208397	1331.2898	NaN
2020-03-02	2020-03-02	1202.959	1259.076	1194.937	1252.624	497973601	1318.3969	NaN
2020-03-03	2020-03-03	1278.269	1332.835	1272.063	1285.034	512293927	1306.9619	NaN
2020-03-04	2020-03-04	1268.834	1277.686	1226.802	1255.723	381952408	1295.1898	NaN
2020-03-05	2020-03-05	1277.706	1277.706	1246.714	1264.788	431261562	1286.8348	NaN
2020-03-06	2020-03-06	1246.096	1255.074	1237.623	1243.373	326121340	1276.4858	NaN
2020-03-09	2020-03-09	1216.035	1223.246	1171.859	1171.859	387270267	1258.5007	NaN
2020-03-10	2020-03-10	1148.397	1216.328	1135.209	1216.328	406492040	1243.3967	NaN
2020-03-11	2020-03-11	1218.516	1233.426	1184.710	1184.710	424850927	1233.0435	NaN
2020-03-12	2020-03-12	1161.922	1177.360	1151.785	1167.065	280538369	1223.2559	NaN
2020-03-13	2020-03-13	1103.956	1187.701	1103.956	1175.282	338330986	1221.6786	NaN
2020-03-16	2020-03-16	1188.078	1200.464	1115.084	1115.084	318118708	1207.9246	NaN
2020-03-17	2020-03-17	1132.949	1141.388	1079.838	1131.547	297267785	1192.5759	NaN
2020-03-18	2020-03-18	1145.302	1173.234	1115.019	1115.019	299956033	1178.5055	1208.04424
2020-03-19	2020-03-19	1116.263	1133.695	1091.240	1128.973	278503183	1164.9240	1210.62370
2020-03-20	2020-03-20	1140.092	1148.090	1114.291	1127.369	235596103	1153.3236	1212.77662
2020-03-23	2020-03-23	1092.501	1096.400	1037.528	1039.884	283047441	1140.1261	1213.08466
2020-03-24	2020-03-24	1067.014	1071.340	1020.249	1060.843	236834177	1124.5776	1213.75598
2020-03-25	2020-03-25	1097.309	1110.099	1094.163	1101.319	249632576	1116.2385	1214.92070
2020-03-26	2020-03-26	1093.331	1111.484	1091.004	1095.338	211377899	1109.0658	1215.80426
2020-03-27	2020-03-27	1109.542	1110.537	1082.932	1084.505	229201253	1099.9881	1215.92812
2020-03-30	2020-03-30	1062.899	1062.899	1021.499	1035.827	226087562	1092.0624	1215.11590
2020-03-31	2020-03-31	1048.736	1055.268	1027.224	1029.749	170367165	1081.8826	1213.59392
2020-04-01	2020-04-01	1026.575	1060.708	1026.575	1037.927	175056079	1074.1734	1211.98634

In [277]:

# 设置mplfinance的蜡烛颜色，up为阳线颜色，down为阴线颜色
my_color = mpf.make_marketcolors(up='r',
                                 down='g',
                                 edge='inherit',
                                 wick='inherit',
                                 volume='inherit')
# 设置图表的背景色
my_style = mpf.make_mpf_style(marketcolors=my_color,
                              figcolor='(0.82, 0.83, 0.85)',
                              gridcolor='(0.82, 0.83, 0.85)')

In [278]:

# 取半年的数据
df = df[51:51+180]
df.head()

Out[278]:

	date	open	high	low	close	volume	ma10	ma50
date
2020-03-20	2020-03-20	1140.092	1148.090	1114.291	1127.369	235596103	1153.3236	1212.77662
2020-03-23	2020-03-23	1092.501	1096.400	1037.528	1039.884	283047441	1140.1261	1213.08466
2020-03-24	2020-03-24	1067.014	1071.340	1020.249	1060.843	236834177	1124.5776	1213.75598
2020-03-25	2020-03-25	1097.309	1110.099	1094.163	1101.319	249632576	1116.2385	1214.92070
2020-03-26	2020-03-26	1093.331	1111.484	1091.004	1095.338	211377899	1109.0658	1215.80426

In [279]:

# 画K线，蜡烛图
mpf.plot(df,type='candle',style=my_style,ylabel='price(RMB)')

In [280]:

# 画带成交量的蜡烛图
mpf.plot(df,type='candle',style=my_style,ylabel='price(RMB)',volume=True,ylabel_lower="vol")

In [281]:

df[["ma10","ma50"]]

Out[281]:

	ma10	ma50
date
2020-03-20	1153.3236	1212.77662
2020-03-23	1140.1261	1213.08466
2020-03-24	1124.5776	1213.75598
2020-03-25	1116.2385	1214.92070
2020-03-26	1109.0658	1215.80426
...	...	...
2020-12-08	1398.0271	1416.62582
2020-12-09	1395.6945	1416.12486
2020-12-10	1393.1819	1414.85046
2020-12-11	1390.7011	1414.21116
2020-12-14	1389.6592	1414.43194

180 rows × 2 columns

In [282]:

plot_add = [mpf.make_addplot(df[["ma10","ma50"]])]

In [283]:

# 画MA10 MA50
mpf.plot(df,type='candle',style=my_style,ylabel='Price(RMB)',volume=True,ylabel_lower="Volumn",addplot=plot_add)

In [284]:

#ma10 = df.iloc[:,7]
ma10 = df['ma10']
ma50 = df['ma50']
s1 = ma10 < ma50
s2 = ma10 > ma50

death_ex = s1 & s2.shift(1) #判断死叉的条件，一组BOOL值
death_date = df.loc[death_ex].index

golden_ex = -(s1 | s2.shift(1)) #判断金叉的条件，一组BOOL值
golden_date = df.loc[golden_ex].index
death_date

Out[284]:

DatetimeIndex(['2020-08-24', '2020-10-27', '2020-11-30'], dtype='datetime64[ns]', name='date', freq=None)

In [285]:

g = pd.Series(data=np.nan,index=df.index)
g[golden_date] = df.loc[golden_date]['close']
g[golden_date] 

Out[285]:

date
2020-05-07    1224.105
2020-10-19    1412.148
2020-11-05    1475.375
dtype: float64

In [286]:

d = pd.Series(data=np.nan,index=df.index)
d[death_date] = df.loc[death_date]['close']
d

Out[286]:

date
2020-03-20   NaN
2020-03-23   NaN
2020-03-24   NaN
2020-03-25   NaN
2020-03-26   NaN
              ..
2020-12-08   NaN
2020-12-09   NaN
2020-12-10   NaN
2020-12-11   NaN
2020-12-14   NaN
Length: 180, dtype: float64

In [287]:

df[['ma10','ma50']]

Out[287]:

	ma10	ma50
date
2020-03-20	1153.3236	1212.77662
2020-03-23	1140.1261	1213.08466
2020-03-24	1124.5776	1213.75598
2020-03-25	1116.2385	1214.92070
2020-03-26	1109.0658	1215.80426
...	...	...
2020-12-08	1398.0271	1416.62582
2020-12-09	1395.6945	1416.12486
2020-12-10	1393.1819	1414.85046
2020-12-11	1390.7011	1414.21116
2020-12-14	1389.6592	1414.43194

180 rows × 2 columns

In [288]:

g.to_frame(name='golden')

Out[288]:

	golden
date
2020-03-20	NaN
2020-03-23	NaN
2020-03-24	NaN
2020-03-25	NaN
2020-03-26	NaN
...	...
2020-12-08	NaN
2020-12-09	NaN
2020-12-10	NaN
2020-12-11	NaN
2020-12-14	NaN

180 rows × 1 columns

In [289]:

g.to_frame(name='golden').loc['2020-05-07']

Out[289]:

golden    1224.105
Name: 2020-05-07 00:00:00, dtype: float64

In [290]:

plot_add=[mpf.make_addplot(df[['ma10','ma50']]),
          mpf.make_addplot(g.to_frame(name='golden'),type='scatter',markersize=100,marker='^',color='r'),
          mpf.make_addplot(d.to_frame(name='death'),type='scatter',markersize=100,marker='v',color='b')]

In [291]:

mpf.plot(df,type='candle',style=my_style,ylabel='Price(RMB)',volume=True,ylabel_lower="Volumn",addplot=plot_add)

In [ ]:

In [ ]: