股票交易软件接口有哪些-学习记录655@python双均线突破量化回测实战以比亚迪为例

学习记录655@python双均线突破量化回测实战以比亚迪为例

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,看净值情况,查看回测的效果。根据净值，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

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

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

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

# 计算趋势，0代表没有趋势不做操作，1代表多头趋势，第二日买入,-1代表空头趋势,第二日卖出平仓,或者不做操作
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

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

# 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

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

# 把开盘价作为交易价格
df["entry_p"]=df["open"]
df

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

# 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

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

# 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

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

# 绘制净值曲线
df=df.set_index("trade_date")
df.plot(figsize=(12,6),y=["net_value"])

<AxesSubplot:xlabel="trade_date">

文章为作者独立观点，不代表股票交易接口观点