# 技术分析【3】—— 众星拱月，众口铄金?
> 来源：https://uqer.io/community/share/55498c0af9f06c1c3d68806e
很多股民都习惯于看新闻，根据新闻中对某只股票的评价（或好或坏），进行买卖操作。这里新闻对于股票的评价我们称之为新闻情感。本篇中，我们将做一个小实验，看看这样的操作手法是否合理。
## 1. 数据准备
在我们的量化实验室中，用户可以通过数据API: `NewsSentimentIndexGet` 获取某只股票对应的新闻情感。
```py
res = DataAPI.NewsSentimentIndexGet(secID = '600000.XSHG',field=['secID', 'newsPublishDate', 'sentimentIndex'])
res.tail()
```
| | secID | newsPublishDate | sentimentIndex |
| --- | --- |
| 26 | 600000.XSHG | 2015-05-02 | -0.008371 |
| 27 | 600000.XSHG | 2015-05-03 | -0.016820 |
| 28 | 600000.XSHG | 2015-05-04 | -0.013082 |
| 29 | 600000.XSHG | 2015-05-05 | 0.004557 |
| 30 | 600000.XSHG | 2015-05-06 | -0.026943 |
上面的API调用，获得了最近的浦发银行的每日新闻情感：
+ `secID` 证券代码
+ `newsPublishDate` 交易日
+ `sentimentIndex` 当时交易日的总体新闻情感指标，正的表示评价总体正面，负值表示评价总体负面
默认情况下，会获取最近30天的情感指标。
## 2. 操作手法
我们这里使用程序化的方法，执行如下的操作手法：
+ 获取上证50成分股的最近30日新闻情感
+ 将新闻情感按照 `secID` 分组，每组取平均；即获取每只股票最近30日情感均值
+ 取情感最正面的5只股票
```py
from quartz.api import set_universe
universe = set_universe('SH50')
res = DataAPI.NewsSentimentIndexGet(secID=universe, field=['secID', 'newsPublishDate', 'sentimentIndex'])
res = res.groupby('secID')
res.mean().sort('sentimentIndex', ascending=False).head(5)
```
| | sentimentIndex |
| --- | --- |
| secID | |
| 600406.XSHG | 0.153961 |
| 600372.XSHG | 0.130670 |
| 600018.XSHG | 0.119349 |
| 600887.XSHG | 0.116333 |
| 600196.XSHG | 0.108185 |
## 3. 策略实现
+ 投资域 ：沪深300成分股
+ 业绩基准 ：沪深300指数
+ 调仓频率 ：60个交易日
+ 开仓信号 ：评价最正面的10%股票
+ 清仓信号 ：每个调仓日前一个工作日，清空当前仓位
+ 买入方式 ：等比例买入
+ 回测周期 ：2010年1月1日至2015年4月28日
```py
from CAL.PyCAL import Date
start = '2010-01-01' # 回测起始时间
end = '2015-05-05' # 回测结束时间
benchmark = 'HS300' # 策略参考标准
universe = set_universe('HS300') # 证券池，支持股票和基金
capital_base = 1000000 # 起始资金
longest_history = 0 # handle_data 函数中可以使用的历史数据最长窗口长度
refresh_rate = 1 # 调仓频率，即每 refresh_rate 个交易日执行一次 handle_data() 函数
longest_history = 1
def initialize(account): # 初始化虚拟账户状态
account.isBuyPeriod = False
account.dayCount = 0
def handle_data(account): # 每个交易日的买入卖出指令
account.dayCount += 1
if account.isBuyPeriod: # 每60个工作日（3个月）调仓
hist = account.get_history(longest_history)
endDate = Date.fromDateTime(account.current_date)
startDate = endDate - 30
res = DataAPI.NewsSentimentIndexGet(secID=account.universe, field=['secID', 'newsPublishDate', 'sentimentIndex'], beginDate=startDate.strftime('%Y%m%d'),endDate=endDate.strftime('%Y%m%d'))
res = res.groupby('secID')
# top 10%
top10 = res.mean().sort('sentimentIndex', ascending=False).head(int(0.1*len(res)))
buyList = list(top10.index)
print u"%s 买入 : %s" % (endDate, buyList)
# 等权重买入
if len(buyList) != 0:
singleCash = account.cash / len(buyList)
for stock in buyList:
approximationAmount = int(singleCash / hist[stock]['closePrice'][-1]/100.0) * 100
order(stock, approximationAmount)
account.isBuyPeriod = False
account.dayCount = 0
elif account.dayCount == 59: # 调仓日前一日清空当前仓位
for stock in account.valid_secpos:
order_to(stock,0)
account.isBuyPeriod = True
```
```
2010-04-06 买入 : ['601888.XSHG', '000338.XSHE', '000401.XSHE', '000425.XSHE', '600880.XSHG', '601179.XSHG', '600066.XSHG', '601299.XSHG', '000983.XSHE', '601186.XSHG', '600010.XSHG', '601866.XSHG', '002146.XSHE', '000630.XSHE']
2010-07-05 买入 : ['601006.XSHG', '600660.XSHG', '600188.XSHG', '600498.XSHG', '600585.XSHG', '601168.XSHG', '000878.XSHE', '601766.XSHG', '600060.XSHG', '000630.XSHE', '000625.XSHE']
2010-09-30 买入 : ['600718.XSHG', '600703.XSHG', '000400.XSHE', '600583.XSHG', '002230.XSHE', '601766.XSHG', '601808.XSHG', '600406.XSHG', '600795.XSHG', '600875.XSHG', '600066.XSHG', '600196.XSHG', '000559.XSHE', '601018.XSHG']
2010-12-30 买入 : ['000883.XSHE', '600642.XSHG', '600998.XSHG', '600276.XSHG', '002008.XSHE', '600066.XSHG', '600880.XSHG', '002292.XSHE', '600100.XSHG', '600741.XSHG', '601766.XSHG', '300070.XSHE', '002146.XSHE', '600018.XSHG', '601299.XSHG', '000400.XSHE']
2011-04-01 买入 : ['600583.XSHG', '601179.XSHG', '000402.XSHE', '600058.XSHG', '600900.XSHG', '601299.XSHG', '000623.XSHE', '601766.XSHG', '600118.XSHG', '601117.XSHG', '601006.XSHG', '600038.XSHG', '600893.XSHG', '000559.XSHE', '000937.XSHE', '000876.XSHE']
2011-06-30 买入 : ['600252.XSHG', '600276.XSHG', '600066.XSHG', '601117.XSHG', '600079.XSHG', '600583.XSHG', '601808.XSHG', '000963.XSHE', '600535.XSHG', '600741.XSHG', '600068.XSHG', '600196.XSHG', '600688.XSHG', '600585.XSHG', '002353.XSHE', '601299.XSHG', '601933.XSHG']
2011-09-23 买入 : ['600079.XSHG', '600660.XSHG', '600276.XSHG', '000581.XSHE', '002375.XSHE', '002465.XSHE', '600066.XSHG', '002081.XSHE', '600170.XSHG', '600009.XSHG', '600267.XSHG', '600588.XSHG', '600893.XSHG', '600648.XSHG', '002400.XSHE', '600655.XSHG', '000869.XSHE', '000999.XSHE', '600741.XSHG', '600637.XSHG', '300017.XSHE', '000778.XSHE', '600196.XSHG', '601888.XSHG']
2011-12-23 买入 : ['600079.XSHG', '600863.XSHG', '000963.XSHE', '000581.XSHE', '600741.XSHG', '000400.XSHE', '600660.XSHG', '002475.XSHE', '600060.XSHG', '600271.XSHG', '002415.XSHE', '002081.XSHE', '600256.XSHG', '600009.XSHG', '002465.XSHE', '600166.XSHG', '000338.XSHE', '600068.XSHG', '600674.XSHG', '000630.XSHE', '600066.XSHG', '002422.XSHE', '000999.XSHE', '600340.XSHG']
2012-03-27 买入 : ['000338.XSHE', '002450.XSHE', '600893.XSHG', '601098.XSHG', '600741.XSHG', '601179.XSHG', '300015.XSHE', '002353.XSHE', '601299.XSHG', '600060.XSHG', '600348.XSHG', '002375.XSHE', '600066.XSHG', '600863.XSHG', '002470.XSHE', '600588.XSHG', '600655.XSHG', '000826.XSHE', '002065.XSHE', '600570.XSHG', '002230.XSHE', '300133.XSHE', '600880.XSHG', '000400.XSHE', '600157.XSHG']
2012-06-27 买入 : ['600660.XSHG', '600316.XSHG', '601333.XSHG', '002353.XSHE', '002400.XSHE', '600741.XSHG', '600372.XSHG', '300251.XSHE', '002470.XSHE', '600089.XSHG', '002038.XSHE', '002310.XSHE', '002603.XSHE', '601216.XSHG', '601669.XSHG', '601117.XSHG', '601766.XSHG', '601299.XSHG', '002252.XSHE', '000883.XSHE', '000027.XSHE', '600893.XSHG', '000963.XSHE', '600038.XSHG', '300133.XSHE']
2012-09-19 买入 : ['002465.XSHE', '002470.XSHE', '600660.XSHG', '600718.XSHG', '600583.XSHG', '600079.XSHG', '600633.XSHG', '000963.XSHE', '000338.XSHE', '000826.XSHE', '600570.XSHG', '600372.XSHG', '002146.XSHE', '600436.XSHG', '600867.XSHG', '600832.XSHG', '600498.XSHG', '601231.XSHG', '000400.XSHE', '300017.XSHE', '600271.XSHG', '002051.XSHE', '002450.XSHE', '600588.XSHG', '601158.XSHG', '002129.XSHE', '000792.XSHE']
2012-12-19 买入 : ['600170.XSHG', '300124.XSHE', '002475.XSHE', '600741.XSHG', '002292.XSHE', '600718.XSHG', '601766.XSHG', '002465.XSHE', '002241.XSHE', '002081.XSHE', '002400.XSHE', '600166.XSHG', '000826.XSHE', '600633.XSHG', '600373.XSHG', '601231.XSHG', '000725.XSHE', '000338.XSHE', '601299.XSHG', '600649.XSHG', '600535.XSHG', '601118.XSHG', '600547.XSHG', '600340.XSHG', '600637.XSHG', '002065.XSHE', '300133.XSHE']
2013-03-25 买入 : ['600880.XSHG', '601158.XSHG', '000963.XSHE', '600867.XSHG', '600316.XSHG', '600718.XSHG', '002294.XSHE', '600271.XSHG', '600372.XSHG', '601928.XSHG', '600340.XSHG', '002410.XSHE', '002292.XSHE', '601098.XSHG', '002465.XSHE', '002385.XSHE', '000598.XSHE', '600498.XSHG', '002146.XSHE', '002603.XSHE', '600373.XSHG', '600886.XSHG', '600633.XSHG', '600118.XSHG', '000917.XSHE', '600535.XSHG', '600038.XSHG']
2013-06-27 买入 : ['600741.XSHG', '600660.XSHG', '600900.XSHG', '600008.XSHG', '600018.XSHG', '002400.XSHE', '600066.XSHG', '601333.XSHG', '002292.XSHE', '000826.XSHE', '002465.XSHE', '600703.XSHG', '601299.XSHG', '000401.XSHE', '600583.XSHG', '600276.XSHG', '000917.XSHE', '600079.XSHG', '601098.XSHG', '002475.XSHE', '300124.XSHE', '600633.XSHG', '300015.XSHE', '600827.XSHG', '601800.XSHG', '600373.XSHG', '600637.XSHG']
2013-09-23 买入 : ['300015.XSHE', '000963.XSHE', '600660.XSHG', '601098.XSHG', '600741.XSHG', '600066.XSHG', '600886.XSHG', '600703.XSHG', '600373.XSHG', '002465.XSHE', '601231.XSHG', '601299.XSHG', '300133.XSHE', '601158.XSHG', '000623.XSHE', '600170.XSHG', '600009.XSHG', '002008.XSHE', '600827.XSHG', '600863.XSHG', '601928.XSHG', '600516.XSHG', '002051.XSHE', '000778.XSHE', '600406.XSHG', '300146.XSHE', '000826.XSHE', '300124.XSHE']
2013-12-23 买入 : ['600741.XSHG', '002400.XSHE', '300015.XSHE', '002292.XSHE', '601929.XSHG', '600660.XSHG', '600900.XSHG', '000156.XSHE', '601299.XSHG', '600886.XSHG', '000963.XSHE', '600066.XSHG', '600633.XSHG', '600018.XSHG', '600578.XSHG', '600498.XSHG', '000338.XSHE', '601098.XSHG', '600372.XSHG', '600583.XSHG', '002450.XSHE', '600703.XSHG', '600170.XSHG', '601179.XSHG', '600406.XSHG', '002465.XSHE', '600079.XSHG', '601766.XSHG']
2014-03-25 买入 : ['600900.XSHG', '601299.XSHG', '002292.XSHE', '600886.XSHG', '300133.XSHE', '002400.XSHE', '600741.XSHG', '002465.XSHE', '600497.XSHG', '000963.XSHE', '600018.XSHG', '300015.XSHE', '600633.XSHG', '601231.XSHG', '000338.XSHE', '600066.XSHG', '600585.XSHG', '600583.XSHG', '601928.XSHG', '600578.XSHG', '600703.XSHG', '600170.XSHG', '600498.XSHG', '002146.XSHE', '002241.XSHE', '601607.XSHG', '600372.XSHG', '601929.XSHG']
2014-06-23 买入 : ['600578.XSHG', '600741.XSHG', '600170.XSHG', '600660.XSHG', '601158.XSHG', '600886.XSHG', '002292.XSHE', '002146.XSHE', '600066.XSHG', '600703.XSHG', '600485.XSHG', '300015.XSHE', '002465.XSHE', '000598.XSHE', '600718.XSHG', '601231.XSHG', '601098.XSHG', '002400.XSHE', '600018.XSHG', '601179.XSHG', '600079.XSHG', '600498.XSHG', '600783.XSHG', '600089.XSHG', '000826.XSHE', '600497.XSHG', '600583.XSHG', '000400.XSHE']
2014-09-16 买入 : ['600741.XSHG', '300015.XSHE', '600578.XSHG', '002400.XSHE', '600886.XSHG', '600079.XSHG', '002465.XSHE', '600170.XSHG', '002292.XSHE', '600660.XSHG', '601158.XSHG', '600703.XSHG', '601299.XSHG', '600633.XSHG', '600718.XSHG', '000963.XSHE', '600066.XSHG', '601179.XSHG', '600900.XSHG', '601929.XSHG', '000598.XSHE', '601231.XSHG', '600585.XSHG', '002146.XSHE', '600089.XSHG', '002241.XSHE', '600583.XSHG', '601098.XSHG']
2014-12-16 买入 : ['600741.XSHG', '002400.XSHE', '601929.XSHG', '600578.XSHG', '600079.XSHG', '002292.XSHE', '600170.XSHG', '300015.XSHE', '601158.XSHG', '600718.XSHG', '600783.XSHG', '601098.XSHG', '600900.XSHG', '600583.XSHG', '600703.XSHG', '000963.XSHE', '601179.XSHG', '600018.XSHG', '600660.XSHG', '601800.XSHG', '600585.XSHG', '600886.XSHG', '600066.XSHG', '002146.XSHE', '000338.XSHE', '600497.XSHG', '002465.XSHE', '601607.XSHG']
2015-03-19 买入 : ['601098.XSHG', '600718.XSHG', '000400.XSHE', '600066.XSHG', '600900.XSHG', '600018.XSHG', '600886.XSHG', '000963.XSHE', '600089.XSHG', '600583.XSHG', '600373.XSHG', '002051.XSHE', '002292.XSHE', '002400.XSHE', '002465.XSHE', '600703.XSHG', '600316.XSHG', '600153.XSHG', '600118.XSHG', '300124.XSHE', '002450.XSHE', '600485.XSHG', '601158.XSHG', '600893.XSHG', '601929.XSHG', '600498.XSHG', '600276.XSHG', '000598.XSHE']
```
我们还可以试一下反向操作，选取评价最负面的10%
+ 投资域 ：沪深300成分股
+ 业绩基准 ：沪深300指数
+ 调仓频率 ：60个交易日
+ 开仓信号 ：评价最负面的10%股票
+ 清仓信号 ：每个调仓日前一个工作日，清空当前仓位
+ 买入方式 ：等比例买入
+ 回测周期 ：2010年1月1日至2015年4月28日
```py
from CAL.PyCAL import Date
start = '2010-01-01' # 回测起始时间
end = '2015-05-05' # 回测结束时间
benchmark = 'HS300' # 策略参考标准
universe = set_universe('HS300') # 证券池，支持股票和基金
capital_base = 1000000 # 起始资金
longest_history = 0 # handle_data 函数中可以使用的历史数据最长窗口长度
refresh_rate = 1 # 调仓频率，即每 refresh_rate 个交易日执行一次 handle_data() 函数
longest_history = 1
def initialize(account): # 初始化虚拟账户状态
account.isBuyPeriod = False
account.dayCount = 0
def handle_data(account): # 每个交易日的买入卖出指令
account.dayCount += 1
if account.isBuyPeriod: # 每60个工作日（3个月）调仓
hist = account.get_history(longest_history)
endDate = Date.fromDateTime(account.current_date)
startDate = endDate - 30
res = DataAPI.NewsSentimentIndexGet(secID=account.universe, field=['secID', 'newsPublishDate', 'sentimentIndex'], beginDate=startDate.strftime('%Y%m%d'),endDate=endDate.strftime('%Y%m%d'))
res = res.groupby('secID')
# Bottom 10%
top10 = res.mean().sort('sentimentIndex', ascending=True).head(int(0.1*len(res)))
buyList = list(top10.index)
print u"%s 买入 : %s" % (endDate, buyList)
# 等权重买入
if len(buyList) != 0:
singleCash = account.cash / len(buyList)
for stock in buyList:
approximationAmount = int(singleCash / hist[stock]['closePrice'][-1]/100.0) * 100
order(stock, approximationAmount)
account.isBuyPeriod = False
account.dayCount = 0
elif account.dayCount == 59: # 调仓日前一日清空当前仓位
for stock in account.valid_secpos:
order_to(stock,0)
account.isBuyPeriod = True
```
```
2010-04-06 买入 : ['600143.XSHG', '600588.XSHG', '600900.XSHG', '300017.XSHE', '000825.XSHE', '600276.XSHG', '600839.XSHG', '000503.XSHE', '601898.XSHG', '600497.XSHG', '000878.XSHE', '601166.XSHG', '000876.XSHE', '600036.XSHG']
2010-07-05 买入 : ['600649.XSHG', '601333.XSHG', '000425.XSHE', '000423.XSHE', '002304.XSHE', '002310.XSHE', '000069.XSHE', '000793.XSHE', '601898.XSHG', '600029.XSHG', '300024.XSHE']
2010-09-30 买入 : ['600497.XSHG', '000960.XSHE', '002310.XSHE', '601899.XSHG', '600060.XSHG', '300017.XSHE', '601006.XSHG', '300027.XSHE', '002399.XSHE', '600118.XSHG', '000878.XSHE', '600519.XSHG', '600315.XSHG', '000776.XSHE']
2010-12-30 买入 : ['601006.XSHG', '600208.XSHG', '600497.XSHG', '600436.XSHG', '601899.XSHG', '000793.XSHE', '000783.XSHE', '600804.XSHG', '600583.XSHG', '600369.XSHG', '000858.XSHE', '601166.XSHG', '000538.XSHE', '600597.XSHG', '600011.XSHG', '600058.XSHG']
2011-04-01 买入 : ['601888.XSHG', '300058.XSHE', '601899.XSHG', '600886.XSHG', '600415.XSHG', '000060.XSHE', '000009.XSHE', '600674.XSHG', '002292.XSHE', '600663.XSHG', '600362.XSHG', '601998.XSHG', '601601.XSHG', '000686.XSHE', '000768.XSHE', '601939.XSHG']
2011-06-30 买入 : ['002385.XSHE', '000009.XSHE', '600085.XSHG', '300017.XSHE', '600485.XSHG', '300015.XSHE', '000858.XSHE', '600664.XSHG', '002500.XSHE', '601899.XSHG', '600497.XSHG', '000630.XSHE', '000895.XSHE', '300070.XSHE', '600036.XSHG', '600089.XSHG', '000778.XSHE']
2011-09-23 买入 : ['600415.XSHG', '600783.XSHG', '600832.XSHG', '002007.XSHE', '601179.XSHG', '002470.XSHE', '002399.XSHE', '601899.XSHG', '000009.XSHE', '601866.XSHG', '000792.XSHE', '601216.XSHG', '600153.XSHG', '600664.XSHG', '600867.XSHG', '002429.XSHE', '002008.XSHE', '601098.XSHG', '600108.XSHG', '601958.XSHG', '000629.XSHE', '601898.XSHG', '600436.XSHG', '600583.XSHG']
2011-12-23 买入 : ['002456.XSHE', '000793.XSHE', '600362.XSHG', '600583.XSHG', '600348.XSHG', '600588.XSHG', '000895.XSHE', '002252.XSHE', '000402.XSHE', '002399.XSHE', '002429.XSHE', '601866.XSHG', '600352.XSHG', '002603.XSHE', '600031.XSHG', '002385.XSHE', '601899.XSHG', '000970.XSHE', '000002.XSHE', '600369.XSHG', '000725.XSHE', '000060.XSHE', '000009.XSHE', '600649.XSHG']
2012-03-27 买入 : ['600832.XSHG', '601899.XSHG', '000009.XSHE', '600998.XSHG', '600315.XSHG', '000536.XSHE', '600664.XSHG', '002422.XSHE', '002399.XSHE', '002400.XSHE', '600485.XSHG', '000060.XSHE', '600029.XSHG', '002570.XSHE', '002603.XSHE', '000623.XSHE', '601699.XSHG', '000538.XSHE', '600221.XSHG', '000895.XSHE', '601818.XSHG', '600068.XSHG', '000002.XSHE', '600398.XSHG', '600153.XSHG']
2012-06-27 买入 : ['000793.XSHE', '600827.XSHG', '002416.XSHE', '002570.XSHE', '000858.XSHE', '000400.XSHE', '600348.XSHG', '601607.XSHG', '300015.XSHE', '000536.XSHE', '002385.XSHE', '600369.XSHG', '000009.XSHE', '000895.XSHE', '002594.XSHE', '002001.XSHE', '600880.XSHG', '603000.XSHG', '002007.XSHE', '600398.XSHG', '000983.XSHE', '000725.XSHE', '600015.XSHG', '300017.XSHE', '601231.XSHG']
2012-09-19 买入 : ['600516.XSHG', '600398.XSHG', '002399.XSHE', '000869.XSHE', '000060.XSHE', '000960.XSHE', '601258.XSHG', '601866.XSHG', '600809.XSHG', '300124.XSHE', '600369.XSHG', '002024.XSHE', '600315.XSHG', '002416.XSHE', '601888.XSHG', '002304.XSHE', '002410.XSHE', '002385.XSHE', '600029.XSHG', '600008.XSHG', '600518.XSHG', '000858.XSHE', '600519.XSHG', '603000.XSHG', '600115.XSHG', '600221.XSHG', '601933.XSHG']
2012-12-19 买入 : ['000869.XSHE', '000895.XSHE', '600369.XSHG', '603993.XSHG', '601258.XSHG', '300146.XSHE', '600352.XSHG', '000858.XSHE', '601333.XSHG', '600398.XSHG', '600519.XSHG', '600015.XSHG', '601888.XSHG', '600415.XSHG', '600031.XSHG', '600809.XSHG', '601555.XSHG', '000157.XSHE', '002304.XSHE', '002673.XSHE', '600029.XSHG', '603000.XSHG', '000728.XSHE', '000568.XSHE', '000060.XSHE', '600221.XSHG', '000960.XSHE']
2013-03-25 买入 : ['000623.XSHE', '000536.XSHE', '002375.XSHE', '600406.XSHG', '601699.XSHG', '600143.XSHG', '600015.XSHG', '002310.XSHE', '600832.XSHG', '000869.XSHE', '000858.XSHE', '002399.XSHE', '600549.XSHG', '600518.XSHG', '600348.XSHG', '002416.XSHE', '002051.XSHE', '000792.XSHE', '603000.XSHG', '600519.XSHG', '601258.XSHG', '601168.XSHG', '002653.XSHE', '000400.XSHE', '601898.XSHG', '601888.XSHG', '300124.XSHE']
2013-06-27 买入 : ['000839.XSHE', '002653.XSHE', '000629.XSHE', '601899.XSHG', '600031.XSHG', '002422.XSHE', '600085.XSHG', '000825.XSHE', '600395.XSHG', '603000.XSHG', '600998.XSHG', '600570.XSHG', '603993.XSHG', '000568.XSHE', '000858.XSHE', '300146.XSHE', '600362.XSHG', '600315.XSHG', '601398.XSHG', '000060.XSHE', '600369.XSHG', '600519.XSHG', '601600.XSHG', '601988.XSHG', '000157.XSHE', '600839.XSHG', '600348.XSHG']
2013-09-23 买入 : ['600873.XSHG', '600642.XSHG', '600348.XSHG', '002422.XSHE', '601818.XSHG', '600188.XSHG', '000960.XSHE', '002399.XSHE', '000878.XSHE', '000858.XSHE', '002153.XSHE', '600177.XSHG', '600519.XSHG', '600031.XSHG', '600029.XSHG', '601111.XSHG', '603000.XSHG', '600489.XSHG', '601898.XSHG', '000536.XSHE', '600398.XSHG', '603993.XSHG', '002304.XSHE', '600395.XSHG', '601601.XSHG', '000568.XSHE', '000983.XSHE', '600518.XSHG']
2013-12-23 买入 : ['600348.XSHG', '600664.XSHG', '600315.XSHG', '600489.XSHG', '002653.XSHE', '601899.XSHG', '000960.XSHE', '000709.XSHE', '000536.XSHE', '601600.XSHG', '000858.XSHE', '002422.XSHE', '601866.XSHG', '000629.XSHE', '603000.XSHG', '000825.XSHE', '600519.XSHG', '600518.XSHG', '601111.XSHG', '600188.XSHG', '600177.XSHG', '000983.XSHE', '601888.XSHG', '601618.XSHG', '600011.XSHG', '600383.XSHG', '601258.XSHG', '000878.XSHE']
2014-03-25 买入 : ['600348.XSHG', '000060.XSHE', '600664.XSHG', '000825.XSHE', '000536.XSHE', '000568.XSHE', '600489.XSHG', '603699.XSHG', '601225.XSHG', '601111.XSHG', '600362.XSHG', '601166.XSHG', '000983.XSHE', '000858.XSHE', '603000.XSHG', '002422.XSHE', '600115.XSHG', '600398.XSHG', '600029.XSHG', '601398.XSHG', '600036.XSHG', '601618.XSHG', '600177.XSHG', '601628.XSHG', '600188.XSHG', '603993.XSHG', '000709.XSHE', '601939.XSHG']
2014-06-23 买入 : ['600315.XSHG', '600489.XSHG', '601225.XSHG', '601899.XSHG', '603288.XSHG', '600108.XSHG', '600348.XSHG', '002422.XSHE', '600436.XSHG', '600664.XSHG', '601111.XSHG', '000629.XSHE', '002653.XSHE', '601628.XSHG', '601555.XSHG', '603000.XSHG', '000568.XSHE', '601601.XSHG', '000858.XSHE', '601600.XSHG', '000728.XSHE', '000825.XSHE', '600115.XSHG', '600383.XSHG', '002304.XSHE', '600188.XSHG', '601258.XSHG', '002153.XSHE']
2014-09-16 买入 : ['600348.XSHG', '601225.XSHG', '600489.XSHG', '600664.XSHG', '000825.XSHE', '002653.XSHE', '000536.XSHE', '000983.XSHE', '000060.XSHE', '603000.XSHG', '600362.XSHG', '601111.XSHG', '600518.XSHG', '000858.XSHE', '601898.XSHG', '600177.XSHG', '601899.XSHG', '600143.XSHG', '601600.XSHG', '600115.XSHG', '000568.XSHE', '600519.XSHG', '603288.XSHG', '600029.XSHG', '600315.XSHG', '601618.XSHG', '600383.XSHG', '000800.XSHE']
2014-12-16 买入 : ['600348.XSHG', '600664.XSHG', '600489.XSHG', '600315.XSHG', '000983.XSHE', '600518.XSHG', '000060.XSHE', '000536.XSHE', '600362.XSHG', '600873.XSHG', '600739.XSHG', '603000.XSHG', '601225.XSHG', '000825.XSHE', '000568.XSHE', '603993.XSHG', '601618.XSHG', '000709.XSHE', '601899.XSHG', '600519.XSHG', '000858.XSHE', '601168.XSHG', '601166.XSHG', '000869.XSHE', '600115.XSHG', '601111.XSHG', '601601.XSHG', '603288.XSHG']
2015-03-19 买入 : ['600348.XSHG', '000060.XSHE', '601225.XSHG', '601898.XSHG', '601088.XSHG', '000568.XSHE', '603000.XSHG', '002422.XSHE', '601899.XSHG', '603288.XSHG', '601166.XSHG', '601111.XSHG', '600315.XSHG', '600739.XSHG', '000709.XSHE', '600489.XSHG', '600519.XSHG', '600873.XSHG', '601699.XSHG', '600016.XSHG', '601398.XSHG', '601939.XSHG', '601009.XSHG', '000536.XSHE', '000895.XSHE', '601988.XSHG', '600015.XSHG', '600188.XSHG']
```
嗯？主动收益率还是正的。。。。看来这样的简单正面负面指标还不足以进行有效的区分。
- Python 量化交易教程
- 第一部分 新手入门
- 一 量化投资视频学习课程
- 二 Python 手把手教学
- 量化分析师的Python日记【第1天：谁来给我讲讲Python？】
- 量化分析师的Python日记【第2天：再接着介绍一下Python呗】
- 量化分析师的Python日记【第3天：一大波金融Library来袭之numpy篇】
- 量化分析师的Python日记【第4天：一大波金融Library来袭之scipy篇】
- 量化分析师的Python日记【第5天：数据处理的瑞士军刀pandas】
- 量化分析师的Python日记【第6天：数据处理的瑞士军刀pandas下篇
- 量化分析师的Python日记【第7天：Q Quant 之初出江湖】
- 量化分析师的Python日记【第8天 Q Quant兵器谱之函数插值】
- 量化分析师的Python日记【第9天 Q Quant兵器谱之二叉树】
- 量化分析师的Python日记【第10天 Q Quant兵器谱 -之偏微分方程1】
- 量化分析师的Python日记【第11天 Q Quant兵器谱之偏微分方程2】
- 量化分析师的Python日记【第12天：量化入门进阶之葵花宝典：因子如何产生和回测】
- 量化分析师的Python日记【第13天 Q Quant兵器谱之偏微分方程3】
- 量化分析师的Python日记【第14天：如何在优矿上做Alpha对冲模型】
- 量化分析师的Python日记【第15天：如何在优矿上搞一个wealthfront出来】
- 第二部分 股票量化相关
- 一 基本面分析
- 1.1 alpha 多因子模型
- 破解Alpha对冲策略——观《量化分析师Python日记第14天》有感
- 熔断不要怕， alpha model 为你保驾护航！
- 寻找 alpha 之: alpha 设计
- 1.2 基本面因子选股
- Porfolio（现金比率+负债现金+现金保障倍数）+市盈率
- ROE选股指标
- 成交量因子
- ROIC&cashROIC
- 【国信金工】资产周转率选股模型
- 【基本面指标】Cash Cow
- 量化因子选股——净利润／营业总收入
- 营业收入增长率+市盈率
- 1.3 财报阅读 • [米缸量化读财报] 资产负债表-投资相关资产
- 1.4 股东分析
- 技术分析入门 【2】 —— 大家抢筹码（06年至12年版）
- 技术分析入门 【2】 —— 大家抢筹码（06年至12年版）— 更新版
- 谁是中国A股最有钱的自然人
- 1.5 宏观研究
- 【干货包邮】手把手教你做宏观择时
- 宏观研究：从估值角度看当前市场
- 追寻“国家队”的足迹
- 二 套利
- 2.1 配对交易
- HS300ETF套利（上）
- 【统计套利】配对交易
- 相似公司股票搬砖
- Paired trading
- 2.2 期现套利 • 通过股指期货的期现差与 ETF 对冲套利
- 三 事件驱动
- 3.1 盈利预增
- 盈利预增事件
- 事件驱动策略示例——盈利预增
- 3.2 分析师推荐 • 分析师的金手指？
- 3.3 牛熊转换
- 历史总是相似 牛市还在延续
- 历史总是相似 牛市已经见顶？
- 3.4 熔断机制 • 股海拾贝之 [熔断错杀股]
- 3.5 暴涨暴跌 • [实盘感悟] 遇上暴跌我该怎么做？
- 3.6 兼并重组、举牌收购 • 宝万战-大戏开幕
- 四 技术分析
- 4.1 布林带
- 布林带交易策略
- 布林带回调系统-日内
- Conservative Bollinger Bands
- Even More Conservative Bollinger Bands
- Simple Bollinger Bands
- 4.2 均线系统
- 技术分析入门 —— 双均线策略
- 5日线10日线交易策略
- 用5日均线和10日均线进行判断 --- 改进版
- macross
- 4.3 MACD
- Simple MACD
- MACD quantization trade
- MACD平滑异同移动平均线方法
- 4.4 阿隆指标 • 技术指标阿隆（ Aroon ）全解析
- 4.5 CCI • CCI 顺势指标探索
- 4.6 RSI
- 重写 rsi
- RSI指标策略
- 4.7 DMI • DMI 指标体系的构建及简单应用
- 4.8 EMV • EMV 技术指标的构建及应用
- 4.9 KDJ • KDJ 策略
- 4.10 CMO
- CMO 策略模仿练习 1
- CMO策略模仿练习2
- [技术指标] CMO
- 4.11 FPC • FPC 指标选股
- 4.12 Chaikin Volatility
- 嘉庆离散指标测试
- 4.13 委比 • 实时计算委比
- 4.14 封单量
- 按照封单跟流通股本比例排序，剔除6月上市新股，前50
- 涨停股票封单统计
- 实时计算涨停板股票的封单资金与总流通市值的比例
- 4.15 成交量 • 决战之地， IF1507 ！
- 4.16 K 线分析 • 寻找夜空中最亮的星
- 五 量化模型
- 5.1 动量模型
- Momentum策略
- 【小散学量化】-2-动量模型的简单实践
- 一个追涨的策略(修正版)
- 动量策略（momentum driven）
- 动量策略（momentum driven）——修正版
- 最经典的Momentum和Contrarian在中国市场的测试
- 最经典的Momentum和Contrarian在中国市场的测试-yanheven改进
- ［策略］基于胜率的趋势交易策略
- 策略探讨（更新）：价量结合+动量反转
- 反向动量策略（reverse momentum driven）
- 轻松跑赢大盘 - 主题Momentum策略
- Contrarian strategy
- 5.2 Joseph Piotroski 9 F-Score Value Investing Model · 基本面选股系统：Piotroski F-Score ranking system
- 5.3 SVR · 使用SVR预测股票开盘价 v1.0
- 5.4 决策树、随机树
- 决策树模型（固定模型）
- 基于Random Forest的决策策略
- 5.5 钟摆理论 · 钟摆理论的简单实现——完美躲过股灾和精准抄底
- 5.6 海龟模型
- simple turtle
- 侠之大者 一起赚钱
- 5.7 5217 策略 · 白龙马的新手策略
- 5.8 SMIA · 基于历史状态空间相似性匹配的行业配置 SMIA 模型—取交集
- 5.9 神经网络
- 神经网络交易的训练部分
- 通过神经网络进行交易
- 5.10 PAMR · PAMR ： 基于均值反转的投资组合选择策略 - 修改版
- 5.11 Fisher Transform · Using Fisher Transform Indicator
- 5.12 分型假说， Hurst 指数 · 分形市场假说，一个听起来很美的假说
- 5.13 变点理论 · 变点策略初步
- 5.14 Z-score Model
- Zscore Model Tutorial
- 信用债风险模型初探之：Z-Score Model
- user-defined package
- 5.15 机器学习 · Machine Learning 学习笔记（一） by OTreeWEN
- 5.16 DualTrust 策略和布林强盗策略
- 5.17 卡尔曼滤波
- 5.18 LPPL anti-bubble model
- 今天大盘熔断大跌，后市如何—— based on LPPL anti-bubble model
- 破解股市泡沫之谜——对数周期幂率（LPPL）模型
- 六 大数据模型
- 6.1 市场情绪分析
- 通联情绪指标策略
- 互联网+量化投资 大数据指数手把手
- 6.2 新闻热点
- 如何使用优矿之“新闻热点”？
- 技术分析【3】—— 众星拱月，众口铄金?
- 七 排名选股系统
- 7.1 小市值投资法
- 学习笔记：可模拟（小市值+便宜 的修改版）
- 市值最小300指数
- 流通市值最小股票（新筛选器版）
- 持有市值最小的10只股票
- 10% smallest cap stock
- 7.2 羊驼策略
- 羊驼策略
- 羊驼反转策略（修改版）
- 羊驼反转策略
- 我的羊驼策略，选5只股无脑轮替
- 7.3 低价策略
- 专捡便宜货(新版quartz)
- 策略原理
- 便宜就是 alpha
- 八 轮动模型
- 8.1 大小盘轮动 · 新手上路 -- 二八ETF择时轮动策略2.0
- 8.2 季节性策略
- Halloween Cycle
- Halloween cycle 2
- 夏买电，东买煤？
- 历史的十一月板块涨幅
- 8.3 行业轮动
- 银行股轮动
- 申万二级行业在最近1年、3个月、5个交易日的涨幅统计
- 8.4 主题轮动
- 快速研究主题神器
- recommendation based on subject
- strategy7: recommendation based on theme
- 板块异动类
- 风险因子（离散类）
- 8.5 龙头轮动
- Competitive Securities
- Market Competitiveness
- 主题龙头类
- 九 组合投资
- 9.1 指数跟踪 · [策略] 指数跟踪低成本建仓策略
- 9.2 GMVP · Global Minimum Variance Portfolio (GMVP)
- 9.3 凸优化 · 如何在 Python 中利用 CVXOPT 求解二次规划问题
- 十 波动率
- 10.1 波动率选股 · 风平浪静 风起猪飞
- 10.2 波动率择时
- 基于 VIX 指数的择时策略
- 简单低波动率指数
- 10.3 Arch/Garch 模型 · 如何使用优矿进行 GARCH 模型分析
- 十一 算法交易
- 11.1 VWAP · Value-Weighted Average Price (VWAP)
- 十二 中高频交易
- 12.1 order book 分析 · 基于高频 limit order book 数据的短程价格方向预测—— via multi-class SVM
- 12.2 日内交易 · 大盘日内走势 (for 择时)
- 十三 Alternative Strategy
- 13.1 易经、传统文化 · 老黄历诊股
- 第三部分 基金、利率互换、固定收益类
- 一 分级基金
- “优矿”集思录——分级基金专题
- 基于期权定价的分级基金交易策略
- 基于期权定价的兴全合润基金交易策略
- 二 基金分析
- Alpha 基金“黑天鹅事件” -- 思考以及原因
- 三 债券
- 债券报价中的小陷阱
- 四 利率互换
- Swap Curve Construction
- 中国 Repo 7D 互换的例子
- 第四部分 衍生品相关
- 一 期权数据
- 如何获取期权市场数据快照
- 期权高频数据准备
- 二 期权系列
- [ 50ETF 期权] 1. 历史成交持仓和 PCR 数据
- 【50ETF期权】 2. 历史波动率
- 【50ETF期权】 3. 中国波指 iVIX
- 【50ETF期权】 4. Greeks 和隐含波动率微笑
- 【50ETF期权】 5. 日内即时监控 Greeks 和隐含波动率微笑
- 【50ETF期权】 5. 日内即时监控 Greeks 和隐含波动率微笑
- 三 期权分析
- 【50ETF期权】 期权择时指数 1.0
- 每日期权风险数据整理
- 期权头寸计算
- 期权探秘1
- 期权探秘2
- 期权市场一周纵览
- 基于期权PCR指数的择时策略
- 期权每日成交额PC比例计算
- 四 期货分析
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