def get_total_csv(rightStock, dfname, stockArgX):
rightStocklist = [dfname]
totalProfit = pd.DataFrame({'rule': rightStocklist})
if (stockArgX.strategy == True):
#复制一份原始stock，用于策略买卖收益
rightStockStrategy = copy.deepcopy(rightStock)
totalProfit = rule.add_strategy_income(totalProfit, rightStockStrategy,
stockArgX, dfname)
#复制一份原始stock,用于计算原价卖收益
# rightStockH = copy.deepcopy(rightStock)
#计算原价卖收益
# totalProfit = add_final_income(totalProfit, rightStockH,stockArgX,dfname)
totalProfit = add_right_count(totalProfit, rightStock)
rightStock = group_by_date(rightStock)
# 是否生产中间表
if (stockArgX.mean):
rightStock.to_csv(con.detailPath + str(dateUtil.get_date_date()) +
dateUtil.get_hour_and_minute_str() + dfname +
'mean.csv',
index=False)
# 增加筛选规则对应的样本数量和百分百显示
totalProfit = add_right_count_by_date(totalProfit, rightStock)
# 计算并且增加 评价收益
totalProfit = add_income_mean(totalProfit, rightStock)
totalProfit = add_days_income_percent(totalProfit, rightStock)
# totalProfit = add_high_income_mean(totalProfit,rightStockH)
# totalProfit = add_max_price_day_percent(totalProfit, rightStock)
# 清除临时detail缓存
del rightStock
# 马上重置垃圾清除器
gc.collect()
return totalProfit
def create_stock_csv(codeStr):
log.info("当前开始生成代码" + codeStr + "的CSV文件!")
stock_data = ts.get_k_data(codeStr,
start='2010-01-01',
end=str(dateUtil.get_date_date()))
stock_data.to_csv(con.csvPath + codeStr + '.csv',
index=False,
encoding='gbk')
def create_all_index_csv():
for codeStr in con.baseIndexList:
log.info("当前开始生成指数" + codeStr + "的CSV文件!")
index_data = ts.get_k_data(codeStr,
index=True,
start='2010-01-01',
end=str(dateUtil.get_date_date()))
index_data.to_csv(con.indexCsvPath + codeStr + '.csv',
index=False,
encoding='gbk')
def generate_detail_csv(detailTempList, stockArgX, ruleName):
# 8.如果规则筛选出了样本则进行生成csv等
if (len(detailTempList) > 0):
# 连接所有的detail数据
tempDetail = pd.concat(detailTempList)
# 转换为pandas的dataframe格式
tempDetail = pd.DataFrame(tempDetail)
# 按日期逆向排序
tempDetail = tempDetail.sort_values(by=['date'], ascending=False)
# 10.是否生成detail表
if (stockArgX.detail and len(tempDetail) > 0):
tempDetail.to_csv(con.detailPath + str(dateUtil.get_date_date()) +
dateUtil.get_hour_and_minute_str() + ruleName +
'detail.csv',
index=False)
#为邮件保存临时detail
stockArgX.tempDetail = tempDetail
return tempDetail
def start_stock_generator():
'''
启动股票生成器
'''
# 定义参数类
class stockArg:
# total报表命名
totalCsvName = ''
# code信息
codeInfo = ''
stockArgX = stockArg()
'''股票类型过滤'''
stockArgX.stockTypeTF = False
'''
'''
stockArgX.stockTypeList = []
''' 基础参数 '''
# total表名称
stockArgX.totalCsvName = '今日筛选'
# 是否生成明细表开关
stockArgX.detail = True
# 是否生成平均表开关
stockArgX.mean = False
# 是否生成保本表开关
stockArgX.save = False
# 是否分年开关
stockArgX.groupByDaysTF = False
# 是否使用开始日期参数开关
stockArgX.dateBeginTF = False
# 开始日期参数
stockArgX.dateBeginRange = str(dateUtil.get_date_date())
# stockArgX.dateBeginRange = '2018-05-21'
# 是否使用结束日期参数开关
stockArgX.dateEndTF = False
# 结束日期参数
stockArgX.dateEndRange = '2018-05-21'
''' 规则参数 '''
#成熟规则获取开关,开启时下面定义规则无效
stockArgX.mustByCsvTF = False
stockArgX.mustBmustByCsvTFyCsvName = 'gongshiv2'
stockArgX.mustByCsvRule = [1, 2, 3, 5, 6, 7, 8, 10, 11, 13, 19, 21]
stockArgX.ruleNumListChoose = [10]
stockArgX.ruleNumListMust = [1, 8, 11, 13, 21]
# 大盘规则开关
stockArgX.indexOpen = False
''' 策略参数开始 '''
# 策略开关
stockArgX.strategy = False
'''买入策略'''
#买入策略二
# 买入线 开关
stockArgX.buyLineTF = False
# 买入线 等待天数
stockArgX.buyLineWaitDays = 3
# 买入线 为day0close的加n个百分点
stockArgX.buyLine = -2
# 买入线期望修正
stockArgX.buyLineExpect = 0
'''卖出策略'''
# 按收盘卖开关
stockArgX.sellIncomeByCloseTF = False
# 按收盘卖day几c
stockArgX.sellCloseDay = 2
# 固定收益卖出开关
stockArgX.sellIncomeTF = False
# 固定收益卖出参数(百分点)
stockArgX.sellIncome = 10
# 按low卖出收益开关
stockArgX.sellIncomeByLowTF = False
# 按low卖出收益参数
stockArgX.sellIncomeByLow = 10
# 策略止损线开关
stockArgX.stopLineTF = False
# 策略止损线(百分点)
stockArgX.stopLine = -10
# 策略止损线期望修正(百分点)
stockArgX.stopLineExpect = 0
# 策略失败割肉交易日参数(day几 收盘割肉卖)
stockArgX.cutMeatDay = 15
''' 策略参数结束 '''
''' 是否发送邮件开关'''
stockArgX.TFmail = False
#是否成功筛选
stockArgX.TFHaveResult = False
#是否已经赋值第一排名信息
stockArgX.TFHaveHighStock = False
startDate = dateUtil.print_date()
# 执行规则数据生成器方法！
rule.make_stockData(stockArgX)
dateUtil.print_end_date(startDate)
def add_final_income(df, rightStock, stockArgX, dfname):
rightStockGood = rightStock[rightStock.day2closeIncome >= 0]
rightStockNormal = rightStock[(rightStock.day2closeIncome < 0)
& ((rightStock.day3highIncome >= 0)
| (rightStock.day4highIncome >= 0)
| (rightStock.day5highIncome >= 0)
| (rightStock.day6highIncome >= 0)
| (rightStock.day7highIncome >= 0)
| (rightStock.day8highIncome >= 0)
| (rightStock.day9highIncome >= 0)
| (rightStock.day11highIncome >= 0)
| (rightStock.day12highIncome >= 0)
| (rightStock.day13highIncome >= 0)
| (rightStock.day14highIncome >= 0)
| (rightStock.day15highIncome >= 0)
| (rightStock.day16highIncome >= 0)
| (rightStock.day17highIncome >= 0)
| (rightStock.day18highIncome >= 0)
| (rightStock.day19highIncome >= 0)
| (rightStock.day20highIncome >= 0))]
rightStockLoss = rightStock[(rightStock['day2highIncome'] < 0)
& (rightStock['day3highIncome'] < 0)
& (rightStock['day4highIncome'] < 0)
& (rightStock['day5highIncome'] < 0)
& (rightStock['day6highIncome'] < 0)
& (rightStock['day7highIncome'] < 0)
& (rightStock['day8highIncome'] < 0)
& (rightStock['day9highIncome'] < 0)
& (rightStock['day11highIncome'] < 0)
& (rightStock['day12highIncome'] < 0)
& (rightStock['day13highIncome'] < 0)
& (rightStock['day14highIncome'] < 0)
& (rightStock['day15highIncome'] < 0)
& (rightStock['day16highIncome'] < 0)
& (rightStock['day17highIncome'] < 0)
& (rightStock['day18highIncome'] < 0)
& (rightStock['day19highIncome'] < 0)
& (rightStock['day20highIncome'] < 0)]
# 是否生产保本表
if (stockArgX.save):
rightStockGood.to_csv(con.detailPath + str(dateUtil.get_date_date()) +
dateUtil.get_hour_and_minute_str() + dfname +
'day2收盘卖.csv',
index=False)
rightStockNormal.to_csv(
con.detailPath + str(dateUtil.get_date_date()) +
dateUtil.get_hour_and_minute_str() + dfname + '原价卖.csv',
index=False)
rightStockLoss.to_csv(con.detailPath + str(dateUtil.get_date_date()) +
dateUtil.get_hour_and_minute_str() + dfname +
'割肉卖.csv',
index=False)
#进行日期分组
rightStockGood = group_by_date(rightStockGood)
rightStockNormal = group_by_date(rightStockNormal)
rightStockLoss = group_by_date(rightStockLoss)
#分组后计算收益和概率
totalCount = len(rightStockGood) + len(rightStockNormal) + len(
rightStockLoss)
df['day2收盘卖掉概率'] = round(len(rightStockGood) / totalCount, 3)
df['原价卖概率'] = round(len(rightStockNormal) / totalCount, 3)
df['割肉卖概率'] = round(len(rightStockLoss) / totalCount, 3)
df['day2收盘卖掉平均收益'] = round(rightStockGood.day2closeIncome.mean(), 3)
df['原价卖收益'] = 0
df['割肉卖平均亏损'] = round(rightStockLoss.day20closeIncome.mean(), 3)
df['保本卖法收益'] = round((df['day2收盘卖掉概率'] * (df['day2收盘卖掉平均收益'] + 1)) +
df['原价卖概率'] + (df['割肉卖概率'] * (df['割肉卖平均亏损'] + 1)), 3)
return df
def get_total_csv_by_year(rightStock, ruleName, stockArgX, totalReportForm):
rightStockList = []
'''
先进行按年划分
'''
rightStock1 = rightStock[rightStock['date'] >= '2010-01-01']
rightStock1 = rightStock1[rightStock1['date'] < '2011-01-01']
rightStock2 = rightStock[rightStock['date'] >= '2011-01-01']
rightStock2 = rightStock2[rightStock2['date'] < '2012-01-01']
rightStock3 = rightStock[rightStock['date'] >= '2012-01-01']
rightStock3 = rightStock3[rightStock3['date'] < '2013-01-01']
rightStock4 = rightStock[rightStock['date'] >= '2013-01-01']
rightStock4 = rightStock4[rightStock4['date'] < '2014-01-01']
rightStock5 = rightStock[rightStock['date'] >= '2014-01-01']
rightStock5 = rightStock5[rightStock5['date'] < '2015-01-01']
rightStock6 = rightStock[rightStock['date'] >= '2015-01-01']
rightStock6 = rightStock6[rightStock6['date'] < '2016-01-01']
rightStock7 = rightStock[rightStock['date'] >= '2016-01-01']
rightStock7 = rightStock7[rightStock7['date'] < '2017-01-01']
rightStock8 = rightStock[rightStock['date'] >= '2017-01-01']
rightStock8 = rightStock8[rightStock8['date'] < '2018-01-01']
rightStockList.append(rightStock)
rightStockList.append(rightStock1)
rightStockList.append(rightStock2)
rightStockList.append(rightStock3)
rightStockList.append(rightStock4)
rightStockList.append(rightStock5)
rightStockList.append(rightStock6)
rightStockList.append(rightStock7)
rightStockList.append(rightStock8)
year = -2
'''
再循环添加对应的数据
'''
for rightStockX in rightStockList:
year = year + 1
if len(rightStockX) > 0:
ruleNameyear = ruleName + 'year201' + str(year)
# 如果是全部的rightStock 则重新命名为 year
if (year == -1):
ruleNameyear = ruleName + 'year'
ruleNamelist = [ruleNameyear]
totalProfit = pd.DataFrame({'rule': ruleNamelist})
totalProfit = add_right_count(totalProfit, rightStockX)
rightStockX = group_by_date(rightStockX)
# 是否生产中间表
if (stockArgX.mean):
rightStockX.to_csv(
con.detailPath + str(dateUtil.get_date_date()) +
dateUtil.get_hour_and_minute_str() + ruleNameyear +
'mean.csv',
index=False)
# 增加筛选规则对应的样本数量和百分百显示
totalProfit = add_right_count_by_date(totalProfit, rightStockX)
# 计算并且增加 评价收益
totalProfit = add_income_mean(totalProfit, rightStockX)
totalProfit = add_days_income_percent(totalProfit, rightStockX)
totalReportForm = totalReportForm.append(totalProfit)
# 清除临时detail缓存
del rightStockX
# 马上重置垃圾清除器
gc.collect()
del rightStock
# 马上重置垃圾清除器
gc.collect()
return totalReportForm
def generate_report_form(ChooseCombinations, ruleNumListMust, ruleList,
stockCashList, stockArgX):
# 声明最终的total报表
totalReportForm = pd.DataFrame()
# 规则实例数量
exampleCount = 0
# 1.循环所有的规则数组组合
for inList in ChooseCombinations:
# 2.循环规则数组中的每一个,
for brackets in inList:
# 将必选规则添加到规则中
brackets = list(brackets) + ruleNumListMust
# 对形成的规则排序
brackets.sort()
# 为规则前面加rule前缀
ruleName = 'rule'
# 3.循环每一个数组中的每一个规则
for ruleNum in brackets:
# 4.循环规则列表中的每一个规则属性和数组中的规则匹配,来生成规则命名
for rule in ruleList:
if (ruleNum == rule.num):
# 如果规则定义为Ture增加+号标记
if (rule.tf == True):
ruleName = ruleName + '+' + str(ruleNum)
# 如果规则定义为Ture增加-号标记
if (rule.tf == False):
ruleName = ruleName + '-' + str(ruleNum)
# 规则实例数量+1
exampleCount = exampleCount + 1
print('开始生成规则' + ruleName + '的数据!当前是第' + str(exampleCount) +
'种规则.')
# 打印当前时间
dateUtil.print_date()
'''
开始生成复合规则的股票数据,根据股票标记与规则一一对应筛选
'''
detailTempList = get_TF_stock(stockCashList, brackets, ruleList)
if (len(detailTempList) <= 0):
continue
'''
开始生成detail.csv报表
'''
tempDetail = view.generate_detail_csv(detailTempList, stockArgX,
ruleName)
if (stockArgX.groupByDaysTF):
'''
开始生成某规则的收益total数据
'''
totalReportForm = view.get_total_csv_by_year(
tempDetail, ruleName, stockArgX, totalReportForm)
else:
'''
开始生成某规则的收益total数据
'''
totalProfit = view.get_total_csv(tempDetail, ruleName,
stockArgX)
'''
开始按年生成某规则的收益total数据
'''
# 将某规则的收益total数据添加到total报表中
totalReportForm = totalReportForm.append(totalProfit)
if (len(totalReportForm) > 0):
print('筛选成功,生成totalcsv文件开始!')
dateUtil.print_date()
# 最终生成csv命名
totalCsvName = stockArgX.totalCsvName
totalReportForm.to_csv(con.detailPath + str(dateUtil.get_date_date()) +
dateUtil.get_hour_and_minute_str() + 'total' +
totalCsvName + '.csv',
index=False)
print('生成csv文件结束!')
stockArgX.TFHaveResult = True
if (len(totalReportForm) == 0):
stockArgX.TFHaveResult = False