列举了一些常用的函数，此帖会不断更新~~~
过滤停牌股票、ST股票、退市股票、新股等函数
## 过滤停股票def paused_filter(security_list): if g.filter_paused: current_data = get_current_data() security_list = [stock for stock in security_list if not current_data[stock].paused] # 返回结果 return security_list## 过滤退市股票def delisted_filter(security_list): if g.filter_delisted: current_data = get_current_data() security_list = [stock for stock in security_list if not '退' in current_data[stock].name] # 返回结果 return security_list## 过滤ST股票def st_filter(security_list): if g.only_st: current_data = get_current_data() security_list = [stock for stock in security_list if current_data[stock].is_st] else: if g.filter_st: current_data = get_current_data() security_list = [stock for stock in security_list if not current_data[stock].is_st] # 返回结果 return security_list## 获取上市新股def get_new_stocks(context): df = get_all_securities(types=['stock']) date=context.current_dt.date() new_stocks = list(df[df.start_date == date].index) return new_stocks过滤停牌股票、ST股票、退市股票等函数
## 个股止损def security_stoploss(context,loss=0.1): if len(context.portfolio.positions)>0: for stock in context.portfolio.positions.keys(): avg_cost = context.portfolio.positions[stock].avg_cost current_price = context.portfolio.positions[stock].price if 1 - current_price/avg_cost >= loss: log.info(str(stock) + ' 跌幅达个股止损线，平仓止损！') order_target_value(stock, 0)## 个股止盈def security_stopprofit(context,profit=0.1): if len(context.portfolio.positions)>0: for stock in context.portfolio.positions.keys(): avg_cost = context.portfolio.positions[stock].avg_cost current_price = context.portfolio.positions[stock].price if current_price/avg_cost - 1 >= profit: log.info(str(stock) + ' 涨幅达个股止盈线，平仓止盈！') order_target_value(stock, 0)## 大盘止损# 止损方法1：根据大盘指数N日均线进行止损def index_stoploss_sicha(context,n=60): ''' 当大盘N日均线(默认60日)与昨日收盘价构成“死叉”，则清仓止损 ''' if len(context.portfolio.positions)>0: hist = attribute_history(g.index, n+2, '1d', 'close', df=False) temp1 = mean(hist['close'][1:-1]) temp2 = mean(hist['close'][0:-2]) close1 = hist['close'][-1] close2 = hist['close'][-2] if (close2 > temp2) and (close1 < temp1):="" log.info('大盘触及止损线，清仓！')="" for="" stock="" in="" context.portfolio.positions.keys():="" order_target_value(stock,="" 0)#="" 止损方法2：根据大盘指数跌幅进行止损def="" index_stoploss_diefu(context,n="10,zs=0.03):" '''="" 当大盘n日内跌幅超过zs，则清仓止损="" '''="" if="" len(context.portfolio.positions)="">0: hist = attribute_history(g.index, n, '1d', 'close',df=False) if ((1-float(hist['close'][-1]/hist['close'][0])) >= zs): log.info('大盘触及止损线，清仓！') for stock in context.portfolio.positions.keys(): order_target_value(stock, 0)获取一段时期财务数据的季报数据或者年报数据总和与均值。
def get_fundamentals_sum_mean_value(security='000001.XSHE', search=income.basic_eps, count=5, frequency='quarter'): ''' 输入： security: 要查询股票的代码 search: 要查询的字段，详情参考 API： get_fundamentals-查询财务数据 count: 单位时间长度，表示返回前几多少期的季报或者年报 frequency: 获取数据类型，'quarter'为季报，'year'为年报 输出： sum_num: 总值 mean_num: 平均值 df: 一段时间内季报或者年报的 DataFrame 注： 对于年报数据, 我们目前只有 现金流表 和 利润表, 当查询其他表时, 会返回该年份最后一个季报的数据' ''' def get_quarter(month): if month in (1,2,3): return 1 elif month in (4,5,6): return 2 elif month in (7,8,9): return 3 elif month in (10,11,12): return 4 # 查询条件 q = query( income.code, income.statDate, search, ).filter( income.code.in_([security]) ) # 获取最近一次报表发布的日期 statDate_num = get_fundamentals(q)['statDate'][0] # 获取最近一次报表发布所属的年份 year = datetime.datetime.strptime(statDate_num, '%Y-%m-%d').year # 获取最近一次报表发布所属的月份 month = datetime.datetime.strptime(statDate_num, '%Y-%m-%d').month # 获取最近一次报表发布日期所属的季度 qt = get_quarter(month) # 获取季度列表 qt_list = range(qt,0,-1)+range(4,0,-1)*(count/4+1) qt_list = qt_list[:count] # 查询时间列表 data_list = [] # 获取查询的名称 name = str(search).split('.')[-1] # 列名称 colums_list = [] # 获取拼接后的 DataFrame if frequency == 'quarter': for num in range(len(qt_list)): s = str(year)+'q'+str(qt_list[num]) data_list.append(s) colums_list.append(name + '_' + s) if qt_list[num] == 1: year -= 1 # 拼接列表 df = get_fundamentals(q, statDate = data_list[0]) df.set_index(df.code.values, inplace=True) for t in data_list[1:]: d = get_fundamentals(q, statDate = t) d.set_index(d.code.values, inplace=True) df = pd.concat([df[name],d[name]],axis = 1) elif frequency == 'year': for num in range(count): year -= 1 s = str(year) data_list.append(s) colums_list.append(name + '_' + s) # 拼接列表 df = get_fundamentals(q, statDate = data_list[0]) df.set_index(df.code.values, inplace=True) for t in data_list[1:]: d = get_fundamentals(q, statDate = t) d.set_index(d.code.values, inplace=True) df = pd.concat([df[name],d[name]],axis = 1) else: print '请输入正确的 frequency 参数，'quarter'为季报，'year'为年报. \n对于年报数据, 我们目前只有 现金流表 和 利润表, \n当查询其他表时, 会返回该年份最后一个季报的数据' return # 设置列名称 df.columns = colums_list[:len(df.iloc[0])] # 计算总值 sum_num = sum(df.iloc[0]) # 计算平均值 mean_num = mean(df.iloc[0]) # 返回结果 return sum_num, mean_num, df使用技术指标函数前，请现在策略中导入如下模块，以确保函数能正常运行
from jqdata import *import numpy as npimport talib具体函数如下：
# MACDdef MACD(security_list, fastperiod=12, slowperiod=26, signalperiod=9): # 修复传入为单只股票的情况 if isinstance(security_list, str): security_list = [security_list] # 计算 MACD security_data = history(slowperiod*2, '1d', 'close' , security_list, df=False, skip_paused=True) macd_DIF = {}; macd_DEA = {}; macd_HIST = {} for stock in security_list: macd_DIF[stock], macd_DEA[stock], macd = talib.MACDEXT(security_data[stock], fastperiod=fastperiod, fastmatype=1, slowperiod=slowperiod, slowmatype=1, signalperiod=signalperiod, signalmatype=1) macd_HIST[stock] = macd * 2 return macd_DIF, macd_DEA, macd_HIST# MAdef MA(security_list, timeperiod=5): # 修复传入为单只股票的情况 if isinstance(security_list, str): security_list = [security_list] # 计算 MA security_data = history(timeperiod*2, '1d', 'close' , security_list, df=False, skip_paused=True) ma = {} for stock in security_list: ma[stock] = talib.MA(security_data[stock], timeperiod) return ma# SMAdef SMA(security_list, timeperiod=5): # 修复传入为单只股票的情况 if isinstance(security_list, str): security_list = [security_list] # 计算 SMA security_data = history(timeperiod*2, '1d', 'close' , security_list, df=False, skip_paused=True) sma = {} for stock in security_list: close = np.nan_to_num(security_data[stock]) sma[stock] = reduce(lambda x, y: ((timeperiod - 1) * x + y) / timeperiod, close) return sma# KDJdef KDJ(security_list, fastk_period=5, slowk_period=3, fastd_period=3) : def SMA_CN(close, timeperiod) : close = np.nan_to_num(close) return reduce(lambda x, y: ((timeperiod - 1) * x + y) / timeperiod, close) # 修复传入为单只股票的情况 if isinstance(security_list, str): security_list = [security_list] # 计算 KDJ n = max(fastk_period, slowk_period, fastd_period) k = {}; d = {}; j = {} for stock in security_list: security_data = attribute_history(stock, n*2,'1d',fields=['high', 'low', 'close'], df=False) high = security_data['high'] low = security_data['low'] close = security_data['close'] kValue, dValue = talib.STOCHF(high, low, close, fastk_period, fastd_period, fastd_matype=0) kValue = np.array(map(lambda x : SMA_CN(kValue[:x], slowk_period), range(1, len(kValue) + 1))) dValue = np.array(map(lambda x : SMA_CN(kValue[:x], fastd_period), range(1, len(kValue) + 1))) jValue = 3 * kValue - 2 * dValue func = lambda arr : np.array([0 if x < 0="" else="" (100="" if="" x=""> 100 else x) for x in arr]) k[stock] = func(kValue) d[stock] = func(dValue) j[stock] = func(jValue) return k, d, j# RSIdef RSI(security_list, timeperiod=14): def SMA_CN(close, timeperiod) : close = np.nan_to_num(close) return reduce(lambda x, y: ((timeperiod - 1) * x + y) / timeperiod, close) # 修复传入为单只股票的情况 if isinstance(security_list, str): security_list = [security_list] # 计算 RSI security_data = history(timeperiod*2, '1d', 'close' , security_list, df=False, skip_paused=True) rsi = {} for stock in security_list: close = close = security_data[stock] diff = map(lambda x, y : x - y, close[1:], close[:-1]) diffGt0 = map(lambda x : 0 if x < 0="" else="" x,="" diff)="" diffabs="map(lambda" x="" :="" abs(x),="" diff)="" diff="np.array(diff)" diffgt0="np.array(diffGt0)" diffabs="np.array(diffABS)" diff="np.append(diff[0]," diff)="" diffgt0="np.append(diffGt0[0]," diffgt0)="" diffabs="np.append(diffABS[0]," diffabs)="" rsi[stock]="np.array(map(lambda" x:="" sma_cn(diffgt0[:x],timeperiod)/sma_cn(diffabs[:x],timeperiod)*100,="" range(1,len(diffgt0)+1)))="" return="" rsi#="" ccidef="" cci(security_list,="" timeperiod="14):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" cci="" cci="{}" for="" stock="" in="" security_list:="" security_data="attribute_history(stock," timeperiod*2,="" '1d',['close','high','low']="" ,="" df="False)" close_cci="security_data['close']" high_cci="security_data['high']" low_cci="security_data['low']" cci[stock]="talib.CCI(high_CCI," low_cci,="" close_cci,="" timeperiod)="" return="" cci#="" atrdef="" atr(security_list,="" timeperiod="14):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" atr="" atr="{}" for="" stock="" in="" security_list:="" security_data="attribute_history(stock," timeperiod*2,="" '1d',['close','high','low']="" ,="" df="False)" close_atr="security_data['close']" high_atr="security_data['high']" low_atr="security_data['low']" atr[stock]="talib.ATR(high_ATR," low_atr,="" close_atr,="" timeperiod)="" return="" atr#="" 布林线def="" bollinger_bands(security_list,="" timeperiod="5," nbdevup="2," nbdevdn="2):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" bollinger="" bands="" security_data="history(timeperiod*2," '1d',="" 'close'="" ,="" security_list,="" df="False," skip_paused="True)" upperband="{};" middleband="{};" lowerband="{}" for="" stock="" in="" security_list:="" upperband[stock],="" middleband[stock],="" lowerband[stock]="talib.BBANDS(security_data[stock]," timeperiod,="" nbdevup,="" nbdevdn)="" return="" upperband,="" middleband,="" lowerband#="" 平均成交额def="" ma_money(security_list,="" timeperiod="5):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" n="" 日平均成交额="" security_data="history(timeperiod*2," '1d',="" 'money'="" ,="" security_list,="" df="False," skip_paused="True)" mamoney="{}" for="" stock="" in="" security_list:="" x="security_data[stock]" mamoney[stock]="talib.MA(security_data[stock]," timeperiod)="" return="" mamoney#="" 平均成交量def="" ma_volume(security_list,="" timeperiod="5):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" n="" 日平均成交量="" security_data="history(timeperiod*2," '1d',="" 'volume'="" ,="" security_list,="" df="False," skip_paused="True)" mavol="{}" for="" stock="" in="" security_list:="" x="security_data[stock]" mavol[stock]="talib.MA(security_data[stock]," timeperiod)="" return="" mavol#="" biasdef="" bias(security_list,="" timeperiod="5):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" bias="" security_data="history(timeperiod*2," '1d',="" 'close'="" ,="" security_list,="" df="False," skip_paused="True)" bias="{}" for="" stock="" in="" security_list:="" average_price="security_data[stock][-timeperiod:].mean()" current_price="security_data[stock][-1]" bias[stock]="(current_price-average_price)/average_price" return="" bias#="" bbidef="" bbi(security_list,="" timeperiod1="3," timeperiod2="6," timeperiod3="12," timeperiod4="24):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" bbi="" security_data="history(timeperiod4*2," '1d',="" 'close'="" ,="" security_list,="" df="False," skip_paused="True)" bbi="{}" for="" stock="" in="" security_list:="" x="security_data[stock]" d="(x[-timeperiod1:].mean()+x[-timeperiod2:].mean()+x[-timeperiod3:].mean()+x[-timeperiod4:].mean())/4.0" bbi[stock]="d" return="" bbi#="" trixdef="" trix(security_list,="" timeperiod="30):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" trix="" security_data="history(timeperiod*2," '1d',="" 'close'="" ,="" security_list,="" df="False," skip_paused="True)" trix="{}" for="" stock="" in="" security_list:="" trix[stock]="talib.TRIX(security_data[stock]," timeperiod)="" return="" trix#="" emadef="" ema(security_list,="" timeperiod="30):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" ema="" security_data="history(timeperiod*2," '1d',="" 'close'="" ,="" security_list,="" df="False," skip_paused="True)" ema="{}" for="" stock="" in="" security_list:="" ema[stock]="talib.EMA(security_data[stock]," timeperiod)="" return="" ema#="" dmadef="" dma(security_list,fastperiod="5,slowperiod=60,amaperiod=20):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" dma="" dma="{}" for="" security="" in="" security_list:="" security_data="attribute_history(security," slowperiod*2,="" '1d',="" ['close'],="" skip_paused="True," df="False)['close']" m1="map(lambda" i:="" security_data[i-fastperiod+1:i+1],range(len(security_data))[len(security_data)-slowperiod:])="" m2="map(lambda" i:="" security_data[i-slowperiod+1:i+1],range(len(security_data))[len(security_data)-slowperiod:])="" ma1="map(lambda" x:="" mean(x),="" m1)="" ma2="map(lambda" x:="" mean(x),="" m2)="" dma="array(MA1)" -="" array(ma2)="" dma[security]="DMA" return="" dma#="" amadef="" ama(security_list,fastperiod="5,slowperiod=60,amaperiod=20):" #="" 修复传入为单只股票的情况="" if="" isinstance(security_list,="" str):="" security_list="[security_list]" #="" 计算="" dma="" ama="{}" for="" security="" in="" security_list:="" security_data="attribute_history(security," slowperiod*2,="" '1d',="" ['close'],="" skip_paused="True," df="False)['close']" m1="map(lambda" i:="" security_data[i-fastperiod+1:i+1],range(len(security_data))[len(security_data)-slowperiod:])="" m2="map(lambda" i:="" security_data[i-slowperiod+1:i+1],range(len(security_data))[len(security_data)-slowperiod:])="" ma1="map(lambda" x:="" mean(x),="" m1)="" ma2="map(lambda" x:="" mean(x),="" m2)="" dma="array(MA1)" -="" array(ma2)="" #="" 计算="" ama="" a1="map(lambda" i:="" dma[i-amaperiod+1:i+1],range(len(dma))[-2:])="" ama="map(lambda" x:="" mean(x),="" a1)="" ama[security]="AMA" return="">联系客服
