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布林线追龙头战法
策略
作者: 水滴
```python # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/31867 # 标题：布林线追龙头战法 # 作者：fishsome # 导入函数库 from jqdata import * #1.股价处于盘整状态时，股价下碰支撑线买入，上碰阻力线卖出。 #2.股价连续上涨时，会沿着中线和阻力线形成的通道上升。当股价不能再触及阻力线时，则上涨趋势减弱，应卖出。 #ma25 ma60 ma200全部向上 #3.当股价连续下跌时，会沿着中线和支撑线形成的下降通道下跌，当股价不能再触及支撑线时，下跌趋势减弱，应买入。 # 初始化函数，设定基准等等 def initialize(context): # 设定沪深300作为基准 set_benchmark('000300.XSHG') # 开启动态复权模式(真实价格) set_option('use_real_price', True) # 输出内容到日志 log.info() log.info('初始函数开始运行且全局只运行一次') # 过滤掉order系列API产生的比error级别低的log # log.set_level('order', 'error') g.redbull = False ### 股票相关设定 ### # 股票类每笔交易时的手续费是：买入时佣金万分之1，卖出时佣金万分之1加千分之一印花税, 每笔交易佣金最低扣0块钱 set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0001, close_commission=0.0001, min_commission=0), type='stock') ## 运行函数（reference_security为运行时间的参考标的；传入的标的只做种类区分，因此传入'000300.XSHG'或'510300.XSHG'是一样的） # 开盘前运行 run_daily(before_market_open, time='before_open', reference_security='000300.XSHG') # 开盘时运行 run_daily(market_open, time='open', reference_security='000300.XSHG') # 收盘后运行 run_daily(after_market_close, time='after_close', reference_security='000300.XSHG') g.stocksnum =8 g.period = 20 g.days = 0 g.buy_sell_num = 0 ## 开盘前运行函数 def before_market_open(context): # 输出运行时间 # log.info('函数运行时间(before_market_open)：'+str(context.current_dt.time())) # log.info('函数运行前10天！！！！！！！！！：'+str(context.current_dt+datetime.timedelta(days=-10))) # 给微信发送消息（添加模拟交易，并绑定微信生效） # send_message('美好的一天~') # 要操作的股票：平安银行（g.为全局变量） g.security = '600519.XSHG' ## 开盘时运行函数 def market_open(context): scu = get_index_stocks('000002.XSHG') q = query(valuation.code).filter(valuation.code.in_(scu), valuation.pe_ratio > 15, valuation.pe_ratio < 100 , indicator.gross_profit_margin>40, ).order_by( valuation.market_cap.desc(),valuation.pe_ratio.asc() ).limit(20) df = get_fundamentals(q) buylist = list(df['code']) for stock in context.portfolio.positions: if stock not in buylist: order_target(stock,0) log.info('函数运行时间(market_open):'+str(context.current_dt.time())) for security in buylist: #security = g.security # 获取股票的收盘价 his_201 = his = attribute_history(security, count=201, unit='1d',fields=['close'],skip_paused=True, df=True, fq='pre') #1至201日的均线是昨日的 #0到200日的均线是前日的ma200线 sma200 = round(his_201['close'][1:].mean()-his_201['close'][:-1].mean(),4) sma60 = round(his_201['close'][-60:].mean()-his_201['close'][-61:-1].mean(),4) sma25 = round(his_201['close'][-25:].mean()-his_201['close'][-26:-1].mean(),4) # sma60 = round(get_price(security, count=60, end_date=context.current_dt, frequency='daily', fields=['close'], # skip_paused=True, fq='pre', panel=True).mean()['close'] - get_price(security, count=60, end_date=context.previous_date, frequency='daily', fields=['close'], #skip_paused=True, fq='pre', panel=True).mean()['close'],4) # sma25 = round(get_price(security, count=25, end_date=context.current_dt, frequency='daily', fields=['close'], # skip_paused=True, fq='pre', panel=True).mean()['close'] - get_price(security, count=25, end_date=context.previous_date, frequency='daily', fields=['close'], # skip_paused=True, fq='pre', panel=True).mean()['close'],4) log.info('sma200============:' + str(sma200)) close_data = get_bars(security, count=5, unit='1d', fields=['close']) # 取得过去五天的平均价格 MA5 = close_data['close'].mean() # 取得上一时间点价格 current_price = close_data['close'][-1] log.info('current_price============:' + str(current_price)) # 取得当前的现金 cash = context.portfolio.available_cash ######布林20日均线上下轨计算 price20=attribute_history(security,20,'1d',('close','open','low','high'),skip_paused=True) #创建一个num*days的二维数组来保存收盘价数据 lastLowestprice = price20['low'][-1] lastHiggstprice = price20['high'][-1] if price20['close'][-1] > price20['open'][-1]: pricelower = price20['open'][-1] pricehiger = price20['close'][-1] else: pricelower = price20['close'][-1] pricehiger = price20['open'][-1] #创建一个数组来保存中轨信息 mid=round(np.mean(price20['close']),2) std=round(np.std(price20['close']),2) #用up来保存昨日的上轨线 bollUp=mid+2*std #用down来保存昨日的下轨线 bollDown=mid-2*std minchange = round(current_price/10000,4)#最小价格波动变动误差千元股与几元股的误差不同 avg_cost = context.portfolio.positions[security].avg_cost position_per_stk = context.portfolio.total_value/g.stocksnum # 如果三红线，价格在20日线下方, 则全仓买入 if (sma200 >minchange) and (sma60 >minchange) and (sma25 >minchange): if current_price < mid and (cash > 100*current_price): # 记录这次买入 log.info("价格高于均价 1%%, 买入 %s" % (security)) # 用所有 cash 买入股票 order_target_value(security, position_per_stk) if lastHiggstprice<bollUp and avg_cost*1.2 < current_price and context.portfolio.positions[security].closeable_amount > 0: order_target(security, 0) log.info("全部跌落布林上轨，卖出！ %s" % (security)) # 如果含有两条负线, 则空仓卖出 if ( ((sma200 <-minchange) and (sma60 < -minchange)) or ((sma25 <-minchange) and (sma60 < -minchange)) or ((sma200 <-minchange) and (sma25 < -minchange))) and context.portfolio.positions[security].closeable_amount > 0: # 记录这次卖出 log.info("两条负线, 卖出 %s" % (security)) log.info("sma25 ma60 sma200====" + str(sma25) +str(sma60) + str(sma200) ) # 卖出所有股票,使这只股票的最终持有量为0 order_target(security, 0) #实体掉进中线卖出 #if lastHiggstprice<mid and context.portfolio.positions[security].closeable_amount > 0: # order_target(security, 0) ## 收盘后运行函数 def after_market_close(context): #log.info(str('函数运行时间(after_market_close):'+str(context.current_dt.time()))) #得到当天所有成交记录 trades = get_trades() for _trade in trades.values(): log.info('成交记录：'+str(_trade)) #log.info('一天结束') #log.info('##############################################################') ```
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