@QuantSouth 写的很好，mark一下，感谢

完成作业，数据有点不一样啊180%-33.45%，没有案例的高，是不是时间有误了

# 初始化函数 def initialize(context): # 设置每日运行函数 run_daily(period, time='every_bar') # 设定要交易的股票数量 g.stocksnum = 7 # 每次交易7只股票 # 设定交易周期 g.period = 13 # 每13天进行一次交易 # 记录策略进行天数 g.days = 0 # 初始化天数为0 # 每日运行函数 def period(context): # 策略进行天数加1 g.days += 1 # 止损逻辑：检查持仓股票的盈亏比例 for stock in context.portfolio.positions: # 获取持仓信息 position = context.portfolio.positions[stock] cost_price = position.avg_cost # 持仓成本价 current_price = position.price # 当前价格 # 计算盈亏比例 profit_ratio = (current_price - cost_price) / cost_price * 100 # 如果亏损超过5%，则卖出该股票 if profit_ratio < -5: order_target(stock, 0) # 卖出股票 log.info(f"股票 {stock} 亏损超过5%，已卖出") # 判断是否达到交易周期 if g.days % g.period == 1: # 每 period 天执行一次交易 # 获取上证指数成分股（所有在上海证券交易所上市的股票） scu = get_all_securities(['stock'], date=context.current_dt).index.tolist() scu = [stock for stock in scu if stock.endswith('.XSHG')] # 筛选出上海证券交易所的股票 # 排除 ST 股票 st_info = get_extras('is_st', scu, count=1) # 获取 ST 状态 scu = [stock for stock in scu if not st_info[stock].iloc[-1]] # 过滤掉 ST 股票 # 排除停牌股票 price_info = get_price(scu, count=1, end_date=context.current_dt, frequency='daily', fields=['paused']) scu = [stock for stock in scu if not price_info[stock].iloc[-1]] # 过滤掉停牌股票 # 排除涨停股票 price_info = get_price(scu, count=1, end_date=context.current_dt, frequency='daily', fields=['open', 'close']) scu = [stock for stock in scu if price_info[stock]['close'].iloc[-1] < price_info[stock]['open'].iloc[-1] * 1.1] # 过滤掉涨停股票 # 选出在 scu 内的市值排名最小的前 stocksnum 只股票 q = query( valuation.code ).filter( valuation.code.in_(scu) ).order_by( valuation.market_cap.asc() # 按市值从小到大排序 ).limit(g.stocksnum) # 限制为前 stocksnum 只股票 df = get_fundamentals(q) # 选取股票代码并转为 list buylist = list(df['code']) # 若已持有的股票的市值已经不够小而不在要买入的股票中，则卖出这些股票 for stock in context.portfolio.positions: if stock not in buylist: # 如果 stock 不在 buylist 中 order_target(stock, 0) # 卖出股票 # 买入要买入的股票，买入金额为可用资金的 stocksnum 分之一 position_per_stk = context.portfolio.cash / g.stocksnum # 每只股票的买入金额 for stock in buylist: order_value(stock, position_per_stk) # 买入股票

亏麻了兄弟

@KisaDB 什么策略 想见识一下

@TPK 就这篇文章的小市值策略

亏麻了+1,  ``` # 导入函数库 from jqdata import * def initialize(context): run_daily(period,time='every_bar') # True为开启动态复权模式，使用真实价格交易 set_option('use_real_price', True) # 设定成交量比例 set_option('order_volume_ratio', 1) # 股票类交易手续费是：买入时佣金万分之三，卖出时佣金万分之三加千分之一印花税, 每笔交易佣金最低扣5块钱 set_order_cost(OrderCost(open_tax=0, close_tax=0.001, \ open_commission=0.0003, close_commission=0.0003,\ close_today_commission=0, min_commission=5), type='stock') #这里是用来写初始化代码的地方,例子中就是选定要交易的股票为平安银行 g.stock_num = 7 g.period = 5 g.days = 0 def period(context): for stock in context.portfolio.positions: position = context.portfolio.positions[stock] cost_price = position.avg_cost # 持仓成本价 current_price = position.price # 当前价格 df = get_bars(stock, count=5, unit='1d', fields=['close']) # # 低于五日均价且盈利 if current_price < df['close'].mean(): profit_ratio = (current_price - cost_price) / cost_price * 100 if profit_ratio >15: order_target(stock, 0) log.info(f"股票 {stock} ，已卖出, 收益为{profit_ratio}") if not g.days % g.period: scu = get_all_securities(date=context.current_dt).index.tolist() # 停牌和涨停过滤掉 scu = get_price(scu, end_date=context.current_dt, frequency='daily',panel=False, count=1,fields=['open', 'close','paused']) scu = scu[(scu['paused'] == 0) & (scu['close'] / scu['open'] < 1.1)]['code'].tolist() q=query(valuation.code).filter( valuation.code.in_(scu) ).order_by( valuation.market_cap.asc() ).limit(g.stock_num) df = get_fundamentals(q) buy_list = list(df['code']) for stock in context.portfolio.positions: if stock not in buy_list: order_target(stock, 0) position_per_stk = context.portfolio.cash/g.stock_num for stock in buy_list: order_value(stock, position_per_stk) g.days += 1 ```

@KisaDB 我也亏麻了，为啥呀 ```python # 导入函数库 from jqdata import * def initialize(context): run_daily(period,time='every_bar') # True为开启动态复权模式，使用真实价格交易 set_option('use_real_price', True) # 设定成交量比例 set_option('order_volume_ratio', 1) # 股票类交易手续费是：买入时佣金万分之三，卖出时佣金万分之三加千分之一印花税, 每笔交易佣金最低扣5块钱 set_order_cost(OrderCost(open_tax=0, close_tax=0.001, \ open_commission=0.0003, close_commission=0.0003,\ close_today_commission=0, min_commission=5), type='stock') #这里是用来写初始化代码的地方,例子中就是选定要交易的股票为平安银行 g.stock_num = 7 g.period = 5 g.days = 0 def period(context): for stock in context.portfolio.positions: position = context.portfolio.positions[stock] cost_price = position.avg_cost # 持仓成本价 current_price = position.price # 当前价格 df = get_bars(stock, count=5, unit='1d', fields=['close']) # # 低于五日均价且盈利 if current_price < df['close'].mean(): profit_ratio = (current_price - cost_price) / cost_price * 100 if profit_ratio >15: order_target(stock, 0) log.info(f"股票 {stock} ，已卖出, 收益为{profit_ratio}") if not g.days % g.period: scu = get_all_securities(date=context.current_dt).index.tolist() # 停牌和涨停过滤掉 scu = get_price(scu, end_date=context.current_dt, frequency='daily',panel=False, count=1,fields=['open', 'close','paused']) scu = scu[(scu['paused'] == 0) & (scu['close'] / scu['open'] < 1.1)]['code'].tolist() q=query(valuation.code).filter( valuation.code.in_(scu) ).order_by( valuation.market_cap.asc() ).limit(g.stock_num) df = get_fundamentals(q) buy_list = list(df['code']) for stock in context.portfolio.positions: if stock not in buy_list: order_target(stock, 0) position_per_stk = context.portfolio.cash/g.stock_num for stock in buy_list: order_value(stock, position_per_stk) g.days += 1 ```

@kanechen 为啥过滤掉st, 结果买的都是st

@QuantSouth 棒

交作业

 时不待我，果然是没有一直盈利的策略。

有没有人知道为什么我的这个练习策略回测，盈利次数和亏损次数都是0？这是怎么回事？感谢高手指点。

@向上生长的地软 问题已解决，是刚开始学，我的策略写的有问题。感谢感谢。

打卡，记录学习

加入了止损策略，过滤了ST和停牌股票，欢迎相互学习

@QuantSouth markmark

最新api接口写的小市值策略

修改了有关ST代码的部分，之前的部分，没有current_dt传进去，所以导致使用了现在的名称去做过滤，导致出现了偏差，现在修正了，欢迎指教