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上涨股策略2020年收益123%
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/30863 # 标题：上涨股策略2020年收益123% # 作者：scottchenrui from kuanke.wizard import * from jqdata import * import numpy as np import pandas as pd import talib import datetime import numpy as np import pylab as pl import matplotlib.pyplot as plt import scipy.signal as signal from sklearn.linear_model import LinearRegression ## 初始化函数，设定要操作的股票、基准等等 def initialize(context): # 设定基准 set_benchmark('000300.XSHG') # 设定滑点 set_slippage(FixedSlippage(0.02)) # 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, min_commission=5), type='stock') # 个股最大持仓比重 g.security_max_proportion = 1 # 选股频率 g.check_stocks_refresh_rate = 120 # 买入频率 g.buy_refresh_rate = 10 # 卖出频率 g.sell_refresh_rate = 10 # 最大建仓数量 g.max_hold_stocknum = 10 # 选股频率计数器 g.check_stocks_days = 0 # 买卖交易频率计数器 g.buy_trade_days=0 g.sell_trade_days=0 # 获取未卖出的股票 g.open_sell_securities = [] # 卖出股票的dict g.selled_security_list={} # 股票筛选初始化函数 check_stocks_initialize() # 股票筛选排序初始化函数 check_stocks_sort_initialize() # 出场初始化函数 sell_initialize() # 入场初始化函数 buy_initialize() # 风控初始化函数 risk_management_initialize() # 关闭提示 log.set_level('order', 'info') # 运行函数 run_daily(sell_every_day,'open') #卖出未卖出成功的股票 run_daily(risk_management, 'every_bar') #风险控制 run_daily(check_stocks, 'open') #选股 run_daily(trade, 'open') #交易 run_daily(selled_security_list_count, 'after_close') #卖出股票日期计数 ## 股票筛选初始化函数 def check_stocks_initialize(): # 是否过滤停盘 g.filter_paused = True # 是否过滤退市 g.filter_delisted = True # 是否只有ST g.only_st = False # 是否过滤ST g.filter_st = True # 股票池 g.security_universe_index = ["all_a_securities"] g.security_universe_user_securities = [] # 行业列表 g.industry_list = ["801010","801020","801030","801040","801050","801080","801110","801120","801130","801140","801150","801160","801170","801180","801200","801210","801230","801710","801720","801730","801740","801750","801760","801770","801780","801790","801880","801890"] # 概念列表 g.concept_list = [] ## 股票筛选排序初始化函数 def check_stocks_sort_initialize(): # 总排序准则： desc-降序、asc-升序 g.check_out_lists_ascending = 'desc' ## 出场初始化函数 def sell_initialize(): # 设定是否卖出buy_lists中的股票 g.sell_will_buy = True # 固定出仓的数量或者百分比 g.sell_by_amount = None g.sell_by_percent = None ## 入场初始化函数 def buy_initialize(): # 是否可重复买入 g.filter_holded = False # 委托类型 #每次买入固定股数 g.order_style_str = 'by_amount' g.order_style_value = 500 #等权重买入 #g.order_style_str = 'by_cap_mean' #g.order_style_value = 100 ## 风控初始化函数 def risk_management_initialize(): # 策略风控信号 g.risk_management_signal = True # 策略当日触发风控清仓信号 g.daily_risk_management = True # 单只最大买入股数或金额 g.max_buy_value = None g.max_buy_amount = None ## 卖出未卖出成功的股票 def sell_every_day(context): g.open_sell_securities = list(set(g.open_sell_securities)) open_sell_securities = [s for s in context.portfolio.positions.keys() if s in g.open_sell_securities] if len(open_sell_securities)>0: for stock in open_sell_securities: order_target_value(stock, 0) g.open_sell_securities = [s for s in g.open_sell_securities if s in context.portfolio.positions.keys()] return ## 风控 def risk_management(context): ### _风控函数筛选-开始 ### security_stoploss(context,0.1,g.open_sell_securities) portfolio_stoploss(context,0.2,g.open_sell_securities) portfolio_stopprofit(context,1,g.open_sell_securities) security_stopprofit(context,0.2,g.open_sell_securities) #index_stoploss_sicha(context,60,g.open_sell_securities, '000300.XSHG') ### _风控函数筛选-结束 ### return ## 股票筛选 def check_stocks(context): if g.check_stocks_days%g.check_stocks_refresh_rate != 0: # 计数器加一 g.check_stocks_days += 1 return # 股票池赋值 g.check_out_lists = get_up_security(context)#get_security_universe(context, g.security_universe_index, g.security_universe_user_securities) # print(g.check_out_lists) # 行业过滤 g.check_out_lists = industry_filter(context, g.check_out_lists, g.industry_list) # 概念过滤 g.check_out_lists = concept_filter(context, g.check_out_lists, g.concept_list) # 过滤ST股票 g.check_out_lists = st_filter(context, g.check_out_lists) # 过滤退市股票 g.check_out_lists = delisted_filter(context, g.check_out_lists) # 财务筛选 g.check_out_lists = financial_statements_filter(context, g.check_out_lists) # 行情筛选 g.check_out_lists = situation_filter(context, g.check_out_lists) # 技术指标筛选 g.check_out_lists = technical_indicators_filter(context, g.check_out_lists) # 形态指标筛选函数 g.check_out_lists = pattern_recognition_filter(context, g.check_out_lists) # 其他筛选函数 g.check_out_lists = other_func_filter(context, g.check_out_lists) # 排序 input_dict = get_check_stocks_sort_input_dict() g.check_out_lists = check_stocks_sort(context,g.check_out_lists,input_dict,g.check_out_lists_ascending) # 计数器归一 g.check_stocks_days = 1 return ## 交易函数 def trade(context): # 初始化买入列表 buy_lists = [] # 买入股票筛选 if g.buy_trade_days%g.buy_refresh_rate == 0: # 获取 buy_lists 列表 buy_lists = g.check_out_lists # 过滤ST股票 buy_lists = st_filter(context, buy_lists) # 过滤停牌股票 buy_lists = paused_filter(context, buy_lists) # 过滤退市股票 buy_lists = delisted_filter(context, buy_lists) # 过滤涨停股票 buy_lists = high_limit_filter(context, buy_lists) ### _入场函数筛选-开始 ### ### _入场函数筛选-结束 ### # 卖出操作 if g.sell_trade_days%g.sell_refresh_rate != 0: # 计数器加一 g.sell_trade_days += 1 else: # 卖出股票 sell(context, buy_lists) # 计数器归一 g.sell_trade_days = 1 # 买入操作 if g.buy_trade_days%g.buy_refresh_rate != 0: # 计数器加一 g.buy_trade_days += 1 else: # 卖出股票 buy(context, buy_lists) # 计数器归一 g.buy_trade_days = 1 ## 卖出股票日期计数 def selled_security_list_count(context): g.daily_risk_management = True if len(g.selled_security_list)>0: for stock in g.selled_security_list.keys(): g.selled_security_list[stock] += 1 ################################## 选股函数群 ################################## ## 财务指标筛选函数 def financial_statements_filter(context, security_list): ### _财务指标筛选函数-开始 ### security_list = financial_data_filter_dayu(security_list, indicator.inc_net_profit_year_on_year, 20) ### _财务指标筛选函数-结束 ### # 返回列表 return security_list ## 行情筛选函数 def situation_filter(context, security_list): ### _行情筛选函数-开始 ### ### _行情筛选函数-结束 ### # 返回列表 return security_list ## 技术指标筛选函数 def technical_indicators_filter(context, security_list): ### _技术指标筛选函数-开始 ### security_list = [security for security in security_list if MA_judge_duotou(security, 5, 60)] ### _技术指标筛选函数-结束 ### # 返回列表 return security_list ## 形态指标筛选函数 def pattern_recognition_filter(context, security_list): ### _形态指标筛选函数-开始 ### ### _形态指标筛选函数-结束 ### # 返回列表 return security_list ## 其他方式筛选函数 def other_func_filter(context, security_list): ### _其他方式筛选函数-开始 ### ### _其他方式筛选函数-结束 ### # 返回列表 return security_list # 获取选股排序的 input_dict def get_check_stocks_sort_input_dict(): input_dict = { indicator.eps:('desc',1), } # 返回结果 return input_dict ################################## 交易函数群 ################################## # 交易函数 - 出场 def sell(context, buy_lists): # 获取 sell_lists 列表 init_sl = context.portfolio.positions.keys() sell_lists = context.portfolio.positions.keys() # 判断是否卖出buy_lists中的股票 if not g.sell_will_buy: sell_lists = [security for security in sell_lists if security not in buy_lists] ### _出场函数筛选-开始 ### ### _出场函数筛选-结束 ### # 卖出股票 if len(sell_lists)>0: for stock in sell_lists: sell_by_amount_or_percent_or_none(context,stock, g.sell_by_amount, g.sell_by_percent, g.open_sell_securities) # 获取卖出的股票, 并加入到 g.selled_security_list中 selled_security_list_dict(context,init_sl) return # 交易函数 - 入场 def buy(context, buy_lists): # 风控信号判断 if not g.risk_management_signal: return # 判断当日是否触发风控清仓止损 if not g.daily_risk_management: return # 判断是否可重复买入 buy_lists = holded_filter(context,buy_lists) # 获取最终的 buy_lists 列表 Num = g.max_hold_stocknum - len(context.portfolio.positions) buy_lists = buy_lists[:Num] # 买入股票 if len(buy_lists)>0: # 分配资金 result = order_style(context,buy_lists,g.max_hold_stocknum, g.order_style_str, g.order_style_value) for stock in buy_lists: if len(context.portfolio.positions) < g.max_hold_stocknum: # 获取资金 Cash = result[stock] # 判断个股最大持仓比重 value = judge_security_max_proportion(context,stock,Cash,g.security_max_proportion) # 判断单只最大买入股数或金额 amount = max_buy_value_or_amount(stock,value,g.max_buy_value,g.max_buy_amount) # 下单 order(stock, amount, MarketOrderStyle()) return ################################### 公用函数群 ################################## ## 排序 def check_stocks_sort(context,security_list,input_dict,ascending='desc'): if (len(security_list) == 0) or (len(input_dict) == 0): return security_list else: # 生成 key 的 list idk = list(input_dict.keys()) # 生成矩阵 a = pd.DataFrame() for i in idk: b = get_sort_dataframe(security_list, i, input_dict[i]) a = pd.concat([a,b],axis = 1) # 生成 score 列 a['score'] = a.sum(1,False) # 根据 score 排序 if ascending == 'asc':# 升序 if hasattr(a, 'sort'): a = a.sort(['score'],ascending = True) else: a = a.sort_values(['score'],ascending = True) elif ascending == 'desc':# 降序 if hasattr(a, 'sort'): a = a.sort(['score'],ascending = False) else: a = a.sort_values(['score'],ascending = False) # 返回结果 return list(a.index) ## 过滤同一标的继上次卖出N天不再买入 def filter_n_tradeday_not_buy(security, n=0): try: if (security in g.selled_security_list.keys()) and (g.selled_security_list[security]<n): return False return True except: return True ## 是否可重复买入 def holded_filter(context,security_list): if not g.filter_holded: security_list = [stock for stock in security_list if stock not in context.portfolio.positions.keys()] # 返回结果 return security_list ## 卖出股票加入dict def selled_security_list_dict(context,security_list): selled_sl = [s for s in security_list if s not in context.portfolio.positions.keys()] if len(selled_sl)>0: for stock in selled_sl: g.selled_security_list[stock] = 0 ## 过滤停牌股票 def paused_filter(context, 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(context, 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) or ('*' in current_data[stock].name))] # 返回结果 return security_list ## 过滤ST股票 def st_filter(context, 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 # 过滤涨停股票 def high_limit_filter(context, security_list): current_data = get_current_data() security_list = [stock for stock in security_list if not (current_data[stock].day_open >= current_data[stock].high_limit)] # 返回结果 return security_list # 获取股票股票池 def get_security_universe(context, security_universe_index, security_universe_user_securities): temp_index = [] for s in security_universe_index: if s == 'all_a_securities': temp_index += list(get_all_securities(['stock'], context.current_dt.date()).index) else: temp_index += get_index_stocks(s) for x in security_universe_user_securities: temp_index += x return sorted(list(set(temp_index))) #获取上涨趋势的股票 def get_up_security(context): #dfs=get_all_securities(types=['stock'], date=context.previous_date) dfs300=get_index_stocks("000300.XSHG", date=context.previous_date) #dfs=get_index_stocks("000300.XSHG", date='2020-12-20') temp_index = list() for s in dfs300:#dfs.index: df=get_price(s, end_date=context.previous_date, frequency='daily', fields=["close"], skip_paused=False, fq='pre',count=g.check_stocks_refresh_rate) #print(df['low'].tolist()) cl=df['close'].tolist() #if len(cl)<=10: # continue x=np.array(df['close'].tolist()) Y=x[signal.argrelextrema(x, np.less)] if len(Y)<=0: #print("------------"+s+"-------------------") continue X=range(len(Y)) #转换成numpy的ndarray数据格式，n行1列,LinearRegression需要列格式数据，如下： X_train = np.array(X).reshape((len(X), 1)) Y_train = np.array(Y).reshape((len(Y), 1)) #新建一个线性回归模型，并把数据放进去对模型进行训练 lineModel = LinearRegression() lineModel.fit(X_train, Y_train) #用训练后的模型，进行预测 Y_predict = lineModel.predict(X_train) #coef_是系数，intercept_是截距 a1 = lineModel.coef_[0][0] b = lineModel.intercept_[0] score=lineModel.score(X_train, Y_train) #print("y=%.4f*x+%.4f" % (a1,b)) if a1>1.25 and score>0.7: temp_index.append(s) print("y=%.4f*x+%.4f stock:%s" % (a1,b,s)) #print("%s %s"%(s,dfs.loc[s]['display_name'])) #print('temp_index') print(str(context.previous_date)+":".join(temp_index)) return sorted(list(set(temp_index))) def get_up_stocks(context): dfs300=get_index_stocks("000300.XSHG", date=context.previous_date) #dfs=get_index_stocks("000300.XSHG", date='2020-12-20') temp_index = list() for s in dfs300:#dfs.index: df=get_price(s, end_date=context.previous_date, frequency='daily', fields=["close"], skip_paused=False, fq='pre',count=g.check_stocks_refresh_rate) cl=df['close'].tolist() #if len(cl)<=10: # continue x=np.array(df['close'].tolist()) #Y=x[signal.argrelextrema(x, np.less_equal)] #if len(Y)<=0: #print("------------"+s+"-------------------") # continue X=range(len(x)) #转换成numpy的ndarray数据格式，n行1列,LinearRegression需要列格式数据，如下： X_train = np.array(X).reshape((len(X), 1)) Y_train = np.array(x).reshape((len(x), 1)) #新建一个线性回归模型，并把数据放进去对模型进行训练 lineModel = LinearRegression() lineModel.fit(X_train, Y_train) #用训练后的模型，进行预测 Y_predict = lineModel.predict(X_train) #coef_是系数，intercept_是截距 a1 = lineModel.coef_[0][0] b = lineModel.intercept_[0] score=lineModel.score(X_train, Y_train) #print("y=%.4f*x+%.4f" % (a1,b)) if a1>1.5 and lineModel.score(X_train, Y_train)>0.7: temp_index.append(s) return sorted(list(set(temp_index))) # 行业过滤 def industry_filter(context, security_list, industry_list): if len(industry_list) == 0: # 返回股票列表 return security_list else: securities = [] for s in industry_list: temp_securities = get_industry_stocks(s) securities += temp_securities security_list = [stock for stock in security_list if stock in securities] # 返回股票列表 return security_list # 概念过滤 def concept_filter(context, security_list, concept_list): if len(concept_list) == 0: return security_list else: securities = [] for s in concept_list: temp_securities = get_concept_stocks(s) securities += temp_securities security_list = [stock for stock in security_list if stock in securities] # 返回股票列表 return security_list #自定义函数 ```
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