增加交易策略、交易指标、量化库代码等文件夹

1.交易策略/1.CTA策略/2.VeighNa策略/1.atr_rsi_strategy.py

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+from vnpy_ctastrategy import (
+    CtaTemplate,
+    StopOrder,
+    TickData,
+    BarData,
+    TradeData,
+    OrderData,
+    BarGenerator,
+    ArrayManager,
+)
+
+
+class AtrRsiStrategy(CtaTemplate):
+    """"""
+
+    author = "用Python的交易员"
+
+    atr_length = 22
+    atr_ma_length = 10
+    rsi_length = 5
+    rsi_entry = 16
+    trailing_percent = 0.8
+    fixed_size = 1
+
+    atr_value = 0
+    atr_ma = 0
+    rsi_value = 0
+    rsi_buy = 0
+    rsi_sell = 0
+    intra_trade_high = 0
+    intra_trade_low = 0
+
+    parameters = [
+        "atr_length",
+        "atr_ma_length",
+        "rsi_length",
+        "rsi_entry",
+        "trailing_percent",
+        "fixed_size",
+    ]
+    variables = [
+        "atr_value",
+        "atr_ma",
+        "rsi_value",
+        "rsi_buy",
+        "rsi_sell",
+        "intra_trade_high",
+        "intra_trade_low",
+    ]
+
+    def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
+        """"""
+        super().__init__(cta_engine, strategy_name, vt_symbol, setting)
+        self.bg = BarGenerator(self.on_bar)
+        self.am = ArrayManager()
+
+    def on_init(self):
+        """
+        Callback when strategy is inited.
+        """
+        self.write_log("策略初始化")
+
+        self.rsi_buy = 50 + self.rsi_entry
+        self.rsi_sell = 50 - self.rsi_entry
+
+        self.load_bar(10)
+
+    def on_start(self):
+        """
+        Callback when strategy is started.
+        """
+        self.write_log("策略启动")
+
+    def on_stop(self):
+        """
+        Callback when strategy is stopped.
+        """
+        self.write_log("策略停止")
+
+    def on_tick(self, tick: TickData):
+        """
+        Callback of new tick data update.
+        """
+        self.bg.update_tick(tick)
+
+    def on_bar(self, bar: BarData):
+        """
+        Callback of new bar data update.
+        """
+        self.cancel_all()
+
+        am = self.am
+        am.update_bar(bar)
+        if not am.inited:
+            return
+
+        atr_array = am.atr(self.atr_length, array=True)
+        self.atr_value = atr_array[-1]
+        self.atr_ma = atr_array[-self.atr_ma_length :].mean()
+        self.rsi_value = am.rsi(self.rsi_length)
+
+        if self.pos == 0:
+            self.intra_trade_high = bar.high_price
+            self.intra_trade_low = bar.low_price
+
+            if self.atr_value > self.atr_ma:
+                if self.rsi_value > self.rsi_buy:
+                    self.buy(bar.close_price + 5, self.fixed_size)
+                elif self.rsi_value < self.rsi_sell:
+                    self.short(bar.close_price - 5, self.fixed_size)
+
+        elif self.pos > 0:
+            self.intra_trade_high = max(self.intra_trade_high, bar.high_price)
+            self.intra_trade_low = bar.low_price
+
+            long_stop = self.intra_trade_high * (1 - self.trailing_percent / 100)
+            self.sell(long_stop, abs(self.pos), stop=True)
+
+        elif self.pos < 0:
+            self.intra_trade_low = min(self.intra_trade_low, bar.low_price)
+            self.intra_trade_high = bar.high_price
+
+            short_stop = self.intra_trade_low * (1 + self.trailing_percent / 100)
+            self.cover(short_stop, abs(self.pos), stop=True)
+
+        self.put_event()
+
+    def on_order(self, order: OrderData):
+        """
+        Callback of new order data update.
+        """
+        pass
+
+    def on_trade(self, trade: TradeData):
+        """
+        Callback of new trade data update.
+        """
+        self.put_event()
+
+    def on_stop_order(self, stop_order: StopOrder):
+        """
+        Callback of stop order update.
+        """
+        pass

1.交易策略/1.CTA策略/2.VeighNa策略/Y03 基于布林带的股票量化择时策略/布林策略.py

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+import numpy as np
+import pandas as pd    
+from pandas import Series,DataFrame
+
+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) 
+    # 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')
+def handle_data(context, data):
+    #获取沪深300股票池
+    stock_set=get_index_stocks('000300.XSHG')
+    #此处可增加选股条件
+    q = query(
+        valuation.code, # 股票代码
+    ).filter(
+        valuation.code.in_(stock_set),#只对设定股票池执行
+    )
+    #获取财务数据，指定日期为回测当天
+    current_date=context.current_dt.strftime('%Y-%m-%d')
+    fdf = get_fundamentals(q,current_date)
+    #取前50只股
+    fdf=fdf.head(100)
+    #获取股票列表
+    get_stocks=list(fdf['code'])
+    # 去除ST，*ST
+    st=get_extras('is_st',get_stocks,current_date,current_date, df=True)
+    st=st.loc[current_date]
+    get_stocks=list(st[st==False].index)
+    #考虑5天的历史数据
+    num=5  
+    #轨线用前面20天数据计算
+    days=20
+    #每只股票可用资金为当前资金除以50
+    cash=context.portfolio.available_cash/100
+    #获取所有股票前num+days天收盘价数据
+    price=history(num+days,'1d','close',get_stocks,skip_paused=True)
+    where_are_nan = np.isnan(price)  
+    where_are_inf = np.isinf(price)  
+    price[where_are_nan] = 0  
+    price[where_are_inf] = 0
+    #循环每只股
+    for security in get_stocks:
+        #用数组保存均值，大小为num
+        mid=np.arange(num)
+        #标准差
+        std=np.arange(num)
+        #定义数组大小
+        close_data=np.arange(num*days).reshape(num,days)
+        for i in range(0,num):
+            for j in range(0,days):
+                close_data[i][j]=price[security][i+j]
+            #中轨线即均值为days天收盘价数据平均
+            mid[i]=np.mean(close_data[i])
+            #计算标准差
+            std[i]=np.std(close_data[i])
+        #上轨线=中轨线+两倍的标准差
+        up=mid[num-1]+2*std[num-1]
+        #下轨线
+        down=mid[num-1]-2*std[num-1]
+        #保存num天数据，判断开口收口或平口
+        boll=0
+        for i in range(0,num-1):
+            if std[i]>std[i+1]:
+                boll=boll+1
+            else:
+                boll=boll-1
+                
+        #判断目前股票是否停牌
+        paused=data[security].paused
+        
+        #取得当前股票价格
+        current_price=data[security].price
+        #如果连续num天开口
+        if boll==-4:
+                #如果当前价格超过昨日的上轨且价格高于均线
+            if current_price>up and current_price>mid[num-1]:
+                #计算可以买多少股票
+                num_of_shares=int(cash/current_price)
+                    #如果可以买的数量超过0并且股票未停牌
+                if num_of_shares>0 and paused==False:
+                    #购买股票
+                    order(security,+num_of_shares)
+                #如果当前价格跌破了昨日的下轨且价格低于均线
+            elif current_price<down and current_price<mid[num-1]:
+                #如果股票未停牌
+                if paused==False:
+                    #将股票卖空
+                    order_target(security,0)
+        #如果连续num天收口
+        if boll==4:
+            #股价超过上轨且价格低于均线时卖
+            if current_price>up and current_price<mid[num-1]:
+                if paused==False:
+                    order_target(security,0)
+            #跌破下轨且价格高于均线时买
+            elif current_price<down and current_price>mid[num-1]:
+                num_of_shares=int(cash/current_price)
+                if num_of_shares>0 and paused==False:
+                    order(security,+num_of_shares)
+        #连续平口
+        if boll in range(-1,1):
+            #价格在中轨线上且昨日均价高于三天前
+            if current_price>mid[num-1] and mid[num-1]>mid[num-3]:
+                num_of_shares=int(cash/current_price)
+                if num_of_shares>0 and paused==False:
+                    order(security,+num_of_shares)
+            if current_price<mid[num-1] and mid[num-1]<mid[num-3]:
+                if paused==False:
+                    order_target(security,0)

1.交易策略/1.CTA策略/2.VeighNa策略/Y04 基于趋势判断的股票量化择时策略/趋势择时策略.py

@@ -0,0 +1,97 @@
+
+# coding: utf-8
+
+# In[ ]:
+
+# 导入函数库
+import jqdata
+import pandas as pd
+import numpy as np
+
+# 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)
+    # 股票类每笔交易时的手续费是：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(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
+
+#获取沪深300股票池
+stock_set=get_index_stocks('000300.XSHG')
+#此处可增加选股条件
+q = query(
+    valuation.code, # 股票代码
+).filter(
+    valuation.code.in_(stock_set),#只对设定股票池执行
+)
+fdf = get_fundamentals(q)
+#取前50只股
+fdf=fdf.head(50)
+#获取股票列表
+stock_list=list(fdf['code'])
+trend={}
+for security in stock_list:
+    trend[security]='None'
+    
+def handle_data(context, data):
+    N = 20   # 计算TR时的N
+    M = 49   # 计算MATRIX时的M
+    num=3
+    length_of_data = N+M+num  # 取closeprice的天数，为了足够计算MATRIX、TRIX
+    
+    cash=context.portfolio.available_cash/50
+    for security in stock_list:
+        close_price=attribute_history(security,length_of_data,'1d',('close'),skip_paused=True)
+        where_are_nan = np.isnan(close_price)  
+        where_are_inf = np.isinf(close_price)  
+        close_price[where_are_nan] = 0  
+        close_price[where_are_inf] = 0
+        
+        MA5=close_price['close'][-5:].mean()
+        MA10=close_price['close'][-10:].mean()
+        ma5=close_price['close'][-6:-1].mean()
+        ma10=close_price['close'][-11:-1].mean()
+        
+        price_array={}
+        for i in range(0,M+num):
+            price_array[i]=close_price['close'][i:i+N]
+        #TR=收盘价的N日指数移动平均
+        TR={}
+        for i in range(0,M+num):
+            TR[i]=np.mean(price_array[i])  
+        #TRIX=(TR-昨日TR)/昨日TR*100
+        TRIX={}
+        for i in range(1,M+num):
+            if TR[i]==0:
+                TRIX[i]=0
+                continue
+            TRIX[i]=(TR[i]-TR[i-1])/TR[i]*100  
+        #MATRIX=TRIX的M日简单移动平均
+        MATRIX={}
+        for i in range(0,num):
+            TRIX_sum=0
+            for j in range(1,M):
+                TRIX_sum=TRIX_sum+TRIX[i+j]
+            MATRIX[i]=TRIX_sum/M
+        current_price=data[security].price
+        
+        length=0
+        for i in range(0,num-1):
+            if TRIX[M+i]>MATRIX[i]:
+                length=length+1
+            else:
+                length=length-1
+        if length>0 and MATRIX[num-1]>MATRIX[0]:
+            trend[security]='up'
+        elif length<0 and MATRIX[num-1]<MATRIX[0]:
+            trend[security]='down'
+        
+        if trend[security]=='up':
+            order_value(security,cash)
+        elif trend[security]=='down' and security in context.portfolio.positions:
+            if MA5<MA10:
+                order_target(security,0)
+            elif MA5>MA10 and ma5<ma10 :
+                order_value(security,cash)
+