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5.16 DualTrust 策略和布林强盗策略

来源：https://uqer.io/community/share/564737ddf9f06c4446b48133

谁能够帮忙实现DualTrust策略和布林强盗策略(BollingerBandit)？@薛昆Kelvin

@lookis：

DualTrust：

start = '2014-01-01'                       # 回测起始时间
end = '2015-01-01'                         # 回测结束时间
benchmark = 'HS300'                        # 策略参考标准
universe = set_universe("CYB") # 证券池，支持股票和基金
capital_base = 100000                      # 起始资金
freq = 'm'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 1                           # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟

def initialize(account):                   # 初始化虚拟账户状态
    account.k1 = 0.7
    account.k2 = 0.7
    account.cache = {}
    account.holding_max = 10
    account.holding = 0
    account.buy_sell_line = {}
    pass

def handle_data(account):                  # 每个交易日的买入卖出指令
    #准备数据
    if not account.current_date.strftime('%Y%m%d') in account.cache:
        account.cache = {}
        account.cache[account.current_date.strftime('%Y%m%d')] = account.get_daily_history(1)
    if account.current_minute == "09:30":
        return
    #每天画一次线
    if account.current_minute == "09:31":
        account.buy_sell_line = {}
        for stock in account.cache[account.current_date.strftime('%Y%m%d')]:
            if stock in account.universe:
                close = account.cache[account.current_date.strftime('%Y%m%d')][stock]["closePrice"][0]
                low = account.cache[account.current_date.strftime('%Y%m%d')][stock]["lowPrice"][0]
                high = account.cache[account.current_date.strftime('%Y%m%d')][stock]["highPrice"][0]
                o = account.referencePrice[stock]
                r = max(high - low, close - low)
                account.buy_sell_line[stock] = {"buy": o + account.k1 * r, "sell": o - account.k2 * r}
    else:
        #每天剩余的时间根据画线买卖
        for stock in account.buy_sell_line:
            if stock in account.universe and stock in account.referencePrice and stock in account.valid_secpos:
                if account.referencePrice[stock] < account.buy_sell_line[stock]["sell"]:
                    order_to(stock, 0)
                    account.holding -= 1
        for stock in account.buy_sell_line:
            if stock in account.universe and stock in account.referencePrice and not stock in account.valid_secpos:
                if account.holding < account.holding_max and account.referencePrice[stock] > account.buy_sell_line[stock]["buy"]:
                    account.holding += 1
                    order_pct(stock, 1.0/account.holding_max)
    return

回测看效果不是特别好…… LZ自己调一下参数吧

@JasonYichuan：

BollingerBandit很一般，不过没怎么调参数，看着办吧

import numpy as np
import pandas as pd

start = '2015-01-01'                       # 回测起始时间
end = '2015-11-26'                         # 回测结束时间
benchmark = 'HS300'                        # 策略参考标准
universe = set_universe('HS300')  # 证券池，支持股票和基金
capital_base = 100000                      # 起始资金
#commission = Commission(buycost=0.00025,sellcost=0.00025)  # 佣金
freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 1                           # 调仓频率

# 全局参数
## Boll线参数
N = 20
k = 2
## ROC变动率参数
M = 20
## 平仓参数
E = 20

def initialize(account):                   # 初始化虚拟账户状态
    # 持股代码以及持股时间
    account.duration = pd.DataFrame(np.array([0]*len(universe)), index=universe, columns=['duration']) 
    account.amount = 400

def handle_data(account):                  # 每个交易日的买入卖出指令
    hist = account.get_attribute_history('closePrice',50)
    ticker_name = []                       # 符合买入要求股票代码
    for stk in account.universe:           # 遍历股票池内所有股票，选出符合要求的股票   
        if np.isnan(account.referencePrice[stk]) or account.referencePrice[stk] == 0:  # 停牌或是还没有上市等原因不能交易
            continue      

        # 计算股票的BOLL线上下轨
        ## 计算MA
        MA = np.mean(hist[stk][-N:])
        ## 计算标准差MD
        MD = np.sqrt((sum(hist[stk][-N:] - MA)**2) / N)
        ## 计算MB、UP、DN线
        MB =np.mean(hist[stk][-(N-1):])
        UP = MB + k * MD
        DN = MB - k * MD

        # 计算股票的ROC
        ROC = float(hist[stk][-1] - hist[stk][-M])/float(hist[stk][-M])

        # 开仓条件
        if (hist[stk][-1] > UP) and (ROC > 0):
            ticker_name.append(stk)
    # 若股票符合开仓条件且尚未持有，则买入 
    for stk in ticker_name:
        if stk not in account.valid_secpos:
            order(stk,account.amount) 
            account.duration.loc[stk]['duration'] = 1   
    # 对于持有的股票，若股票不符合平仓条件，则将持仓时间加1，否则卖出，并删除该持仓时间记录
    for stk in account.valid_secpos:
        T = max(E - account.duration.loc[stk]['duration'],10)
        if hist[stk][-1] > np.mean(hist[stk][-T:]):
            account.duration.loc[stk]['duration'] = account.duration.loc[stk]['duration'] + 1
        else:
            order_to(stk,0)
            account.duration.loc[stk]['duration'] = 0      
    return
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