5.16 DualTrust 策略和布林强盗策略
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