# 羊駝策略 ## 策略實現 羊駝做為上古十大神獸之一, 選股祥瑞, 名號響亮, 本策略由一個羊駝類負責每周生成買入賣出信號, 驗證羊駝是否名實相符. + 投資域 ：滬深300成分股 + 業績基準 ：滬深300指數 + 調倉頻率 ：5個交易日 + 買入賣出信號 ：初始時任意買10只羊駝,每次調倉時,剔除收益最差的一只羊駝,再任意買一只羊駝. + 回測周期 ：2014年1月1日至2015年5月5日  ```py import numpy as np import operator from datetime import datetime start = datetime(2010, 1, 1) end = datetime(2015, 5, 5) benchmark = 'HS300' universe = set_universe('HS300') capital_base = 100000 longest_history = 10 refresh_rate = 5 def initialize(account): account.stocks_num = 10 def handle_data(account): hist_prices = account.get_attribute_history('closePrice', 5) yangtuos = list(YangTuo(set(account.universe)-set(account.valid_secpos.keys()), account.stocks_num)) cash = account.cash if account.stocks_num == 1: hist_returns = {} for stock in account.valid_secpos: hist_returns[stock] = hist_prices[stock][-1]/hist_prices[stock][0] sorted_returns = sorted(hist_returns.items(), key=operator.itemgetter(1)) sell_stock = sorted_returns[0][0] cash = account.cash + hist_prices[sell_stock][-1]*account.valid_secpos.get(sell_stock) order_to(sell_stock, 0) else: account.stocks_num = 1 for stock in yangtuos: order(stock, cash/len(yangtuos)/hist_prices[stock][-1]) class YangTuo: def __init__(self, caoyuan=[], count=10): self.count = count self.i = 0 self.caoyuan = list(caoyuan) def __iter__(self): return self def next(self): if self.i < self.count: self.i += 1 return self.caoyuan.pop(np.random.randint(len(self.caoyuan))) else: raise StopIteration() ```  也許你會說,這只是運氣好,并不能說明羊駝的厲害啊!好,接下來我們運行100次,看看羊駝的威力. ```py start = datetime(2010, 1, 1) end = datetime(2015, 5, 5) benchmark = 'HS300' universe = set_universe('HS300') capital_base = 100000 sim_params = quartz.sim_condition.env.SimulationParameters(start, end, benchmark, universe, capital_base) idxmap_all, data_all = quartz.sim_condition.data_generator.get_daily_data(sim_params) ``` ```py import numpy as np import operator longest_history = 10 refresh_rate = 5 def initialize(account): account.stocks_num = 10 def handle_data(account): hist_prices = account.get_attribute_history('closePrice', 5) yangtuos = list(YangTuo(set(account.universe)-set(account.valid_secpos.keys()), account.stocks_num)) cash = account.cash if account.stocks_num == 1: hist_returns = {} for stock in account.valid_secpos: hist_returns[stock] = hist_prices[stock][-1]/hist_prices[stock][0] sorted_returns = sorted(hist_returns.items(), key=operator.itemgetter(1)) sell_stock = sorted_returns[0][0] cash = account.cash + hist_prices[sell_stock][-1]*account.valid_secpos.get(sell_stock) order_to(sell_stock, 0) else: account.stocks_num = 1 for stock in yangtuos: order(stock, cash/len(yangtuos)/hist_prices[stock][-1]) class YangTuo: def __init__(self, caoyuan=[], count=10): self.count = count self.i = 0 self.caoyuan = list(caoyuan) def __iter__(self): return self def next(self): if self.i < self.count: self.i += 1 return self.caoyuan.pop(np.random.randint(len(self.caoyuan))) else: raise StopIteration() strategy = quartz.sim_condition.strategy.TradingStrategy(initialize, handle_data) perfs = [] for i in xrange(100): bt, acct = quartz.quick_backtest(sim_params, strategy, idxmap_all, data_all, refresh_rate = refresh_rate, longest_history=longest_history) perf = quartz.perf_parse(bt, acct) perfs.append(perf) ``` ```py from matplotlib import pylab import seaborn x = sorted([p['annualized_return']-p['benchmark_annualized_return'] for p in perfs]) pylab.plot(x) pylab.plot([0]*len(x)) [<matplotlib.lines.Line2D at 0x7702a10>] ```  100%的勝率! 大家閉著眼睛,跟著羊駝買就行了! 接下來的工作: 由于指數并沒有分紅等概念, 直接拿HS300指數做benchmark, 對HS300并不公平. 所以接下來考慮把benchmark換成某只指數基金, 再做對比.