羊駝策略1

基本原理

• 在本策略中，每天按照收益率從小到大對股票池中的所有股票進行排序，起始時買入num_of_stocks只股票，然后每天在整個股票池中選出收益率前num_of_stocks，如果這些股票已持有，則繼續持有，如果未持有則買入，并賣掉收益率不是排在前num_of_stocks的股票

策略實現

• 選取市盈率在0~20之間的股票，作為待選股（若用所有股票，計算量過于龐大），一共332支股票

• 初始資金100萬，時間段為：2016-01-01~2018-05-01

• 設置策略參數，初始買入的股票數num_of_stocks，收益率計算所用天數period

• 其中收益率=昨天的收盤價/period天之前的收盤價

• 將股票池內的股票按照收益率排序，買入收益率最高的num_of_stocks只股票（num_of_stocks默認為10)各1000股。

• 之后的每天都將所有股票按收益率排序，如果股票池中有處于收益率前num_of_stocks而未持有的則買入，并賣掉收益率不處于前num_of_stocks的

• （一天操作股票數量為20）運行截圖：
  

• （一天操作股票數量為10）運行截圖：
  

代碼如下：

# coding: utf-8 # @author: lin # @date: 2018/11/9import QUANTAXIS as QA import datetime import pandas as pd import time import matplotlib.pyplot as plt import numpy as nppd.set_option('max_colwidth', 5000) pd.set_option('display.max_columns', 5000) pd.set_option('display.max_rows', 5000)class Alpaca:def __init__(self, start_time, stop_time, n_stock=10, stock_init_cash=1000000, n_days_before=1):self.Account = QA.QA_Account() # 初始化賬戶self.Account.reset_assets(stock_init_cash) # 初始化賬戶self.Account.account_cookie = 'alpaca'self.Broker = QA.QA_BacktestBroker()self.time_quantum_list = ['-12-31', '-09-30', '-06-30', '-03-31']self.start_time = start_timeself.stop_time = stop_timeself.n_days_before = n_days_beforeself.stock_pool = []self.data = Noneself.ind = Noneself.n_stock = n_stockself.get_stock_pool()def get_financial_time(self):"""得到此日期前一個財務數據的日期:return:"""year = self.start_time[0:4]while (True):for day in self.time_quantum_list:the_financial_time = year + dayif the_financial_time <= self.start_time:return the_financial_timeyear = str(int(year) - 1)@staticmethoddef get_assets_eps(stock_code, the_financial_time):"""得到高級財務數據:param stock_code::param the_financial_time: 離開始時間最近的財務數據的時間:return:"""financial_report = QA.QA_fetch_financial_report(stock_code, the_financial_time)if financial_report is not None:return financial_report.iloc[0]['totalAssets'], financial_report.iloc[0]['EPS']return None, Nonedef get_stock_pool(self):"""選取哪些股票"""stock_code_list = QA.QA_fetch_stock_list_adv().code.tolist()the_financial_time = self.get_financial_time()for stock_code in stock_code_list:# print(stock_code)assets, EPS = self.get_assets_eps(stock_code, the_financial_time)if assets is not None and EPS != 0:data = QA.QA_fetch_stock_day_adv(stock_code, self.start_time, self.stop_time)if data is None:continueprice = data.to_pd().iloc[0]['close']if 0 < price / EPS < 20: # 滿足條件才添加進行排序# print(price / EPS)self.stock_pool.append(stock_code)# 成交量因子def alpaca(self, data):data['yesterday_price'] = 0data['previous_n_price'] = 0data.reset_index(inplace=True) # 重置后，索引以數字for index, row in data.iterrows():yes_index = index - 1pre_n_index = index - (self.n_days_before+1)if yes_index >= 0:data.loc[index, 'yesterday_price'] = data.loc[yes_index, 'close']if pre_n_index >= 0:data.loc[index, 'previous_n_price'] = data.loc[pre_n_index, 'close']data['yield_rate'] = 0data['yield_rate'] = data['yesterday_price'] / data['previous_n_price']data.set_index(['date', 'code'], inplace=True)return datadef solve_data(self):self.data = QA.QA_fetch_stock_day_adv(self.stock_pool, self.start_time, self.stop_time)self.ind = self.data.add_func(self.alpaca)def run(self):self.solve_data()for items in self.data.panel_gen:today_time = items.index[0][0]one_day_data = self.ind.loc[today_time] # 得到有包含因子的DataFrameone_day_data['date'] = items.index[0][0]one_day_data.reset_index(inplace=True)one_day_data.sort_values(by='yield_rate', axis=0, ascending=False, inplace=True)today_stock = list(one_day_data.iloc[0:self.n_stock]['code'])one_day_data.set_index(['date', 'code'], inplace=True)one_day_data = QA.QA_DataStruct_Stock_day(one_day_data) # 轉換格式，便于計算bought_stock_list = list(self.Account.hold.index)print("SELL:")for stock_code in bought_stock_list:# 如果直接在循環中對bought_stock_list操作，會跳過一些元素if stock_code not in today_stock:try:item = one_day_data.select_day(str(today_time)).select_code(stock_code)order = self.Account.send_order(code=stock_code,time=today_time,amount=self.Account.sell_available.get(stock_code, 0),towards=QA.ORDER_DIRECTION.SELL,price=0,order_model=QA.ORDER_MODEL.MARKET,amount_model=QA.AMOUNT_MODEL.BY_AMOUNT)self.Broker.receive_order(QA.QA_Event(order=order, market_data=item))trade_mes = self.Broker.query_orders(self.Account.account_cookie, 'filled')res = trade_mes.loc[order.account_cookie, order.realorder_id]order.trade(res.trade_id, res.trade_price, res.trade_amount, res.trade_time)except Exception as e:print(e)print('BUY:')for stock_code in today_stock:try:item = one_day_data.select_day(str(today_time)).select_code(stock_code)order = self.Account.send_order(code=stock_code,time=today_time,amount=1000,towards=QA.ORDER_DIRECTION.BUY,price=0,order_model=QA.ORDER_MODEL.CLOSE,amount_model=QA.AMOUNT_MODEL.BY_AMOUNT)self.Broker.receive_order(QA.QA_Event(order=order, market_data=item))trade_mes = self.Broker.query_orders(self.Account.account_cookie, 'filled')res = trade_mes.loc[order.account_cookie, order.realorder_id]order.trade(res.trade_id, res.trade_price, res.trade_amount, res.trade_time)except Exception as e:print(e)self.Account.settle()Risk = QA.QA_Risk(self.Account)print(Risk.message)# plt.show()Risk.assets.plot() # 總資產plt.show()Risk.benchmark_assets.plot() # 基準收益的資產plt.show()Risk.plot_assets_curve() # 兩個合起來的對比圖plt.show()Risk.plot_dailyhold() # 每只股票每天的買入量plt.show()start = time.time() sss = Alpaca('2017-01-01', '2018-01-01', 10) stop = time.time() print(stop - start) print(len(sss.stock_pool)) sss.run() stop2 = time.time() print(stop2 - stop)

總結

以上是生活随笔為你收集整理的[QUANTAXIS量化分析]羊驼策略1的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。