[QUANTAXIS量化分析]羊驼策略1
羊驼策略1
基本原理
- 在本策略中，每天按照收益率从小到大对股票池中的所有股票进行排序，起始时买入num_of_stocks只股票，然后每天在整个股票池中选出收益率前num_of_stocks，如果这些股票已持有，则继续持有，如果未持有则买入，并卖掉收益率不是排在前num_of_stocks的股票
策略实现
-
选取市盈率在0~20之间的股票，作为待选股（若用所有股票，计算量过于庞大），一共332支股票
-
初始资金100万，时间段为：2016-01-01~2018-05-01
-
设置策略参数，初始买入的股票数num_of_stocks，收益率计算所用天数period
-
其中收益率=昨天的收盘价/period天之前的收盘价
-
将股票池内的股票按照收益率排序，买入收益率最高的num_of_stocks只股票（num_of_stocks默认为10)各1000股。
-
之后的每天都将所有股票按收益率排序，如果股票池中有处于收益率前num_of_stocks而未持有的则买入，并卖掉收益率不处于前num_of_stocks的
-
（一天操作股票数量为20）运行截图：
-
（一天操作股票数量为10）运行截图：
代码如下：
# coding: utf-8
# @author: lin
# @date: 2018/11/9
import QUANTAXIS as QA
import datetime
import pandas as pd
import time
import matplotlib.pyplot as plt
import numpy as np
pd.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() # 初始化账户
self.Account.reset_assets(stock_init_cash) # 初始化账户
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_time
self.stop_time = stop_time
self.n_days_before = n_days_before
self.stock_pool = []
self.data = None
self.ind = None
self.n_stock = n_stock
self.get_stock_pool()
def get_financial_time(self):
"""
得到此日期前一个财务数据的日期
:return:
"""
year = self.start_time[0:4]
while (True):
for day in self.time_quantum_list:
the_financial_time = year + day
if the_financial_time <= self.start_time:
return the_financial_time
year = str(int(year) - 1)
@staticmethod
def get_assets_eps(stock_code, the_financial_time):
"""
得到高级财务数据
:param stock_code:
:param the_financial_time: 离开始时间最近的财务数据的时间
: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, None
def get_stock_pool(self):
"""
选取哪些股票
"""
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:
continue
price = data.to_pd().iloc[0]['close']
if 0 < price / EPS < 20: # 满足条件才添加进行排序
# print(price / EPS)
self.stock_pool.append(stock_code)
# 成交量因子
def alpaca(self, data):
data['yesterday_price'] = 0
data['previous_n_price'] = 0
data.reset_index(inplace=True) # 重置后，索引以数字
for index, row in data.iterrows():
yes_index = index - 1
pre_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'] = 0
data['yield_rate'] = data['yesterday_price'] / data['previous_n_price']
data.set_index(['date', 'code'], inplace=True)
return data
def 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DataFrame
one_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) # 转换格式，便于计算
bought_stock_list = list(self.Account.hold.index)
print("SELL:")
for stock_code in bought_stock_list:
# 如果直接在循环中对bought_stock_list操作，会跳过一些元素
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() # 总资产
plt.show()
Risk.benchmark_assets.plot() # 基准收益的资产
plt.show()
Risk.plot_assets_curve() # 两个合起来的对比图
plt.show()
Risk.plot_dailyhold() # 每只股票每天的买入量
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)