def algo(context):
date_now = context.now.strftime('%Y-%m-%d')
date_previous = get_trading_date_from_now(date_now, -1,
ql.Days) # 前一个交易日，用于获取因子数据的日期
if date_now not in trading_date_list: # 非调仓日
pass # 预留非调仓日的微调空间
else: # 调仓日执行算法
print(date_now + '日回测程序执行中...')
try:
code_list = list_gm2wind(
list(
get_history_constituents(
INDEX,
start_date=date_previous,
end_date=date_previous)[0]['constituents'].keys()))
except IndexError:
code_list = w.wset("sectorconstituent", "date=" + date_previous +
";windcode=" + INDEX).Data[1]
I = trading_date_list.index(date_now)
trading_dates = trading_date_list[I - HISTORY_LENGTH:I + 1]
data_dfs = []
for i in range(len(trading_dates) - 1):
date_start = get_trading_date_from_now(
trading_dates[i], -1, ql.Days) # 计算因子值的日子，买入前一日的因子值
date_end = get_trading_date_from_now(trading_dates[i + 1], -1,
ql.Days) # 计算收益率到期的日子-收盘
factors_df = get_factor_from_wind(code_list, FACTOR_LIST,
date_start) # 获取因子
return_df = get_return_from_wind(code_list, date_start, date_end)
factors_df_and_return_df = pd.concat(
[factors_df, return_df], axis=1).dropna() # 去掉因子或者回报有空缺值的样本
factors_df_and_return_df = sort_data(
factors_df_and_return_df) # 使用排序数据作为输入
data_dfs.append(factors_df_and_return_df)
factors_return_df = pd.concat(data_dfs,
axis=0) # 获取的最终训练数据拼接，return为目标
# 根据data_df训练模型
model = OrdinaryLinearRegression()
model.fit(factors_return_df)
# 根据factor_date_previous选取股票
factor_date_previous_df = get_factor_from_wind(code_list, FACTOR_LIST,
date_previous).dropna()
sorted_codes = model.predict(
factor_date_previous_df) # 获取预测收益率从小到大排序的股票列表
sorted_codes = list_wind2jq(sorted_codes)
# 根据股票列表下单
stock_codes = sorted_codes[-STOCK_NUMBER:]
stock_now = {}
for stock_code in stock_codes: # 平均持仓持股
stock_now[stock_code] = 1. / STOCK_NUMBER
stock_dict[date_now] = stock_now
def algo(context):
global position_now, position_target
date_now = context.now.strftime('%Y-%m-%d')
date_previous = get_trading_date_from_now(date_now, -1,
ql.Days) # 前一个交易日，用于获取因子数据的日期
select_time_value = select_time_model[date_now] # 择时信号计算
print(date_now + ('日回测程序执行中...，择时值：%.2f' % select_time_value))
if date_now not in trading_date_list: # 非调仓日
pass # 预留非调仓日的微调空间
else: # 调仓日执行算法
position_now = False # 虚拟上，调仓日需要提前清仓
# 根据指数获取股票候选池的代码
code_list = SelectedStockPoolFromListV1(
INCLUDED_INDEX, EXCLUDED_INDEX, date_previous).get_stock_pool()
I = trading_date_list.index(date_now)
trading_dates = trading_date_list[I - HISTORY_LENGTH:I + 1]
# 提取训练数据并训练模型
data_dfs = []
for i in range(len(trading_dates) - 1):
date_start = get_trading_date_from_now(
trading_dates[i], -1, ql.Days) # 计算因子值的日子，买入前一日的因子值
date_end = get_trading_date_from_now(trading_dates[i + 1], -1,
ql.Days) # 计算收益率到期的日子-收盘
# 提取因子和收益数据
factors_df = get_factor_from_wind(code_list, FACTOR_LIST,
date_start) # 获取因子
return_df = get_return_from_wind(code_list, date_start, date_end)
factors_df_and_return_df = pd.concat(
[factors_df, return_df], axis=1).dropna() # 去掉因子或者回报有空缺值的样本
factors_df_and_return_df = factor_and_return_process(
factors_df_and_return_df) # 对因子和回报进行预处理# 使用排序数据作为输入
data_dfs.append(factors_df_and_return_df)
factors_return_df = pd.concat(data_dfs,
axis=0) # 获取的最终训练数据拼接，return为目标
# 根据data_df训练模型
model = OrdinaryLinearRegression(select_number=SELECT_NUMBER)
model.fit(factors_return_df)
# 根据factor_date_previous选取股票，使用模型
factor_date_previous_df = get_factor_from_wind(code_list, FACTOR_LIST,
date_previous).dropna()
factor_date_previous_df = factor_process(
factor_date_previous_df) # 对因子进行预处理
select_code_list = model.predict(
factor_date_previous_df) # 返回选取的股票代码，Wind格式
# 行业轮动部分
sw1_industry = get_SW1_industry(date_now, select_code_list)
industry_wm_result = industry_wheel_movement[date_now]
select_code_list = [
stock for stock in select_code_list
if sw1_industry[stock] is not None
and industry_wm_result[sw1_industry[stock]] == 1
] # 忽略无行业信息的股票并根据行业择时信号选择候选股票
# 转化为聚宽代码格式
select_code_list = list_wind2jq(select_code_list)
# 根据股票列表下单
if len(select_code_list) > 0: # 有可选股票时记录下可选股票
stock_now = WEIGHTS(select_code_list, date_previous).get_weights()
position_target = stock_now
else:
position_target = {}
# 择时判定
if select_time_value >= 0 and not position_now and position_target != {}: # LLT择时信号为正,空仓且有目标持仓状态
stock_dict[date_now] = position_target
position_now = True
elif select_time_value < 0 and position_now and position_target != {}: # LLT择时信号为负且持仓状态:
stock_dict[date_now] = {}
position_now = False