#双人工智能配合，样本外夏普3.9
from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd
import pickle
import pandas as pd
import torch
import torch.nn as nn
from tqdm import tqdm
industry_code = ['HY001', 'HY002', 'HY003', 'HY004', 'HY005', 'HY006', 'HY007', 'HY008', 'HY009', 'HY010', 'HY011']
# 初始化函数
def initialize(context):
# 设定基准
set_benchmark('000985.XSHG')#000037
# 用真实价格交易
set_option('use_real_price', True)
# 打开防未来函数
set_option("avoid_future_data", True)
# 将滑点设置为0
set_slippage(FixedSlippage(0))
# 设置交易成本万分之三，不同滑点影响可在归因分析中查看
set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003,
close_today_commission=0, min_commission=5), type='stock')
# 过滤order中低于error级别的日志
log.set_level('order', 'error')
# 初始化全局变量
g.no_trading_today_signal = False
g.stock_num = 20
g.hold_list = [] # 当前持仓的全部股票
g.yesterday_HL_list = [] # 记录持仓中昨日涨停的股票
g.model = pickle.loads(read_file('model/xgboost_technical.pkl'))
# g.model = pickle.loads(read_file('xgboost_base.pkl'), encoding='iso-8859-1')
# 因子列表
g.factor_list = [
"boll_down",
"boll_up",
"EMA5",
"EMAC10",
"EMAC12",
"EMAC120",
"EMAC20",
"EMAC26",
"MAC10",
"MAC120",
"MAC20",
"MAC5",
"MAC60",
"MACDC",
"MFI14",
"price_no_fq"
]
# 设置交易运行时间
run_daily(prepare_stock_list, '9:05')
# run_daily(weekly_adjustment, '9:30')
run_weekly(weekly_adjustment, 1, '9:30')
# run_monthly(weekly_adjustment, 1, '9:30')
run_daily(check_limit_up, '14:00') # 检查持仓中的涨停股是否需要卖出
# run_daily(zheshi_trade, time='14:00')
run_daily(close_account, '14:50')
def min_max_scaling(lst):
min_val = min(lst)
max_val = max(lst)
scaled_lst = [(x - min_val) / (max_val - min_val) for x in lst]
return scaled_lst
# 1-1 准备股票池
def prepare_stock_list(context):
# 获取已持有列表
g.hold_list = []
for position in list(context.portfolio.positions.values()):
stock = position.security
g.hold_list.append(stock)
# 获取昨日涨停列表
if g.hold_list != []:
df = get_price(g.hold_list, end_date=context.previous_date, frequency='daily', fields=['close', 'high_limit'],
count=1, panel=False, fill_paused=False)
df = df[df['close'] == df['high_limit']]
g.yesterday_HL_list = list(df.code)
else:
g.yesterday_HL_list = []
model_path1 = r'ZCSZX_0.pt'
model_path2 = r'ZCSZX_1.pt'
model_path3 = r'ZCSZX_2.pt'
class model(nn.Module):
def __init__(self,
fc1_size=2000,
fc2_size=1000,
fc3_size=100,
fc1_dropout=0.2,
fc2_dropout=0.2,
fc3_dropout=0.2,
num_of_classes=50):
super(model, self).__init__()
self.f_model = nn.Sequential(
nn.Linear(2816, fc1_size), # 887
nn.BatchNorm1d(fc1_size),
nn.ReLU(),
nn.Dropout(fc1_dropout),
nn.Linear(fc1_size, fc2_size),
nn.BatchNorm1d(fc2_size),
nn.ReLU(),
nn.Dropout(fc2_dropout),
nn.Linear(fc2_size, fc3_size),
nn.BatchNorm1d(fc3_size),
nn.ReLU(),
nn.Dropout(fc3_dropout),
nn.Linear(fc3_size, 2),
)
self.conv_layers1 = nn.Sequential(
nn.Conv1d(6, 16, kernel_size=1),
nn.BatchNorm1d(16),
nn.Dropout(fc3_dropout),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(16, 32, kernel_size=1),
nn.BatchNorm1d(32),
nn.Dropout(fc3_dropout),
nn.ReLU(),
)
self.conv_2D = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=2),
nn.BatchNorm2d(16),
nn.Dropout(fc3_dropout),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(16, 32, kernel_size=2),
nn.BatchNorm2d(32),
nn.Dropout(fc3_dropout),
nn.ReLU(),
)
hidden_dim = 32
self.lstm = nn.LSTM(input_size=hidden_dim, hidden_size=hidden_dim, num_layers=4, batch_first=True,
# dropout=fc3_dropout,
bidirectional=True)
for name, module in self.named_modules():
if isinstance(module, nn.Linear):
nn.init.kaiming_normal_(module.weight, mode='fan_in', nonlinearity='relu')
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_in', nonlinearity='relu')
if isinstance(module, nn.Conv1d):
nn.init.kaiming_normal_(module.weight, mode='fan_in', nonlinearity='relu')
def forward(self, x):
min_vals, _ = torch.min(x, dim=1, keepdim=True)
max_vals, _ = torch.max(x, dim=1, keepdim=True)
x = (x - min_vals) / (max_vals - min_vals + 0.00001)
xx = x.unsqueeze(1)
xx = self.conv_2D(xx)
xx = torch.reshape(xx, (xx.shape[0], xx.shape[1] * xx.shape[2] * xx.shape[3]))
x = x.transpose(1, 2)
x = self.conv_layers1(x)
out = x.transpose(1, 2)
out2, _ = self.lstm(out)
out2 = torch.reshape(out2, (out2.shape[0], out2.shape[1] * out2.shape[2]))
IN = torch.cat((xx, out2), dim=1)
out = self.f_model(IN)
return out
import io
buffer = io.BytesIO(read_file(model_path1))
model_t1 = model()
model_t1.load_state_dict(torch.load(buffer))
model_t1.eval() # 0.54
buffer = io.BytesIO(read_file(model_path2))
model_t2 = model()
model_t2.load_state_dict(torch.load(buffer))
model_t2.eval() # 0.54
buffer = io.BytesIO(read_file(model_path3))
model_t3 = model()
model_t3.load_state_dict(torch.load(buffer))
model_t3.eval() # 0.54
print('模型加载成功')
# 1-2 选股模块
def get_stock_list(context):
# 指定日期防止未来数据
yesterday = context.previous_date
final_list = []
today = context.current_dt
initial_list = get_all_securities('stock', today).index.tolist()
initial_list = filter_all_stock2(context, initial_list)
# initial_list = ['603127.XSHG','600386.XSHG','300042.XSHE']
factor_data = get_factor_values(initial_list, g.factor_list, end_date=yesterday, count=1)
df_jq_factor_value = pd.DataFrame(index=initial_list, columns=g.factor_list)
for factor in g.factor_list:
df_jq_factor_value[factor] = list(factor_data[factor].T.iloc[:, 0])
df_jq_factor_value = data_preprocessing(df_jq_factor_value, initial_list, industry_code, yesterday)
tar = g.model.predict_proba(df_jq_factor_value)
df = df_jq_factor_value
df = df.dropna()
# print('tar is \n%s' % tar)
df['total_score'] = list(tar[:, 1])
df = df[df['total_score'] > 0.5]
df = df.sort_values(by=['total_score'], ascending=False) # 分数越高即预测未来收益越高，排序默认降序
postive_list = list(df.index)[:int(0.1 * len(list(df.index)))]
q = query(valuation.code, valuation.circulating_market_cap, indicator.eps).filter(
valuation.code.in_(postive_list)).order_by(valuation.circulating_market_cap.asc())
df = get_fundamentals(q)
df = df[df['eps'] > 0]
lst = list(df.code)
lst = filter_paused_stock(lst)
lst = filter_limitup_stock(context, lst)
lst = filter_limitdown_stock(context, lst)
print(len(lst))
tensor_list =[]
for i in lst:
df = attribute_history(i, 60, '1d')
df = np.array(df)
df_tensor = torch.Tensor(df)
tensor_list.append(df_tensor)
stacked_tensor = torch.stack(tensor_list)
tensor_list=[]
with torch.no_grad():
output1 = model_t1(stacked_tensor)
output2 = model_t2(stacked_tensor)
output3 = model_t3(stacked_tensor)
output = output1+ output2 + output3
output = output[:, 1]
data = {'ID': lst, 'score': output.squeeze().tolist()}
df = pd.DataFrame(data)
N = 3
top_N_rows = df.nlargest(N, 'score')
top_N_IDs = top_N_rows['ID'].tolist()
return top_N_IDs
# 1-3 整体调整持仓
def weekly_adjustment(context):
if g.no_trading_today_signal == False:
# 获取应买入列表
target_list = get_stock_list(context)
# 调仓卖出
for stock in g.hold_list:
if (stock not in target_list) and (stock not in g.yesterday_HL_list):
log.info("卖出[%s]" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
else:
log.info("已持有[%s]" % (stock))
# 调仓买入
position_count = len(context.portfolio.positions)
print(position_count)
target_num = len(target_list)
print(target_num)
if target_num > position_count:
print(context.portfolio.cash)
value = context.portfolio.cash / (target_num - position_count)
for stock in target_list:
if context.portfolio.positions[stock].total_amount == 0:
if open_position(stock, value):
if len(context.portfolio.positions) == target_num:
break
# 1-4 调整昨日涨停股票
def check_limit_up(context):
now_time = context.current_dt
if g.yesterday_HL_list != []:
# 对昨日涨停股票观察到尾盘如不涨停则提前卖出，如果涨停即使不在应买入列表仍暂时持有
for stock in g.yesterday_HL_list:
current_data = get_price(stock, end_date=now_time, frequency='1m', fields=['close', 'high_limit'],
skip_paused=False, fq='pre', count=1, panel=False, fill_paused=True)
if current_data.iloc[0, 0] < current_data.iloc[0, 1]:
log.info("[%s]涨停打开，卖出" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
else:
log.info("[%s]涨停，继续持有" % (stock))
def zheshi_trade(context):
#每天都运行
#对每支持仓股票进行审视
for stock in context.portfolio.positions:
#计算这只股票的当前价格
price = context.portfolio.positions[stock].price
#获取这只股票近30日最高价
close30 = history(5, unit='1d', field='close', security_list=stock, df=True, skip_paused=False, fq='pre')
max_prices = close30[stock].max()
ret = ((max_prices/price)-1)
# print(ret)
if ret >= 0.15:
log.info("[%s]跌太多，卖出" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
def filter_all_stock2(context, stock_list):
# 过滤次新股（新股、老股的分界日期，两种指定方法）
# 新老股的分界日期, 自然日180天
# by_date = context.previous_date - datetime.timedelta(days=180)
# 新老股的分界日期，120个交易日
by_date = get_trade_days(end_date=context.previous_date, count=180)[0]
all_stocks = get_all_securities(date=by_date).index.tolist()
stock_list = list(set(stock_list).intersection(set(all_stocks)))
curr_data = get_current_data()
print(curr_data)
return [stock for stock in stock_list if not (
stock.startswith(( '3','68', '4', '8')) or # 创业，科创，北交所
curr_data[stock].paused or
curr_data[stock].is_st or # ST
('ST' in curr_data[stock].name) or
('*' in curr_data[stock].name) or
('退' in curr_data[stock].name) or
(curr_data[stock].day_open == curr_data[stock].high_limit) or # 涨停开盘, 其它时间用last_price
(curr_data[stock].day_open == curr_data[stock].low_limit) # 跌停开盘, 其它时间用last_price
)]
def order_target_value_(security, value):
if value == 0:
log.debug("Selling out %s" % (security))
else:
log.debug("Order %s to value %f" % (security, value))
return order_target_value(security, value)
# 2-2 过滤ST及其他具有退市标签的股票
def filter_st_stock(stock_list):
current_data = get_current_data()
return [stock for stock in stock_list
if not current_data[stock].is_st
and 'ST' not in current_data[stock].name
and '*' not in current_data[stock].name
and '退' not in current_data[stock].name]
# 2-3 过滤科创北交股票
def filter_kcbj_stock(stock_list):
for stock in stock_list[:]:
if stock[0] == '4' or stock[0] == '8' or stock[:2] == '68':
stock_list.remove(stock)
return stock_list
# 2-4 过滤涨停的股票
def filter_limitup_stock(context, stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
current_data = get_current_data()
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] < current_data[stock].high_limit]
# 2-5 过滤跌停的股票
def filter_limitdown_stock(context, stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
current_data = get_current_data()
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] > current_data[stock].low_limit]
# 2-6 过滤次新股
def filter_new_stock(context, stock_list):
yesterday = context.previous_date
return [stock for stock in stock_list if
not yesterday - get_security_info(stock).start_date < datetime.timedelta(days=375)]
# 3-2 交易模块-开仓
def open_position(security, value):
order = order_target_value_(security, value)
if order != None and order.filled > 0:
return True
return False
# 3-3 交易模块-平仓
def close_position(position):
security = position.security
order = order_target_value_(security, 0) # 可能会因停牌失败
if order != None:
if order.status == OrderStatus.held and order.filled == order.amount:
return True
return False
# 4-2 清仓后次日资金可转
def close_account(context):
if g.no_trading_today_signal == True:
if len(g.hold_list) != 0:
for stock in g.hold_list:
position = context.portfolio.positions[stock]
close_position(position)
log.info("卖出[%s]" % (stock))
def get_industry_name(i_Constituent_Stocks, value):
return [k for k, v in i_Constituent_Stocks.items() if value in v]
关键函数解锁后查看：
