定义一个函数，要求给出某只股票的简称、股票代码、收盘价格，能够计算明天的涨停价格，提示:股票简称中包含ST的涨跌幅是5％，股票代码以300开头的涨跌幅20％，其余均为10％
该回答引用chatgpt:
def calculate_limit_up_price(short_name, stock_code, closing_price):
if 'ST' in short_name:
increase_percentage = 0.05
elif stock_code.startswith('300'):
increase_percentage = 0.2
else:
increase_percentage = 0.1
limit_up_price = closing_price * (1 + increase_percentage)
return limit_up_price
short_name = 'ST股票'
stock_code = '300001'
closing_price = 10.0
limit_up_price = calculate_limit_up_price(short_name, stock_code, closing_price)
print(f"明天的涨停价格为：{limit_up_price}")
函数接受三个参数：股票的简称（short_name）、股票代码（stock_code）、收盘价格（closing_price）。根据股票简称和股票代码的规则，确定涨跌幅的百分比。然后，根据收盘价格和涨跌幅计算明天的涨停价格（limit_up_price），并将其返回
def calculate_limit_price(name, code, close_price):
limit_percent = 0.05 if 'ST' in name else 0.2 if code.startswith('300') else 0.1
limit_price = close_price * (1 + limit_percent)
return limit_price
for i in range(5): # 10折的话把5改成10，下同
print(i,"times: ")
test=data[int(idx*i*0.2):int(idx*(i+1)*0.2),] # 测试集，如果10折的话把0.2改成0.1，下同
test_y=y[int(idx*i*0.2):int(idx*(i+1)*0.2),] # 测试集合标签
if i+1 <= max(range(5)): # 10折的话把5改成10，下同
val=data[int(idx*(i+1)*0.2):int((i+2)*idx*0.2)] # 验证集，
val_y=y[int(idx*(i+1)*0.2):int((i+2)*idx*0.2)] # 验证集标签
train=np.delete(data,range(int(idx*i*0.2),int(idx*(i+2)*0.2)),axis=0) # 训练集，去掉测试集和验证集剩下部分
train_y=np.delete(y,range(int(idx*i*0.2),int(idx*(i+2)*0.2)),axis=0) # 训练集标签
#train=np.delete(train,range(int(idx*(i+1)*0.2),int((i+2)*idx*0.2)),axis=0)
else: # 最后一个循环：当最后一组为测试集，第一组为验证集，中间为训练集时
val=data[:int(((i+1)%4)*idx*0.2)] # 10折的话把4改成8，0.2改成0.1，下同
val_y=y[:int(((i+1)%4)*idx*0.2)]
train=np.delete(data,range(int(idx*i*0.2),int(idx*(i+1)*0.2)),axis=0)
train=np.delete(train,range(int(((i+1)%4)*idx*0.2)),axis=0)
train_y=np.delete(y,range(int(idx*i*0.2),int(idx*(i+1)*0.2)),axis=0)
train_y=np.delete(train_y,range(int(((i+1)%4)*idx*0.2)),axis=0)
print("test:\n",test,"-----test_y",test_y)
print("val:\n", val,"-----val_y",val_y)
print("train\n", train,"---------train_y", train_y)
print("---------------------------------------")
结果如下：
0 times:
test:
tf.Tensor(
[[0. 0.3 0.7]
[1. 1.3 1.7]
[2. 2.3 2.7]
[3. 3.3 3.7]], shape=(4, 3), dtype=float32) -----test_y tf.Tensor([0 1 2 3], shape=(4,), dtype=int32)
val:
tf.Tensor(
[[4. 4.3 4.7]
[5. 5.3 5.7]
[6. 6.3 6.7]
[7. 7.3 7.7]], shape=(4, 3), dtype=float32) -----val_y tf.Tensor([4 5 6 7], shape=(4,), dtype=int32)
train
[[ 8. 8.3 8.7]
[ 9. 9.3 9.7]
[10. 10.3 10.7]
[11. 11.3 11.7]
[12. 12.3 12.7]
[13. 13.3 13.7]
[14. 14.3 14.7]
[15. 15.3 15.7]
[16. 16.3 16.7]
[17. 17.3 17.7]
[18. 18.3 18.7]
[19. 19.3 19.7]] ---------train_y [ 8 9 10 11 12 13 14 15 16 17 18 19]
---------------------------------------
1 times:
test:
tf.Tensor(
[[4. 4.3 4.7]
[5. 5.3 5.7]
[6. 6.3 6.7]
[7. 7.3 7.7]], shape=(4, 3), dtype=float32) -----test_y tf.Tensor([4 5 6 7], shape=(4,), dtype=int32)
val:
tf.Tensor(
[[ 8. 8.3 8.7]
[ 9. 9.3 9.7]
[10. 10.3 10.7]
[11. 11.3 11.7]], shape=(4, 3), dtype=float32) -----val_y tf.Tensor([ 8 9 10 11], shape=(4,), dtype=int32)
train
[[ 0. 0.3 0.7]
[ 1. 1.3 1.7]
[ 2. 2.3 2.7]
[ 3. 3.3 3.7]
[12. 12.3 12.7]
[13. 13.3 13.7]
[14. 14.3 14.7]
[15. 15.3 15.7]
[16. 16.3 16.7]
[17. 17.3 17.7]
[18. 18.3 18.7]
[19. 19.3 19.7]] ---------train_y [ 0 1 2 3 12 13 14 15 16 17 18 19]
---------------------------------------
2 times:
test:
tf.Tensor(
[[ 8. 8.3 8.7]
[ 9. 9.3 9.7]
[10. 10.3 10.7]
[11. 11.3 11.7]], shape=(4, 3), dtype=float32) -----test_y tf.Tensor([ 8 9 10 11], shape=(4,), dtype=int32)
val:
tf.Tensor(
[[12. 12.3 12.7]
[13. 13.3 13.7]
[14. 14.3 14.7]
[15. 15.3 15.7]], shape=(4, 3), dtype=float32) -----val_y tf.Tensor([12 13 14 15], shape=(4,), dtype=int32)
train
[[ 0. 0.3 0.7]
[ 1. 1.3 1.7]
[ 2. 2.3 2.7]
[ 3. 3.3 3.7]
[ 4. 4.3 4.7]
[ 5. 5.3 5.7]
[ 6. 6.3 6.7]
[ 7. 7.3 7.7]
[16. 16.3 16.7]
[17. 17.3 17.7]
[18. 18.3 18.7]
[19. 19.3 19.7]] ---------train_y [ 0 1 2 3 4 5 6 7 16 17 18 19]
---------------------------------------
3 times:
test:
tf.Tensor(
[[12. 12.3 12.7]
[13. 13.3 13.7]
[14. 14.3 14.7]
[15. 15.3 15.7]], shape=(4, 3), dtype=float32) -----test_y tf.Tensor([12 13 14 15], shape=(4,), dtype=int32)
val:
tf.Tensor(
[[16. 16.3 16.7]
[17. 17.3 17.7]
[18. 18.3 18.7]
[19. 19.3 19.7]], shape=(4, 3), dtype=float32) -----val_y tf.Tensor([16 17 18 19], shape=(4,), dtype=int32)
train
[[ 0. 0.3 0.7]
[ 1. 1.3 1.7]
[ 2. 2.3 2.7]
[ 3. 3.3 3.7]
[ 4. 4.3 4.7]
[ 5. 5.3 5.7]
[ 6. 6.3 6.7]
[ 7. 7.3 7.7]
[ 8. 8.3 8.7]
[ 9. 9.3 9.7]
[10. 10.3 10.7]
[11. 11.3 11.7]] ---------train_y [ 0 1 2 3 4 5 6 7 8 9 10 11]
---------------------------------------
4 times:
test:
tf.Tensor(
[[16. 16.3 16.7]
[17. 17.3 17.7]
[18. 18.3 18.7]
[19. 19.3 19.7]], shape=(4, 3), dtype=float32) -----test_y tf.Tensor([16 17 18 19], shape=(4,), dtype=int32)
val:
tf.Tensor(
[[0. 0.3 0.7]
[1. 1.3 1.7]
[2. 2.3 2.7]
[3. 3.3 3.7]], shape=(4, 3), dtype=float32) -----val_y tf.Tensor([0 1 2 3], shape=(4,), dtype=int32)
train
[[ 4. 4.3 4.7]
[ 5. 5.3 5.7]
[ 6. 6.3 6.7]
[ 7. 7.3 7.7]
[ 8. 8.3 8.7]
[ 9. 9.3 9.7]
[10. 10.3 10.7]
[11. 11.3 11.7]
[12. 12.3 12.7]
[13. 13.3 13.7]
[14. 14.3 14.7]
[15. 15.3 15.7]] ---------train_y [ 4 5 6 7 8 9 10 11 12 13 14 15]
---------------------------------------
可见数据在各组之间循环，而且对应的label也都是对应循环的
这儿是设置的5组，如果是10组的话，把代码中range（5）直接改成range（10），然后把0.2改成0.1，4改成8即可。
这个代码不完美，只是能实现三者循环，有更好的代码可以分享给我