作者: Raoul Malm

描述:

本笔记本演示了如何使用 TensorFlow 中的循环神经网络预测不同股票的未来价格。实现了带有基本单元、LSTM 或 GRU 单元的循环神经网络。

大纲:

1. [库和设置]
2. [分析数据]
3. [操作数据]
4. [建模和验证数据]
5. [预测]

参考:

https://www.kaggle.com/benjibb/lstm-stock-prediction-20170507/notebook

1. Libraries and settings

import numpy as np
import pandas as pd
import math
import sklearn
import sklearn.preprocessing
import datetime
import os
import matplotlib.pyplot as plt
import tensorflow as tf# split data in 80%/10%/10% train/validation/test sets
valid_set_size_percentage = 10 
test_set_size_percentage = 10 #display parent directory and working directory
# print(os.path.dirname(os.getcwd())+':', os.listdir(os.path.dirname(os.getcwd())));
# print(os.getcwd()+':', os.listdir(os.getcwd()));

2. 分析数据

• 从 prices-split-adjusted.csv 加载股票价格
• 分析数据

# import all stock prices 
df = pd.read_csv("./prices-split-adjusted.csv", index_col = 0)
df.info()
df.head()# number of different stocks
print('\nnumber of different stocks: ', len(list(set(df.symbol))))
print(list(set(df.symbol))[:10])

<class 'pandas.core.frame.DataFrame'>
Index: 851264 entries, 2016-01-05 to 2016-12-30
Data columns (total 6 columns):#   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  0   symbol  851264 non-null  object 1   open    851264 non-null  float642   close   851264 non-null  float643   low     851264 non-null  float644   high    851264 non-null  float645   volume  851264 non-null  float64
dtypes: float64(5), object(1)
memory usage: 45.5+ MBnumber of different stocks:  501
['JNJ', 'ZION', 'CNC', 'BBBY', 'CTL', 'PHM', 'FTR', 'MOS', 'TSCO', 'SWN']

In [3]:

df.tail()

Out[3]:

	symbol	open	close	low	high	volume
date
2016-12-30	ZBH	103.309998	103.199997	102.849998	103.930000	973800.0
2016-12-30	ZION	43.070000	43.040001	42.689999	43.310001	1938100.0
2016-12-30	ZTS	53.639999	53.529999	53.270000	53.740002	1701200.0
2016-12-30	AIV	44.730000	45.450001	44.410000	45.590000	1380900.0
2016-12-30	FTV	54.200001	53.630001	53.389999	54.480000	705100.0