贝叶斯网络预测股票价格的python实现

贝叶斯网络是一种概率图模型，可以用来建模多个变量之间的关系。在股票价格预测中，我们可以使用贝叶斯网络来建模多个影响股价的因素之间的关系，例如公司财务状况、行业走势、政治环境等。在这里，我提供一个简单的股票价格预测的Python实现，使用贝叶斯网络建模。
首先，我们需要安装以下库：
!pip install pyagrum pandas numpy matplotlib seaborn
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然后，我们可以使用以下代码来实现：
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import pyAgrum as gum
import pyAgrum.lib.notebook as gnb

# 读取数据
data = pd.read_csv('stock_data.csv')

# 定义贝叶斯网络
bn = gum.BayesNet()

# 定义节点
# 股票价格
price = gum.LabelizedVariable('price', 'price', 3)
price.changeLabel(0, 'low')
price.changeLabel(1, 'medium')
price.changeLabel(2, 'high')
bn.add(price)

# 公司财务状况
finances = gum.LabelizedVariable('finances', 'finances', 3)
finances.changeLabel(0, 'poor')
finances.changeLabel(1, 'average')
finances.changeLabel(2, 'good')
bn.add(finances)

# 行业走势
trend = gum.LabelizedVariable('trend', 'trend', 3)
trend.changeLabel(0, 'down')
trend.changeLabel(1, 'stable')
trend.changeLabel(2, 'up')
bn.add(trend)

# 政治环境
politics = gum.LabelizedVariable('politics', 'politics', 2)
politics.changeLabel(0, 'stable')
politics.changeLabel(1, 'unstable')
bn.add(politics)

# 定义节点之间的关系
bn.addArc(finances, price)
bn.addArc(trend, price)
bn.addArc(politics, price)

# 为节点添加概率表
# 公司财务状况
bn.cpt(finances)[{'poor': 0}] = [0.6, 0.3, 0.1]
bn.cpt(finances)[{'average': 1}] = [0.3, 0.5, 0.2]
bn.cpt(finances)[{'good': 2}] = [0.1, 0.4, 0.5]

# 行业走势
bn.cpt(trend)[{'down': 0}] = [0.6, 0.3, 0.1]
bn.cpt(trend)[{'stable': 1}] = [0.3, 0.5, 0.2]
bn.cpt(trend)[{'up': 2}] = [0.1, 0.4, 0.5]

# 政治环境
bn.cpt(politics)[{'stable': 0}] = [0.7, 0.3]
bn.cpt(politics)[{'unstable': 1}] = [0.3, 0.7]

# 股票价格
bn.cpt(price)[{'poor': 0, 'down': 0, 'stable': 0}] = [0.9, 0.1, 0.0]
bn.cpt(price)[{'poor': 0, 'down': 0, 'stable': 1}] = [0.7, 0.3, 0.0]
bn.cpt(price)[{'poor': 0, 'down': 0, 'stable': 2}] = [0.5, 0.5, 0.0]
bn.cpt(price)[{'poor': 0, 'down': 1, 'stable': 0}] = [0.5, 0.4, 0.1]
bn.cpt(price)[{'poor': 0, 'down': 1, 'stable': 1}] = [0.3, 0.6, 0.1]
bn.cpt(price)[{'poor': 0, 'down': 1, 'stable': 2}] = [0.1, 0.8, 0.1]
bn.cpt(price)[{'poor': 0, 'down': 2, 'stable': 0}] = [0.1, 0.7, 0.2]
bn.cpt(price)[{'poor': 0, 'down': 2, 'stable': 1}] = [0.1, 0.3, 0.6]
bn.cpt(price)[{'poor': 0, 'down': 2, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'poor': 0, 'up': 0, 'stable': 0}] = [0.1, 0.4, 0.5]
bn.cpt(price)[{'poor': 0, 'up': 0, 'stable': 1}] = [0.1, 0.2, 0.7]
bn.cpt(price)[{'poor': 0, 'up': 0, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'poor': 0, 'up': 1, 'stable': 0}] = [0.1, 0.2, 0.7]
bn.cpt(price)[{'poor': 0, 'up': 1, 'stable': 1}] = [0.1, 0.5, 0.4]
bn.cpt(price)[{'poor': 0, 'up': 1, 'stable': 2}] = [0.1, 0.8, 0.1]
bn.cpt(price)[{'poor': 0, 'up': 2, 'stable': 0}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'poor': 0, 'up': 2, 'stable': 1}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'poor': 0, 'up': 2, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'average': 1, 'down': 0, 'stable': 0}] = [0.7, 0.3, 0.0]
bn.cpt(price)[{'average': 1, 'down': 0, 'stable': 1}] = [0.5, 0.5, 0.0]
bn.cpt(price)[{'average': 1, 'down': 0, 'stable': 2}] = [0.3, 0.7, 0.0]
bn.cpt(price)[{'average': 1, 'down': 1, 'stable': 0}] = [0.3, 0.6, 0.1]
bn.cpt(price)[{'average': 1, 'down': 1, 'stable': 1}] = [0.1, 0.7, 0.2]
bn.cpt(price)[{'average': 1, 'down': 1, 'stable': 2}] = [0.1, 0.5, 0.4]
bn.cpt(price)[{'average': 1, 'down': 2, 'stable': 0}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'average': 1, 'down': 2, 'stable': 1}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'average': 1, 'down': 2, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'average': 1, 'up': 0, 'stable': 0}] = [0.1, 0.5, 0.4]
bn.cpt(price)[{'average': 1, 'up': 0, 'stable': 1}] = [0.1, 0.3, 0.6]
bn.cpt(price)[{'average': 1, 'up': 0, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'average': 1, 'up': 1, 'stable': 0}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'average': 1, 'up': 1, 'stable': 1}] = [0.1, 0.5, 0.4]
bn.cpt(price)[{'average': 1, 'up': 1, 'stable': 2}] = [0.1, 0.8, 0.1]
bn.cpt(price)[{'average': 1, 'up': 2, 'stable': 0}] = [0.1, 0.4, 0.5]
bn.cpt(price)[{'average': 1, 'up': 2, 'stable': 1}] = [0.1, 0.2, 0.7]
bn.cpt(price)[{'average': 1, 'up': 2, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'good': 2, 'down': 0, 'stable': 0}] = [0.5, 0.5, 0.0]
bn.cpt(price)[{'good': 2, 'down': 0, 'stable': 1}] = [0.3, 0.7, 0.0]
bn.cpt(price)[{'good': 2, 'down': 0, 'stable': 2}] = [0.1, 0.9, 0.0]
bn.cpt(price)[{'good': 2, 'down': 1, 'stable': 0}] = [0.1, 0.8, 0.1]
bn.cpt(price)[{'good': 2, 'down': 1, 'stable': 1}] = [0.1, 0.7, 0.2]
bn.cpt(price)[{'good': 2, 'down': 1, 'stable': 2}] = [0.1, 0.5, 0.4]
bn.cpt(price)[{'good': 2, 'down': 2, 'stable': 0}] = [0.1, 0.2, 0.7]
bn.cpt(price)[{'good': 2, 'down': 2, 'stable': 1}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'good': 2, 'down': 2, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'good': 2, 'up': 0, 'stable': 0}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'good': 2, 'up': 0, 'stable': 1}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'good': 2, 'up': 0, 'stable': 2}] = [0.1, 0.1, 0.8]
bn.cpt(price)[{'good': 2, 'up': 1, 'stable': 0}] = [0.1, 0.2, 0.7]
bn.cpt(price)[{'good': 2, 'up': 1, 'stable': 1}] = [0.1, 0.7, 0.2]
bn.cpt(price)[{'good': 2, 'up': 1, 'stable': 2}] = [0.1, 0.9, 0.0]
bn.cpt(price)[{'good': 2, 'up': 2, 'stable': 0}] = [0.1, 0.5, 0.4]
bn.cpt(price)[{'good': 2, 'up': 2, 'stable': 1}] = [0.1, 0.3, 0.6]
bn.cpt(price)[{'good': 2, 'up': 2, 'stable': 2}] = [0.1, 0.1, 0.8]

# 绘制贝叶斯网络
gnb.showBN(bn)

# 预测股票价格
# 公司财务状况：good
# 行业走势：up
# 政治环境：stable
evs = gum.Evidence(bn)
evs[finances] = 2
evs[trend] = 2
evs[politics] = 0
ie = gum.LazyPropagation(bn)
ie.setEvidence(evs)
ie.makeInference()
print('预测股票价格：', price.label(ie.argmax(price)))
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在这个例子中，我们使用了三个影响股票价格的因素：公司财务状况、行业走势和政治环境。我们假设每个因素有三个状态（好、中、差），股票价格有三个状态（低、中、高）。我们定义了贝叶斯网络，并为节点之间的关系添加了概率表。然后，我们使用贝叶斯网络预测了股票价格，给定了公司财务状况为好、行业走势为上升、政治环境为稳定的情况下的股票价格。