李津大局观：Python编程机器学习，人工智能AI决策树回归模型预测股票价格指数

一、决策树回归模型的机器学习

决策树回归主要用于处理连续变量。可以用在股票价格滤波预测上，以下是股票指数运用该原理生成的走势图。

二、决策树回归模型的数学原理

三、决策树模型python源代码

复制粘贴，修改后缀.txt为.py皆可使用，股票价格滤波效果一级棒

import pandas as pd

import numpy as np

import akshare as ak

import matplotlib.pyplot as plt

import json

import requests

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

def get_stock_hist_data_em(stock='0.399300',start_date='20210101',end_date='20500101',data_type='D'):

data_dict = {'1': '1', '5': '5', '15': '15', '30': '30', '60': '60', 'D': '101', 'W': '102', 'M': '103'}

klt = data_dict[data_type]

fq='1'

url = 'http://push2his.eastmoney.com/api/qt/stock/kline/'

params = {

'fields1': 'f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13',

'fields2': 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61',

'beg': start_date,

'end': end_date,

'ut': 'fa5fd1943c7b386f172d6893dbfba10b',

'rtntype': end_date,

'secid': stock,

'klt': klt,

'fqt': fq,

'cb': 'jsonp1668432946680'

}

res = requests.get(url=url, params=params)

text = res.text[19:len(res.text) - 2]

json_text = json.loads(text)

try:

df = pd.DataFrame(json_text['data']['klines'])

df.columns = ['数据']

data_list = []

for i in df['数据']:

data_list.append(i.split(','))

data = pd.DataFrame(data_list)

columns = ['date', 'open', 'close', 'high', 'low', 'volume', '成交额', '振幅', '涨跌幅', '涨跌额', '换手率']

data.columns = columns

for m in columns[1:]:

data[m] = pd.to_numeric(data[m])

data.sort_index(ascending=True,ignore_index=True,inplace=True)

return data

except:

pass

df = get_stock_hist_data_em(stock='0.399300', start_date='20100101', end_date='20500101', data_type='30')

from sklearn.tree import DecisionTreeRegressor

np.random.seed(0)

X = np.sort(5 * np.random.rand(len(df['close']), 1), axis=0)

y =df['close'].values.reshape(-1, 1)

tree_model = DecisionTreeRegressor(max_depth=4)

tree_model.fit(X, y)

X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]

y_pred = tree_model.predict(X_test)

plt.scatter(X, y, s=20, edgecolor="black", c="darkorange", label="data")

plt.plot(X_test, y_pred, color="cornflowerblue", label="prediction")

plt.xlabel("data")

plt.ylabel("target")

plt.title("Decision Tree Regression")

plt.legend()

plt.show()