[Data Mining and Business Intelligence Decision-Making] Chapter 9 Random Forest Model

Enterprise 2023-05-06 00:10:29 views: null

9.1.3 Code Implementation of Random Forest Model

Like the decision tree model, the random forest model can be used for both classification analysis and regression analysis.

The corresponding models are random forest classification model (RandomForestClassifier) ​​and random forest regression model (RandomForestRegressor). The base model of the random forest classification model is the classification decision tree model (see section 5.1.2 for details), and the base model of the random forest regression model is the regression decision tree model (see section 5.1.3 for details).

# 随机森林分类模型简单代码演示如下所示：
from sklearn.ensemble import RandomForestClassifier
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [0, 0, 0, 1, 1]

model = RandomForestClassifier(n_estimators=10, random_state=123)
model.fit(X, y)

print(model.predict([[5, 5]]))

[0]

# 随机森林回归模型简单代码演示如下所示：
from sklearn.ensemble import RandomForestRegressor
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [1, 2, 3, 4, 5]

model = RandomForestRegressor(n_estimators=10, random_state=123)
model.fit(X, y)

print(model.predict([[5, 5]]))

[2.8]

9.2 Quantitative Finance - Stock Data Acquisition

9.2.1 Acquisition of stock basic data

Here is a free Python interface package for financial data: Tushare library, through which we can freely call historical market data for analysis. Its official address is: http://tushare.org/
If you want to view stock market data, you can visit the corresponding website: http://tushare.org/trading.html

1. Basic introduction of Tushare library

It is recommended to install the Tushare library through the PIP installation method. Take the Windows system as an example. The specific method is: use the Win + R key combination to call out the run box, enter cmd and press Enter, then enter pip install tushare in the pop-up box and press Enter Press the Enter key to install. If you install it in the Jupyter Notebook editor mentioned in Section 1.2.3, you only need to enter !pip instll tushare in the code box (note that it is in English format!) and then run the line of code box.

(1) Obtain daily market data

import tushare as ts
df = ts.get_hist_data('000002', start='2018-01-01', end='2019-01-31')
df.head()

本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover
date

Note that if you do not write the start and end dates, directly writing ts.get_hist_data('000002') will retrieve the data from the current day to the previous 3 years by default. In addition, the above code can also be abbreviated as:

df = ts.get_hist_data('000002','2018-01-01', '2019-01-31')
df.head()
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20
date
2019-01-31 27.39 28.15 27.75 27.00 411857.59 0.54 1.99 26.800 26.153 25.641 426579.02 351523.31 320269.20
2019-01-30 26.70 27.82 27.21 26.63 592303.19 0.33 1.23 26.332 25.875 25.457 391193.72 334927.14 310794.00
2019-01-29 25.91 26.88 26.88 25.87 368071.62 0.82 3.15 25.952 25.696 25.292 302102.48 302443.43 293529.36
2019-01-28 26.20 26.62 26.06 25.86 308906.56 -0.04 -0.15 25.656 25.524 25.139 304355.52 302512.15 291266.32
2019-01-25 25.51 26.35 26.10 25.49 451756.16 0.69 2.71 25.574 25.420 25.008 293674.18 289949.63 293446.08

Supplementary knowledge points: get_k_data() function

Because the get_hist_data() function not only obtains the basic price information of the stock, but also obtains derivative variables such as price changes and moving average prices, so it can only retrieve the data of the previous 3 years at most. If you want to retrieve the data of more than 3 years Daily level data, you have to use the ts.get_k_data() function, which only gets the basic data of the stock price, the code is as follows:

df = ts.get_k_data('000002', start='2000-01-01', end='2019-01-31')
df.head()
date open close high low volume code
0 2000-01-04 0.584 0.614 0.620 0.572 45747.08 000002
1 2000-01-05 0.617 0.599 0.623 0.596 46136.73 000002
2 2000-01-06 0.596 0.627 0.632 0.587 71920.31 000002
3 2000-01-07 0.631 0.655 0.656 0.624 136349.36 000002
4 2000-01-10 0.673 0.721 0.721 0.665 142424.86 000002

The data obtained through the get_k_data() function does not set the date as the row index by default like the get_hist_data() function. The date here is still used as an ordinary column (date column). If you want to convert the date column here into a row index, you can Use the set_index() function to set the index, the code is as follows:

df = df.set_index('date')  # 或者写成：df.set_index('date', inplace=True)
df.head()
open close high low volume code
date
2000-01-04 0.584 0.614 0.620 0.572 45747.08 000002
2000-01-05 0.617 0.599 0.623 0.596 46136.73 000002
2000-01-06 0.596 0.627 0.632 0.587 71920.31 000002
2000-01-07 0.631 0.655 0.656 0.624 136349.36 000002
2000-01-10 0.673 0.721 0.721 0.665 142424.86 000002

(2) Obtain minute-level data

Minute-level data can be obtained by setting the ktype parameter, the code is as follows:

df = ts.get_hist_data('000002', ktype='5')
df.head()
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover
date
2020-01-03 15:00:00 32.06 32.07 32.06 32.05 3920.32 0.00 0.00 32.122 32.113 32.0350 15322.7 17669.5 13041.0 0.00
2020-01-03 14:55:00 32.11 32.11 32.07 32.03 8377.52 -0.04 -0.12 32.136 32.103 32.0290 19359.3 17817.5 13428.9 0.01
2020-01-03 14:50:00 32.20 32.21 32.12 32.11 13402.00 -0.08 -0.25 32.154 32.093 32.0175 23136.3 17962.0 13959.7 0.01
2020-01-03 14:45:00 32.16 32.21 32.20 32.12 24470.90 0.04 0.12 32.160 32.078 32.0050 24442.3 17137.9 13903.3 0.03
2020-01-03 14:40:00 32.13 32.18 32.16 32.13 26443.00 0.03 0.09 32.132 32.056 31.9880 23976.3 15128.1 13491.1 0.03

(3) 获得实时行情数据

通过如下代码可以实时取得股票当前报价和成交信息：

df = ts.get_realtime_quotes('000002') 
df
name open pre_close price high low bid ask volume amount ... a2_p a3_v a3_p a4_v a4_p a5_v a5_p date time code
0 万 科Ａ 32.710 32.560 32.050 32.810 31.780 32.040 32.050 80553629 2584309903.290 ... 32.060 3005 32.070 119 32.080 344 32.090 2020-01-03 15:00:03 000002

1 rows × 33 columns

其运行结果就是当时的股价信息，如果收盘后运行的话获得的就是当日收盘价相关信息。如果觉得列数过多，可以通过DataFrame选取列的方法选取相应的列，代码如下：

df = df[['code','name','price','bid','ask','volume','amount','time']]
df
code name price bid ask volume amount time
0 000002 万 科Ａ 32.050 32.040 32.050 80553629 2584309903.290 15:00:03

如果想同时获得多个股票代码的实时数据，可以用如下代码：

df = ts.get_realtime_quotes(['000002','000980','000981'])
df
name open pre_close price high low bid ask volume amount ... a2_p a3_v a3_p a4_v a4_p a5_v a5_p date time code
0 万 科Ａ 32.710 32.560 32.050 32.810 31.780 32.040 32.050 80553629 2584309903.290 ... 32.060 3005 32.070 119 32.080 344 32.090 2020-01-03 15:00:03 000002
1 众泰汽车 3.010 3.000 3.020 3.040 2.970 3.010 3.020 32495074 97566972.190 ... 3.030 4849 3.040 3840 3.050 2811 3.060 2020-01-03 15:00:03 000980
2 ST银亿 1.870 1.890 1.810 1.920 1.800 1.810 1.820 40518670 74744476.400 ... 1.830 2939 1.840 4163 1.850 1449 1.860 2020-01-03 15:00:03 000981

3 rows × 33 columns

(4) 获得分笔数据

通过如下代码可以获得历史分笔数据，分笔数据也即每笔成交的信息：

df = ts.get_tick_data('000002', date='2018-12-12', src='tt')
df.head()

D:\Anaconda\Anaconda\lib\site-packages\tushare\stock\trading.py:182: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\t'.
  skiprows=[0])
time price change volume amount type
0 09:25:04 26.31 0.34 6077 15988903 卖盘
1 09:30:00 26.33 0.02 197 518651 买盘
2 09:30:04 26.33 0.00 4623 12173863 卖盘
3 09:30:06 26.34 0.01 391 1030134 买盘
4 09:30:09 26.35 0.01 3289 8664911 买盘

(5) 获得指数信息

通过如下代码可以获得上证指数等指数信息：

df = ts.get_index()
df.head()  # 目前的tushare获得的指数的列名有点错乱-2020-01-04备注
code name change open preclose close high low volume amount
1 00上证指数 3089.0220 0.33 3085.1976 3083.7858 3093.8192 3074.5178 0.0 2.899917e+11 0.0
2 00Ａ股指数 3236.7077 0.33 3232.6892 3231.1885 3241.7436 3221.4906 0.0 2.899041e+11 0.0
3 00Ｂ股指数 261.0510 0.00 261.1236 261.7619 261.7619 260.2429 0.0 8.764934e+07 0.0
8 00综合指数 3006.0295 0.39 2999.1744 3006.5318 3018.1699 2998.4266 0.0 6.499701e+10 0.0
9 0上证380 4885.0267 0.23 4881.7235 4879.5471 4890.8838 4858.4325 0.0 5.888844e+10 0.0

9.2.2 股票衍生变量生成

1.生成股票基本数据

这里首先通过上一节的get_k_data()函数获取从2015-01-01到2019-12-31的股票基本数据：

df = ts.get_k_data('000002',start='2015-01-01',end='2019-12-31')
df.head()
date open close high low volume code
0 2015-01-05 12.436 12.885 13.214 12.289 6560835.0 000002
1 2015-01-06 12.617 12.410 12.954 12.142 3346346.0 000002
2 2015-01-07 12.324 12.298 12.531 12.099 2642051.0 000002
3 2015-01-08 12.375 11.745 12.419 11.632 2639394.0 000002
4 2015-01-09 11.701 11.624 12.289 11.485 3294584.0 000002 
# 通过set_index()函数可以将日期列设置为行索引：
df = df.set_index('date')
df.head()
open close high low volume code
date
2015-01-05 12.436 12.885 13.214 12.289 6560835.0 000002
2015-01-06 12.617 12.410 12.954 12.142 3346346.0 000002
2015-01-07 12.324 12.298 12.531 12.099 2642051.0 000002
2015-01-08 12.375 11.745 12.419 11.632 2639394.0 000002
2015-01-09 11.701 11.624 12.289 11.485 3294584.0 000002

2.简单衍生变量的计算

通过如下代码我们可以先构造一些简单的衍生变量：

df['close-open'] = (df['close'] - df['open'])/df['open']
df['high-low'] = (df['high'] - df['low'])/df['low']

df['pre_close'] = df['close'].shift(1)  # 该列所有往下移一行形成昨日收盘价
df['price_change'] = df['close']-df['pre_close']
df['p_change'] = (df['close']-df['pre_close'])/df['pre_close']*100

df.head()
open close high low volume code close-open high-low pre_close price_change p_change
date
2015-01-05 12.436 12.885 13.214 12.289 6560835.0 000002 0.036105 0.075271 NaN NaN NaN
2015-01-06 12.617 12.410 12.954 12.142 3346346.0 000002 -0.016406 0.066875 12.885 -0.475 -3.686457
2015-01-07 12.324 12.298 12.531 12.099 2642051.0 000002 -0.002110 0.035705 12.410 -0.112 -0.902498
2015-01-08 12.375 11.745 12.419 11.632 2639394.0 000002 -0.050909 0.067658 12.298 -0.553 -4.496666
2015-01-09 11.701 11.624 12.289 11.485 3294584.0 000002 -0.006581 0.070004 11.745 -0.121 -1.030226

3.移动平均线指标MA值

通过如下代码可以获得股价的5日移动平均值和10日移动平均值：

df['MA5'] = df['close'].rolling(5).mean()
df['MA10'] = df['close'].rolling(10).mean()

df.head(15)  # head(15)表示展示前15行，因为要展示10行以上，才能看到MA10有值
open close high low volume code close-open high-low pre_close price_change p_change MA5 MA10
date
2015-01-05 12.436 12.885 13.214 12.289 6560835.0 000002 0.036105 0.075271 NaN NaN NaN NaN NaN
2015-01-06 12.617 12.410 12.954 12.142 3346346.0 000002 -0.016406 0.066875 12.885 -0.475 -3.686457 NaN NaN
2015-01-07 12.324 12.298 12.531 12.099 2642051.0 000002 -0.002110 0.035705 12.410 -0.112 -0.902498 NaN NaN
2015-01-08 12.375 11.745 12.419 11.632 2639394.0 000002 -0.050909 0.067658 12.298 -0.553 -4.496666 NaN NaN
2015-01-09 11.701 11.624 12.289 11.485 3294584.0 000002 -0.006581 0.070004 11.745 -0.121 -1.030226 12.1924 NaN
2015-01-12 11.511 11.338 11.511 11.019 2436341.0 000002 -0.015029 0.044650 11.624 -0.286 -2.460427 11.8830 NaN
2015-01-13 11.278 11.295 11.563 11.209 1664610.0 000002 0.001507 0.031582 11.338 -0.043 -0.379256 11.6600 NaN
2015-01-14 11.295 11.321 11.494 11.122 1646818.0 000002 0.002302 0.033447 11.295 0.026 0.230190 11.4646 NaN
2015-01-15 11.347 11.900 11.952 11.235 2429686.0 000002 0.048735 0.063818 11.321 0.579 5.114389 11.4956 NaN
2015-01-16 11.900 11.684 11.900 11.572 2129475.0 000002 -0.018151 0.028344 11.900 -0.216 -1.815126 11.5076 11.8500
2015-01-19 10.803 10.517 11.148 10.517 3603625.0 000002 -0.026474 0.059998 11.684 -1.167 -9.988018 11.3434 11.6132
2015-01-20 10.543 10.673 10.889 10.422 2914688.0 000002 0.012330 0.044809 10.517 0.156 1.483313 11.2190 11.4395
2015-01-21 10.656 11.278 11.407 10.457 3555294.0 000002 0.058371 0.090848 10.673 0.605 5.668509 11.2104 11.3375
2015-01-22 11.252 11.736 11.796 11.166 3224727.0 000002 0.043015 0.056421 11.278 0.458 4.061004 11.1776 11.3366
2015-01-23 11.727 12.030 12.177 11.494 3310408.0 000002 0.025838 0.059422 11.736 0.294 2.505112 11.2468 11.3772 
# 删除空值
df.dropna(inplace=True)  # 删除空值行，也可以写成df = df.dropna()
df.head()
open close high low volume code close-open high-low pre_close price_change p_change MA5 MA10
date
2015-01-16 11.900 11.684 11.900 11.572 2129475.0 000002 -0.018151 0.028344 11.900 -0.216 -1.815126 11.5076 11.8500
2015-01-19 10.803 10.517 11.148 10.517 3603625.0 000002 -0.026474 0.059998 11.684 -1.167 -9.988018 11.3434 11.6132
2015-01-20 10.543 10.673 10.889 10.422 2914688.0 000002 0.012330 0.044809 10.517 0.156 1.483313 11.2190 11.4395
2015-01-21 10.656 11.278 11.407 10.457 3555294.0 000002 0.058371 0.090848 10.673 0.605 5.668509 11.2104 11.3375
2015-01-22 11.252 11.736 11.796 11.166 3224727.0 000002 0.043015 0.056421 11.278 0.458 4.061004 11.1776 11.3366

4.股票衍生变量生成库：TA-Lib库的安装

下面要讲的衍生变量指标都是通过股票衍生变量生成库：TA-Lib库生成的，所以这里我们先讲解一下如何安装Ta-Lib库：

以Windows操作系统为例，如果你的系统是Windows的64位系统，直接使用pip install talib语句会报错，原因在于python pip源中TA-Lib是32位的，不能安装在64位系统平台上。

正确的方法是下载64位的安装包后本地安装，下载推荐使用加州大学的python扩展库，地址：https://www.lfd.uci.edu/~gohlke/pythonlibs/

进入网址后Ctrl + F键搜索“ta_lib”，如下图所示，
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-FpvDHWnj-1681175692727)( https://uploader.shimo.im/f/rd7iXLJw6RMZPkbV.png!thumbnail)]

选择对应的文件TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl（cp后的37表示的是Python3.7版本）下载到自己选择的文件夹，读者在下载时也要根据自己Python的版本进行下载。

如何查看自己Python的版本，可以通过Win + R键调出运行框，然后输入cmd，在弹出界面中输入python，然后按一下Enter回车键即可查看相关版本，如下图所示：

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-6KXw9yAF-1681175692728)( https://uploader.shimo.im/f/90luFuZqHt46OZko.png)]

下载完成后，在自己选择的文件夹中（例如笔者保存在的文件夹“E:\机器学习与大数据分析\随机森林”），如下图所示，在搜索框中输入cmd后按一下Enter回车键搜索：

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Wp2cI7Zm-1681175692728)( https://uploader.shimo.im/f/EnabNoMQKT0tYdaz.png!thumbnail)]

在弹出框中输入如下内容，然后Enter回车键安装即可。

pip install TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl

5.通过TA-Lib库生成相对强弱指标RSI值

import talib
df['RSI'] = talib.RSI(df['close'], timeperiod=12)

6.通过TA-Lib库生成动量指标MOM值

df['MOM'] = talib.MOM(df['close'], timeperiod=5)

7.通过TA-Lib库生成指数移动平均值EMA

df['EMA12'] = talib.EMA(df['close'], timeperiod=12)  # 12日指数移动平均线
df['EMA26'] = talib.EMA(df['close'], timeperiod=26)  # 26日指数移动平均线

8.通过TA-Lib库生成异同移动平均线MACD值

df['MACD'], df['MACDsignal'], df['MACDhist'] = talib.MACD(df['close'], fastperiod=12, slowperiod=26, signalperiod=9) 

df.dropna(inplace=True)  # 删除空行
df.tail()  # 和head()相对，通过tail()函数可以查看后五行
open close high low volume code close-open high-low pre_close price_change p_change MA5 MA10 RSI MOM EMA12 EMA26 MACD MACDsignal MACDhist
date
2019-12-25 30.40 30.29 30.63 30.18 685037.0 000002 -0.003618 0.014911 30.38 -0.09 -0.296248 30.878 30.075 63.075563 -0.02 29.908556 28.973211 0.935345 0.772958 0.162387
2019-12-26 30.50 31.12 31.30 30.50 888790.0 000002 0.020328 0.026230 30.29 0.83 2.740178 30.896 30.387 68.890164 0.09 30.094932 29.132233 0.962699 0.810906 0.151793
2019-12-27 31.23 31.00 31.32 30.81 703096.0 000002 -0.007365 0.016553 31.12 -0.12 -0.385604 30.760 30.672 67.220611 -0.68 30.234173 29.270586 0.963587 0.841442 0.122145
2019-12-30 31.35 31.57 31.79 31.02 915751.0 000002 0.007018 0.024823 31.00 0.57 1.838710 30.872 30.884 70.877814 0.56 30.439685 29.440913 0.998772 0.872908 0.125864
2019-12-31 31.35 32.18 32.45 31.32 663497.0 000002 0.026475 0.036079 31.57 0.61 1.932214 31.232 31.057 74.233951 1.80 30.707426 29.643808 1.063618 0.911050 0.152567

补充内容：Talib库的一些验证

RSI指标的验证

import pandas as pd
import talib

data = pd.DataFrame()
data['close'] = [10, 12, 11, 13, 12, 14, 13]
data['RSI'] = talib.RSI(data['close'], timeperiod=6)

data
close RSI
0 10 NaN
1 12 NaN
2 11 NaN
3 13 NaN
4 12 NaN
5 14 NaN
6 13 66.666667

9.3 量化金融 - 股票涨跌预测模型搭建

9.3.1 多因子模型搭建

1.引入之后需要用到的库

import tushare as ts  # 股票基本数据相关库
import numpy as np  # 科学计算相关库
import pandas as pd  # 科学计算相关库  
import talib  # 股票衍生变量数据相关库
import matplotlib.pyplot as plt  # 引入绘图相关库
from sklearn.ensemble import RandomForestClassifier  # 引入分类决策树模型
from sklearn.metrics import accuracy_score  # 引入准确度评分函数
import warnings
warnings.filterwarnings("ignore") # 忽略警告信息，警告非报错，不影响代码执行

2.股票数据处理与衍生变量生成

我们这里将8.2节股票基本数据和股票衍生变量数据的相关代码汇总，方便之后的股票涨跌预测模型的搭建：

# 1.股票基本数据获取
df = ts.get_k_data('000002',start='2015-01-01',end='2019-12-31')
df = df.set_index('date')  # 设置日期为索引

# 2.简单衍生变量构造
df['close-open'] = (df['close'] - df['open'])/df['open']
df['high-low'] = (df['high'] - df['low'])/df['low']

df['pre_close'] = df['close'].shift(1)  # 该列所有往下移一行形成昨日收盘价
df['price_change'] = df['close']-df['pre_close']
df['p_change'] = (df['close']-df['pre_close'])/df['pre_close']*100

# 3.移动平均线相关数据构造
df['MA5'] = df['close'].rolling(5).mean()
df['MA10'] = df['close'].rolling(10).mean()
df.dropna(inplace=True)  # 删除空值

# 4.通过Ta_lib库构造衍生变量
df['RSI'] = talib.RSI(df['close'], timeperiod=12)  # 相对强弱指标
df['MOM'] = talib.MOM(df['close'], timeperiod=5)  # 动量指标
df['EMA12'] = talib.EMA(df['close'], timeperiod=12)  # 12日指数移动平均线
df['EMA26'] = talib.EMA(df['close'], timeperiod=26)  # 26日指数移动平均线
df['MACD'], df['MACDsignal'], df['MACDhist'] = talib.MACD(df['close'], fastperiod=12, slowperiod=26, signalperiod=9)  # MACD值
df.dropna(inplace=True)  # 删除空值

本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2

# 查看此时的df后五行
df.tail()
open close high low volume code close-open high-low pre_close price_change p_change MA5 MA10 RSI MOM EMA12 EMA26 MACD MACDsignal MACDhist
date
2019-12-25 27.165 27.055 27.395 26.945 685037.0 000002 -0.004049 0.016701 27.145 -0.09 -0.331553 27.643 26.840 63.081344 -0.02 26.673555 25.737103 0.936452 0.774585 0.161867
2019-12-26 27.265 27.885 28.065 27.265 888790.0 000002 0.022740 0.029342 27.055 0.83 3.067825 27.661 27.152 68.895291 0.09 26.859932 25.896207 0.963725 0.812413 0.151311
2019-12-27 27.995 27.765 28.085 27.575 703096.0 000002 -0.008216 0.018495 27.885 -0.12 -0.430339 27.525 27.437 67.225542 -0.68 26.999173 26.034636 0.964537 0.842838 0.121699
2019-12-30 28.115 28.335 28.555 27.785 915751.0 000002 0.007825 0.027713 27.765 0.57 2.052944 27.637 27.649 70.882335 0.56 27.204685 26.205033 0.999651 0.874201 0.125451
2019-12-31 28.115 28.945 29.215 28.085 663497.0 000002 0.029522 0.040235 28.335 0.61 2.152815 27.997 27.822 74.238064 1.80 27.472426 26.407994 1.064432 0.912247 0.152185

3.特征变量和目标变量提取

X = df[['close', 'volume', 'close-open', 'MA5', 'MA10', 'high-low', 'RSI', 'MOM', 'EMA12', 'MACD', 'MACDsignal', 'MACDhist']]
y = np.where(df['price_change'].shift(-1)> 0, 1, -1)

首先强调最核心的一点：应该是今天的股价信息预测下一天的股价涨跌情况，所以y应该是下一天的股价变化情况。

其中Numpy库中的where()函数的使用方法如下所示：
np.where(判断条件,满足条件的赋值,不满足条件的赋值)

其中df[‘price_change’].shift(-1)则是利用shift()函数将price_change（股价变化）这一列往上移动一行，这样就获得了每一行对应的下一天股价涨跌情况。

因此这里的判断条件就是下一天股价是否大于0，如果下一天股价涨了的我们则y赋值为数字1，下一天股价跌了的，则y赋值为数字-1。这个下一天的股价涨跌情况就是我们根据当天股票基本数据以及衍生变量预测的内容。

3.训练集和测试集数据划分

接下来，我们要将原始数据集进行分割，我们要注意到一点，训练集与测试集的划分要按照时间序列划分，而不是像之前利用train_test_split()函数进行划分。原因在于股票价格的变化趋势具有时间性，如果我们随机划分，则会破坏时间性特征，因为我们是根据当天数据来预测下一天的股价涨跌情况，而不是任意一天的股票数据来预测下一天的股价涨跌情况。
因此，我们将前90%的数据作为训练集，后10%的数据作为测试集，代码如下：

X_length = X.shape[0]  # shape属性获取X的行数和列数，shape[0]即表示行数 
split = int(X_length * 0.9)

X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]

4.模型搭建

model = RandomForestClassifier(max_depth=3, n_estimators=10, min_samples_leaf=10, random_state=1)
model.fit(X_train, y_train)

RandomForestClassifier(max_depth=3, min_samples_leaf=10, n_estimators=10,

                   random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier(max_depth=3, min_samples_leaf=10, n_estimators=10,
                   random_state=1)</pre></div></div></div></div></div>

9.3.2 模型使用与评估

1.预测下一天的涨跌情况

y_pred = model.predict(X_test)
print(y_pred)

[-1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1
  1  1  1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  1  1  1  1  1  1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1
 -1 -1 -1 -1 -1 -1 -1 -1 -1]

a = pd.DataFrame()  # 创建一个空DataFrame 
a['预测值'] = list(y_pred)
a['实际值'] = list(y_test)
a.head()
预测值 实际值
0 -1 -1
1 1 -1
2 -1 -1
3 1 -1
4 1 1 
# 查看预测概率
y_pred_proba = model.predict_proba(X_test)
y_pred_proba[0:5]

array([[0.53462409, 0.46537591],
       [0.49852513, 0.50147487],
       [0.53687766, 0.46312234],
       [0.49733765, 0.50266235],
       [0.49733765, 0.50266235]])

2.模型准确度评估

from sklearn.metrics import accuracy_score
score = accuracy_score(y_pred, y_test)
print(score)

0.5428571428571428

# 此外，我们还可以通过模型自带的score()函数记性打分，代码如下：
model.score(X_test, y_test)

0.5428571428571428

3.分析数据特征的重要性

model.feature_importances_

array([0.15132672, 0.09957677, 0.05021545, 0.06514831, 0.079073  ,
       0.11447561, 0.04576496, 0.17559964, 0.04713332, 0.07061667,
       0.08866083, 0.01240873])

# 通过如下代码可以更好的展示特征及其特征重要性：
features = X.columns  
importances = model.feature_importances_
a = pd.DataFrame()
a['特征'] = features
a['特征重要性'] = importances
a = a.sort_values('特征重要性', ascending=False)
a
特征 特征重要性
7 MOM 0.175600
0 close 0.151327
5 high-low 0.114476
1 volume 0.099577
10 MACDsignal 0.088661
4 MA10 0.079073
9 MACD 0.070617
3 MA5 0.065148
2 close-open 0.050215
8 EMA12 0.047133
6 RSI 0.045765
11 MACDhist 0.012409

9.3.3 参数调优

from sklearn.model_selection import GridSearchCV  # 网格搜索合适的超参数
# 指定分类器中参数的范围
parameters = {
    
    'n_estimators':[5, 10, 20], 'max_depth':[2, 3, 4, 5], 'min_samples_leaf':[5, 10, 20, 30]}
new_model = RandomForestClassifier(random_state=1)  # 构建分类器
grid_search = GridSearchCV(new_model, parameters, cv=6, scoring='accuracy')  # cv=6表示交叉验证6次，scoring='roc_auc'表示以ROC曲线的AUC评分作为模型评价准则, 默认为'accuracy', 即按准确度评分

grid_search.fit(X_train, y_train)  # 传入数据
grid_search.best_params_  # 输出参数的最优值

{'max_depth': 2, 'min_samples_leaf': 20, 'n_estimators': 10}

9.3.4 收益回测曲线绘制

X_test['prediction'] = model.predict(X_test)
X_test['p_change'] = (X_test['close'] - X_test['close'].shift(1)) / X_test['close'].shift(1)

X_test['origin'] = (X_test['p_change'] + 1).cumprod()
X_test['strategy'] = (X_test['prediction'].shift(1) * X_test['p_change'] + 1).cumprod()

X_test[['strategy', 'origin']].tail()
strategy origin
date
2019-12-25 1.248484 1.059319
2019-12-26 1.210183 1.091817
2019-12-27 1.215391 1.087118
2019-12-30 1.190439 1.109436
2019-12-31 1.164811 1.133320 
# 通过如下代码将收益情况删除空值后可视化，并设置X轴刻度自动倾斜：
X_test[['strategy', 'origin']].dropna().plot()
plt.gcf().autofmt_xdate()
plt.show()

​
在这里插入图片描述

Guess you like

Origin blog.csdn.net/Algernon98/article/details/130075678
Recommended
Ranking
ElasticSearch-- data modeling best practices
Permission Maintenance - Shadow User Backdoor
Refactor the code using MVP mode
Quantitative investment-fundamental model-PVC multi-factor model
Spark Big Data Processing Lecture Notes 3.2 Mastering RDD Operators
Blazor page components (2)
Erlernen von Kenntnissen zur Android-Entwicklung – Kodierung, Verschlüsselung, Hash, Serialisierung und Zeichensätze
About Qi high in JAVA study notes SORM summary detailed personal explanation
Will you calculate the accuracy of the rope displacement sensor in the measurement?
OPENJTAG debugging learning (3): debugging using the gdb command line
Daily
More
2024-05-01(4)
2024-04-30(36)
2024-04-29(5)
2024-04-28(12)
2024-04-27(29)
2024-04-26(22)
2024-04-25(32)
2024-04-24(30)
2024-04-23(30)
2024-04-22(5)

Code World

© 2018 codetd.com