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1 +{
2 + "cells": [
3 + {
4 + "cell_type": "markdown",
5 + "metadata": {},
6 + "source": [
7 + "# KOSPI 200 지수 예측 프로젝트\n",
8 + "\n",
9 + "---\n",
10 + "\n",
11 + "🏝[중간보고서링크](http://khuhub.khu.ac.kr/2020-2-capstone-design2/2016103208/tree/master/%E1%84%87%E1%85%A9%E1%84%80%E1%85%A9%E1%84%89%E1%85%A5)"
12 + ]
13 + }
14 + ],
15 + "metadata": {
16 + "kernelspec": {
17 + "display_name": "Python 3",
18 + "language": "python",
19 + "name": "python3"
20 + },
21 + "language_info": {
22 + "codemirror_mode": {
23 + "name": "ipython",
24 + "version": 3
25 + },
26 + "file_extension": ".py",
27 + "mimetype": "text/x-python",
28 + "name": "python",
29 + "nbconvert_exporter": "python",
30 + "pygments_lexer": "ipython3",
31 + "version": "3.7.3"
32 + }
33 + },
34 + "nbformat": 4,
35 + "nbformat_minor": 2
36 +}
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1 +{
2 + "cells": [
3 + {
4 + "cell_type": "markdown",
5 + "metadata": {},
6 + "source": [
7 + "# KOSPI 200 지수 예측 프로젝트\n",
8 + "\n",
9 + "---\n",
10 + "\n",
11 + "🏝[중간보고서링크](http://khuhub.khu.ac.kr/2020-2-capstone-design2/2016103208/tree/master/%E1%84%87%E1%85%A9%E1%84%80%E1%85%A9%E1%84%89%E1%85%A5)"
12 + ]
13 + }
14 + ],
15 + "metadata": {
16 + "kernelspec": {
17 + "display_name": "Python 3",
18 + "language": "python",
19 + "name": "python3"
20 + },
21 + "language_info": {
22 + "codemirror_mode": {
23 + "name": "ipython",
24 + "version": 3
25 + },
26 + "file_extension": ".py",
27 + "mimetype": "text/x-python",
28 + "name": "python",
29 + "nbconvert_exporter": "python",
30 + "pygments_lexer": "ipython3",
31 + "version": "3.7.3"
32 + }
33 + },
34 + "nbformat": 4,
35 + "nbformat_minor": 2
36 +}
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1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 1,
6 + "metadata": {},
7 + "outputs": [],
8 + "source": [
9 + "# module\n",
10 + "import pandas as pd\n",
11 + "from tqdm import tqdm\n",
12 + "import matplotlib.pyplot as plt\n",
13 + "import seaborn as sns"
14 + ]
15 + },
16 + {
17 + "cell_type": "code",
18 + "execution_count": 2,
19 + "metadata": {},
20 + "outputs": [],
21 + "source": [
22 + "test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv')\n",
23 + "validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv')\n"
24 + ]
25 + },
26 + {
27 + "cell_type": "code",
28 + "execution_count": 5,
29 + "metadata": {},
30 + "outputs": [],
31 + "source": [
32 + "del test['Unnamed: 0']\n",
33 + "del validation['Unnamed: 0']"
34 + ]
35 + },
36 + {
37 + "cell_type": "markdown",
38 + "metadata": {},
39 + "source": [
40 + "## 지수이동평균 - 종가"
41 + ]
42 + },
43 + {
44 + "cell_type": "code",
45 + "execution_count": 8,
46 + "metadata": {},
47 + "outputs": [],
48 + "source": [
49 + "##----test_data----##\n",
50 + "test_EMA_5 = test['close'].ewm(span=5).mean()\n",
51 + "test_EMA_10 = test['close'].ewm(span=10).mean()\n",
52 + "test_EMA_20 = test['close'].ewm(span=20).mean()\n",
53 + "test_EMA_60 = test['close'].ewm(span=60).mean()\n",
54 + "test_EMA_120 = test['close'].ewm(span=120).mean()\n",
55 + "\n",
56 + "test_EMA = pd.DataFrame({'5':test_EMA_5,'10':test_EMA_10,'20':test_EMA_20,'60':test_EMA_60,'120':test_EMA_120})\n",
57 + "test_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EMA.csv\")"
58 + ]
59 + },
60 + {
61 + "cell_type": "code",
62 + "execution_count": 9,
63 + "metadata": {},
64 + "outputs": [],
65 + "source": [
66 + "##----val_data----##\n",
67 + "val_EMA_5 = validation['close'].ewm(span=5).mean()\n",
68 + "val_EMA_10 = validation['close'].ewm(span=10).mean()\n",
69 + "val_EMA_20 = validation['close'].ewm(span=20).mean()\n",
70 + "val_EMA_60 = validation['close'].ewm(span=60).mean()\n",
71 + "val_EMA_120 = validation['close'].ewm(span=120).mean()\n",
72 + "\n",
73 + "val_EMA = pd.DataFrame({'5':val_EMA_5,'10':val_EMA_10,'20':val_EMA_20,'60':val_EMA_60,'120':val_EMA_120})\n",
74 + "val_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EMA.csv\")"
75 + ]
76 + },
77 + {
78 + "cell_type": "markdown",
79 + "metadata": {},
80 + "source": [
81 + "## 지수이동평균 - 거래량"
82 + ]
83 + },
84 + {
85 + "cell_type": "code",
86 + "execution_count": 10,
87 + "metadata": {},
88 + "outputs": [],
89 + "source": [
90 + "##----test_data----##\n",
91 + "test_EVMA_5 = test['vol'].ewm(span=5).mean()\n",
92 + "test_EVMA_10 = test['vol'].ewm(span=10).mean()\n",
93 + "test_EVMA_20 = test['vol'].ewm(span=20).mean()\n",
94 + "test_EVMA_60 = test['vol'].ewm(span=60).mean()\n",
95 + "test_EVMA_120 = test['vol'].ewm(span=120).mean()\n",
96 + "\n",
97 + "test_EVMA = pd.DataFrame({'5':test_EVMA_5,'10':test_EVMA_10,'20':test_EVMA_20,'60':test_EVMA_60,'120':test_EVMA_120})\n",
98 + "test_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EVMA.csv\")\n"
99 + ]
100 + },
101 + {
102 + "cell_type": "code",
103 + "execution_count": 11,
104 + "metadata": {},
105 + "outputs": [],
106 + "source": [
107 + "##----val_data----##\n",
108 + "val_EVMA_5 = validation['vol'].ewm(span=5).mean()\n",
109 + "val_EVMA_10 = validation['vol'].ewm(span=10).mean()\n",
110 + "val_EVMA_20 = validation['vol'].ewm(span=20).mean()\n",
111 + "val_EVMA_60 = validation['vol'].ewm(span=60).mean()\n",
112 + "val_EVMA_120 = validation['vol'].ewm(span=120).mean()\n",
113 + "\n",
114 + "val_EVMA = pd.DataFrame({'5':val_EVMA_5,'10':val_EVMA_10,'20':val_EVMA_20,'60':val_EVMA_60,'120':val_EVMA_120})\n",
115 + "val_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv\")"
116 + ]
117 + },
118 + {
119 + "cell_type": "code",
120 + "execution_count": 12,
121 + "metadata": {},
122 + "outputs": [],
123 + "source": [
124 + "from sklearn.preprocessing import MinMaxScaler\n",
125 + "scaler = MinMaxScaler()\n",
126 + "scale_cols = ['close','open','high','low','vol']\n",
127 + "scale_cols2 = ['5','10','20','60','120']"
128 + ]
129 + },
130 + {
131 + "cell_type": "code",
132 + "execution_count": 13,
133 + "metadata": {},
134 + "outputs": [],
135 + "source": [
136 + "\n",
137 + "##----test_data----##\n",
138 + "test_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EMA.csv')\n",
139 + "\n",
140 + "test_EMA_scaled = scaler.fit_transform(test_EMA[scale_cols2])\n",
141 + "test_EMA_scaled = pd.DataFrame(test_EMA_scaled)\n",
142 + "test_EMA_scaled.columns = ['5','10','20','60','120']\n",
143 + "\n",
144 + "##----val_data----##\n",
145 + "val_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EMA.csv')\n",
146 + "\n",
147 + "val_EMA_scaled = scaler.fit_transform(val_EMA[scale_cols2])\n",
148 + "val_EMA_scaled = pd.DataFrame(val_EMA_scaled)\n",
149 + "val_EMA_scaled.columns = ['5','10','20','60','120']"
150 + ]
151 + },
152 + {
153 + "cell_type": "code",
154 + "execution_count": 14,
155 + "metadata": {},
156 + "outputs": [],
157 + "source": [
158 + "##----test_data----##\n",
159 + "test_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EVMA.csv')\n",
160 + "\n",
161 + "test_EVMA_scaled = scaler.fit_transform(test_EVMA[scale_cols2])\n",
162 + "test_EVMA_scaled = pd.DataFrame(test_EVMA_scaled)\n",
163 + "test_EVMA_scaled.columns = ['5','10','20','60','120']\n",
164 + "\n",
165 + "##----val_data----##\n",
166 + "val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv')\n",
167 + "\n",
168 + "val_EVMA_scaled = scaler.fit_transform(val_EVMA[scale_cols2])\n",
169 + "val_EVMA_scaled = pd.DataFrame(val_EVMA_scaled)\n",
170 + "val_EVMA_scaled.columns = ['5','10','20','60','120']"
171 + ]
172 + },
173 + {
174 + "cell_type": "code",
175 + "execution_count": 15,
176 + "metadata": {},
177 + "outputs": [],
178 + "source": [
179 + "test_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/test_EMA_scaled.csv\")\n",
180 + "val_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/val_EMA_scaled.csv\")\n",
181 + "test_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/test_EVMA_scaled.csv\")\n",
182 + "val_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/val_EVMA_scaled.csv\")"
183 + ]
184 + },
185 + {
186 + "cell_type": "code",
187 + "execution_count": null,
188 + "metadata": {},
189 + "outputs": [],
190 + "source": []
191 + }
192 + ],
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207 + "nbconvert_exporter": "python",
208 + "pygments_lexer": "ipython3",
209 + "version": "3.7.3"
210 + }
211 + },
212 + "nbformat": 4,
213 + "nbformat_minor": 2
214 +}
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