├── AW Monte Carlo (3).pdf
├── HMM MULTIPLE STATES.ipynb
├── Images
    ├── inflationxgboost.png
    ├── info.txt
    ├── ke.png
    ├── mc_sim.png
    ├── output.png
    ├── output1.png
    ├── regimes.png
    ├── w.png
    └── wealth.png
├── Inflation XGBoost.ipynb
├── LICENSE
├── MPT+BL+Bayesian.pdf
├── RBAA HMM.ipynb
├── README.md
├── Recession Prediction XGBoost.ipynb
├── Residual Income Model and abnormal returns-checkpoint.ipynb
├── Vanilla_Mean_Variance.ipynb
└── matchprediction_tes.ipynb


/AW Monte Carlo (3).pdf:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/AW Monte Carlo (3).pdf


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/Images/inflationxgboost.png:
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/Images/info.txt:
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1 | Images for readme
2 | 


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/Images/ke.png:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/Images/ke.png


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/Images/mc_sim.png:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/Images/mc_sim.png


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/Images/output.png:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/Images/output.png


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/Images/output1.png:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/Images/output1.png


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/Images/regimes.png:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/Images/regimes.png


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/Images/w.png:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/Images/w.png


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/Images/wealth.png:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/Images/wealth.png


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/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2022 enexqnt
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/MPT+BL+Bayesian.pdf:
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https://raw.githubusercontent.com/enexqnt/quantallocation/9547581cf1324d4853c2840aba4f63dc29d24196/MPT+BL+Bayesian.pdf


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/README.md:
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 1 | # quantallocation
 2 | A collection of quantitative finance notebooks, including:
 3 | 
 4 | 1. [Monte Carlo Simulation](https://github.com/enexqnt/quantallocation/blob/main/AW%20Monte%20Carlo%20(3).pdf) of lazy multi-asset portfolio + Historical Analysis with Pyfolio 
 5 | 2. [Residual Income Model and Abnormal returns](https://github.com/enexqnt/quantallocation/blob/main/Residual%20Income%20Model%20and%20abnormal%20returns-checkpoint.ipynb) - Empirical Backtesting
 6 | 3. [Mean Variance, Black & Litterman, Pure Bayesian and Jorion Rule for in-sample optimization](https://github.com/enexqnt/quantallocation/blob/main/MPT%2BBL%2BBayesian.pdf) using PyPortfolioOpt
 7 | 4. [Regime switching Mean Variance Optimization with Hidden Markov Model and Trend Filtering algorithm](https://github.com/enexqnt/quantallocation/blob/main/HMM%20MULTIPLE%20STATES.ipynb)
 8 | 5. [Inflation forecast with XGBoost](https://github.com/enexqnt/quantallocation/blob/main/Inflation%20XGBoost.ipynb)
 9 | 6. [Recession forecast with XGBoost](https://github.com/enexqnt/quantallocation/blob/main/Recession%20Prediction%20XGBoost.ipynb)
10 | 
11 | Preview:
12 | 
13 | 
14 | ![alt text](https://github.com/enexqnt/quantallocation/blob/main/Images/inflationxgboost.png)
15 | ![alt text](https://github.com/enexqnt/quantallocation/blob/main/Images/regimes.png)
16 | ![alt text](https://github.com/enexqnt/quantallocation/blob/main/Images/w.png)
17 | ![alt text](https://github.com/enexqnt/quantallocation/blob/main/Images/wealth.png)
18 | ![alt text](https://github.com/enexqnt/quantallocation/blob/main/Images/ke.png)
19 | 


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/matchprediction_tes.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "from tqdm import tqdm\n",
 12 |     "import itertools\n",
 13 |     "\n",
 14 |     "import matplotlib.pyplot as plt\n",
 15 |     "import seaborn as sns\n",
 16 |     "\n",
 17 |     "from os import listdir\n",
 18 |     "from os.path import isfile, join\n",
 19 |     "\n",
 20 |     "from sklearn.ensemble import RandomForestClassifier\n",
 21 |     "from sklearn.model_selection import TimeSeriesSplit\n",
 22 |     "from sklearn.metrics import balanced_accuracy_score\n",
 23 |     "from sklearn.preprocessing import LabelEncoder\n",
 24 |     "\n",
 25 |     "\n",
 26 |     "import warnings\n",
 27 |     "warnings.filterwarnings(\"ignore\")"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "Code inspired by:\n",
 35 |     "**Stübinger, Johannes and Julian Knoll. “Beat the Bookmaker - Winning Football Bets with Machine Learning** (Best Application Paper).” SGAI Conf. (2018)."
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "markdown",
 40 |    "metadata": {},
 41 |    "source": [
 42 |     "# Import data\n",
 43 |     "\n",
 44 |     "Source: https://www.football-data.co.uk/"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 2,
 50 |    "metadata": {},
 51 |    "outputs": [],
 52 |    "source": [
 53 |     "# merge all csv files into one pandas dataframe:\n",
 54 |     "onlyfiles = [f for f in listdir('premier') if isfile(join('premier', f))]\n",
 55 |     "\n",
 56 |     "for i in onlyfiles:\n",
 57 |     "    if i==onlyfiles[0]:\n",
 58 |     "        data=pd.read_csv('premier/'+i,parse_dates=True,index_col='Date',dayfirst=True)\n",
 59 |     "    else:\n",
 60 |     "        data=pd.concat([pd.read_csv('premier/'+i,parse_dates=True,index_col='Date',dayfirst=True),data],axis=0)\n",
 61 |     "\n",
 62 |     "# sort dataframe\n",
 63 |     "data=data.sort_index().iloc[:-1]"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "markdown",
 68 |    "metadata": {},
 69 |    "source": [
 70 |     "# Data cleaning"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": 3,
 76 |    "metadata": {},
 77 |    "outputs": [],
 78 |    "source": [
 79 |     "data_=data.iloc[:,2:data.columns.to_list().index('B365H')].drop(['HTHG','HTAG','HTR'],axis=1)\n",
 80 |     "data_['y']=data_['FTAG']-data['FTHG']\n",
 81 |     "data_=data_.drop(['FTHG','FTAG'],axis=1)\n",
 82 |     "data_.dropna(inplace=True)"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 4,
 88 |    "metadata": {},
 89 |    "outputs": [
 90 |     {
 91 |      "data": {
 92 |       "text/html": [
 93 |        "<div>\n",
 94 |        "<style scoped>\n",
 95 |        "    .dataframe tbody tr th:only-of-type {\n",
 96 |        "        vertical-align: middle;\n",
 97 |        "    }\n",
 98 |        "\n",
 99 |        "    .dataframe tbody tr th {\n",
100 |        "        vertical-align: top;\n",
101 |        "    }\n",
102 |        "\n",
103 |        "    .dataframe thead th {\n",
104 |        "        text-align: right;\n",
105 |        "    }\n",
106 |        "</style>\n",
107 |        "<table border=\"1\" class=\"dataframe\">\n",
108 |        "  <thead>\n",
109 |        "    <tr style=\"text-align: right;\">\n",
110 |        "      <th></th>\n",
111 |        "      <th>HomeTeam</th>\n",
112 |        "      <th>AwayTeam</th>\n",
113 |        "      <th>FTR</th>\n",
114 |        "      <th>HS</th>\n",
115 |        "      <th>AS</th>\n",
116 |        "      <th>HST</th>\n",
117 |        "      <th>AST</th>\n",
118 |        "      <th>HF</th>\n",
119 |        "      <th>AF</th>\n",
120 |        "      <th>HC</th>\n",
121 |        "      <th>AC</th>\n",
122 |        "      <th>HY</th>\n",
123 |        "      <th>AY</th>\n",
124 |        "      <th>HR</th>\n",
125 |        "      <th>AR</th>\n",
126 |        "      <th>y</th>\n",
127 |        "    </tr>\n",
128 |        "    <tr>\n",
129 |        "      <th>Date</th>\n",
130 |        "      <th></th>\n",
131 |        "      <th></th>\n",
132 |        "      <th></th>\n",
133 |        "      <th></th>\n",
134 |        "      <th></th>\n",
135 |        "      <th></th>\n",
136 |        "      <th></th>\n",
137 |        "      <th></th>\n",
138 |        "      <th></th>\n",
139 |        "      <th></th>\n",
140 |        "      <th></th>\n",
141 |        "      <th></th>\n",
142 |        "      <th></th>\n",
143 |        "      <th></th>\n",
144 |        "      <th></th>\n",
145 |        "      <th></th>\n",
146 |        "    </tr>\n",
147 |        "  </thead>\n",
148 |        "  <tbody>\n",
149 |        "    <tr>\n",
150 |        "      <th>2007-08-10</th>\n",
151 |        "      <td>126</td>\n",
152 |        "      <td>115</td>\n",
153 |        "      <td>1</td>\n",
154 |        "      <td>16.0</td>\n",
155 |        "      <td>10.0</td>\n",
156 |        "      <td>7.0</td>\n",
157 |        "      <td>4.0</td>\n",
158 |        "      <td>19.0</td>\n",
159 |        "      <td>23.0</td>\n",
160 |        "      <td>9.0</td>\n",
161 |        "      <td>0.0</td>\n",
162 |        "      <td>4.0</td>\n",
163 |        "      <td>3.0</td>\n",
164 |        "      <td>0.0</td>\n",
165 |        "      <td>0.0</td>\n",
166 |        "      <td>0.0</td>\n",
167 |        "    </tr>\n",
168 |        "    <tr>\n",
169 |        "      <th>2007-08-11</th>\n",
170 |        "      <td>10</td>\n",
171 |        "      <td>61</td>\n",
172 |        "      <td>2</td>\n",
173 |        "      <td>27.0</td>\n",
174 |        "      <td>6.0</td>\n",
175 |        "      <td>13.0</td>\n",
176 |        "      <td>0.0</td>\n",
177 |        "      <td>14.0</td>\n",
178 |        "      <td>13.0</td>\n",
179 |        "      <td>4.0</td>\n",
180 |        "      <td>1.0</td>\n",
181 |        "      <td>0.0</td>\n",
182 |        "      <td>3.0</td>\n",
183 |        "      <td>0.0</td>\n",
184 |        "      <td>0.0</td>\n",
185 |        "      <td>-3.0</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>2007-08-11</th>\n",
189 |        "      <td>17</td>\n",
190 |        "      <td>140</td>\n",
191 |        "      <td>1</td>\n",
192 |        "      <td>17.0</td>\n",
193 |        "      <td>20.0</td>\n",
194 |        "      <td>4.0</td>\n",
195 |        "      <td>9.0</td>\n",
196 |        "      <td>15.0</td>\n",
197 |        "      <td>17.0</td>\n",
198 |        "      <td>7.0</td>\n",
199 |        "      <td>6.0</td>\n",
200 |        "      <td>1.0</td>\n",
201 |        "      <td>2.0</td>\n",
202 |        "      <td>0.0</td>\n",
203 |        "      <td>0.0</td>\n",
204 |        "      <td>0.0</td>\n",
205 |        "    </tr>\n",
206 |        "    <tr>\n",
207 |        "      <th>2007-08-11</th>\n",
208 |        "      <td>43</td>\n",
209 |        "      <td>63</td>\n",
210 |        "      <td>2</td>\n",
211 |        "      <td>19.0</td>\n",
212 |        "      <td>10.0</td>\n",
213 |        "      <td>5.0</td>\n",
214 |        "      <td>3.0</td>\n",
215 |        "      <td>13.0</td>\n",
216 |        "      <td>13.0</td>\n",
217 |        "      <td>6.0</td>\n",
218 |        "      <td>5.0</td>\n",
219 |        "      <td>2.0</td>\n",
220 |        "      <td>1.0</td>\n",
221 |        "      <td>0.0</td>\n",
222 |        "      <td>0.0</td>\n",
223 |        "      <td>-1.0</td>\n",
224 |        "    </tr>\n",
225 |        "    <tr>\n",
226 |        "      <th>2007-08-11</th>\n",
227 |        "      <td>60</td>\n",
228 |        "      <td>59</td>\n",
229 |        "      <td>0</td>\n",
230 |        "      <td>10.0</td>\n",
231 |        "      <td>14.0</td>\n",
232 |        "      <td>2.0</td>\n",
233 |        "      <td>8.0</td>\n",
234 |        "      <td>20.0</td>\n",
235 |        "      <td>18.0</td>\n",
236 |        "      <td>6.0</td>\n",
237 |        "      <td>4.0</td>\n",
238 |        "      <td>3.0</td>\n",
239 |        "      <td>1.0</td>\n",
240 |        "      <td>0.0</td>\n",
241 |        "      <td>0.0</td>\n",
242 |        "      <td>1.0</td>\n",
243 |        "    </tr>\n",
244 |        "    <tr>\n",
245 |        "      <th>...</th>\n",
246 |        "      <td>...</td>\n",
247 |        "      <td>...</td>\n",
248 |        "      <td>...</td>\n",
249 |        "      <td>...</td>\n",
250 |        "      <td>...</td>\n",
251 |        "      <td>...</td>\n",
252 |        "      <td>...</td>\n",
253 |        "      <td>...</td>\n",
254 |        "      <td>...</td>\n",
255 |        "      <td>...</td>\n",
256 |        "      <td>...</td>\n",
257 |        "      <td>...</td>\n",
258 |        "      <td>...</td>\n",
259 |        "      <td>...</td>\n",
260 |        "      <td>...</td>\n",
261 |        "      <td>...</td>\n",
262 |        "    </tr>\n",
263 |        "    <tr>\n",
264 |        "      <th>2022-09-17</th>\n",
265 |        "      <td>145</td>\n",
266 |        "      <td>88</td>\n",
267 |        "      <td>0</td>\n",
268 |        "      <td>6.0</td>\n",
269 |        "      <td>16.0</td>\n",
270 |        "      <td>1.0</td>\n",
271 |        "      <td>7.0</td>\n",
272 |        "      <td>7.0</td>\n",
273 |        "      <td>9.0</td>\n",
274 |        "      <td>5.0</td>\n",
275 |        "      <td>7.0</td>\n",
276 |        "      <td>2.0</td>\n",
277 |        "      <td>1.0</td>\n",
278 |        "      <td>1.0</td>\n",
279 |        "      <td>0.0</td>\n",
280 |        "      <td>3.0</td>\n",
281 |        "    </tr>\n",
282 |        "    <tr>\n",
283 |        "      <th>2022-09-17</th>\n",
284 |        "      <td>93</td>\n",
285 |        "      <td>20</td>\n",
286 |        "      <td>1</td>\n",
287 |        "      <td>20.0</td>\n",
288 |        "      <td>10.0</td>\n",
289 |        "      <td>7.0</td>\n",
290 |        "      <td>3.0</td>\n",
291 |        "      <td>10.0</td>\n",
292 |        "      <td>11.0</td>\n",
293 |        "      <td>8.0</td>\n",
294 |        "      <td>1.0</td>\n",
295 |        "      <td>2.0</td>\n",
296 |        "      <td>2.0</td>\n",
297 |        "      <td>0.0</td>\n",
298 |        "      <td>0.0</td>\n",
299 |        "      <td>0.0</td>\n",
300 |        "    </tr>\n",
301 |        "    <tr>\n",
302 |        "      <th>2022-09-17</th>\n",
303 |        "      <td>131</td>\n",
304 |        "      <td>79</td>\n",
305 |        "      <td>2</td>\n",
306 |        "      <td>16.0</td>\n",
307 |        "      <td>19.0</td>\n",
308 |        "      <td>11.0</td>\n",
309 |        "      <td>7.0</td>\n",
310 |        "      <td>9.0</td>\n",
311 |        "      <td>9.0</td>\n",
312 |        "      <td>6.0</td>\n",
313 |        "      <td>1.0</td>\n",
314 |        "      <td>1.0</td>\n",
315 |        "      <td>2.0</td>\n",
316 |        "      <td>0.0</td>\n",
317 |        "      <td>0.0</td>\n",
318 |        "      <td>-4.0</td>\n",
319 |        "    </tr>\n",
320 |        "    <tr>\n",
321 |        "      <th>2022-09-18</th>\n",
322 |        "      <td>22</td>\n",
323 |        "      <td>2</td>\n",
324 |        "      <td>0</td>\n",
325 |        "      <td>5.0</td>\n",
326 |        "      <td>13.0</td>\n",
327 |        "      <td>2.0</td>\n",
328 |        "      <td>7.0</td>\n",
329 |        "      <td>10.0</td>\n",
330 |        "      <td>10.0</td>\n",
331 |        "      <td>3.0</td>\n",
332 |        "      <td>3.0</td>\n",
333 |        "      <td>0.0</td>\n",
334 |        "      <td>2.0</td>\n",
335 |        "      <td>0.0</td>\n",
336 |        "      <td>0.0</td>\n",
337 |        "      <td>3.0</td>\n",
338 |        "    </tr>\n",
339 |        "    <tr>\n",
340 |        "      <th>2022-09-18</th>\n",
341 |        "      <td>47</td>\n",
342 |        "      <td>142</td>\n",
343 |        "      <td>2</td>\n",
344 |        "      <td>7.0</td>\n",
345 |        "      <td>14.0</td>\n",
346 |        "      <td>2.0</td>\n",
347 |        "      <td>4.0</td>\n",
348 |        "      <td>8.0</td>\n",
349 |        "      <td>11.0</td>\n",
350 |        "      <td>5.0</td>\n",
351 |        "      <td>14.0</td>\n",
352 |        "      <td>3.0</td>\n",
353 |        "      <td>1.0</td>\n",
354 |        "      <td>0.0</td>\n",
355 |        "      <td>0.0</td>\n",
356 |        "      <td>-1.0</td>\n",
357 |        "    </tr>\n",
358 |        "  </tbody>\n",
359 |        "</table>\n",
360 |        "<p>20995 rows × 16 columns</p>\n",
361 |        "</div>"
362 |       ],
363 |       "text/plain": [
364 |        "            HomeTeam  AwayTeam  FTR    HS    AS   HST  AST    HF    AF   HC  \\\n",
365 |        "Date                                                                          \n",
366 |        "2007-08-10       126       115    1  16.0  10.0   7.0  4.0  19.0  23.0  9.0   \n",
367 |        "2007-08-11        10        61    2  27.0   6.0  13.0  0.0  14.0  13.0  4.0   \n",
368 |        "2007-08-11        17       140    1  17.0  20.0   4.0  9.0  15.0  17.0  7.0   \n",
369 |        "2007-08-11        43        63    2  19.0  10.0   5.0  3.0  13.0  13.0  6.0   \n",
370 |        "2007-08-11        60        59    0  10.0  14.0   2.0  8.0  20.0  18.0  6.0   \n",
371 |        "...              ...       ...  ...   ...   ...   ...  ...   ...   ...  ...   \n",
372 |        "2022-09-17       145        88    0   6.0  16.0   1.0  7.0   7.0   9.0  5.0   \n",
373 |        "2022-09-17        93        20    1  20.0  10.0   7.0  3.0  10.0  11.0  8.0   \n",
374 |        "2022-09-17       131        79    2  16.0  19.0  11.0  7.0   9.0   9.0  6.0   \n",
375 |        "2022-09-18        22         2    0   5.0  13.0   2.0  7.0  10.0  10.0  3.0   \n",
376 |        "2022-09-18        47       142    2   7.0  14.0   2.0  4.0   8.0  11.0  5.0   \n",
377 |        "\n",
378 |        "              AC   HY   AY   HR   AR    y  \n",
379 |        "Date                                       \n",
380 |        "2007-08-10   0.0  4.0  3.0  0.0  0.0  0.0  \n",
381 |        "2007-08-11   1.0  0.0  3.0  0.0  0.0 -3.0  \n",
382 |        "2007-08-11   6.0  1.0  2.0  0.0  0.0  0.0  \n",
383 |        "2007-08-11   5.0  2.0  1.0  0.0  0.0 -1.0  \n",
384 |        "2007-08-11   4.0  3.0  1.0  0.0  0.0  1.0  \n",
385 |        "...          ...  ...  ...  ...  ...  ...  \n",
386 |        "2022-09-17   7.0  2.0  1.0  1.0  0.0  3.0  \n",
387 |        "2022-09-17   1.0  2.0  2.0  0.0  0.0  0.0  \n",
388 |        "2022-09-17   1.0  1.0  2.0  0.0  0.0 -4.0  \n",
389 |        "2022-09-18   3.0  0.0  2.0  0.0  0.0  3.0  \n",
390 |        "2022-09-18  14.0  3.0  1.0  0.0  0.0 -1.0  \n",
391 |        "\n",
392 |        "[20995 rows x 16 columns]"
393 |       ]
394 |      },
395 |      "execution_count": 4,
396 |      "metadata": {},
397 |      "output_type": "execute_result"
398 |     }
399 |    ],
400 |    "source": [
401 |     "# convert categorical variables into numbers\n",
402 |     "l=LabelEncoder()\n",
403 |     "l.fit(np.union1d(data_.iloc[:,0].values,data_.iloc[:,1].values))\n",
404 |     "data_.iloc[:,0]=l.transform(data_.iloc[:,0].values)\n",
405 |     "data_.iloc[:,1]=l.transform(data_.iloc[:,1].values)\n",
406 |     "\n",
407 |     "ll=LabelEncoder()\n",
408 |     "ll.fit(data_.iloc[:,2].values)\n",
409 |     "data_.iloc[:,2]=ll.transform(data_.iloc[:,2].values)\n",
410 |     "data_"
411 |    ]
412 |   },
413 |   {
414 |    "cell_type": "markdown",
415 |    "metadata": {},
416 |    "source": [
417 |     "# Model traning"
418 |    ]
419 |   },
420 |   {
421 |    "cell_type": "code",
422 |    "execution_count": 5,
423 |    "metadata": {},
424 |    "outputs": [],
425 |    "source": [
426 |     "# define a percentage split between train and test\n",
427 |     "percentage=0.15\n",
428 |     "\n",
429 |     "df_train=data_.iloc[:int(len(data_)*percentage)]\n",
430 |     "\n",
431 |     "# input hyperparameters for model fitting:\n",
432 |     "parameters={'random_state': [100],'max_depth': [4,8,10,12],'n_estimators': [20,40]}\n",
433 |     "parameters_=list((dict(zip(parameters, x)) for x in itertools.product(*parameters.values())))"
434 |    ]
435 |   },
436 |   {
437 |    "cell_type": "code",
438 |    "execution_count": 6,
439 |    "metadata": {},
440 |    "outputs": [],
441 |    "source": [
442 |     "d=np.array(data_) # transorm the dataset into numpy matrix \n",
443 |     "roll=100 # define a rolling window for model fitting"
444 |    ]
445 |   },
446 |   {
447 |    "cell_type": "code",
448 |    "execution_count": 7,
449 |    "metadata": {},
450 |    "outputs": [
451 |     {
452 |      "name": "stderr",
453 |      "output_type": "stream",
454 |      "text": [
455 |       "100%|██████████| 3049/3049 [01:27<00:00, 34.92it/s]\n"
456 |      ]
457 |     },
458 |     {
459 |      "name": "stdout",
460 |      "output_type": "stream",
461 |      "text": [
462 |       "0.089427238880926 {'random_state': 100, 'max_depth': 4, 'n_estimators': 20}\n"
463 |      ]
464 |     },
465 |     {
466 |      "name": "stderr",
467 |      "output_type": "stream",
468 |      "text": [
469 |       "100%|██████████| 3049/3049 [02:32<00:00, 19.96it/s]\n"
470 |      ]
471 |     },
472 |     {
473 |      "name": "stdout",
474 |      "output_type": "stream",
475 |      "text": [
476 |       "0.08907483433331322 {'random_state': 100, 'max_depth': 4, 'n_estimators': 40}\n"
477 |      ]
478 |     },
479 |     {
480 |      "name": "stderr",
481 |      "output_type": "stream",
482 |      "text": [
483 |       "100%|██████████| 3049/3049 [00:39<00:00, 77.71it/s]\n"
484 |      ]
485 |     },
486 |     {
487 |      "name": "stdout",
488 |      "output_type": "stream",
489 |      "text": [
490 |       "0.09061612915346963 {'random_state': 100, 'max_depth': 8, 'n_estimators': 20}\n"
491 |      ]
492 |     },
493 |     {
494 |      "name": "stderr",
495 |      "output_type": "stream",
496 |      "text": [
497 |       "100%|██████████| 3049/3049 [01:13<00:00, 41.33it/s]\n"
498 |      ]
499 |     },
500 |     {
501 |      "name": "stdout",
502 |      "output_type": "stream",
503 |      "text": [
504 |       "0.09677554965449649 {'random_state': 100, 'max_depth': 8, 'n_estimators': 40}\n"
505 |      ]
506 |     },
507 |     {
508 |      "name": "stderr",
509 |      "output_type": "stream",
510 |      "text": [
511 |       "100%|██████████| 3049/3049 [00:37<00:00, 80.28it/s]\n"
512 |      ]
513 |     },
514 |     {
515 |      "name": "stdout",
516 |      "output_type": "stream",
517 |      "text": [
518 |       "0.09079908308287721 {'random_state': 100, 'max_depth': 10, 'n_estimators': 20}\n"
519 |      ]
520 |     },
521 |     {
522 |      "name": "stderr",
523 |      "output_type": "stream",
524 |      "text": [
525 |       "100%|██████████| 3049/3049 [01:13<00:00, 41.23it/s]\n"
526 |      ]
527 |     },
528 |     {
529 |      "name": "stdout",
530 |      "output_type": "stream",
531 |      "text": [
532 |       "0.0951668871973804 {'random_state': 100, 'max_depth': 10, 'n_estimators': 40}\n"
533 |      ]
534 |     },
535 |     {
536 |      "name": "stderr",
537 |      "output_type": "stream",
538 |      "text": [
539 |       "100%|██████████| 3049/3049 [00:42<00:00, 72.44it/s]\n"
540 |      ]
541 |     },
542 |     {
543 |      "name": "stdout",
544 |      "output_type": "stream",
545 |      "text": [
546 |       "0.09224591663016006 {'random_state': 100, 'max_depth': 12, 'n_estimators': 20}\n"
547 |      ]
548 |     },
549 |     {
550 |      "name": "stderr",
551 |      "output_type": "stream",
552 |      "text": [
553 |       "100%|██████████| 3049/3049 [01:12<00:00, 42.07it/s]"
554 |      ]
555 |     },
556 |     {
557 |      "name": "stdout",
558 |      "output_type": "stream",
559 |      "text": [
560 |       "0.09764736813271888 {'random_state': 100, 'max_depth': 12, 'n_estimators': 40}\n"
561 |      ]
562 |     },
563 |     {
564 |      "name": "stderr",
565 |      "output_type": "stream",
566 |      "text": [
567 |       "\n"
568 |      ]
569 |     },
570 |     {
571 |      "data": {
572 |       "text/plain": [
573 |        "{'random_state': 100, 'max_depth': 12, 'n_estimators': 40}"
574 |       ]
575 |      },
576 |      "execution_count": 7,
577 |      "metadata": {},
578 |      "output_type": "execute_result"
579 |     }
580 |    ],
581 |    "source": [
582 |     "# train RF and find the best hyperparameter:\n",
583 |     "accuracy=[]\n",
584 |     "for param in parameters_:\n",
585 |     "    pr=[]\n",
586 |     "    model=RandomForestClassifier(**param)\n",
587 |     "    for i in tqdm(range(roll,len(df_train))):\n",
588 |     "        try:\n",
589 |     "            X_test=np.round(np.median(d[np.where(np.logical_and(d[:i,1]==d[i,1],d[:i,0]==d[i,0]))][-3:],axis=0))[:-1].reshape(1, -1)\n",
590 |     "        \n",
591 |     "            model.fit(d[i-roll:i,:-1],d[i-roll:i,-1])    \n",
592 |     "            pr.append(np.array([d[i,-1],model.predict(X_test)[0]]))\n",
593 |     "\n",
594 |     "        except:\n",
595 |     "            pr.append(np.array([d[i,-1],float('Nan')]))\n",
596 |     "        \n",
597 |     "    results=pd.DataFrame(pr,columns=['Actual','Predicted'])\n",
598 |     "        \n",
599 |     "    accuracy.append(balanced_accuracy_score(results.dropna()['Actual'],results.dropna()['Predicted']))\n",
600 |     "    print(accuracy[-1],param)\n",
601 |     "\n",
602 |     "best_param = parameters_[np.argmax(accuracy)]\n",
603 |     "best_param\n",
604 |     "\n",
605 |     "\n"
606 |    ]
607 |   },
608 |   {
609 |    "cell_type": "markdown",
610 |    "metadata": {},
611 |    "source": [
612 |     "# Model evaluation"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": 8,
618 |    "metadata": {},
619 |    "outputs": [
620 |     {
621 |      "name": "stderr",
622 |      "output_type": "stream",
623 |      "text": [
624 |       "100%|██████████| 17846/17846 [07:11<00:00, 41.39it/s]\n"
625 |      ]
626 |     }
627 |    ],
628 |    "source": [
629 |     "# evaluate model performance with test data\n",
630 |     "pr=[]\n",
631 |     "\n",
632 |     "for i in tqdm(range(len(df_train),len(data_))):\n",
633 |     "    try:\n",
634 |     "        X_test=np.round(np.median(d[np.where(np.logical_and(d[:i,1]==d[i,1],d[:i,0]==d[i,0]))][-3:],axis=0))[:-1].reshape(1, -1)\n",
635 |     "        model=RandomForestClassifier(**best_param)\n",
636 |     "        model.fit(d[i-roll:i,:-1],d[i-roll:i,-1])\n",
637 |     "        \n",
638 |     "        pr.append(np.array([d[i,-1],model.predict(X_test)[0]]))\n",
639 |     "\n",
640 |     "    except:\n",
641 |     "        pr.append(np.array([d[i,-1],float('Nan')]))\n",
642 |     " \n",
643 |     "results=pd.DataFrame(pr,columns=['Actual','Predicted'])"
644 |    ]
645 |   },
646 |   {
647 |    "cell_type": "code",
648 |    "execution_count": 9,
649 |    "metadata": {},
650 |    "outputs": [
651 |     {
652 |      "data": {
653 |       "image/png": 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",
654 |       "text/plain": [
655 |        "<Figure size 432x288 with 1 Axes>"
656 |       ]
657 |      },
658 |      "metadata": {
659 |       "needs_background": "light"
660 |      },
661 |      "output_type": "display_data"
662 |     }
663 |    ],
664 |    "source": [
665 |     "# display a violinplot to see the accuracy of predictions:\n",
666 |     "\n",
667 |     "sns.violinplot(data=results,x='Predicted',y='Actual')\n",
668 |     "plt.axhline(0)\n",
669 |     "plt.ylabel('Actual')\n",
670 |     "plt.xlabel('Predicted')\n",
671 |     "plt.title('Away minus Home goals')\n",
672 |     "plt.show()"
673 |    ]
674 |   },
675 |   {
676 |    "cell_type": "code",
677 |    "execution_count": 10,
678 |    "metadata": {},
679 |    "outputs": [],
680 |    "source": [
681 |     "dd=data[['B365H','B365A']].iloc[len(df_train):-1].dropna()\n",
682 |     "rr=results.reset_index(drop=True).join(dd.reset_index(drop=True))\n"
683 |    ]
684 |   },
685 |   {
686 |    "cell_type": "code",
687 |    "execution_count": 27,
688 |    "metadata": {},
689 |    "outputs": [
690 |     {
691 |      "name": "stdout",
692 |      "output_type": "stream",
693 |      "text": [
694 |       "Mean <-2 odd:  2.7515533980582494\n",
695 |       "Mean <-2 implied probability:  0.3634310715923931\n",
696 |       "TP/(TP+FP):  0.627831715210356\n",
697 |       "--------\n",
698 |       "Mean >2 odd:  4.588035714285714\n",
699 |       "Mean >2 implied probability:  0.21795819873117195\n",
700 |       "TP/(TP+FP):  0.5357142857142857\n"
701 |      ]
702 |     }
703 |    ],
704 |    "source": [
705 |     "# Print some stats\n",
706 |     "print('Mean <-2 odd: ',rr[rr['Predicted']<-2]['B365H'].mean())\n",
707 |     "print('Mean <-2 implied probability: ',1/rr[rr['Predicted']<-2]['B365H'].mean())\n",
708 |     "print('TP/(TP+FP): ',rr[np.logical_and(rr['Predicted']<-2,rr['Actual']<0)].shape[0]/rr[rr['Predicted']<-2].shape[0])\n",
709 |     "print('--------')\n",
710 |     "\n",
711 |     "print('Mean >2 odd: ',rr[rr['Predicted']>2]['B365A'].mean())\n",
712 |     "print('Mean >2 implied probability: ',1/(rr[rr['Predicted']>2]['B365A'].mean()))\n",
713 |     "print('TP/(TP+FP): ',rr[np.logical_and(rr['Predicted']>2,rr['Actual']>0)].shape[0]/rr[rr['Predicted']>2].shape[0])\n"
714 |    ]
715 |   },
716 |   {
717 |    "cell_type": "markdown",
718 |    "metadata": {},
719 |    "source": [
720 |     "# Strategy backtest\n",
721 |     "\n",
722 |     "* When predicted >=2 and odd for Away win >1/0.53 --> Bet on away team\n",
723 |     "* When predicted <=-2 and odd for Home win >1/0.6 --> Bet on home team"
724 |    ]
725 |   },
726 |   {
727 |    "cell_type": "code",
728 |    "execution_count": 29,
729 |    "metadata": {},
730 |    "outputs": [
731 |     {
732 |      "data": {
733 |       "text/plain": [
734 |        "1.6129032258064517"
735 |       ]
736 |      },
737 |      "execution_count": 29,
738 |      "metadata": {},
739 |      "output_type": "execute_result"
740 |     }
741 |    ],
742 |    "source": [
743 |     "1/0.62"
744 |    ]
745 |   },
746 |   {
747 |    "cell_type": "code",
748 |    "execution_count": 31,
749 |    "metadata": {},
750 |    "outputs": [],
751 |    "source": [
752 |     "# backtest the strategy\n",
753 |     "ptf=pd.DataFrame(100,columns=['Wealth'],index=rr.index)\n",
754 |     "betted=pd.DataFrame(0,columns=['Wealth'],index=rr.index)\n",
755 |     "bet=10\n",
756 |     "c=0\n",
757 |     "for a,b in rr.iterrows():\n",
758 |     "\n",
759 |     "    if (b['Predicted']>2)and(b['B365A']>1/0.53):\n",
760 |     "        betted.iloc[a]=betted.iloc[a-1]+bet\n",
761 |     "        c+=1\n",
762 |     "        if b['Predicted']>0:\n",
763 |     "            ptf.iloc[a]=ptf.iloc[a-1]+b['B365A']*bet\n",
764 |     "            \n",
765 |     "        else:\n",
766 |     "            ptf.iloc[a]=ptf.iloc[a-1]-bet\n",
767 |     "    if b['Predicted']<-2 and (b['B365H']>1/0.62):\n",
768 |     "        betted.iloc[a]=betted.iloc[a-1]+bet\n",
769 |     "        c+=1\n",
770 |     "        if b['Actual']<0:\n",
771 |     "            ptf.iloc[a]=ptf.iloc[a-1]+b['B365H']*bet\n",
772 |     "            \n",
773 |     "        else:\n",
774 |     "            ptf.iloc[a]=ptf.iloc[a-1]-bet\n",
775 |     "    else:\n",
776 |     "        betted.iloc[a]=betted.iloc[a-1]\n",
777 |     "        ptf.iloc[a]=ptf.iloc[a-1]\n"
778 |    ]
779 |   },
780 |   {
781 |    "cell_type": "code",
782 |    "execution_count": 32,
783 |    "metadata": {},
784 |    "outputs": [
785 |     {
786 |      "name": "stdout",
787 |      "output_type": "stream",
788 |      "text": [
789 |       "Number of bets:  619\n"
790 |      ]
791 |     }
792 |    ],
793 |    "source": [
794 |     "print('Number of bets: ', c)"
795 |    ]
796 |   },
797 |   {
798 |    "cell_type": "code",
799 |    "execution_count": 33,
800 |    "metadata": {},
801 |    "outputs": [
802 |     {
803 |      "data": {
804 |       "image/png": 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",
805 |       "text/plain": [
806 |        "<Figure size 432x288 with 1 Axes>"
807 |       ]
808 |      },
809 |      "metadata": {
810 |       "needs_background": "light"
811 |      },
812 |      "output_type": "display_data"
813 |     }
814 |    ],
815 |    "source": [
816 |     "ptf.index=data_.iloc[len(df_train):].index\n",
817 |     "betted.index=data_.iloc[len(df_train):].index\n",
818 |     "\n",
819 |     "plt.plot(ptf,label='Wealth index')\n",
820 |     "plt.plot(betted,label='Cumulative bets')\n",
821 |     "plt.legend()\n",
822 |     "plt.show()"
823 |    ]
824 |   },
825 |   {
826 |    "cell_type": "code",
827 |    "execution_count": 34,
828 |    "metadata": {},
829 |    "outputs": [
830 |     {
831 |      "data": {
832 |       "image/png": 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",
833 |       "text/plain": [
834 |        "<Figure size 432x288 with 1 Axes>"
835 |       ]
836 |      },
837 |      "metadata": {
838 |       "needs_background": "light"
839 |      },
840 |      "output_type": "display_data"
841 |     }
842 |    ],
843 |    "source": [
844 |     "plt.plot(ptf-betted)\n",
845 |     "plt.title('Total bet P&L')\n",
846 |     "plt.show()"
847 |    ]
848 |   },
849 |   {
850 |    "cell_type": "code",
851 |    "execution_count": 35,
852 |    "metadata": {},
853 |    "outputs": [
854 |     {
855 |      "data": {
856 |       "image/png": 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",
857 |       "text/plain": [
858 |        "<Figure size 432x288 with 1 Axes>"
859 |       ]
860 |      },
861 |      "metadata": {
862 |       "needs_background": "light"
863 |      },
864 |      "output_type": "display_data"
865 |     }
866 |    ],
867 |    "source": [
868 |     "plt.plot(ptf-ptf.cummax())\n",
869 |     "plt.title('Drawdown')\n",
870 |     "plt.show()"
871 |    ]
872 |   },
873 |   {
874 |    "cell_type": "code",
875 |    "execution_count": null,
876 |    "metadata": {},
877 |    "outputs": [],
878 |    "source": []
879 |   }
880 |  ],
881 |  "metadata": {
882 |   "kernelspec": {
883 |    "display_name": "Python 3.9.12 ('base')",
884 |    "language": "python",
885 |    "name": "python3"
886 |   },
887 |   "language_info": {
888 |    "codemirror_mode": {
889 |     "name": "ipython",
890 |     "version": 3
891 |    },
892 |    "file_extension": ".py",
893 |    "mimetype": "text/x-python",
894 |    "name": "python",
895 |    "nbconvert_exporter": "python",
896 |    "pygments_lexer": "ipython3",
897 |    "version": "3.9.12"
898 |   },
899 |   "orig_nbformat": 4,
900 |   "vscode": {
901 |    "interpreter": {
902 |     "hash": "4f7527b0034a603a2f6ced09004f8a888be406f0f25f6fb9b2bc8cc71cf9bc4b"
903 |    }
904 |   }
905 |  },
906 |  "nbformat": 4,
907 |  "nbformat_minor": 2
908 | }
909 | 


--------------------------------------------------------------------------------