├── Calculando a expectativa de retorno com CAPM.ipynb
├── Calculando o Beta de um Ação .ipynb
├── Calculando o Índice de Sharpe.ipynb
├── Calculo de Covariancias entre ativos.ipynb
├── Calculo de Risco de ativos.ipynb
├── Coletado dados do yahoo finance.ipynb
├── Exportando dados da Bolsa.ipynb
├── Machine learnig - Prevendo o preço das ações com Python.ipynb
├── Monte Carlo Simulação de preços de ações.ipynb
├── Obtendo a Fronteira Eficiente de Markowitz.ipynb
├── Ocr - concluido.ipynb
├── Prevendo_preço_de_ações_o_Facebook_Prophet_em_Python.ipynb
├── README.md
├── Retorno logaritimo de uma ação.ipynb
├── Retorno simples de uma ação.ipynb
├── Taxa de Retorno de uma Carteria de Açoes.ipynb
└── detecção de anomalias outliers.ipynb
/Calculando a expectativa de retorno com CAPM.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 4,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "#vamos caregar o modulo pandas\n",
10 | "import pandas as pd\n",
11 | "#importar biblioteca do pandas datareader\n",
12 | "from pandas_datareader import data as pdr\n",
13 | "#importar a bliblioteca Numpy\n",
14 | "import numpy as np\n",
15 | "import matplotlib.pyplot as plt\n",
16 | "%matplotlib inline"
17 | ]
18 | },
19 | {
20 | "cell_type": "code",
21 | "execution_count": 5,
22 | "metadata": {},
23 | "outputs": [
24 | {
25 | "name": "stdout",
26 | "output_type": "stream",
27 | "text": [
28 | " VVAR3.SA ^BVSP\n",
29 | "Date \n",
30 | "2015-01-02 NaN NaN\n",
31 | "2015-01-05 0.000000 -0.020724\n",
32 | "2015-01-06 0.000000 0.010134\n",
33 | "2015-01-07 0.079137 0.030003\n",
34 | "2015-01-08 0.000000 0.009657\n"
35 | ]
36 | },
37 | {
38 | "data": {
39 | "text/plain": [
40 | "0.07148356738680711"
41 | ]
42 | },
43 | "execution_count": 5,
44 | "metadata": {},
45 | "output_type": "execute_result"
46 | }
47 | ],
48 | "source": [
49 | "# tempo ideal para calc um beta é de 5 anos por isso eu coloquei (data atual - 1) - menos 5 anos\n",
50 | "carteira = ['VVAR3.SA','^BVSP']\n",
51 | "mdata = pd.DataFrame()\n",
52 | "for t in carteira:\n",
53 | " mdata[t] = pdr.DataReader(t,data_source='yahoo',start='2015-1-1', end = '2020-03-19')['Adj Close']\n",
54 | "\n",
55 | "#vamos criar um data frame novo com os dados de retorno em log... sabemos que em log é o melhor jeito se for ativos individuais\n",
56 | "df_log= np.log(mdata / mdata.shift(1))\n",
57 | "print(df_log.head())\n",
58 | " \n",
59 | "#vamos cria uma matriz de covariancai com o metodo (.cov)\n",
60 | "cov = df_log.cov()*250\n",
61 | "cov\n",
62 | "\n",
63 | "#vamos obter a covariancia com o mercador, dando o numero floot\n",
64 | "cov_com_mercado = cov.iloc[0,1]\n",
65 | "cov_com_mercado\n",
66 | "\n",
67 | "# vamos obter a variancia anualizado o nosso indice Ibov( Nossa carteria de Mercado)\n",
68 | "var_mercado = df_log['^BVSP'].var() * 250\n",
69 | "var_mercado"
70 | ]
71 | },
72 | {
73 | "cell_type": "markdown",
74 | "metadata": {},
75 | "source": [
76 | "# Formula:\n",
77 | "** Beta: **\n",
78 | "### $$ \n",
79 | "\\beta_{Ação} = \\frac{\\sigma_{Ação,m}}{\\sigma_{m}^2}\n",
80 | "$$"
81 | ]
82 | },
83 | {
84 | "cell_type": "code",
85 | "execution_count": 7,
86 | "metadata": {},
87 | "outputs": [
88 | {
89 | "data": {
90 | "text/plain": [
91 | "1.107204057750166"
92 | ]
93 | },
94 | "execution_count": 7,
95 | "metadata": {},
96 | "output_type": "execute_result"
97 | }
98 | ],
99 | "source": [
100 | "beta_acao = cov_com_mercado / var_mercado\n",
101 | "beta_acao"
102 | ]
103 | },
104 | {
105 | "cell_type": "markdown",
106 | "metadata": {},
107 | "source": [
108 | "**Calculando a expectativa de retorno de uma Ação (CAPM):**\n",
109 | "\n",
110 | "**No Brasil eu considero como taxa livre a Selic**\n",
111 | "\n",
112 | "**Premio de risco 2(Valor da Inflação)**\n",
113 | "\n",
114 | "### $$\n",
115 | "\\overline{r_{ação}} = r_f + \\beta_{ação}(\\overline{r_{m}} - r_f) \n",
116 | "$$"
117 | ]
118 | },
119 | {
120 | "cell_type": "code",
121 | "execution_count": 8,
122 | "metadata": {},
123 | "outputs": [
124 | {
125 | "name": "stdout",
126 | "output_type": "stream",
127 | "text": [
128 | "12.608%\n"
129 | ]
130 | }
131 | ],
132 | "source": [
133 | "retorno_esp_min = 0.0375 + beta_acao * 0.08\n",
134 | "#agora vou tranformar em porcetagem\n",
135 | "print (str(round(retorno_esp_min ,5)*100) + '%')"
136 | ]
137 | },
138 | {
139 | "cell_type": "code",
140 | "execution_count": null,
141 | "metadata": {},
142 | "outputs": [],
143 | "source": []
144 | },
145 | {
146 | "cell_type": "code",
147 | "execution_count": null,
148 | "metadata": {},
149 | "outputs": [],
150 | "source": []
151 | },
152 | {
153 | "cell_type": "code",
154 | "execution_count": null,
155 | "metadata": {},
156 | "outputs": [],
157 | "source": []
158 | },
159 | {
160 | "cell_type": "code",
161 | "execution_count": null,
162 | "metadata": {},
163 | "outputs": [],
164 | "source": []
165 | },
166 | {
167 | "cell_type": "code",
168 | "execution_count": null,
169 | "metadata": {},
170 | "outputs": [],
171 | "source": []
172 | }
173 | ],
174 | "metadata": {
175 | "kernelspec": {
176 | "display_name": "Python 3",
177 | "language": "python",
178 | "name": "python3"
179 | },
180 | "language_info": {
181 | "codemirror_mode": {
182 | "name": "ipython",
183 | "version": 3
184 | },
185 | "file_extension": ".py",
186 | "mimetype": "text/x-python",
187 | "name": "python",
188 | "nbconvert_exporter": "python",
189 | "pygments_lexer": "ipython3",
190 | "version": "3.6.5"
191 | }
192 | },
193 | "nbformat": 4,
194 | "nbformat_minor": 2
195 | }
196 |
--------------------------------------------------------------------------------
/Calculando o Beta de um Ação .ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "#vamos caregar o modulo pandas\n",
10 | "import pandas as pd\n",
11 | "#importar biblioteca do pandas datareader\n",
12 | "from pandas_datareader import data as pdr\n",
13 | "#importar a bliblioteca Numpy\n",
14 | "import numpy as np\n",
15 | "import matplotlib.pyplot as plt\n",
16 | "%matplotlib inline"
17 | ]
18 | },
19 | {
20 | "cell_type": "code",
21 | "execution_count": 10,
22 | "metadata": {},
23 | "outputs": [],
24 | "source": [
25 | "# tempo ideal para calc um beta é de 5 anos por isso eu coloquei (data atual - 1) - menos 5 anos\n",
26 | "carteira = ['VVAR3.SA','^BVSP']\n",
27 | "mdata = pd.DataFrame()\n",
28 | "for t in carteira:\n",
29 | " mdata[t] = pdr.DataReader(t,data_source='yahoo',start='2015-1-1', end = '2020-03-19')['Adj Close']"
30 | ]
31 | },
32 | {
33 | "cell_type": "code",
34 | "execution_count": 11,
35 | "metadata": {},
36 | "outputs": [
37 | {
38 | "name": "stdout",
39 | "output_type": "stream",
40 | "text": [
41 | " VVAR3.SA ^BVSP\n",
42 | "Date \n",
43 | "2015-01-02 NaN NaN\n",
44 | "2015-01-05 0.000000 -0.020724\n",
45 | "2015-01-06 0.000000 0.010134\n",
46 | "2015-01-07 0.079137 0.030003\n",
47 | "2015-01-08 0.000000 0.009657\n"
48 | ]
49 | }
50 | ],
51 | "source": [
52 | "#vamos criar um data frame novo com os dados de retorno em log... sabemos que em log é o melhor jeito se for ativos individuais\n",
53 | "df_log= np.log(mdata / mdata.shift(1))\n",
54 | "print(df_log.head())"
55 | ]
56 | },
57 | {
58 | "cell_type": "code",
59 | "execution_count": 12,
60 | "metadata": {},
61 | "outputs": [
62 | {
63 | "data": {
64 | "text/html": [
65 | "\n",
66 | "\n",
79 | "
\n",
80 | " \n",
81 | " \n",
82 | " | \n",
83 | " VVAR3.SA | \n",
84 | " ^BVSP | \n",
85 | "
\n",
86 | " \n",
87 | " \n",
88 | " \n",
89 | " | VVAR3.SA | \n",
90 | " 0.588919 | \n",
91 | " 0.079147 | \n",
92 | "
\n",
93 | " \n",
94 | " | ^BVSP | \n",
95 | " 0.079147 | \n",
96 | " 0.071484 | \n",
97 | "
\n",
98 | " \n",
99 | "
\n",
100 | "
\n",
46 | "\n",
59 | "
\n",
60 | " \n",
61 | " \n",
62 | " | \n",
63 | " FB | \n",
64 | " MSFT | \n",
65 | "
\n",
66 | " \n",
67 | " | Date | \n",
68 | " | \n",
69 | " | \n",
70 | "
\n",
71 | " \n",
72 | " \n",
73 | " \n",
74 | " | 2013-01-02 | \n",
75 | " 28.000000 | \n",
76 | " 23.362539 | \n",
77 | "
\n",
78 | " \n",
79 | " | 2013-01-03 | \n",
80 | " 27.770000 | \n",
81 | " 23.049566 | \n",
82 | "
\n",
83 | " \n",
84 | " | 2013-01-04 | \n",
85 | " 28.760000 | \n",
86 | " 22.618179 | \n",
87 | "
\n",
88 | " \n",
89 | " | 2013-01-07 | \n",
90 | " 29.420000 | \n",
91 | " 22.575893 | \n",
92 | "
\n",
93 | " \n",
94 | " | 2013-01-08 | \n",
95 | " 29.059999 | \n",
96 | " 22.457466 | \n",
97 | "
\n",
98 | " \n",
99 | "
\n",
100 | "
"
138 | ]
139 | },
140 | "metadata": {},
141 | "output_type": "display_data"
142 | }
143 | ],
144 | "source": [
145 | "#normalizar valores pegos os valores da primeira possição e transformo na base 100 ai todos vao sair da msm origem\n",
146 | "#va\n",
147 | "(mdata / mdata.iloc[0] * 100).plot(figsize = (16,8));"
148 | ]
149 | },
150 | {
151 | "cell_type": "code",
152 | "execution_count": 23,
153 | "metadata": {},
154 | "outputs": [
155 | {
156 | "name": "stdout",
157 | "output_type": "stream",
158 | "text": [
159 | " FB MSFT\n",
160 | "Date \n",
161 | "2013-01-02 NaN NaN\n",
162 | "2013-01-03 -0.008248 -0.013487\n",
163 | "2013-01-04 0.035029 -0.018893\n",
164 | "2013-01-07 0.022689 -0.001871\n",
165 | "2013-01-08 -0.012312 -0.005260\n"
166 | ]
167 | }
168 | ],
169 | "source": [
170 | "#vamos criar um data frame novo com os dados de retorno em log... sabemos que em log é o melhor jeito se for ativos individuais\n",
171 | "df_log= np.log(mdata / mdata.shift(1))\n",
172 | "print(df_log.head())"
173 | ]
174 | },
175 | {
176 | "cell_type": "markdown",
177 | "metadata": {},
178 | "source": [
179 | "# Facebook"
180 | ]
181 | },
182 | {
183 | "cell_type": "code",
184 | "execution_count": 9,
185 | "metadata": {},
186 | "outputs": [
187 | {
188 | "data": {
189 | "text/plain": [
190 | "0.0009139779362826145"
191 | ]
192 | },
193 | "execution_count": 9,
194 | "metadata": {},
195 | "output_type": "execute_result"
196 | }
197 | ],
198 | "source": [
199 | "#media diaria de variação \n",
200 | "df_log['FB'].mean()"
201 | ]
202 | },
203 | {
204 | "cell_type": "code",
205 | "execution_count": 10,
206 | "metadata": {},
207 | "outputs": [
208 | {
209 | "data": {
210 | "text/plain": [
211 | "0.22849448407065365"
212 | ]
213 | },
214 | "execution_count": 10,
215 | "metadata": {},
216 | "output_type": "execute_result"
217 | }
218 | ],
219 | "source": [
220 | "#media anual de variação \n",
221 | "df_log['FB'].mean() * 250"
222 | ]
223 | },
224 | {
225 | "cell_type": "code",
226 | "execution_count": 12,
227 | "metadata": {},
228 | "outputs": [
229 | {
230 | "data": {
231 | "text/plain": [
232 | "0.02093348306141575"
233 | ]
234 | },
235 | "execution_count": 12,
236 | "metadata": {},
237 | "output_type": "execute_result"
238 | }
239 | ],
240 | "source": [
241 | "#desvio padrao\n",
242 | "df_log['FB'].std()"
243 | ]
244 | },
245 | {
246 | "cell_type": "code",
247 | "execution_count": 13,
248 | "metadata": {},
249 | "outputs": [
250 | {
251 | "data": {
252 | "text/plain": [
253 | "0.330987429173141"
254 | ]
255 | },
256 | "execution_count": 13,
257 | "metadata": {},
258 | "output_type": "execute_result"
259 | }
260 | ],
261 | "source": [
262 | "#volatilidade\n",
263 | "df_log['FB'].std() *250 **0.5"
264 | ]
265 | },
266 | {
267 | "cell_type": "markdown",
268 | "metadata": {},
269 | "source": [
270 | "# Microsoft"
271 | ]
272 | },
273 | {
274 | "cell_type": "code",
275 | "execution_count": 17,
276 | "metadata": {},
277 | "outputs": [
278 | {
279 | "data": {
280 | "text/plain": [
281 | "0.000988622708668693"
282 | ]
283 | },
284 | "execution_count": 17,
285 | "metadata": {},
286 | "output_type": "execute_result"
287 | }
288 | ],
289 | "source": [
290 | "#media diaria de variação \n",
291 | "df_log['MSFT'].mean()\n"
292 | ]
293 | },
294 | {
295 | "cell_type": "code",
296 | "execution_count": 18,
297 | "metadata": {},
298 | "outputs": [
299 | {
300 | "data": {
301 | "text/plain": [
302 | "0.01603944822004159"
303 | ]
304 | },
305 | "execution_count": 18,
306 | "metadata": {},
307 | "output_type": "execute_result"
308 | }
309 | ],
310 | "source": [
311 | "#desvio pasrao\n",
312 | "df_log['MSFT'].std()\n"
313 | ]
314 | },
315 | {
316 | "cell_type": "code",
317 | "execution_count": 19,
318 | "metadata": {},
319 | "outputs": [
320 | {
321 | "data": {
322 | "text/plain": [
323 | "0.253605943938325"
324 | ]
325 | },
326 | "execution_count": 19,
327 | "metadata": {},
328 | "output_type": "execute_result"
329 | }
330 | ],
331 | "source": [
332 | "#volatilidade\n",
333 | "df_log['MSFT'].std() *250 **0.5"
334 | ]
335 | },
336 | {
337 | "cell_type": "code",
338 | "execution_count": 21,
339 | "metadata": {},
340 | "outputs": [
341 | {
342 | "data": {
343 | "text/plain": [
344 | "0.24715567716717324"
345 | ]
346 | },
347 | "execution_count": 21,
348 | "metadata": {},
349 | "output_type": "execute_result"
350 | }
351 | ],
352 | "source": [
353 | "#media anual \n",
354 | "df_log['MSFT'].mean() * 250\n"
355 | ]
356 | },
357 | {
358 | "cell_type": "code",
359 | "execution_count": 26,
360 | "metadata": {},
361 | "outputs": [
362 | {
363 | "data": {
364 | "text/plain": [
365 | "MSFT 0.253606\n",
366 | "FB 0.330987\n",
367 | "dtype: float64"
368 | ]
369 | },
370 | "execution_count": 26,
371 | "metadata": {},
372 | "output_type": "execute_result"
373 | }
374 | ],
375 | "source": [
376 | "#volatilidade dos dois no msm codigo\n",
377 | "#Obs: Cuidado isso so deu certo pq coloquei dois cochetes falando para o numpy qye se trata de duas matrizes,\n",
378 | "#Obs: se fosse apenas um cochetes daria erro pq nao tem como ter dois espaço vetorial em um espaço \n",
379 | "df_log[['MSFT','FB']].std() *250 **0.5"
380 | ]
381 | },
382 | {
383 | "cell_type": "code",
384 | "execution_count": null,
385 | "metadata": {},
386 | "outputs": [],
387 | "source": []
388 | }
389 | ],
390 | "metadata": {
391 | "kernelspec": {
392 | "display_name": "Python 3",
393 | "language": "python",
394 | "name": "python3"
395 | },
396 | "language_info": {
397 | "codemirror_mode": {
398 | "name": "ipython",
399 | "version": 3
400 | },
401 | "file_extension": ".py",
402 | "mimetype": "text/x-python",
403 | "name": "python",
404 | "nbconvert_exporter": "python",
405 | "pygments_lexer": "ipython3",
406 | "version": "3.6.5"
407 | }
408 | },
409 | "nbformat": 4,
410 | "nbformat_minor": 2
411 | }
412 |
--------------------------------------------------------------------------------
/Exportando dados da Bolsa.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "id": "9b169460",
7 | "metadata": {},
8 | "outputs": [],
9 | "source": [
10 | "import yfinance as yf\n",
11 | "import pandas as pd"
12 | ]
13 | },
14 | {
15 | "cell_type": "code",
16 | "execution_count": 2,
17 | "id": "837a39ec",
18 | "metadata": {},
19 | "outputs": [
20 | {
21 | "name": "stdout",
22 | "output_type": "stream",
23 | "text": [
24 | "[*********************100%***********************] 1 of 1 completed\n"
25 | ]
26 | }
27 | ],
28 | "source": [
29 | "tick = ['mglu3.SA']\n",
30 | "ativo = yf.download(tickers=tick, start=\"2021-01-24\", end=\"2022-01-23\", interval= \"1d\")"
31 | ]
32 | },
33 | {
34 | "cell_type": "code",
35 | "execution_count": null,
36 | "id": "f5cb2603",
37 | "metadata": {},
38 | "outputs": [],
39 | "source": [
40 | "ativo['Adj Close'].to_excel('dados1y-Magalu.xlsx')"
41 | ]
42 | },
43 | {
44 | "cell_type": "code",
45 | "execution_count": null,
46 | "id": "5a8aef1e",
47 | "metadata": {},
48 | "outputs": [],
49 | "source": [
50 | "ativo"
51 | ]
52 | }
53 | ],
54 | "metadata": {
55 | "kernelspec": {
56 | "display_name": "Python 3 (ipykernel)",
57 | "language": "python",
58 | "name": "python3"
59 | },
60 | "language_info": {
61 | "codemirror_mode": {
62 | "name": "ipython",
63 | "version": 3
64 | },
65 | "file_extension": ".py",
66 | "mimetype": "text/x-python",
67 | "name": "python",
68 | "nbconvert_exporter": "python",
69 | "pygments_lexer": "ipython3",
70 | "version": "3.9.7"
71 | }
72 | },
73 | "nbformat": 4,
74 | "nbformat_minor": 5
75 | }
76 |
--------------------------------------------------------------------------------
/Ocr - concluido.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 3,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "from pdf2image import convert_from_path\n",
10 | "import pandas as pd\n",
11 | "import glob as gb\n",
12 | "import requests\n",
13 | "import json\n",
14 | "import os\n",
15 | "import time\n",
16 | " \n",
17 | "def ocr_space_file(filename, overlay=False, api_key='8b1d9d74aa88957', language='por'):\n",
18 | "\n",
19 | " payload = {'isOverlayRequired': overlay,\n",
20 | " 'apikey': api_key,\n",
21 | " 'language': language,\n",
22 | " }\n",
23 | " with open(filename, 'rb') as f:\n",
24 | " r = requests.post('https://api.ocr.space/parse/image',\n",
25 | " files={filename: f},\n",
26 | " data=payload,\n",
27 | " )\n",
28 | " return r.content.decode()"
29 | ]
30 | },
31 | {
32 | "cell_type": "code",
33 | "execution_count": 2,
34 | "metadata": {},
35 | "outputs": [
36 | {
37 | "name": "stdout",
38 | "output_type": "stream",
39 | "text": [
40 | "(0) - relatorio_follow.PDF --> relatorio_follow.PNG\n"
41 | ]
42 | }
43 | ],
44 | "source": [
45 | "count = 0\n",
46 | "df = pd.DataFrame(columns=['caminho'])\n",
47 | "\n",
48 | "for imFilename in gb.glob(r\"C:\\Users\\alisson.oliveira\\Documents\\pdf_converte\\*.pdf\"):\n",
49 | " imFilename = imFilename\n",
50 | " \n",
51 | " images = convert_from_path(imFilename, dpi = 300)\n",
52 | " imFilename = imFilename[:len(imFilename)-4]\n",
53 | " ultimo = imFilename.split(\"\\\\\")\n",
54 | " print(\"(\" + str(count) + \") - \" + ultimo[-1] + \".PDF --> \" + ultimo[-1] +\".PNG\")\n",
55 | "\n",
56 | " \n",
57 | " im = \"C:\\\\Users\\\\alisson.oliveira\\\\Documents\\\\pdf_converte\\\\convertidos\\\\\" + ultimo[-1]\n",
58 | " for i, image in enumerate(images):\n",
59 | " fname = im + \"_\" + \"(\" + str(i + 1) + '-' + str(len(images)) + ')' '.png'\n",
60 | " image.save(fname,\"PNG\")\n",
61 | " df.loc[len(df)] = fname\n",
62 | " \n",
63 | " \n",
64 | " "
65 | ]
66 | },
67 | {
68 | "cell_type": "code",
69 | "execution_count": 4,
70 | "metadata": {},
71 | "outputs": [],
72 | "source": [
73 | "for fname in df['caminho']:\n",
74 | " test_file = ocr_space_file(filename=fname, language='por')\n",
75 | " resultado = json.loads(test_file)\n",
76 | " detectar_text = resultado.get(\"ParsedResults\")[0].get(\"ParsedText\")\n",
77 | " nome_area = detectar_text.splitlines() \n",
78 | " os.rename(fname, 'C:\\\\Users\\\\alisson.oliveira\\\\Documents\\\\pdf_converte\\\\convertidos\\\\'+nome_area[0]+'.png')\n",
79 | " time.sleep(5)\n"
80 | ]
81 | },
82 | {
83 | "cell_type": "code",
84 | "execution_count": null,
85 | "metadata": {},
86 | "outputs": [],
87 | "source": [
88 | "fname"
89 | ]
90 | },
91 | {
92 | "cell_type": "code",
93 | "execution_count": null,
94 | "metadata": {},
95 | "outputs": [],
96 | "source": []
97 | }
98 | ],
99 | "metadata": {
100 | "kernelspec": {
101 | "display_name": "Python 3",
102 | "language": "python",
103 | "name": "python3"
104 | },
105 | "language_info": {
106 | "codemirror_mode": {
107 | "name": "ipython",
108 | "version": 3
109 | },
110 | "file_extension": ".py",
111 | "mimetype": "text/x-python",
112 | "name": "python",
113 | "nbconvert_exporter": "python",
114 | "pygments_lexer": "ipython3",
115 | "version": "3.8.3"
116 | }
117 | },
118 | "nbformat": 4,
119 | "nbformat_minor": 4
120 | }
121 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Finanças Quantitativas: Com Python
2 | Finanças Quantitativas: Com Python
3 |
4 | # Sobre o projeto.
5 | A iniciativa desse projeto veio pelo simples fato que toda vez que eu abria meu youtube, tinha inumeras propagandas de pessoas prometendo milagres com seus dinheiro através de vendas de cursos de como operar na bolsa, com compra de ações ou de indices e opções. Como forma de ajudar essas pessoas venho através desse repositorio compartilhar diversos algoritimos para iniciar no mundo de finanças quantitativas.
6 |
7 | Algoritimos que, SIM, lhe ajudaram a criar uma carteria que lhe dê retornos.
8 |
9 | # O que é Finanças Quantitadivas ?
10 |
11 | Finanças quantitativas e engenharia financeira são as áreas de finanças que envolvem
12 | a aplicação de ferramentas e métodos de finanças tradicionais, matemática, física,
13 | computação, economia e econometria à solução de problemas de interesse em áreas
14 | como gestão de investimentos, finanças corporativas, gestão de riscos, apreçamento
15 | e hedging de instrumentos derivativos, trading, finanças econômicas, produtos
16 | estruturados e asset allocation.
17 |
18 | # Oque temos nesse repositorio ?
19 | **Todos os Cods são comentados linha a linha, apenas os muitos obvios que não**
20 |
21 | *Calculando a expectativa de retorno com CAPM*
22 |
23 | *Calculando o Beta de um Ação*
24 |
25 | *Calculando o Índice de Sharpe*
26 |
27 | *Calculo de Covariancias entre ativos*
28 |
29 | *Calculo de Risco de ativos*
30 |
31 | *Monte Carlo Simulação de preços de ações*
32 |
33 | *Obtendo a Fronteira Eficiente de Markowitz*
34 |
35 | *Retorno logaritimo de uma ação*
36 |
37 | *Retorno simples de uma ação*
38 |
39 | *Taxa de Retorno de uma Carteria de Açoes*
40 |
41 | *Machine learnig - Prevendo o preço das ações com Python*
42 |
43 | *Detecção de anomalias outliers*
44 |
45 | *Prevendo preços de ações com o algoritmo do facebook Prophet*
46 |
47 |
48 | # OBS:
49 | **Esse repositorio sempre será atualizado, para trazer novidades e novos jeitos de otimizar seus ganhos**
50 |
51 |
52 |
--------------------------------------------------------------------------------
/Retorno logaritimo de uma ação.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "#vamos caregar o modulo pandas\n",
10 | "import pandas as pd\n",
11 | "#importar biblioteca do pandas datareader\n",
12 | "from pandas_datareader import data as pdr\n",
13 | "#importar a bliblioteca Numpy\n",
14 | "import numpy as np\n",
15 | "import matplotlib.pyplot as plt"
16 | ]
17 | },
18 | {
19 | "cell_type": "code",
20 | "execution_count": 2,
21 | "metadata": {},
22 | "outputs": [],
23 | "source": [
24 | "df_PG = pdr.DataReader('PG',data_source='yahoo',start='1995-1-1')"
25 | ]
26 | },
27 | {
28 | "cell_type": "code",
29 | "execution_count": 3,
30 | "metadata": {},
31 | "outputs": [
32 | {
33 | "data": {
34 | "text/html": [
35 | "\n",
36 | "\n",
49 | "
\n",
50 | " \n",
51 | " \n",
52 | " | \n",
53 | " High | \n",
54 | " Low | \n",
55 | " Open | \n",
56 | " Close | \n",
57 | " Volume | \n",
58 | " Adj Close | \n",
59 | "
\n",
60 | " \n",
61 | " | Date | \n",
62 | " | \n",
63 | " | \n",
64 | " | \n",
65 | " | \n",
66 | " | \n",
67 | " | \n",
68 | "
\n",
69 | " \n",
70 | " \n",
71 | " \n",
72 | " | 1995-01-03 | \n",
73 | " 15.62500 | \n",
74 | " 15.43750 | \n",
75 | " 15.46875 | \n",
76 | " 15.59375 | \n",
77 | " 3318400.0 | \n",
78 | " 6.362402 | \n",
79 | "
\n",
80 | " \n",
81 | " | 1995-01-04 | \n",
82 | " 15.65625 | \n",
83 | " 15.31250 | \n",
84 | " 15.53125 | \n",
85 | " 15.46875 | \n",
86 | " 2218800.0 | \n",
87 | " 6.311406 | \n",
88 | "
\n",
89 | " \n",
90 | " | 1995-01-05 | \n",
91 | " 15.43750 | \n",
92 | " 15.21875 | \n",
93 | " 15.37500 | \n",
94 | " 15.25000 | \n",
95 | " 2319600.0 | \n",
96 | " 6.222151 | \n",
97 | "
\n",
98 | " \n",
99 | " | 1995-01-06 | \n",
100 | " 15.40625 | \n",
101 | " 15.15625 | \n",
102 | " 15.15625 | \n",
103 | " 15.28125 | \n",
104 | " 3438000.0 | \n",
105 | " 6.234900 | \n",
106 | "
\n",
107 | " \n",
108 | " | 1995-01-09 | \n",
109 | " 15.40625 | \n",
110 | " 15.18750 | \n",
111 | " 15.34375 | \n",
112 | " 15.21875 | \n",
113 | " 1795200.0 | \n",
114 | " 6.209402 | \n",
115 | "
\n",
116 | " \n",
117 | "
\n",
118 | "
\n",
148 | "\n",
161 | "
\n",
162 | " \n",
163 | " \n",
164 | " | \n",
165 | " High | \n",
166 | " Low | \n",
167 | " Open | \n",
168 | " Close | \n",
169 | " Volume | \n",
170 | " Adj Close | \n",
171 | "
\n",
172 | " \n",
173 | " | Date | \n",
174 | " | \n",
175 | " | \n",
176 | " | \n",
177 | " | \n",
178 | " | \n",
179 | " | \n",
180 | "
\n",
181 | " \n",
182 | " \n",
183 | " \n",
184 | " | 2020-03-12 | \n",
185 | " 111.580002 | \n",
186 | " 101.000000 | \n",
187 | " 103.000000 | \n",
188 | " 101.839996 | \n",
189 | " 20988000.0 | \n",
190 | " 101.839996 | \n",
191 | "
\n",
192 | " \n",
193 | " | 2020-03-13 | \n",
194 | " 114.629997 | \n",
195 | " 105.410004 | \n",
196 | " 106.500000 | \n",
197 | " 114.070000 | \n",
198 | " 17378700.0 | \n",
199 | " 114.070000 | \n",
200 | "
\n",
201 | " \n",
202 | " | 2020-03-16 | \n",
203 | " 115.949997 | \n",
204 | " 101.000000 | \n",
205 | " 101.750000 | \n",
206 | " 108.500000 | \n",
207 | " 17367400.0 | \n",
208 | " 108.500000 | \n",
209 | "
\n",
210 | " \n",
211 | " | 2020-03-17 | \n",
212 | " 119.699997 | \n",
213 | " 110.769997 | \n",
214 | " 111.449997 | \n",
215 | " 118.239998 | \n",
216 | " 19431100.0 | \n",
217 | " 118.239998 | \n",
218 | "
\n",
219 | " \n",
220 | " | 2020-03-18 | \n",
221 | " 121.480003 | \n",
222 | " 112.150002 | \n",
223 | " 113.019997 | \n",
224 | " 112.184998 | \n",
225 | " 13538597.0 | \n",
226 | " 112.184998 | \n",
227 | "
\n",
228 | " \n",
229 | "
\n",
230 | "
"
356 | ]
357 | },
358 | "metadata": {},
359 | "output_type": "display_data"
360 | }
361 | ],
362 | "source": [
363 | "df_PG['Retorno Logaritimo'].plot(figsize=(16,8))\n",
364 | "plt.show()"
365 | ]
366 | },
367 | {
368 | "cell_type": "code",
369 | "execution_count": 13,
370 | "metadata": {
371 | "scrolled": true
372 | },
373 | "outputs": [
374 | {
375 | "name": "stdout",
376 | "output_type": "stream",
377 | "text": [
378 | "0.00045221293719987615\n"
379 | ]
380 | }
381 | ],
382 | "source": [
383 | "#media no tempo toodo\n",
384 | "log_medio = df_PG['Retorno Logaritimo'].mean()\n",
385 | "print (log_medio)"
386 | ]
387 | },
388 | {
389 | "cell_type": "code",
390 | "execution_count": 15,
391 | "metadata": {},
392 | "outputs": [
393 | {
394 | "name": "stdout",
395 | "output_type": "stream",
396 | "text": [
397 | "0.11305323429996904\n"
398 | ]
399 | }
400 | ],
401 | "source": [
402 | "#media no tempo toodo\n",
403 | "log_medio = df_PG['Retorno Logaritimo'].mean()*250\n",
404 | "print (log_medio)"
405 | ]
406 | },
407 | {
408 | "cell_type": "code",
409 | "execution_count": 16,
410 | "metadata": {},
411 | "outputs": [
412 | {
413 | "name": "stdout",
414 | "output_type": "stream",
415 | "text": [
416 | "11.305%\n"
417 | ]
418 | }
419 | ],
420 | "source": [
421 | "#agora vou tranformar em porcetagem\n",
422 | "print (str(round(log_medio ,5)*100) + '%')"
423 | ]
424 | },
425 | {
426 | "cell_type": "code",
427 | "execution_count": null,
428 | "metadata": {},
429 | "outputs": [],
430 | "source": []
431 | }
432 | ],
433 | "metadata": {
434 | "kernelspec": {
435 | "display_name": "Python 3",
436 | "language": "python",
437 | "name": "python3"
438 | },
439 | "language_info": {
440 | "codemirror_mode": {
441 | "name": "ipython",
442 | "version": 3
443 | },
444 | "file_extension": ".py",
445 | "mimetype": "text/x-python",
446 | "name": "python",
447 | "nbconvert_exporter": "python",
448 | "pygments_lexer": "ipython3",
449 | "version": "3.6.5"
450 | }
451 | },
452 | "nbformat": 4,
453 | "nbformat_minor": 2
454 | }
455 |
--------------------------------------------------------------------------------
/Retorno simples de uma ação.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "#vamos caregar o modulo pandas\n",
10 | "import pandas as pd\n",
11 | "#importar biblioteca do pandas datareader\n",
12 | "from pandas_datareader import data as pdr\n",
13 | "#importar a bliblioteca Numpy\n",
14 | "import numpy as np\n",
15 | "import matplotlib.pyplot as plt"
16 | ]
17 | },
18 | {
19 | "cell_type": "code",
20 | "execution_count": 3,
21 | "metadata": {},
22 | "outputs": [],
23 | "source": [
24 | "df_PG = pdr.DataReader('PG',data_source='yahoo',start='1995-1-1')"
25 | ]
26 | },
27 | {
28 | "cell_type": "code",
29 | "execution_count": 6,
30 | "metadata": {},
31 | "outputs": [
32 | {
33 | "data": {
34 | "text/html": [
35 | "\n",
36 | "\n",
49 | "
\n",
50 | " \n",
51 | " \n",
52 | " | \n",
53 | " High | \n",
54 | " Low | \n",
55 | " Open | \n",
56 | " Close | \n",
57 | " Volume | \n",
58 | " Adj Close | \n",
59 | "
\n",
60 | " \n",
61 | " | Date | \n",
62 | " | \n",
63 | " | \n",
64 | " | \n",
65 | " | \n",
66 | " | \n",
67 | " | \n",
68 | "
\n",
69 | " \n",
70 | " \n",
71 | " \n",
72 | " | 1995-01-03 | \n",
73 | " 15.62500 | \n",
74 | " 15.43750 | \n",
75 | " 15.46875 | \n",
76 | " 15.59375 | \n",
77 | " 3318400.0 | \n",
78 | " 6.362402 | \n",
79 | "
\n",
80 | " \n",
81 | " | 1995-01-04 | \n",
82 | " 15.65625 | \n",
83 | " 15.31250 | \n",
84 | " 15.53125 | \n",
85 | " 15.46875 | \n",
86 | " 2218800.0 | \n",
87 | " 6.311406 | \n",
88 | "
\n",
89 | " \n",
90 | " | 1995-01-05 | \n",
91 | " 15.43750 | \n",
92 | " 15.21875 | \n",
93 | " 15.37500 | \n",
94 | " 15.25000 | \n",
95 | " 2319600.0 | \n",
96 | " 6.222151 | \n",
97 | "
\n",
98 | " \n",
99 | " | 1995-01-06 | \n",
100 | " 15.40625 | \n",
101 | " 15.15625 | \n",
102 | " 15.15625 | \n",
103 | " 15.28125 | \n",
104 | " 3438000.0 | \n",
105 | " 6.234900 | \n",
106 | "
\n",
107 | " \n",
108 | " | 1995-01-09 | \n",
109 | " 15.40625 | \n",
110 | " 15.18750 | \n",
111 | " 15.34375 | \n",
112 | " 15.21875 | \n",
113 | " 1795200.0 | \n",
114 | " 6.209402 | \n",
115 | "
\n",
116 | " \n",
117 | "
\n",
118 | "
\n",
148 | "\n",
161 | "
\n",
162 | " \n",
163 | " \n",
164 | " | \n",
165 | " High | \n",
166 | " Low | \n",
167 | " Open | \n",
168 | " Close | \n",
169 | " Volume | \n",
170 | " Adj Close | \n",
171 | "
\n",
172 | " \n",
173 | " | Date | \n",
174 | " | \n",
175 | " | \n",
176 | " | \n",
177 | " | \n",
178 | " | \n",
179 | " | \n",
180 | "
\n",
181 | " \n",
182 | " \n",
183 | " \n",
184 | " | 2020-03-12 | \n",
185 | " 111.580002 | \n",
186 | " 101.000000 | \n",
187 | " 103.000000 | \n",
188 | " 101.839996 | \n",
189 | " 20988000.0 | \n",
190 | " 101.839996 | \n",
191 | "
\n",
192 | " \n",
193 | " | 2020-03-13 | \n",
194 | " 114.629997 | \n",
195 | " 105.410004 | \n",
196 | " 106.500000 | \n",
197 | " 114.070000 | \n",
198 | " 17378700.0 | \n",
199 | " 114.070000 | \n",
200 | "
\n",
201 | " \n",
202 | " | 2020-03-16 | \n",
203 | " 115.949997 | \n",
204 | " 101.000000 | \n",
205 | " 101.750000 | \n",
206 | " 108.500000 | \n",
207 | " 17367400.0 | \n",
208 | " 108.500000 | \n",
209 | "
\n",
210 | " \n",
211 | " | 2020-03-17 | \n",
212 | " 119.699997 | \n",
213 | " 110.769997 | \n",
214 | " 111.449997 | \n",
215 | " 118.239998 | \n",
216 | " 19431100.0 | \n",
217 | " 118.239998 | \n",
218 | "
\n",
219 | " \n",
220 | " | 2020-03-18 | \n",
221 | " 121.480003 | \n",
222 | " 113.010002 | \n",
223 | " 113.019997 | \n",
224 | " 115.154999 | \n",
225 | " 12285921.0 | \n",
226 | " 115.154999 | \n",
227 | "
\n",
228 | " \n",
229 | "
\n",
230 | "