├── Full_Retracement.ipynb
├── Images
    ├── DJI_fib.png
    ├── Fib_Future.png
    ├── Fib_Gif.gif
    ├── NASDAQ_fib.png
    ├── SP500_fib.png
    ├── fib_formula.png
    └── fib_ratio.png
├── Projects
    ├── .ipynb_checkpoints
    │   ├── Fibonacci_Test-checkpoint.ipynb
    │   └── Historical_WIP-checkpoint.ipynb
    ├── Fibonacci_Test.ipynb
    └── Historical_WIP.ipynb
└── README.md


/Images/DJI_fib.png:
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https://raw.githubusercontent.com/brandonlatherow/Fibonacci-Retracement-with-Python/16bf31cf769bd0ca22a9b88a0d0002c445b1cfad/Images/DJI_fib.png


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/Images/Fib_Future.png:
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https://raw.githubusercontent.com/brandonlatherow/Fibonacci-Retracement-with-Python/16bf31cf769bd0ca22a9b88a0d0002c445b1cfad/Images/Fib_Future.png


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/Images/Fib_Gif.gif:
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https://raw.githubusercontent.com/brandonlatherow/Fibonacci-Retracement-with-Python/16bf31cf769bd0ca22a9b88a0d0002c445b1cfad/Images/Fib_Gif.gif


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/Images/NASDAQ_fib.png:
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https://raw.githubusercontent.com/brandonlatherow/Fibonacci-Retracement-with-Python/16bf31cf769bd0ca22a9b88a0d0002c445b1cfad/Images/NASDAQ_fib.png


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/Images/SP500_fib.png:
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https://raw.githubusercontent.com/brandonlatherow/Fibonacci-Retracement-with-Python/16bf31cf769bd0ca22a9b88a0d0002c445b1cfad/Images/SP500_fib.png


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/Images/fib_formula.png:
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https://raw.githubusercontent.com/brandonlatherow/Fibonacci-Retracement-with-Python/16bf31cf769bd0ca22a9b88a0d0002c445b1cfad/Images/fib_formula.png


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/Images/fib_ratio.png:
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https://raw.githubusercontent.com/brandonlatherow/Fibonacci-Retracement-with-Python/16bf31cf769bd0ca22a9b88a0d0002c445b1cfad/Images/fib_ratio.png


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/Projects/.ipynb_checkpoints/Fibonacci_Test-checkpoint.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 46,
  6 |    "id": "2d375a08",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "import pandas as pd\n",
 13 |     "# !yes | pip install yfinance\n",
 14 |     "import yfinance as yf"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 47,
 20 |    "id": "352eda08",
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "# SELF DOCUMENTING\n",
 25 |     "ticker_list = ['SQ',\n",
 26 |     "'^GSPC',\n",
 27 |     "'GDOT',\n",
 28 |     "'MELI',\n",
 29 |     "'BAC',\n",
 30 |     "'PYPL',\n",
 31 |     "'NVDA',\n",
 32 |     "'CME',\n",
 33 |     "'DOCU']\n",
 34 |     "# ticker_list\n",
 35 |     "#CREATES A DATAFRAME OF THE CLOSE PRICE OF THE ticker_list STARTING FROM 2018"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "code",
 40 |    "execution_count": 48,
 41 |    "id": "1d6bd55b",
 42 |    "metadata": {},
 43 |    "outputs": [
 44 |     {
 45 |      "data": {
 46 |       "text/html": [
 47 |        "<div>\n",
 48 |        "<style scoped>\n",
 49 |        "    .dataframe tbody tr th:only-of-type {\n",
 50 |        "        vertical-align: middle;\n",
 51 |        "    }\n",
 52 |        "\n",
 53 |        "    .dataframe tbody tr th {\n",
 54 |        "        vertical-align: top;\n",
 55 |        "    }\n",
 56 |        "\n",
 57 |        "    .dataframe thead th {\n",
 58 |        "        text-align: right;\n",
 59 |        "    }\n",
 60 |        "</style>\n",
 61 |        "<table border=\"1\" class=\"dataframe\">\n",
 62 |        "  <thead>\n",
 63 |        "    <tr style=\"text-align: right;\">\n",
 64 |        "      <th></th>\n",
 65 |        "      <th>SQ</th>\n",
 66 |        "      <th>^GSPC</th>\n",
 67 |        "      <th>GDOT</th>\n",
 68 |        "      <th>MELI</th>\n",
 69 |        "      <th>BAC</th>\n",
 70 |        "      <th>PYPL</th>\n",
 71 |        "      <th>NVDA</th>\n",
 72 |        "      <th>CME</th>\n",
 73 |        "      <th>DOCU</th>\n",
 74 |        "    </tr>\n",
 75 |        "    <tr>\n",
 76 |        "      <th>Date</th>\n",
 77 |        "      <th></th>\n",
 78 |        "      <th></th>\n",
 79 |        "      <th></th>\n",
 80 |        "      <th></th>\n",
 81 |        "      <th></th>\n",
 82 |        "      <th></th>\n",
 83 |        "      <th></th>\n",
 84 |        "      <th></th>\n",
 85 |        "      <th></th>\n",
 86 |        "    </tr>\n",
 87 |        "  </thead>\n",
 88 |        "  <tbody>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>1997-01-02 00:00:00-05:00</th>\n",
 91 |        "      <td>NaN</td>\n",
 92 |        "      <td>737.010010</td>\n",
 93 |        "      <td>NaN</td>\n",
 94 |        "      <td>NaN</td>\n",
 95 |        "      <td>12.589018</td>\n",
 96 |        "      <td>NaN</td>\n",
 97 |        "      <td>NaN</td>\n",
 98 |        "      <td>NaN</td>\n",
 99 |        "      <td>NaN</td>\n",
100 |        "    </tr>\n",
101 |        "    <tr>\n",
102 |        "      <th>1997-01-03 00:00:00-05:00</th>\n",
103 |        "      <td>NaN</td>\n",
104 |        "      <td>748.030029</td>\n",
105 |        "      <td>NaN</td>\n",
106 |        "      <td>NaN</td>\n",
107 |        "      <td>12.750618</td>\n",
108 |        "      <td>NaN</td>\n",
109 |        "      <td>NaN</td>\n",
110 |        "      <td>NaN</td>\n",
111 |        "      <td>NaN</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>1997-01-06 00:00:00-05:00</th>\n",
115 |        "      <td>NaN</td>\n",
116 |        "      <td>747.650024</td>\n",
117 |        "      <td>NaN</td>\n",
118 |        "      <td>NaN</td>\n",
119 |        "      <td>12.766783</td>\n",
120 |        "      <td>NaN</td>\n",
121 |        "      <td>NaN</td>\n",
122 |        "      <td>NaN</td>\n",
123 |        "      <td>NaN</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>1997-01-07 00:00:00-05:00</th>\n",
127 |        "      <td>NaN</td>\n",
128 |        "      <td>753.229980</td>\n",
129 |        "      <td>NaN</td>\n",
130 |        "      <td>NaN</td>\n",
131 |        "      <td>12.782941</td>\n",
132 |        "      <td>NaN</td>\n",
133 |        "      <td>NaN</td>\n",
134 |        "      <td>NaN</td>\n",
135 |        "      <td>NaN</td>\n",
136 |        "    </tr>\n",
137 |        "    <tr>\n",
138 |        "      <th>1997-01-08 00:00:00-05:00</th>\n",
139 |        "      <td>NaN</td>\n",
140 |        "      <td>748.409973</td>\n",
141 |        "      <td>NaN</td>\n",
142 |        "      <td>NaN</td>\n",
143 |        "      <td>13.089993</td>\n",
144 |        "      <td>NaN</td>\n",
145 |        "      <td>NaN</td>\n",
146 |        "      <td>NaN</td>\n",
147 |        "      <td>NaN</td>\n",
148 |        "    </tr>\n",
149 |        "  </tbody>\n",
150 |        "</table>\n",
151 |        "</div>"
152 |       ],
153 |       "text/plain": [
154 |        "                           SQ       ^GSPC  GDOT  MELI        BAC  PYPL  NVDA  \\\n",
155 |        "Date                                                                           \n",
156 |        "1997-01-02 00:00:00-05:00 NaN  737.010010   NaN   NaN  12.589018   NaN   NaN   \n",
157 |        "1997-01-03 00:00:00-05:00 NaN  748.030029   NaN   NaN  12.750618   NaN   NaN   \n",
158 |        "1997-01-06 00:00:00-05:00 NaN  747.650024   NaN   NaN  12.766783   NaN   NaN   \n",
159 |        "1997-01-07 00:00:00-05:00 NaN  753.229980   NaN   NaN  12.782941   NaN   NaN   \n",
160 |        "1997-01-08 00:00:00-05:00 NaN  748.409973   NaN   NaN  13.089993   NaN   NaN   \n",
161 |        "\n",
162 |        "                           CME  DOCU  \n",
163 |        "Date                                  \n",
164 |        "1997-01-02 00:00:00-05:00  NaN   NaN  \n",
165 |        "1997-01-03 00:00:00-05:00  NaN   NaN  \n",
166 |        "1997-01-06 00:00:00-05:00  NaN   NaN  \n",
167 |        "1997-01-07 00:00:00-05:00  NaN   NaN  \n",
168 |        "1997-01-08 00:00:00-05:00  NaN   NaN  "
169 |       ]
170 |      },
171 |      "execution_count": 48,
172 |      "metadata": {},
173 |      "output_type": "execute_result"
174 |     }
175 |    ],
176 |    "source": [
177 |     "x = 0\n",
178 |     "while x in range(len(ticker_list)):\n",
179 |     "    if x == 0:\n",
180 |     "        df = yf.Ticker(ticker_list[x]).history(start='1997-01-01')['Close'].rename(ticker_list[x])\n",
181 |     "    else:\n",
182 |     "        df1 = yf.Ticker(ticker_list[x]).history(start='1997-01-01')['Close'].rename(ticker_list[x])\n",
183 |     "        df = pd.concat([df, df1], axis=1)\n",
184 |     "    x += 1\n",
185 |     "df.head()"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 77,
191 |    "id": "0dbe3334",
192 |    "metadata": {},
193 |    "outputs": [
194 |     {
195 |      "name": "stdout",
196 |      "output_type": "stream",
197 |      "text": [
198 |       "2009-03-06 00:00:00-05:00\n"
199 |      ]
200 |     },
201 |     {
202 |      "data": {
203 |       "image/png": 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\n",
204 |       "text/plain": [
205 |        "<Figure size 1368x648 with 1 Axes>"
206 |       ]
207 |      },
208 |      "metadata": {
209 |       "needs_background": "light"
210 |      },
211 |      "output_type": "display_data"
212 |     }
213 |    ],
214 |    "source": [
215 |     "name = \"^GSPC\"\n",
216 |     "ticker = yf.Ticker(name)\n",
217 |     "df = ticker.history(interval=\"1d\",start=\"1997-01-01\",end=\"2022-10-31\")\n",
218 |     "df.head()\n",
219 |     "\n",
220 |     "highest_swing = -1\n",
221 |     "lowest_swing = -1\n",
222 |     "for i in range(1,df.shape[0]-1):\n",
223 |     "    if df['High'][i] > df['High'][i-1] and df['High'][i] > df['High'][i+1] and (highest_swing == -1 or df['High'][i] > df['High'][highest_swing]) :\n",
224 |     "        highest_swing = i\n",
225 |     "    if df['Low'][i] < df['Low'][i-1] and df['Low'][i] < df['Low'][i+1] and (lowest_swing == -1 or df['Low'][i] < df['Low'][lowest_swing]) :\n",
226 |     "        lowest_swing = i\n",
227 |     "\n",
228 |     "ratios = [0,0.236, 0.382, 0.5 , 0.618, 0.786,1]\n",
229 |     "colors = [\"black\",\"r\",\"g\",\"b\",\"cyan\",\"magenta\",\"yellow\"]\n",
230 |     "levels = []\n",
231 |     "max_level = df['High'][highest_swing]\n",
232 |     "min_level = df['Low'][lowest_swing]\n",
233 |     "for ratio in ratios:\n",
234 |     "    if highest_swing > lowest_swing: # Uptrend\n",
235 |     "        levels.append(max_level - (max_level-min_level)*ratio)\n",
236 |     "    else: # Downtrend\n",
237 |     "        levels.append(min_level + (max_level-min_level)*ratio)\n",
238 |     "\n",
239 |     "plt.rcParams['figure.figsize'] = [19, 9]\n",
240 |     "plt.rc('font', size=14)\n",
241 |     "plt.plot(df['Close'])\n",
242 |     "start_date = df.index[min(highest_swing,lowest_swing)]\n",
243 |     "end_date = df.index[max(highest_swing,lowest_swing)]\n",
244 |     "print(start_date)\n",
245 |     "for i in range(len(levels)):\n",
246 |     "#     plt.hlines(levels[i],start_date, end_date,label=\"{:.1f}%\".format(ratios[i]*100),colors=colors[i], linestyles=\"dashed\")\n",
247 |     "    plt.hlines(levels[i],df.index[0], df.index[-1],label=\"{:.1f}%\".format(ratios[i]*100),colors=colors[i], linestyles=\"dashed\")\n",
248 |     "\n",
249 |     "plt.legend()\n",
250 |     "plt.show()"
251 |    ]
252 |   },
253 |   {
254 |    "cell_type": "code",
255 |    "execution_count": 57,
256 |    "id": "7c4daa50",
257 |    "metadata": {},
258 |    "outputs": [
259 |     {
260 |      "data": {
261 |       "text/html": [
262 |        "<div>\n",
263 |        "<style scoped>\n",
264 |        "    .dataframe tbody tr th:only-of-type {\n",
265 |        "        vertical-align: middle;\n",
266 |        "    }\n",
267 |        "\n",
268 |        "    .dataframe tbody tr th {\n",
269 |        "        vertical-align: top;\n",
270 |        "    }\n",
271 |        "\n",
272 |        "    .dataframe thead th {\n",
273 |        "        text-align: right;\n",
274 |        "    }\n",
275 |        "</style>\n",
276 |        "<table border=\"1\" class=\"dataframe\">\n",
277 |        "  <thead>\n",
278 |        "    <tr style=\"text-align: right;\">\n",
279 |        "      <th></th>\n",
280 |        "      <th>Open</th>\n",
281 |        "      <th>High</th>\n",
282 |        "      <th>Low</th>\n",
283 |        "      <th>Close</th>\n",
284 |        "      <th>Volume</th>\n",
285 |        "      <th>Dividends</th>\n",
286 |        "      <th>Stock Splits</th>\n",
287 |        "    </tr>\n",
288 |        "    <tr>\n",
289 |        "      <th>Date</th>\n",
290 |        "      <th></th>\n",
291 |        "      <th></th>\n",
292 |        "      <th></th>\n",
293 |        "      <th></th>\n",
294 |        "      <th></th>\n",
295 |        "      <th></th>\n",
296 |        "      <th></th>\n",
297 |        "    </tr>\n",
298 |        "  </thead>\n",
299 |        "  <tbody>\n",
300 |        "    <tr>\n",
301 |        "      <th>1997-01-02 00:00:00-05:00</th>\n",
302 |        "      <td>740.739990</td>\n",
303 |        "      <td>742.809998</td>\n",
304 |        "      <td>729.549988</td>\n",
305 |        "      <td>737.010010</td>\n",
306 |        "      <td>463230000</td>\n",
307 |        "      <td>0</td>\n",
308 |        "      <td>0</td>\n",
309 |        "    </tr>\n",
310 |        "    <tr>\n",
311 |        "      <th>1997-01-03 00:00:00-05:00</th>\n",
312 |        "      <td>737.010010</td>\n",
313 |        "      <td>748.239990</td>\n",
314 |        "      <td>737.010010</td>\n",
315 |        "      <td>748.030029</td>\n",
316 |        "      <td>452970000</td>\n",
317 |        "      <td>0</td>\n",
318 |        "      <td>0</td>\n",
319 |        "    </tr>\n",
320 |        "    <tr>\n",
321 |        "      <th>1997-01-06 00:00:00-05:00</th>\n",
322 |        "      <td>748.030029</td>\n",
323 |        "      <td>753.309998</td>\n",
324 |        "      <td>743.820007</td>\n",
325 |        "      <td>747.650024</td>\n",
326 |        "      <td>531350000</td>\n",
327 |        "      <td>0</td>\n",
328 |        "      <td>0</td>\n",
329 |        "    </tr>\n",
330 |        "    <tr>\n",
331 |        "      <th>1997-01-07 00:00:00-05:00</th>\n",
332 |        "      <td>747.650024</td>\n",
333 |        "      <td>753.260010</td>\n",
334 |        "      <td>742.179993</td>\n",
335 |        "      <td>753.229980</td>\n",
336 |        "      <td>538220000</td>\n",
337 |        "      <td>0</td>\n",
338 |        "      <td>0</td>\n",
339 |        "    </tr>\n",
340 |        "    <tr>\n",
341 |        "      <th>1997-01-08 00:00:00-05:00</th>\n",
342 |        "      <td>753.229980</td>\n",
343 |        "      <td>755.719971</td>\n",
344 |        "      <td>747.710022</td>\n",
345 |        "      <td>748.409973</td>\n",
346 |        "      <td>557510000</td>\n",
347 |        "      <td>0</td>\n",
348 |        "      <td>0</td>\n",
349 |        "    </tr>\n",
350 |        "    <tr>\n",
351 |        "      <th>...</th>\n",
352 |        "      <td>...</td>\n",
353 |        "      <td>...</td>\n",
354 |        "      <td>...</td>\n",
355 |        "      <td>...</td>\n",
356 |        "      <td>...</td>\n",
357 |        "      <td>...</td>\n",
358 |        "      <td>...</td>\n",
359 |        "    </tr>\n",
360 |        "    <tr>\n",
361 |        "      <th>2022-10-24 00:00:00-04:00</th>\n",
362 |        "      <td>3762.010010</td>\n",
363 |        "      <td>3810.739990</td>\n",
364 |        "      <td>3741.649902</td>\n",
365 |        "      <td>3797.340088</td>\n",
366 |        "      <td>4747930000</td>\n",
367 |        "      <td>0</td>\n",
368 |        "      <td>0</td>\n",
369 |        "    </tr>\n",
370 |        "    <tr>\n",
371 |        "      <th>2022-10-25 00:00:00-04:00</th>\n",
372 |        "      <td>3799.439941</td>\n",
373 |        "      <td>3862.850098</td>\n",
374 |        "      <td>3799.439941</td>\n",
375 |        "      <td>3859.110107</td>\n",
376 |        "      <td>4843120000</td>\n",
377 |        "      <td>0</td>\n",
378 |        "      <td>0</td>\n",
379 |        "    </tr>\n",
380 |        "    <tr>\n",
381 |        "      <th>2022-10-26 00:00:00-04:00</th>\n",
382 |        "      <td>3825.969971</td>\n",
383 |        "      <td>3886.149902</td>\n",
384 |        "      <td>3824.070068</td>\n",
385 |        "      <td>3830.600098</td>\n",
386 |        "      <td>4817310000</td>\n",
387 |        "      <td>0</td>\n",
388 |        "      <td>0</td>\n",
389 |        "    </tr>\n",
390 |        "    <tr>\n",
391 |        "      <th>2022-10-27 00:00:00-04:00</th>\n",
392 |        "      <td>3834.689941</td>\n",
393 |        "      <td>3859.949951</td>\n",
394 |        "      <td>3803.790039</td>\n",
395 |        "      <td>3807.300049</td>\n",
396 |        "      <td>4687320000</td>\n",
397 |        "      <td>0</td>\n",
398 |        "      <td>0</td>\n",
399 |        "    </tr>\n",
400 |        "    <tr>\n",
401 |        "      <th>2022-10-28 00:00:00-04:00</th>\n",
402 |        "      <td>3808.260010</td>\n",
403 |        "      <td>3905.419922</td>\n",
404 |        "      <td>3808.260010</td>\n",
405 |        "      <td>3901.060059</td>\n",
406 |        "      <td>4459410000</td>\n",
407 |        "      <td>0</td>\n",
408 |        "      <td>0</td>\n",
409 |        "    </tr>\n",
410 |        "  </tbody>\n",
411 |        "</table>\n",
412 |        "<p>6501 rows × 7 columns</p>\n",
413 |        "</div>"
414 |       ],
415 |       "text/plain": [
416 |        "                                  Open         High          Low        Close  \\\n",
417 |        "Date                                                                            \n",
418 |        "1997-01-02 00:00:00-05:00   740.739990   742.809998   729.549988   737.010010   \n",
419 |        "1997-01-03 00:00:00-05:00   737.010010   748.239990   737.010010   748.030029   \n",
420 |        "1997-01-06 00:00:00-05:00   748.030029   753.309998   743.820007   747.650024   \n",
421 |        "1997-01-07 00:00:00-05:00   747.650024   753.260010   742.179993   753.229980   \n",
422 |        "1997-01-08 00:00:00-05:00   753.229980   755.719971   747.710022   748.409973   \n",
423 |        "...                                ...          ...          ...          ...   \n",
424 |        "2022-10-24 00:00:00-04:00  3762.010010  3810.739990  3741.649902  3797.340088   \n",
425 |        "2022-10-25 00:00:00-04:00  3799.439941  3862.850098  3799.439941  3859.110107   \n",
426 |        "2022-10-26 00:00:00-04:00  3825.969971  3886.149902  3824.070068  3830.600098   \n",
427 |        "2022-10-27 00:00:00-04:00  3834.689941  3859.949951  3803.790039  3807.300049   \n",
428 |        "2022-10-28 00:00:00-04:00  3808.260010  3905.419922  3808.260010  3901.060059   \n",
429 |        "\n",
430 |        "                               Volume  Dividends  Stock Splits  \n",
431 |        "Date                                                            \n",
432 |        "1997-01-02 00:00:00-05:00   463230000          0             0  \n",
433 |        "1997-01-03 00:00:00-05:00   452970000          0             0  \n",
434 |        "1997-01-06 00:00:00-05:00   531350000          0             0  \n",
435 |        "1997-01-07 00:00:00-05:00   538220000          0             0  \n",
436 |        "1997-01-08 00:00:00-05:00   557510000          0             0  \n",
437 |        "...                               ...        ...           ...  \n",
438 |        "2022-10-24 00:00:00-04:00  4747930000          0             0  \n",
439 |        "2022-10-25 00:00:00-04:00  4843120000          0             0  \n",
440 |        "2022-10-26 00:00:00-04:00  4817310000          0             0  \n",
441 |        "2022-10-27 00:00:00-04:00  4687320000          0             0  \n",
442 |        "2022-10-28 00:00:00-04:00  4459410000          0             0  \n",
443 |        "\n",
444 |        "[6501 rows x 7 columns]"
445 |       ]
446 |      },
447 |      "execution_count": 57,
448 |      "metadata": {},
449 |      "output_type": "execute_result"
450 |     }
451 |    ],
452 |    "source": [
453 |     "df"
454 |    ]
455 |   },
456 |   {
457 |    "cell_type": "code",
458 |    "execution_count": 59,
459 |    "id": "4586a959",
460 |    "metadata": {},
461 |    "outputs": [
462 |     {
463 |      "data": {
464 |       "text/plain": [
465 |        "6501"
466 |       ]
467 |      },
468 |      "execution_count": 59,
469 |      "metadata": {},
470 |      "output_type": "execute_result"
471 |     }
472 |    ],
473 |    "source": [
474 |     "df.shape[0]"
475 |    ]
476 |   },
477 |   {
478 |    "cell_type": "code",
479 |    "execution_count": 63,
480 |    "id": "2d260303",
481 |    "metadata": {},
482 |    "outputs": [
483 |     {
484 |      "name": "stdout",
485 |      "output_type": "stream",
486 |      "text": [
487 |       "2009-03-06 00:00:00-05:00\n"
488 |      ]
489 |     }
490 |    ],
491 |    "source": [
492 |     "print(start_date)"
493 |    ]
494 |   },
495 |   {
496 |    "cell_type": "code",
497 |    "execution_count": 64,
498 |    "id": "21511249",
499 |    "metadata": {},
500 |    "outputs": [
501 |     {
502 |      "data": {
503 |       "text/plain": [
504 |        "pandas._libs.tslibs.timestamps.Timestamp"
505 |       ]
506 |      },
507 |      "execution_count": 64,
508 |      "metadata": {},
509 |      "output_type": "execute_result"
510 |     }
511 |    ],
512 |    "source": [
513 |     "type(start_date)"
514 |    ]
515 |   },
516 |   {
517 |    "cell_type": "code",
518 |    "execution_count": 66,
519 |    "id": "d1e6e62c",
520 |    "metadata": {},
521 |    "outputs": [
522 |     {
523 |      "data": {
524 |       "text/plain": [
525 |        "6294"
526 |       ]
527 |      },
528 |      "execution_count": 66,
529 |      "metadata": {},
530 |      "output_type": "execute_result"
531 |     }
532 |    ],
533 |    "source": [
534 |     "highest_swing"
535 |    ]
536 |   },
537 |   {
538 |    "cell_type": "code",
539 |    "execution_count": 67,
540 |    "id": "deb6dfbe",
541 |    "metadata": {},
542 |    "outputs": [
543 |     {
544 |      "data": {
545 |       "text/plain": [
546 |        "3063"
547 |       ]
548 |      },
549 |      "execution_count": 67,
550 |      "metadata": {},
551 |      "output_type": "execute_result"
552 |     }
553 |    ],
554 |    "source": [
555 |     "lowest_swing"
556 |    ]
557 |   },
558 |   {
559 |    "cell_type": "code",
560 |    "execution_count": 76,
561 |    "id": "d7594513",
562 |    "metadata": {},
563 |    "outputs": [
564 |     {
565 |      "data": {
566 |       "text/plain": [
567 |        "Timestamp('2022-10-28 00:00:00-0400', tz='America/New_York')"
568 |       ]
569 |      },
570 |      "execution_count": 76,
571 |      "metadata": {},
572 |      "output_type": "execute_result"
573 |     }
574 |    ],
575 |    "source": [
576 |     "df.index[-1]"
577 |    ]
578 |   },
579 |   {
580 |    "cell_type": "code",
581 |    "execution_count": 65,
582 |    "id": "02a1e35e",
583 |    "metadata": {},
584 |    "outputs": [],
585 |    "source": [
586 |     "start_date = df.index[min(highest_swing,lowest_swing)]\n"
587 |    ]
588 |   },
589 |   {
590 |    "cell_type": "code",
591 |    "execution_count": null,
592 |    "id": "f098708f",
593 |    "metadata": {},
594 |    "outputs": [],
595 |    "source": [
596 |     "end_date = df.index[max(highest_swing,lowest_swing)]"
597 |    ]
598 |   }
599 |  ],
600 |  "metadata": {
601 |   "kernelspec": {
602 |    "display_name": "Python [conda env:project1env] *",
603 |    "language": "python",
604 |    "name": "conda-env-project1env-py"
605 |   },
606 |   "language_info": {
607 |    "codemirror_mode": {
608 |     "name": "ipython",
609 |     "version": 3
610 |    },
611 |    "file_extension": ".py",
612 |    "mimetype": "text/x-python",
613 |    "name": "python",
614 |    "nbconvert_exporter": "python",
615 |    "pygments_lexer": "ipython3",
616 |    "version": "3.7.13"
617 |   }
618 |  },
619 |  "nbformat": 4,
620 |  "nbformat_minor": 5
621 | }
622 | 


--------------------------------------------------------------------------------
/Projects/.ipynb_checkpoints/Historical_WIP-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 5
6 | }
7 | 


--------------------------------------------------------------------------------
/Projects/Fibonacci_Test.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 46,
  6 |    "id": "023d6e71",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "import pandas as pd\n",
 13 |     "# !yes | pip install yfinance\n",
 14 |     "import yfinance as yf"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 47,
 20 |    "id": "94fb8df1",
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "# Ticker List\n",
 25 |     "ticker_list = ['^GSPC',\n",
 26 |     "'^IXIC',\n",
 27 |     "'^DJI']"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 48,
 33 |    "id": "84e0676e",
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "data": {
 38 |       "text/html": [
 39 |        "<div>\n",
 40 |        "<style scoped>\n",
 41 |        "    .dataframe tbody tr th:only-of-type {\n",
 42 |        "        vertical-align: middle;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe tbody tr th {\n",
 46 |        "        vertical-align: top;\n",
 47 |        "    }\n",
 48 |        "\n",
 49 |        "    .dataframe thead th {\n",
 50 |        "        text-align: right;\n",
 51 |        "    }\n",
 52 |        "</style>\n",
 53 |        "<table border=\"1\" class=\"dataframe\">\n",
 54 |        "  <thead>\n",
 55 |        "    <tr style=\"text-align: right;\">\n",
 56 |        "      <th></th>\n",
 57 |        "      <th>SQ</th>\n",
 58 |        "      <th>^GSPC</th>\n",
 59 |        "      <th>GDOT</th>\n",
 60 |        "      <th>MELI</th>\n",
 61 |        "      <th>BAC</th>\n",
 62 |        "      <th>PYPL</th>\n",
 63 |        "      <th>NVDA</th>\n",
 64 |        "      <th>CME</th>\n",
 65 |        "      <th>DOCU</th>\n",
 66 |        "    </tr>\n",
 67 |        "    <tr>\n",
 68 |        "      <th>Date</th>\n",
 69 |        "      <th></th>\n",
 70 |        "      <th></th>\n",
 71 |        "      <th></th>\n",
 72 |        "      <th></th>\n",
 73 |        "      <th></th>\n",
 74 |        "      <th></th>\n",
 75 |        "      <th></th>\n",
 76 |        "      <th></th>\n",
 77 |        "      <th></th>\n",
 78 |        "    </tr>\n",
 79 |        "  </thead>\n",
 80 |        "  <tbody>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>1997-01-02 00:00:00-05:00</th>\n",
 83 |        "      <td>NaN</td>\n",
 84 |        "      <td>737.010010</td>\n",
 85 |        "      <td>NaN</td>\n",
 86 |        "      <td>NaN</td>\n",
 87 |        "      <td>12.589018</td>\n",
 88 |        "      <td>NaN</td>\n",
 89 |        "      <td>NaN</td>\n",
 90 |        "      <td>NaN</td>\n",
 91 |        "      <td>NaN</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>1997-01-03 00:00:00-05:00</th>\n",
 95 |        "      <td>NaN</td>\n",
 96 |        "      <td>748.030029</td>\n",
 97 |        "      <td>NaN</td>\n",
 98 |        "      <td>NaN</td>\n",
 99 |        "      <td>12.750618</td>\n",
100 |        "      <td>NaN</td>\n",
101 |        "      <td>NaN</td>\n",
102 |        "      <td>NaN</td>\n",
103 |        "      <td>NaN</td>\n",
104 |        "    </tr>\n",
105 |        "    <tr>\n",
106 |        "      <th>1997-01-06 00:00:00-05:00</th>\n",
107 |        "      <td>NaN</td>\n",
108 |        "      <td>747.650024</td>\n",
109 |        "      <td>NaN</td>\n",
110 |        "      <td>NaN</td>\n",
111 |        "      <td>12.766783</td>\n",
112 |        "      <td>NaN</td>\n",
113 |        "      <td>NaN</td>\n",
114 |        "      <td>NaN</td>\n",
115 |        "      <td>NaN</td>\n",
116 |        "    </tr>\n",
117 |        "    <tr>\n",
118 |        "      <th>1997-01-07 00:00:00-05:00</th>\n",
119 |        "      <td>NaN</td>\n",
120 |        "      <td>753.229980</td>\n",
121 |        "      <td>NaN</td>\n",
122 |        "      <td>NaN</td>\n",
123 |        "      <td>12.782941</td>\n",
124 |        "      <td>NaN</td>\n",
125 |        "      <td>NaN</td>\n",
126 |        "      <td>NaN</td>\n",
127 |        "      <td>NaN</td>\n",
128 |        "    </tr>\n",
129 |        "    <tr>\n",
130 |        "      <th>1997-01-08 00:00:00-05:00</th>\n",
131 |        "      <td>NaN</td>\n",
132 |        "      <td>748.409973</td>\n",
133 |        "      <td>NaN</td>\n",
134 |        "      <td>NaN</td>\n",
135 |        "      <td>13.089993</td>\n",
136 |        "      <td>NaN</td>\n",
137 |        "      <td>NaN</td>\n",
138 |        "      <td>NaN</td>\n",
139 |        "      <td>NaN</td>\n",
140 |        "    </tr>\n",
141 |        "  </tbody>\n",
142 |        "</table>\n",
143 |        "</div>"
144 |       ],
145 |       "text/plain": [
146 |        "                           SQ       ^GSPC  GDOT  MELI        BAC  PYPL  NVDA  \\\n",
147 |        "Date                                                                           \n",
148 |        "1997-01-02 00:00:00-05:00 NaN  737.010010   NaN   NaN  12.589018   NaN   NaN   \n",
149 |        "1997-01-03 00:00:00-05:00 NaN  748.030029   NaN   NaN  12.750618   NaN   NaN   \n",
150 |        "1997-01-06 00:00:00-05:00 NaN  747.650024   NaN   NaN  12.766783   NaN   NaN   \n",
151 |        "1997-01-07 00:00:00-05:00 NaN  753.229980   NaN   NaN  12.782941   NaN   NaN   \n",
152 |        "1997-01-08 00:00:00-05:00 NaN  748.409973   NaN   NaN  13.089993   NaN   NaN   \n",
153 |        "\n",
154 |        "                           CME  DOCU  \n",
155 |        "Date                                  \n",
156 |        "1997-01-02 00:00:00-05:00  NaN   NaN  \n",
157 |        "1997-01-03 00:00:00-05:00  NaN   NaN  \n",
158 |        "1997-01-06 00:00:00-05:00  NaN   NaN  \n",
159 |        "1997-01-07 00:00:00-05:00  NaN   NaN  \n",
160 |        "1997-01-08 00:00:00-05:00  NaN   NaN  "
161 |       ]
162 |      },
163 |      "execution_count": 48,
164 |      "metadata": {},
165 |      "output_type": "execute_result"
166 |     }
167 |    ],
168 |    "source": [
169 |     "#CREATES A DATAFRAME OF THE CLOSE PRICE OF THE ticker_list STARTING FROM 1997\n",
170 |     "x = 0\n",
171 |     "while x in range(len(ticker_list)):\n",
172 |     "    if x == 0:\n",
173 |     "        df = yf.Ticker(ticker_list[x]).history(start='1997-01-01')['Close'].rename(ticker_list[x])\n",
174 |     "    else:\n",
175 |     "        df1 = yf.Ticker(ticker_list[x]).history(start='1997-01-01')['Close'].rename(ticker_list[x])\n",
176 |     "        df = pd.concat([df, df1], axis=1)\n",
177 |     "    x += 1\n",
178 |     "df.head()"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 77,
184 |    "id": "036c0f40",
185 |    "metadata": {},
186 |    "outputs": [
187 |     {
188 |      "name": "stdout",
189 |      "output_type": "stream",
190 |      "text": [
191 |       "2009-03-06 00:00:00-05:00\n"
192 |      ]
193 |     },
194 |     {
195 |      "data": {
196 |       "image/png": 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\n",
197 |       "text/plain": [
198 |        "<Figure size 1368x648 with 1 Axes>"
199 |       ]
200 |      },
201 |      "metadata": {
202 |       "needs_background": "light"
203 |      },
204 |      "output_type": "display_data"
205 |     }
206 |    ],
207 |    "source": [
208 |     "name = \"^GSPC\"\n",
209 |     "ticker = yf.Ticker(name)\n",
210 |     "df = ticker.history(interval=\"1d\",start=\"1997-01-01\",end=\"2022-10-31\")\n",
211 |     "df.head()\n",
212 |     "\n",
213 |     "highest_swing = -1\n",
214 |     "lowest_swing = -1\n",
215 |     "for i in range(1,df.shape[0]-1):\n",
216 |     "    if df['High'][i] > df['High'][i-1] and df['High'][i] > df['High'][i+1] and (highest_swing == -1 or df['High'][i] > df['High'][highest_swing]) :\n",
217 |     "        highest_swing = i\n",
218 |     "    if df['Low'][i] < df['Low'][i-1] and df['Low'][i] < df['Low'][i+1] and (lowest_swing == -1 or df['Low'][i] < df['Low'][lowest_swing]) :\n",
219 |     "        lowest_swing = i\n",
220 |     "\n",
221 |     "ratios = [0,0.236, 0.382, 0.5 , 0.618, 0.786,1]\n",
222 |     "colors = [\"black\",\"r\",\"g\",\"b\",\"cyan\",\"magenta\",\"yellow\"]\n",
223 |     "levels = []\n",
224 |     "max_level = df['High'][highest_swing]\n",
225 |     "min_level = df['Low'][lowest_swing]\n",
226 |     "for ratio in ratios:\n",
227 |     "    if highest_swing > lowest_swing: # Uptrend\n",
228 |     "        levels.append(max_level - (max_level-min_level)*ratio)\n",
229 |     "    else: # Downtrend\n",
230 |     "        levels.append(min_level + (max_level-min_level)*ratio)\n",
231 |     "\n",
232 |     "plt.rcParams['figure.figsize'] = [19, 9]\n",
233 |     "plt.rc('font', size=14)\n",
234 |     "plt.plot(df['Close'])\n",
235 |     "start_date = df.index[min(highest_swing,lowest_swing)]\n",
236 |     "end_date = df.index[max(highest_swing,lowest_swing)]\n",
237 |     "print(start_date)\n",
238 |     "for i in range(len(levels)):\n",
239 |     "#     plt.hlines(levels[i],start_date, end_date,label=\"{:.1f}%\".format(ratios[i]*100),colors=colors[i], linestyles=\"dashed\")\n",
240 |     "    plt.hlines(levels[i],df.index[0], df.index[-1],label=\"{:.1f}%\".format(ratios[i]*100),colors=colors[i], linestyles=\"dashed\")\n",
241 |     "\n",
242 |     "plt.legend()\n",
243 |     "plt.show()"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": 57,
249 |    "id": "e702e806",
250 |    "metadata": {},
251 |    "outputs": [
252 |     {
253 |      "data": {
254 |       "text/html": [
255 |        "<div>\n",
256 |        "<style scoped>\n",
257 |        "    .dataframe tbody tr th:only-of-type {\n",
258 |        "        vertical-align: middle;\n",
259 |        "    }\n",
260 |        "\n",
261 |        "    .dataframe tbody tr th {\n",
262 |        "        vertical-align: top;\n",
263 |        "    }\n",
264 |        "\n",
265 |        "    .dataframe thead th {\n",
266 |        "        text-align: right;\n",
267 |        "    }\n",
268 |        "</style>\n",
269 |        "<table border=\"1\" class=\"dataframe\">\n",
270 |        "  <thead>\n",
271 |        "    <tr style=\"text-align: right;\">\n",
272 |        "      <th></th>\n",
273 |        "      <th>Open</th>\n",
274 |        "      <th>High</th>\n",
275 |        "      <th>Low</th>\n",
276 |        "      <th>Close</th>\n",
277 |        "      <th>Volume</th>\n",
278 |        "      <th>Dividends</th>\n",
279 |        "      <th>Stock Splits</th>\n",
280 |        "    </tr>\n",
281 |        "    <tr>\n",
282 |        "      <th>Date</th>\n",
283 |        "      <th></th>\n",
284 |        "      <th></th>\n",
285 |        "      <th></th>\n",
286 |        "      <th></th>\n",
287 |        "      <th></th>\n",
288 |        "      <th></th>\n",
289 |        "      <th></th>\n",
290 |        "    </tr>\n",
291 |        "  </thead>\n",
292 |        "  <tbody>\n",
293 |        "    <tr>\n",
294 |        "      <th>1997-01-02 00:00:00-05:00</th>\n",
295 |        "      <td>740.739990</td>\n",
296 |        "      <td>742.809998</td>\n",
297 |        "      <td>729.549988</td>\n",
298 |        "      <td>737.010010</td>\n",
299 |        "      <td>463230000</td>\n",
300 |        "      <td>0</td>\n",
301 |        "      <td>0</td>\n",
302 |        "    </tr>\n",
303 |        "    <tr>\n",
304 |        "      <th>1997-01-03 00:00:00-05:00</th>\n",
305 |        "      <td>737.010010</td>\n",
306 |        "      <td>748.239990</td>\n",
307 |        "      <td>737.010010</td>\n",
308 |        "      <td>748.030029</td>\n",
309 |        "      <td>452970000</td>\n",
310 |        "      <td>0</td>\n",
311 |        "      <td>0</td>\n",
312 |        "    </tr>\n",
313 |        "    <tr>\n",
314 |        "      <th>1997-01-06 00:00:00-05:00</th>\n",
315 |        "      <td>748.030029</td>\n",
316 |        "      <td>753.309998</td>\n",
317 |        "      <td>743.820007</td>\n",
318 |        "      <td>747.650024</td>\n",
319 |        "      <td>531350000</td>\n",
320 |        "      <td>0</td>\n",
321 |        "      <td>0</td>\n",
322 |        "    </tr>\n",
323 |        "    <tr>\n",
324 |        "      <th>1997-01-07 00:00:00-05:00</th>\n",
325 |        "      <td>747.650024</td>\n",
326 |        "      <td>753.260010</td>\n",
327 |        "      <td>742.179993</td>\n",
328 |        "      <td>753.229980</td>\n",
329 |        "      <td>538220000</td>\n",
330 |        "      <td>0</td>\n",
331 |        "      <td>0</td>\n",
332 |        "    </tr>\n",
333 |        "    <tr>\n",
334 |        "      <th>1997-01-08 00:00:00-05:00</th>\n",
335 |        "      <td>753.229980</td>\n",
336 |        "      <td>755.719971</td>\n",
337 |        "      <td>747.710022</td>\n",
338 |        "      <td>748.409973</td>\n",
339 |        "      <td>557510000</td>\n",
340 |        "      <td>0</td>\n",
341 |        "      <td>0</td>\n",
342 |        "    </tr>\n",
343 |        "    <tr>\n",
344 |        "      <th>...</th>\n",
345 |        "      <td>...</td>\n",
346 |        "      <td>...</td>\n",
347 |        "      <td>...</td>\n",
348 |        "      <td>...</td>\n",
349 |        "      <td>...</td>\n",
350 |        "      <td>...</td>\n",
351 |        "      <td>...</td>\n",
352 |        "    </tr>\n",
353 |        "    <tr>\n",
354 |        "      <th>2022-10-24 00:00:00-04:00</th>\n",
355 |        "      <td>3762.010010</td>\n",
356 |        "      <td>3810.739990</td>\n",
357 |        "      <td>3741.649902</td>\n",
358 |        "      <td>3797.340088</td>\n",
359 |        "      <td>4747930000</td>\n",
360 |        "      <td>0</td>\n",
361 |        "      <td>0</td>\n",
362 |        "    </tr>\n",
363 |        "    <tr>\n",
364 |        "      <th>2022-10-25 00:00:00-04:00</th>\n",
365 |        "      <td>3799.439941</td>\n",
366 |        "      <td>3862.850098</td>\n",
367 |        "      <td>3799.439941</td>\n",
368 |        "      <td>3859.110107</td>\n",
369 |        "      <td>4843120000</td>\n",
370 |        "      <td>0</td>\n",
371 |        "      <td>0</td>\n",
372 |        "    </tr>\n",
373 |        "    <tr>\n",
374 |        "      <th>2022-10-26 00:00:00-04:00</th>\n",
375 |        "      <td>3825.969971</td>\n",
376 |        "      <td>3886.149902</td>\n",
377 |        "      <td>3824.070068</td>\n",
378 |        "      <td>3830.600098</td>\n",
379 |        "      <td>4817310000</td>\n",
380 |        "      <td>0</td>\n",
381 |        "      <td>0</td>\n",
382 |        "    </tr>\n",
383 |        "    <tr>\n",
384 |        "      <th>2022-10-27 00:00:00-04:00</th>\n",
385 |        "      <td>3834.689941</td>\n",
386 |        "      <td>3859.949951</td>\n",
387 |        "      <td>3803.790039</td>\n",
388 |        "      <td>3807.300049</td>\n",
389 |        "      <td>4687320000</td>\n",
390 |        "      <td>0</td>\n",
391 |        "      <td>0</td>\n",
392 |        "    </tr>\n",
393 |        "    <tr>\n",
394 |        "      <th>2022-10-28 00:00:00-04:00</th>\n",
395 |        "      <td>3808.260010</td>\n",
396 |        "      <td>3905.419922</td>\n",
397 |        "      <td>3808.260010</td>\n",
398 |        "      <td>3901.060059</td>\n",
399 |        "      <td>4459410000</td>\n",
400 |        "      <td>0</td>\n",
401 |        "      <td>0</td>\n",
402 |        "    </tr>\n",
403 |        "  </tbody>\n",
404 |        "</table>\n",
405 |        "<p>6501 rows × 7 columns</p>\n",
406 |        "</div>"
407 |       ],
408 |       "text/plain": [
409 |        "                                  Open         High          Low        Close  \\\n",
410 |        "Date                                                                            \n",
411 |        "1997-01-02 00:00:00-05:00   740.739990   742.809998   729.549988   737.010010   \n",
412 |        "1997-01-03 00:00:00-05:00   737.010010   748.239990   737.010010   748.030029   \n",
413 |        "1997-01-06 00:00:00-05:00   748.030029   753.309998   743.820007   747.650024   \n",
414 |        "1997-01-07 00:00:00-05:00   747.650024   753.260010   742.179993   753.229980   \n",
415 |        "1997-01-08 00:00:00-05:00   753.229980   755.719971   747.710022   748.409973   \n",
416 |        "...                                ...          ...          ...          ...   \n",
417 |        "2022-10-24 00:00:00-04:00  3762.010010  3810.739990  3741.649902  3797.340088   \n",
418 |        "2022-10-25 00:00:00-04:00  3799.439941  3862.850098  3799.439941  3859.110107   \n",
419 |        "2022-10-26 00:00:00-04:00  3825.969971  3886.149902  3824.070068  3830.600098   \n",
420 |        "2022-10-27 00:00:00-04:00  3834.689941  3859.949951  3803.790039  3807.300049   \n",
421 |        "2022-10-28 00:00:00-04:00  3808.260010  3905.419922  3808.260010  3901.060059   \n",
422 |        "\n",
423 |        "                               Volume  Dividends  Stock Splits  \n",
424 |        "Date                                                            \n",
425 |        "1997-01-02 00:00:00-05:00   463230000          0             0  \n",
426 |        "1997-01-03 00:00:00-05:00   452970000          0             0  \n",
427 |        "1997-01-06 00:00:00-05:00   531350000          0             0  \n",
428 |        "1997-01-07 00:00:00-05:00   538220000          0             0  \n",
429 |        "1997-01-08 00:00:00-05:00   557510000          0             0  \n",
430 |        "...                               ...        ...           ...  \n",
431 |        "2022-10-24 00:00:00-04:00  4747930000          0             0  \n",
432 |        "2022-10-25 00:00:00-04:00  4843120000          0             0  \n",
433 |        "2022-10-26 00:00:00-04:00  4817310000          0             0  \n",
434 |        "2022-10-27 00:00:00-04:00  4687320000          0             0  \n",
435 |        "2022-10-28 00:00:00-04:00  4459410000          0             0  \n",
436 |        "\n",
437 |        "[6501 rows x 7 columns]"
438 |       ]
439 |      },
440 |      "execution_count": 57,
441 |      "metadata": {},
442 |      "output_type": "execute_result"
443 |     }
444 |    ],
445 |    "source": [
446 |     "df"
447 |    ]
448 |   },
449 |   {
450 |    "cell_type": "code",
451 |    "execution_count": 59,
452 |    "id": "daac79e8",
453 |    "metadata": {},
454 |    "outputs": [
455 |     {
456 |      "data": {
457 |       "text/plain": [
458 |        "6501"
459 |       ]
460 |      },
461 |      "execution_count": 59,
462 |      "metadata": {},
463 |      "output_type": "execute_result"
464 |     }
465 |    ],
466 |    "source": [
467 |     "df.shape[0]"
468 |    ]
469 |   },
470 |   {
471 |    "cell_type": "code",
472 |    "execution_count": 63,
473 |    "id": "62fbeee1",
474 |    "metadata": {},
475 |    "outputs": [
476 |     {
477 |      "name": "stdout",
478 |      "output_type": "stream",
479 |      "text": [
480 |       "2009-03-06 00:00:00-05:00\n"
481 |      ]
482 |     }
483 |    ],
484 |    "source": [
485 |     "print(start_date)"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "code",
490 |    "execution_count": 64,
491 |    "id": "e29d7153",
492 |    "metadata": {},
493 |    "outputs": [
494 |     {
495 |      "data": {
496 |       "text/plain": [
497 |        "pandas._libs.tslibs.timestamps.Timestamp"
498 |       ]
499 |      },
500 |      "execution_count": 64,
501 |      "metadata": {},
502 |      "output_type": "execute_result"
503 |     }
504 |    ],
505 |    "source": [
506 |     "type(start_date)"
507 |    ]
508 |   },
509 |   {
510 |    "cell_type": "code",
511 |    "execution_count": 66,
512 |    "id": "e8fb6ca2",
513 |    "metadata": {},
514 |    "outputs": [
515 |     {
516 |      "data": {
517 |       "text/plain": [
518 |        "6294"
519 |       ]
520 |      },
521 |      "execution_count": 66,
522 |      "metadata": {},
523 |      "output_type": "execute_result"
524 |     }
525 |    ],
526 |    "source": [
527 |     "highest_swing"
528 |    ]
529 |   },
530 |   {
531 |    "cell_type": "code",
532 |    "execution_count": 67,
533 |    "id": "cbf12b57",
534 |    "metadata": {},
535 |    "outputs": [
536 |     {
537 |      "data": {
538 |       "text/plain": [
539 |        "3063"
540 |       ]
541 |      },
542 |      "execution_count": 67,
543 |      "metadata": {},
544 |      "output_type": "execute_result"
545 |     }
546 |    ],
547 |    "source": [
548 |     "lowest_swing"
549 |    ]
550 |   },
551 |   {
552 |    "cell_type": "code",
553 |    "execution_count": 76,
554 |    "id": "cb796b15",
555 |    "metadata": {},
556 |    "outputs": [
557 |     {
558 |      "data": {
559 |       "text/plain": [
560 |        "Timestamp('2022-10-28 00:00:00-0400', tz='America/New_York')"
561 |       ]
562 |      },
563 |      "execution_count": 76,
564 |      "metadata": {},
565 |      "output_type": "execute_result"
566 |     }
567 |    ],
568 |    "source": [
569 |     "df.index[-1]"
570 |    ]
571 |   },
572 |   {
573 |    "cell_type": "code",
574 |    "execution_count": 65,
575 |    "id": "dad1a772",
576 |    "metadata": {},
577 |    "outputs": [],
578 |    "source": [
579 |     "start_date = df.index[min(highest_swing,lowest_swing)]\n"
580 |    ]
581 |   },
582 |   {
583 |    "cell_type": "code",
584 |    "execution_count": 1,
585 |    "id": "91e9a7f5",
586 |    "metadata": {},
587 |    "outputs": [
588 |     {
589 |      "ename": "NameError",
590 |      "evalue": "name 'df' is not defined",
591 |      "output_type": "error",
592 |      "traceback": [
593 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
594 |       "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
595 |       "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_7660\\451166535.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mend_date\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhighest_swing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlowest_swing\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
596 |       "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
597 |      ]
598 |     }
599 |    ],
600 |    "source": [
601 |     "end_date = df.index[max(highest_swing,lowest_swing)]\n"
602 |    ]
603 |   },
604 |   {
605 |    "cell_type": "code",
606 |    "execution_count": null,
607 |    "id": "92e9d4e5",
608 |    "metadata": {},
609 |    "outputs": [],
610 |    "source": [
611 |     "df.hvplot.line(x='Date',y=['^GSPC', '^IXIC', '^DJI'],\n",
612 |     "              value_label='Close', legend='top', height=500, width=1000)"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": null,
618 |    "id": "b7a93a57",
619 |    "metadata": {},
620 |    "outputs": [],
621 |    "source": []
622 |   }
623 |  ],
624 |  "metadata": {
625 |   "kernelspec": {
626 |    "display_name": "Python 3 (ipykernel)",
627 |    "language": "python",
628 |    "name": "python3"
629 |   },
630 |   "language_info": {
631 |    "codemirror_mode": {
632 |     "name": "ipython",
633 |     "version": 3
634 |    },
635 |    "file_extension": ".py",
636 |    "mimetype": "text/x-python",
637 |    "name": "python",
638 |    "nbconvert_exporter": "python",
639 |    "pygments_lexer": "ipython3",
640 |    "version": "3.7.13"
641 |   }
642 |  },
643 |  "nbformat": 4,
644 |  "nbformat_minor": 5
645 | }
646 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # Fibonacci Retracement and Analysis
  2 | 
  3 | * Fibonacci analysis of 3 major indices over the past 25 years. Using this data we will analyze and detail entry points historically, as well as potential future entry points.
  4 | 
  5 | 
  6 | 
  7 | # Installion Guide
  8 | 
  9 | ```
 10 | conda activate base
 11 | conda create -n project1env python=3.7 -y
 12 | conda activate project1env
 13 | conda install -c conda-forge python-dotenv -y
 14 | conda install -c anaconda nb_conda -y
 15 | conda install -c conda-forge nodejs=12 -y
 16 | conda install -c pyviz holoviz -y
 17 | conda install -c plotly plotly -y
 18 | conda install -c conda-forge jupyterlab=2.2 -y
 19 | conda install -c anaconda numpy==1.19 -y
 20 | conda install -c conda-forge matplotlib==3.0.3 -y
 21 | jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
 22 | jupyter labextension install jupyterlab-plotly --no-build
 23 | jupyter labextension install plotlywidget --no-build
 24 | jupyter labextension install @pyviz/jupyterlab_pyviz --no-build
 25 | jupyter lab build
 26 | pip install yfinance
 27 | ```
 28 | 
 29 | # Timeline
 30 |     - Day 1 : Proposal and Research
 31 |     - Day 2 : Building  Fibonacci Retracement in Python
 32 |     - Day 3 : Building and Finishing Retracement Code
 33 |     - Day 4 : Markdown and Documentation
 34 |     - Day 5 : Final touches to Markdown and Presentation Prep
 35 | 
 36 | 
 37 | # Project Team
 38 | * Tim Clemens 
 39 | * Max Heatter 
 40 | * Brandon Latherow
 41 | * Gregory Neubel Jr.
 42 | 
 43 | # Fibonacci Background
 44 | * A Fibonacci Sequence is a series of numbers, starting with zero and one, in which each number is the sum of the previous two numbers. This sequence extends to infinity and is summarized with the formula: 
 45 | ![](Images/fib_formula.png)
 46 | * Fibonacci is based around the Golden Ratio which can be found all around in nature. The Golden Ratio is equal to 0.618 or 1.618. This ratio is created using the following formula: a/b = (a+b)/a =1.618... This ratio is then used to create a percentage is associates with being: 23.6%, 38.2%, 61.8%, and 78.6%. These percentage levels are used in retracement along with a 50% level. Although 50% is not a true Fibonacci Level or number it is used with the retracement to add accuracy for support and resistance.
 47 | 
 48 |   ![](Images/Fib_Gif.gif)
 49 | 
 50 | # Data Analysis
 51 | * Using the Retracement of the total 25 year analysis, we were able to see how the historical crashes of these indicies correlated with the support levels of Fibonacci. On the other hand, as the indicies rose in value, and hit high points, the closing price had strong and consistant correspondance to the resistance levels over the time frame analyzed.
 52 | ## Dow Jones
 53 | ![](Images/DJI_fib.png)
 54 | ## Nasdaq Composite
 55 | ![](Images/NASDAQ_fib.png)
 56 | ## S&P 500
 57 | ![](Images/SP500_fib.png)
 58 | 
 59 | # Challenges
 60 | * There were multiple challenges we had with this project, one of such challenges was expanding the Fibonacci Retracement Lines to future dates. We thought of a way of manually adding dates to the table with "null" values except for the "Date", but do to time constraints we could test the idea or put it into practice. 
 61 | * We also tried to make the retracement using hvplot instead of pyplot, but again due to time constraints we were unable to finish the retracement code.
 62 | * Lastly we had difficulty using our code to easily and accurately find trends using smaller sample sizes/data frames.
 63 | ![](Images/Fib_Future.png)
 64 | 
 65 | # Investing
 66 | ## Investment Strategies
 67 |  
 68 | ### Single Option Strategies
 69 | - Buying Calls 
 70 | - Writing Calls
 71 | - Buying Puts
 72 | - Writing Puts
 73 |  
 74 | ### Buying Calls  (Owner/Long/Bullish)
 75 | - Investor speculate the underline price of stock will rise
 76 | - Can profit on upward price movement by only paying the premium 
 77 | - Provides investor w/ greater leverage (higher potential percentage return)
 78 | - Deferring to purchase in the future, great way to lock in XP/Sales price of stock
 79 | - Investors also use calls to protect a short stock position, a popular way to hedge.
 80 |   - Note: Short/position holders lose when the stock price rises, facing potential unlimited loss. 
 81 |  
 82 | ### Writing Calls (Seller/Short/Bearish)
 83 | - Investor believes stock’s price will decline or stay the same
 84 | - Does not own stock, protection does not exist. (Naked, Uncovered)
 85 | - Has obligation to sell
 86 | - Unlimited potential risk of loss
 87 | 	- Note: Call writers profit if the stock’s price falls below or stays at the strike price.
 88 | 		Most often a strategy for an uncovered writer to earn the premium.
 89 |  
 90 | ### Buying Puts (Owner/Long/Bearish)
 91 | - Investor can profit from a decrease in the stock price, while investing in small amounts
 92 | - If stock falls below the strike price, while in the money, may result in a profit
 93 | - Locks in stock sales price until the option expires.
 94 | - Protects appreciation potential until expiration date
 95 |  
 96 | ### Writing Puts (Seller/Short/Bullish)
 97 | - Investors believe the stock is going to rise
 98 | - Can profit/earn premium if stock price rises above strike price 
 99 | - Investors hope the put option expires to keep premium  
100 | - Huge Risk!
101 |  
102 |  
103 |  
104 | ## Order Type Strategies
105 |  
106 | - Market Orders
107 | - Limit Order
108 | - Stop Order (Stop Loss)
109 |  
110 | ### Market Orders 
111 | - Are sent immediately to the trading floor for execution without restrictions or limits. Executed immediately at the current market price and has priority over all orders. 
112 | - Buy order: executed at the lowest offering price available 
113 | - Sell order: executed at the highest bid price available 
114 |  
115 | ### Limit Orders
116 | - Investors limits the acceptable purchase or selling price
117 | - Executed only at specified price or better.
118 | - Buy Limit Orders: Investors believe stock is overpriced and would like to buy low with the dip in the market. Placed below the current market.
119 | - Sell Limit Orders: Investors who own the stock believe the stock they own is currently undervalued, would place sell limit orders to sell the stock if it should rise to desired level.
120 |  
121 | ### Stop Orders
122 | - Designed to protect profit or prevent loss if stock moves in wrong direction
123 | - Sell Stop: Entered below current market
124 |   - Protects profit or loss in a long stock position.
125 |   - Technical analysis believes that if a stock breaks through the support, it will fall like a rock.
126 | - Buy Stop: Entered above current market
127 |   - Protects a profit or loss in a short stock position
128 |   - Triggered when the market price touches or goes through the buy stop price
129 |   - Used by technical analysis who track support and resistance levels for stock.
130 | 
131 | # Findings and Possible Courses of Action
132 | ## S&P 500
133 | - Through analysis of Fibonacci Retracement we are predicting a support line and bottom for the S&P 500. 
134 | - Price $3200
135 | - As early as Late Feb, 2023
136 | ## Nasdaq 
137 | - Using the Fibonacci Retracement the Nasdaq is currently at a level of support. It has been at this support level for roughly a month trying to rebound positively, but has just been hovering.
138 | - Price $10462.71
139 | - Now could be the perfect time to enter.
140 | ## Dow Jones 
141 | - 23rd of September the DJI reached its support level and proceeded to hover until the middle of October. From October 14th to Present Day (November 7th), the DJI has risen from slightly beneath the support level at $29634.33 to $32830.89. From this retracement and analysis it is hard to make an accurate prediction with the path of the DJI, the time to invest, at least for short term, looks to have passed.
142 | 
143 | # Future Expanse 
144 | 
145 | ### EXPANSION OF PYTHON LIBRARIES
146 | 
147 | * Python Technical Analysis 
148 |   - TA-Lib
149 | * Machine Learning Libraries
150 |   - Sci-kit-learn
151 |   - Keras
152 |   - Tensor Flow
153 | 
154 | * Python Trading Libraries For Backtesting
155 |   - PyAlgoTrade
156 |   - Zipline
157 |   - PyBacktest (BACKTESTING)
158 | 
159 | * Python Data Collection
160 |   - Ultrafinance
161 |   - TWP (Trading WIth Python)
162 |   - IBridgePy
163 | 
164 | ### MODELING AND COMPARISON (LIKE THE BIG BANKS)
165 | 
166 | * Comparing Fibonacci Retracement with Machine Learning models such as
167 |   - Monte Carlo Simulation
168 |   - Markov Chain Simulation
169 |   - Bootstrap
170 |   - Black Scholes
171 |   - Discrete Event Simulation
172 | 
173 | * insert image
174 | 
175 | # Resources
176 | * https://www.investopedia.com/terms/f/fibonacciretracement.asp 
177 | * https://blog.quantinsti.com/fibonacci-retracement-trading-strategy-python/
178 | * https://towardsdatascience.com/fibonacci-retracements-in-python-470eb33b6362  
179 | 
180 | 


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