├── Gold Futures Historical Data.xlsx
├── README.md
└── FIBONACCI RETRACEMENT.ipynb


/Gold Futures Historical Data.xlsx:
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https://raw.githubusercontent.com/Ranjitkumarsahu1436/Fibonacci-Retracement/HEAD/Gold Futures Historical Data.xlsx


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/README.md:
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1 | # Fibonacci-Retracement
2 | 
3 | 
4 | Fibonacci Retracements are ratios used to identify potential reversal levels. These ratios are found in the Fibonacci sequence. The most popular Fibonacci Retracements are 61.8% and 38.2%. Note that 38.2% is often rounded to 38% and 61.8 is rounded to 62%. After an advance, chartists apply Fibonacci ratios to define retracement levels and forecast the extent of a correction or pullback. Fibonacci Retracements can also be applied after a decline to forecast the length of a counter-trend bounce. These retracements can be combined with other indicators and price patterns to create an overall strategy.
5 | 
6 | The Fibonacci Retracements Tool at StockCharts shows four common retracements: 23.6%, 38.2%, 50%, and 61.8%. From the Fibonacci section above, it is clear that 23.6%, 38.2%, and 61.8% stem from ratios found within the Fibonacci sequence. The 50% retracement is not based on a Fibonacci number.
7 | 
8 | Unlike moving averages, Fibonacci retracement levels are static prices. They do not change. This allows quick and simple identification and allows traders and investors to react when price levels are tested. Because these levels are inflection points, traders expect some type of price action, either a break or a rejection. The 0.618 Fibonacci retracement that is often used by stock analysts approximates to the "golden ratio".
9 | 


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/FIBONACCI RETRACEMENT.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# FIBONACCI RETRACEMENT"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "Import Library and Data set"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 27,
 20 |    "metadata": {},
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "import pandas as pd\n",
 24 |     "import numpy as np\n",
 25 |     "import seaborn as sns\n",
 26 |     "import matplotlib.pyplot as plt\n",
 27 |     "%matplotlib inline\n",
 28 |     "\n",
 29 |     "df = pd.read_excel(r'F:\\fibonacci retracement\\Gold Futures Historical Data.xlsx')\n",
 30 |     "df = df.set_index('Date',drop = False)"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 28,
 36 |    "metadata": {},
 37 |    "outputs": [
 38 |     {
 39 |      "data": {
 40 |       "text/html": [
 41 |        "<div>\n",
 42 |        "<style scoped>\n",
 43 |        "    .dataframe tbody tr th:only-of-type {\n",
 44 |        "        vertical-align: middle;\n",
 45 |        "    }\n",
 46 |        "\n",
 47 |        "    .dataframe tbody tr th {\n",
 48 |        "        vertical-align: top;\n",
 49 |        "    }\n",
 50 |        "\n",
 51 |        "    .dataframe thead th {\n",
 52 |        "        text-align: right;\n",
 53 |        "    }\n",
 54 |        "</style>\n",
 55 |        "<table border=\"1\" class=\"dataframe\">\n",
 56 |        "  <thead>\n",
 57 |        "    <tr style=\"text-align: right;\">\n",
 58 |        "      <th></th>\n",
 59 |        "      <th>Date</th>\n",
 60 |        "      <th>Price</th>\n",
 61 |        "      <th>Open</th>\n",
 62 |        "      <th>High</th>\n",
 63 |        "      <th>Low</th>\n",
 64 |        "      <th>Vol.</th>\n",
 65 |        "      <th>Change %</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 |        "    </tr>\n",
 77 |        "  </thead>\n",
 78 |        "  <tbody>\n",
 79 |        "    <tr>\n",
 80 |        "      <td>2020-01-30</td>\n",
 81 |        "      <td>2020-01-30</td>\n",
 82 |        "      <td>1578.85</td>\n",
 83 |        "      <td>1576.65</td>\n",
 84 |        "      <td>1582.45</td>\n",
 85 |        "      <td>1575.65</td>\n",
 86 |        "      <td>-</td>\n",
 87 |        "      <td>0.0014</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <td>2020-01-29</td>\n",
 91 |        "      <td>2020-01-29</td>\n",
 92 |        "      <td>1576.70</td>\n",
 93 |        "      <td>1567.65</td>\n",
 94 |        "      <td>1577.70</td>\n",
 95 |        "      <td>1562.35</td>\n",
 96 |        "      <td>-</td>\n",
 97 |        "      <td>0.0044</td>\n",
 98 |        "    </tr>\n",
 99 |        "    <tr>\n",
100 |        "      <td>2020-01-28</td>\n",
101 |        "      <td>2020-01-28</td>\n",
102 |        "      <td>1569.80</td>\n",
103 |        "      <td>1580.60</td>\n",
104 |        "      <td>1582.20</td>\n",
105 |        "      <td>1564.60</td>\n",
106 |        "      <td>378.01K</td>\n",
107 |        "      <td>-0.0048</td>\n",
108 |        "    </tr>\n",
109 |        "    <tr>\n",
110 |        "      <td>2020-01-27</td>\n",
111 |        "      <td>2020-01-27</td>\n",
112 |        "      <td>1577.40</td>\n",
113 |        "      <td>1580.50</td>\n",
114 |        "      <td>1588.40</td>\n",
115 |        "      <td>1575.30</td>\n",
116 |        "      <td>398.06K</td>\n",
117 |        "      <td>-0.0029</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <td>2020-01-26</td>\n",
121 |        "      <td>2020-01-26</td>\n",
122 |        "      <td>1581.95</td>\n",
123 |        "      <td>1571.65</td>\n",
124 |        "      <td>1588.10</td>\n",
125 |        "      <td>1571.65</td>\n",
126 |        "      <td>-</td>\n",
127 |        "      <td>0.0064</td>\n",
128 |        "    </tr>\n",
129 |        "    <tr>\n",
130 |        "      <td>...</td>\n",
131 |        "      <td>...</td>\n",
132 |        "      <td>...</td>\n",
133 |        "      <td>...</td>\n",
134 |        "      <td>...</td>\n",
135 |        "      <td>...</td>\n",
136 |        "      <td>...</td>\n",
137 |        "      <td>...</td>\n",
138 |        "    </tr>\n",
139 |        "    <tr>\n",
140 |        "      <td>2014-02-05</td>\n",
141 |        "      <td>2014-02-05</td>\n",
142 |        "      <td>1413.00</td>\n",
143 |        "      <td>1413.00</td>\n",
144 |        "      <td>1413.00</td>\n",
145 |        "      <td>1413.00</td>\n",
146 |        "      <td>1.62K</td>\n",
147 |        "      <td>0.0041</td>\n",
148 |        "    </tr>\n",
149 |        "    <tr>\n",
150 |        "      <td>2014-02-04</td>\n",
151 |        "      <td>2014-02-04</td>\n",
152 |        "      <td>1407.30</td>\n",
153 |        "      <td>1407.30</td>\n",
154 |        "      <td>1407.30</td>\n",
155 |        "      <td>1407.30</td>\n",
156 |        "      <td>1.75K</td>\n",
157 |        "      <td>-0.0069</td>\n",
158 |        "    </tr>\n",
159 |        "    <tr>\n",
160 |        "      <td>2014-02-03</td>\n",
161 |        "      <td>2014-02-03</td>\n",
162 |        "      <td>1417.10</td>\n",
163 |        "      <td>1417.10</td>\n",
164 |        "      <td>1417.10</td>\n",
165 |        "      <td>1417.10</td>\n",
166 |        "      <td>2.91K</td>\n",
167 |        "      <td>0.0130</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <td>2014-01-31</td>\n",
171 |        "      <td>2014-01-31</td>\n",
172 |        "      <td>1398.90</td>\n",
173 |        "      <td>1398.90</td>\n",
174 |        "      <td>1398.90</td>\n",
175 |        "      <td>1398.90</td>\n",
176 |        "      <td>5.82K</td>\n",
177 |        "      <td>-0.0028</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <td>2014-01-30</td>\n",
181 |        "      <td>2014-01-30</td>\n",
182 |        "      <td>1402.80</td>\n",
183 |        "      <td>1402.80</td>\n",
184 |        "      <td>1402.80</td>\n",
185 |        "      <td>1402.80</td>\n",
186 |        "      <td>45.28K</td>\n",
187 |        "      <td>-0.0136</td>\n",
188 |        "    </tr>\n",
189 |        "  </tbody>\n",
190 |        "</table>\n",
191 |        "<p>1587 rows × 7 columns</p>\n",
192 |        "</div>"
193 |       ],
194 |       "text/plain": [
195 |        "                 Date    Price     Open     High      Low     Vol.  Change %\n",
196 |        "Date                                                                        \n",
197 |        "2020-01-30 2020-01-30  1578.85  1576.65  1582.45  1575.65        -    0.0014\n",
198 |        "2020-01-29 2020-01-29  1576.70  1567.65  1577.70  1562.35        -    0.0044\n",
199 |        "2020-01-28 2020-01-28  1569.80  1580.60  1582.20  1564.60  378.01K   -0.0048\n",
200 |        "2020-01-27 2020-01-27  1577.40  1580.50  1588.40  1575.30  398.06K   -0.0029\n",
201 |        "2020-01-26 2020-01-26  1581.95  1571.65  1588.10  1571.65        -    0.0064\n",
202 |        "...               ...      ...      ...      ...      ...      ...       ...\n",
203 |        "2014-02-05 2014-02-05  1413.00  1413.00  1413.00  1413.00    1.62K    0.0041\n",
204 |        "2014-02-04 2014-02-04  1407.30  1407.30  1407.30  1407.30    1.75K   -0.0069\n",
205 |        "2014-02-03 2014-02-03  1417.10  1417.10  1417.10  1417.10    2.91K    0.0130\n",
206 |        "2014-01-31 2014-01-31  1398.90  1398.90  1398.90  1398.90    5.82K   -0.0028\n",
207 |        "2014-01-30 2014-01-30  1402.80  1402.80  1402.80  1402.80   45.28K   -0.0136\n",
208 |        "\n",
209 |        "[1587 rows x 7 columns]"
210 |       ]
211 |      },
212 |      "execution_count": 28,
213 |      "metadata": {},
214 |      "output_type": "execute_result"
215 |     }
216 |    ],
217 |    "source": [
218 |     "df"
219 |    ]
220 |   },
221 |   {
222 |    "cell_type": "code",
223 |    "execution_count": 29,
224 |    "metadata": {},
225 |    "outputs": [
226 |     {
227 |      "data": {
228 |       "text/plain": [
229 |        "Date        datetime64[ns]\n",
230 |        "Price              float64\n",
231 |        "Open               float64\n",
232 |        "High               float64\n",
233 |        "Low                float64\n",
234 |        "Vol.                object\n",
235 |        "Change %           float64\n",
236 |        "dtype: object"
237 |       ]
238 |      },
239 |      "execution_count": 29,
240 |      "metadata": {},
241 |      "output_type": "execute_result"
242 |     }
243 |    ],
244 |    "source": [
245 |     "df.dtypes"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "markdown",
250 |    "metadata": {},
251 |    "source": [
252 |     "Find Maximum and minimum Value "
253 |    ]
254 |   },
255 |   {
256 |    "cell_type": "code",
257 |    "execution_count": 30,
258 |    "metadata": {},
259 |    "outputs": [
260 |     {
261 |      "data": {
262 |       "text/plain": [
263 |        "1551.8"
264 |       ]
265 |      },
266 |      "execution_count": 30,
267 |      "metadata": {},
268 |      "output_type": "execute_result"
269 |     }
270 |    ],
271 |    "source": [
272 |     "df['Price']['2019-08-28':'2018-04-01'].max()"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 31,
278 |    "metadata": {},
279 |    "outputs": [
280 |     {
281 |      "data": {
282 |       "text/plain": [
283 |        "1204.9"
284 |       ]
285 |      },
286 |      "execution_count": 31,
287 |      "metadata": {},
288 |      "output_type": "execute_result"
289 |     }
290 |    ],
291 |    "source": [
292 |     "df['Price']['2019-08-28':'2018-04-01'].min()"
293 |    ]
294 |   },
295 |   {
296 |    "cell_type": "code",
297 |    "execution_count": 32,
298 |    "metadata": {},
299 |    "outputs": [],
300 |    "source": [
301 |     "df1=df['2019-08-28':'2019-04-01']"
302 |    ]
303 |   },
304 |   {
305 |    "cell_type": "code",
306 |    "execution_count": 33,
307 |    "metadata": {},
308 |    "outputs": [
309 |     {
310 |      "data": {
311 |       "text/html": [
312 |        "<div>\n",
313 |        "<style scoped>\n",
314 |        "    .dataframe tbody tr th:only-of-type {\n",
315 |        "        vertical-align: middle;\n",
316 |        "    }\n",
317 |        "\n",
318 |        "    .dataframe tbody tr th {\n",
319 |        "        vertical-align: top;\n",
320 |        "    }\n",
321 |        "\n",
322 |        "    .dataframe thead th {\n",
323 |        "        text-align: right;\n",
324 |        "    }\n",
325 |        "</style>\n",
326 |        "<table border=\"1\" class=\"dataframe\">\n",
327 |        "  <thead>\n",
328 |        "    <tr style=\"text-align: right;\">\n",
329 |        "      <th></th>\n",
330 |        "      <th>Date</th>\n",
331 |        "      <th>Price</th>\n",
332 |        "      <th>Open</th>\n",
333 |        "      <th>High</th>\n",
334 |        "      <th>Low</th>\n",
335 |        "      <th>Vol.</th>\n",
336 |        "      <th>Change %</th>\n",
337 |        "    </tr>\n",
338 |        "    <tr>\n",
339 |        "      <th>Date</th>\n",
340 |        "      <th></th>\n",
341 |        "      <th></th>\n",
342 |        "      <th></th>\n",
343 |        "      <th></th>\n",
344 |        "      <th></th>\n",
345 |        "      <th></th>\n",
346 |        "      <th></th>\n",
347 |        "    </tr>\n",
348 |        "  </thead>\n",
349 |        "  <tbody>\n",
350 |        "    <tr>\n",
351 |        "      <td>2019-08-28</td>\n",
352 |        "      <td>2019-08-28</td>\n",
353 |        "      <td>1549.1</td>\n",
354 |        "      <td>1551.8</td>\n",
355 |        "      <td>1556.6</td>\n",
356 |        "      <td>1541.4</td>\n",
357 |        "      <td>353.91K</td>\n",
358 |        "      <td>-0.0017</td>\n",
359 |        "    </tr>\n",
360 |        "    <tr>\n",
361 |        "      <td>2019-08-27</td>\n",
362 |        "      <td>2019-08-27</td>\n",
363 |        "      <td>1551.8</td>\n",
364 |        "      <td>1537.3</td>\n",
365 |        "      <td>1554.5</td>\n",
366 |        "      <td>1535.3</td>\n",
367 |        "      <td>347.05K</td>\n",
368 |        "      <td>0.0095</td>\n",
369 |        "    </tr>\n",
370 |        "    <tr>\n",
371 |        "      <td>2019-08-26</td>\n",
372 |        "      <td>2019-08-26</td>\n",
373 |        "      <td>1537.2</td>\n",
374 |        "      <td>1545.4</td>\n",
375 |        "      <td>1565.0</td>\n",
376 |        "      <td>1534.8</td>\n",
377 |        "      <td>411.67K</td>\n",
378 |        "      <td>-0.0003</td>\n",
379 |        "    </tr>\n",
380 |        "    <tr>\n",
381 |        "      <td>2019-08-23</td>\n",
382 |        "      <td>2019-08-23</td>\n",
383 |        "      <td>1537.6</td>\n",
384 |        "      <td>1508.8</td>\n",
385 |        "      <td>1540.3</td>\n",
386 |        "      <td>1503.0</td>\n",
387 |        "      <td>473.00K</td>\n",
388 |        "      <td>0.0193</td>\n",
389 |        "    </tr>\n",
390 |        "    <tr>\n",
391 |        "      <td>2019-08-22</td>\n",
392 |        "      <td>2019-08-22</td>\n",
393 |        "      <td>1508.5</td>\n",
394 |        "      <td>1512.4</td>\n",
395 |        "      <td>1514.6</td>\n",
396 |        "      <td>1502.1</td>\n",
397 |        "      <td>279.41K</td>\n",
398 |        "      <td>-0.0048</td>\n",
399 |        "    </tr>\n",
400 |        "    <tr>\n",
401 |        "      <td>...</td>\n",
402 |        "      <td>...</td>\n",
403 |        "      <td>...</td>\n",
404 |        "      <td>...</td>\n",
405 |        "      <td>...</td>\n",
406 |        "      <td>...</td>\n",
407 |        "      <td>...</td>\n",
408 |        "      <td>...</td>\n",
409 |        "    </tr>\n",
410 |        "    <tr>\n",
411 |        "      <td>2019-04-05</td>\n",
412 |        "      <td>2019-04-05</td>\n",
413 |        "      <td>1313.7</td>\n",
414 |        "      <td>1315.0</td>\n",
415 |        "      <td>1315.0</td>\n",
416 |        "      <td>1306.6</td>\n",
417 |        "      <td>1.64K</td>\n",
418 |        "      <td>0.0011</td>\n",
419 |        "    </tr>\n",
420 |        "    <tr>\n",
421 |        "      <td>2019-04-04</td>\n",
422 |        "      <td>2019-04-04</td>\n",
423 |        "      <td>1312.2</td>\n",
424 |        "      <td>1313.1</td>\n",
425 |        "      <td>1316.4</td>\n",
426 |        "      <td>1302.8</td>\n",
427 |        "      <td>2.29K</td>\n",
428 |        "      <td>-0.0008</td>\n",
429 |        "    </tr>\n",
430 |        "    <tr>\n",
431 |        "      <td>2019-04-03</td>\n",
432 |        "      <td>2019-04-03</td>\n",
433 |        "      <td>1313.3</td>\n",
434 |        "      <td>1314.0</td>\n",
435 |        "      <td>1316.6</td>\n",
436 |        "      <td>1310.6</td>\n",
437 |        "      <td>1.44K</td>\n",
438 |        "      <td>-0.0001</td>\n",
439 |        "    </tr>\n",
440 |        "    <tr>\n",
441 |        "      <td>2019-04-02</td>\n",
442 |        "      <td>2019-04-02</td>\n",
443 |        "      <td>1313.4</td>\n",
444 |        "      <td>1310.7</td>\n",
445 |        "      <td>1314.7</td>\n",
446 |        "      <td>1308.0</td>\n",
447 |        "      <td>1.41K</td>\n",
448 |        "      <td>0.0008</td>\n",
449 |        "    </tr>\n",
450 |        "    <tr>\n",
451 |        "      <td>2019-04-01</td>\n",
452 |        "      <td>2019-04-01</td>\n",
453 |        "      <td>1312.4</td>\n",
454 |        "      <td>1314.4</td>\n",
455 |        "      <td>1319.3</td>\n",
456 |        "      <td>1309.5</td>\n",
457 |        "      <td>0.77K</td>\n",
458 |        "      <td>-0.0031</td>\n",
459 |        "    </tr>\n",
460 |        "  </tbody>\n",
461 |        "</table>\n",
462 |        "<p>109 rows × 7 columns</p>\n",
463 |        "</div>"
464 |       ],
465 |       "text/plain": [
466 |        "                 Date   Price    Open    High     Low     Vol.  Change %\n",
467 |        "Date                                                                    \n",
468 |        "2019-08-28 2019-08-28  1549.1  1551.8  1556.6  1541.4  353.91K   -0.0017\n",
469 |        "2019-08-27 2019-08-27  1551.8  1537.3  1554.5  1535.3  347.05K    0.0095\n",
470 |        "2019-08-26 2019-08-26  1537.2  1545.4  1565.0  1534.8  411.67K   -0.0003\n",
471 |        "2019-08-23 2019-08-23  1537.6  1508.8  1540.3  1503.0  473.00K    0.0193\n",
472 |        "2019-08-22 2019-08-22  1508.5  1512.4  1514.6  1502.1  279.41K   -0.0048\n",
473 |        "...               ...     ...     ...     ...     ...      ...       ...\n",
474 |        "2019-04-05 2019-04-05  1313.7  1315.0  1315.0  1306.6    1.64K    0.0011\n",
475 |        "2019-04-04 2019-04-04  1312.2  1313.1  1316.4  1302.8    2.29K   -0.0008\n",
476 |        "2019-04-03 2019-04-03  1313.3  1314.0  1316.6  1310.6    1.44K   -0.0001\n",
477 |        "2019-04-02 2019-04-02  1313.4  1310.7  1314.7  1308.0    1.41K    0.0008\n",
478 |        "2019-04-01 2019-04-01  1312.4  1314.4  1319.3  1309.5    0.77K   -0.0031\n",
479 |        "\n",
480 |        "[109 rows x 7 columns]"
481 |       ]
482 |      },
483 |      "execution_count": 33,
484 |      "metadata": {},
485 |      "output_type": "execute_result"
486 |     }
487 |    ],
488 |    "source": [
489 |     "df1"
490 |    ]
491 |   },
492 |   {
493 |    "cell_type": "code",
494 |    "execution_count": 34,
495 |    "metadata": {},
496 |    "outputs": [
497 |     {
498 |      "data": {
499 |       "text/plain": [
500 |        "1277.9"
501 |       ]
502 |      },
503 |      "execution_count": 34,
504 |      "metadata": {},
505 |      "output_type": "execute_result"
506 |     }
507 |    ],
508 |    "source": [
509 |     "Price_Min =df1['Low']['2019-08-28':'2019-04-01'].min()\n",
510 |     "Price_Min"
511 |    ]
512 |   },
513 |   {
514 |    "cell_type": "code",
515 |    "execution_count": 35,
516 |    "metadata": {},
517 |    "outputs": [
518 |     {
519 |      "data": {
520 |       "text/plain": [
521 |        "1565.0"
522 |       ]
523 |      },
524 |      "execution_count": 35,
525 |      "metadata": {},
526 |      "output_type": "execute_result"
527 |     }
528 |    ],
529 |    "source": [
530 |     "Price_Max =df1['High']['2019-08-28':'2019-04-01'].max()\n",
531 |     "Price_Max"
532 |    ]
533 |   },
534 |   {
535 |    "cell_type": "markdown",
536 |    "metadata": {},
537 |    "source": [
538 |     "Find the Difference and find the level as per the fibonacci Retracement Model"
539 |    ]
540 |   },
541 |   {
542 |    "cell_type": "code",
543 |    "execution_count": 36,
544 |    "metadata": {},
545 |    "outputs": [],
546 |    "source": [
547 |     "Diff = Price_Max-Price_Min"
548 |    ]
549 |   },
550 |   {
551 |    "cell_type": "code",
552 |    "execution_count": 37,
553 |    "metadata": {},
554 |    "outputs": [],
555 |    "source": [
556 |     "level1 = Price_Max - 0.236 * Diff\n",
557 |     "level2 = Price_Max - 0.382 * Diff\n",
558 |     "level3 = Price_Max - 0.618 * Diff"
559 |    ]
560 |   },
561 |   {
562 |    "cell_type": "code",
563 |    "execution_count": 38,
564 |    "metadata": {},
565 |    "outputs": [
566 |     {
567 |      "name": "stdout",
568 |      "output_type": "stream",
569 |      "text": [
570 |       "Level   PRICE\n",
571 |       "0         1565.0\n",
572 |       "0.236     1497.2444\n",
573 |       "0.382     1455.3278\n",
574 |       "0.618     1387.5722\n",
575 |       "1         1277.9\n"
576 |      ]
577 |     }
578 |    ],
579 |    "source": [
580 |     "\n",
581 |     "print (\"Level\", \" \", \"PRICE\")\n",
582 |     "\n",
583 |     "print (\"0 \", \"      \" , Price_Max)\n",
584 |     "print (\"0.236\", \"   \" ,level1)\n",
585 |     "print (\"0.382\",  \"   \",level2)\n",
586 |     "print (\"0.618\",\"   \",  level3)\n",
587 |     "print (\"1 \",   \"      \", Price_Min)"
588 |    ]
589 |   },
590 |   {
591 |    "cell_type": "markdown",
592 |    "metadata": {},
593 |    "source": [
594 |     "Ploting Graph"
595 |    ]
596 |   },
597 |   {
598 |    "cell_type": "code",
599 |    "execution_count": 39,
600 |    "metadata": {},
601 |    "outputs": [
602 |     {
603 |      "data": {
604 |       "image/png": 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\n",
605 |       "text/plain": [
606 |        "<Figure size 1080x360 with 1 Axes>"
607 |       ]
608 |      },
609 |      "metadata": {
610 |       "needs_background": "light"
611 |      },
612 |      "output_type": "display_data"
613 |     }
614 |    ],
615 |    "source": [
616 |     "fig, ax = plt.subplots(figsize=(15,5))\n",
617 |     "\n",
618 |     "ax.plot(df1.Date, df1.Price)\n",
619 |     "\n",
620 |     "ax.axhspan(level1, Price_Min, alpha=0.4, color='lightsalmon')\n",
621 |     "ax.axhspan(level2, level1, alpha=0.5, color='palegoldenrod')\n",
622 |     "ax.axhspan(level3, level2, alpha=0.5, color='palegreen')\n",
623 |     "ax.axhspan(Price_Max, level3, alpha=0.5, color='powderblue')\n",
624 |     "\n",
625 |     "plt.ylabel(\"Price\")\n",
626 |     "plt.xlabel(\"Date\")\n",
627 |     "\n",
628 |     "plt.title('Fibonacci')\n",
629 |     "\n",
630 |     "plt.show()"
631 |    ]
632 |   },
633 |   {
634 |    "cell_type": "code",
635 |    "execution_count": null,
636 |    "metadata": {},
637 |    "outputs": [],
638 |    "source": []
639 |   }
640 |  ],
641 |  "metadata": {
642 |   "kernelspec": {
643 |    "display_name": "Python 3",
644 |    "language": "python",
645 |    "name": "python3"
646 |   },
647 |   "language_info": {
648 |    "codemirror_mode": {
649 |     "name": "ipython",
650 |     "version": 3
651 |    },
652 |    "file_extension": ".py",
653 |    "mimetype": "text/x-python",
654 |    "name": "python",
655 |    "nbconvert_exporter": "python",
656 |    "pygments_lexer": "ipython3",
657 |    "version": "3.7.1"
658 |   }
659 |  },
660 |  "nbformat": 4,
661 |  "nbformat_minor": 2
662 | }
663 | 


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