├── README.md
├── TradingviewData.ipynb
└── TradingviewData
    ├── __init__.py
    ├── __pycache__
        ├── __init__.cpython-39.pyc
        └── main.cpython-39.pyc
    └── main.py


/README.md:
--------------------------------------------------------------------------------
 1 | 
 2 | # TradingView Data For any Indexes and Stocks
 3 | 
 4 | A Simple TradingView Data Downloader. TradinViewData allows downloading upto 5000 Candles on any of the supported timeframe.
 5 | 
 6 |     
 7 | # Usage
 8 | 
 9 | ```Python
10 | from TradingviewData import TradingViewData,Interval
11 | 
12 | request = TradingViewData()
13 | ```
14 | 
15 | 
16 | # Get Symbol
17 | 
18 | 
19 | To find the exact symbols for an instrument you can use ``` request.search_symbol ``` method.
20 | 
21 | ```Python
22 | 
23 | request.search('METAL','MCX')
24 | ```
25 | 
26 | Other method is check Manually via [Tradingview Search]("https://www.tradingview.com/markets/indices/").
27 | 
28 | 
29 | # Getting Data
30 | 
31 | 
32 | ## Index
33 | 
34 | ```Python
35 | nifty_data = request.get_hist(symbol='NIFTY',exchange='NSE',interval=Interval.hour_1,n_bars=1000)
36 | ```
37 | ## Futures continuous contract
38 | 
39 | ```Python
40 | nifty_futures = request.get_hist(symbol='NIFTY',exchange='NSE',interval=Interval.hour_1,n_bars=1000,fut_contract=1)
41 | ```
42 | 
43 | ## Stocks
44 | 
45 | ```Python
46 | relience_data = request.get_hist(symbol='RELIANCE',exchange='NSE',interval=Interval.min_5,n_bars=5000)
47 | ```
48 | 
49 | ## MCX
50 | 
51 | ```Python
52 | crudeoil_data = request.get_hist(symbol='CRUDEOIL',exchange='MCX',interval=Interval.hour_1,n_bars=5000)
53 | ```
54 | 
55 | ## Downloading data for extended market hours
56 | 
57 | 
58 | ```Python
59 | extended_data = request.get_hist(symbol="EICHERMOT",exchange="NSE",interval=Interval.hour_1,n_bars=500, extended_session=False)
60 | ```
61 | 
62 | 
63 | ## Supported Time Frames
64 | 
65 | 
66 | #####  1 Minute = min_1
67 | #####  3 Minute = min_3
68 | #####  5 Minute = min_5
69 | #####  15 Minute = min_15
70 | #####  30 Minute = min_30
71 | #####  45 Minute = min_45
72 | #####  1 Hour = hour_1
73 | #####  2 Hour = hour_2
74 | #####  3 Hour = hour_3
75 | #####  4 Hour = hour_4
76 | #####  1 Day = daily
77 | #####  1 Week = weekly
78 | #####  1 Month = monthy
79 | 


--------------------------------------------------------------------------------
/TradingviewData.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "from TradingviewData import TradingViewData,Interval\n",
 10 |     "\n",
 11 |     "request = TradingViewData()"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 14,
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "data": {
 21 |       "text/plain": [
 22 |        "[{'symbol': 'MCXMETLDEX',\n",
 23 |        "  'description': 'MCX ICOMDEX BASE METAL',\n",
 24 |        "  'type': 'index',\n",
 25 |        "  'exchange': 'MCX',\n",
 26 |        "  'currency_code': 'INR',\n",
 27 |        "  'provider_id': 'ice',\n",
 28 |        "  'country': 'IN'},\n",
 29 |        " {'symbol': 'MCXMETLDEX',\n",
 30 |        "  'description': 'MCX ICOMDEX BASE METAL FUTURES',\n",
 31 |        "  'type': 'futures',\n",
 32 |        "  'exchange': 'MCX',\n",
 33 |        "  'currency_code': 'INR',\n",
 34 |        "  'provider_id': 'ice',\n",
 35 |        "  'country': 'IN',\n",
 36 |        "  'contracts': [{'symbol': 'MCXMETLDEX1!',\n",
 37 |        "    'typespecs': ['continuous', 'synthetic'],\n",
 38 |        "    'description': 'CONTINUOUS: CURRENT CONTRACT IN FRONT'},\n",
 39 |        "   {'symbol': 'MCXMETLDEX2!',\n",
 40 |        "    'typespecs': ['continuous', 'synthetic'],\n",
 41 |        "    'description': 'CONTINUOUS: NEXT CONTRACT IN FRONT'},\n",
 42 |        "   {'symbol': 'MCXMETLDEXG2023', 'description': 'FEB 2023'},\n",
 43 |        "   {'symbol': 'MCXMETLDEXH2023', 'description': 'MAR 2023'},\n",
 44 |        "   {'symbol': 'MCXMETLDEXJ2023', 'description': 'APR 2023'}]}]"
 45 |       ]
 46 |      },
 47 |      "execution_count": 14,
 48 |      "metadata": {},
 49 |      "output_type": "execute_result"
 50 |     }
 51 |    ],
 52 |    "source": [
 53 |     "request.search('METAL','MCX')"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": 4,
 59 |    "metadata": {},
 60 |    "outputs": [
 61 |     {
 62 |      "data": {
 63 |       "text/html": [
 64 |        "<div>\n",
 65 |        "<style scoped>\n",
 66 |        "    .dataframe tbody tr th:only-of-type {\n",
 67 |        "        vertical-align: middle;\n",
 68 |        "    }\n",
 69 |        "\n",
 70 |        "    .dataframe tbody tr th {\n",
 71 |        "        vertical-align: top;\n",
 72 |        "    }\n",
 73 |        "\n",
 74 |        "    .dataframe thead th {\n",
 75 |        "        text-align: right;\n",
 76 |        "    }\n",
 77 |        "</style>\n",
 78 |        "<table border=\"1\" class=\"dataframe\">\n",
 79 |        "  <thead>\n",
 80 |        "    <tr style=\"text-align: right;\">\n",
 81 |        "      <th></th>\n",
 82 |        "      <th>symbol</th>\n",
 83 |        "      <th>open</th>\n",
 84 |        "      <th>high</th>\n",
 85 |        "      <th>low</th>\n",
 86 |        "      <th>close</th>\n",
 87 |        "      <th>volume</th>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>datetime</th>\n",
 91 |        "      <th></th>\n",
 92 |        "      <th></th>\n",
 93 |        "      <th></th>\n",
 94 |        "      <th></th>\n",
 95 |        "      <th></th>\n",
 96 |        "      <th></th>\n",
 97 |        "    </tr>\n",
 98 |        "  </thead>\n",
 99 |        "  <tbody>\n",
100 |        "    <tr>\n",
101 |        "      <th>2022-07-15 12:15:00</th>\n",
102 |        "      <td>NSE:NIFTY</td>\n",
103 |        "      <td>15949.45</td>\n",
104 |        "      <td>15983.05</td>\n",
105 |        "      <td>15927.30</td>\n",
106 |        "      <td>15979.50</td>\n",
107 |        "      <td>21558646.0</td>\n",
108 |        "    </tr>\n",
109 |        "    <tr>\n",
110 |        "      <th>2022-07-15 13:15:00</th>\n",
111 |        "      <td>NSE:NIFTY</td>\n",
112 |        "      <td>15980.05</td>\n",
113 |        "      <td>15992.25</td>\n",
114 |        "      <td>15945.10</td>\n",
115 |        "      <td>15992.25</td>\n",
116 |        "      <td>25357416.0</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>2022-07-15 14:15:00</th>\n",
120 |        "      <td>NSE:NIFTY</td>\n",
121 |        "      <td>15993.65</td>\n",
122 |        "      <td>16054.30</td>\n",
123 |        "      <td>15987.90</td>\n",
124 |        "      <td>16053.25</td>\n",
125 |        "      <td>39190692.0</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>2022-07-15 15:15:00</th>\n",
129 |        "      <td>NSE:NIFTY</td>\n",
130 |        "      <td>16052.90</td>\n",
131 |        "      <td>16066.95</td>\n",
132 |        "      <td>16044.10</td>\n",
133 |        "      <td>16052.85</td>\n",
134 |        "      <td>19423958.0</td>\n",
135 |        "    </tr>\n",
136 |        "    <tr>\n",
137 |        "      <th>2022-07-18 09:15:00</th>\n",
138 |        "      <td>NSE:NIFTY</td>\n",
139 |        "      <td>16152.30</td>\n",
140 |        "      <td>16199.70</td>\n",
141 |        "      <td>16142.20</td>\n",
142 |        "      <td>16184.80</td>\n",
143 |        "      <td>53958590.0</td>\n",
144 |        "    </tr>\n",
145 |        "    <tr>\n",
146 |        "      <th>...</th>\n",
147 |        "      <td>...</td>\n",
148 |        "      <td>...</td>\n",
149 |        "      <td>...</td>\n",
150 |        "      <td>...</td>\n",
151 |        "      <td>...</td>\n",
152 |        "      <td>...</td>\n",
153 |        "    </tr>\n",
154 |        "    <tr>\n",
155 |        "      <th>2023-02-10 11:15:00</th>\n",
156 |        "      <td>NSE:NIFTY</td>\n",
157 |        "      <td>17866.60</td>\n",
158 |        "      <td>17876.95</td>\n",
159 |        "      <td>17820.35</td>\n",
160 |        "      <td>17823.75</td>\n",
161 |        "      <td>21562199.0</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>2023-02-10 12:15:00</th>\n",
165 |        "      <td>NSE:NIFTY</td>\n",
166 |        "      <td>17824.15</td>\n",
167 |        "      <td>17842.95</td>\n",
168 |        "      <td>17815.90</td>\n",
169 |        "      <td>17822.70</td>\n",
170 |        "      <td>24809266.0</td>\n",
171 |        "    </tr>\n",
172 |        "    <tr>\n",
173 |        "      <th>2023-02-10 13:15:00</th>\n",
174 |        "      <td>NSE:NIFTY</td>\n",
175 |        "      <td>17822.85</td>\n",
176 |        "      <td>17859.25</td>\n",
177 |        "      <td>17815.10</td>\n",
178 |        "      <td>17850.55</td>\n",
179 |        "      <td>38897505.0</td>\n",
180 |        "    </tr>\n",
181 |        "    <tr>\n",
182 |        "      <th>2023-02-10 14:15:00</th>\n",
183 |        "      <td>NSE:NIFTY</td>\n",
184 |        "      <td>17850.10</td>\n",
185 |        "      <td>17865.15</td>\n",
186 |        "      <td>17823.65</td>\n",
187 |        "      <td>17861.80</td>\n",
188 |        "      <td>38252608.0</td>\n",
189 |        "    </tr>\n",
190 |        "    <tr>\n",
191 |        "      <th>2023-02-10 15:15:00</th>\n",
192 |        "      <td>NSE:NIFTY</td>\n",
193 |        "      <td>17862.10</td>\n",
194 |        "      <td>17864.40</td>\n",
195 |        "      <td>17840.00</td>\n",
196 |        "      <td>17843.35</td>\n",
197 |        "      <td>17747095.0</td>\n",
198 |        "    </tr>\n",
199 |        "  </tbody>\n",
200 |        "</table>\n",
201 |        "<p>1000 rows × 6 columns</p>\n",
202 |        "</div>"
203 |       ],
204 |       "text/plain": [
205 |        "                        symbol      open      high       low     close  \\\n",
206 |        "datetime                                                                 \n",
207 |        "2022-07-15 12:15:00  NSE:NIFTY  15949.45  15983.05  15927.30  15979.50   \n",
208 |        "2022-07-15 13:15:00  NSE:NIFTY  15980.05  15992.25  15945.10  15992.25   \n",
209 |        "2022-07-15 14:15:00  NSE:NIFTY  15993.65  16054.30  15987.90  16053.25   \n",
210 |        "2022-07-15 15:15:00  NSE:NIFTY  16052.90  16066.95  16044.10  16052.85   \n",
211 |        "2022-07-18 09:15:00  NSE:NIFTY  16152.30  16199.70  16142.20  16184.80   \n",
212 |        "...                        ...       ...       ...       ...       ...   \n",
213 |        "2023-02-10 11:15:00  NSE:NIFTY  17866.60  17876.95  17820.35  17823.75   \n",
214 |        "2023-02-10 12:15:00  NSE:NIFTY  17824.15  17842.95  17815.90  17822.70   \n",
215 |        "2023-02-10 13:15:00  NSE:NIFTY  17822.85  17859.25  17815.10  17850.55   \n",
216 |        "2023-02-10 14:15:00  NSE:NIFTY  17850.10  17865.15  17823.65  17861.80   \n",
217 |        "2023-02-10 15:15:00  NSE:NIFTY  17862.10  17864.40  17840.00  17843.35   \n",
218 |        "\n",
219 |        "                         volume  \n",
220 |        "datetime                         \n",
221 |        "2022-07-15 12:15:00  21558646.0  \n",
222 |        "2022-07-15 13:15:00  25357416.0  \n",
223 |        "2022-07-15 14:15:00  39190692.0  \n",
224 |        "2022-07-15 15:15:00  19423958.0  \n",
225 |        "2022-07-18 09:15:00  53958590.0  \n",
226 |        "...                         ...  \n",
227 |        "2023-02-10 11:15:00  21562199.0  \n",
228 |        "2023-02-10 12:15:00  24809266.0  \n",
229 |        "2023-02-10 13:15:00  38897505.0  \n",
230 |        "2023-02-10 14:15:00  38252608.0  \n",
231 |        "2023-02-10 15:15:00  17747095.0  \n",
232 |        "\n",
233 |        "[1000 rows x 6 columns]"
234 |       ]
235 |      },
236 |      "execution_count": 4,
237 |      "metadata": {},
238 |      "output_type": "execute_result"
239 |     }
240 |    ],
241 |    "source": [
242 |     "nifty_data = request.get_hist(symbol='NIFTY',exchange='NSE',interval=Interval.hour_1,n_bars=1000)\n",
243 |     "\n",
244 |     "nifty_data"
245 |    ]
246 |   },
247 |   {
248 |    "cell_type": "code",
249 |    "execution_count": 6,
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>symbol</th>\n",
274 |        "      <th>open</th>\n",
275 |        "      <th>high</th>\n",
276 |        "      <th>low</th>\n",
277 |        "      <th>close</th>\n",
278 |        "      <th>volume</th>\n",
279 |        "    </tr>\n",
280 |        "    <tr>\n",
281 |        "      <th>datetime</th>\n",
282 |        "      <th></th>\n",
283 |        "      <th></th>\n",
284 |        "      <th></th>\n",
285 |        "      <th></th>\n",
286 |        "      <th></th>\n",
287 |        "      <th></th>\n",
288 |        "    </tr>\n",
289 |        "  </thead>\n",
290 |        "  <tbody>\n",
291 |        "    <tr>\n",
292 |        "      <th>2022-07-15 12:15:00</th>\n",
293 |        "      <td>NSE:NIFTY1!</td>\n",
294 |        "      <td>15959.35</td>\n",
295 |        "      <td>15995.00</td>\n",
296 |        "      <td>15930.30</td>\n",
297 |        "      <td>15987.70</td>\n",
298 |        "      <td>1003100.0</td>\n",
299 |        "    </tr>\n",
300 |        "    <tr>\n",
301 |        "      <th>2022-07-15 13:15:00</th>\n",
302 |        "      <td>NSE:NIFTY1!</td>\n",
303 |        "      <td>15987.10</td>\n",
304 |        "      <td>16009.60</td>\n",
305 |        "      <td>15955.00</td>\n",
306 |        "      <td>16009.15</td>\n",
307 |        "      <td>821700.0</td>\n",
308 |        "    </tr>\n",
309 |        "    <tr>\n",
310 |        "      <th>2022-07-15 14:15:00</th>\n",
311 |        "      <td>NSE:NIFTY1!</td>\n",
312 |        "      <td>16008.45</td>\n",
313 |        "      <td>16070.00</td>\n",
314 |        "      <td>16000.00</td>\n",
315 |        "      <td>16068.90</td>\n",
316 |        "      <td>2255300.0</td>\n",
317 |        "    </tr>\n",
318 |        "    <tr>\n",
319 |        "      <th>2022-07-15 15:15:00</th>\n",
320 |        "      <td>NSE:NIFTY1!</td>\n",
321 |        "      <td>16069.55</td>\n",
322 |        "      <td>16083.90</td>\n",
323 |        "      <td>16062.10</td>\n",
324 |        "      <td>16074.00</td>\n",
325 |        "      <td>1096150.0</td>\n",
326 |        "    </tr>\n",
327 |        "    <tr>\n",
328 |        "      <th>2022-07-18 09:15:00</th>\n",
329 |        "      <td>NSE:NIFTY1!</td>\n",
330 |        "      <td>16199.00</td>\n",
331 |        "      <td>16520.00</td>\n",
332 |        "      <td>16170.45</td>\n",
333 |        "      <td>16207.00</td>\n",
334 |        "      <td>2582300.0</td>\n",
335 |        "    </tr>\n",
336 |        "    <tr>\n",
337 |        "      <th>...</th>\n",
338 |        "      <td>...</td>\n",
339 |        "      <td>...</td>\n",
340 |        "      <td>...</td>\n",
341 |        "      <td>...</td>\n",
342 |        "      <td>...</td>\n",
343 |        "      <td>...</td>\n",
344 |        "    </tr>\n",
345 |        "    <tr>\n",
346 |        "      <th>2023-02-10 11:15:00</th>\n",
347 |        "      <td>NSE:NIFTY1!</td>\n",
348 |        "      <td>17892.00</td>\n",
349 |        "      <td>17899.90</td>\n",
350 |        "      <td>17845.00</td>\n",
351 |        "      <td>17848.65</td>\n",
352 |        "      <td>1115850.0</td>\n",
353 |        "    </tr>\n",
354 |        "    <tr>\n",
355 |        "      <th>2023-02-10 12:15:00</th>\n",
356 |        "      <td>NSE:NIFTY1!</td>\n",
357 |        "      <td>17849.35</td>\n",
358 |        "      <td>17872.75</td>\n",
359 |        "      <td>17847.00</td>\n",
360 |        "      <td>17850.65</td>\n",
361 |        "      <td>369950.0</td>\n",
362 |        "    </tr>\n",
363 |        "    <tr>\n",
364 |        "      <th>2023-02-10 13:15:00</th>\n",
365 |        "      <td>NSE:NIFTY1!</td>\n",
366 |        "      <td>17851.35</td>\n",
367 |        "      <td>17884.00</td>\n",
368 |        "      <td>17844.70</td>\n",
369 |        "      <td>17877.00</td>\n",
370 |        "      <td>486700.0</td>\n",
371 |        "    </tr>\n",
372 |        "    <tr>\n",
373 |        "      <th>2023-02-10 14:15:00</th>\n",
374 |        "      <td>NSE:NIFTY1!</td>\n",
375 |        "      <td>17877.40</td>\n",
376 |        "      <td>17888.00</td>\n",
377 |        "      <td>17849.30</td>\n",
378 |        "      <td>17886.00</td>\n",
379 |        "      <td>819950.0</td>\n",
380 |        "    </tr>\n",
381 |        "    <tr>\n",
382 |        "      <th>2023-02-10 15:15:00</th>\n",
383 |        "      <td>NSE:NIFTY1!</td>\n",
384 |        "      <td>17886.00</td>\n",
385 |        "      <td>17887.00</td>\n",
386 |        "      <td>17869.00</td>\n",
387 |        "      <td>17870.00</td>\n",
388 |        "      <td>368700.0</td>\n",
389 |        "    </tr>\n",
390 |        "  </tbody>\n",
391 |        "</table>\n",
392 |        "<p>1000 rows × 6 columns</p>\n",
393 |        "</div>"
394 |       ],
395 |       "text/plain": [
396 |        "                          symbol      open      high       low     close  \\\n",
397 |        "datetime                                                                   \n",
398 |        "2022-07-15 12:15:00  NSE:NIFTY1!  15959.35  15995.00  15930.30  15987.70   \n",
399 |        "2022-07-15 13:15:00  NSE:NIFTY1!  15987.10  16009.60  15955.00  16009.15   \n",
400 |        "2022-07-15 14:15:00  NSE:NIFTY1!  16008.45  16070.00  16000.00  16068.90   \n",
401 |        "2022-07-15 15:15:00  NSE:NIFTY1!  16069.55  16083.90  16062.10  16074.00   \n",
402 |        "2022-07-18 09:15:00  NSE:NIFTY1!  16199.00  16520.00  16170.45  16207.00   \n",
403 |        "...                          ...       ...       ...       ...       ...   \n",
404 |        "2023-02-10 11:15:00  NSE:NIFTY1!  17892.00  17899.90  17845.00  17848.65   \n",
405 |        "2023-02-10 12:15:00  NSE:NIFTY1!  17849.35  17872.75  17847.00  17850.65   \n",
406 |        "2023-02-10 13:15:00  NSE:NIFTY1!  17851.35  17884.00  17844.70  17877.00   \n",
407 |        "2023-02-10 14:15:00  NSE:NIFTY1!  17877.40  17888.00  17849.30  17886.00   \n",
408 |        "2023-02-10 15:15:00  NSE:NIFTY1!  17886.00  17887.00  17869.00  17870.00   \n",
409 |        "\n",
410 |        "                        volume  \n",
411 |        "datetime                        \n",
412 |        "2022-07-15 12:15:00  1003100.0  \n",
413 |        "2022-07-15 13:15:00   821700.0  \n",
414 |        "2022-07-15 14:15:00  2255300.0  \n",
415 |        "2022-07-15 15:15:00  1096150.0  \n",
416 |        "2022-07-18 09:15:00  2582300.0  \n",
417 |        "...                        ...  \n",
418 |        "2023-02-10 11:15:00  1115850.0  \n",
419 |        "2023-02-10 12:15:00   369950.0  \n",
420 |        "2023-02-10 13:15:00   486700.0  \n",
421 |        "2023-02-10 14:15:00   819950.0  \n",
422 |        "2023-02-10 15:15:00   368700.0  \n",
423 |        "\n",
424 |        "[1000 rows x 6 columns]"
425 |       ]
426 |      },
427 |      "execution_count": 6,
428 |      "metadata": {},
429 |      "output_type": "execute_result"
430 |     }
431 |    ],
432 |    "source": [
433 |     "nifty_futures = request.get_hist(symbol='NIFTY',exchange='NSE',interval=Interval.hour_1,n_bars=1000,fut_contract=1)\n",
434 |     "\n",
435 |     "nifty_futures"
436 |    ]
437 |   },
438 |   {
439 |    "cell_type": "code",
440 |    "execution_count": 15,
441 |    "metadata": {},
442 |    "outputs": [
443 |     {
444 |      "data": {
445 |       "text/html": [
446 |        "<div>\n",
447 |        "<style scoped>\n",
448 |        "    .dataframe tbody tr th:only-of-type {\n",
449 |        "        vertical-align: middle;\n",
450 |        "    }\n",
451 |        "\n",
452 |        "    .dataframe tbody tr th {\n",
453 |        "        vertical-align: top;\n",
454 |        "    }\n",
455 |        "\n",
456 |        "    .dataframe thead th {\n",
457 |        "        text-align: right;\n",
458 |        "    }\n",
459 |        "</style>\n",
460 |        "<table border=\"1\" class=\"dataframe\">\n",
461 |        "  <thead>\n",
462 |        "    <tr style=\"text-align: right;\">\n",
463 |        "      <th></th>\n",
464 |        "      <th>symbol</th>\n",
465 |        "      <th>open</th>\n",
466 |        "      <th>high</th>\n",
467 |        "      <th>low</th>\n",
468 |        "      <th>close</th>\n",
469 |        "      <th>volume</th>\n",
470 |        "    </tr>\n",
471 |        "    <tr>\n",
472 |        "      <th>datetime</th>\n",
473 |        "      <th></th>\n",
474 |        "      <th></th>\n",
475 |        "      <th></th>\n",
476 |        "      <th></th>\n",
477 |        "      <th></th>\n",
478 |        "      <th></th>\n",
479 |        "    </tr>\n",
480 |        "  </thead>\n",
481 |        "  <tbody>\n",
482 |        "    <tr>\n",
483 |        "      <th>2022-11-09 11:20:00</th>\n",
484 |        "      <td>NSE:RELIANCE</td>\n",
485 |        "      <td>2607.90</td>\n",
486 |        "      <td>2607.95</td>\n",
487 |        "      <td>2605.60</td>\n",
488 |        "      <td>2606.65</td>\n",
489 |        "      <td>22114.0</td>\n",
490 |        "    </tr>\n",
491 |        "    <tr>\n",
492 |        "      <th>2022-11-09 11:25:00</th>\n",
493 |        "      <td>NSE:RELIANCE</td>\n",
494 |        "      <td>2606.20</td>\n",
495 |        "      <td>2606.85</td>\n",
496 |        "      <td>2604.85</td>\n",
497 |        "      <td>2605.95</td>\n",
498 |        "      <td>25276.0</td>\n",
499 |        "    </tr>\n",
500 |        "    <tr>\n",
501 |        "      <th>2022-11-09 11:30:00</th>\n",
502 |        "      <td>NSE:RELIANCE</td>\n",
503 |        "      <td>2605.75</td>\n",
504 |        "      <td>2607.00</td>\n",
505 |        "      <td>2603.95</td>\n",
506 |        "      <td>2606.45</td>\n",
507 |        "      <td>31536.0</td>\n",
508 |        "    </tr>\n",
509 |        "    <tr>\n",
510 |        "      <th>2022-11-09 11:35:00</th>\n",
511 |        "      <td>NSE:RELIANCE</td>\n",
512 |        "      <td>2606.00</td>\n",
513 |        "      <td>2607.85</td>\n",
514 |        "      <td>2605.05</td>\n",
515 |        "      <td>2607.50</td>\n",
516 |        "      <td>27541.0</td>\n",
517 |        "    </tr>\n",
518 |        "    <tr>\n",
519 |        "      <th>2022-11-09 11:40:00</th>\n",
520 |        "      <td>NSE:RELIANCE</td>\n",
521 |        "      <td>2607.50</td>\n",
522 |        "      <td>2610.00</td>\n",
523 |        "      <td>2606.35</td>\n",
524 |        "      <td>2609.50</td>\n",
525 |        "      <td>25894.0</td>\n",
526 |        "    </tr>\n",
527 |        "    <tr>\n",
528 |        "      <th>...</th>\n",
529 |        "      <td>...</td>\n",
530 |        "      <td>...</td>\n",
531 |        "      <td>...</td>\n",
532 |        "      <td>...</td>\n",
533 |        "      <td>...</td>\n",
534 |        "      <td>...</td>\n",
535 |        "    </tr>\n",
536 |        "    <tr>\n",
537 |        "      <th>2023-02-10 15:05:00</th>\n",
538 |        "      <td>NSE:RELIANCE</td>\n",
539 |        "      <td>2336.70</td>\n",
540 |        "      <td>2337.35</td>\n",
541 |        "      <td>2335.00</td>\n",
542 |        "      <td>2336.00</td>\n",
543 |        "      <td>67412.0</td>\n",
544 |        "    </tr>\n",
545 |        "    <tr>\n",
546 |        "      <th>2023-02-10 15:10:00</th>\n",
547 |        "      <td>NSE:RELIANCE</td>\n",
548 |        "      <td>2336.40</td>\n",
549 |        "      <td>2337.00</td>\n",
550 |        "      <td>2335.45</td>\n",
551 |        "      <td>2336.50</td>\n",
552 |        "      <td>94976.0</td>\n",
553 |        "    </tr>\n",
554 |        "    <tr>\n",
555 |        "      <th>2023-02-10 15:15:00</th>\n",
556 |        "      <td>NSE:RELIANCE</td>\n",
557 |        "      <td>2336.50</td>\n",
558 |        "      <td>2338.00</td>\n",
559 |        "      <td>2335.30</td>\n",
560 |        "      <td>2336.20</td>\n",
561 |        "      <td>134330.0</td>\n",
562 |        "    </tr>\n",
563 |        "    <tr>\n",
564 |        "      <th>2023-02-10 15:20:00</th>\n",
565 |        "      <td>NSE:RELIANCE</td>\n",
566 |        "      <td>2336.75</td>\n",
567 |        "      <td>2337.70</td>\n",
568 |        "      <td>2335.00</td>\n",
569 |        "      <td>2337.35</td>\n",
570 |        "      <td>109691.0</td>\n",
571 |        "    </tr>\n",
572 |        "    <tr>\n",
573 |        "      <th>2023-02-10 15:25:00</th>\n",
574 |        "      <td>NSE:RELIANCE</td>\n",
575 |        "      <td>2337.35</td>\n",
576 |        "      <td>2337.65</td>\n",
577 |        "      <td>2336.00</td>\n",
578 |        "      <td>2336.90</td>\n",
579 |        "      <td>68028.0</td>\n",
580 |        "    </tr>\n",
581 |        "  </tbody>\n",
582 |        "</table>\n",
583 |        "<p>5000 rows × 6 columns</p>\n",
584 |        "</div>"
585 |       ],
586 |       "text/plain": [
587 |        "                           symbol     open     high      low    close  \\\n",
588 |        "datetime                                                                \n",
589 |        "2022-11-09 11:20:00  NSE:RELIANCE  2607.90  2607.95  2605.60  2606.65   \n",
590 |        "2022-11-09 11:25:00  NSE:RELIANCE  2606.20  2606.85  2604.85  2605.95   \n",
591 |        "2022-11-09 11:30:00  NSE:RELIANCE  2605.75  2607.00  2603.95  2606.45   \n",
592 |        "2022-11-09 11:35:00  NSE:RELIANCE  2606.00  2607.85  2605.05  2607.50   \n",
593 |        "2022-11-09 11:40:00  NSE:RELIANCE  2607.50  2610.00  2606.35  2609.50   \n",
594 |        "...                           ...      ...      ...      ...      ...   \n",
595 |        "2023-02-10 15:05:00  NSE:RELIANCE  2336.70  2337.35  2335.00  2336.00   \n",
596 |        "2023-02-10 15:10:00  NSE:RELIANCE  2336.40  2337.00  2335.45  2336.50   \n",
597 |        "2023-02-10 15:15:00  NSE:RELIANCE  2336.50  2338.00  2335.30  2336.20   \n",
598 |        "2023-02-10 15:20:00  NSE:RELIANCE  2336.75  2337.70  2335.00  2337.35   \n",
599 |        "2023-02-10 15:25:00  NSE:RELIANCE  2337.35  2337.65  2336.00  2336.90   \n",
600 |        "\n",
601 |        "                       volume  \n",
602 |        "datetime                       \n",
603 |        "2022-11-09 11:20:00   22114.0  \n",
604 |        "2022-11-09 11:25:00   25276.0  \n",
605 |        "2022-11-09 11:30:00   31536.0  \n",
606 |        "2022-11-09 11:35:00   27541.0  \n",
607 |        "2022-11-09 11:40:00   25894.0  \n",
608 |        "...                       ...  \n",
609 |        "2023-02-10 15:05:00   67412.0  \n",
610 |        "2023-02-10 15:10:00   94976.0  \n",
611 |        "2023-02-10 15:15:00  134330.0  \n",
612 |        "2023-02-10 15:20:00  109691.0  \n",
613 |        "2023-02-10 15:25:00   68028.0  \n",
614 |        "\n",
615 |        "[5000 rows x 6 columns]"
616 |       ]
617 |      },
618 |      "execution_count": 15,
619 |      "metadata": {},
620 |      "output_type": "execute_result"
621 |     }
622 |    ],
623 |    "source": [
624 |     "relience_data = request.get_hist(symbol='RELIANCE',exchange='NSE',interval=Interval.min_5,n_bars=5000)\n",
625 |     "\n",
626 |     "relience_data"
627 |    ]
628 |   },
629 |   {
630 |    "cell_type": "code",
631 |    "execution_count": 16,
632 |    "metadata": {},
633 |    "outputs": [
634 |     {
635 |      "name": "stderr",
636 |      "output_type": "stream",
637 |      "text": [
638 |       "ERROR:TradingviewData.main:The read operation timed out\n",
639 |       "ERROR:TradingviewData.main:no data, please check the exchange and symbol\n"
640 |      ]
641 |     }
642 |    ],
643 |    "source": [
644 |     "crudeoil_data = request.get_hist(symbol='MCXMETLDEX',exchange='MCX',interval=Interval.hour_1,n_bars=5000)\n",
645 |     "\n",
646 |     "crudeoil_data"
647 |    ]
648 |   },
649 |   {
650 |    "cell_type": "code",
651 |    "execution_count": 17,
652 |    "metadata": {},
653 |    "outputs": [
654 |     {
655 |      "data": {
656 |       "text/plain": [
657 |        "<AxesSubplot:>"
658 |       ]
659 |      },
660 |      "execution_count": 17,
661 |      "metadata": {},
662 |      "output_type": "execute_result"
663 |     },
664 |     {
665 |      "data": {
666 |       "image/png": 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667 |       "text/plain": [
668 |        "<Figure size 1500x1000 with 1 Axes>"
669 |       ]
670 |      },
671 |      "metadata": {},
672 |      "output_type": "display_data"
673 |     }
674 |    ],
675 |    "source": [
676 |     "nifty_data.close.plot(figsize=(15,10),use_index=False)"
677 |    ]
678 |   }
679 |  ],
680 |  "metadata": {
681 |   "kernelspec": {
682 |    "display_name": "Python 3",
683 |    "language": "python",
684 |    "name": "python3"
685 |   },
686 |   "language_info": {
687 |    "codemirror_mode": {
688 |     "name": "ipython",
689 |     "version": 3
690 |    },
691 |    "file_extension": ".py",
692 |    "mimetype": "text/x-python",
693 |    "name": "python",
694 |    "nbconvert_exporter": "python",
695 |    "pygments_lexer": "ipython3",
696 |    "version": "3.11.2"
697 |   },
698 |   "vscode": {
699 |    "interpreter": {
700 |     "hash": "6569da2fa2341b5b424b9ad55f4c985f26aa6aa2f4b6f6ccd931f2c67a6ca5d7"
701 |    }
702 |   }
703 |  },
704 |  "nbformat": 4,
705 |  "nbformat_minor": 2
706 | }
707 | 


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/TradingviewData/__init__.py:
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1 | from .main import TradingViewData, Interval
2 | 
3 | __version__ = "2.1.0"
4 | 


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/TradingviewData/main.py:
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  1 | import datetime
  2 | import enum
  3 | import json
  4 | import logging
  5 | import random
  6 | import re
  7 | import string
  8 | import pandas as pd
  9 | from websocket import create_connection
 10 | import requests
 11 | import json
 12 | 
 13 | logger = logging.getLogger(__name__)
 14 | 
 15 | 
 16 | class Interval(enum.Enum):
 17 |     min_1 = "1"
 18 |     min_3 = "3"
 19 |     min_5 = "5"
 20 |     min_15 = "15"
 21 |     min_30 = "30"
 22 |     min_45 = "45"
 23 |     hour_1 = "1H"
 24 |     hour_2 = "2H"
 25 |     hour_3 = "3H"
 26 |     hour_4 = "4H"
 27 |     daily = "1D"
 28 |     weekly = "1W"
 29 |     monthly = "1M"
 30 | 
 31 | 
 32 | class TradingViewData:
 33 |     __sign_in_url = 'https://www.tradingview.com/accounts/signin/'
 34 |     __search_url = 'https://symbol-search.tradingview.com/symbol_search/?text={}&hl=1&exchange={}&lang=en&type=&domain=production'
 35 |     __ws_headers = json.dumps({"Origin": "https://data.tradingview.com"})
 36 |     __signin_headers = {'Referer': 'https://www.tradingview.com'}
 37 |     __ws_timeout = 5
 38 | 
 39 |     def __init__(
 40 |         self,
 41 |         username: str = None,
 42 |         password: str = None,
 43 |     ) -> None:
 44 | 
 45 |         self.ws_debug = False
 46 | 
 47 |         self.token = self.__auth(username, password)
 48 | 
 49 |         if self.token is None:
 50 |             self.token = "unauthorized_user_token"
 51 | 
 52 | 
 53 |         self.ws = None
 54 |         self.session = self.__generate_session()
 55 |         self.chart_session = self.__generate_chart_session()
 56 | 
 57 |     def __auth(self, username, password):
 58 | 
 59 |         if (username is None or password is None):
 60 |             token = None
 61 | 
 62 |         else:
 63 |             data = {"username": username,
 64 |                     "password": password,
 65 |                     "remember": "on"}
 66 |             try:
 67 |                 response = requests.post(
 68 |                     url=self.__sign_in_url, data=data, headers=self.__signin_headers)
 69 |                 token = response.json()['user']['auth_token']
 70 |             except Exception as e:
 71 |                 logger.error('error while signin')
 72 |                 token = None
 73 | 
 74 |         return token
 75 | 
 76 |     def __create_connection(self):
 77 |         logging.debug("creating websocket connection")
 78 |         self.ws = create_connection(
 79 |             "wss://data.tradingview.com/socket.io/websocket", headers=self.__ws_headers, timeout=self.__ws_timeout
 80 |         )
 81 | 
 82 |     @staticmethod
 83 |     def __filter_raw_message(text):
 84 |         try:
 85 |             found = re.search('"m":"(.+?)",', text).group(1)
 86 |             found2 = re.search('"p":(.+?"}"])}', text).group(1)
 87 | 
 88 |             return found, found2
 89 |         except AttributeError:
 90 |             logger.error("error in filter_raw_message")
 91 | 
 92 |     @staticmethod
 93 |     def __generate_session():
 94 |         stringLength = 12
 95 |         letters = string.ascii_lowercase
 96 |         random_string = "".join(random.choice(letters)
 97 |                                 for i in range(stringLength))
 98 |         return "qs_" + random_string
 99 | 
100 |     @staticmethod
101 |     def __generate_chart_session():
102 |         stringLength = 12
103 |         letters = string.ascii_lowercase
104 |         random_string = "".join(random.choice(letters)
105 |                                 for i in range(stringLength))
106 |         return "cs_" + random_string
107 | 
108 |     @staticmethod
109 |     def __prepend_header(st):
110 |         return "~m~" + str(len(st)) + "~m~" + st
111 | 
112 |     @staticmethod
113 |     def __construct_message(func, param_list):
114 |         return json.dumps({"m": func, "p": param_list}, separators=(",", ":"))
115 | 
116 |     def __create_message(self, func, paramList):
117 |         return self.__prepend_header(self.__construct_message(func, paramList))
118 | 
119 |     def __send_message(self, func, args):
120 |         m = self.__create_message(func, args)
121 |         if self.ws_debug:
122 |             print(m)
123 |         self.ws.send(m)
124 | 
125 |     @staticmethod
126 |     def __create_df(raw_data, symbol):
127 |         try:
128 |             out = re.search('"s":\[(.+?)\}\]', raw_data).group(1)
129 |             x = out.split(',{"')
130 |             data = list()
131 |             volume_data = True
132 | 
133 |             for xi in x:
134 |                 xi = re.split("\[|:|,|\]", xi)
135 |                 ts = datetime.datetime.fromtimestamp(float(xi[4]))
136 | 
137 |                 row = [ts]
138 | 
139 |                 for i in range(5, 10):
140 | 
141 |                     # skip converting volume data if does not exists
142 |                     if not volume_data and i == 9:
143 |                         row.append(0.0)
144 |                         continue
145 |                     try:
146 |                         row.append(float(xi[i]))
147 | 
148 |                     except ValueError:
149 |                         volume_data = False
150 |                         row.append(0.0)
151 |                         logger.debug('no volume data')
152 | 
153 |                 data.append(row)
154 | 
155 |             data = pd.DataFrame(
156 |                 data, columns=["datetime", "open",
157 |                                "high", "low", "close", "volume"]
158 |             ).set_index("datetime")
159 |             data.insert(0, "symbol", value=symbol)
160 |             return data
161 |         except AttributeError:
162 |             logger.error("no data, please check the exchange and symbol")
163 | 
164 |     @staticmethod
165 |     def __format_symbol(symbol, exchange, contract: int = None):
166 | 
167 |         if ":" in symbol:
168 |             pass
169 |         elif contract is None:
170 |             symbol = f"{exchange}:{symbol}"
171 | 
172 |         elif isinstance(contract, int):
173 |             symbol = f"{exchange}:{symbol}{contract}!"
174 | 
175 |         else:
176 |             raise ValueError("not a valid contract")
177 | 
178 |         return symbol
179 | 
180 |     def get_hist(
181 |         self,
182 |         symbol: str,
183 |         exchange: str = "NSE",
184 |         interval: Interval = Interval.daily,
185 |         n_bars: int = 10,
186 |         fut_contract: int = None,
187 |         extended_session: bool = False,
188 |     ) -> pd.DataFrame:
189 |         symbol = self.__format_symbol(
190 |             symbol=symbol, exchange=exchange, contract=fut_contract
191 |         )
192 | 
193 |         interval = interval.value
194 | 
195 |         self.__create_connection()
196 | 
197 |         self.__send_message("set_auth_token", [self.token])
198 |         self.__send_message("chart_create_session", [self.chart_session, ""])
199 |         self.__send_message("quote_create_session", [self.session])
200 |         self.__send_message(
201 |             "quote_set_fields",
202 |             [
203 |                 self.session,
204 |                 "ch",
205 |                 "chp",
206 |                 "current_session",
207 |                 "description",
208 |                 "local_description",
209 |                 "language",
210 |                 "exchange",
211 |                 "fractional",
212 |                 "is_tradable",
213 |                 "lp",
214 |                 "lp_time",
215 |                 "minmov",
216 |                 "minmove2",
217 |                 "original_name",
218 |                 "pricescale",
219 |                 "pro_name",
220 |                 "short_name",
221 |                 "type",
222 |                 "update_mode",
223 |                 "volume",
224 |                 "currency_code",
225 |                 "rchp",
226 |                 "rtc",
227 |             ],
228 |         )
229 | 
230 |         self.__send_message(
231 |             "quote_add_symbols", [self.session, symbol,
232 |                                   {"flags": ["force_permission"]}]
233 |         )
234 |         self.__send_message("quote_fast_symbols", [self.session, symbol])
235 | 
236 |         self.__send_message(
237 |             "resolve_symbol",
238 |             [
239 |                 self.chart_session,
240 |                 "symbol_1",
241 |                 '={"symbol":"'
242 |                 + symbol
243 |                 + '","adjustment":"splits","session":'
244 |                 + ('"regular"' if not extended_session else '"extended"')
245 |                 + "}",
246 |             ],
247 |         )
248 |         self.__send_message(
249 |             "create_series",
250 |             [self.chart_session, "s1", "s1", "symbol_1", interval, n_bars],
251 |         )
252 |         self.__send_message("switch_timezone", [
253 |                             self.chart_session, "exchange"])
254 | 
255 |         raw_data = ""
256 | 
257 |         logger.debug(f"getting data for {symbol}...")
258 |         while True:
259 |             try:
260 |                 result = self.ws.recv()
261 |                 raw_data = raw_data + result + "\n"
262 |             except Exception as e:
263 |                 logger.error(e)
264 |                 break
265 | 
266 |             if "series_completed" in result:
267 |                 break
268 | 
269 |         return self.__create_df(raw_data, symbol)
270 | 
271 |     def search(self, text: str, exchange: str = ''):
272 |         url = self.__search_url.format(text, exchange)
273 | 
274 |         symbols_list = []
275 |         try:
276 |             resp = requests.get(url)
277 | 
278 |             symbols_list = json.loads(resp.text.replace(
279 |                 '</em>', '').replace('<em>', ''))
280 |         except Exception as e:
281 |             logger.error(e)
282 | 
283 |         return symbols_list
284 | 
285 | 
286 | if __name__ == "__main__":
287 |     logging.basicConfig(level=logging.DEBUG)
288 |     tv = TradingViewData()
289 |     print(tv.get_hist("CRUDEOIL", "MCX", fut_contract=1))
290 |     print(tv.get_hist("NIFTY", "NSE", fut_contract=1))
291 |     print(
292 |         tv.get_hist(
293 |             "EICHERMOT",
294 |             "NSE",
295 |             interval=Interval.hour_1,
296 |             n_bars=500,
297 |             extended_session=False,
298 |         )
299 |     )
300 | 


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