如何判断所有列的最后一个值是否大于n
How to determine if the last value in all columns is greater than n
我有一个模拟器，目前可以获取股票代码并将其潜在价格点绘制在 matplotlib 图表上：
ticker = 'PDN'
style.use('ggplot')
start = dt.datetime(2016, 6, 21)
end = dt.datetime.today()
prices = web.DataReader(ticker, 'yahoo', start, end)['Close']
returns = prices.pct_change()
last_price = prices[-1]
number_of_simulations = 500
num_days = 252
simulation_df = pd.DataFrame()
for x in range(number_of_simulations):
counter = 0
daily_vol = returns.std()
price_series = []
price = last_price * (1 + np.random.normal(0, daily_vol))
price_series.append(price)
for y in range(num_days):
if counter == 251:
break
price = price_series[counter] * (1 + np.random.normal(0, daily_vol))
price_series.append(price)
counter += 1
simulation_df[x] = price_series
fig = plt.figure()
fig.suptitle('Monte Carlo Sim: ' + ticker)
plt.plot(simulation_df)
plt.axhline(y = last_price, color = 'r', linestyle = '-')
plt.xlabel('Day')
plt.ylabel('Price')
plt.show()
如何显示上面出现的模拟量 last_price？
编辑：
我正在尝试根据模拟结束时高于 last_price 的价格数量获得概率。
例如，如果 last_price 是 4 并且我 运行 模拟了 500 次，我想知道 price_series 中超过 4 的价格在完成时的数量模拟。
因此，如果 price_series 中的 250 个价格高于 last_price，我希望它打印 250 prices above last price 等
- 使用
.gt 到 return 一个 Boolean 的值大于 last_price
- 使用
.iloc[-1, :] 获取所有列的最后一行
- 使用
.value_counts得到True和False 的计数
res = simulation_df.iloc[-1, :].gt(last_price).value_counts()
[res]:
False 259
True 241
Name: 251, dtype: int64
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 6))
simulation_df.plot(ax=ax1, xlabel='Day', ylabel='Price', title=f'Monte Carlo Sim: {ticker}', legend=False)
ax1.axhline(y = last_price, color = 'r', linestyle = '-')
res.plot(kind='bar', rot=0, title=f'Simulations with Final Price \nGreater Than {last_price:0.2f}', ax=ax2)
ax2.bar_label(ax.containers[0], label_type='edge')
- 在这里我们可以看到每个模拟的值和
Boolean
simulation_df.iloc[-1:, :]
[out]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
251 40.855915 25.640301 50.693303 44.023614 29.942804 41.920965 39.430077 30.287864 46.901486 40.062016 34.413895 29.344321 38.129262 35.562871 43.878346 36.285428 40.344195 38.597264 45.799781 35.118803 32.83081 35.600166 31.418348 35.756048 34.056794 32.377976 33.453087 26.727197 45.164206 40.158221 49.263849 24.238894 39.240774 42.817753 45.943722 30.942606 40.726633 26.540864 38.005849 25.825681 39.991136 30.09845 47.677947 36.321087 42.869729 36.40385 36.78647 32.70447 44.153209 44.789974 29.362195 42.138189 37.631511 33.924148 30.591147 44.016539 42.98707 25.007736 34.115459 47.054218 53.145284 40.189995 44.731036 40.570369 44.762215 46.457627 39.830706 33.202047 35.433265 42.063941 39.102714 41.064516 25.851312 48.018641 42.969446 46.504681 38.108579 46.28159 35.738122 37.231233 27.252866 35.53253 38.031299 36.126276 40.563356 40.54352 34.04269 39.809459 40.288753 36.269328 37.791311 43.832391 36.447865 26.920694 38.421288 39.042681 31.215561 34.815419 53.112216 45.066397 36.169979 40.886545 42.582102 38.72763 48.431439 31.814367 35.740232 49.684038 34.213517 37.605503 28.643752 34.568333 29.841837 36.203857 36.425014 38.918877 49.890237 28.826627 52.237877 40.793962 38.614708 38.122995 37.598773 40.947161 33.51841 31.823795 33.236405 31.779156 37.249006 46.192083 32.72734 52.978747 43.900892 25.323094 41.483599 50.010067 33.590545 43.348554 40.043626 27.539256 54.355179 35.651842 33.592941 29.638326 44.659418 41.700115 39.957892 38.863896 34.16594 43.371738 46.344883 33.523403 31.234028 40.628669 28.829392 32.199562 48.291964 33.784433 31.7044 48.56218 38.596052 38.025864 32.670343 38.086486 49.829264 35.210974 52.233563 30.255959 41.567659 47.571851 34.832187 33.141691 41.156277 53.633788 34.710377 35.350705 30.378631 31.906148 44.135636 41.575441 45.154519 30.471229 31.179558 41.406688 49.135382 49.786557 50.232863 35.446094 39.563712 50.208991 36.253971 35.040563 36.600378 37.112624 33.848333 46.733337 36.944805 34.331206 34.944138 39.723268 39.659194 37.88296 32.45357 36.154124 29.074326 42.737439 36.284944 40.678968 41.146663 44.093906 33.126406 45.824828 41.37231 34.441153 33.495475 37.618837 29.569943 22.647538 33.444178 60.173991 26.842564 51.167586 40.185917 45.180057 41.342309 31.781825 41.612493 40.117101 39.535119 27.371335 28.184017 33.962494 29.39672 32.117928 39.411364 39.268301 45.013207 45.649696 39.823967 31.780107 33.742755 43.23369 35.877269 29.668803 32.718458 35.220299 41.197486 46.445858 41.99952 53.128441 42.868252 42.73364 41.16791 35.707717 41.756247 33.38655 32.007061 44.104624 48.931682 31.24579 56.493701 27.6561 38.950942 34.029482 34.857725 40.179171 35.110987 31.525206 36.252121 27.403918 35.832982 39.875417 38.057676 40.688748 32.400066 32.730502 34.775845 32.959225 34.084925 36.780572 31.888236 25.841151 42.76338 40.626112 53.101078 33.808038 39.023637 37.868076 29.315238 53.661011 41.102963 45.444108 42.141841 33.043901 31.653144 25.713386 32.363687 31.517243 51.831122 41.678572 30.390078 31.37178 34.106836 35.269581 44.72275 47.347668 35.176777 39.662001 40.351962 41.767312 30.630819 40.547984 39.728632 39.911709 46.810056 44.472718 35.706436 31.800223 46.617443 39.138519 46.172246 39.162627 39.846782 36.249235 36.023444 27.743788 33.152005 39.706168 31.118545 38.35658 44.03662 30.500257 41.087599 54.213662 42.207963 31.046056 24.262964 46.519919 38.608789 42.230698 43.133583 39.824389 44.945088 35.102184 43.778711 38.458147 29.468663 40.146654 35.785838 26.282946 32.012363 45.123286 33.567002 37.308091 31.785537 41.515803 38.514655 34.188416 46.942958 39.491936 35.421798 52.588823 23.719545 29.011848 38.014521 42.558341 41.823499 45.0463 44.76837 37.289019 28.106492 37.934772 31.001834 45.393037 31.439607 36.820191 36.579662 39.91516 32.837042 48.398255 32.27708 42.606464 40.813862 31.247971 48.819663 40.972179 49.028832 43.711345 41.011618 33.65084 33.060376 35.276123 39.268903 34.282186 42.466964 34.532674 47.015342 37.052616 28.151835 42.695932 37.244537 39.238347 31.272434 62.710279 49.628861 35.112308 35.881238 36.516698 33.808105 36.561232 40.935168 35.305627 34.337012 32.214898 51.598472 36.718695 46.546647 32.769513 36.272245 33.216237 42.610709 30.385475 51.857045 45.530473 24.058967 33.723272 44.112363 51.182444 49.708666 47.038852 37.247625 26.486865 51.113427 44.462653 33.523164 36.112279 34.999821 45.791838 43.472233 33.405719 39.206421 25.295449 29.965772 36.976642 32.327498 37.549199 41.718816 42.771669 38.155392 36.436803 35.317047 33.275542 34.469672 41.227772 40.807429 25.825658 47.18341 39.141284 45.039577 49.116326 37.17708 36.892309 32.776463 34.499641 44.079595 26.026153 34.850848 46.515759 33.491574 47.712159 66.767126 28.815782 38.25052 35.89885 43.677117 45.624847 39.02742 39.236109 40.590124 55.893069 39.629833 65.119539 33.180381 31.056857 37.401845 48.850696 32.05405 34.450518 40.810174 39.074104 37.322891 35.385588 35.677069 31.185545 61.429976 40.00021 24.436327 27.219327 39.769648 42.179461
simulation_df.iloc[-1:, :].gt(last_price)
[out]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
251 True False True True False True True False True True False False False False True False True True True False False False False False False False False False True True True False True True True False True False False False True False True False True False False False True True False True False False False True True False False True True True True True True True True False False True True True False True True True False True False False False False False False True True False True True False False True False False True True False False True True False True True True True False False True False False False False False False False True True False True True True False False True False False False False False True False True True False True True False True True False True False False False True True True True False True True False False True False False True False False True True False False False True False True False True True False False True True False False False False True True True False False True True True True False True True False False False False False True False False False True True False False False False True False True True True False True True False False False False False False True False True True True True False True True True False False False False False True True True True True False False True False False False False True True True True True True True False True False False True True False True False True False False True False False False False False True False True False False False False False False False False True True True False True False False True True True True False False False False False True True False False False False True True False True True True False True True True True True False False True True True True True False False False False True False True True False True True True False False True True True True True True False True True False True False False False True False False False True True False True True False True False False False True True True True False False False False True False False False True False True False True True False True True True True True False False False True False True False True False False True False True False True True False False False False False True False False False True False True False False False True False True True False False True True True True False False True True False False False True True False True False False False False False True True False False False False False True True False True True True True False False False False True False False True False True True False False False True True True True True True True True False False False True False False True True False False False False True True False False True True
我有一个模拟器，目前可以获取股票代码并将其潜在价格点绘制在 matplotlib 图表上：
ticker = 'PDN'
style.use('ggplot')
start = dt.datetime(2016, 6, 21)
end = dt.datetime.today()
prices = web.DataReader(ticker, 'yahoo', start, end)['Close']
returns = prices.pct_change()
last_price = prices[-1]
number_of_simulations = 500
num_days = 252
simulation_df = pd.DataFrame()
for x in range(number_of_simulations):
counter = 0
daily_vol = returns.std()
price_series = []
price = last_price * (1 + np.random.normal(0, daily_vol))
price_series.append(price)
for y in range(num_days):
if counter == 251:
break
price = price_series[counter] * (1 + np.random.normal(0, daily_vol))
price_series.append(price)
counter += 1
simulation_df[x] = price_series
fig = plt.figure()
fig.suptitle('Monte Carlo Sim: ' + ticker)
plt.plot(simulation_df)
plt.axhline(y = last_price, color = 'r', linestyle = '-')
plt.xlabel('Day')
plt.ylabel('Price')
plt.show()
如何显示上面出现的模拟量 last_price？
编辑：
我正在尝试根据模拟结束时高于 last_price 的价格数量获得概率。
例如，如果 last_price 是 4 并且我 运行 模拟了 500 次，我想知道 price_series 中超过 4 的价格在完成时的数量模拟。
因此，如果 price_series 中的 250 个价格高于 last_price，我希望它打印 250 prices above last price 等
- 使用
.gt到 return 一个Boolean的值大于last_price - 使用
.iloc[-1, :]获取所有列的最后一行
- 使用
.value_counts得到True和False的计数
res = simulation_df.iloc[-1, :].gt(last_price).value_counts()
[res]:
False 259
True 241
Name: 251, dtype: int64
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 6))
simulation_df.plot(ax=ax1, xlabel='Day', ylabel='Price', title=f'Monte Carlo Sim: {ticker}', legend=False)
ax1.axhline(y = last_price, color = 'r', linestyle = '-')
res.plot(kind='bar', rot=0, title=f'Simulations with Final Price \nGreater Than {last_price:0.2f}', ax=ax2)
ax2.bar_label(ax.containers[0], label_type='edge')
- 在这里我们可以看到每个模拟的值和
Boolean
simulation_df.iloc[-1:, :]
[out]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
251 40.855915 25.640301 50.693303 44.023614 29.942804 41.920965 39.430077 30.287864 46.901486 40.062016 34.413895 29.344321 38.129262 35.562871 43.878346 36.285428 40.344195 38.597264 45.799781 35.118803 32.83081 35.600166 31.418348 35.756048 34.056794 32.377976 33.453087 26.727197 45.164206 40.158221 49.263849 24.238894 39.240774 42.817753 45.943722 30.942606 40.726633 26.540864 38.005849 25.825681 39.991136 30.09845 47.677947 36.321087 42.869729 36.40385 36.78647 32.70447 44.153209 44.789974 29.362195 42.138189 37.631511 33.924148 30.591147 44.016539 42.98707 25.007736 34.115459 47.054218 53.145284 40.189995 44.731036 40.570369 44.762215 46.457627 39.830706 33.202047 35.433265 42.063941 39.102714 41.064516 25.851312 48.018641 42.969446 46.504681 38.108579 46.28159 35.738122 37.231233 27.252866 35.53253 38.031299 36.126276 40.563356 40.54352 34.04269 39.809459 40.288753 36.269328 37.791311 43.832391 36.447865 26.920694 38.421288 39.042681 31.215561 34.815419 53.112216 45.066397 36.169979 40.886545 42.582102 38.72763 48.431439 31.814367 35.740232 49.684038 34.213517 37.605503 28.643752 34.568333 29.841837 36.203857 36.425014 38.918877 49.890237 28.826627 52.237877 40.793962 38.614708 38.122995 37.598773 40.947161 33.51841 31.823795 33.236405 31.779156 37.249006 46.192083 32.72734 52.978747 43.900892 25.323094 41.483599 50.010067 33.590545 43.348554 40.043626 27.539256 54.355179 35.651842 33.592941 29.638326 44.659418 41.700115 39.957892 38.863896 34.16594 43.371738 46.344883 33.523403 31.234028 40.628669 28.829392 32.199562 48.291964 33.784433 31.7044 48.56218 38.596052 38.025864 32.670343 38.086486 49.829264 35.210974 52.233563 30.255959 41.567659 47.571851 34.832187 33.141691 41.156277 53.633788 34.710377 35.350705 30.378631 31.906148 44.135636 41.575441 45.154519 30.471229 31.179558 41.406688 49.135382 49.786557 50.232863 35.446094 39.563712 50.208991 36.253971 35.040563 36.600378 37.112624 33.848333 46.733337 36.944805 34.331206 34.944138 39.723268 39.659194 37.88296 32.45357 36.154124 29.074326 42.737439 36.284944 40.678968 41.146663 44.093906 33.126406 45.824828 41.37231 34.441153 33.495475 37.618837 29.569943 22.647538 33.444178 60.173991 26.842564 51.167586 40.185917 45.180057 41.342309 31.781825 41.612493 40.117101 39.535119 27.371335 28.184017 33.962494 29.39672 32.117928 39.411364 39.268301 45.013207 45.649696 39.823967 31.780107 33.742755 43.23369 35.877269 29.668803 32.718458 35.220299 41.197486 46.445858 41.99952 53.128441 42.868252 42.73364 41.16791 35.707717 41.756247 33.38655 32.007061 44.104624 48.931682 31.24579 56.493701 27.6561 38.950942 34.029482 34.857725 40.179171 35.110987 31.525206 36.252121 27.403918 35.832982 39.875417 38.057676 40.688748 32.400066 32.730502 34.775845 32.959225 34.084925 36.780572 31.888236 25.841151 42.76338 40.626112 53.101078 33.808038 39.023637 37.868076 29.315238 53.661011 41.102963 45.444108 42.141841 33.043901 31.653144 25.713386 32.363687 31.517243 51.831122 41.678572 30.390078 31.37178 34.106836 35.269581 44.72275 47.347668 35.176777 39.662001 40.351962 41.767312 30.630819 40.547984 39.728632 39.911709 46.810056 44.472718 35.706436 31.800223 46.617443 39.138519 46.172246 39.162627 39.846782 36.249235 36.023444 27.743788 33.152005 39.706168 31.118545 38.35658 44.03662 30.500257 41.087599 54.213662 42.207963 31.046056 24.262964 46.519919 38.608789 42.230698 43.133583 39.824389 44.945088 35.102184 43.778711 38.458147 29.468663 40.146654 35.785838 26.282946 32.012363 45.123286 33.567002 37.308091 31.785537 41.515803 38.514655 34.188416 46.942958 39.491936 35.421798 52.588823 23.719545 29.011848 38.014521 42.558341 41.823499 45.0463 44.76837 37.289019 28.106492 37.934772 31.001834 45.393037 31.439607 36.820191 36.579662 39.91516 32.837042 48.398255 32.27708 42.606464 40.813862 31.247971 48.819663 40.972179 49.028832 43.711345 41.011618 33.65084 33.060376 35.276123 39.268903 34.282186 42.466964 34.532674 47.015342 37.052616 28.151835 42.695932 37.244537 39.238347 31.272434 62.710279 49.628861 35.112308 35.881238 36.516698 33.808105 36.561232 40.935168 35.305627 34.337012 32.214898 51.598472 36.718695 46.546647 32.769513 36.272245 33.216237 42.610709 30.385475 51.857045 45.530473 24.058967 33.723272 44.112363 51.182444 49.708666 47.038852 37.247625 26.486865 51.113427 44.462653 33.523164 36.112279 34.999821 45.791838 43.472233 33.405719 39.206421 25.295449 29.965772 36.976642 32.327498 37.549199 41.718816 42.771669 38.155392 36.436803 35.317047 33.275542 34.469672 41.227772 40.807429 25.825658 47.18341 39.141284 45.039577 49.116326 37.17708 36.892309 32.776463 34.499641 44.079595 26.026153 34.850848 46.515759 33.491574 47.712159 66.767126 28.815782 38.25052 35.89885 43.677117 45.624847 39.02742 39.236109 40.590124 55.893069 39.629833 65.119539 33.180381 31.056857 37.401845 48.850696 32.05405 34.450518 40.810174 39.074104 37.322891 35.385588 35.677069 31.185545 61.429976 40.00021 24.436327 27.219327 39.769648 42.179461
simulation_df.iloc[-1:, :].gt(last_price)
[out]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
251 True False True True False True True False True True False False False False True False True True True False False False False False False False False False True True True False True True True False True False False False True False True False True False False False True True False True False False False True True False False True True True True True True True True False False True True True False True True True False True False False False False False False True True False True True False False True False False True True False False True True False True True True True False False True False False False False False False False True True False True True True False False True False False False False False True False True True False True True False True True False True False False False True True True True False True True False False True False False True False False True True False False False True False True False True True False False True True False False False False True True True False False True True True True False True True False False False False False True False False False True True False False False False True False True True True False True True False False False False False False True False True True True True False True True True False False False False False True True True True True False False True False False False False True True True True True True True False True False False True True False True False True False False True False False False False False True False True False False False False False False False False True True True False True False False True True True True False False False False False True True False False False False True True False True True True False True True True True True False False True True True True True False False False False True False True True False True True True False False True True True True True True False True True False True False False False True False False False True True False True True False True False False False True True True True False False False False True False False False True False True False True True False True True True True True False False False True False True False True False False True False True False True True False False False False False True False False False True False True False False False True False True True False False True True True True False False True True False False False True True False True False False False False False True True False False False False False True True False True True True True False False False False True False False True False True True False False False True True True True True True True True False False False True False False True True False False False False True True False False True True