Numba_Crossover_All.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "unusual-ground",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "hawaiian-principle",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:59:37.260982Z",
     "start_time": "2021-04-13T07:59:37.241657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132090, 102)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numba import njit, prange\n",
    "\n",
    "np.random.seed(42)\n",
    "N,M,K = np.random.choice(np.arange(1000),3)\n",
    "arr2 = np.array(N*[M*[0]+K*[1]]).ravel() - .5\n",
    "arr1 = np.zeros(len(arr2))\n",
    "len(arr2), N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "invisible-dayton",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:59:43.558800Z",
     "start_time": "2021-04-13T07:59:40.108879Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42 ms ± 522 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "def crossover(signal, price):\n",
    "    L = price.shape[0]\n",
    "    out = np.empty(L, dtype=np.int64)\n",
    "    out[0] = 0\n",
    "    for i in range(1,L):\n",
    "        if signal[i-1] < price[i-1] and signal[i] > price[i]:\n",
    "            out[i] =1\n",
    "        else:\n",
    "            out[i] =0\n",
    "    return out  \n",
    "\n",
    "%timeit crossover(arr2,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "documentary-measurement",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:59:52.760983Z",
     "start_time": "2021-04-13T07:59:52.704089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crossover(arr2,arr1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "exact-render",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:54:38.240420Z",
     "start_time": "2021-04-13T07:54:16.521659Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n",
      "203 µs ± 8.95 µs per loop (mean ± std. dev. of 1000 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "@njit\n",
    "def nb_crossover(signal, price):\n",
    "    L = price.shape[0]\n",
    "    out = np.empty(L, dtype=np.int64)\n",
    "    out[0] = 0\n",
    "    for i in range(1, L):\n",
    "        if signal[i - 1] < price[i - 1] and signal[i] > price[i]:\n",
    "            out[i] = 1\n",
    "        else:\n",
    "            out[i] = 0\n",
    "    return out\n",
    "\n",
    "\n",
    "print(nb_crossover(arr2, arr1).sum())\n",
    "%timeit -n100 -r1000 nb_crossover(arr2,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "contained-sociology",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:54:43.604663Z",
     "start_time": "2021-04-13T07:54:38.241877Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n",
      "The slowest run took 22.15 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "47.1 µs ± 43 µs per loop (mean ± std. dev. of 1000 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "@njit(parallel=True)\n",
    "def nbp_crossover(signal, price):\n",
    "    L = price.shape[0]\n",
    "    out = np.empty(L, dtype=np.int32)\n",
    "    out[0] = 0\n",
    "    for i in prange(1, L):\n",
    "        if signal[i - 1] < price[i - 1] and signal[i] > price[i]:\n",
    "            out[i] = 1\n",
    "        else:\n",
    "            out[i] = 0\n",
    "    return out\n",
    "\n",
    "print(nbp_crossover(arr2,arr1).sum())\n",
    "%timeit -n100 -r1000 nbp_crossover(arr2,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "casual-scenario",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:57:19.913822Z",
     "start_time": "2021-04-13T07:56:19.299984Z"
    }
   },
   "outputs": [],
   "source": [
    "for _ in range(1000):\n",
    "    N,M,K = np.random.choice(np.arange(1,1000),3)\n",
    "    arr2 = np.array(N*[M*[0]+K*[1]]).ravel() - .5\n",
    "    arr1 = np.zeros(len(arr2))\n",
    "    assert np.allclose(nbp_crossover(arr2,arr1), nb_crossover(arr2,arr1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "earlier-jefferson",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:44.887940Z",
     "start_time": "2021-04-12T21:41:43.285Z"
    }
   },
   "outputs": [],
   "source": [
    "for _ in range(1000):\n",
    "    N,M,K = np.random.choice(np.arange(1,1000),3)\n",
    "    arr2 = np.array(N*[M*[0]+K*[1]]).ravel() - .5\n",
    "    arr1 = np.zeros(len(arr2))\n",
    "    assert N == nb_crossover(arr2,arr1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unique-cocktail",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:44.888466Z",
     "start_time": "2021-04-12T21:41:43.287Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "acc = 0\n",
    "for _ in range(1000):\n",
    "    N,M,K = np.random.choice(np.arange(1,1000),3)\n",
    "    arr2 = np.array(N*[M*[0]+K*[1]]).ravel() - .5\n",
    "    arr1 = np.zeros(len(arr2))\n",
    "    start = time.time()\n",
    "    out = nb_crossover(arr2,arr1)\n",
    "    finish = time.time()\n",
    "    acc +=  finish - start\n",
    "    assert N == out.sum()\n",
    "\n",
    "print(acc)\n",
    "# 12.3147554397583"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nominated-oriental",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:44.889014Z",
     "start_time": "2021-04-12T21:41:43.290Z"
    }
   },
   "outputs": [],
   "source": [
    "acc = 0\n",
    "for _ in range(1000):\n",
    "    N,M,K = np.random.choice(np.arange(1,1000),3)\n",
    "    arr2 = np.array(N*[M*[0]+K*[1]]).ravel() - .5\n",
    "    arr1 = np.zeros(len(arr2))\n",
    "    start = time.time()\n",
    "    out = nbp_crossover(arr2,arr1)\n",
    "    finish = time.time()\n",
    "    acc +=  finish - start\n",
    "    assert N == out.sum()\n",
    "\n",
    "print(acc)\n",
    "# 7.125715970993042"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "psychological-accountability",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:44.889614Z",
     "start_time": "2021-04-12T21:41:43.292Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%timeit -n100 -r100\n",
    "N,M,K = np.random.choice(np.arange(1,1000),3)\n",
    "arr2 = np.array(N*[M*[0]+K*[1]]).ravel() - .5\n",
    "arr1 = np.zeros(len(arr2))\n",
    "%timeit nb_crossover(arr2,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "radio-sixth",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:44.890103Z",
     "start_time": "2021-04-12T21:41:43.295Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%timeit -n100 -r100\n",
    "N,M,K = np.random.choice(np.arange(1,1000),3)\n",
    "arr2 = np.array(N*[M*[0]+K*[1]]).ravel() - .5\n",
    "arr1 = np.zeros(len(arr2))\n",
    "%timeit nbp_crossover(arr2,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dangerous-mexico",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:44.890762Z",
     "start_time": "2021-04-12T21:41:43.298Z"
    }
   },
   "outputs": [],
   "source": [
    "from talib import BBANDS\n",
    "np.random.seed(42)\n",
    "ret = np.random.randn(1000)/100\n",
    "price = np.cumprod(1+ ret)\n",
    "upper, middle, lower = BBANDS(price, 30, 1)\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(price, c=\"black\")\n",
    "plt.plot(upper, c=\"red\")\n",
    "plt.plot(lower, c=\"green\")\n",
    "plt.plot(middle, c=\"blue\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fresh-freeze",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:52.896948Z",
     "start_time": "2021-04-12T21:41:50.835132Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.3 µs ± 7.12 µs per loop (mean ± std. dev. of 100 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "from talib import BBANDS\n",
    "from numba import njit, prange\n",
    "\n",
    "np.random.seed(42)\n",
    "ret = np.random.randn(100000) / 100\n",
    "price = np.cumprod(1 + ret)\n",
    "\n",
    "lb = 100\n",
    "std = 1\n",
    "upper, middle, lower = BBANDS(price, lb, std)\n",
    "price = price[lb:]\n",
    "upper = upper[lb:]\n",
    "middle = middle[lb:]\n",
    "lower = lower[lb:]\n",
    "\n",
    "\n",
    "@njit(parallel=True)\n",
    "def gen_signal(price, upper, middle, lower):\n",
    "\n",
    "    L = price.shape[0]\n",
    "    signal = np.empty(L, dtype=np.int32)\n",
    "\n",
    "    lower_crossup = 0\n",
    "    upper_crossdown = 0\n",
    "\n",
    "    signal[0] = 0\n",
    "    for i in prange(1, L):\n",
    "\n",
    "        # lower crossup\n",
    "        if lower[i - 1] > price[i - 1] and lower[i] < price[i]:\n",
    "            lower_crossup = 1\n",
    "\n",
    "        # upper crossdown\n",
    "        if upper[i - 1] < price[i - 1] and upper[i] > price[i]:\n",
    "            upper_crossdown = 1\n",
    "\n",
    "        # enter long\n",
    "        if lower_crossup:\n",
    "            signal[i] = 1\n",
    "            if (middle[i - 1] > price[i - 1] and middle[i] < price[i]) or (\n",
    "                lower[i - 1] < price[i - 1] and lower[i] > price[i]\n",
    "            ):\n",
    "                lower_crossup = 0\n",
    "                signal[i] = 0\n",
    "\n",
    "        elif upper_crossdown:\n",
    "            signal[i] = -1\n",
    "            if (price[i - 1] > middle[i - 1] and price[i] < middle[i]) or (\n",
    "                price[i - 1] < upper[i - 1] and price[i] > upper[i]\n",
    "            ):\n",
    "                signal[i] = 0\n",
    "                upper_crossdown = 0\n",
    "        else:\n",
    "            signal[i] = 0\n",
    "\n",
    "    return signal\n",
    "\n",
    "\n",
    "out=gen_signal(price, upper, middle, lower)\n",
    "%timeit -n100 -r100 gen_signal(price, upper, middle, lower)\n",
    "\n",
    "# 88.4 µs ± 10.2 µs per loop (mean ± std. dev. of 100 runs, 100 loops each)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ef02970",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:53.811232Z",
     "start_time": "2021-04-12T21:41:52.898490Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 11.11 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "90.4 µs ± 79.9 µs per loop (mean ± std. dev. of 100 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "out = gen_signal(price, upper, middle, lower)\n",
    "%timeit -n100 -r100 gen_signal(price, upper, middle, lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "complicated-median",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:54.014042Z",
     "start_time": "2021-04-12T21:41:53.813018Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out[:300]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e1b9a63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:57.879038Z",
     "start_time": "2021-04-12T21:41:54.015124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350 µs ± 6.42 µs per loop (mean ± std. dev. of 100 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "@njit\n",
    "def crossup(x, y, i):\n",
    "    if x[i - 1] < y[i - 1] and x[i] > y[i]:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "\n",
    "@njit\n",
    "def crossdown(x, y, i):\n",
    "    if x[i - 1] > y[i - 1] and x[i] < y[i]:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "\n",
    "@njit\n",
    "def gen_signal2(price, upper, middle, lower):\n",
    "\n",
    "    L = price.shape[0]\n",
    "    out = np.empty(L, dtype=np.int64)\n",
    "    out[0] = 0\n",
    "    i = 1\n",
    "\n",
    "    while i < L:\n",
    "                \n",
    "        if crossup(price, lower, i):\n",
    "            while not (crossup(price, middle, i) or crossdown(price, lower, i)) and i < L:\n",
    "                out[i] = 1\n",
    "                i += 1\n",
    "\n",
    "        elif crossdown(price, upper, i):\n",
    "            while not (crossdown(price, middle, i) or crossup(price, upper, i)) and i < L:\n",
    "                out[i] = -1\n",
    "                i += 1\n",
    "\n",
    "        \n",
    "        out[i] = 0      \n",
    "        i +=1\n",
    "          \n",
    "    return out\n",
    "\n",
    "out2 = gen_signal2(price, upper, middle, lower)\n",
    "%timeit -n100 -r100 gen_signal2(price, upper, middle, lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4dff653",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:14:55.047169Z",
     "start_time": "2021-04-12T21:14:55.043218Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0, -1, -1, -1, -1,  0,  0,  0,  0,\n",
       "        0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out2[:35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6c1674d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:58.001927Z",
     "start_time": "2021-04-12T21:41:57.880118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 192,
       "width": 886
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 3))\n",
    "plt.plot(price[8320:8330], c=\"black\")\n",
    "plt.plot(upper[8320:8330], c=\"red\")\n",
    "plt.plot(lower[8320:8330], c=\"green\")\n",
    "plt.plot(middle[8320:8330], c=\"blue\")\n",
    "plt.xticks(range(10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a1817c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:47:05.527504Z",
     "start_time": "2021-04-12T21:47:04.131071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 4.15 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "90 µs ± 40.7 µs per loop (mean ± std. dev. of 100 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "@njit(parallel=True)\n",
    "def gen_signal3(price, upper, middle, lower):\n",
    "\n",
    "    L = price.shape[0]\n",
    "    out = np.empty(L, dtype=np.int64)\n",
    "    out[0] = 0\n",
    "    lower_crossup = 0\n",
    "    upper_crossdown = 0\n",
    "    \n",
    "    for i in prange(1,L):\n",
    "\n",
    "        if crossup(price, lower, i):\n",
    "            crossup_lower = 1\n",
    "            \n",
    "        if crossdown(price, upper, i):\n",
    "            crossdown_upper = 1\n",
    "           \n",
    "        if crossup_lower:\n",
    "            out[i] = 1 \n",
    "            if crossup(price, middle, i) or crossdown(price, lower, i):\n",
    "                out[i] = 0\n",
    "                crossup_lower = 0\n",
    "            \n",
    "        elif crossdown_upper:\n",
    "            out[i] = - 1\n",
    "            if crossdown(price, middle, i) or crossup(price, upper, i):\n",
    "                out[i] = 0\n",
    "                crossdown_upper = 0\n",
    "                \n",
    "        else:\n",
    "            out[i] = 0\n",
    "          \n",
    "    return out\n",
    "\n",
    "out3 = gen_signal3(price, upper, middle, lower)\n",
    "%timeit -n100 -r100 gen_signal3(price, upper, middle, lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8fd4cce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:48:04.229714Z",
     "start_time": "2021-04-12T21:47:59.530941Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "442 µs ± 8.27 µs per loop (mean ± std. dev. of 100 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "@njit\n",
    "def gen_signal4(price, upper, middle, lower):\n",
    "\n",
    "    L = price.shape[0]\n",
    "    out = np.empty(L, dtype=np.int64)\n",
    "    out[0] = 0\n",
    "    lower_crossup = 0\n",
    "    upper_crossdown = 0\n",
    "    \n",
    "    for i in range(1,L):\n",
    "\n",
    "        if crossup(price, lower, i):\n",
    "            crossup_lower = 1\n",
    "            \n",
    "        if crossdown(price, upper, i):\n",
    "            crossdown_upper = 1\n",
    "           \n",
    "        if crossup_lower:\n",
    "            out[i] = 1 \n",
    "            if crossup(price, middle, i) or crossdown(price, lower, i):\n",
    "                out[i] = 0\n",
    "                crossup_lower = 0\n",
    "            \n",
    "        elif crossdown_upper:\n",
    "            out[i] = - 1\n",
    "            if crossdown(price, middle, i) or crossup(price, upper, i):\n",
    "                out[i] = 0\n",
    "                crossdown_upper = 0\n",
    "                \n",
    "        else:\n",
    "            out[i] = 0\n",
    "          \n",
    "    return out\n",
    "\n",
    "out4 = gen_signal4(price, upper, middle, lower)\n",
    "%timeit -n100 -r100 gen_signal4(price, upper, middle, lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b13f186",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:48:38.142591Z",
     "start_time": "2021-04-12T21:48:38.134844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(out2, out3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f578a50d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:46:14.167786Z",
     "start_time": "2021-04-12T21:46:14.159990Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(out, out3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dda7d205",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:48:32.819291Z",
     "start_time": "2021-04-12T21:48:32.811159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(out4, out3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c94aa6b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:49:00.466048Z",
     "start_time": "2021-04-12T21:49:00.458135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(out4, out2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3109824",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:59.537159Z",
     "start_time": "2021-04-12T21:41:59.430944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8326"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(out != out2).argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f546068f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:59.645819Z",
     "start_time": "2021-04-12T21:41:59.538068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0, -1, -1, -1, -1,  0,  0,  0,  0], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out[8320:8330]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "683f7088",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:59.746030Z",
     "start_time": "2021-04-12T21:41:59.647513Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0, -1, -1, -1, -1, -1, -1,  0,  0])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out2[8320:8330]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1fd4fb4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:41:59.871374Z",
     "start_time": "2021-04-12T21:41:59.747882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(price > upper) | (price < middle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7a86db94",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-12T21:42:00.000729Z",
     "start_time": "2021-04-12T21:41:59.873360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>upper</th>\n",
       "      <th>middle</th>\n",
       "      <th>crossup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8320</th>\n",
       "      <td>0.501111</td>\n",
       "      <td>0.505457</td>\n",
       "      <td>0.495143</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>0.505840</td>\n",
       "      <td>0.505383</td>\n",
       "      <td>0.495587</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8322</th>\n",
       "      <td>0.502814</td>\n",
       "      <td>0.505389</td>\n",
       "      <td>0.495917</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8323</th>\n",
       "      <td>0.498446</td>\n",
       "      <td>0.505419</td>\n",
       "      <td>0.496127</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8324</th>\n",
       "      <td>0.501504</td>\n",
       "      <td>0.505486</td>\n",
       "      <td>0.496364</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8325</th>\n",
       "      <td>0.501341</td>\n",
       "      <td>0.505592</td>\n",
       "      <td>0.496520</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8326</th>\n",
       "      <td>0.505504</td>\n",
       "      <td>0.505766</td>\n",
       "      <td>0.496726</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8327</th>\n",
       "      <td>0.500925</td>\n",
       "      <td>0.505852</td>\n",
       "      <td>0.496897</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8328</th>\n",
       "      <td>0.492012</td>\n",
       "      <td>0.505852</td>\n",
       "      <td>0.496977</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8329</th>\n",
       "      <td>0.489389</td>\n",
       "      <td>0.505841</td>\n",
       "      <td>0.497036</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         price     upper    middle  crossup\n",
       "8320  0.501111  0.505457  0.495143    False\n",
       "8321  0.505840  0.505383  0.495587     True\n",
       "8322  0.502814  0.505389  0.495917    False\n",
       "8323  0.498446  0.505419  0.496127    False\n",
       "8324  0.501504  0.505486  0.496364    False\n",
       "8325  0.501341  0.505592  0.496520    False\n",
       "8326  0.505504  0.505766  0.496726    False\n",
       "8327  0.500925  0.505852  0.496897    False\n",
       "8328  0.492012  0.505852  0.496977     True\n",
       "8329  0.489389  0.505841  0.497036     True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\"price\": price, \"upper\": upper, \"middle\": middle, \"crossup\": (price > upper)|(price < middle)}\n",
    ")[8320:8330]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}