diff --git a/notebooks/00.04_mjc_example_realdata_magpie.ipynb b/notebooks/00.04_mjc_example_realdata_magpie.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ce655bc0",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-07-16T07:26:15.161022Z",
+ "start_time": "2022-07-16T07:26:15.142775Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning no robinhood utils.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import torch\n",
+ "import pandas as pd\n",
+ "import gpytorch\n",
+ "import matplotlib.style as style\n",
+ "from matplotlib.lines import Line2D\n",
+ "\n",
+ "palette = [\"red\", \"blue\", \"green\", \"orange\", \"purple\", \"#AF7595\", \"#E6480F\", \"#FA9500\"]\n",
+ "plt.style.use('ggplot')\n",
+ "plt.rcParams['figure.figsize'] = (14.0, 6)\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "from loguru import logger\n",
+ "logger.remove()\n",
+ "logger.add(os.sys.stdout, colorize=True, format=\"{time} | {message}\")\n",
+ "\n",
+ "from voltron.likelihoods import VolatilityGaussianLikelihood\n",
+ "from voltron.models import SingleTaskVariationalGP\n",
+ "from voltron.kernels import BMKernel, VolatilityKernel\n",
+ "from voltron.train_utils import TrainVolModel, TrainVoltMagpieModel, LearnGPCV, TrainDataModel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e4fb0fb7-c533-4474-8b74-ef4f7c38da76",
+ "metadata": {},
+ "source": [
+ "## Generate Some Synthetic SDE Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "3b74d19b",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-07-16T07:23:41.627613Z",
+ "start_time": "2022-07-16T07:23:41.610314Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "np.random.seed(2019)\n",
+ "torch.random.manual_seed(2019)\n",
+ "\n",
+ "# F0 = 10 ## init price\n",
+ "# V0 = 0.2 ## init price\n",
+ "# mu = 0.05 ## rate of return\n",
+ "\n",
+ "# alpha = 1.25\n",
+ "# beta = 0.9\n",
+ "# rho = -0.2\n",
+ "\n",
+ "T = 1 ## Time of Simulation\n",
+ "steps = 400 ## steps per time. Changing this seems to mess everything up\n",
+ "# dt = T/(steps) ## delta t\n",
+ "dt = 1./252\n",
+ "\n",
+ "# dW = np.random.normal(0, np.sqrt(dt), steps*T)\n",
+ "# dZ = rho * dW + np.sqrt(1 - rho **2) * np.random.normal(0, np.sqrt(dt), steps*T)\n",
+ "\n",
+ "\n",
+ "# train_x = time = torch.linspace(0, T, steps-1) + dt\n",
+ "test_x = torch.linspace(T + dt, 1.5*T, int(.5*steps)-1) + dt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0389e233",
+ "metadata": {},
+ "source": [
+ "# Replace with real data\n",
+ "\n",
+ "It should be \n",
+ "- steps = 400\n",
+ "- full_x: a float tensor, of steps from 0 to 1\n",
+ "- full_y: the log returns\n",
+ "\n",
+ "and note that\n",
+ "- F was price\n",
+ "- V was vol"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ede9e3fd",
+ "metadata": {},
+ "outputs": [
+ {
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