trade-for-real.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trade\n",
    "This notebook trades with the hyperparameters selected in the previous over the full set of trading data.\n",
    "\n",
    "Note that in reality the byperparameter tunning could be repeated every trading day but we will ignore that complication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import boto3\n",
    "from IPython.display import Image\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.estimator import Estimator\n",
    "from sagemaker.session import Session\n",
    "import seaborn as sns; sns.set()\n",
    "import shutil\n",
    "from time import sleep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set for local parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_instances = 1\n",
    "#image_type = 'cpu'\n",
    "#instance_type = 'ml.m5.large'\n",
    "\n",
    "image_type = 'gpu'\n",
    "instance_type = 'ml.g4dn.xlarge'\n",
    "\n",
    "hyperparameters = {'prices_name': 'prices-2019.csv',\n",
    "                   'signals_name': 'signals-2019.csv',\n",
    "                   'start_day': 2418,\n",
    "                   'days_per_epoch': 40,\n",
    "                   'fc1': 16,\n",
    "                   'fc2': 0,\n",
    "                   'lr_actor': 0.00092,\n",
    "                   'lr_critic': 0.00431}\n",
    "\n",
    "train_use_spot_instances = True\n",
    "train_max_run = 14400\n",
    "train_max_wait = 14400 if train_use_spot_instances else None\n",
    "\n",
    "sagemaker_session = Session()\n",
    "bucket_name = sagemaker_session.default_bucket()\n",
    "role = get_execution_role()\n",
    "account = boto3.client('sts').get_caller_identity()['Account']\n",
    "region = boto3.Session().region_name\n",
    "image_name = '{}.dkr.ecr.{}.amazonaws.com/portfolio-optimization-{}:latest'.format(account, region, image_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit the job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-29 04:10:13 Starting - Starting the training job...\n",
      "2020-07-29 04:10:15 Starting - Launching requested ML instances.........\n",
      "2020-07-29 04:11:57 Starting - Preparing the instances for training......\n",
      "2020-07-29 04:12:55 Downloading - Downloading input data\n",
      "2020-07-29 04:12:55 Training - Downloading the training image........\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[34mbash: no job control in this shell\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:26,444 sagemaker-containers INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:26,445 sagemaker-containers INFO     Failed to parse hyperparameter prices_name value real-prices.csv to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:26,464 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\n",
      "2020-07-29 04:14:26 Training - Training image download completed. Training in progress.\u001b[34m2020-07-29 04:14:32,694 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:32,695 sagemaker-containers INFO     Module train does not provide a setup.py. \u001b[0m\n",
      "\u001b[34mGenerating setup.py\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:32,695 sagemaker-containers INFO     Generating setup.cfg\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:32,696 sagemaker-containers INFO     Generating MANIFEST.in\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:32,696 sagemaker-containers INFO     Installing module with the following command:\u001b[0m\n",
      "\u001b[34m/usr/bin/python -m pip install . \u001b[0m\n",
      "\u001b[34mProcessing /opt/ml/code\u001b[0m\n",
      "\u001b[34mBuilding wheels for collected packages: train\n",
      "  Building wheel for train (setup.py): started\n",
      "  Building wheel for train (setup.py): finished with status 'done'\n",
      "  Created wheel for train: filename=train-1.0.0-py2.py3-none-any.whl size=1398600 sha256=6ced47c8300fe5c9902f7f6ded508d5aecb8a2f8b6dc470de6df3714b4a1473d\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-jghxja7c/wheels/95/c1/85/65aaf48b35aba88c6e896d2fd04a4b69f1cee0d81ea32993ca\u001b[0m\n",
      "\u001b[34mSuccessfully built train\u001b[0m\n",
      "\u001b[34mInstalling collected packages: train\u001b[0m\n",
      "\u001b[34mSuccessfully installed train-1.0.0\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:34,367 sagemaker-containers INFO     Failed to parse hyperparameter prices_name value real-prices.csv to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2020-07-29 04:14:34,388 sagemaker-containers INFO     Invoking user script\n",
      "\u001b[0m\n",
      "\u001b[34mTraining Env:\n",
      "\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {},\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"lr_critic\": 0.00431,\n",
      "        \"fc2\": 0,\n",
      "        \"days_per_epoch\": 40,\n",
      "        \"fc1\": 16,\n",
      "        \"start_day\": 2418,\n",
      "        \"prices_name\": \"real-prices.csv\",\n",
      "        \"lr_actor\": 0.00092\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {},\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"portfolio-optimization-gpu-2020-07-29-04-10-13-452\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"/opt/ml/code\",\n",
      "    \"module_name\": \"train\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 1,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"train.py\"\u001b[0m\n",
      "\u001b[34m}\n",
      "\u001b[0m\n",
      "\u001b[34mEnvironment variables:\n",
      "\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"days_per_epoch\":40,\"fc1\":16,\"fc2\":0,\"lr_actor\":0.00092,\"lr_critic\":0.00431,\"prices_name\":\"real-prices.csv\",\"start_day\":2418}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=train.py\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=train\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=4\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=1\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=/opt/ml/code\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"days_per_epoch\":40,\"fc1\":16,\"fc2\":0,\"lr_actor\":0.00092,\"lr_critic\":0.00431,\"prices_name\":\"real-prices.csv\",\"start_day\":2418},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"portfolio-optimization-gpu-2020-07-29-04-10-13-452\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"/opt/ml/code\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":1,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--days_per_epoch\",\"40\",\"--fc1\",\"16\",\"--fc2\",\"0\",\"--lr_actor\",\"0.00092\",\"--lr_critic\",\"0.00431\",\"--prices_name\",\"real-prices.csv\",\"--start_day\",\"2418\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_HP_LR_CRITIC=0.00431\u001b[0m\n",
      "\u001b[34mSM_HP_FC2=0\u001b[0m\n",
      "\u001b[34mSM_HP_DAYS_PER_EPOCH=40\u001b[0m\n",
      "\u001b[34mSM_HP_FC1=16\u001b[0m\n",
      "\u001b[34mSM_HP_START_DAY=2418\u001b[0m\n",
      "\u001b[34mSM_HP_PRICES_NAME=real-prices.csv\u001b[0m\n",
      "\u001b[34mSM_HP_LR_ACTOR=0.00092\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/usr/local/bin:/usr/lib/python36.zip:/usr/lib/python3.6:/usr/lib/python3.6/lib-dynload:/usr/local/lib/python3.6/dist-packages:/usr/lib/python3/dist-packages\n",
      "\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\n",
      "\u001b[0m\n",
      "\u001b[34m/usr/bin/python -m train --days_per_epoch 40 --fc1 16 --fc2 0 --lr_actor 0.00092 --lr_critic 0.00431 --prices_name real-prices.csv --start_day 2418\n",
      "\n",
      "\u001b[0m\n",
      "\u001b[34mGPU activated.\u001b[0m\n",
      "\u001b[34mSetting up the environment.\u001b[0m\n",
      "\u001b[34mNumber of assets: 10\u001b[0m\n",
      "\u001b[34mSignals per asset: 6\u001b[0m\n",
      "\u001b[34mTraining the agent.\u001b[0m\n",
      "\u001b[34mBeginning initial training.\u001b[0m\n",
      "\u001b[34mDay 2420 p/m ratio: 0.97\u001b[0m\n",
      "\u001b[34mDay 2440 p/m ratio: 0.94\u001b[0m\n",
      "\u001b[34mDay 2460 p/m ratio: 0.95\u001b[0m\n",
      "\u001b[34mDay 2480 p/m ratio: 0.95\u001b[0m\n",
      "\u001b[34mDay 2500 p/m ratio: 0.94\u001b[0m\n",
      "\u001b[34mDay 2520 p/m ratio: 0.97\u001b[0m\n",
      "\u001b[34mDay 2540 p/m ratio: 0.98\u001b[0m\n",
      "\u001b[34mDay 2560 p/m ratio: 0.99\u001b[0m\n",
      "\u001b[34mDay 2580 p/m ratio: 1.02\u001b[0m\n",
      "\u001b[34mDay 2600 p/m ratio: 1.03\u001b[0m\n",
      "\u001b[34mDay 2620 p/m ratio: 1.03\u001b[0m\n",
      "\n",
      "2020-07-29 05:29:43 Interrupted - Training job interrupted\n",
      "2020-07-29 05:32:33 Starting - Launching requested ML instances\n",
      "2020-07-29 05:37:25 Starting - Preparing the instances for training\n",
      "2020-07-29 05:38:26 Downloading - Downloading input data\n",
      "2020-07-29 05:38:26 Training - Downloading the training image\n",
      "2020-07-29 05:39:58 Training - Training image download completed. Training in progress.\u001b[32mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[32mbash: no job control in this shell\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,660 sagemaker-containers INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,661 sagemaker-containers INFO     Failed to parse hyperparameter prices_name value real-prices.csv to Json.\u001b[0m\n",
      "\u001b[32mReturning the value itself\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,681 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,682 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,682 sagemaker-containers INFO     Module train does not provide a setup.py. \u001b[0m\n",
      "\u001b[32mGenerating setup.py\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,683 sagemaker-containers INFO     Generating setup.cfg\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,683 sagemaker-containers INFO     Generating MANIFEST.in\u001b[0m\n",
      "\u001b[32m2020-07-29 05:39:59,683 sagemaker-containers INFO     Installing module with the following command:\u001b[0m\n",
      "\u001b[32m/usr/bin/python -m pip install . \u001b[0m\n",
      "\u001b[32mProcessing /opt/ml/code\u001b[0m\n",
      "\u001b[32mBuilding wheels for collected packages: train\n",
      "  Building wheel for train (setup.py): started\n",
      "  Building wheel for train (setup.py): finished with status 'done'\n",
      "  Created wheel for train: filename=train-1.0.0-py2.py3-none-any.whl size=1398600 sha256=52c01ac7a1a05094a4161519583839cc2704bfd64718eb19aa61b11b1b48f4ec\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-4qivy5op/wheels/95/c1/85/65aaf48b35aba88c6e896d2fd04a4b69f1cee0d81ea32993ca\u001b[0m\n",
      "\u001b[32mSuccessfully built train\u001b[0m\n",
      "\u001b[32mInstalling collected packages: train\u001b[0m\n",
      "\u001b[32mSuccessfully installed train-1.0.0\u001b[0m\n",
      "\u001b[32m2020-07-29 05:40:01,436 sagemaker-containers INFO     Failed to parse hyperparameter prices_name value real-prices.csv to Json.\u001b[0m\n",
      "\u001b[32mReturning the value itself\u001b[0m\n",
      "\u001b[32m2020-07-29 05:40:01,457 sagemaker-containers INFO     Invoking user script\n",
      "\u001b[0m\n",
      "\u001b[32mTraining Env:\n",
      "\u001b[0m\n",
      "\u001b[32m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {},\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"lr_critic\": 0.00431,\n",
      "        \"fc2\": 0,\n",
      "        \"days_per_epoch\": 40,\n",
      "        \"fc1\": 16,\n",
      "        \"start_day\": 2418,\n",
      "        \"prices_name\": \"real-prices.csv\",\n",
      "        \"lr_actor\": 0.00092\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {},\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"portfolio-optimization-gpu-2020-07-29-04-10-13-452\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"/opt/ml/code\",\n",
      "    \"module_name\": \"train\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 1,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"train.py\"\u001b[0m\n",
      "\u001b[32m}\n",
      "\u001b[0m\n",
      "\u001b[32mEnvironment variables:\n",
      "\u001b[0m\n",
      "\u001b[32mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[32mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[32mSM_HPS={\"days_per_epoch\":40,\"fc1\":16,\"fc2\":0,\"lr_actor\":0.00092,\"lr_critic\":0.00431,\"prices_name\":\"real-prices.csv\",\"start_day\":2418}\u001b[0m\n",
      "\u001b[32mSM_USER_ENTRY_POINT=train.py\u001b[0m\n",
      "\u001b[32mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[32mSM_RESOURCE_CONFIG={\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[32mSM_INPUT_DATA_CONFIG={}\u001b[0m\n",
      "\u001b[32mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[32mSM_CHANNELS=[]\u001b[0m\n",
      "\u001b[32mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[32mSM_MODULE_NAME=train\u001b[0m\n",
      "\u001b[32mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[32mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[32mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[32mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[32mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[32mSM_NUM_CPUS=4\u001b[0m\n",
      "\u001b[32mSM_NUM_GPUS=1\u001b[0m\n",
      "\u001b[32mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[32mSM_MODULE_DIR=/opt/ml/code\u001b[0m\n",
      "\u001b[32mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"days_per_epoch\":40,\"fc1\":16,\"fc2\":0,\"lr_actor\":0.00092,\"lr_critic\":0.00431,\"prices_name\":\"real-prices.csv\",\"start_day\":2418},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"portfolio-optimization-gpu-2020-07-29-04-10-13-452\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"/opt/ml/code\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":1,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
      "\u001b[32mSM_USER_ARGS=[\"--days_per_epoch\",\"40\",\"--fc1\",\"16\",\"--fc2\",\"0\",\"--lr_actor\",\"0.00092\",\"--lr_critic\",\"0.00431\",\"--prices_name\",\"real-prices.csv\",\"--start_day\",\"2418\"]\u001b[0m\n",
      "\u001b[32mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[32mSM_HP_LR_CRITIC=0.00431\u001b[0m\n",
      "\u001b[32mSM_HP_FC2=0\u001b[0m\n",
      "\u001b[32mSM_HP_DAYS_PER_EPOCH=40\u001b[0m\n",
      "\u001b[32mSM_HP_FC1=16\u001b[0m\n",
      "\u001b[32mSM_HP_START_DAY=2418\u001b[0m\n",
      "\u001b[32mSM_HP_PRICES_NAME=real-prices.csv\u001b[0m\n",
      "\u001b[32mSM_HP_LR_ACTOR=0.00092\u001b[0m\n",
      "\u001b[32mPYTHONPATH=/usr/local/bin:/usr/lib/python36.zip:/usr/lib/python3.6:/usr/lib/python3.6/lib-dynload:/usr/local/lib/python3.6/dist-packages:/usr/lib/python3/dist-packages\n",
      "\u001b[0m\n",
      "\u001b[32mInvoking script with the following command:\n",
      "\u001b[0m\n",
      "\u001b[32m/usr/bin/python -m train --days_per_epoch 40 --fc1 16 --fc2 0 --lr_actor 0.00092 --lr_critic 0.00431 --prices_name real-prices.csv --start_day 2418\n",
      "\n",
      "\u001b[0m\n",
      "\u001b[32mGPU activated.\u001b[0m\n",
      "\u001b[32mSetting up the environment.\u001b[0m\n",
      "\u001b[32mNumber of assets: 10\u001b[0m\n",
      "\u001b[32mSignals per asset: 6\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mTraining the agent.\u001b[0m\n",
      "\u001b[32mBeginning initial training.\u001b[0m\n",
      "\u001b[32mDay 2420 p/m ratio: 1.01\u001b[0m\n",
      "\u001b[32mDay 2440 p/m ratio: 1.07\u001b[0m\n",
      "\n",
      "2020-07-29 06:02:58 Interrupted - Training job interrupted\n",
      "2020-07-29 06:06:13 Starting - Launching requested ML instances\n",
      "2020-07-29 06:13:31 Starting - Preparing the instances for training\n",
      "2020-07-29 06:14:26 Downloading - Downloading input data\n",
      "2020-07-29 06:14:26 Training - Downloading the training image\n",
      "2020-07-29 06:15:58 Training - Training image download completed. Training in progress.\u001b[36mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[36mbash: no job control in this shell\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,378 sagemaker-containers INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,379 sagemaker-containers INFO     Failed to parse hyperparameter prices_name value real-prices.csv to Json.\u001b[0m\n",
      "\u001b[36mReturning the value itself\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,399 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,399 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,400 sagemaker-containers INFO     Module train does not provide a setup.py. \u001b[0m\n",
      "\u001b[36mGenerating setup.py\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,400 sagemaker-containers INFO     Generating setup.cfg\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,400 sagemaker-containers INFO     Generating MANIFEST.in\u001b[0m\n",
      "\u001b[36m2020-07-29 06:15:59,401 sagemaker-containers INFO     Installing module with the following command:\u001b[0m\n",
      "\u001b[36m/usr/bin/python -m pip install . \u001b[0m\n",
      "\u001b[36mProcessing /opt/ml/code\u001b[0m\n",
      "\u001b[36mBuilding wheels for collected packages: train\n",
      "  Building wheel for train (setup.py): started\n",
      "  Building wheel for train (setup.py): finished with status 'done'\n",
      "  Created wheel for train: filename=train-1.0.0-py2.py3-none-any.whl size=1398600 sha256=85bb144116ee9db01e7013738af3e508fc12f2ead6f4c637e976fc66e65e7adb\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-g5nf5dwi/wheels/95/c1/85/65aaf48b35aba88c6e896d2fd04a4b69f1cee0d81ea32993ca\u001b[0m\n",
      "\u001b[36mSuccessfully built train\u001b[0m\n",
      "\u001b[36mInstalling collected packages: train\u001b[0m\n",
      "\u001b[36mSuccessfully installed train-1.0.0\u001b[0m\n",
      "\u001b[36m2020-07-29 06:16:01,122 sagemaker-containers INFO     Failed to parse hyperparameter prices_name value real-prices.csv to Json.\u001b[0m\n",
      "\u001b[36mReturning the value itself\u001b[0m\n",
      "\u001b[36m2020-07-29 06:16:01,144 sagemaker-containers INFO     Invoking user script\n",
      "\u001b[0m\n",
      "\u001b[36mTraining Env:\n",
      "\u001b[0m\n",
      "\u001b[36m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {},\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"lr_critic\": 0.00431,\n",
      "        \"fc2\": 0,\n",
      "        \"days_per_epoch\": 40,\n",
      "        \"fc1\": 16,\n",
      "        \"start_day\": 2418,\n",
      "        \"prices_name\": \"real-prices.csv\",\n",
      "        \"lr_actor\": 0.00092\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {},\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"portfolio-optimization-gpu-2020-07-29-04-10-13-452\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"/opt/ml/code\",\n",
      "    \"module_name\": \"train\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 1,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"train.py\"\u001b[0m\n",
      "\u001b[36m}\n",
      "\u001b[0m\n",
      "\u001b[36mEnvironment variables:\n",
      "\u001b[0m\n",
      "\u001b[36mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[36mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[36mSM_HPS={\"days_per_epoch\":40,\"fc1\":16,\"fc2\":0,\"lr_actor\":0.00092,\"lr_critic\":0.00431,\"prices_name\":\"real-prices.csv\",\"start_day\":2418}\u001b[0m\n",
      "\u001b[36mSM_USER_ENTRY_POINT=train.py\u001b[0m\n",
      "\u001b[36mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[36mSM_RESOURCE_CONFIG={\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[36mSM_INPUT_DATA_CONFIG={}\u001b[0m\n",
      "\u001b[36mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[36mSM_CHANNELS=[]\u001b[0m\n",
      "\u001b[36mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[36mSM_MODULE_NAME=train\u001b[0m\n",
      "\u001b[36mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[36mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[36mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[36mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[36mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[36mSM_NUM_CPUS=4\u001b[0m\n",
      "\u001b[36mSM_NUM_GPUS=1\u001b[0m\n",
      "\u001b[36mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[36mSM_MODULE_DIR=/opt/ml/code\u001b[0m\n",
      "\u001b[36mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"days_per_epoch\":40,\"fc1\":16,\"fc2\":0,\"lr_actor\":0.00092,\"lr_critic\":0.00431,\"prices_name\":\"real-prices.csv\",\"start_day\":2418},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"portfolio-optimization-gpu-2020-07-29-04-10-13-452\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"/opt/ml/code\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":1,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
      "\u001b[36mSM_USER_ARGS=[\"--days_per_epoch\",\"40\",\"--fc1\",\"16\",\"--fc2\",\"0\",\"--lr_actor\",\"0.00092\",\"--lr_critic\",\"0.00431\",\"--prices_name\",\"real-prices.csv\",\"--start_day\",\"2418\"]\u001b[0m\n",
      "\u001b[36mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[36mSM_HP_LR_CRITIC=0.00431\u001b[0m\n",
      "\u001b[36mSM_HP_FC2=0\u001b[0m\n",
      "\u001b[36mSM_HP_DAYS_PER_EPOCH=40\u001b[0m\n",
      "\u001b[36mSM_HP_FC1=16\u001b[0m\n",
      "\u001b[36mSM_HP_START_DAY=2418\u001b[0m\n",
      "\u001b[36mSM_HP_PRICES_NAME=real-prices.csv\u001b[0m\n",
      "\u001b[36mSM_HP_LR_ACTOR=0.00092\u001b[0m\n",
      "\u001b[36mPYTHONPATH=/usr/local/bin:/usr/lib/python36.zip:/usr/lib/python3.6:/usr/lib/python3.6/lib-dynload:/usr/local/lib/python3.6/dist-packages:/usr/lib/python3/dist-packages\n",
      "\u001b[0m\n",
      "\u001b[36mInvoking script with the following command:\n",
      "\u001b[0m\n",
      "\u001b[36m/usr/bin/python -m train --days_per_epoch 40 --fc1 16 --fc2 0 --lr_actor 0.00092 --lr_critic 0.00431 --prices_name real-prices.csv --start_day 2418\n",
      "\n",
      "\u001b[0m\n",
      "\u001b[36mGPU activated.\u001b[0m\n",
      "\u001b[36mSetting up the environment.\u001b[0m\n",
      "\u001b[36mNumber of assets: 10\u001b[0m\n",
      "\u001b[36mSignals per asset: 6\u001b[0m\n",
      "\u001b[36mTraining the agent.\u001b[0m\n",
      "\u001b[36mBeginning initial training.\u001b[0m\n",
      "\u001b[36mDay 2420 p/m ratio: 0.99\u001b[0m\n",
      "\u001b[36mDay 2440 p/m ratio: 1.00\u001b[0m\n",
      "\u001b[36mResetting agent.\u001b[0m\n",
      "\u001b[36mDay 2460 p/m ratio: 1.02\u001b[0m\n",
      "\u001b[36mDay 2480 p/m ratio: 1.03\u001b[0m\n",
      "\u001b[36mDay 2500 p/m ratio: 1.05\u001b[0m\n",
      "\u001b[36mDay 2520 p/m ratio: 1.14\u001b[0m\n",
      "\u001b[36mDay 2540 p/m ratio: 1.20\u001b[0m\n",
      "\u001b[36mDay 2560 p/m ratio: 1.21\u001b[0m\n",
      "\u001b[36mDay 2580 p/m ratio: 1.37\u001b[0m\n",
      "\u001b[36mResetting agent.\u001b[0m\n",
      "\u001b[36mDay 2600 p/m ratio: 1.33\u001b[0m\n",
      "\u001b[36mDay 2620 p/m ratio: 1.45\u001b[0m\n",
      "\u001b[36mDay 2640 p/m ratio: 1.57\u001b[0m\n",
      "\u001b[36mDay 2660 p/m ratio: 1.49\u001b[0m\n",
      "\u001b[36mResetting agent.\u001b[0m\n",
      "\u001b[36mDay 2680 p/m ratio: 1.48\u001b[0m\n",
      "\u001b[36mDay 2700 p/m ratio: 1.54\u001b[0m\n",
      "\u001b[36mDay 2720 p/m ratio: 1.47\u001b[0m\n",
      "\u001b[36mResetting agent.\u001b[0m\n",
      "\u001b[36mDay 2740 p/m ratio: 1.58\u001b[0m\n",
      "\u001b[36mResetting agent.\u001b[0m\n",
      "\u001b[36mDay 2760 p/m ratio: 1.56\u001b[0m\n",
      "\u001b[36mDay 2780 p/m ratio: 1.57\u001b[0m\n",
      "\u001b[36mDay 2800 p/m ratio: 1.57\u001b[0m\n",
      "\u001b[36mDay 2820 p/m ratio: 1.61\u001b[0m\n",
      "\u001b[36mDay 2840 p/m ratio: 1.56\u001b[0m\n",
      "\u001b[36mDay 2860 p/m ratio: 1.57\u001b[0m\n",
      "\u001b[36mDay 2880 p/m ratio: 1.52\u001b[0m\n",
      "\u001b[36mDay 2900 p/m ratio: 1.52\u001b[0m\n",
      "\u001b[36mDay 2920 p/m ratio: 1.58\u001b[0m\n",
      "\u001b[36m0.28 annualized alpha.\u001b[0m\n",
      "\u001b[36m50.39 minutes of training.\u001b[0m\n",
      "\u001b[36m-175.80 training objective.\u001b[0m\n",
      "\u001b[36mTraining Time:  50.4 minutes\u001b[0m\n",
      "\u001b[36mMake training plot.\u001b[0m\n",
      "\u001b[36m2020-07-29 07:06:32,076 sagemaker-containers INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2020-07-29 07:06:47 Uploading - Uploading generated training model\n",
      "2020-07-29 07:06:47 Completed - Training job completed\n",
      "Training seconds: 7756\n",
      "Billable seconds: 2327\n",
      "Managed Spot Training savings: 70.0%\n"
     ]
    }
   ],
   "source": [
    "estimator = Estimator(role=role,\n",
    "                      train_instance_count=n_instances,\n",
    "                      train_instance_type=instance_type,\n",
    "                      image_name=image_name,\n",
    "                      train_use_spot_instances=train_use_spot_instances,\n",
    "                      train_max_run=train_max_run,\n",
    "                      train_max_wait=train_max_wait,\n",
    "                      hyperparameters=hyperparameters)\n",
    "estimator.fit()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3 = boto3.resource('s3')\n",
    "key = '{}/output/output.tar.gz'.format(estimator._current_job_name)\n",
    "s3.Bucket(bucket_name).download_file(key, 'output.tar.gz')\n",
    "shutil.unpack_archive('output.tar.gz')\n",
    "Image(filename='history.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference\n",
    "- [Tennis Sensitivity](https://github.com/daniel-fudge/sagemaker-tennis/blob/master/sensitivity.ipynb)\n",
    "- [Tennis Repo](https://github.com/daniel-fudge/sagemaker-tennis)\n",
    "#### SageMaker\n",
    "- [SageMaker Instance types](https://aws.amazon.com/sagemaker/pricing/instance-types/)\n",
    "- [SageMaker Instance prices](https://aws.amazon.com/sagemaker/pricing/)\n",
    "- [SageMaker Estimator SDK](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_pytorch_p36",
   "language": "python",
   "name": "conda_pytorch_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}