15_Goku.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abdulazeezb/1_SparkFundsInvestmentAnalysis/blob/master/15_Goku.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking GPU Version"
      ],
      "metadata": {
        "id": "NAjscLW7Sk9V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "metadata": {
        "id": "t8nxH9ZvSaY6",
        "outputId": "fc2ad37a-9750-48d7-b504-6e8c5d537f23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed May  3 10:16:25 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Not using a high-RAM runtime')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "metadata": {
        "id": "KEPzY7qCS2Lc",
        "outputId": "d66b4d7e-b2a8-43c4-d2af-caab9cf0aee4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 27.3 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMMqmdiYMkvi"
      },
      "source": [
        "## Part 1: Getting Started - Install Python Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23TOba33L4qf",
        "outputId": "a860cafc-db35-4103-9d30-f071757ed201"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wrds\n",
            "  Downloading wrds-3.1.6-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from wrds) (1.22.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from wrds) (1.5.3)\n",
            "Collecting psycopg2-binary\n",
            "  Downloading psycopg2_binary-2.9.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from wrds) (1.10.1)\n",
            "Collecting sqlalchemy<2\n",
            "  Downloading SQLAlchemy-1.4.48-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
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            "\u001b[?25hRequirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy<2->wrds) (2.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->wrds) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->wrds) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->wrds) (1.16.0)\n",
            "Installing collected packages: sqlalchemy, psycopg2-binary, wrds\n",
            "  Attempting uninstall: sqlalchemy\n",
            "    Found existing installation: SQLAlchemy 2.0.10\n",
            "    Uninstalling SQLAlchemy-2.0.10:\n",
            "      Successfully uninstalled SQLAlchemy-2.0.10\n",
            "Successfully installed psycopg2-binary-2.9.6 sqlalchemy-1.4.48 wrds-3.1.6\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting swig\n",
            "  Downloading swig-4.1.1-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.8 MB)\n",
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            "\u001b[?25hInstalling collected packages: swig\n",
            "Successfully installed swig-4.1.1\n",
            "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.1.0-1/Mambaforge-23.1.0-1-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:12\n",
            "🔁 Restarting kernel...\n",
            "Selecting previously unselected package libgl1-mesa-glx:amd64.\n",
            "(Reading database ... 122518 files and directories currently installed.)\n",
            "Preparing to unpack .../libgl1-mesa-glx_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ...\n",
            "Unpacking libgl1-mesa-glx:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\n",
            "Selecting previously unselected package swig4.0.\n",
            "Preparing to unpack .../swig4.0_4.0.1-5build1_amd64.deb ...\n",
            "Unpacking swig4.0 (4.0.1-5build1) ...\n",
            "Selecting previously unselected package swig.\n",
            "Preparing to unpack .../swig_4.0.1-5build1_all.deb ...\n",
            "Unpacking swig (4.0.1-5build1) ...\n",
            "Setting up libgl1-mesa-glx:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\n",
            "Setting up swig4.0 (4.0.1-5build1) ...\n",
            "Setting up swig (4.0.1-5build1) ...\n",
            "Processing triggers for man-db (2.9.1-1) ...\n"
          ]
        }
      ],
      "source": [
        "## install finrl library\n",
        "!pip install wrds\n",
        "!pip install swig\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()\n",
        "!apt-get update -y -qq && apt-get install -y -qq cmake libopenmpi-dev python3-dev zlib1g-dev libgl1-mesa-glx swig"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git\n"
      ],
      "metadata": {
        "id": "haJsmd15TCdH",
        "outputId": "d634e3eb-b1b6-4286-ccc4-98e540e92573",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/AI4Finance-Foundation/FinRL.git\n",
            "  Cloning https://github.com/AI4Finance-Foundation/FinRL.git to /tmp/pip-req-build-kghd2c2q\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-Foundation/FinRL.git /tmp/pip-req-build-kghd2c2q\n",
            "  Resolved https://github.com/AI4Finance-Foundation/FinRL.git to commit a3598dae504bcd834d12b17110b5aa91c1c5305d\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-mo0oou19/pyfolio_50c0b2fea63f45b89ee7b47db6a887e8\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/quantopian/pyfolio.git /tmp/pip-install-mo0oou19/pyfolio_50c0b2fea63f45b89ee7b47db6a887e8\n",
            "  Resolved https://github.com/quantopian/pyfolio.git to commit 4b901f6d73aa02ceb6d04b7d83502e5c6f2e81aa\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl\n",
            "  Cloning https://github.com/AI4Finance-Foundation/ElegantRL.git to /tmp/pip-install-mo0oou19/elegantrl_30aea606f62048b59c5c4dd0dde8f352\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-Foundation/ElegantRL.git /tmp/pip-install-mo0oou19/elegantrl_30aea606f62048b59c5c4dd0dde8f352\n",
            "  Resolved https://github.com/AI4Finance-Foundation/ElegantRL.git to commit b974a806e6235f59055c954418e54640fa549331\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting stable-baselines3<2.0.0,>=1.6.2\n",
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            "\u001b[?25hBuilding wheels for collected packages: finrl, exchange_calendars, gym, elegantrl, gputil, pyfolio, empyrical, gpustat, thriftpy2, box2d-py, lit\n",
            "  Building wheel for finrl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.5-py3-none-any.whl size=4668722 sha256=b5d9990feddfce4dac1109677bd1616fcf78abb54bcf1b30c280638a7696b8d0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-o1ri3xm8/wheels/72/3b/1a/0fc805a8cc65ecd5bfe4f74a3c586b6075678b8ba53fd8f749\n",
            "  Building wheel for exchange_calendars (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for exchange_calendars: filename=exchange_calendars-3.6.3-py3-none-any.whl size=182636 sha256=23f096d03830ef288577bdfe3d565944669463917f5dd94ee9e6b0f3ac389c18\n",
            "  Stored in directory: /root/.cache/pip/wheels/93/ca/8f/1e1c90cc79fb3ca9b5413ff58e3fccf3baf2182c994c6dfd37\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for gym (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for gym\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for gym\n",
            "  Building wheel for elegantrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for elegantrl: filename=elegantrl-0.3.6-py3-none-any.whl size=195067 sha256=0789852c45710b7487ad7f8c787906a832371da5364d4beb8b432436bec3090a\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-o1ri3xm8/wheels/c0/51/a5/b05f165548221bc570f7223babd33e2992fa873cdcebe2d229\n",
            "  Building wheel for gputil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gputil: filename=GPUtil-1.4.0-py3-none-any.whl size=7408 sha256=43e5e00b64330ceca82b0b2332a55029631afd35b9eaa8cc4d707613f9482a34\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/8a/bd/81082387151853ab8b6b3ef33426e98f5cbfebc3c397a9d4d0\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-py3-none-any.whl size=75775 sha256=20701b364f22ff791c94f70c98771137c58f37cd70be901db94f0347119b406d\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-o1ri3xm8/wheels/c4/1d/91/8ab5d1c88a11b06a63dcd6a69ea81547e2247123232949bb26\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-py3-none-any.whl size=39779 sha256=c578b7489ee7afb8225a9b08a994523f91ace1d8158d73439aed4bb57a97ea77\n",
            "  Stored in directory: /root/.cache/pip/wheels/0e/2e/f2/d6d2d9a1eb8fbbd9949bb5d4c00f753e3b74e5bd7ed10b1d36\n",
            "  Building wheel for gpustat (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gpustat: filename=gpustat-1.1-py3-none-any.whl size=26298 sha256=850b0fd864ce416107c0d0b9767d57e9cdf9ff3bfde28198282522a39b54e4df\n",
            "  Stored in directory: /root/.cache/pip/wheels/ee/d0/2c/1e02440645c2318ba03aea99993a44a9108dc8f74de0bd370b\n",
            "  Building wheel for thriftpy2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for thriftpy2: filename=thriftpy2-0.4.16-cp310-cp310-linux_x86_64.whl size=522576 sha256=3fc1e580c849dad53dd870db6113764321778d37943e080e91ba649187f354b2\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/f5/1d/fe404692e1c8aaea45220c322d1d0f32c9fd40eb0e2bdd571e\n",
            "  Building wheel for box2d-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for box2d-py: filename=box2d_py-2.3.5-cp310-cp310-linux_x86_64.whl size=494649 sha256=6c45d1a8612e8d313e6ad0cec227d2e960d69438b46b43768f1a2329279b4286\n",
            "  Stored in directory: /root/.cache/pip/wheels/db/8f/6a/eaaadf056fba10a98d986f6dce954e6201ba3126926fc5ad9e\n",
            "  Building wheel for lit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lit: filename=lit-16.0.2-py3-none-any.whl size=88190 sha256=79ee9f70ccdf2114f6d535e42be62da6c2718265d0841cdb0f06aee40707ef47\n",
            "  Stored in directory: /root/.cache/pip/wheels/4d/31/6f/140862d5c69ddd665b3ed9485f4c93aa9c84cb34b359cba3ce\n",
            "Successfully built finrl exchange_calendars elegantrl gputil pyfolio empyrical gpustat thriftpy2 box2d-py lit\n",
            "Failed to build gym\n",
            "Installing collected packages: webencodings, wcwidth, pytz, pyglet, py-spy, pure-eval, ptyprocess, ply, pickleshare, opencensus-context, nvidia-ml-py, multitasking, msgpack, mpmath, lit, korean_lunar_calendar, gputil, executing, distlib, colorful, cmake, box2d-py, backcall, appdirs, zipp, websockets, websocket-client, tzdata, typing-extensions, traitlets, threadpoolctl, tabulate, sympy, soupsieve, smart-open, six, PyYAML, pyrsistent, pyparsing, pymysql, pyluach, pygments, pyasn1, psycopg2-binary, psutil, protobuf, prompt-toolkit, prometheus-client, platformdirs, pillow, pexpect, parso, packaging, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, numpy, networkx, multidict, MarkupSafe, lz4, lxml, kiwisolver, joblib, grpcio, greenlet, frozenlist, frozendict, fonttools, filelock, decorator, cycler, cloudpickle, click, charset-normalizer, cachetools, attrs, async-timeout, yarl, virtualenv, thriftpy2, tensorboardX, sqlalchemy, scipy, rsa, python-dateutil, pydantic, pycares, pyasn1-modules, nvidia-cusolver-cu11, nvidia-cudnn-cu11, matplotlib-inline, jsonschema, jinja2, jedi, importlib-metadata, html5lib, gym, googleapis-common-protos, deprecation, contourpy, blessed, beautifulsoup4, asttokens, aiosignal, stack-data, scikit-learn, ray, pandas, matplotlib, gpustat, google-auth, aiohttp, aiodns, yfinance, wrds, stockstats, seaborn, pandas-datareader, jqdatasdk, ipython, google-api-core, exchange_calendars, ccxt, alpaca_trade_api, aiohttp-cors, opencensus, empyrical, pyfolio, triton, torch, stable-baselines3, elegantrl, finrl\n",
            "  Attempting uninstall: charset-normalizer\n",
            "    Found existing installation: charset-normalizer 3.1.0\n",
            "    Uninstalling charset-normalizer-3.1.0:\n",
            "      Successfully uninstalled charset-normalizer-3.1.0\n",
            "  Running setup.py install for gym ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33m  DEPRECATION: gym was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. pip 23.1 will enforce this behaviour change. A possible replacement is to fix the wheel build issue reported above. Discussion can be found at https://github.com/pypa/pip/issues/8368\u001b[0m\u001b[33m\n",
            "\u001b[0mSuccessfully installed MarkupSafe-2.1.2 PyYAML-6.0 aiodns-3.0.0 aiohttp-3.8.1 aiohttp-cors-0.7.0 aiosignal-1.3.1 alpaca_trade_api-3.0.0 appdirs-1.4.4 asttokens-2.2.1 async-timeout-4.0.2 attrs-23.1.0 backcall-0.2.0 beautifulsoup4-4.12.2 blessed-1.20.0 box2d-py-2.3.5 cachetools-5.3.0 ccxt-3.0.90 charset-normalizer-2.1.1 click-8.1.3 cloudpickle-2.2.1 cmake-3.26.3 colorful-0.5.5 contourpy-1.0.7 cycler-0.11.0 decorator-5.1.1 deprecation-2.1.0 distlib-0.3.6 elegantrl-0.3.6 empyrical-0.5.5 exchange_calendars-3.6.3 executing-1.2.0 filelock-3.12.0 finrl-0.3.5 fonttools-4.39.3 frozendict-2.3.8 frozenlist-1.3.3 google-api-core-2.11.0 google-auth-2.17.3 googleapis-common-protos-1.59.0 gpustat-1.1 gputil-1.4.0 greenlet-2.0.2 grpcio-1.51.3 gym-0.21.0 html5lib-1.1 importlib-metadata-4.13.0 ipython-8.13.1 jedi-0.18.2 jinja2-3.1.2 joblib-1.2.0 jqdatasdk-1.8.11 jsonschema-4.17.3 kiwisolver-1.4.4 korean_lunar_calendar-0.3.1 lit-16.0.2 lxml-4.9.2 lz4-4.3.2 matplotlib-3.7.1 matplotlib-inline-0.1.6 mpmath-1.3.0 msgpack-1.0.3 multidict-6.0.4 multitasking-0.0.11 networkx-3.1 numpy-1.24.3 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-ml-py-11.525.112 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 opencensus-0.11.2 opencensus-context-0.1.3 packaging-23.1 pandas-2.0.1 pandas-datareader-0.10.0 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 pillow-9.5.0 platformdirs-3.5.0 ply-3.11 prometheus-client-0.16.0 prompt-toolkit-3.0.38 protobuf-3.20.3 psutil-5.9.5 psycopg2-binary-2.9.6 ptyprocess-0.7.0 pure-eval-0.2.2 py-spy-0.3.14 pyasn1-0.5.0 pyasn1-modules-0.3.0 pycares-4.3.0 pydantic-1.10.7 pyfolio-0.9.2+75.g4b901f6 pyglet-2.0.6 pygments-2.15.1 pyluach-2.2.0 pymysql-1.0.3 pyparsing-3.0.9 pyrsistent-0.19.3 python-dateutil-2.8.2 pytz-2023.3 ray-2.4.0 rsa-4.9 scikit-learn-1.2.2 scipy-1.10.1 seaborn-0.12.2 six-1.16.0 smart-open-6.3.0 soupsieve-2.4.1 sqlalchemy-1.4.48 stable-baselines3-1.8.0 stack-data-0.6.2 stockstats-0.5.2 sympy-1.11.1 tabulate-0.9.0 tensorboardX-2.6 threadpoolctl-3.1.0 thriftpy2-0.4.16 torch-2.0.0 traitlets-5.9.0 triton-2.0.0 typing-extensions-4.5.0 tzdata-2023.3 virtualenv-20.21.0 wcwidth-0.2.6 webencodings-0.5.1 websocket-client-1.5.1 websockets-10.4 wrds-3.1.6 yarl-1.9.2 yfinance-0.2.18 zipp-3.15.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.2 Check if the additional packages needed are present, if not install them"
      ],
      "metadata": {
        "id": "DMaIlOcVTJfp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install trading_calendars\n",
        "# !pip install alpaca_trade_api\n",
        "# !pip install ccxt\n",
        "# !pip install jqdatasdk\n",
        "# !pip install wrds\n",
        "\n",
        "# !pip install lz4\n",
        "# !pip install ray[tune]\n",
        "# !pip install tensorboardX\n",
        "# !pip install gputil\n",
        "\n",
        "#%%capture\n",
        "if True:\n",
        "    # installing packages\n",
        "    !pip install pyfolio-reloaded  #original pyfolio no longer maintained\n",
        "    !pip install optuna\n",
        "    !pip install -U \"ray[rllib]\"\n",
        "    !pip install plotly\n",
        "    !pip install ipywidgets\n",
        "    !pip install -U kaleido   # enables saving plots to file\n",
        "    \n",
        "    "
      ],
      "metadata": {
        "id": "UfmXkH1rTFM7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9c4bdf1-f8a1-446e-8be1-566dd0884c31"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyfolio-reloaded\n",
            "  Downloading pyfolio_reloaded-0.9.5-py3-none-any.whl (6.3 MB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas>=0.18.1 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (2.0.1)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (1.10.1)\n",
            "Requirement already satisfied: pytz>=2014.10 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (2023.3)\n",
            "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (8.13.1)\n",
            "Requirement already satisfied: scikit-learn>=0.16.1 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (1.2.2)\n",
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            "Collecting empyrical-reloaded>=0.5.8\n",
            "  Downloading empyrical_reloaded-0.5.9-py3-none-any.whl (32 kB)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (1.24.3)\n",
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            "Collecting bottleneck>=1.3.0\n",
            "  Downloading Bottleneck-1.3.7-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (354 kB)\n",
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            "\u001b[?25hRequirement already satisfied: yfinance>=0.1.63 in /usr/local/lib/python3.10/site-packages (from empyrical-reloaded>=0.5.8->pyfolio-reloaded) (0.2.18)\n",
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            "Successfully installed PyWavelets-1.4.1 dm-tree-0.1.8 gymnasium-0.26.3 gymnasium-notices-0.0.1 imageio-2.28.1 lazy_loader-0.2 markdown-it-py-2.2.0 mdurl-0.1.2 rich-13.3.5 scikit-image-0.20.0 tifffile-2023.4.12 typer-0.9.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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            "Installing collected packages: widgetsnbextension, tornado, pyzmq, nest-asyncio, jupyterlab-widgets, jupyter-core, debugpy, comm, jupyter-client, ipykernel, ipywidgets\n",
            "Successfully installed comm-0.1.3 debugpy-1.6.7 ipykernel-6.22.0 ipywidgets-8.0.6 jupyter-client-8.2.0 jupyter-core-5.3.0 jupyterlab-widgets-3.0.7 nest-asyncio-1.5.6 pyzmq-25.0.2 tornado-6.3.1 widgetsnbextension-4.0.7\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kaleido\n",
            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: kaleido\n",
            "Successfully installed kaleido-0.2.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas==1.5.3\n",
        "!pip install gymnasium"
      ],
      "metadata": {
        "id": "9Zkmtvr-TFn_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d145d08-719a-4818-e4b6-cfd184dd207e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pandas==1.5.3\n",
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            "Installing collected packages: pandas\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.0.1\n",
            "    Uninstalling pandas-2.0.1:\n",
            "      Successfully uninstalled pandas-2.0.1\n",
            "Successfully installed pandas-1.5.3\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")"
      ],
      "metadata": {
        "id": "cMn1VNKCR0ld"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3 Import packages"
      ],
      "metadata": {
        "id": "BbOMJnGdTRYX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing the libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "import optuna\n",
        "from pathlib import Path\n",
        "from google.colab import files\n",
        "%matplotlib inline\n",
        "from finrl import config\n",
        "from finrl import config_tickers\n",
        "from optuna.integration import PyTorchLightningPruningCallback\n",
        "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv as StockTradingEnv_numpy\n",
        "from finrl.agents.stablebaselines3.models import DRLAgent\n",
        "from finrl.agents.rllib.models import DRLAgent as DRLAgent_rllib\n",
        "from finrl.meta.data_processor import DataProcessor\n",
        "import joblib\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "import ray\n",
        "from pprint import pprint\n",
        "import kaleido\n",
        "\n",
        "\n",
        "\n",
        "import itertools\n",
        "\n",
        "import torch\n",
        "if torch.cuda.is_available():\n",
        "  device = torch.device(\"cuda\")\n",
        "else:\n",
        "  device = torch.device(\"cpu\")\n",
        "print(f'Torch device: {device}')"
      ],
      "metadata": {
        "id": "tG5qIkVHTNv8",
        "outputId": "088562e5-f206-4124-ff37-804df28600bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Torch device: cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config_tickers.DOW_30_TICKER"
      ],
      "metadata": {
        "id": "DB11ygQLVLUD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "132392c9-dd99-4170-9e92-10eef2349689"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['AXP',\n",
              " 'AMGN',\n",
              " 'AAPL',\n",
              " 'BA',\n",
              " 'CAT',\n",
              " 'CSCO',\n",
              " 'CVX',\n",
              " 'GS',\n",
              " 'HD',\n",
              " 'HON',\n",
              " 'IBM',\n",
              " 'INTC',\n",
              " 'JNJ',\n",
              " 'KO',\n",
              " 'JPM',\n",
              " 'MCD',\n",
              " 'MMM',\n",
              " 'MRK',\n",
              " 'MSFT',\n",
              " 'NKE',\n",
              " 'PG',\n",
              " 'TRV',\n",
              " 'UNH',\n",
              " 'CRM',\n",
              " 'VZ',\n",
              " 'V',\n",
              " 'WBA',\n",
              " 'WMT',\n",
              " 'DIS',\n",
              " 'DOW']"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fMaPm8MsUo0R"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "metadata": {
        "id": "FAZpwTH3VVi-"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Collecting data and preprocessing"
      ],
      "metadata": {
        "id": "cqJ8ngFWVcNG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#config_tickers.DOW_30_TICKER = [\"PYPL\"]"
      ],
      "metadata": {
        "id": "4M4UwcP4Ap_o"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Custom ticker list dataframe download\n",
        "#TODO save df to avoid download\n",
        "path_pf = '/content/ticker_data.csv'\n",
        "if Path(path_pf).is_file():\n",
        "  print('Reading ticker data')\n",
        "  df = pd.read_csv(path_pf)\n",
        "  \n",
        "else:\n",
        "  print('Downloading ticker data')\n",
        "  ticker_list = config_tickers.DOW_30_TICKER\n",
        "  df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2023-04-30',\n",
        "                     ticker_list = ticker_list).fetch_data()\n",
        "  df.to_csv('ticker_data.csv')"
      ],
      "metadata": {
        "id": "7BJNlsAuVZ-o",
        "outputId": "6549acc1-5d7a-444e-bdf3-774a745b12e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading ticker data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_processed_full(processed):\n",
        "  list_ticker = processed[\"tic\"].unique().tolist()\n",
        "  list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "  combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "  processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "  processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "  processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "  processed_full = processed_full.fillna(0)\n",
        "  processed_full.sort_values(['date','tic'],ignore_index=True).head(5)\n",
        "\n",
        "  processed_full.to_csv('processed_full.csv')\n",
        "  return processed_full"
      ],
      "metadata": {
        "id": "RD6wwtGSVZVU"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#You can add technical indicators and turbulence factor to dataframe\n",
        "#Just set the use_technical_indicator=True, use_vix=True and use_turbulence=True\n",
        "def create_techind():\n",
        "  fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.INDICATORS,\n",
        "                    use_vix=True,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "  processed = fe.preprocess_data(df)\n",
        "  return processed"
      ],
      "metadata": {
        "id": "UNqEALmbVoJo"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load price and technical indicator data from file if available\n",
        "path_pf = '/content/processed_full.csv'\n",
        "if Path(path_pf).is_file():\n",
        "  print('Reading processed_full data')\n",
        "  processed_full = pd.read_csv(path_pf)\n",
        "\n",
        "else:\n",
        "  print('Creating processed_full file')\n",
        "  processed=create_techind()\n",
        "  processed_full=create_processed_full(processed)"
      ],
      "metadata": {
        "id": "Qr1Qd-lMVpzg",
        "outputId": "ddf3da27-6448-4130-a3d8-926a86eebb09",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading processed_full data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date_col = \"date\"\n",
        "tic_col = \"tic\"\n",
        "\n",
        "init_train_trade_data = processed_full.sort_values([date_col, tic_col])\n",
        "\n",
        "init_train_trade_data = processed_full.fillna(0)\n",
        "\n",
        "init_train_data = data_split(\n",
        "    init_train_trade_data, '2020-01-01', '2020-05-01')\n",
        "init_trade_data = data_split(\n",
        "    init_train_trade_data, '2021-05-01','2021-10-01')\n",
        "\n",
        "print(f'Number of training samples: {len(init_train_data)}')\n",
        "print(f'Number of testing samples: {len(init_train_trade_data)}')"
      ],
      "metadata": {
        "id": "KFs4AjWIVr2s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1511de7-d889-418b-c81a-0cda7bb8bf8d"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training samples: 2407\n",
            "Number of testing samples: 104516\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "init_trade_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "GI2qgPGFTerU",
        "outputId": "d371c550-bb8c-4416-f550-d09c7a686e9c"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0        date   tic        open        high         low  \\\n",
              "0      130616  2021-05-03  AAPL  132.039993  134.070007  131.830002   \n",
              "0      130617  2021-05-03  AMGN  240.669998  247.020004  240.550003   \n",
              "0      130618  2021-05-03   AXP  154.589996  156.050003  154.009995   \n",
              "0      130619  2021-05-03    BA  234.110001  237.100006  233.809998   \n",
              "0      130620  2021-05-03   CAT  230.000000  230.929993  227.210007   \n",
              "\n",
              "        close      volume  day      macd     boll_ub     boll_lb     rsi_30  \\\n",
              "0  130.963547  75135100.0  0.0  1.805506  135.539561  126.477478  53.856512   \n",
              "0  230.412491   3587700.0  0.0 -0.410712  248.223095  221.790974  50.842034   \n",
              "0  150.957855   2726200.0  0.0  2.754005  151.027342  139.179352  62.209653   \n",
              "0  235.190002   9887800.0  0.0 -2.593339  260.290298  227.831702  49.445883   \n",
              "0  218.290512   3182900.0  0.0  1.167007  223.005762  216.688293  56.081921   \n",
              "\n",
              "       cci_30      dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
              "0   50.478426  20.583396    127.527259    126.185906  18.309999   20.080451  \n",
              "0  -81.617873   5.785497    234.734409    226.585521  18.309999   20.080451  \n",
              "0  175.796203  22.874057    142.745663    138.440772  18.309999   20.080451  \n",
              "0 -102.331978  17.827725    245.819000    237.101333  18.309999   20.080451  \n",
              "0  -17.678374   2.780977    218.974402    211.601994  18.309999   20.080451  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c9119e2-2abc-4b6b-8557-eeeab6cdf5be\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>vix</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>130616</td>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>132.039993</td>\n",
              "      <td>134.070007</td>\n",
              "      <td>131.830002</td>\n",
              "      <td>130.963547</td>\n",
              "      <td>75135100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.805506</td>\n",
              "      <td>135.539561</td>\n",
              "      <td>126.477478</td>\n",
              "      <td>53.856512</td>\n",
              "      <td>50.478426</td>\n",
              "      <td>20.583396</td>\n",
              "      <td>127.527259</td>\n",
              "      <td>126.185906</td>\n",
              "      <td>18.309999</td>\n",
              "      <td>20.080451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>130617</td>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>AMGN</td>\n",
              "      <td>240.669998</td>\n",
              "      <td>247.020004</td>\n",
              "      <td>240.550003</td>\n",
              "      <td>230.412491</td>\n",
              "      <td>3587700.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.410712</td>\n",
              "      <td>248.223095</td>\n",
              "      <td>221.790974</td>\n",
              "      <td>50.842034</td>\n",
              "      <td>-81.617873</td>\n",
              "      <td>5.785497</td>\n",
              "      <td>234.734409</td>\n",
              "      <td>226.585521</td>\n",
              "      <td>18.309999</td>\n",
              "      <td>20.080451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>130618</td>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>AXP</td>\n",
              "      <td>154.589996</td>\n",
              "      <td>156.050003</td>\n",
              "      <td>154.009995</td>\n",
              "      <td>150.957855</td>\n",
              "      <td>2726200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.754005</td>\n",
              "      <td>151.027342</td>\n",
              "      <td>139.179352</td>\n",
              "      <td>62.209653</td>\n",
              "      <td>175.796203</td>\n",
              "      <td>22.874057</td>\n",
              "      <td>142.745663</td>\n",
              "      <td>138.440772</td>\n",
              "      <td>18.309999</td>\n",
              "      <td>20.080451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>130619</td>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>BA</td>\n",
              "      <td>234.110001</td>\n",
              "      <td>237.100006</td>\n",
              "      <td>233.809998</td>\n",
              "      <td>235.190002</td>\n",
              "      <td>9887800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.593339</td>\n",
              "      <td>260.290298</td>\n",
              "      <td>227.831702</td>\n",
              "      <td>49.445883</td>\n",
              "      <td>-102.331978</td>\n",
              "      <td>17.827725</td>\n",
              "      <td>245.819000</td>\n",
              "      <td>237.101333</td>\n",
              "      <td>18.309999</td>\n",
              "      <td>20.080451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>130620</td>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>CAT</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>230.929993</td>\n",
              "      <td>227.210007</td>\n",
              "      <td>218.290512</td>\n",
              "      <td>3182900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.167007</td>\n",
              "      <td>223.005762</td>\n",
              "      <td>216.688293</td>\n",
              "      <td>56.081921</td>\n",
              "      <td>-17.678374</td>\n",
              "      <td>2.780977</td>\n",
              "      <td>218.974402</td>\n",
              "      <td>211.601994</td>\n",
              "      <td>18.309999</td>\n",
              "      <td>20.080451</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c9119e2-2abc-4b6b-8557-eeeab6cdf5be')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c9119e2-2abc-4b6b-8557-eeeab6cdf5be button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c9119e2-2abc-4b6b-8557-eeeab6cdf5be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating Environment"
      ],
      "metadata": {
        "id": "L3VlkfBMW8e7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Portfolio:\n",
        "    def __init__(self):\n",
        "        self.num_shares = 0\n",
        "        self.total_cost = 0.0\n",
        "        self.avg_cost = 0.0\n",
        "\n",
        "    def buy(self, num_shares, price):\n",
        "        self.total_cost += num_shares * price\n",
        "        self.num_shares += num_shares\n",
        "        self.avg_cost = self.total_cost / self.num_shares\n",
        "\n",
        "    def sell(self, num_shares):\n",
        "        self.total_cost -= self.avg_cost * num_shares\n",
        "        self.num_shares -= num_shares\n",
        "\n",
        "    def get_num_shares(self):\n",
        "        return self.num_shares\n",
        "\n",
        "    def get_total_cost(self):\n",
        "        return self.total_cost\n",
        "\n",
        "    def get_avg_cost(self):\n",
        "        return self.avg_cost\n",
        "\n",
        "# Example usage\n",
        "portfolio = Portfolio()\n",
        "\n",
        "# Buy 50 shares at $126.81\n",
        "portfolio.buy(50, 126.81)\n",
        "\n",
        "# Sell 25 shares\n",
        "portfolio.sell(25)\n",
        "\n",
        "# Buy 30 shares at $125.03\n",
        "portfolio.buy(30, 125.03)\n",
        "\n",
        "# Sell 10 shares\n",
        "portfolio.sell(10)\n",
        "\n",
        "\n",
        "print(\"Number of shares: \", portfolio.get_num_shares())\n",
        "print(\"Total cost: \", round(portfolio.get_total_cost(), 2))\n",
        "print(\"Average cost: \", round(portfolio.get_avg_cost(), 2))\n"
      ],
      "metadata": {
        "id": "_henJDi45WQV",
        "outputId": "bc980972-f59c-45a5-c86f-646a6ebe0c13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of shares:  45\n",
            "Total cost:  5662.76\n",
            "Average cost:  125.84\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import annotations\n",
        "import copy\n",
        "from typing import List\n",
        "\n",
        "import gym\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "np.set_printoptions(linewidth=np.inf)\n",
        "import pandas as pd\n",
        "from gym import spaces\n",
        "from gym.utils import seeding\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "\n",
        "matplotlib.use(\"Agg\")\n",
        "\n",
        "# from stable_baselines3.common.logger import Logger, KVWriter, CSVOutputFormat\n",
        "\n",
        "\n",
        "class GokuEnv(gym.Env):\n",
        "    \"\"\"A stock trading environment for OpenAI gym\"\"\"\n",
        "\n",
        "    metadata = {\"render.modes\": [\"human\"]}\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        df: pd.DataFrame,\n",
        "        stock_dim: int,\n",
        "        hmax: int,\n",
        "        initial_amount: int,\n",
        "        num_stock_shares: list[int],\n",
        "        buy_cost_pct: list[float],\n",
        "        sell_cost_pct: list[float],\n",
        "        reward_scaling: float,\n",
        "        state_space: int,\n",
        "        action_space: int,\n",
        "        tech_indicator_list: list[str],\n",
        "        turbulence_threshold=None,\n",
        "        risk_indicator_col=\"turbulence\",\n",
        "        make_plots: bool = False,\n",
        "        print_verbosity=10,\n",
        "        day=0,\n",
        "        initial=True,\n",
        "        previous_state=[],\n",
        "        model_name=\"\",\n",
        "        mode=\"\",\n",
        "        iteration=\"\",\n",
        "    ):\n",
        "        self.day = day\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.num_stock_shares = num_stock_shares\n",
        "        self.initial_amount = initial_amount  # get the initial cash\n",
        "        self.buy_cost_pct = buy_cost_pct\n",
        "        self.sell_cost_pct = sell_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "        self.action_space = spaces.Box(low=-1, high=1, shape=(self.action_space,))\n",
        "        self.observation_space = spaces.Box(\n",
        "            low=-np.inf, high=np.inf, shape=(self.state_space,)\n",
        "        )\n",
        "        self.data = self.df.loc[self.day, :]\n",
        "        self.terminal = False\n",
        "        self.make_plots = make_plots\n",
        "        self.print_verbosity = print_verbosity\n",
        "        self.turbulence_threshold = turbulence_threshold\n",
        "        self.risk_indicator_col = risk_indicator_col\n",
        "        self.initial = initial\n",
        "        self.previous_state = previous_state\n",
        "        self.model_name = model_name\n",
        "        self.mode = mode\n",
        "        self.iteration = iteration\n",
        "        # initalize state\n",
        "        self.state = self._initiate_state()\n",
        "\n",
        "        # initialize reward\n",
        "        self.reward = 0\n",
        "        self.turbulence = 0\n",
        "        self.cost = 0\n",
        "        self.trades = 0\n",
        "        self.episode = 0\n",
        "        # memorize all the total balance change\n",
        "        self.asset_memory = [\n",
        "            self.initial_amount\n",
        "            + np.sum(\n",
        "                np.array(self.num_stock_shares)\n",
        "                * np.array(self.state[1 : 1 + self.stock_dim])\n",
        "            )\n",
        "        ]  # the initial total asset is calculated by cash + sum (num_share_stock_i * price_stock_i)\n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory = []\n",
        "        self.state_memory = (\n",
        "            []\n",
        "        )  # we need sometimes to preserve the state in the middle of trading process\n",
        "        self.date_memory = [self._get_date()]\n",
        "        #         self.logger = Logger('results',[CSVOutputFormat])\n",
        "        # self.reset()\n",
        "        self._seed()\n",
        "        self.total_price = np.array([0.0] * 29)\n",
        "        self.avg_price = np.array([0.0] * 29)\n",
        "        self.total_stockss = np.array([0.0] * 29)\n",
        "        self.frame  = pd.DataFrame()\n",
        "        self.portfolios = [Portfolio() for _ in range(29)]\n",
        "        self.count = 0\n",
        "\n",
        "    def _sell_stock(self, index, action):\n",
        "        def _do_sell_normal():\n",
        "            if (\n",
        "                self.state[index + 2 * self.stock_dim + 1] != True\n",
        "            ):  # check if the stock is able to sell, for simlicity we just add it in techical index\n",
        "                # if self.state[index + 1] > 0: # if we use price<0 to denote a stock is unable to trade in that day, the total asset calculation may be wrong for the price is unreasonable\n",
        "                # Sell only if the price is > 0 (no missing data in this particular date)\n",
        "                # perform sell action based on the sign of the action\n",
        "                if self.state[index + self.stock_dim + 1] > 0:\n",
        "                    # Sell only if current asset is > 0\n",
        "                    sell_num_shares = min(\n",
        "                        abs(action), self.state[index + self.stock_dim + 1]\n",
        "                    )\n",
        "                    sell_amount = (\n",
        "                        self.state[index + 1]\n",
        "                        * sell_num_shares\n",
        "                        * (1 - self.sell_cost_pct[index])\n",
        "                    )\n",
        "                    # update balance\n",
        "                    self.state[0] += sell_amount\n",
        "\n",
        "                    self.state[index + self.stock_dim + 1] -= sell_num_shares\n",
        "                    self.cost += (\n",
        "                        self.state[index + 1]\n",
        "                        * sell_num_shares\n",
        "                        * self.sell_cost_pct[index]\n",
        "                    )\n",
        "                    self.trades += 1\n",
        "                    #if sell_num_shares >0:\n",
        "                    #  print(\"stocks Sold sell_num_shares\", sell_num_shares)\n",
        "                else:\n",
        "                    sell_num_shares = 0\n",
        "            else:\n",
        "                sell_num_shares = 0\n",
        "            return sell_num_shares\n",
        "\n",
        "        # perform sell action based on the sign of the action\n",
        "        if self.turbulence_threshold is not None:\n",
        "            if self.turbulence >= self.turbulence_threshold:\n",
        "                if self.state[index + 1] > 0:\n",
        "                    # Sell only if the price is > 0 (no missing data in this particular date)\n",
        "                    # if turbulence goes over threshold, just clear out all positions\n",
        "                    if self.state[index + self.stock_dim + 1] > 0:\n",
        "                        # Sell only if current asset is > 0\n",
        "                        sell_num_shares = self.state[index + self.stock_dim + 1]\n",
        "                        sell_amount = (\n",
        "                            self.state[index + 1]\n",
        "                            * sell_num_shares\n",
        "                            * (1 - self.sell_cost_pct[index])\n",
        "                        )\n",
        "                        # update balance\n",
        "                        self.state[0] += sell_amount\n",
        "                        self.state[index + self.stock_dim + 1] = 0\n",
        "                        self.cost += (\n",
        "                            self.state[index + 1]\n",
        "                            * sell_num_shares\n",
        "                            * self.sell_cost_pct[index]\n",
        "                        )\n",
        "                        self.trades += 1\n",
        "                    else:\n",
        "                        sell_num_shares = 0\n",
        "                else:\n",
        "                    sell_num_shares = 0\n",
        "            else:\n",
        "                sell_num_shares = _do_sell_normal()\n",
        "        else:\n",
        "            sell_num_shares = _do_sell_normal()\n",
        "        #print(\"stocks Sold sell_num_shares\", sell_num_shares)\n",
        "        return sell_num_shares\n",
        "\n",
        "    def _buy_stock(self, index, action):\n",
        "        def _do_buy():\n",
        "            if (self.state[index + 2 * self.stock_dim + 1] != True):  # check if the stock is able to buy\n",
        "                # if self.state[index + 1] >0:\n",
        "                # Buy only if the price is > 0 (no missing data in this particular date)\n",
        "                available_amount = self.state[0] // (self.state[index + 1] * (1 + self.buy_cost_pct[index]))\n",
        "                # when buying stocks, we should consider the cost of trading when calculating available_amount, or we may be have cash<0\n",
        "                # print('available_amount:{}'.format(available_amount))\n",
        "                # update balance\n",
        "                buy_num_shares = min(available_amount, action)\n",
        "                buy_amount = (self.state[index + 1] * buy_num_shares * (1 + self.buy_cost_pct[index]))\n",
        "                self.state[0] -= buy_amount\n",
        "                self.state[index + self.stock_dim + 1] += buy_num_shares\n",
        "                self.cost += (self.state[index + 1] * buy_num_shares * self.buy_cost_pct[index])\n",
        "                self.trades += 1\n",
        "            else:\n",
        "                buy_num_shares = 0\n",
        "\n",
        "            return buy_num_shares\n",
        "\n",
        "        # perform buy action based on the sign of the action\n",
        "        if self.turbulence_threshold is None:\n",
        "            buy_num_shares = _do_buy()\n",
        "        else:\n",
        "            if self.turbulence < self.turbulence_threshold:\n",
        "                buy_num_shares = _do_buy()\n",
        "            else:\n",
        "                buy_num_shares = 0\n",
        "                pass\n",
        "\n",
        "        return buy_num_shares\n",
        "\n",
        "    def _make_plot(self):\n",
        "        plt.plot(self.asset_memory, \"r\")\n",
        "        plt.savefig(f\"results/account_value_trade_{self.episode}.png\")\n",
        "        plt.close()\n",
        "\n",
        "    def step(self, actions):\n",
        "        self.terminal = self.day >= len(self.df.index.unique()) - 1\n",
        "        if self.terminal:\n",
        "            # print(f\"Episode: {self.episode}\")\n",
        "            if self.make_plots:\n",
        "                self._make_plot()\n",
        "            end_total_asset = self.state[0] + sum(np.array(self.state[1 : (self.stock_dim + 1)]) * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]))\n",
        "            df_total_value = pd.DataFrame(self.asset_memory)\n",
        "            tot_reward = (self.state[0] + \n",
        "                          sum(np.array(self.state[1 : (self.stock_dim + 1)]) \n",
        "                          * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])) \n",
        "                          - self.asset_memory[0])  # initial_amount is only cash part of our initial asset\n",
        "            df_total_value.columns = [\"account_value\"]\n",
        "            df_total_value[\"date\"] = self.date_memory\n",
        "            df_total_value[\"daily_return\"] = df_total_value[\"account_value\"].pct_change(1)\n",
        "            if df_total_value[\"daily_return\"].std() != 0:\n",
        "                sharpe = ((252**0.5) \n",
        "                * df_total_value[\"daily_return\"].mean() \n",
        "                / df_total_value[\"daily_return\"].std())\n",
        "            df_rewards = pd.DataFrame(self.rewards_memory)\n",
        "            df_rewards.columns = [\"account_rewards\"]\n",
        "            df_rewards[\"date\"] = self.date_memory[:-1]\n",
        "            \n",
        "            if self.episode % self.print_verbosity == 0:\n",
        "                print(f\"day: {self.day}, episode: {self.episode}\")\n",
        "                print(f\"begin_total_asset: {self.asset_memory[0]:0.2f}\")\n",
        "                print(f\"end_total_asset: {end_total_asset:0.2f}\")\n",
        "                print(f\"total_reward: {tot_reward:0.2f}\")\n",
        "                print(f\"total_cost: {self.cost:0.2f}\")\n",
        "                print(f\"total_trades: {self.trades}\")\n",
        "                if df_total_value[\"daily_return\"].std() != 0:\n",
        "                    print(f\"Sharpe: {sharpe:0.3f}\")\n",
        "                print(\"=================================\")\n",
        "\n",
        "            if (self.model_name != \"\") and (self.mode != \"\"):\n",
        "                df_actions = self.save_action_memory()\n",
        "                df_actions.to_csv(\"results/actions_{}_{}_{}.csv\".format(self.mode, self.model_name, self.iteration))\n",
        "                df_total_value.to_csv(\"results/account_value_{}_{}_{}.csv\".format(self.mode, self.model_name, self.iteration),index=False,)\n",
        "                df_rewards.to_csv(\"results/account_rewards_{}_{}_{}.csv\".format(self.mode, self.model_name, self.iteration),index=False,)\n",
        "                plt.plot(self.asset_memory, \"r\")\n",
        "                plt.savefig(\"results/account_value_{}_{}_{}.png\".format(self.mode, self.model_name, self.iteration))\n",
        "                plt.close()\n",
        "            return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "        else:\n",
        "            actions = actions * self.hmax  # actions initially is scaled between 0 to 1\n",
        "            actions = actions.astype(int)  # convert into integer because we can't by fraction of shares\n",
        "\n",
        "\n",
        "            if self.turbulence_threshold is not None:\n",
        "                if self.turbulence >= self.turbulence_threshold:\n",
        "                    actions = np.array([-self.hmax] * self.stock_dim)\n",
        "            \n",
        "            current_price = np.array(self.state[1 : (self.stock_dim + 1)])\n",
        "            actions = np.where(((current_price > ( self.avg_price * 0.6  + self.avg_price))& (self.avg_price >0.0)), self.total_stockss*-1,actions  )\n",
        "            actions = np.where(((current_price < (self.avg_price - self.avg_price * 0.2))& (self.avg_price >0)), self.total_stockss*-1,actions  )\n",
        "            \n",
        "            if self.count > 20:\n",
        "               actions = np.where(self.total_stockss >0.0, self.total_stockss*-1,0.00)\n",
        "            \n",
        "            # # # Mandatory Selling Profits and Losses\n",
        "            # for i in range(0,len(actions)):\n",
        "            #   # self.total_price\n",
        "            #   # self.total_stockss\n",
        "            #   # self.avg_price\n",
        "            #   # print(\"Current Price\")\n",
        "            #   # print(np.array(self.state[1 : (self.stock_dim + 1)])[i])\n",
        "            #   current_price  = np.array(self.state[1 : (self.stock_dim + 1)])[i]\n",
        "            #   avg_price = self.avg_price[i]\n",
        "            #   #print(avg_price)\n",
        "            #   if (current_price > ( avg_price * 0.6  + avg_price))  and (avg_price > 0.0):\n",
        "            #     actions[i] = self.total_stockss[i] * -1\n",
        "            #     #print(\"Updated actions\")\n",
        "            #     #self.total_price[i] = 0.0\n",
        "            #     print(round(current_price) , \"|\", \" avg_price\", avg_price, \"Profit\", current_price -  avg_price)\n",
        "            #   elif current_price < (avg_price - avg_price * 0.2)  and avg_price > 0.0:\n",
        "            #     actions[i] = self.total_stockss[i] * -1\n",
        "            #     print(round(current_price) , \"|\", \" avg_price\", avg_price,  \"loss\", current_price -  avg_price )\n",
        "            #     #self.total_price[i] = 0.0\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "            begin_total_asset = self.state[0] + sum(np.array(self.state[1 : (self.stock_dim + 1)])* np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]))\n",
        "            # print(\"begin_total_asset:{}\".format(begin_total_asset))\n",
        "\n",
        "\n",
        "            argsort_actions = np.argsort(actions)\n",
        "            sell_index = argsort_actions[: np.where(actions < 0)[0].shape[0]]\n",
        "            buy_index = argsort_actions[::-1][: np.where(actions > 0)[0].shape[0]]\n",
        "\n",
        "            for index in sell_index:\n",
        "              actions[index] = self._sell_stock(index, actions[index]) * (-1)\n",
        "\n",
        "            for index in buy_index:\n",
        "                actions[index] = self._buy_stock(index, actions[index])\n",
        "\n",
        "\n",
        "            self.actions_memory.append(actions)\n",
        "\n",
        "            # state: s -> s+1\n",
        "            self.day += 1\n",
        "            self.count += 1\n",
        "            self.data = self.df.loc[self.day, :]\n",
        "            if self.turbulence_threshold is not None:\n",
        "                if len(self.df.tic.unique()) == 1:\n",
        "                    self.turbulence = self.data[self.risk_indicator_col]\n",
        "                elif len(self.df.tic.unique()) > 1:\n",
        "                    self.turbulence = self.data[self.risk_indicator_col].values[0]\n",
        "\n",
        "\n",
        "\n",
        "            \n",
        "            recent_price = copy.deepcopy(self.state[1 : (self.stock_dim + 1)])\n",
        "            recent_buy_sell = copy.deepcopy(actions)\n",
        "            recent_buy_sell_price = np.where(recent_buy_sell != 0.0, recent_buy_sell * recent_price, 0.0)\n",
        "\n",
        "\n",
        "            \n",
        "            self.total_stockss = copy.deepcopy(recent_buy_sell) + copy.deepcopy(self.total_stockss)\n",
        "            self.total_price = copy.deepcopy(recent_buy_sell_price)+ copy.deepcopy(self.total_price)\n",
        "            \n",
        "            def _def_find_avg(x):\n",
        "                index, val = x\n",
        "                recent_action, recent_price = val\n",
        "                if recent_action > 0:\n",
        "                  self.portfolios[index].buy(recent_action, recent_price)\n",
        "                elif recent_action < 0:\n",
        "                  self.portfolios[index].sell(abs(recent_action))\n",
        "                return round(self.portfolios[index].get_avg_cost(), 2)\n",
        "              \n",
        "              \n",
        "            indexed = enumerate(zip(recent_buy_sell, recent_price))\n",
        "            \n",
        "            self.avg_price = np.fromiter(map(_def_find_avg, indexed, ), dtype=float)\n",
        "            \n",
        "            #print(self.avg_price )\n",
        "            # self.avg_price = np.divide(self.total_price,\n",
        "            #                            self.total_stockss,\n",
        "            #                            out=np.zeros_like(self.total_price),\n",
        "            #                            #where=((recent_buy_sell!=0.0)) &(self.total_stockss >0.0) )\n",
        "            #                            where=recent_buy_sell!=0.0)\n",
        "\n",
        "            self.avg_price = np.where(~np.isfinite(self.avg_price), 0.0, self.avg_price)\n",
        "            self.avg_price = np.where(self.avg_price<0.0, 0.0, self.avg_price)\n",
        "\n",
        "            self.avg_price = np.where(self.total_stockss <=0.0,0.0,self.avg_price)\n",
        "\n",
        "\n",
        "            # new_df = pd.DataFrame([recent_price])\n",
        "            # self.frame = pd.concat([self.frame, pd.DataFrame(new_df)],ignore_index=True,axis = 0)\n",
        "            \n",
        "            # new_df = pd.DataFrame([recent_buy_sell_price])\n",
        "            # self.frame = pd.concat([self.frame, pd.DataFrame(new_df)],ignore_index=True,axis = 0)\n",
        "            \n",
        "            # new_df = pd.DataFrame([self.total_stockss])\n",
        "            # self.frame = pd.concat([self.frame, pd.DataFrame(new_df)],ignore_index=True,axis = 0)\n",
        "\n",
        "            # new_df = pd.DataFrame([actions])\n",
        "            # self.frame = pd.concat([self.frame, pd.DataFrame(new_df)],ignore_index=True,axis = 0)\n",
        "\n",
        "            # new_df = pd.DataFrame([self.total_price])\n",
        "            # self.frame = pd.concat([self.frame, pd.DataFrame(new_df)],ignore_index=True,axis = 0)\n",
        "\n",
        "            # new_df = pd.DataFrame([self.avg_price])\n",
        "            # self.frame = pd.concat([self.frame, pd.DataFrame(new_df)],ignore_index=True,axis = 0)\n",
        "\n",
        "            # self.frame.to_csv(\"test.csv\")\n",
        "            \n",
        "            \n",
        "            \n",
        "            # dx = np.where(((self.avg_price < 0.00)))\n",
        "            # print(dx)\n",
        "            #if len(dx) > 0:\n",
        "            #print(self.total_stockss)\n",
        "            #print(self.total_price )\n",
        "            #print(self.avg_price)\n",
        "\n",
        "\n",
        "            \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "            # self.total_price = np.add(recent_buy_sell_price,\n",
        "            #                           self.total_price,\n",
        "            #                           out=np.zeros_like(self.total_price),\n",
        "            #                                where=(recent_buy_sell!=0.0  )\n",
        "            \n",
        "            # #self.total_price = np.where(self.total_price<0.0, 0.0, self.total_price)\n",
        "            # self.total_stockss = actions + self.total_stockss\n",
        "            \n",
        "            # self.avg_price = np.divide(self.total_price,\n",
        "            #                            self.total_stockss,\n",
        "            #                            out=np.zeros_like(self.total_price),\n",
        "            #                            where=recent_buy_sell!=0.0)\n",
        "          \n",
        "            # print(\"Total Stocks\")\n",
        "            # print(self.total_stockss)\n",
        "            # print(\"Price\")\n",
        "            # print(recent_price)\n",
        "\n",
        "            # print(\"Avg Price\")\n",
        "            # print(self.avg_price)\n",
        "            # print(\"Total Price\")\n",
        "            # print(self.total_price)\n",
        "\n",
        "\n",
        "            self.state = self._update_state()\n",
        "\n",
        "            end_total_asset = self.state[0] + sum(\n",
        "                np.array(self.state[1 : (self.stock_dim + 1)])\n",
        "                * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
        "            )\n",
        "            self.asset_memory.append(end_total_asset)\n",
        "            self.date_memory.append(self._get_date())\n",
        "            self.reward = end_total_asset - begin_total_asset\n",
        "            self.rewards_memory.append(self.reward)\n",
        "            self.reward = self.reward * self.reward_scaling\n",
        "            self.state_memory.append(\n",
        "                self.state\n",
        "            )  # add current state in state_recorder for each step\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):\n",
        "        # initiate state\n",
        "        self.state = self._initiate_state()\n",
        "\n",
        "        if self.initial:\n",
        "            self.asset_memory = [\n",
        "                self.initial_amount\n",
        "                + np.sum(\n",
        "                    np.array(self.num_stock_shares)\n",
        "                    * np.array(self.state[1 : 1 + self.stock_dim])\n",
        "                )\n",
        "            ]\n",
        "        else:\n",
        "            previous_total_asset = self.previous_state[0] + sum(\n",
        "                np.array(self.state[1 : (self.stock_dim + 1)])\n",
        "                * np.array(\n",
        "                    self.previous_state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]\n",
        "                )\n",
        "            )\n",
        "            self.asset_memory = [previous_total_asset]\n",
        "\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day, :]\n",
        "        self.turbulence = 0\n",
        "        self.cost = 0\n",
        "        self.trades = 0\n",
        "        self.terminal = False\n",
        "        # self.iteration=self.iteration\n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory = []\n",
        "        self.date_memory = [self._get_date()]\n",
        "        if self.total_price[5] > 0.0:\n",
        "          dd = pd.DataFrame(data = self.avg_price)\n",
        "          dd.to_csv(\"avg\")\n",
        "          dd = pd.DataFrame(data = self.total_price)\n",
        "          dd.to_csv(\"tp\")\n",
        "        \n",
        "        self.episode += 1\n",
        "        self.total_price = np.array([0.0] * 29)\n",
        "        self.avg_price = np.array([0.0] * 29)\n",
        "        self.total_stockss = np.array([0.0] * 29)\n",
        "        self.portfolios = [Portfolio() for _ in range(29)]\n",
        "        self.count = 0\n",
        "        #print(\"Resteting Account\")\n",
        "        return self.state\n",
        "\n",
        "    def render(self, mode=\"human\", close=False):\n",
        "        return self.state\n",
        "\n",
        "    def _initiate_state(self):\n",
        "        if self.initial:\n",
        "            # For Initial State\n",
        "            if len(self.df.tic.unique()) > 1:\n",
        "                # for multiple stock\n",
        "                state = (\n",
        "                    [self.initial_amount]\n",
        "                    + self.data.close.values.tolist()\n",
        "                    + self.num_stock_shares\n",
        "                    + sum(\n",
        "                        (\n",
        "                            self.data[tech].values.tolist()\n",
        "                            for tech in self.tech_indicator_list\n",
        "                        ),\n",
        "                        [],\n",
        "                    )\n",
        "                )  # append initial stocks_share to initial state, instead of all zero\n",
        "            else:\n",
        "                # for single stock\n",
        "                state = (\n",
        "                    [self.initial_amount]\n",
        "                    + [self.data.close]\n",
        "                    + [0] * self.stock_dim\n",
        "                    + sum(([self.data[tech]] for tech in self.tech_indicator_list), [])\n",
        "                )\n",
        "        else:\n",
        "            # Using Previous State\n",
        "            if len(self.df.tic.unique()) > 1:\n",
        "                # for multiple stock\n",
        "                state = (\n",
        "                    [self.previous_state[0]]\n",
        "                    + self.data.close.values.tolist()\n",
        "                    + self.previous_state[\n",
        "                        (self.stock_dim + 1) : (self.stock_dim * 2 + 1)\n",
        "                    ]\n",
        "                    + sum(\n",
        "                        (\n",
        "                            self.data[tech].values.tolist()\n",
        "                            for tech in self.tech_indicator_list\n",
        "                        ),\n",
        "                        [],\n",
        "                    )\n",
        "                )\n",
        "            else:\n",
        "                # for single stock\n",
        "                state = (\n",
        "                    [self.previous_state[0]]\n",
        "                    + [self.data.close]\n",
        "                    + self.previous_state[\n",
        "                        (self.stock_dim + 1) : (self.stock_dim * 2 + 1)\n",
        "                    ]\n",
        "                    + sum(([self.data[tech]] for tech in self.tech_indicator_list), [])\n",
        "                )\n",
        "        return state\n",
        "\n",
        "    def _update_state(self):\n",
        "        if len(self.df.tic.unique()) > 1:\n",
        "            # for multiple stock\n",
        "            state = (\n",
        "                [self.state[0]]\n",
        "                + self.data.close.values.tolist()\n",
        "                + list(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
        "                + sum((self.data[tech].values.tolist() for tech in self.tech_indicator_list),[],))\n",
        "\n",
        "        else:\n",
        "            # for single stock\n",
        "            state = ([self.state[0]] + [self.data.close] \n",
        "                     + list(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
        "                     + sum(([self.data[tech]] for tech in self.tech_indicator_list), []))\n",
        "\n",
        "        return state\n",
        "\n",
        "    def _get_date(self):\n",
        "        if len(self.df.tic.unique()) > 1:\n",
        "            date = self.data.date.unique()[0]\n",
        "        else:\n",
        "            date = self.data.date\n",
        "        return date\n",
        "\n",
        "\n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        asset_list = self.asset_memory\n",
        "        # print(len(date_list))\n",
        "        # print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({\"date\": date_list, \"account_value\": asset_list})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        if len(self.df.tic.unique()) > 1:\n",
        "            # date and close price length must match actions length\n",
        "            date_list = self.date_memory[:-1]\n",
        "            df_date = pd.DataFrame(date_list)\n",
        "            df_date.columns = [\"date\"]\n",
        "\n",
        "            action_list = self.actions_memory\n",
        "            df_actions = pd.DataFrame(action_list)\n",
        "            df_actions.columns = self.data.tic.values\n",
        "            df_actions.index = df_date.date\n",
        "            # df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        else:\n",
        "            date_list = self.date_memory[:-1]\n",
        "            action_list = self.actions_memory\n",
        "            df_actions = pd.DataFrame({\"date\": date_list, \"actions\": action_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]\n",
        "\n",
        "    def get_sb_env(self):\n",
        "        e = DummyVecEnv([lambda: self])\n",
        "        obs = e.reset()\n",
        "        return e, obs\n"
      ],
      "metadata": {
        "id": "4p02b2cx_dj5"
      },
      "execution_count": 377,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k5e9VO3mBLhm"
      },
      "execution_count": 377,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock_dimension = len(init_train_data.tic.unique())\n",
        "state_space = 1 + 2 * stock_dimension + len(config.INDICATORS) * stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n",
        "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
        "num_stock_shares = [0] * stock_dimension"
      ],
      "metadata": {
        "id": "rg7DTZbVV10n",
        "outputId": "701bc1ab-aeb8-47a9-dfe5-e833261f0c46",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock Dimension: 29, State Space: 291\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Defining the environment kwargs\n",
        "\n",
        "initial_amount = 500000\n",
        "env_kwargs = {\n",
        "    \"hmax\": 100,\n",
        "    \"initial_amount\": initial_amount,\n",
        "    \"num_stock_shares\": num_stock_shares,\n",
        "    \"buy_cost_pct\": buy_cost_list,\n",
        "    \"sell_cost_pct\": sell_cost_list,\n",
        "    \"state_space\": state_space,\n",
        "    \"stock_dim\": stock_dimension,\n",
        "    \"tech_indicator_list\": config.INDICATORS,\n",
        "    \"action_space\": stock_dimension,\n",
        "    \"reward_scaling\": 1e-4\n",
        "}"
      ],
      "metadata": {
        "id": "zODPmc5hV42J"
      },
      "execution_count": 379,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Instantiate the training gym compatible environment\n",
        "e_train_gym = GokuEnv(df = init_train_data, **env_kwargs)\n",
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "agent = DRLAgent(env = env_train)"
      ],
      "metadata": {
        "id": "7n2QYGP5V58p"
      },
      "execution_count": 380,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Instantiate the trading environment\n",
        "e_trade_gym = GokuEnv(df = init_trade_data, turbulence_threshold = None, **env_kwargs)"
      ],
      "metadata": {
        "id": "xoBM74XEX1oI"
      },
      "execution_count": 381,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Trade performance code\n",
        "The following code calculates trade performance metrics, which are then used as an objective for optimizing hyperparameter values.\n",
        "\n",
        "There are several available metrics. In this tutorial, the default choice is the ratio of average value of winning to losing trades."
      ],
      "metadata": {
        "id": "Dq6zJ14ZYsTK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Main method\n",
        "# Calculates Trade Performance for Objective\n",
        "# Called from objective method\n",
        "# Returns selected trade perf metric(s)\n",
        "# Requires actions and associated prices\n",
        "\n",
        "def calc_trade_perf_metric(df_actions, \n",
        "                           df_prices_trade,\n",
        "                           tp_metric,\n",
        "                           dbg=False):\n",
        "  \n",
        "    df_actions_p, df_prices_p, tics = prep_data(df_actions.copy(),\n",
        "                                                df_prices_trade.copy())\n",
        "    # actions predicted by trained model on trade data\n",
        "    df_actions_p.to_csv('df_actions.csv') \n",
        "\n",
        "    \n",
        "    # Confirms that actions, prices and tics are consistent\n",
        "    df_actions_s, df_prices_s, tics_prtfl = \\\n",
        "        sync_tickers(df_actions_p.copy(),df_prices_p.copy(),tics)\n",
        "    \n",
        "    # copy to ensure that tics from portfolio remains unchanged\n",
        "    tics = tics_prtfl.copy()\n",
        "    \n",
        "    # Analysis is performed on each portfolio ticker\n",
        "    perf_data= collect_performance_data(df_actions_s, df_prices_s, tics)\n",
        "    # profit/loss for each ticker\n",
        "    pnl_all = calc_pnl_all(perf_data, tics)\n",
        "    # values for trade performance metrics\n",
        "    perf_results = calc_trade_perf(pnl_all)\n",
        "    df = pd.DataFrame.from_dict(perf_results, orient='index')\n",
        "    \n",
        "    # calculate and return trade metric value as objective\n",
        "    m = calc_trade_metric(df,tp_metric)\n",
        "    print(f'Ratio Avg Win/Avg Loss: {m}')\n",
        "    k = str(len(tpm_hist)+1)\n",
        "    # save metric value\n",
        "    tpm_hist[k] = m\n",
        "    return m\n",
        "\n",
        "\n",
        "# Supporting methods\n",
        "def calc_trade_metric(df,metric='avgwl'):\n",
        "    '''# trades', '# wins', '# losses', 'wins total value', 'wins avg value',\n",
        "       'losses total value', 'losses avg value'''\n",
        "    # For this tutorial, the only metric available is the ratio of \n",
        "    #  average values of winning to losing trades. Others are in development.\n",
        "    \n",
        "    # some test cases produce no losing trades.\n",
        "    # The code below assigns a value as a multiple of the highest value during\n",
        "    # previous hp optimization runs. If the first run experiences no losses,\n",
        "    # a fixed value is assigned for the ratio\n",
        "    tpm_mult = 1.0\n",
        "    avgwl_no_losses = 25\n",
        "    if metric == 'avgwl':\n",
        "        if sum(df['# losses']) == 0:\n",
        "          try:\n",
        "            return max(tpm_hist.values())*tpm_mult\n",
        "          except ValueError:\n",
        "            return avgwl_no_losses\n",
        "        avg_w = sum(df['wins total value'])/sum(df['# wins'])\n",
        "        avg_l = sum(df['losses total value'])/sum(df['# losses'])\n",
        "        m = abs(avg_w/avg_l)\n",
        "\n",
        "    return m\n",
        "\n",
        "\n",
        "def prep_data(df_actions,\n",
        "              df_prices_trade):\n",
        "    \n",
        "    df=df_prices_trade[['date','close','tic']]\n",
        "    df['Date'] = pd.to_datetime(df['date'])\n",
        "    df = df.set_index('Date')\n",
        "    # set indices on both df to datetime\n",
        "    idx = pd.to_datetime(df_actions.index, infer_datetime_format=True)\n",
        "    df_actions.index=idx\n",
        "    tics = np.unique(df.tic)\n",
        "    n_tics = len(tics)\n",
        "    print(f'Number of tickers: {n_tics}')\n",
        "    print(f'Tickers: {tics}')\n",
        "    dategr = df.groupby('tic')\n",
        "    p_d={t:dategr.get_group(t).loc[:,'close'] for t in tics}\n",
        "    df_prices = pd.DataFrame.from_dict(p_d)\n",
        "    df_prices.index = df_prices.index.normalize()\n",
        "    return df_actions, df_prices, tics\n",
        "\n",
        "\n",
        "# prepares for integrating action and price files\n",
        "def link_prices_actions(df_a,\n",
        "                        df_p):\n",
        "    cols_a = [t + '_a' for t in df_a.columns]\n",
        "    df_a.columns = cols_a\n",
        "    cols_p = [t + '_p' for t in df_p.columns]\n",
        "    df_p.columns = cols_p\n",
        "    return df_a, df_p\n",
        "\n",
        "\n",
        "def sync_tickers(df_actions,df_tickers_p,tickers):\n",
        "    # Some DOW30 components may not be included in portfolio\n",
        "    # passed tickers includes all DOW30 components\n",
        "    # actions and ticker files may have different length indices\n",
        "    if len(df_actions) != len(df_tickers_p):\n",
        "      msng_dates = set(df_actions.index)^set(df_tickers_p.index)\n",
        "      try:\n",
        "        #assumption is prices has one additional timestamp (row)\n",
        "        df_tickers_p.drop(msng_dates,inplace=True)\n",
        "      except:\n",
        "        df_actions.drop(msng_dates,inplace=True)\n",
        "    df_actions, df_tickers_p = link_prices_actions(df_actions,df_tickers_p)\n",
        "    # identify any DOW components not in portfolio\n",
        "    t_not_in_a = [t for t in tickers if t + '_a' not in list(df_actions.columns)]\n",
        "  \n",
        "    # remove t_not_in_a from df_tickers_p\n",
        "    drop_cols = [t + '_p' for t in t_not_in_a]\n",
        "    df_tickers_p.drop(columns=drop_cols,inplace=True)\n",
        "    \n",
        "    # Tickers in portfolio\n",
        "    tickers_prtfl = [c.split('_')[0] for c in df_actions.columns]\n",
        "    return df_actions,df_tickers_p, tickers_prtfl\n",
        "\n",
        "def collect_performance_data(dfa,dfp,tics, dbg=False):\n",
        "    \n",
        "    perf_data = {}\n",
        "    # In current version, files columns include secondary identifier\n",
        "    for t in tics:\n",
        "        # actions: purchase/sale of DOW equities\n",
        "        acts = dfa['_'.join([t,'a'])].values\n",
        "        # ticker prices\n",
        "        prices = dfp['_'.join([t,'p'])].values\n",
        "        # market value of purchases/sales\n",
        "        tvals_init = np.multiply(acts,prices)\n",
        "        d={'actions':acts, 'prices':prices,'init_values':tvals_init}\n",
        "        perf_data[t]=d\n",
        "\n",
        "    return perf_data\n",
        "\n",
        "\n",
        "def calc_pnl_all(perf_dict, tics_all):\n",
        "    # calculate profit/loss for each ticker\n",
        "    print(f'Calculating profit/loss for each ticker')\n",
        "    pnl_all = {}\n",
        "    for tic in tics_all:\n",
        "        pnl_t = []\n",
        "        tic_data = perf_dict[tic]\n",
        "        init_values = tic_data['init_values']\n",
        "        acts = tic_data['actions']\n",
        "        prices = tic_data['prices']\n",
        "        cs = np.cumsum(acts)\n",
        "        args_s = [i + 1 for i in range(len(cs) - 1) if cs[i + 1] < cs[i]]\n",
        "        # tic actions with no sales\n",
        "        if not args_s:\n",
        "            pnl = complete_calc_buyonly(acts, prices, init_values)\n",
        "            pnl_all[tic] = pnl\n",
        "            continue\n",
        "        # copy acts: acts_rev will be revised based on closing/reducing init positions\n",
        "        pnl_all = execute_position_sales(tic,acts,prices,args_s,pnl_all)\n",
        "\n",
        "    return pnl_all\n",
        "\n",
        "\n",
        "def complete_calc_buyonly(actions, prices, init_values):\n",
        "    # calculate final pnl for each ticker assuming no sales\n",
        "    fnl_price = prices[-1]\n",
        "    final_values = np.multiply(fnl_price, actions)\n",
        "    pnl = np.subtract(final_values, init_values)\n",
        "    return pnl\n",
        "\n",
        "\n",
        "def execute_position_sales(tic,acts,prices,args_s,pnl_all):\n",
        "  # calculate final pnl for each ticker with sales\n",
        "    pnl_t = []\n",
        "    acts_rev = acts.copy()\n",
        "    # location of sales transactions\n",
        "    for s in args_s:  # s is scaler\n",
        "        # price_s = [prices[s]]\n",
        "        act_s = [acts_rev[s]]\n",
        "        args_b = [i for i in range(s) if acts_rev[i] > 0]\n",
        "        prcs_init_trades = prices[args_b]\n",
        "        acts_init_trades = acts_rev[args_b]\n",
        "  \n",
        "        # update actions for sales\n",
        "        # reduce/eliminate init values through trades\n",
        "        # always start with earliest purchase that has not been closed through sale\n",
        "        # selectors for purchase and sales trades\n",
        "        # find earliest remaining purchase\n",
        "        arg_sel = min(args_b)\n",
        "        # sel_s = len(acts_trades) - 1\n",
        "\n",
        "        # closing part/all of earliest init trade not yet closed\n",
        "        # sales actions are negative\n",
        "        # in this test case, abs_val of init and sales share counts are same\n",
        "        # zero-out sales actions\n",
        "        # market value of sale\n",
        "        # max number of shares to be closed: may be less than # originally purchased\n",
        "        acts_shares = min(abs(act_s.pop()), acts_rev[arg_sel])\n",
        "\n",
        "        # mv of shares when purchased\n",
        "        mv_p = abs(acts_shares * prices[arg_sel])\n",
        "        # mv of sold shares\n",
        "        mv_s = abs(acts_shares * prices[s])\n",
        "\n",
        "        # calc pnl\n",
        "        pnl = mv_s - mv_p\n",
        "        # reduce init share count\n",
        "        # close all/part of init purchase\n",
        "        acts_rev[arg_sel] -= acts_shares\n",
        "        acts_rev[s] += acts_shares\n",
        "        # calculate pnl for trade\n",
        "        # value of associated purchase\n",
        "        \n",
        "        # find earliest non-zero positive act in acts_revs\n",
        "        pnl_t.append(pnl)\n",
        "    \n",
        "    pnl_op = calc_pnl_for_open_positions(acts_rev, prices)\n",
        "    #pnl_op is list\n",
        "    # add pnl_op results (if any) to pnl_t (both lists)\n",
        "    pnl_t.extend(pnl_op)\n",
        "    #print(f'Total pnl for {tic}: {np.sum(pnl_t)}')\n",
        "    pnl_all[tic] = np.array(pnl_t)\n",
        "    return pnl_all\n",
        "\n",
        "\n",
        "def calc_pnl_for_open_positions(acts,prices):\n",
        "    # identify any positive share values after accounting for sales\n",
        "    pnl = []\n",
        "    fp = prices[-1] # last price\n",
        "    open_pos_arg = np.argwhere(acts>0)\n",
        "    if len(open_pos_arg)==0:return pnl # no open positions\n",
        "\n",
        "    mkt_vals_open = np.multiply(acts[open_pos_arg], prices[open_pos_arg])\n",
        "    # mkt val at end of testing period\n",
        "    # treat as trades for purposes of calculating pnl at end of testing period\n",
        "    mkt_vals_final = np.multiply(fp, acts[open_pos_arg])\n",
        "    pnl_a = np.subtract(mkt_vals_final, mkt_vals_open)\n",
        "    #convert to list\n",
        "    pnl = [i[0] for i in pnl_a.tolist()]\n",
        "    #print(f'Market value of open positions at end of testing {pnl}')\n",
        "    return pnl\n",
        "\n",
        "\n",
        "def calc_trade_perf(pnl_d):\n",
        "    # calculate trade performance metrics\n",
        "    perf_results = {}\n",
        "    for t,pnl in pnl_d.items():\n",
        "        wins = pnl[pnl>0]  # total val\n",
        "        losses = pnl[pnl<0]\n",
        "        n_wins = len(wins)\n",
        "        n_losses = len(losses)\n",
        "        n_trades = n_wins + n_losses\n",
        "        wins_val = np.sum(wins)\n",
        "        losses_val = np.sum(losses)\n",
        "        wins_avg = 0 if n_wins==0 else np.mean(wins)\n",
        "        #print(f'{t} n_wins: {n_wins} n_losses: {n_losses}')\n",
        "        losses_avg = 0 if n_losses==0 else np.mean(losses)\n",
        "        d = {'# trades':n_trades,'# wins':n_wins,'# losses':n_losses,\n",
        "             'wins total value':wins_val, 'wins avg value':wins_avg,\n",
        "             'losses total value':losses_val, 'losses avg value':losses_avg,}\n",
        "        perf_results[t] = d\n",
        "    return perf_results"
      ],
      "metadata": {
        "id": "QydaFexDX5BQ"
      },
      "execution_count": 382,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tuning hyperparameters using Optuna"
      ],
      "metadata": {
        "id": "c_AT1dsuZApj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sample_ddpg_params(trial:optuna.Trial):\n",
        "  # Size of the replay buffer\n",
        "  buffer_size = trial.suggest_categorical(\"buffer_size\", [int(1e4), int(1e5), int(1e6)])\n",
        "  learning_rate = trial.suggest_loguniform(\"learning_rate\", 1e-5, 1)\n",
        "  batch_size = trial.suggest_categorical(\"batch_size\", [32, 64, 128, 256, 512])\n",
        "  \n",
        "  return {\"buffer_size\": buffer_size,\n",
        "          \"learning_rate\":learning_rate,\n",
        "          \"batch_size\":batch_size}"
      ],
      "metadata": {
        "id": "fKGkKHV7Y9AA"
      },
      "execution_count": 383,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Variables\n",
        "## Fixed\n",
        "tpm_hist = {}  # record tp metric values for trials\n",
        "tp_metric = 'avgwl'  # specified trade_param_metric: ratio avg value win/loss\n",
        "## Settable by User\n",
        "n_trials = 100  # number of HP optimization runs\n",
        "total_timesteps = 2000 # per HP optimization run\n",
        "## Logging callback params\n",
        "lc_threshold=10e-5\n",
        "lc_patience=25\n",
        "lc_trial_number=100"
      ],
      "metadata": {
        "id": "N6blMWpz-pR0"
      },
      "execution_count": 419,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "OPTIONAL CODE FOR SAMPLING HYPERPARAMETERS\n",
        "\n",
        "Replace current call in function objective with\n",
        "\n",
        "hyperparameters = sample_ddpg_params_all(trial)"
      ],
      "metadata": {
        "id": "fHET-odKZShg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sample_ddpg_params_all(trial:optuna.Trial):\n",
        "\n",
        "    buffer_size = trial.suggest_categorical(\"buffer_size\", [int(1e4), int(1e5), int(1e6)])\n",
        "    learning_rate = trial.suggest_float(\"learning_rate\", 1e-5, 1)\n",
        "    batch_size = trial.suggest_categorical(\"batch_size\", [32, 64, 128, 256, 512])\n",
        "\n",
        "    gamma = trial.suggest_float(\"gamma\", 0.8, 0.999)\n",
        "    # Polyak coeff\n",
        "    tau = trial.suggest_float(\"tau\", 0.001, 0.1)\n",
        "\n",
        "    train_freq = trial.suggest_int(\"train_freq\", 1, 1024)\n",
        "    \n",
        "    gradient_steps = trial.suggest_int(\"gradient_steps\", 1, 1024)\n",
        "    \n",
        "    noise_type = trial.suggest_categorical(\"noise_type\", [\"ornstein-uhlenbeck\", \"normal\", None])\n",
        "    noise_std = trial.suggest_categorical(\"noise_std\", [.1,.2,.3] )\n",
        "\n",
        "    # NOTE: Add \"verybig\" to net_arch when tuning HER (see TD3)\n",
        "    net_arch = trial.suggest_categorical(\"net_arch\", [\"small\", \"big\"])\n",
        "    # activation_fn = trial.suggest_categorical('activation_fn', [nn.Tanh, nn.ReLU, nn.ELU, nn.LeakyReLU])\n",
        "\n",
        "    net_arch = {\n",
        "        \"small\": [64, 64],\n",
        "        \"medium\": [256, 256],\n",
        "        \"big\": [512, 512],\n",
        "    }[net_arch]\n",
        "  \n",
        "    hyperparams = {\n",
        "        \"batch_size\": batch_size,\n",
        "        \"buffer_size\": buffer_size,\n",
        "        \"gamma\": gamma,\n",
        "        \"gradient_steps\": gradient_steps,\n",
        "        \"learning_rate\": learning_rate,\n",
        "        \"tau\": tau,\n",
        "        \"train_freq\": train_freq,\n",
        "        #\"noise_std\": noise_std,\n",
        "        #\"noise_type\": noise_type,\n",
        "        \n",
        "        \"policy_kwargs\": dict(net_arch=net_arch)\n",
        "    }\n",
        "    return hyperparams"
      ],
      "metadata": {
        "id": "htLdZHKTZKPJ"
      },
      "execution_count": 386,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dJYdh-E4ZQKP"
      },
      "execution_count": 386,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Callbacks\n",
        "\n",
        "\n",
        "1. The callback will terminate if the improvement margin is below certain point\n",
        "2. It will terminate after certain number of trial_number are reached, not before that\n",
        "3. It will hold its patience to reach the threshold"
      ],
      "metadata": {
        "id": "mWEoJMPLZY4o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LoggingCallback:\n",
        "    def __init__(self,threshold,trial_number,patience):\n",
        "      '''\n",
        "      threshold:int tolerance for increase in objective\n",
        "      trial_number: int Prune after minimum number of trials\n",
        "      patience: int patience for the threshold\n",
        "      '''\n",
        "      self.threshold = threshold\n",
        "      self.trial_number  = trial_number\n",
        "      self.patience = patience\n",
        "      print(f'Callback threshold {self.threshold}, \\\n",
        "            trial_number {self.trial_number}, \\\n",
        "            patience {self.patience}')\n",
        "      self.cb_list = [] #Trials list for which threshold is reached\n",
        "    def __call__(self,study:optuna.study, frozen_trial:optuna.Trial):\n",
        "      #Setting the best value in the current trial\n",
        "      study.set_user_attr(\"previous_best_value\", study.best_value)\n",
        "      \n",
        "      #Checking if the minimum number of trials have pass\n",
        "      if frozen_trial.number >self.trial_number:\n",
        "          previous_best_value = study.user_attrs.get(\"previous_best_value\",None)\n",
        "          #Checking if the previous and current objective values have the same sign\n",
        "          if previous_best_value * study.best_value >=0:\n",
        "              #Checking for the threshold condition\n",
        "              if abs(previous_best_value-study.best_value) < self.threshold: \n",
        "                  self.cb_list.append(frozen_trial.number)\n",
        "                  #If threshold is achieved for the patience amount of time\n",
        "                  if len(self.cb_list)>self.patience:\n",
        "                      print('The study stops now...')\n",
        "                      print('With number',frozen_trial.number ,'and value ',frozen_trial.value)\n",
        "                      print('The previous and current best values are {} and {} respectively'\n",
        "                              .format(previous_best_value, study.best_value))\n",
        "                      study.stop()"
      ],
      "metadata": {
        "id": "He7GTZZUZWP8"
      },
      "execution_count": 420,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate the Sharpe ratio\n",
        "#This is our objective for tuning\n",
        "def calculate_sharpe(df):\n",
        "  df['daily_return'] = df['account_value'].pct_change(1)\n",
        "  if df['daily_return'].std() !=0:\n",
        "    sharpe = (252**0.5)*df['daily_return'].mean()/ \\\n",
        "          df['daily_return'].std()\n",
        "    return sharpe\n",
        "  else:\n",
        "    return 0"
      ],
      "metadata": {
        "id": "s9orU-WplQ70"
      },
      "execution_count": 421,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import clear_output\n",
        "import sys   \n",
        "\n",
        "os.makedirs(\"models\",exist_ok=True)\n",
        "\n",
        "def objective(trial:optuna.Trial):\n",
        "  #Trial will suggest a set of hyperparamters from the specified range\n",
        "\n",
        "  # Optional to optimize larger set of parameters\n",
        "  # hyperparameters = sample_ddpg_params_all(trial)\n",
        "  \n",
        "  # Optimize buffer size, batch size, learning rate\n",
        "  hyperparameters = sample_ddpg_params_all(trial)\n",
        "  print(f'Hyperparameters from objective: {hyperparameters.keys()}')\n",
        "  policy_kwargs = None  # default\n",
        "  \n",
        "  if 'policy_kwargs' in hyperparameters.keys():\n",
        "    policy_kwargs = hyperparameters['policy_kwargs']\n",
        "    del hyperparameters['policy_kwargs']\n",
        "    print(f'Policy keyword arguments {policy_kwargs}')\n",
        "  \n",
        "  \n",
        "  model_ddpg = agent.get_model(\"ddpg\",\n",
        "                               policy_kwargs = policy_kwargs,\n",
        "                               model_kwargs = hyperparameters )\n",
        "  \n",
        "  #You can increase it for better comparison\n",
        "  trained_ddpg = agent.train_model(model=model_ddpg,\n",
        "                                   tb_log_name=\"ddpg\",\n",
        "                                   total_timesteps=total_timesteps)\n",
        "  trained_ddpg.save('models/ddpg_{}.pth'.format(trial.number))\n",
        "  clear_output(wait=True)\n",
        "  \n",
        "  #For the given hyperparamters, determine the account value in the trading period\n",
        "  df_account_value, df_actions = DRLAgent.DRL_prediction(model=trained_ddpg, \n",
        "                                                         environment = e_trade_gym)\n",
        " \n",
        "  # Calculate trade performance metric\n",
        "  # Currently ratio of average win and loss market values\n",
        "  #tpm = calc_trade_perf_metric(df_actions,init_trade_data,tp_metric)\n",
        "  tpm = calculate_sharpe(df_account_value)\n",
        "  return tpm\n",
        "\n",
        "#Create a study object and specify the direction as 'maximize'\n",
        "#As you want to maximize sharpe\n",
        "#Pruner stops not promising iterations\n",
        "#Use a pruner, else you will get error related to divergence of model\n",
        "#You can also use Multivariate samplere\n",
        "#sampler = optuna.samplers.TPESampler(multivarite=True,seed=42)\n",
        "sampler = optuna.samplers.TPESampler()\n",
        "\n",
        "study = optuna.create_study(study_name=\"ddpg_study\",direction='maximize',\n",
        "                            sampler = sampler, pruner=optuna.pruners.HyperbandPruner())\n",
        "\n",
        "logging_callback = LoggingCallback(threshold=lc_threshold,\n",
        "                                   patience=lc_patience,\n",
        "                                   trial_number=lc_trial_number)\n",
        "#You can increase the n_trials for a better search space scanning\n",
        "study.optimize(objective, n_trials=n_trials,catch=(ValueError,),callbacks=[logging_callback])"
      ],
      "metadata": {
        "id": "b6EiwiiIZpRU",
        "outputId": "af64f199-d690-456b-e7ec-7fd72f489f2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-05-03 20:04:09,199]\u001b[0m Trial 9 finished with value: 1.2732697610370216 and parameters: {'buffer_size': 1000000, 'learning_rate': 0.6116794015778626, 'batch_size': 64, 'gamma': 0.8823117603557012, 'tau': 0.029935903658646547, 'train_freq': 159, 'gradient_steps': 742, 'noise_type': 'ornstein-uhlenbeck', 'noise_std': 0.3, 'net_arch': 'small'}. Best is trial 6 with value: 1.45090258536926.\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hit end!\n",
            "Hyperparameters from objective: dict_keys(['batch_size', 'buffer_size', 'gamma', 'gradient_steps', 'learning_rate', 'tau', 'train_freq', 'policy_kwargs'])\n",
            "Policy keyword arguments {'net_arch': [512, 512]}\n",
            "{'batch_size': 64, 'buffer_size': 100000, 'gamma': 0.8031629281164495, 'gradient_steps': 492, 'learning_rate': 0.12202298571357845, 'tau': 0.008023311618610161, 'train_freq': 403}\n",
            "Using cuda device\n",
            "day: 82, episode: 1450\n",
            "begin_total_asset: 500000.00\n",
            "end_total_asset: 539982.86\n",
            "total_reward: 39982.86\n",
            "total_cost: 1434.60\n",
            "total_trades: 355\n",
            "Sharpe: 1.807\n",
            "=================================\n",
            "----------------------------\n",
            "| time/              |     |\n",
            "|    episodes        | 4   |\n",
            "|    fps             | 540 |\n",
            "|    time_elapsed    | 0   |\n",
            "|    total_timesteps | 332 |\n",
            "| train/             |     |\n",
            "|    reward          | 0.0 |\n",
            "----------------------------\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 168       |\n",
            "|    time_elapsed    | 3         |\n",
            "|    total_timesteps | 664       |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -7.59e+04 |\n",
            "|    critic_loss     | 2.61e+12  |\n",
            "|    learning_rate   | 0.122     |\n",
            "|    n_updates       | 492       |\n",
            "|    reward          | 0.0       |\n",
            "----------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 136      |\n",
            "|    time_elapsed    | 7        |\n",
            "|    total_timesteps | 996      |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 338      |\n",
            "|    critic_loss     | 3.78e+09 |\n",
            "|    learning_rate   | 0.122    |\n",
            "|    n_updates       | 984      |\n",
            "|    reward          | 0.0      |\n",
            "---------------------------------\n",
            "day: 82, episode: 1460\n",
            "begin_total_asset: 500000.00\n",
            "end_total_asset: 531721.59\n",
            "total_reward: 31721.59\n",
            "total_cost: 1097.75\n",
            "total_trades: 286\n",
            "Sharpe: 1.473\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 16       |\n",
            "|    fps             | 126      |\n",
            "|    time_elapsed    | 10       |\n",
            "|    total_timesteps | 1328     |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -634     |\n",
            "|    critic_loss     | 1.24e+04 |\n",
            "|    learning_rate   | 0.122    |\n",
            "|    n_updates       | 1476     |\n",
            "|    reward          | 0.0      |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 121      |\n",
            "|    time_elapsed    | 13       |\n",
            "|    total_timesteps | 1660     |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -464     |\n",
            "|    critic_loss     | 6.24e+03 |\n",
            "|    learning_rate   | 0.122    |\n",
            "|    n_updates       | 1968     |\n",
            "|    reward          | 0.0      |\n",
            "---------------------------------\n",
            "day: 82, episode: 1470\n",
            "begin_total_asset: 500000.00\n",
            "end_total_asset: 531721.59\n",
            "total_reward: 31721.59\n",
            "total_cost: 1097.75\n",
            "total_trades: 286\n",
            "Sharpe: 1.473\n",
            "=================================\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    episodes        | 24   |\n",
            "|    fps             | 139  |\n",
            "|    time_elapsed    | 14   |\n",
            "|    total_timesteps | 1992 |\n",
            "| train/             |      |\n",
            "|    reward          | 0.0  |\n",
            "-----------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(study, \"final_ddpg_study__.pkl\")"
      ],
      "metadata": {
        "id": "1yIXC6W2ZqPP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the best hyperparamters\n",
        "print('Hyperparameters after tuning',study.best_params)\n",
        "print('Hyperparameters before tuning',config.DDPG_PARAMS)"
      ],
      "metadata": {
        "id": "gXzfenQTdqq_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f393b02f-a24f-49c5-e47a-6684e789d2ea"
      },
      "execution_count": 391,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hyperparameters after tuning {'buffer_size': 10000, 'learning_rate': 0.5339270926819014, 'batch_size': 512, 'gamma': 0.8859012104842331, 'tau': 0.010947929253002708, 'train_freq': 1016, 'gradient_steps': 932, 'noise_type': None, 'noise_std': 0.3, 'net_arch': 'small'}\n",
            "Hyperparameters before tuning {'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bi9ixeII_AIv"
      },
      "execution_count": 391,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "study.best_trial"
      ],
      "metadata": {
        "id": "VQcuj-yidu0O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90aaff15-948b-40db-a1eb-16a6bb99f4f5"
      },
      "execution_count": 392,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FrozenTrial(number=35, state=TrialState.COMPLETE, values=[1.515769330559795], datetime_start=datetime.datetime(2023, 5, 3, 19, 28, 9, 134350), datetime_complete=datetime.datetime(2023, 5, 3, 19, 28, 24, 251752), params={'buffer_size': 10000, 'learning_rate': 0.5339270926819014, 'batch_size': 512, 'gamma': 0.8859012104842331, 'tau': 0.010947929253002708, 'train_freq': 1016, 'gradient_steps': 932, 'noise_type': None, 'noise_std': 0.3, 'net_arch': 'small'}, user_attrs={}, system_attrs={}, intermediate_values={}, distributions={'buffer_size': CategoricalDistribution(choices=(10000, 100000, 1000000)), 'learning_rate': FloatDistribution(high=1.0, log=False, low=1e-05, step=None), 'batch_size': CategoricalDistribution(choices=(32, 64, 128, 256, 512)), 'gamma': FloatDistribution(high=0.999, log=False, low=0.8, step=None), 'tau': FloatDistribution(high=0.1, log=False, low=0.001, step=None), 'train_freq': IntDistribution(high=1024, log=False, low=1, step=1), 'gradient_steps': IntDistribution(high=1024, log=False, low=1, step=1), 'noise_type': CategoricalDistribution(choices=('ornstein-uhlenbeck', 'normal', None)), 'noise_std': CategoricalDistribution(choices=(0.1, 0.2, 0.3)), 'net_arch': CategoricalDistribution(choices=('small', 'big'))}, trial_id=35, value=None)"
            ]
          },
          "metadata": {},
          "execution_count": 392
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from stable_baselines3 import DDPG\n",
        "tuned_model_ddpg = DDPG.load('models/ddpg_{}.pth'.format(study.best_trial.number),env=env_train)"
      ],
      "metadata": {
        "id": "ZjYe9lOIdw3i"
      },
      "execution_count": 393,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Trading period account value with tuned model\n",
        "df_account_value_tuned, df_actions_tuned = DRLAgent.DRL_prediction(\n",
        "    model=tuned_model_ddpg, \n",
        "    environment = e_trade_gym)"
      ],
      "metadata": {
        "id": "3bnbx87vdzHZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "837d60d9-beb5-4d76-86e3-88e6ad24c3b3"
      },
      "execution_count": 394,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hit end!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_account_value_tuned"
      ],
      "metadata": {
        "id": "3vHSPqsU7Jp7",
        "outputId": "26064129-2d36-48a0-dea9-4e478f0ebfa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 395,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  account_value\n",
              "0    2021-05-03  500000.000000\n",
              "1    2021-05-04  499696.864414\n",
              "2    2021-05-05  501267.426095\n",
              "3    2021-05-06  505740.664321\n",
              "4    2021-05-07  509621.533690\n",
              "..          ...            ...\n",
              "101  2021-09-24  515462.473832\n",
              "102  2021-09-27  515462.473832\n",
              "103  2021-09-28  515462.473832\n",
              "104  2021-09-29  515462.473832\n",
              "105  2021-09-30  515462.473832\n",
              "\n",
              "[106 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d84c55f-663a-43f9-bff8-8a8626692584\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>date</th>\n",
              "      <th>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>500000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-05-04</td>\n",
              "      <td>499696.864414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-05-05</td>\n",
              "      <td>501267.426095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-05-06</td>\n",
              "      <td>505740.664321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-05-07</td>\n",
              "      <td>509621.533690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2021-09-24</td>\n",
              "      <td>515462.473832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>2021-09-27</td>\n",
              "      <td>515462.473832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>2021-09-28</td>\n",
              "      <td>515462.473832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>2021-09-29</td>\n",
              "      <td>515462.473832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>2021-09-30</td>\n",
              "      <td>515462.473832</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d84c55f-663a-43f9-bff8-8a8626692584')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-5d84c55f-663a-43f9-bff8-8a8626692584 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5d84c55f-663a-43f9-bff8-8a8626692584');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 395
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_account_value_tuned[105:106][\"account_value\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1Y1jjMLf0qF",
        "outputId": "644a5f5b-ba32-4dd9-d3d4-3de207b9326d"
      },
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "105    515462.473832\n",
              "Name: account_value, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 396
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Backtesting with our pruned model\n",
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all_tuned = backtest_stats(account_value=df_account_value_tuned)\n",
        "perf_stats_all_tuned = pd.DataFrame(perf_stats_all_tuned)\n",
        "perf_stats_all_tuned.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_tuned_\"+now+'.csv')"
      ],
      "metadata": {
        "id": "FJrv144od3G_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59e4d648-1629-4157-cf43-f6db4a018c3b"
      },
      "execution_count": 397,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.075092\n",
            "Cumulative returns     0.030925\n",
            "Annual volatility      0.049015\n",
            "Sharpe ratio           1.515769\n",
            "Calmar ratio           2.985289\n",
            "Stability              0.374084\n",
            "Max drawdown          -0.025154\n",
            "Omega ratio            1.731046\n",
            "Sortino ratio          2.339298\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             6.951638\n",
            "Daily value at risk   -0.005881\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "edxFoAXweYkC"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline"
      ],
      "metadata": {
        "id": "W6csypxKeWFu"
      },
      "execution_count": 288,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Now train with not tuned hyperaparameters\n",
        "#Default config.ddpg_PARAMS\n",
        "non_tuned_model_ddpg = agent.get_model(\"ddpg\",model_kwargs = config.DDPG_PARAMS )\n",
        "trained_ddpg = agent.train_model(model=non_tuned_model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "metadata": {
        "id": "asWsWzemd5dk",
        "outputId": "343da582-64be-4b0a-80e4-3c4cb771a8e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "328 |  avg_price 141.02 Profit 186.94868896484368\n",
            "43 |  avg_price 38.64 Profit 3.931975708007812\n",
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            "228 |  avg_price 171.31 Profit 56.3236669921875\n",
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            "43 |  avg_price 38.64 Profit 4.528136596679687\n",
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            "100 |  avg_price 84.79 Profit 15.03730865478515\n",
            "328 |  avg_price 141.02 Profit 186.82939575195311\n",
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            "51 |  avg_price 41.82 Profit 9.624988250732422\n",
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            "223 |  avg_price 171.31 Profit 51.598493041992185\n",
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            "52 |  avg_price 41.82 Profit 9.688228302001953\n",
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            "97 |  avg_price 84.79 Profit 12.049866638183587\n",
            "200 |  avg_price 184.55 Profit 15.901766967773426\n",
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            "43 |  avg_price 38.64 Profit 4.031340942382812\n",
            "96 |  avg_price 80.08 Profit 15.765359802246095\n",
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            "73 |  avg_price 68.69 Profit 4.6740823364257835\n",
            "93 |  avg_price 84.79 Profit 8.616249999999994\n",
            "260 |  avg_price 270.59 loss -10.536166992187475\n",
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            "74 |  avg_price 68.69 Profit 5.00895935058594\n",
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            "339 |  avg_price 141.02 Profit 198.39061401367186\n",
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            "328 |  avg_price 141.02 Profit 186.82939575195311\n",
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            "------------------------------------\n",
            "| time/              |             |\n",
            "|    episodes        | 600         |\n",
            "|    fps             | 36          |\n",
            "|    time_elapsed    | 1356        |\n",
            "|    total_timesteps | 49800       |\n",
            "| train/             |             |\n",
            "|    actor_loss      | 2.43        |\n",
            "|    critic_loss     | 0.0135      |\n",
            "|    learning_rate   | 0.001       |\n",
            "|    n_updates       | 49634       |\n",
            "|    reward          | -0.27574354 |\n",
            "------------------------------------\n",
            "331 |  avg_price 141.02 Profit 189.77190063476561\n",
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            "215 |  avg_price 171.31 Profit 43.67065185546875\n",
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            "332 |  avg_price 141.02 Profit 190.74608276367186\n",
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            "335 |  avg_price 141.02 Profit 194.26515625\n",
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            "334 |  avg_price 141.02 Profit 193.3307080078125\n",
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            "328 |  avg_price 141.02 Profit 186.94868896484368\n",
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            "328 |  avg_price 141.02 Profit 187.24690673828124\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_ddpg, \n",
        "    environment = e_trade_gym)"
      ],
      "metadata": {
        "id": "mv1zgwOgd9FY",
        "outputId": "43eb738e-e26b-4af8-a85e-0c8f941e7cca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "258 |  avg_price 210.54 Profit 47.68000122070313\n",
            "53 |  avg_price 49.87 Profit 3.1306904602050807\n",
            "364 |  avg_price 334.05 Profit 29.820880126953114\n",
            "119 |  avg_price 124.3 loss -5.039654541015622\n",
            "50 |  avg_price 53.05 loss -3.053635406494138\n",
            "69 |  avg_price 72.47 loss -3.911802978515624\n",
            "152 |  avg_price 131.91 Profit 19.99341186523438\n",
            "137 |  avg_price 128.95 Profit 7.898709106445324\n",
            "218 |  avg_price 232.74 loss -15.152017822265606\n",
            "209 |  avg_price 238.22 loss -29.710005493164033\n",
            "258 |  avg_price 210.54 Profit 47.43000122070313\n",
            "362 |  avg_price 334.05 Profit 27.617205810546864\n",
            "118 |  avg_price 124.3 loss -6.229512023925778\n",
            "50 |  avg_price 53.05 loss -3.1574485778808565\n",
            "69 |  avg_price 72.47 loss -3.8736712646484364\n",
            "153 |  avg_price 131.91 Profit 20.75171264648438\n",
            "137 |  avg_price 128.95 Profit 8.109417724609386\n",
            "218 |  avg_price 232.74 loss -15.033395996093759\n",
            "217 |  avg_price 238.22 loss -21.240004272460965\n",
            "259 |  avg_price 210.54 Profit 48.63001342773438\n",
            "371 |  avg_price 334.05 Profit 36.88408203124999\n",
            "120 |  avg_price 124.3 loss -4.755513000488278\n",
            "50 |  avg_price 53.05 loss -2.5629280090332003\n",
            "69 |  avg_price 72.47 loss -3.8069598388671864\n",
            "155 |  avg_price 131.91 Profit 23.134876098632816\n",
            "138 |  avg_price 128.95 Profit 8.578701782226574\n",
            "220 |  avg_price 232.74 loss -12.482248535156288\n",
            "221 |  avg_price 238.22 loss -17.119993896484402\n",
            "278 |  avg_price 210.54 Profit 67.31998535156251\n",
            "54 |  avg_price 49.87 Profit 3.88933456420898\n",
            "377 |  avg_price 334.05 Profit 43.03312988281249\n",
            "51 |  avg_price 53.05 loss -2.0627716064453097\n",
            "70 |  avg_price 72.47 loss -2.844298095703124\n",
            "157 |  avg_price 131.91 Profit 25.24232849121094\n",
            "138 |  avg_price 128.95 Profit 9.143750000000011\n",
            "226 |  avg_price 232.74 loss -6.984461059570322\n",
            "221 |  avg_price 238.22 loss -16.83000061035156\n",
            "286 |  avg_price 210.54 Profit 75.09000488281251\n",
            "54 |  avg_price 49.87 Profit 3.927264099121089\n",
            "376 |  avg_price 334.05 Profit 42.061236572265614\n",
            "51 |  avg_price 53.05 loss -1.8834693908691378\n",
            "70 |  avg_price 72.47 loss -2.3105609130859364\n",
            "147 |  avg_price 131.91 Profit 15.404331054687503\n",
            "137 |  avg_price 128.95 Profit 8.530819702148449\n",
            "224 |  avg_price 238.22 loss -14.059996337890652\n",
            "280 |  avg_price 210.54 Profit 69.13999267578126\n",
            "53 |  avg_price 49.87 Profit 3.443632965087893\n",
            "97 |  avg_price 91.17 Profit 6.2232266235351545\n",
            "385 |  avg_price 334.05 Profit 50.68333740234374\n",
            "70 |  avg_price 72.47 loss -2.548826904296874\n",
            "146 |  avg_price 131.91 Profit 13.877887573242191\n",
            "218 |  avg_price 238.22 loss -19.809996337890652\n",
            "272 |  avg_price 210.54 Profit 61.73999877929688\n",
            "98 |  avg_price 91.17 Profit 6.592107849121092\n",
            "375 |  avg_price 334.05 Profit 40.762103271484364\n",
            "51 |  avg_price 53.05 loss -2.0910842895507784\n",
            "50 |  avg_price 52.43 loss -2.0822445678710935\n",
            "70 |  avg_price 72.47 loss -2.5869662475585926\n",
            "280 |  avg_price 291.07 loss -11.395439453124993\n",
            "143 |  avg_price 131.91 Profit 11.179584350585941\n",
            "391 |  avg_price 407.58 loss -16.67225463867186\n",
            "223 |  avg_price 232.74 loss -9.703668823242197\n",
            "225 |  avg_price 238.22 loss -12.859999389648408\n",
            "270 |  avg_price 210.54 Profit 59.96000000000001\n",
            "98 |  avg_price 91.17 Profit 6.563726501464842\n",
            "370 |  avg_price 334.05 Profit 36.32597656249999\n",
            "50 |  avg_price 53.05 loss -2.5723579406738253\n",
            "51 |  avg_price 52.43 loss -1.7761799621582028\n",
            "280 |  avg_price 291.07 loss -10.921898193359368\n",
            "144 |  avg_price 131.91 Profit 11.888614501953128\n",
            "393 |  avg_price 407.58 loss -14.916914062499984\n",
            "224 |  avg_price 232.74 loss -8.596216430664043\n",
            "hit end!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Backtesting for not tuned hyperparamters\n",
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "# perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "metadata": {
        "id": "DuZL52qDeEz6",
        "outputId": "c082435f-275f-4013-944a-bc2f5f53ffcf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return         -0.093379\n",
            "Cumulative returns    -0.040397\n",
            "Annual volatility      0.113727\n",
            "Sharpe ratio          -0.813524\n",
            "Calmar ratio          -1.527834\n",
            "Stability              0.006414\n",
            "Max drawdown          -0.061119\n",
            "Omega ratio            0.871818\n",
            "Sortino ratio         -1.035397\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.710234\n",
            "Daily value at risk   -0.014695\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#You can see with trial, our sharpe ratio is increasing\n",
        "#Certainly you can afford more number of trials for further optimization\n",
        "from optuna.visualization import plot_optimization_history\n",
        "plot_optimization_history(study)"
      ],
      "metadata": {
        "id": "5aSsEmpZeI5C",
        "outputId": "7bc29965-f911-46cb-ae75-27feb610d18c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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      },
      "execution_count": 118,
      "outputs": [
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from optuna.visualization import plot_contour\n",
        "from optuna.visualization import plot_edf\n",
        "from optuna.visualization import plot_intermediate_values\n",
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_slice"
      ],
      "metadata": {
        "id": "K9zcey_BeJMe"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Hyperparamters importance\n",
        "#Ent_coef is the most important\n",
        "plot_param_importances(study)"
      ],
      "metadata": {
        "id": "J6WmB5DneOgh",
        "outputId": "457bcb6b-525c-4b9e-c1fa-eb906bd9b004",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": 120,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s_xcVpBSeOyE"
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      "cell_type": "code",
      "source": [
        "import gym\n",
        "import torch\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "\n",
        "class StockTradingEnv_A_lookback(gym.Env):\n",
        "    def __init__(self,config):\n",
        "        self.df = config[\"data\"]\n",
        "        self.stock_dim = config[\"stock_dim\"]                   # number of unique stocks\n",
        "        self.hmax = config[\"hmax\"]                             # maximum number of shares to trade\n",
        "        self.buy_cost_pct = config[\"buy_cost_pct\"]             # buy cost\n",
        "        self.sell_cost_pct = config[\"sell_cost_pct\"]           # sell cost\n",
        "        self.reward_scaling = config[\"reward_scaling\"]         # scaling factor for reward, good for training\n",
        "        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "        self.force_zero_cash = config[\"force_zero_cash\"] if \"force_zero_cash\" in config.keys() else True\n",
        "        self.initial_amount = torch.tensor(config[\"initial_amount\"],device=self.device) if \"initial_amount\" in config.keys() else torch.tensor(1e6,device=self.device)\n",
        "\n",
        "        self.observation_space = gym.spaces.Box(low=-np.inf, high = np.inf, shape=(config[\"state_space\"],))\n",
        "        self.action_space = gym.spaces.Box(low=-1, high=1, shape=(config[\"action_space\"], ))\n",
        "                 \n",
        "        self._read_file()\n",
        "        self.reset()                            \n",
        "    \n",
        "    def _read_file(self):\n",
        "        self.data = self.df.to_numpy()[:,3:].astype(\"float\")\n",
        "        self.data = torch.tensor(self.data.reshape(-1, self.stock_dim, self.data.shape[1]), dtype=float)   # (ts,sn,fn) \n",
        "        print(f\"Total Steps: {self.data.shape[0]}\")\n",
        "        self.buy_price = torch.tensor(self.df.close.to_numpy().reshape(-1, self.stock_dim).astype(\"float\") , dtype=float)      # (ts,sn)\n",
        "        self.sell_price =  torch.tensor(self.df.close.to_numpy().reshape(-1, self.stock_dim).astype(\"float\") , dtype=float)      # (ts,sn)\n",
        "        # self.buy_price = self.buy_price/self.sell_price[0]\n",
        "        # self.sell_price = self.sell_price/self.sell_price[0]\n",
        "\n",
        "    def reset(self):\n",
        "        self.today = [0]\n",
        "        self.input_data = self.data[self.today].to(self.device)\n",
        "        self.buy_data = self.buy_price[self.today].to(self.device)\n",
        "        self.sell_data = self.sell_price[self.today].to(self.device)\n",
        "        self.state = torch.concat([\n",
        "            self.initial_amount.reshape(-1),\n",
        "            torch.zeros(self.stock_dim, device=self.device),\n",
        "            self.input_data.reshape(-1)],dim = 0)\n",
        "            \n",
        "        self.day_count = 0                           \n",
        "        self.reward = 0                            \n",
        "        self.cost = 0                              \n",
        "        self.trades = 0                              \n",
        "        self.episode = 0  \n",
        "        \n",
        "        self.episode+=1  \n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.rewards_memory = []  \n",
        "        self.actions_memory = []\n",
        "        self.state_memory = [torch.zeros(self.stock_dim, device=self.device).cpu().numpy()]\n",
        "        \n",
        "        return self.state.reshape(-1).cpu().numpy()\n",
        "\n",
        "    def step(self,actions):\n",
        "        with torch.no_grad():\n",
        "            self.terminal = self.today[-1] >= self.data.shape[0]-1\n",
        "            # print(self.today)\n",
        "            # print(self.terminal)\n",
        "            self.day_count += 1\n",
        "\n",
        "            actions = torch.tensor(actions,device=self.device)\n",
        "            actions = actions.to(dtype = torch.float64)\n",
        "            # actions -= torch.mean(actions,keepdim=True,dim = 0)\n",
        "\n",
        "            start_cash = self.state[0]\n",
        "            start_hold = self.state[1:(self.stock_dim+1)]\n",
        "            mid_data = (self.buy_data + self.sell_data)/2\n",
        "            begin_asset =  self.asset_memory[-1] # start asset\n",
        "\n",
        "            # check stock > 0\n",
        "            next_hold = start_hold + actions\n",
        "            zero_hold = torch.zeros_like(next_hold,dtype=torch.float64 ,device=self.device)\n",
        "            next_hold = torch.where( next_hold <= 0, zero_hold, next_hold)  # >0\n",
        "\n",
        "            # check hold change\n",
        "            hold_change = next_hold - start_hold\n",
        "\n",
        "            zero_actions =  torch.zeros_like( actions,dtype=torch.float64 ).to(self.device)\n",
        "            sell_actions = torch.where( hold_change >= 0, zero_actions, hold_change) # <0\n",
        "            buy_actions = torch.where( hold_change <= 0, zero_actions, hold_change)  # >0\n",
        "\n",
        "            sell_money = torch.sum( -self.sell_data * sell_actions * ( 1 - self.sell_cost_pct ))\n",
        "            buy_money = torch.sum( self.buy_data * buy_actions * (1 + self.buy_cost_pct))\n",
        "\n",
        "            sell_money += start_cash\n",
        "\n",
        "            # force zero cash\n",
        "            if self.force_zero_cash:\n",
        "                if buy_money <  sell_money:\n",
        "                    if buy_money != 0:\n",
        "                        factor = sell_money / buy_money\n",
        "                        zero_factor = torch.zeros_like(factor,device=self.device)\n",
        "                        factor = torch.where(torch.isnan(factor), zero_factor, factor)\n",
        "                        factor = torch.where(torch.isinf(factor), zero_factor, factor)\n",
        "                        buy_actions *= factor\n",
        "                        buy_money *= factor\n",
        "                    else:\n",
        "                        factor = sell_money/torch.sum(self.buy_data* (1 + self.buy_cost_pct))\n",
        "                        zero_factor = torch.zeros_like(factor,device=self.device)\n",
        "                        factor = torch.where(torch.isnan(factor), zero_factor, factor)\n",
        "                        factor = torch.where(torch.isinf(factor), zero_factor, factor)\n",
        "                        buy_actions = factor.reshape(-1).repeat_interleave(self.stock_dim,dim = 0)\n",
        "                        buy_money = sell_money\n",
        "                if buy_money >  sell_money:\n",
        "                    if sell_money != 0:\n",
        "                        factor = buy_money / sell_money \n",
        "                        zero_factor = torch.zeros_like(factor,device=self.device)\n",
        "                        factor = torch.where(torch.isnan(factor), zero_factor, factor)\n",
        "                        factor = torch.where(torch.isinf(factor), zero_factor, factor)\n",
        "                        sell_actions *= factor\n",
        "                        sell_money *= factor\n",
        "                    else:\n",
        "                        factor = - buy_money/torch.sum(self.sell_data* (1 - self.sell_cost_pct))\n",
        "                        zero_factor = torch.zeros_like(factor,device=self.device)\n",
        "                        factor = torch.where(torch.isnan(factor), zero_factor, factor)\n",
        "                        factor = torch.where(torch.isinf(factor), zero_factor, factor)\n",
        "                        sell_actions = factor.reshape(-1).repeat_interleave(self.stock_dim,dim = 0)\n",
        "                        sell_money = buy_money\n",
        "            else:  # check cash > 0\n",
        "                if buy_money >  sell_money:\n",
        "                    factor = sell_money / buy_money\n",
        "                    zero_factor = torch.zeros_like(factor,device=self.device)\n",
        "                    factor = torch.where(torch.isnan(factor), zero_factor, factor)\n",
        "                    factor = torch.where(torch.isinf(factor), zero_factor, factor)  \n",
        "                    buy_actions *= factor\n",
        "                    buy_money *= factor\n",
        "            \n",
        "            hold_change = sell_actions + buy_actions\n",
        "            next_cash = sell_money - buy_money\n",
        "            next_hold = start_hold + hold_change\n",
        "            \n",
        "            next_asset = torch.sum( next_hold* mid_data) + next_cash\n",
        "            self.reward = next_asset - begin_asset   \n",
        "            self.reward = self.reward * self.reward_scaling\n",
        "            self.rewards_memory.append(self.reward)\n",
        "            self.asset_memory.append(next_asset) \n",
        "            self.actions_memory.append(actions.cpu().numpy())\n",
        "            self.state_memory.append(next_hold.cpu().numpy())\n",
        "\n",
        "            if self.terminal:\n",
        "                print(f\"Episode: {self.episode}\")\n",
        "                total_reward =  torch.sum(torch.stack(self.rewards_memory,dim = -1))\n",
        "                total_reward_pct =total_reward/self.asset_memory[0]\n",
        "                print(f\"Cumulated Return:  {total_reward}, Cumulated Return Rate:  {total_reward_pct}\")\n",
        "\n",
        "            else:\n",
        "                # update everything\n",
        "                self.today = [i+1 for i in self.today]    # day\n",
        "                self.input_data = self.data[self.today].to(self.device)\n",
        "                self.buy_data = self.buy_price[self.today].to(self.device)\n",
        "                self.sell_data = self.sell_price[self.today].to(self.device)\n",
        "\n",
        "                self.state = torch.concat([\n",
        "                    next_cash.reshape(-1),next_hold,self.input_data.reshape(-1)],dim = 0)\n",
        "                \n",
        "            return self.state.reshape(-1).cpu().numpy(), self.reward.cpu().numpy(), self.terminal,{}\n",
        "    \n",
        "    def render(self, figsize= (20,5)):\n",
        "        pd.DataFrame([0]+[i.cpu().item() for i in self.rewards_memory]).plot(title =\"rewards_memory\",figsize = figsize,legend = False)\n",
        "        plt.show()\n",
        "        pd.DataFrame([i.cpu().item() for i in self.asset_memory]).plot(title =\"asset_memory\",figsize = figsize,legend = False)\n",
        "        plt.show()\n",
        "        sns.heatmap(self.actions_memory)\n",
        "        plt.show()\n",
        "        sns.heatmap(self.state_memory)\n",
        "        plt.show()\n",
        "\n",
        "    def save_asset_memory(self):\n",
        "        date_list = [i for i in range(len(self.asset_memory ))]      # 日期列表\n",
        "        asset_list = [i.cpu().item() for i in self.asset_memory]     # 财产列表\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'account_value':asset_list})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        # date and close price length must match actions length\n",
        "        date_list = [i for i in range(len(self.actions_memory ))] \n",
        "        df_date = pd.DataFrame(date_list)\n",
        "        df_date.columns = ['date']\n",
        "\n",
        "        action_list = self.actions_memory\n",
        "        df_actions = pd.DataFrame(action_list)\n",
        "        df_actions.columns = range(self.stock_dim)\n",
        "        df_actions.index = df_date.date\n",
        "        #df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "\n",
        "        return df_actions\n",
        "\n",
        "    def save_state_memory(self):\n",
        "        # date and close price length must match actions length\n",
        "        date_list = [i for i in range(len(self.state_memory ))] \n",
        "        df_date = pd.DataFrame(date_list)\n",
        "        df_date.columns = ['date']\n",
        "\n",
        "        state_list = self.state_memory\n",
        "        df_state = pd.DataFrame(state_list)\n",
        "        df_state.columns = range(self.stock_dim)\n",
        "        df_state.index = df_date.date\n",
        "        #df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "\n",
        "        return df_state\n",
        "    \n",
        "    def save_reward_memory(self):\n",
        "        date_list = [i for i in range(len(self.rewards_memory ))]      # 日期列表\n",
        "        rewards_list = [i.cpu().item() for i in self.rewards_memory]     # 财产列表\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'account_value':rewards_list})\n",
        "        return df_account_value\n",
        "\n",
        "    def get_sb_env(self):\n",
        "        e = DummyVecEnv([lambda: self])\n",
        "        obs = e.reset()\n",
        "        return e, obs"
      ],
      "metadata": {
        "id": "S15p3oh8smm1"
      },
      "execution_count": 398,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_env_kwargs = {\n",
        "    \"data\":init_train_data,\n",
        "    \"stock_dim\":29,\n",
        "    \"hmax\":100,\n",
        "    \"buy_cost_pct\":0.001,\n",
        "    \"sell_cost_pct\":0.001,\n",
        "    \"reward_scaling\":1e-2,\n",
        "    \"state_space\": 1+30*15,\n",
        "    \"action_space\":15,\n",
        "    \"force_zero_cash\":False\n",
        "}\n",
        "e_train_gym = StockTradingEnv_A_lookback(train_env_kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p25N1SSvRL01",
        "outputId": "e859f4b0-f89a-4fd3-cf81-abba25439480"
      },
      "execution_count": 417,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Steps: 83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "metadata": {
        "id": "ErDMt1XmH416",
        "outputId": "c49acabc-4774-482e-a295-016c08d1fc05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "execution_count": 418,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-418-575def09184a>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0menv_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me_train_gym\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_sb_env\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-398-711fe79f7916>\u001b[0m in \u001b[0;36mget_sb_env\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_sb_env\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDummyVecEnv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         \u001b[0mobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36mreset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0mobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menvs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_obs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_obs_from_buf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36m_save_obs\u001b[0;34m(self, env_idx, obs)\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuf_obs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuf_obs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (494,) into shape (451,)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BvcSVWFGIW1R"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Making the most of your colab subscription",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}