Chapter_10_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chapter10의 사본",
      "provenance": [],
      "collapsed_sections": [
        "YUIHQOy47wen",
        "smNpvef2C--y",
        "pGdUV_EduJo2",
        "Y-roeVAvvGMB",
        "HvGOgCIR1rdY",
        "-85Hh9mp3FBQ"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jo-cho/advances_in_financial_machine_learning/blob/master/Chapter_10_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QiSb3F511Su",
        "colab_type": "text"
      },
      "source": [
        "Chapter 10 Bet Sizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDcDcrunOQkm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "4a1f84c2-a455-4441-bebe-1bd3cc644a1f"
      },
      "source": [
        "! pip install -q mlfinlab\n",
        "from mlfinlab import bet_sizing as bs"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |██▏                             | 10kB 38.6MB/s eta 0:00:01\r\u001b[K     |████▍                           | 20kB 2.2MB/s eta 0:00:01\r\u001b[K     |██████▋                         | 30kB 3.2MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 40kB 2.1MB/s eta 0:00:01\r\u001b[K     |███████████                     | 51kB 2.6MB/s eta 0:00:01\r\u001b[K     |█████████████▏                  | 61kB 3.1MB/s eta 0:00:01\r\u001b[K     |███████████████▍                | 71kB 3.6MB/s eta 0:00:01\r\u001b[K     |█████████████████▌              | 81kB 4.1MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 92kB 4.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 102kB 3.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 112kB 3.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▎     | 122kB 3.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 133kB 3.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 143kB 3.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 153kB 3.5MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.bagging module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUIHQOy47wen",
        "colab_type": "text"
      },
      "source": [
        "# Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfsbu-CRINIY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from matplotlib import cm\n",
        "import seaborn as sns; sns.set()\n",
        "\n",
        "import scipy.stats as ss\n",
        "from scipy.stats import norm, moment\n",
        "\n",
        "from dask.diagnostics import ProgressBar\n",
        "ProgressBar().register()  # ready progress bar once in notebook for later calculations\n",
        "\n",
        "from sklearn.neighbors import KernelDensity"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LeLOdvLDMWqY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlfinlab import data_structures, features, filters, labeling, util"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2w4Uv1LvMq_Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZlB7ZrNVC6Xc",
        "colab_type": "text"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smNpvef2C--y",
        "colab_type": "text"
      },
      "source": [
        "### First option (bet_size_reserve)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxO8KK1FFOl0",
        "colab_type": "text"
      },
      "source": [
        "$c_t = c_{t,l} - c_{t,s} $ \n",
        "\n",
        "where $c_{t,l}$ is the number of concurrent long bets at\n",
        "time $t$, and $c_{t,s}$ is the number of concurrent short bets at time $t$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohMwVApcGUID",
        "colab_type": "text"
      },
      "source": [
        "Recall label concurrency in Chapter 4 Sample Weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RcdtvMZJDN1I",
        "colab_type": "text"
      },
      "source": [
        "$$m_t = \\begin{cases}\n",
        "  \\frac{F[c_t]-F[0]}{1-F[0]} & \\text{if}\\ c_t \\ge 0 \\\\\n",
        "  \\frac{F[c_t]-F[0]}{F[0]} & \\text{if}\\ c_t < 0\n",
        "\\end{cases}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZE3LvrrKtz1h",
        "colab_type": "text"
      },
      "source": [
        "where $F[x]$ is CDF of the fitted mixture of two Gaussians for a value x.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pGdUV_EduJo2",
        "colab_type": "text"
      },
      "source": [
        "### Second Option (bet_size_budget)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIBJNEoZufsV",
        "colab_type": "text"
      },
      "source": [
        "$$ m_t = c_{t,l}\\frac{1}{max_i[c_{i,l}]} − c_{t,s}\\frac{1}{max_i[c_{i,s}]} $$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Li06RzAWu-DI",
        "colab_type": "text"
      },
      "source": [
        "### Third Option (bet_size_probability)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flyYYQ8pvFKl",
        "colab_type": "text"
      },
      "source": [
        "Apply meta-labeling. Fit a classifier, such as an SVC or RF, to determine the probability of misclassification, and use that probability to derive the bet size.\n",
        "\n",
        "- Advantages\n",
        " - the ML algorithm that decides the bet sizes is independent of the primary model, allowing for the incorporation of features predictive of false positives.\n",
        " - the predicted probability can be directly translated into bet size. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjRGqF4OvGaX",
        "colab_type": "text"
      },
      "source": [
        "When num_classes = 2,\n",
        "\n",
        "$$z = \\frac{p[x=1]−\\frac{1}{2}}{\\sqrt{p[x=1](1−p[x=1])}} $$\n",
        "\n",
        "$$m = 2Z [z] − 1$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOb6JcsIIWl8",
        "colab_type": "code",
        "outputId": "9a4bc09c-2cdc-4c2e-9052-3d00c7b0d46b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "# Figure 10.1\n",
        "\n",
        "p = np.linspace(0.001,1,1000,endpoint=False)\n",
        "z = (p-0.5)/(np.sqrt(p*(1-p)))\n",
        "Z_z = norm.cdf(z)\n",
        "m = (2 * Z_z -1) # np.sign(x)=1 always? increasing function\n",
        "plt.plot(z,m)\n",
        "plt.xlim(-3,3)\n",
        "plt.xlabel('z:standardized p');\n",
        "plt.ylabel('m:betting size');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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utXHm98+Z++aQx3CEEPYpr6iCf645QoCPO9NGyoWd4reTnxwhxHVVVtfx9uojqFQqnh3T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\nUnCEEEJYxf8DQZ/Mp/U/7vMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CiNZ5l90wSdh",
        "colab_type": "text"
      },
      "source": [
        "When num_classes > 2,\n",
        "\n",
        " $$ p = max_i[p_i]$$\n",
        "\n",
        " $$ z = \\frac{p − \\frac{1}{‖X‖}}{\\sqrt{p(1−p)}}$$\n",
        " \n",
        " $$ m = x (2Z [z] − 1) $$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AYjBMVpxMJj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "bc2411c6-fd7e-44a8-d8bc-374a0af5d703"
      },
      "source": [
        "n, p = 1000, 0.5\n",
        "\n",
        "mock_data = pd.DataFrame([np.random.binomial(n, p)/n for _ in range(1,n)], columns=['p']) #max p\n",
        "mock_data = mock_data.assign(z=(mock_data.p - 0.5) / (mock_data.p * (1-mock_data.p))**0.5)\n",
        "\n",
        "x =np.where(mock_data.p.diff()>0, 1, -1) #side\n",
        "tmp1 = 2*norm.cdf(mock_data.z) -1 #size\n",
        "mock_data['m'] = x * tmp1\n",
        "\n",
        "plt.figure(figsize=(12,4.6))\n",
        "ax1 = plt.subplot(121)\n",
        "mock_data.p.plot(ax=ax1).axhline(0.5, c='r', ls='--', )\n",
        "ax1.set_title(\"Predicted Prob.\")\n",
        "ax2=plt.subplot(122)\n",
        "mock_data[['z','m']].sort_values(by='z').plot(x='z', y='m', ax=ax2)\n",
        "ax2.set_title(\"Betting size\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Betting size')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hQwe89NJLqKysxJNPPok333xT0hOR0FBxZWndwYjHgnHn7SH4bNUpvP/dIcx6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\n",
            "text/plain": [
              "<Figure size 864x331.2 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-roeVAvvGMB",
        "colab_type": "text"
      },
      "source": [
        "### Dynamic bet sizing, limit price"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jj_GhTKy0W92",
        "colab_type": "text"
      },
      "source": [
        "TBA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AM7JlBP2D3p",
        "colab_type": "text"
      },
      "source": [
        "# Exercises"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvGOgCIR1rdY",
        "colab_type": "text"
      },
      "source": [
        "## 1. Using the formulation in Section 10.3, plot the bet size (m) as a function of the maximum predicted probability $(\\tilde{p})$ when ‖X‖ = 2, 3,…, 10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IE6xgy33xACB",
        "colab_type": "text"
      },
      "source": [
        "$$ z = \\frac{\\tilde{p}− \\frac{1}{‖X‖}}{\\sqrt{p(1-\\tilde{p})}} ∼Z $$\n",
        "\n",
        "$$ m = x (2Z [z] − 1) $$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOR-kwy4kU6T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "71ccb968-9ce9-401f-8521-5277ebe662fb"
      },
      "source": [
        "# This code is partly cloned from github.com/hudson-and-thames/research\n",
        "%matplotlib inline\n",
        "\n",
        "listX = range(2, 11)  # array of IIXII\n",
        "n = 10000\n",
        "\n",
        "fig_10_1, ax_10_1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "for X in listX:\n",
        "    # possible range for maximum predicted probability, [1/||X||, 1]\n",
        "    p = np.linspace(1/X, 1, n, endpoint=False)  # range of maximum predicted probabilities to plot\n",
        "    z = (p - 1/X) / (p*(1-p))**0.5\n",
        "    m = 2 * norm.cdf(z) - 1\n",
        "    ax_10_1.plot(p, m, label=f\"||X||={X}\")\n",
        "\n",
        "ax_10_1.set_ylabel(\"Bet Size $m=2Z[z]-1$\", fontsize=10)\n",
        "ax_10_1.set_xlabel(r\"Maximum Predicted Probability $\\tilde{p}=max_i${$p_i$}\", fontsize=10)\n",
        "ax_10_1.set_title(\"Bet Size vs. Maximum Predicted Probability\", fontsize=12)\n",
        "ax_10_1.legend(loc=\"upper left\", fontsize=8, title=\"Number of bet size labels\", title_fontsize=9)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XFeKXlEzCXfcQOmgIOqMRVVXZWb6Hz7O/ocJeRa8ohZldppIW2viPfwCPuRD7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\n54EH+sMdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-q4pt8pYpVHV",
        "colab_type": "text"
      },
      "source": [
        "예측된 최대 확률로 나타낸 bet size의 함수이다. 예측된 최대 확률이 높으면 bet size가 커진다. 최대확률이 $\\frac{1}{||X||}$ 보다 작으면 베팅하지 않는다. \n",
        "\n",
        "--Note--\n",
        "\n",
        "$||X||$가 클수록 bet size를 1로 놓는 순간이 빠르다, since the greater number of alternative bets spreads the remaining probability much thinner, leading to a greater confidence in that with the maximum predicted probability."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KY45YPZ-wn5r",
        "colab_type": "text"
      },
      "source": [
        "## 2. Draw 10,000 random numbers from a uniform distribution with bounds U[.5, 1.]. (Author's note: These exercises are intended to simulate dynamic bet sizing of a long-only strategy.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txH3mDaOw43a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# draw random numbers from a uniform distribution (all bets are long)\n",
        "np.random.seed(123)\n",
        "n = 10000\n",
        "P_t = np.random.uniform(0.01, 1., n)  # array of random from uniform dist.\n",
        "\n",
        "#0.5보다 큰 것만 뽑는다는건 long만 생각한다는 것이다.\n",
        "#그래서 그냥 U[0,1]로 간다. 0이 나오면 안돼서 0.01로 간다."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3L0YhkDywy8r",
        "colab_type": "text"
      },
      "source": [
        "### (a) Compute bet sizes m for $||X||=2$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89IbAEegwnNb",
        "colab_type": "code",
        "outputId": "2ddee919-0174-42e3-94fe-7a59a92a5723",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "z_t = (P_t - 1/2) /  (P_t*(1-P_t))**0.5\n",
        "m_t = 2 * norm.cdf(z_t) - 1\n",
        "m_t"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.33654605, -0.35022155, -0.46892829, ...,  0.99994458,\n",
              "       -0.48222868,  0.19047552])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmBI2gSUxjFp",
        "colab_type": "text"
      },
      "source": [
        "### (b) Assign 10,000 consecutive calendar days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pudhwe_DxfkK",
        "colab_type": "code",
        "outputId": "df6d88c9-0341-4c1c-d11c-9a994ff581f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "start_date = dt.datetime(1992, 8, 22)\n",
        "date_step = dt.timedelta(days=1)\n",
        "dates = np.array([start_date + i*date_step for i in range(n)])\n",
        "\n",
        "bet_sizes = pd.Series(data=m_t, index=dates)\n",
        "bet_sizes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1992-08-22    0.336546\n",
              "1992-08-23   -0.350222\n",
              "1992-08-24   -0.468928\n",
              "1992-08-25    0.089418\n",
              "1992-08-26    0.380306\n",
              "                ...   \n",
              "2020-01-03   -0.227356\n",
              "2020-01-04    0.143890\n",
              "2020-01-05    0.999945\n",
              "2020-01-06   -0.482229\n",
              "2020-01-07    0.190476\n",
              "Length: 10000, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzIk_IdGxeaR",
        "colab_type": "text"
      },
      "source": [
        "### (c) Draw 10,000 random numbers from a uniform distribution with bounds U[1, 25]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUzMT9ndymc-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rn = np.random.uniform(1, 25, n)\n",
        "rn = rn.round()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h595_oehy_UA",
        "colab_type": "text"
      },
      "source": [
        "### (d) Form a pandas.Series indexed by the dates in 2.b, and with values equal to the index shifted forward the number of days in 2.c. This is a t1 object similar to the ones we used in Chapter 3."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXyMtL49zC44",
        "colab_type": "code",
        "outputId": "2d0b4355-b9e8-4f21-855c-9125850a9737",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "shift_day = np.array([dt.timedelta(days=ndays) for ndays in rn])\n",
        "shift_day"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([datetime.timedelta(17), datetime.timedelta(21),\n",
              "       datetime.timedelta(21), ..., datetime.timedelta(12),\n",
              "       datetime.timedelta(8), datetime.timedelta(8)], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmWYKV0wz7_B",
        "colab_type": "code",
        "outputId": "56a93948-976d-4137-d26d-a53659f28ae6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "dates_shifted = dates + shift_day\n",
        "\n",
        "t1 = pd.Series(data=dates_shifted, index=dates)\n",
        "t1"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1992-08-22   1992-09-08\n",
              "1992-08-23   1992-09-13\n",
              "1992-08-24   1992-09-14\n",
              "1992-08-25   1992-09-04\n",
              "1992-08-26   1992-08-31\n",
              "                ...    \n",
              "2020-01-03   2020-01-23\n",
              "2020-01-04   2020-01-08\n",
              "2020-01-05   2020-01-17\n",
              "2020-01-06   2020-01-14\n",
              "2020-01-07   2020-01-15\n",
              "Length: 10000, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JEOZ3t-Z7cA",
        "colab_type": "code",
        "outputId": "2b515a42-fc31-4075-dea8-66d20813135d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "df_events = pd.DataFrame({'t1':t1, 'p':P_t, 'bet_size':bet_sizes})\n",
        "\n",
        "df_events.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>p</th>\n",
              "      <th>bet_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-08-22</th>\n",
              "      <td>1992-09-08</td>\n",
              "      <td>0.699504</td>\n",
              "      <td>0.336546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-23</th>\n",
              "      <td>1992-09-13</td>\n",
              "      <td>0.293278</td>\n",
              "      <td>-0.350222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-24</th>\n",
              "      <td>1992-09-14</td>\n",
              "      <td>0.234583</td>\n",
              "      <td>-0.468928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-25</th>\n",
              "      <td>1992-09-04</td>\n",
              "      <td>0.555802</td>\n",
              "      <td>0.089418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-26</th>\n",
              "      <td>1992-08-31</td>\n",
              "      <td>0.722274</td>\n",
              "      <td>0.380306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   t1         p  bet_size\n",
              "1992-08-22 1992-09-08  0.699504  0.336546\n",
              "1992-08-23 1992-09-13  0.293278 -0.350222\n",
              "1992-08-24 1992-09-14  0.234583 -0.468928\n",
              "1992-08-25 1992-09-04  0.555802  0.089418\n",
              "1992-08-26 1992-08-31  0.722274  0.380306"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4MR_xv0Zxyi",
        "colab_type": "text"
      },
      "source": [
        "### (e) Compute the resulting average active bets, following Section 10.4."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2KH6zWeaY5K",
        "colab_type": "text"
      },
      "source": [
        "what is average active bets?\n",
        "\n",
        "현재 bet 되어있는 모든 bet들의 평균"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ed9070tVekob",
        "colab_type": "code",
        "outputId": "82fa6a74-b3b4-4443-ca72-b02891635ee6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "df_events_2 = df_events.rename(columns={'bet_size': 'signal'})\n",
        "df_events_2.head()\n",
        "# 아래의 함수를 돌리기 위해 잠시 이름을 바꾸겠음."
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>p</th>\n",
              "      <th>signal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-08-22</th>\n",
              "      <td>1992-09-08</td>\n",
              "      <td>0.699504</td>\n",
              "      <td>0.336546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-23</th>\n",
              "      <td>1992-09-13</td>\n",
              "      <td>0.293278</td>\n",
              "      <td>-0.350222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-24</th>\n",
              "      <td>1992-09-14</td>\n",
              "      <td>0.234583</td>\n",
              "      <td>-0.468928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-25</th>\n",
              "      <td>1992-09-04</td>\n",
              "      <td>0.555802</td>\n",
              "      <td>0.089418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-26</th>\n",
              "      <td>1992-08-31</td>\n",
              "      <td>0.722274</td>\n",
              "      <td>0.380306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   t1         p    signal\n",
              "1992-08-22 1992-09-08  0.699504  0.336546\n",
              "1992-08-23 1992-09-13  0.293278 -0.350222\n",
              "1992-08-24 1992-09-14  0.234583 -0.468928\n",
              "1992-08-25 1992-09-04  0.555802  0.089418\n",
              "1992-08-26 1992-08-31  0.722274  0.380306"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JF5oArqZhATQ",
        "colab_type": "code",
        "outputId": "70e56afc-61b6-40fb-b97c-c58ec51ac41d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "avg_active_signals = avgActiveSignals(signals=df_events_2,numThreads=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-16 11:58:57.580270 100.0% mpAvgActiveSignals done after 0.34 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZ5CVya-ROu1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ef9793a1-0893-4d33-f642-e07b7bc46371"
      },
      "source": [
        "#mlfinlab\n",
        "avg_active_signals = bs.bet_sizing.avg_active_signals(signals = df_events_2, num_threads=10)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-16 12:16:52.019024 100.0% mp_avg_active_signals done after 0.5 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5HgEg4FbeHo",
        "colab_type": "code",
        "outputId": "4bfac37f-5644-4ccc-c520-7667a78af09a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "df_events['avg_active_bets'] = avg_active_signals\n",
        "df_events.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>p</th>\n",
              "      <th>bet_size</th>\n",
              "      <th>avg_active_bets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-08-22</th>\n",
              "      <td>1992-09-08</td>\n",
              "      <td>0.699504</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.336546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-23</th>\n",
              "      <td>1992-09-13</td>\n",
              "      <td>0.293278</td>\n",
              "      <td>-0.350222</td>\n",
              "      <td>-0.006838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-24</th>\n",
              "      <td>1992-09-14</td>\n",
              "      <td>0.234583</td>\n",
              "      <td>-0.468928</td>\n",
              "      <td>-0.160868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-25</th>\n",
              "      <td>1992-09-04</td>\n",
              "      <td>0.555802</td>\n",
              "      <td>0.089418</td>\n",
              "      <td>-0.098296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-26</th>\n",
              "      <td>1992-08-31</td>\n",
              "      <td>0.722274</td>\n",
              "      <td>0.380306</td>\n",
              "      <td>-0.002576</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   t1         p  bet_size  avg_active_bets\n",
              "1992-08-22 1992-09-08  0.699504  0.336546         0.336546\n",
              "1992-08-23 1992-09-13  0.293278 -0.350222        -0.006838\n",
              "1992-08-24 1992-09-14  0.234583 -0.468928        -0.160868\n",
              "1992-08-25 1992-09-04  0.555802  0.089418        -0.098296\n",
              "1992-08-26 1992-08-31  0.722274  0.380306        -0.002576"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqQC12ial-vy",
        "colab_type": "text"
      },
      "source": [
        "## 3. Using the $t1$ object from exercise 2.d:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX2-vS5PmN0j",
        "colab_type": "text"
      },
      "source": [
        "### (a) Determine the maximum number of concurrent long bets, $\\bar{c}_l$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvvnN2mSuXOa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_events_3 = df_events.copy()\n",
        "c_l = pd.Series()\n",
        "\n",
        "for idx in df_events_3.index:    \n",
        "    # long bets are defined as having a prediction probability greater than or equal to 0.5\n",
        "    df_long_active_idx = set(df_events_3[(df_events_3.index<=idx) & (df_events_3.t1>idx) & (df_events_3.p>=0.5)].index)\n",
        "    c_l[idx] = len(df_long_active_idx)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Akhm4bN6SfTD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bs.bet_sizing.get_concurrent_sides()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6aj8elwDyn9",
        "colab_type": "code",
        "outputId": "25221c65-b444-4f5b-9250-85c68e9fa6fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "print(c_l.head(), '\\n','max number of concurrent long :',c_l.max())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1992-08-22    1\n",
            "1992-08-23    1\n",
            "1992-08-24    1\n",
            "1992-08-25    2\n",
            "1992-08-26    2\n",
            "dtype: int64 \n",
            " max number of concurrent long : 13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMMKUf6pEted",
        "colab_type": "text"
      },
      "source": [
        "### (b) Determine the maximum number of concurrent short bets, $c_s$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3QGeDVrByza",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "c_s = pd.Series()\n",
        "\n",
        "for idx in df_events_3.index:    \n",
        "    # short bets are defined as having a prediction probability less than 0.5\n",
        "    df_short_active_idx = set(df_events_3[(df_events_3.index<=idx) & (df_events_3.t1>idx) & (df_events_3.p<0.5)].index)\n",
        "    c_s[idx] = len(df_short_active_idx)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1bDyLzjClih",
        "colab_type": "code",
        "outputId": "afddfcd1-a531-4bfd-9f8a-ea25a5415e91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "print(c_s.head(),'\\n', 'max number of concurrent short :', c_s.max())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1992-08-22    0\n",
            "1992-08-23    1\n",
            "1992-08-24    2\n",
            "1992-08-25    2\n",
            "1992-08-26    1\n",
            "dtype: int64 \n",
            " max number of concurrent short : 15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-_4COVUE_lB",
        "colab_type": "text"
      },
      "source": [
        "### (c) Derive the bet size as $m_t = c_{t,l} \\frac1{\\bar{c}_l}- c_{t,s}\\frac1{\\bar{c}_s}$, where $c_{t,l}$ is the number of concurrent long bets at time t, and $c_{t,s}$ is the number of concurrent short bets at time t."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhYTbVLlE__0",
        "colab_type": "code",
        "outputId": "1f3236d6-c248-4c26-e31f-f1efa77c4078",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "bet_size_2 = c_l/c_l.max() - c_s/c_s.max()\n",
        "bet_size_2.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1992-08-22    0.076923\n",
              "1992-08-23    0.010256\n",
              "1992-08-24   -0.056410\n",
              "1992-08-25    0.020513\n",
              "1992-08-26    0.087179\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tj_-wjCMIUAV",
        "colab_type": "code",
        "outputId": "02df3b87-a5fc-4938-97f4-a70c05238400",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "#overview\n",
        "\n",
        "df_events= df_events.assign(bet_size_2=bet_size_2)\n",
        "df_events.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>p</th>\n",
              "      <th>bet_size</th>\n",
              "      <th>avg_active_bets</th>\n",
              "      <th>bet_size_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-08-22</th>\n",
              "      <td>1992-09-04</td>\n",
              "      <td>0.699504</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.076923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-23</th>\n",
              "      <td>1992-09-11</td>\n",
              "      <td>0.293278</td>\n",
              "      <td>-0.350222</td>\n",
              "      <td>-0.006838</td>\n",
              "      <td>0.010256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-24</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.234583</td>\n",
              "      <td>-0.468928</td>\n",
              "      <td>-0.160868</td>\n",
              "      <td>-0.056410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-25</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.555802</td>\n",
              "      <td>0.089418</td>\n",
              "      <td>-0.098296</td>\n",
              "      <td>0.020513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-26</th>\n",
              "      <td>1992-09-14</td>\n",
              "      <td>0.722274</td>\n",
              "      <td>0.380306</td>\n",
              "      <td>0.122210</td>\n",
              "      <td>0.087179</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   t1         p  bet_size  avg_active_bets  bet_size_2\n",
              "1992-08-22 1992-09-04  0.699504  0.336546         0.336546    0.076923\n",
              "1992-08-23 1992-09-11  0.293278 -0.350222        -0.006838    0.010256\n",
              "1992-08-24 1992-08-26  0.234583 -0.468928        -0.160868   -0.056410\n",
              "1992-08-25 1992-08-26  0.555802  0.089418        -0.098296    0.020513\n",
              "1992-08-26 1992-09-14  0.722274  0.380306         0.122210    0.087179"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bursIBu8Mxlb",
        "colab_type": "text"
      },
      "source": [
        "## 4. Using the $t1$ object from exercise 2.d:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-YeJ1f0ND7H",
        "colab_type": "text"
      },
      "source": [
        "###(a) Compute the series $c_t = c_{t,l} − c_{t,s}$, where $c_{t,l}$ is the number of concurrent long bets at time $t$, and $c_{t,s}$ is the number of concurrent short bets at time $t$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQHzOrszNYtW",
        "colab_type": "code",
        "outputId": "1124672f-e9e0-4af6-ce66-11ae8e315ddb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "c = c_l - c_s\n",
        "c.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1992-08-22    1\n",
              "1992-08-23    0\n",
              "1992-08-24   -1\n",
              "1992-08-25    0\n",
              "1992-08-26    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeMK1qL-X4S2",
        "colab_type": "code",
        "outputId": "48c2b997-1247-444a-94ee-a8a3803bf0a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "df_events_4 = df_events.copy()\n",
        "df_events_4['c_t'] = c\n",
        "df_events_4.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>p</th>\n",
              "      <th>bet_size</th>\n",
              "      <th>avg_active_bets</th>\n",
              "      <th>bet_size_2</th>\n",
              "      <th>c_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-08-22</th>\n",
              "      <td>1992-09-04</td>\n",
              "      <td>0.699504</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-23</th>\n",
              "      <td>1992-09-11</td>\n",
              "      <td>0.293278</td>\n",
              "      <td>-0.350222</td>\n",
              "      <td>-0.006838</td>\n",
              "      <td>0.010256</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-24</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.234583</td>\n",
              "      <td>-0.468928</td>\n",
              "      <td>-0.160868</td>\n",
              "      <td>-0.056410</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-25</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.555802</td>\n",
              "      <td>0.089418</td>\n",
              "      <td>-0.098296</td>\n",
              "      <td>0.020513</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-26</th>\n",
              "      <td>1992-09-14</td>\n",
              "      <td>0.722274</td>\n",
              "      <td>0.380306</td>\n",
              "      <td>0.122210</td>\n",
              "      <td>0.087179</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   t1         p  bet_size  avg_active_bets  bet_size_2  c_t\n",
              "1992-08-22 1992-09-04  0.699504  0.336546         0.336546    0.076923    1\n",
              "1992-08-23 1992-09-11  0.293278 -0.350222        -0.006838    0.010256    0\n",
              "1992-08-24 1992-08-26  0.234583 -0.468928        -0.160868   -0.056410   -1\n",
              "1992-08-25 1992-08-26  0.555802  0.089418        -0.098296    0.020513    0\n",
              "1992-08-26 1992-09-14  0.722274  0.380306         0.122210    0.087179    1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wp3M2Kf0NsNB",
        "colab_type": "text"
      },
      "source": [
        "### (b) Fit a mixture of two Gaussians on {$c_t$}. You may want to use the method described in Lopez de Prado and Foreman [2014]."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAfa1py_VhZN",
        "colab_type": "text"
      },
      "source": [
        "from Lopez de Prado and Foreman [2014]\n",
        "\n",
        "I'll use EF3M algo, check https://github.com/jo-cho/research/tree/master/Chapter10/EF3M\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhVIOJ1LZTsf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from ef3m import rawMoment, M2N"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3VeOQByYVPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#F(x)함수 : x의 두 가우스분포 mixture의 CDF 함수\n",
        "\n",
        "def cdf_mixture(x, parameters):\n",
        "    # the CDF of a mixture of 2 normal distributions, evaluated at x\n",
        "    # :param x: (float) x-value\n",
        "    # :param parameters: (list) mixture parameters, [mu1, mu2, sigma1, sigma2, p1]\n",
        "    # :return: (float) CDF of the mixture\n",
        "    # ===================================\n",
        "    mu1, mu2, sigma1, sigma2, p1 = parameters  # for clarity\n",
        "    return p1*norm.cdf(x, mu1, sigma1) + (1-p1)*norm.cdf(x, mu2, sigma2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkrDSf_mcKSS",
        "colab_type": "code",
        "outputId": "04b06caa-615a-4df7-896d-60c5a0473e6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "# compute the first 5 centered moments\n",
        "# ======================================================\n",
        "print(f\"Mean (first raw moment): {df_events_4.c_t.mean()}\")\n",
        "print(\"First 5 centered moments\")\n",
        "mmnts = [moment(df_events_4.c_t.to_numpy(), moment=i) for i in range(1, 6)]\n",
        "for i, mnt in enumerate(mmnts):\n",
        "    print(f\"E[r^{i+1}] = {mnt}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean (first raw moment): 0.2665\n",
            "First 5 centered moments\n",
            "E[r^1] = 0.0\n",
            "E[r^2] = 13.74007775\n",
            "E[r^3] = -6.54721959075\n",
            "E[r^4] = 563.7430845018648\n",
            "E[r^5] = -949.945165248134\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amq3kotOcUV4",
        "colab_type": "code",
        "outputId": "1737f51a-4670-4a7f-ef44-d3dcf4f9b2a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "# Calculate raw moments from centered moments\n",
        "# ======================================================\n",
        "raw_mmnts = rawMoment(central_moments=mmnts, dist_mean=df_events_4.c_t.mean())\n",
        "for i, mnt in enumerate(raw_mmnts):\n",
        "    print(f\"E_Raw[r^{i+1}]={mnt}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "E_Raw[r^1]=0.2665\n",
            "E_Raw[r^2]=13.8111\n",
            "E_Raw[r^3]=4.456900000000001\n",
            "E_Raw[r^4]=562.6239\n",
            "E_Raw[r^5]=-200.80550000000028\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5tPQ8KvfcYHL",
        "colab_type": "code",
        "outputId": "c45dc317-2972-4fe6-a747-4d0b8afcf8c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "n_runs = 20\n",
        "m2n = M2N(raw_mmnts)\n",
        "df_10_4 = m2n.mpFit(raw_mmnts, epsilon=10**-5, factor=5, n_runs=n_runs, variant=2, maxIter=10_000_000)\n",
        "\n",
        "# 참고\n",
        "# https://github.com/jo-cho/research/blob/master/Chapter10/Chapter10_Exercises.ipynb\n",
        "\n",
        "# Visualize results and determine the most likely values from a KDE plot\n",
        "# ======================================================\n",
        "fig_10_4b, ax_10_4b = plt.subplots(nrows=5, ncols=1, figsize=(8,12))\n",
        "cols = ['mu1', 'mu2', 'sigma1', 'sigma2', 'p1']\n",
        "bins = int(n_runs / 4)  # to minimize number of bins without results, choose at own discretion\n",
        "fit_parameters = []\n",
        "\n",
        "print(f\"=== Values chosen based on the mode of the distribution of results from EF3M ({n_runs} runs) ===\")\n",
        "\n",
        "for col_i, ax in enumerate(ax_10_4b.flatten()):\n",
        "    col = cols[col_i]\n",
        "    df = df_10_4.copy()\n",
        "    df[col+'_bin'] = pd.cut(df[col], bins=bins)\n",
        "    df = df.groupby([col+'_bin']).count()\n",
        "    ax = sns.distplot(df_10_4[col], bins=bins, kde=True, ax=ax)\n",
        "    dd = ax.get_lines()[0].get_data()\n",
        "    most_probable_val = dd[0][np.argmax(dd[1])]\n",
        "    fit_parameters.append(most_probable_val)\n",
        "    ax.set_ylabel(\"Value count\")\n",
        "    ax.set_xlabel(f\"Estimated value of parameter: {col}\")\n",
        "    ax.axvline(most_probable_val, color='red', alpha=0.6)\n",
        "    ax.set_title(f\"{col}: {round(most_probable_val,3)}\", color='red')\n",
        "    print(f\"Most probable estimate for parameter '{col}':\", round(dd[0][np.argmax(dd[1])], 3))\n",
        "fig_10_4b.suptitle(f\"Figure 10.4(b): Results of running {n_runs} EF3M fitting rounds\", fontsize=14)\n",
        "fig_10_4b.tight_layout(pad=3.5)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[                                        ] | 0% Completed |  0.2s"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/ef3m.py:205: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  (1-p1 ) )**.5\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[########################################] | 100% Completed |  4.6s\n",
            "[########################################] | 100% Completed |  4.6s\n",
            "[                                        ] | 0% Completed |  0.1s"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/ef3m.py:205: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  (1-p1 ) )**.5\n",
            "/content/ef3m.py:205: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  (1-p1 ) )**.5\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[########################################] | 100% Completed |  1min 29.4s\n",
            "[########################################] | 100% Completed |  1min 29.5s\n",
            "=== Values chosen based on the mode of the distribution of results from EF3M (20 runs) ===\n",
            "Most probable estimate for parameter 'mu1': -1.278\n",
            "Most probable estimate for parameter 'mu2': 1.265\n",
            "Most probable estimate for parameter 'sigma1': 3.72\n",
            "Most probable estimate for parameter 'sigma2': 3.289\n",
            "Most probable estimate for parameter 'p1': 0.413\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gc2Ftpgsbcz3YdTCAtoDXQqUhhNRG7UCflpaGd999FxwORxP5IYQ0ITljyC4o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\ndwy3omvXrpw6dYpjx45hNBqtbkQEMJvNGI1Gqqu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TGT9+fGPHJ0SbU1xWxY6UdLYfSCev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z+IjIykvr6eV199lUWLFjXbELjBwc7dswsN9W2W7bmL9loeSmktvj4eDaYdO1fKtz9k4+2pY8xd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\n",
            "text/plain": [
              "<Figure size 576x864 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm7GPADNZ3us",
        "colab_type": "code",
        "outputId": "6146c2b3-defa-4944-9188-1557f436a4ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "# ef3m 알고리즘을 사용하여 parameter를 구한 결과\n",
        "\n",
        "fit_parameters "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-1.2778220443000434,\n",
              " 1.2649434463226872,\n",
              " 3.719519248817824,\n",
              " 3.2885615850438676,\n",
              " 0.41257874054295174]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-W8QroZacVa",
        "colab_type": "code",
        "outputId": "9a939673-f1b7-41cf-9418-04affbd54a38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "cdf_mixture(c,fit_parameters)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.57597653, 0.46748578, 0.36278127, ..., 0.36278127, 0.26875338,\n",
              "       0.57597653])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kzkQsvpW3Dx",
        "colab_type": "text"
      },
      "source": [
        "### (c) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcIU2Z3gYrWU",
        "colab_type": "text"
      },
      "source": [
        "$$m_t = \\begin{cases}\n",
        "  \\frac{F[c_t]-F[0]}{1-F[0]} & \\text{if}\\ c_t \\ge 0 \\\\\n",
        "  \\frac{F[c_t]-F[0]}{F[0]} & \\text{if}\\ c_t < 0\n",
        "\\end{cases}$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2b9x8P2YnQ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#위의 식\n",
        "\n",
        "def bet_size_mixed(c_t, parameters):\n",
        "    # return the bet size based on the description provided in\n",
        "    # question 10.4(c).\n",
        "    # :param c_t: (int) different of the number of concurrent long bets minus short bets\n",
        "    # :param parameters: (list) mixture parameters, [mu1, mu2, sigma1, sigma2, p1]\n",
        "    # :return: (float) bet size\n",
        "    # =========================\n",
        "    if c_t >= 0:\n",
        "        return ( cdf_mixture(c_t, parameters) - cdf_mixture(0, parameters) ) / ( 1 - cdf_mixture(0, parameters) )\n",
        "    else:\n",
        "        ( cdf_mixture(c_t, parameters) - cdf_mixture(0, parameters) ) / cdf_mixture(0, parameters)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHeM5hH8N_lC",
        "colab_type": "code",
        "outputId": "4c4bd9cc-5727-4063-aca0-839aa4f6abb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "bet_size_3 = pd.Series()\n",
        "bet_size_3 = df_events_4.c_t.apply(lambda c: bet_size_mixed(c, fit_parameters))\n",
        "bet_size_3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1992-08-22    0.203733\n",
              "1992-08-23    0.000000\n",
              "1992-08-24         NaN\n",
              "1992-08-25    0.000000\n",
              "1992-08-26    0.203733\n",
              "                ...   \n",
              "2020-01-03         NaN\n",
              "2020-01-04         NaN\n",
              "2020-01-05         NaN\n",
              "2020-01-06         NaN\n",
              "2020-01-07    0.203733\n",
              "Name: c_t, Length: 10000, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mARE6fLVbWsv",
        "colab_type": "code",
        "outputId": "5655ad85-cadc-452b-ee9c-ffd7ae971052",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "df_events['bet_size_3'] = bet_size_3\n",
        "df_events.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>p</th>\n",
              "      <th>bet_size</th>\n",
              "      <th>avg_active_bets</th>\n",
              "      <th>bet_size_2</th>\n",
              "      <th>bet_size_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-08-22</th>\n",
              "      <td>1992-09-04</td>\n",
              "      <td>0.699504</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.203733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-23</th>\n",
              "      <td>1992-09-11</td>\n",
              "      <td>0.293278</td>\n",
              "      <td>-0.350222</td>\n",
              "      <td>-0.006838</td>\n",
              "      <td>0.010256</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-24</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.234583</td>\n",
              "      <td>-0.468928</td>\n",
              "      <td>-0.160868</td>\n",
              "      <td>-0.056410</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-25</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.555802</td>\n",
              "      <td>0.089418</td>\n",
              "      <td>-0.098296</td>\n",
              "      <td>0.020513</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-26</th>\n",
              "      <td>1992-09-14</td>\n",
              "      <td>0.722274</td>\n",
              "      <td>0.380306</td>\n",
              "      <td>0.122210</td>\n",
              "      <td>0.087179</td>\n",
              "      <td>0.203733</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   t1         p  ...  bet_size_2  bet_size_3\n",
              "1992-08-22 1992-09-04  0.699504  ...    0.076923    0.203733\n",
              "1992-08-23 1992-09-11  0.293278  ...    0.010256    0.000000\n",
              "1992-08-24 1992-08-26  0.234583  ...   -0.056410         NaN\n",
              "1992-08-25 1992-08-26  0.555802  ...    0.020513    0.000000\n",
              "1992-08-26 1992-09-14  0.722274  ...    0.087179    0.203733\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCNrAJ7HXtKc",
        "colab_type": "code",
        "outputId": "99d83452-3f90-487b-a278-1dee1da2134a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "# bet size distrubutions\n",
        "\n",
        "fig_10_4c, ax_10_4c = plt.subplots(figsize=(10,6))\n",
        "for c in ['bet_size','bet_size_2', 'bet_size_3']:\n",
        "    ax_10_4c.hist(df_events[c],  label=c, alpha=0.6, bins=100)\n",
        "ax_10_4c.legend(loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax_10_4c.set_xlabel(\"Bet Size, $m_t$\", fontsize=12)\n",
        "ax_10_4c.set_ylabel(\"Value count\", fontsize=12)\n",
        "ax_10_4c.set_title(\"Figure 10.4(c): Bet Size distributions\", fontsize=14)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Figure 10.4(c): Bet Size distributions')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qFREREfzyyy/O9t9//53u3bvTvn17unfvzsmTJ295mYiIiIivKJJCrnXr1qxY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\nRESkUwzkiIiIiHTq/wFVt2lVPZJ5AwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2I4VkBbezB5",
        "colab_type": "text"
      },
      "source": [
        "## 5. Repeat exercise 1, where you discretize m with a stepSize=.01 stepSize=.05, and stepSize=.1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rrbLo0We4YG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def discreteSignal(signal0,stepSize):\n",
        "  \n",
        "  # discretize signal\n",
        "  signal1=(signal0/stepSize).round()*stepSize # discretize\n",
        "  signal1[signal1>1]=1 # cap\n",
        "  signal1[signal1<-1]=-1 # floor\n",
        "  \n",
        "  return signal1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwrrwX7Pe_TP",
        "colab_type": "code",
        "outputId": "48475752-f165-4abd-88be-92185a4e58b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# exercise 1\n",
        "listX = range(2, 11)  # array of IIXII\n",
        "n = 10000\n",
        "\n",
        "fig_10_5, ax_10_5 = plt.subplots(2, 2, figsize=(20, 16))\n",
        "ax_10_5 = fig_10_5.get_axes()\n",
        "d_list = [None, 0.01, 0.05, 0.1]\n",
        "sub_fig_num = ['i', 'ii', 'iii', 'iv']\n",
        "\n",
        "for i, axi in enumerate(ax_10_5):\n",
        "    #colors = iter(cm.coolwarm(np.linspace(0,1,len(num_classes_list))))\n",
        "    for X in listX:\n",
        "        d = d_list[i]\n",
        "        # possible range for maximum predicted probability, [1/||X||, 1]\n",
        "        P = np.linspace( 1/X, 1, n, endpoint=False)  # range of maximum predicted probabilities to plot\n",
        "        z = (P - 1/X) / (P*(1-P))**0.5\n",
        "        m = 2 * norm.cdf(z) - 1\n",
        "        if not isinstance(d, type(None)):\n",
        "            m = (m/d).round()*d\n",
        "        axi.plot(P, m, label=f\"||X||={X}\")\n",
        "\n",
        "    axi.set_ylabel(\"Bet Size $m=2Z[z]-1$\", fontsize=14)\n",
        "    axi.set_xlabel(r\"Maximum Predicted Probability $\\tilde{p}=max_i${$p_i$}\", fontsize=14)\n",
        "    #axi.set_xticks([0.1*i for i in range(11)])\n",
        "    #axi.set_yticks([0.1*i for i in range(11)])\n",
        "    axi.legend(loc=\"upper left\", fontsize=10, title=\"Number of bet size labels\", title_fontsize=11)\n",
        "    #axi.set_ylim((0,1.05))\n",
        "    #axi.set_xlim((0, 1.05))\n",
        "    if not isinstance(d, type(None)):\n",
        "        axi.set_title(f\"({sub_fig_num[i]}) Discretized Bet Size, d={d}\", fontsize=16)\n",
        "    else:\n",
        "        axi.set_title(f\"({sub_fig_num[i]}) Continuous Bet Size\", fontsize=16)\n",
        "    axi.grid(linewidth=1, linestyle=':')\n",
        "\n",
        "fig_10_5.suptitle(\"Figure 10.5: Plots where bet size $m$ is discretized\", fontsize=18)\n",
        "fig_10_5.tight_layout(pad=4)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Li99++41p06YRERHBRx99lKXytm/fzowZMxg2bBg1a9YkMTGRy5cvm1y/Nb32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\napq2sPFgMbtOlmMySgxPdYat1FV3iRRbFoFTQTeJLIMShAeLJTImKkH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It6Td2KPV83Cg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\nMqry1qFIjcSkrcIRPxdN0zhy5ACXLp0jMTGZxYuXY7MFgkal0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gAAIABJREFU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\nvaI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gi5gNZh7Pe5h0R2rM79FTgkfWI+ZvwDxlKZZpgzd40ZW2/OGOs1Q3+PnZN/N00fwB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\no+/FeE1SO1yd4wnV14Dv09+iNlbhuPNpzLndLIm7wekrjS9XuHlh9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jHn/id75Gm6e/M5\njuy/yoRpedy+oEjXpOnV536P79RJcr77PZJu61n1094gqzKvHP0bh6qOcd+I5cwb3PMlgKHvRev2\nfX1Nu6TpEJE01YtINQ5ICs++f5jaxgD/+NAUBqVHFn+C2BAvsSzoO4TGsUWMb/s3iuyj7OSfUGVv\nKIq00IxNo7E1ebl+/SdUVlY0vQrFmsXS/qeUZ0sx0tXGNtsMXSRhZCnIutf+FZ+7ydNUC7VtjiBx\nevViLZ+tOobaVAFcVTXMFpMu49vqNR9Ts+bjlteaouAcM7bX7UbClks7+Oj8upbXiqqQn5jb4/bi\n4Xrp3/s20rFNrRsUGXOy8DTVi0g0/vp0JS99chylyRNYUTWsInHaL4iHWBb0LfGicdxfEb744gue\neeYZ/vM//zPiYx2O0FO71FQXJpMBs9lISkoogedy2Vr2p6W5MBoNWCwmkpNDibuEBBt2e2h/enoC\nBgNYrSaSkkL7ExPt2GyhwVJmZqiCu81mJjExNFsyKcmB1WrCYAgdD2C3W0hompmVnOzAYjFhNBpI\nS3O19NflCu1PSXFiNhsxmQykpob2O53WlqpisfxM3tqjVF34BHtiIXmjHyIx0dnmM9XV1bB581qs\nVivLl99NZmZah59JcQfxbDiHQdVIWFRERmFGt5+p6upZdn70Iknpmcy5/8ekZ6ZF9Jk0TWPv1vMc\n2X+VidPzWHLveGw2sy46JTlMXH32v/CdOknBD39I0m23R10nSZV59eSbHKo6xgMjV7By8pJefabm\nPsfyb08L+gis/y/Uqos4Fj5J9tRZvfpM8RZPsb5GJCbaw/5MTqeVl9Ycp7ikgX98bBrD85Pj8jMN\nRJ16+5kSEx0D7jMNRJ1685kSEuxR+Uw2m5gRpQdifBtfsWbUGlGkRpIzx5CUeQupuTPJKfoGRrO9\n5TNVVpaTlZXFmDHjmThxMjfddAtZWYPafSa5yos51U7CpGxsYzNIvjkf+/DUTj+T3argqa8mZ8go\nJsxYwMhpc5k65xvkDRsZ9meqrvQgBRUm3zKYiTflc/OsYcxbOkoXnZSrlzA6nGQuWULmkiWkLl7C\n0EcfiqpOFxuvYDNZWTBkFvMGz2T5qIWsKFzSr8e3amUxJmcy9kl3Yp2wiKRb78Y66a4BEU/xcI2I\nZHxbUe8nICksnJ7PitmFLJ0xhDnT8uPuMw1EncT49sbQaSCMbw2apkU1fVtdXc3ixYvZt28fJpMJ\nRVG45ZZb2LBhQ7sn8gcOHOBnP/sZL7zwAuPGjevBudztqkkKeoe3/hRV59/D5sonc/ijGK9bJlNX\nV8uGDZ9gNJpYvHg5iYkdV7hUPRLudWfRggquxUWY07qfaVp59Rw7PvgjzsRU5q58CrsrsuqZ13qa\nTpiex+3zdZxp6vdz9Znf4Tt7hpzvfZ+kW2bo0m4kSIrES0df51j1SR4ceQ+z86PfB73Rgj68n/0n\nakUx9oU/wjK0Z15fgt6jaRqvbzjNtgNXeXThSOZPy491lwQCQQwwGg0tA11BK2J827/xuy9RceZV\nMosexZFU1G6/qqq88cbLTJ48nYkTuy7YU/fmEazD03DeHF6xovrqUtb9978yY+l3KBjVs2JAX++5\nxL7txTzx85mYLfoWsbnyu39H9fsp+L/+H13bjYSXjrxGhbeK/3XLP8SsD3rjWf3/YrA6cd71i1h3\n5YZn1fZzrNt3iZd+2TNrM4FA0P/pbnwb9Rmn6enpjBkzhjVr1gCwZs0axowZ025QefjwYZ5++mme\nffbZHg0qBfrjazhHVfH7WJ05DJnw7XZJ04aGOjZuXIPBYGDRomWdJ019Eu6N51D9Mq4Fw8JKmlaV\nFLPjgz/hSEhhTg+Tpvu2F7d4muqbNPWFkqbnzjLoiR/ELGn64pHXOFZ9kkdG3adb0rT5yU4saJM0\nXSCSpn1FuBp/uuci2w5cZcmtBSJp2g+JZSwLooPQOLaI8W3/ptnT1Onq2H6m2dPUbA7D5UxWu1ya\nfz3NxaBM5p7P5m4uBqV3ISgIFTw1WHvut6oHQVXCbNTPYS4urpeKjEF4mvYZkWgsPE37L3ERy4I+\nJV40jkkZ5H/+53/mV7/6FS+88AJJSUn85je/AeCJJ57gJz/5CRMmTOBf/uVf8Pv9/PrXv2457re/\n/S2jRo2KRZdvePzui1SdfweLLZ3MokcxGK2A1LLf7XazYcOnaJrGokXLSUpK7rAdNSDj3nge1R0k\nYUEh5szu/RGrSy+y/YMXsLuSmLvyKRwRJk0Bvtx1kQN7LzN28iBmLtSxEJTfz9Vn/iuUNP3+D0ic\nfrMu7UaCpMq8ePQ1jtec4tHR93Nbrn59UFVVt7YiQQv68H32O9SK86Gk6TCRNO0rwtF495FSPthx\nnlvHZXPfHe1n4gjin1jFsiB6CI1jjxjf9i/KT79K0N/kWaopof8bWn8aff75di5dKg7tblqg11Ex\nKN/+EoJnrikCpnXtaappGpvf+T0N1WWh102xG4mnaenletZ/eAxFafJklBXMFqM+nqaffETtxvUt\nr1WfD9eEib1uNxI2XNzKhovbWl4HlACFyUN0az8W18vAF+8TPL6ldUPQhzFZ+Bv3FV1prKgq//vV\n/VTV+wEIygpOW0zSIoJeIsY+A5940TgmV4iioiLee++9dttfeumlln+vWrUqml0SdEHAW0Llubcw\nWZPJGv4YJrMDn681aerzedm48VMkSWLx4mWkpKR22I4WVPBsPI9aH8A1fxjmnO6X+tWUX2L7By9g\ncyQwd+WTOBI6Tsh2xVe7L/LV7ouMnpjD7MUj9C0E9Wzsk6YvH3mN49WneGTUfbomTYE2OkcLTfLj\nW/dfKBXnsM//IZZh06PehxuJ7jQ+VlzDq5+dZMyQVB6/awxGneJHEF1iEcuC6CI0jj1ifNt/0DSV\ngOcSVmceVldoSb3RZEc1pNF8lysvL8Vud5CbOzi032ikoGBou7bkCg9YjFgKQmNUg8GAZVhKp+dW\nVYXqkmLSc4eRll0AgMVqI33QsLD7X1XuxueVGDt5ECZTKEmbnqVPsUbf2TMYLJY249qEadEdi52v\nv4DJYGR6dmsx0PEZY3RrPxbXS6X8DAaLHfM1kwEsw2+Nej9uFLrS2BdQuFThZtTgFAZnhX6PDhsU\n+cQcQewRY5+BT7xoLB6tCLpE8lVSefZvGM1OsoZ/E5MldHNJS3NRU+MhGAywadNn+HweFiy4i7S0\njA7b0SQF9+ZilBofrrlDseQmdnvuusqrbH//Baw2B3NXPoUzseOEbFcc2HuJL3ZeYOS4bO64c6S+\nSdPnfo/vzGlyvvf3MUyavs7R6pM8POpebs+7RfdzNOscLTQ5gG/d71HKz2Kf9wMshTdF7dw3Kl1p\nfKm8kec/PMKgdCc/vmcCZpNYxtRfiXYsC6KP0FggCJ+WpfkpY0nKbrU3ujaOZFkmL6+Am2++reu2\nZBVzmiNsT1NFCgIweORkRk3tmaeiLIdmyN42rwiLVV9PUy0YxJoziKyHH9W13UiQFJlMRzorR67o\nk/Zjcb3UZAljWh7222L3vd5IdKWx1GRtccu4bOZMDi9uBfGJGPsMfOJFY/ErWNApcrCeinNvgMFE\n1vDHMFtbn8TV1XmRJInNm9dRX1/LnDmLyMrqeLmJpqh4tl5AqfTgnD0Ey+DuZ4021JSzbdULmCxW\n5qx8CldSWrfHXM+hLy6zd1sxw8dmMXfpKIxGnZKmUpCS55/Fd+okOY8/QdIt0X9aLKsyfzn6Bker\nT/DQqHuYmdc3fair8/ZJux2hyUF8659BKTuFfe73sRRFPxl9I9KZxlV1Pv7rvUM4bGZ+tnISTrt4\nztafiWYsC2KD0FggCJ/mxOn1HpPXxpEsy2F7mtIDT1Ozuee+obLUvLy/DzxNJQlDBLYBfUFQlbAY\n+87/MybXS0USnqZRpCuNpaYHDxYxIaDfI8Y+A5940Vj8EhZ0iCJ5qDj7BqoaJHvEd7DYrk9camzb\ntpGqqgpmz55Pbm7HxWI0RcWz7SJyqRvn7YOxDu186VIznvpqtr3/PABz7v8xCcnpEff/yP6rfL7l\nPIWjMpi/bLSOSVOJkuefw3viONnfeZykGV3PQugLQknTv3Gk6jgPjrybWXl9V4zKZDKiqkqftd+M\nJgfxbXgW5eoJ7HO+J5YuRZGONG7wBvnPdw8hSSr/87GppCXZY9Q7gV5EK5YFsUNoLBB0TUP5bhor\n9gGgEfIGNRhaE1nFxWf56qt9LX6mkhTsMHEaPFeL76uSlteaX+7WfmrP2v+m4vKZ0PvV5kJO4Scn\nqyvcfLbqGIoSOjYYkDGZ9fE0rd2wjpr161peK40NJEyZ2ut2I2HzpR1svrS95XWj5GFM2sg+O180\nrpeBg2uQjm5qea35GjCm5vbpOQWtXK/xhi8v89m+iwCoaijGRUGo/o8Y+wx84kVjkTgVtENVAlSe\nfwslWE/m8EexOrLb7ldVdu/eRmnpFWbMmM2QIYUdtqOpGt5dl5CvNOC4JQ/r8O5njfrc9Wx9/w/I\nUoC5D/yEpLTsbo+5nmMHSti16SzDRqSz4BtjdEuaarJM6R//gPfoEbK/9V2Sb5+lS7uRoKgKrxx7\nk8NVx1g5cgWz8/s2cet0Wqmv9/XpOTRFwrfpeZQrR7HPfhzLyNv79HyCtlyvsT8o88x7h6hp8POL\nhyaTl9m9F7Eg/olGLAtii9BYIOgaf2MxGhqO5FAhLoPRjCNpeMv+iooyAgE/hYUjQvsNhpZ/X4tc\n4UaTVKzNPqYGsI7o+iF/2YWT2F1JZOSGfExNJjM5Q0eH3feqcjeN9X6Gj8nEYg39fMvQydPUe/IE\nKAoJU1uTpYm3RndiwOnacyiayqTMcS3bpmZN6rPzReN6qZScBE3FPGRKyzazGONGjes1P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wfJe+vO6OZra99QL2NBcr7/sqFmt0Kw8DfpV3Xz2KEtRYu24erkz70AdFgKHrNLz0G7zH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\nLNUPO6Ze95Zd1m81HNbD7pdrB31ULdXX95j7KssQ9IHlOrehjFPj6GwMF52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MU9eeeXvh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\nIiIiIiIiIiI9XDglIiIiIiIiIiIi0vN/nuErEjdbZ+IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x1152 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccqRTH4FkKmq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def discreteSignal(signal0,stepSize):\n",
        "  # discretize signal\n",
        "  signal1=(signal0/stepSize).round()*stepSize # discretize\n",
        "  signal1[signal1>1]=1 # cap\n",
        "  signal1[signal1<-1]=-1 # floor\n",
        "  return signal1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvZ8nzULkLD2",
        "colab_type": "code",
        "outputId": "f605e509-c1eb-4324-c655-507c514c0930",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "# 한 김에 위에서 구한 bet size의 discrete version도 구해보자\n",
        "\n",
        "signal0 = df_events['avg_active_bets']\n",
        "discrete_signal = discreteSignal(signal0, stepSize=0.05)\n",
        "df_events['discrete_signal'] = discrete_signal\n",
        "df_events.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>p</th>\n",
              "      <th>bet_size</th>\n",
              "      <th>avg_active_bets</th>\n",
              "      <th>bet_size_2</th>\n",
              "      <th>bet_size_3</th>\n",
              "      <th>discrete_signal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-08-22</th>\n",
              "      <td>1992-09-04</td>\n",
              "      <td>0.699504</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.336546</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.203733</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-23</th>\n",
              "      <td>1992-09-11</td>\n",
              "      <td>0.293278</td>\n",
              "      <td>-0.350222</td>\n",
              "      <td>-0.006838</td>\n",
              "      <td>0.010256</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-24</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.234583</td>\n",
              "      <td>-0.468928</td>\n",
              "      <td>-0.160868</td>\n",
              "      <td>-0.056410</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-25</th>\n",
              "      <td>1992-08-26</td>\n",
              "      <td>0.555802</td>\n",
              "      <td>0.089418</td>\n",
              "      <td>-0.098296</td>\n",
              "      <td>0.020513</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-26</th>\n",
              "      <td>1992-09-14</td>\n",
              "      <td>0.722274</td>\n",
              "      <td>0.380306</td>\n",
              "      <td>0.122210</td>\n",
              "      <td>0.087179</td>\n",
              "      <td>0.203733</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   t1         p  ...  bet_size_3  discrete_signal\n",
              "1992-08-22 1992-09-04  0.699504  ...    0.203733             0.35\n",
              "1992-08-23 1992-09-11  0.293278  ...    0.000000            -0.00\n",
              "1992-08-24 1992-08-26  0.234583  ...         NaN            -0.15\n",
              "1992-08-25 1992-08-26  0.555802  ...    0.000000            -0.10\n",
              "1992-08-26 1992-09-14  0.722274  ...    0.203733             0.10\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTyLCRjJh8Lx",
        "colab_type": "text"
      },
      "source": [
        "## 6. Rewrite the equations in Section 10.6, so that the bet size is determined by a power function rather than a sigmoid function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjlBKuCZimf4",
        "colab_type": "text"
      },
      "source": [
        "Let's see what we got in Section 10.6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2B90YxHirOT",
        "colab_type": "text"
      },
      "source": [
        "Let $q_t$ be the current position, $Q$ the maximum absolute position size, and ̂$q_{i,t}$ the target position size associated with forecast $f_i$, such that\n",
        "\n",
        "$$\\hat{q}_{i,t} = \\text{int}[m[\\omega,f_i − p_t]Q]\\\\\n",
        "m[\\omega, x] = \\frac{x}{\\sqrt{𝜔 + x^2}}$$\n",
        "\n",
        "where m[𝜔, x] is the bet size, x = fi − pt is the divergence between the current market\n",
        "price and the forecast, 𝜔 is a coefficient that regulates the width of the sigmoid function,\n",
        "and Int [x] is the integer value of x. Note that for a real-valued price divergence\n",
        "x, −1 < m[𝜔, x] < 1, the integer value $\\hat{q}_{i,t}$ is bounded −Q < ̂qi,t < Q."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzyfRfXjkKZB",
        "colab_type": "text"
      },
      "source": [
        "The target position size ̂qi,t can be dynamically adjusted as pt changes. In particular,\n",
        "as pt → fi we get ̂qi,t → 0, because the algorithm wants to realize the gains. This implies a breakeven limit price ̄p for the order size ̂qi,t − qt, to avoid realizing losses. In particular,\n",
        "\n",
        "$$ \\hat{p} = \\frac{1}{q_{i,t} − q_{t}} \\displaystyle\\sum_{j=\\mid q_t+sgn[\\hat{q}_{i,t}-q_t]\\mid}^{\\mid\\hat{q}_{i,t}\\mid}{L[f_i,\\omega,\\frac{j}{Q}]} $$\n",
        "\n",
        "where L[fi,𝜔,m] is the inverse function of m[𝜔, fi − pt] with respect to pt,\n",
        "\n",
        "$$ L[f_i,𝜔,m] = f_i − m\\sqrt{\\frac{𝜔}{1−m^2}} $$\n",
        "\n",
        "We do not need to worry about the case m^2 = 1, because $|\\hat{q}_i,t| < 1$. Since this\n",
        "function is monotonic, the algorithm cannot realize losses as pt → fi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TINY6prdm6O7",
        "colab_type": "text"
      },
      "source": [
        "Let us calibrate $𝜔$. Given a user-defined pair $(x,m^∗)$, such that $x = f_i − p_t$ and $m^∗ = m[𝜔, x]$, the inverse function of m[𝜔, x] with respect to 𝜔 is\n",
        "$$\n",
        "𝜔 = x^2(m^{∗−2} − 1)\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2TlDuNl3kBgA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Now let us rewrite\n",
        "# 이것도 역시 hudson-thames 출처"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UX_ieumZiXfv",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "We can substitute a power function to calculate bet size, $\\tilde{m}$:\n",
        "\n",
        "$$\\tilde{m}[\\omega, x] = sgn[x]|x|^\\omega$$\n",
        "$L[f_i, \\omega, \\tilde{m}]$, the inverse function of $\\tilde{m}[\\omega, x]$ with respect to the market price $p_t$, can be rewritten as:\n",
        "\n",
        "$$L[f_i, \\omega, \\tilde{m}] = f_i - sgn[\\tilde{m}]|\\tilde{m}|^{1/\\omega}$$\n",
        "The inverse of $\\tilde{m}[\\omega, x]$ with respect to $\\omega$ can be rewritten as:\n",
        "\n",
        "$$\\omega = \\frac{log[\\frac{\\tilde{m}}{sgn(x)}]}{log[|x|]}$$\n",
        "Where $x = f_i - p_t$ is still the divergence between the current market price, $p_t$, and the price forecast, $f_i$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9wxdhn-iG3Z",
        "colab_type": "text"
      },
      "source": [
        "## 7. Modify Snippet 10.4 so that it implements the equations you derived in exercise 6\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHUs20BBoN5m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Snippet 10.4\n",
        "\n",
        "def betSize(w,x):\n",
        "  return x*(w+x**2)**-.5\n",
        "#———————————————————————————————————————\n",
        "def getTPos(w,f,mP,maxPos):\n",
        "  return int(betSize(w,f-mP)*maxPos)\n",
        "#———————————————————————————————————————\n",
        "def invPrice(f,w,m):\n",
        "  return f-m*(w/(1-m**2))**.5\n",
        "#———————————————————————————————————————\n",
        "def limitPrice(tPos,pos,f,w,maxPos):\n",
        "  sgn=(1 if tPos>=pos else -1)\n",
        "  lP=0\n",
        "  for j in range(abs(pos+sgn),abs(tPos+1)):\n",
        "    lP+=invPrice(f,w,j/float(maxPos))\n",
        "  lP/=tPos-pos\n",
        "  return lP\n",
        "#———————————————————————————————————————\n",
        "def getW(x,m):\n",
        "  # 0<alpha<1\n",
        "  return (x**2)*((m**(-2))-1)\n",
        "#———————————————————————————————————————\n",
        "def main():\n",
        "  pos,maxPos,mP,f,wParams=0,100,100,115,{'divergence':10,'m':.95}\n",
        "  w=getW(wParams['divergence'],wParams['m']) # calibrate w\n",
        "  tPos=getTPos(w,f,mP,maxPos) # get tPos\n",
        "  lP=limitPrice(tPos,pos,f,w,maxPos) # limit price for order\n",
        "  return w, tPos, lP\n",
        "#———————————————————————————————————————\n",
        "if __name__=='__main__':main()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjhxImp9qoTu",
        "colab_type": "code",
        "outputId": "e1730563-6d95-41fc-f779-9b145bcd1eee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "a = ['w', 'tPos', 'lP']\n",
        "for i in range(len(a)):\n",
        "    print(a[i],'=',main()[i])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "w = 10.803324099723\n",
            "tPos = 97\n",
            "lP = 112.36573855883363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpkYtrxTrxme",
        "colab_type": "text"
      },
      "source": [
        "Let us modify"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsqDol5mo4Nm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# New one\n",
        "\n",
        "def betSize2(w,x):\n",
        "  return np.sign(x)*(np.abs(x)**w)\n",
        "#———————————————————————————————————————\n",
        "def getTPos2(w,f,mP,maxPos):\n",
        "  return int(betSize2(w,f-mP)*maxPos)\n",
        "#———————————————————————————————————————\n",
        "def invPrice2(f,w,m):\n",
        "  return f-np.sign(m)*(np.abs(m)**(1/w))\n",
        "#———————————————————————————————————————\n",
        "def limitPrice2(tPos,pos,f,w,maxPos):\n",
        "  sgn=(1 if tPos>=pos else -1)\n",
        "  lP=0\n",
        "  for j in range(abs(pos+sgn),abs(tPos+1)):\n",
        "    lP+=invPrice2(f,w,j/float(maxPos))\n",
        "  lP/=tPos-pos\n",
        "  return lP\n",
        "#———————————————————————————————————————\n",
        "def getW2(x,m):\n",
        "  # 0<alpha<1\n",
        "  return np.log(m/np.sign(x))/np.log(np.abs(x))\n",
        "#———————————————————————————————————————\n",
        "def main2():\n",
        "  pos,maxPos,mP,f,wParams=0,100,100,115,{'divergence':10,'m':.95}\n",
        "  w=getW2(wParams['divergence'],wParams['m']) # calibrate w\n",
        "  tPos=getTPos2(w,f,mP,maxPos) # get tPos\n",
        "  lP=limitPrice2(tPos,pos,f,w,maxPos) # limit price for order\n",
        "  return w, tPos, lP\n",
        "#———————————————————————————————————————\n",
        "if __name__=='__main__':main()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhqz0pgprf4L",
        "colab_type": "code",
        "outputId": "7dd12c51-ce17-4582-b802-e40c0f4451f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "a = ['w', 'tPos', 'lP']\n",
        "for i in range(len(a)):\n",
        "    print(a[i],'=',main2()[i])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "w = -0.02227639471115225\n",
            "tPos = 94\n",
            "lP = -6.426982348112109e+87\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_o8fSFBBkQ9-",
        "colab_type": "text"
      },
      "source": [
        "# Further study"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNqN5qeyiUO9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 실제 E-mini future data를 가지고 실험해보자."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7Ebw8mFrdq6",
        "colab_type": "code",
        "outputId": "158c24de-f088-4252-c454-48667fb0a484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqOwRz-zreZn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlfinlab import data_structures, features, filters, labeling, util"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92OkNR0fsJMP",
        "colab_type": "code",
        "outputId": "80d97db9-fc33-42ad-c26a-cb09c0195259",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "raw_dollar_bars = data_structures.get_dollar_bars('/content/drive/My Drive/Colab Notebooks/csv/clean_IVE_tickbidask2.csv', threshold=1000000)\n",
        "dollar_bars = raw_dollar_bars.set_index(pd.to_datetime(raw_dollar_bars.date_time))\n",
        "dollar_bars = dollar_bars.drop(columns='date_time')\n",
        "dollar_bars = dollar_bars.reset_index().drop_duplicates(subset='date_time', keep='last').set_index('date_time')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading data in batches:\n",
            "Batch number: 0\n",
            "Returning bars \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7k0jUueJsOBS",
        "colab_type": "code",
        "outputId": "99510f27-1fd5-4629-f2fa-3bd4322b09d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "close = dollar_bars.close\n",
        "pt_sl = [1,1]\n",
        "target = util.get_daily_vol(close)\n",
        "h = target.mean()\n",
        "t_events = filters.cusum_filter(close,h)\n",
        "vertical_barrier_times = labeling.add_vertical_barrier(t_events,close,num_days=1)\n",
        "\n",
        "# side\n",
        "fast_window = 20\n",
        "slow_window = 50\n",
        "df = pd.DataFrame()\n",
        "df['fast_mavg'] = close.rolling(window=fast_window, min_periods=fast_window, center=False).mean()\n",
        "df['slow_mavg'] = close.rolling(window=slow_window, min_periods=slow_window, center=False).mean()\n",
        "df['side'] = np.nan\n",
        "long_signals = df['fast_mavg'] >= df['slow_mavg'] \n",
        "short_signals = df['fast_mavg'] < df['slow_mavg'] \n",
        "df.loc[long_signals, 'side'] = 1\n",
        "df.loc[short_signals, 'side'] = -1\n",
        "df['side'] = df['side'].shift(1)\n",
        "side = df['side']\n",
        "\n",
        "events = labeling.get_events(close, t_events, pt_sl, target, min_ret=0.005, num_threads=1, vertical_barrier_times = vertical_barrier_times ,side_prediction = side)\n",
        "bin = labeling.get_bins(events,close)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlfinlab/labeling/labeling.py:124: FutureWarning: \n",
            "Passing list-likes to .loc or [] with any missing label will raise\n",
            "KeyError in the future, you can use .reindex() as an alternative.\n",
            "\n",
            "See the documentation here:\n",
            "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
            "  target = target.loc[t_events]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8saOCx8tSW9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "events = events.dropna()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLe-NmZa8rDK",
        "colab_type": "text"
      },
      "source": [
        "### 1st"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49wRtOHv2OMc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "24eb7b03-708e-49a7-e703-6d1eba149b2b"
      },
      "source": [
        "# 1st option\n",
        "m_reserve = bs.bet_sizing.bet_size_reserve(events_t1=events['t1'], sides=events.side, return_parameters=True)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "|#########################| Completed 100 of 100 fitting rounds."
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qt0hu96x3qbn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "8aedd345-00c7-4665-8965-82dd43e6b9d2"
      },
      "source": [
        "m_reserve[0].head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        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>t1</th>\n",
              "      <th>side</th>\n",
              "      <th>active_long</th>\n",
              "      <th>active_short</th>\n",
              "      <th>c_t</th>\n",
              "      <th>bet_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:05:38</th>\n",
              "      <td>2009-10-01 10:00:33</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.388644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:52:26</th>\n",
              "      <td>2009-10-01 11:16:04</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-2</td>\n",
              "      <td>-0.686534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 11:16:04</th>\n",
              "      <td>2009-10-01 15:51:53</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.388644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 15:51:53</th>\n",
              "      <td>2009-10-02 09:32:42</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.388644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 16:16:36</th>\n",
              "      <td>2009-10-02 09:32:42</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-2</td>\n",
              "      <td>-0.686534</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1  side  ...  c_t  bet_size\n",
              "2009-09-30 15:05:38 2009-10-01 10:00:33  -1.0  ...   -1 -0.388644\n",
              "2009-10-01 09:52:26 2009-10-01 11:16:04  -1.0  ...   -2 -0.686534\n",
              "2009-10-01 11:16:04 2009-10-01 15:51:53  -1.0  ...   -1 -0.388644\n",
              "2009-10-01 15:51:53 2009-10-02 09:32:42  -1.0  ...   -1 -0.388644\n",
              "2009-10-01 16:16:36 2009-10-02 09:32:42  -1.0  ...   -2 -0.686534\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2t1BzcE3wEu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "3163142e-4e27-4cfd-ba86-dbe7c75fe2d6"
      },
      "source": [
        "m_reserve[1]\n",
        "#parameter for two Gaussian mixture"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mu_1': -1.36551,\n",
              " 'mu_2': 0.02715,\n",
              " 'p_1': 0.1819,\n",
              " 'sigma_1': 2.21412,\n",
              " 'sigma_2': 1.72257}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-gFR6tD8uj3",
        "colab_type": "text"
      },
      "source": [
        "### 2nd (budget)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqw7q28x2qnR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2nd option\n",
        "m_budget = bs.bet_sizing.bet_size_budget(events.t1,events.side)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wP2FDCtT3F8m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "bed2d056-0044-42b5-a5f8-3dc150296e57"
      },
      "source": [
        "m_budget"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>side</th>\n",
              "      <th>active_long</th>\n",
              "      <th>active_short</th>\n",
              "      <th>bet_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:05:38</th>\n",
              "      <td>2009-10-01 10:00:33</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:52:26</th>\n",
              "      <td>2009-10-01 11:16:04</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 11:16:04</th>\n",
              "      <td>2009-10-01 15:51:53</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 15:51:53</th>\n",
              "      <td>2009-10-02 09:32:42</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 16:16:36</th>\n",
              "      <td>2009-10-02 09:32:42</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-12 10:18:41</th>\n",
              "      <td>2019-06-13 10:27:19</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-17 09:57:13</th>\n",
              "      <td>2019-06-18 09:39:43</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 09:39:43</th>\n",
              "      <td>2019-06-18 11:06:41</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-20 09:30:00</th>\n",
              "      <td>2019-06-20 12:20:48</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-20 12:20:48</th>\n",
              "      <td>2019-06-20 14:32:11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2662 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1  side  ...  active_short  bet_size\n",
              "2009-09-30 15:05:38 2009-10-01 10:00:33  -1.0  ...             1 -0.100000\n",
              "2009-10-01 09:52:26 2009-10-01 11:16:04  -1.0  ...             2 -0.200000\n",
              "2009-10-01 11:16:04 2009-10-01 15:51:53  -1.0  ...             1 -0.100000\n",
              "2009-10-01 15:51:53 2009-10-02 09:32:42  -1.0  ...             1 -0.100000\n",
              "2009-10-01 16:16:36 2009-10-02 09:32:42  -1.0  ...             2 -0.200000\n",
              "...                                 ...   ...  ...           ...       ...\n",
              "2019-06-12 10:18:41 2019-06-13 10:27:19   1.0  ...             0  0.111111\n",
              "2019-06-17 09:57:13 2019-06-18 09:39:43   1.0  ...             0  0.111111\n",
              "2019-06-18 09:39:43 2019-06-18 11:06:41  -1.0  ...             1 -0.100000\n",
              "2019-06-20 09:30:00 2019-06-20 12:20:48   1.0  ...             0  0.111111\n",
              "2019-06-20 12:20:48 2019-06-20 14:32:11   1.0  ...             0  0.111111\n",
              "\n",
              "[2662 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUTyaKnE8xJD",
        "colab_type": "text"
      },
      "source": [
        "### 3rd (prob) -- prob을 구하는 것이 과제 using ML or others"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "patUX-jE3FvY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 3rd option\n",
        "\n",
        "# 1. get probabilities(confidence) ------>> 이것이 과제.\n",
        "# 일단 임의로 해보겠음.\n",
        "n=2662\n",
        "events_p = events.copy()\n",
        "events_p['prob'] = np.random.uniform(0.01, 1., n)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4dk18wc77Ob",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "d379701e-bdc2-4fce-82e4-3ea44183e504"
      },
      "source": [
        "events_p.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>trgt</th>\n",
              "      <th>side</th>\n",
              "      <th>pt</th>\n",
              "      <th>sl</th>\n",
              "      <th>prob</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:05:38</th>\n",
              "      <td>2009-10-01 10:00:33</td>\n",
              "      <td>0.008165</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.945966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:52:26</th>\n",
              "      <td>2009-10-01 11:16:04</td>\n",
              "      <td>0.008035</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.779903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 11:16:04</th>\n",
              "      <td>2009-10-01 15:51:53</td>\n",
              "      <td>0.006958</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.077566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 15:51:53</th>\n",
              "      <td>2009-10-02 09:32:42</td>\n",
              "      <td>0.009684</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.992109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 16:16:36</th>\n",
              "      <td>2009-10-02 09:32:42</td>\n",
              "      <td>0.009791</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.341500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1      trgt  side  pt  sl      prob\n",
              "2009-09-30 15:05:38 2009-10-01 10:00:33  0.008165  -1.0   1   1  0.945966\n",
              "2009-10-01 09:52:26 2009-10-01 11:16:04  0.008035  -1.0   1   1  0.779903\n",
              "2009-10-01 11:16:04 2009-10-01 15:51:53  0.006958  -1.0   1   1  0.077566\n",
              "2009-10-01 15:51:53 2009-10-02 09:32:42  0.009684  -1.0   1   1  0.992109\n",
              "2009-10-01 16:16:36 2009-10-02 09:32:42  0.009791  -1.0   1   1  0.341500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXlZOQaq471P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m_prob = bs.bet_size_probability(events = events,prob = events_p.prob, num_classes=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9r4KxbC5yaL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "de4e009d-282f-4803-b2f4-04a60bb10c29"
      },
      "source": [
        "m_prob"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2009-09-30 15:05:38    0.951454\n",
              "2009-10-01 09:52:26    0.500697\n",
              "2009-10-01 11:16:04   -0.885726\n",
              "2009-10-01 15:51:53    1.000000\n",
              "2009-10-01 16:16:36   -0.261801\n",
              "                         ...   \n",
              "2019-06-12 10:18:41    0.910438\n",
              "2019-06-17 09:57:13   -0.884660\n",
              "2019-06-18 09:39:43    0.987647\n",
              "2019-06-20 09:30:00   -0.225067\n",
              "2019-06-20 12:20:48   -0.667210\n",
              "Name: signal, Length: 2662, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8FiuVSk8NR4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m_prob_avg = bs.bet_size_probability(events = events,prob = events_p.prob, num_classes=2, average_active=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRXXzJid8Z1G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "550371d7-5c89-41e9-e412-f359089688c8"
      },
      "source": [
        "m_prob_avg"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2009-09-30 15:05:38    0.951454\n",
              "2009-10-01 09:52:26    0.726075\n",
              "2009-10-01 10:00:33    0.500697\n",
              "2009-10-01 11:16:04   -0.885726\n",
              "2009-10-01 15:51:53    1.000000\n",
              "                         ...   \n",
              "2019-06-18 09:39:43    0.987647\n",
              "2019-06-18 11:06:41    0.000000\n",
              "2019-06-20 09:30:00   -0.225067\n",
              "2019-06-20 12:20:48   -0.667210\n",
              "2019-06-20 14:32:11    0.000000\n",
              "Length: 4318, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biKTk0mb8VOM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "dffbcc73-4e27-4c76-cd19-a9c4beebd8c6"
      },
      "source": [
        "m_prob_dis = bs.bet_size_probability(events = events,prob = events_p.prob, num_classes=2, average_active=True, step_size=0.05)\n",
        "m_prob_dis"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2009-09-30 15:05:38    0.95\n",
              "2009-10-01 09:52:26    0.75\n",
              "2009-10-01 10:00:33    0.50\n",
              "2009-10-01 11:16:04   -0.90\n",
              "2009-10-01 15:51:53    1.00\n",
              "                       ... \n",
              "2019-06-18 09:39:43    1.00\n",
              "2019-06-18 11:06:41    0.00\n",
              "2019-06-20 09:30:00   -0.25\n",
              "2019-06-20 12:20:48   -0.65\n",
              "2019-06-20 14:32:11    0.00\n",
              "Length: 4318, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-85Hh9mp3FBQ",
        "colab_type": "text"
      },
      "source": [
        "### 4th (dynamic)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4tSxhMi88ql",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#######################################\n",
        "m_dyn = bs.bet_size_dynamic()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}