reouno/small-edge-search-02-use-other-indexes.ipynb

## small-edge-search-02-use-other-indexes.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 小さいエッジの大量探索第一段階 - 他銘柄のデータを利用\n",
    "\n",
    "常にターゲットはE-mini S&P500先物（ES）。足の単位は自由。予測の方法も自由。予測の内容も自由（次の足の上昇を予測するとか、N足以内の下落を予測するとか）。ただし点推定ではなく分布推定。\n",
    "\n",
    "[前回の分析](https://gist.github.com/reouno/e67e01064b916de20b78a2bc0ac4e397)で、上昇日の翌日の収益率分布を全期間分布と比較した。今回は金先物が下落した日の翌日のES収益率分布が全期間分布と比較する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 金先物が下落した翌日にESは上昇するか\n",
    "\n",
    "### 仮説\n",
    "\n",
    "株式市場と金は逆相関しやすいと言われている。金先物が下落した翌日はESは上昇すると仮定すると、その収益率分布は全期間収益率分布より右に移動すると予想できる。\n",
    "\n",
    "### 結論・考察\n",
    "\n",
    "推定・比較結果については最後の方のセルを参照。\n",
    "\n",
    "金先物が下落した日の翌日のES収益率分布は、無条件の全期間分布と全くと言って良いほど差がなかったため、金先物の上昇や下落をシグナルとして次の日のESの値動きを予測することはほぼ不可能だと思われる（もちろん、日足データを使った今回の条件では、という意味）。\n",
    "\n",
    "しかしこの結果は、前提としていた「株式市場と金は逆相関しやすい」という主張を否定する根拠には全くならない。そもそも今回は相関を直接測ったわけではない。また何よりも、今回の比較から推測できることは、「金先物」と「翌日のES」の収益率の関係であって、同日の金先物とESの関係ではない。\n",
    "\n",
    "ESと他銘柄の相関・逆相関関係をシグナルに利用するなら、今回のような1足のみを見るのではなく、移動平均など複数足の統計データをもとに現在の他銘柄のトレンドを推定し、ESがそれに順行・逆行するという仮説をもとにルールを作るという方法もある。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 準備"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'en_US.UTF-8'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.api.types import CategoricalDtype\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Hiragino Maru Gothic Pro', 'Yu Gothic', 'Meirio', 'Takao', 'IPAexGothic', 'IPAPGothic', 'VL PGothic', 'Noto Sans CJK JP']\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import datetime as dt\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import locale\n",
    "\n",
    "# 月や曜日を英語で取得するためこの設定をしておく\n",
    "locale.setlocale(locale.LC_TIME, 'en_US.UTF-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E-Mini S&P500先物と金先物データ読み込み\n",
    "\n",
    "データは、[TradeStationのDesktop Platform](https://www.tradestation.com/platforms-and-tools/desktop/)を使って出力したCSVファイル。公開は禁止されているので公開できないが、Quandlからも[E-mini S&P500先物](https://www.quandl.com/data/CHRIS/CME_ES1-E-mini-S-P-500-Futures-Continuous-Contract-1-ES1-Front-Month)と[金先物](https://www.quandl.com/data/CHRIS/CME_GC1-Gold-Futures-Continuous-Contract-1-GC1-Front-Month)の最新の日足データ（今回の分析で使っているのと同じ）をAPI経由で取得できるので、そのデータを使用可能。ただしquandl経由で取得したDFは若干構造が違うから、以下のセルは多少修正が必要。\n",
    "- quandl経由で取得したDFはすでにDatetimeindexになっている。また、終値は'Close'ではなく'Last'。おそらくこの2点だけ違う。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leo/src/pyproject/py-envs/py383env/lib/python3.6/site-packages/pandas/core/series.py:679: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "dfsp_tmp = pd.read_csv('data/e-mini-sp500-200530/e-mini-sp500-daily.csv')\n",
    "dfg_tmp = pd.read_csv(f'data/gold-200626/gold-daily.csv')\n",
    "dfs = [dfsp_tmp, dfg_tmp]\n",
    "\n",
    "prods = ['S&P500先物', '金先物']\n",
    "\n",
    "# datetime indexに変換\n",
    "def to_datetime_index(df):\n",
    "    # DateTime列を追加\n",
    "    df['datetime'] = (df['Date'] + '-' + df['Time']).map(lambda s: dt.datetime.strptime(s, '%m/%d/%Y-%H:%M'))\n",
    "    df = df.set_index('datetime', drop=True)\n",
    "    df = df.drop(columns=['Date', 'Time'])\n",
    "    return df\n",
    "\n",
    "dfs = [to_datetime_index(df) for df in dfs]\n",
    "\n",
    "# 対数変換した列を追加\n",
    "def add_log_values(df):\n",
    "    df['logO'] = np.log(df['Open'])\n",
    "    df['logH'] = np.log(df['High'])\n",
    "    df['logL'] = np.log(df['Low'])\n",
    "    df['logC'] = np.log(df['Close'])\n",
    "    df['logV'] = np.log(df['Vol'])\n",
    "    df['logOI'] = np.log(df['OI'])\n",
    "\n",
    "_ = [add_log_values(df) for df in dfs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Vol</th>\n",
       "      <th>OI</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/1997</td>\n",
       "      <td>17:00</td>\n",
       "      <td>1071.25</td>\n",
       "      <td>1082.25</td>\n",
       "      <td>1062.75</td>\n",
       "      <td>1068.50</td>\n",
       "      <td>11825</td>\n",
       "      <td>2909</td>\n",
       "      <td>1997-09-11 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/12/1997</td>\n",
       "      <td>17:00</td>\n",
       "      <td>1070.50</td>\n",
       "      <td>1089.00</td>\n",
       "      <td>1066.00</td>\n",
       "      <td>1071.25</td>\n",
       "      <td>9759</td>\n",
       "      <td>4059</td>\n",
       "      <td>1997-09-12 17:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Time     Open     High      Low    Close    Vol    OI  \\\n",
       "0  09/11/1997  17:00  1071.25  1082.25  1062.75  1068.50  11825  2909   \n",
       "1  09/12/1997  17:00  1070.50  1089.00  1066.00  1071.25   9759  4059   \n",
       "\n",
       "             datetime  \n",
       "0 1997-09-11 17:00:00  \n",
       "1 1997-09-12 17:00:00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考：生のデータフレーム（ES）\n",
    "dfsp_tmp.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Vol</th>\n",
       "      <th>OI</th>\n",
       "      <th>logO</th>\n",
       "      <th>logH</th>\n",
       "      <th>logL</th>\n",
       "      <th>logC</th>\n",
       "      <th>logV</th>\n",
       "      <th>logOI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-09-11 17:00:00</th>\n",
       "      <td>1071.25</td>\n",
       "      <td>1082.25</td>\n",
       "      <td>1062.75</td>\n",
       "      <td>1068.50</td>\n",
       "      <td>11825</td>\n",
       "      <td>2909</td>\n",
       "      <td>6.976581</td>\n",
       "      <td>6.986797</td>\n",
       "      <td>6.968615</td>\n",
       "      <td>6.974011</td>\n",
       "      <td>9.377971</td>\n",
       "      <td>7.975565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-12 17:00:00</th>\n",
       "      <td>1070.50</td>\n",
       "      <td>1089.00</td>\n",
       "      <td>1066.00</td>\n",
       "      <td>1071.25</td>\n",
       "      <td>9759</td>\n",
       "      <td>4059</td>\n",
       "      <td>6.975881</td>\n",
       "      <td>6.993015</td>\n",
       "      <td>6.971669</td>\n",
       "      <td>6.976581</td>\n",
       "      <td>9.185945</td>\n",
       "      <td>8.308692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close    Vol    OI  \\\n",
       "datetime                                                               \n",
       "1997-09-11 17:00:00  1071.25  1082.25  1062.75  1068.50  11825  2909   \n",
       "1997-09-12 17:00:00  1070.50  1089.00  1066.00  1071.25   9759  4059   \n",
       "\n",
       "                         logO      logH      logL      logC      logV  \\\n",
       "datetime                                                                \n",
       "1997-09-11 17:00:00  6.976581  6.986797  6.968615  6.974011  9.377971   \n",
       "1997-09-12 17:00:00  6.975881  6.993015  6.971669  6.976581  9.185945   \n",
       "\n",
       "                        logOI  \n",
       "datetime                       \n",
       "1997-09-11 17:00:00  7.975565  \n",
       "1997-09-12 17:00:00  8.308692  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考：対数変換データ追加後のデータフレーム（ES）\n",
    "dfs[0].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 価格、対数価格、価格階差、対数差収益率（100倍）のDFを作成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leo/src/pyproject/py-envs/py383env/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "def to_log_return_ratio_df(df):\n",
    "    diff_df = df.diff()\n",
    "    close_df = df[['Close', 'logC']]\n",
    "    diff_df = diff_df.rename(columns={'Close': 'CloseDiff', 'logC': 'logCDiff'})\n",
    "    close_diff_df = diff_df[['CloseDiff', 'logCDiff']]\n",
    "    close_diff_df['logCDiff'] = close_diff_df['logCDiff'] * 100\n",
    "    rr_df = pd.concat([close_df, close_diff_df], axis=1)\n",
    "    rr_df = rr_df.dropna()\n",
    "    return rr_df\n",
    "\n",
    "rr_dfs = [to_log_return_ratio_df(df) for df in dfs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Close</th>\n",
       "      <th>logC</th>\n",
       "      <th>CloseDiff</th>\n",
       "      <th>logCDiff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-09-12 17:00:00</th>\n",
       "      <td>1071.25</td>\n",
       "      <td>6.976581</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.257040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-15 17:00:00</th>\n",
       "      <td>1083.75</td>\n",
       "      <td>6.988183</td>\n",
       "      <td>12.50</td>\n",
       "      <td>1.160106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Close      logC  CloseDiff  logCDiff\n",
       "datetime                                                   \n",
       "1997-09-12 17:00:00  1071.25  6.976581       2.75  0.257040\n",
       "1997-09-15 17:00:00  1083.75  6.988183      12.50  1.160106"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考：対数差分をとった後は、初日のデータはなくなる（ES）\n",
    "rr_dfs[0].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分布の推定と比較\n",
    "\n",
    "まず、分析用DF作成。次に全期間のES収益率分布と、金先物下落日の翌日のES収益率分布を推定・比較"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析用DF作成\n",
    "分析では対数差収益率しか使わないからそれ以外の列は不要\n",
    "\n",
    "1. ESデータと金先物データを結合したデータフレームを作る\n",
    "2. ESの「翌足データ」列を追加\n",
    "3. 欠損行を削除\n",
    "    - 最後の行には翌足データがないため欠損\n",
    "    - その他にもESと金先物でどちらかが欠けている日というのがあるかもしれない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "サンプル数： 4759\n"
     ]
    }
   ],
   "source": [
    "# まずESの対数差収益率\n",
    "df = rr_dfs[0][['logCDiff']].copy()\n",
    "df = df.rename(columns={'logCDiff':'ES0'})\n",
    "\n",
    "# 金先物の対数差収益率\n",
    "df['Gold0'] = rr_dfs[1]['logCDiff']\n",
    "\n",
    "# ESの翌足データ列\n",
    "df['ES1'] = df['ES0'][1:].append(pd.Series([np.nan]*1)).to_numpy()\n",
    "\n",
    "# 欠損行削除\n",
    "df = df.dropna()\n",
    "print('サンプル数：', df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ES0</th>\n",
       "      <th>Gold0</th>\n",
       "      <th>ES1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-05-15 17:00:00</th>\n",
       "      <td>0.453609</td>\n",
       "      <td>0.114482</td>\n",
       "      <td>2.518923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16 17:00:00</th>\n",
       "      <td>2.518923</td>\n",
       "      <td>0.635440</td>\n",
       "      <td>0.240433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ES0     Gold0       ES1\n",
       "datetime                                         \n",
       "2001-05-15 17:00:00  0.453609  0.114482  2.518923\n",
       "2001-05-16 17:00:00  2.518923  0.635440  0.240433"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考：分析用データフレーム\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 金先物下落日の翌日のES収益率データ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "金先物下落日の割合 = 2227 / 4759 = 46.80%\n"
     ]
    }
   ],
   "source": [
    "gold_down = df[df['Gold0'] < 0]\n",
    "n_all = df.shape[0]\n",
    "n_down = gold_down.shape[0]\n",
    "print(f'金先物下落日の割合 = {n_down} / {n_all} = {n_down / n_all * 100:.02f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 全期間のES収益率分布と金先物下落日の翌日のES収益率分布を推定・比較"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全期間の収益率 のt分布パラメータ\n",
      "df=2.124709860191633, loc=0.08216264036605168, scale=0.6710383434367057\n",
      "金先物下落日の翌日のES収益率 のt分布パラメータ\n",
      "df=2.034014010825797, loc=0.09135443345659722, scale=0.6607535462539509\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(20, 18))\n",
    "\n",
    "# 全期間の収益率分布を描く\n",
    "# 金先物下落日の翌日の収益率分布を描く\n",
    "# 両者を重ねる\n",
    "# 各々をt分布と仮定してパラメータ推定\n",
    "plot_data = [df['ES1'], gold_down['ES1']]\n",
    "titles = ['全期間の収益率', '金先物下落日の翌日のES収益率']\n",
    "colors=['tab:green','tab:pink']\n",
    "\n",
    "# x軸の範囲を広い方（全期間）に合わせる\n",
    "xmin = plot_data[0].min()\n",
    "xmax = plot_data[0].max()\n",
    "\n",
    "# t分布の描画範囲\n",
    "xs = np.linspace(xmin, xmax, 300)\n",
    "\n",
    "t_params = []\n",
    "t_ys = []\n",
    "for i in range(2):\n",
    "    sns.histplot(plot_data[i], kde=False, stat='density', color='lightblue', ax=ax[i])\n",
    "\n",
    "    # t分布の当てはめ\n",
    "    t_params.append(stats.t.fit(plot_data[i]))\n",
    "    t_ys.append(stats.t.pdf(xs, df=t_params[i][0], loc=t_params[i][1], scale=t_params[i][2]))\n",
    "\n",
    "    ax[i].set_xlim(xmin, xmax)\n",
    "    ax[i].plot(xs, t_ys[i], color=colors[i])\n",
    "\n",
    "    ax[i].set_title(titles[i], fontweight='semibold', fontsize=16)\n",
    "\n",
    "# 推定分布を重ねて比較\n",
    "ax[2].plot(xs, t_ys[0], label=titles[0], color=colors[0])\n",
    "ax[2].plot(xs, t_ys[1], label=titles[1], color=colors[1])\n",
    "ax[2].set_xlim(xmin, xmax)\n",
    "ax[2].legend()\n",
    "\n",
    "# パラメータ推定値の確認\n",
    "for i in range(2):\n",
    "    print(titles[i], 'のt分布パラメータ')\n",
    "    print(f'df={t_params[i][0]}, loc={t_params[i][1]}, scale={t_params[i][2]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 結果\n",
    "\n",
    "金先物が下落した日の翌日のES収益率は、全くと言って良いほど変化がなかった。統計的な判断は不要だろう。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}