va2577/01-turtle-secrets-index.ipynb

## 01-turtle-secrets-index.ipynb

      
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## 02-turtle-secrets-fx.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TURTLE SECRETS (fx)\n",
    "\n",
    "> 単純なタートルズ・ブレイクアウト・システム\n",
    ">\n",
    "> 1. マーケットが過去２０日間（チャート上の２０本の足）の最高値（最安値）を取ったら、新規に買う（新規に売る）。\n",
    "> 1. マーケットが自分の建玉に逆行して過去１０日間（１０本の足）の最安値（最高値）を取ったら、その玉を仕切る。\n",
    "> 1. 新規に建玉した後、もしマーケットが逆行して引かされたなら、資金の２％を厳格な仕切りポイントとして用いて損切りしなさい。\n",
    "> 1. 利益目標を置いてはいけない。利が乗った建玉を手仕舞う唯一の方法は、マーケットが逆行したときだけである。\n",
    ">\n",
    "> <cite>タートルズの秘密 Part 1 基本 The Basics P.56</cite>\n",
    "\n",
    "> Nとは何か？そしてそれの使い方\n",
    ">\n",
    "> Nはマーケット・ボラティリティの単位である。\n",
    "> それは過去１５日間の１日の値動きの平均である。\n",
    ">\n",
    "> この数値を得るためには、まず、高値、安値に窓空け分を加えて、１日の真の値幅を算出する。\n",
    "> そして過去１５日の真の値幅を合計して、それを１５で割るのである。\n",
    ">\n",
    "> …略…\n",
    ">\n",
    "> Nを使ってすべてのトレードにおける厳格な損切りポイントを決めるためには、次のことを心に留めておいて欲しい。\n",
    "> 私たちはどのトレードにおいても、資金の２％以上の損失（もちろん１ユニット当たり）を被ることを望むことは決してない。\n",
    "> したがって、私たちはすべてのトレードにおいて、建玉したときの価格から2Nを差し引く。\n",
    "> あるいは売り玉の場合には2Nだけ上である。\n",
    "> 覚えておいて欲しい。\n",
    "> これらのポイントでは常にマルにするだけである。\n",
    "> 決してドテンしたりしないのだ。\n",
    "> この計算をするときは、単にNを１５日間の値動きであると考えなさい。\n",
    ">\n",
    "> <cite>タートルズの秘密 Part 1 基本 The Basics P.61</cite>\n",
    "\n",
    "> P/Lフィルター\n",
    ">\n",
    "> タートルズが使っているトレード手法で最も重要な原則の１つは、P/Lフィルターとしても知られている「理論上の前回のトレード」を利用するやり方である。\n",
    "> この強力な概念によって、あまり期待値が高くないトレードを避けることができる。\n",
    ">\n",
    "> …略…\n",
    ">\n",
    "> P/Lフィルターのルールは簡単である。\n",
    "> それは利益の上がったトレードの次のトレードは利益になりにくいということである。\n",
    "> なぜなら、マーケットでは、急上昇と暴落が続けて同時に起こることはまれだからである。\n",
    ">\n",
    "> <cite>タートルズの秘密 Part 1 基本 The Basics P.65</cite>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>113.226</td>\n",
       "      <td>113.379</td>\n",
       "      <td>113.147</td>\n",
       "      <td>113.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>113.346</td>\n",
       "      <td>113.348</td>\n",
       "      <td>112.664</td>\n",
       "      <td>112.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>112.866</td>\n",
       "      <td>112.969</td>\n",
       "      <td>112.472</td>\n",
       "      <td>112.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>112.658</td>\n",
       "      <td>112.658</td>\n",
       "      <td>112.658</td>\n",
       "      <td>112.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>112.594</td>\n",
       "      <td>112.789</td>\n",
       "      <td>112.570</td>\n",
       "      <td>112.773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open     high      low    close\n",
       "time                                          \n",
       "2017-12-27  113.226  113.379  113.147  113.347\n",
       "2017-12-28  113.346  113.348  112.664  112.866\n",
       "2017-12-29  112.866  112.969  112.472  112.658\n",
       "2018-01-01  112.658  112.658  112.658  112.658\n",
       "2018-01-02  112.594  112.789  112.570  112.773"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read(filepath):\n",
    "    names = ['time', 'open', 'high', 'low', 'close']\n",
    "    dtype = { 'time': str, 'open': float, 'high': float, 'low': float, 'close': float }\n",
    "    df = pd.read_csv(filepath, header=0, names=names, index_col='time', usecols=names, dtype=dtype, parse_dates=['time'])\n",
    "    return df\n",
    "\n",
    "df = read('~/Documents/1/data/D/USDJPY.csv')\n",
    "df.head()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2017-12-27    0.455752\n",
       "2017-12-28    0.486185\n",
       "2017-12-29    0.487627\n",
       "2018-01-01    0.422610\n",
       "2018-01-02    0.395462\n",
       "Name: atr, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def atr(df, periods=14):\n",
    "    close = df['close'].shift(1)\n",
    "    high = df['high']\n",
    "    low = df['low']\n",
    "    s1 = high - low\n",
    "    s2 = high - close\n",
    "    s3 = close - low\n",
    "    tr = pd.concat([s1, s2, s3], axis=1).max(axis=1)\n",
    "    atr_ = tr.ewm(span=periods).mean()\n",
    "    return atr_.rename('atr')\n",
    "\n",
    "atr(df).head()\n",
    "atr(df).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>113.260</td>\n",
       "      <td>113.350</td>\n",
       "      <td>113.219</td>\n",
       "      <td>113.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>113.225</td>\n",
       "      <td>113.352</td>\n",
       "      <td>113.119</td>\n",
       "      <td>113.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>113.226</td>\n",
       "      <td>113.379</td>\n",
       "      <td>113.147</td>\n",
       "      <td>113.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>113.346</td>\n",
       "      <td>113.348</td>\n",
       "      <td>112.664</td>\n",
       "      <td>112.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>112.866</td>\n",
       "      <td>112.969</td>\n",
       "      <td>112.472</td>\n",
       "      <td>112.658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open     high      low    close\n",
       "time                                          \n",
       "2017-12-25  113.260  113.350  113.219  113.225\n",
       "2017-12-26  113.225  113.352  113.119  113.227\n",
       "2017-12-27  113.226  113.379  113.147  113.347\n",
       "2017-12-28  113.346  113.348  112.664  112.866\n",
       "2017-12-29  112.866  112.969  112.472  112.658"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def drop(df):\n",
    "    df2 = df.dropna()\n",
    "    df3 = df2[(2005 <= df2.index.year) & (df2.index.year <= 2017)]\n",
    "    s1 = df3['open'] == df3['high']\n",
    "    s2 = df3['open'] == df3['low']\n",
    "    s3 = df3['open'] == df3['close']\n",
    "    df4 = df3[~(s1 & s2 & s3)]\n",
    "    return df4\n",
    "\n",
    "df2 = drop(df)\n",
    "df2.head()\n",
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 93.8 ms, sys: 46.9 ms, total: 141 ms\n",
      "Wall time: 164 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1701515f8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test(df, periods1=20, periods2=10, periods3=15, pip=0.01, unit=10000, spread=0.3):\n",
    "    sp = pip * unit * spread\n",
    "    last = lambda x: ([0.0] + x.dropna().values.tolist())[-1]\n",
    "    l = [\n",
    "        df,\n",
    "        df['close'].shift(1),\n",
    "        df['high'].rolling(periods1).max().shift(2),\n",
    "        df['high'].rolling(periods2).max().shift(2),\n",
    "        df['low'].rolling(periods1).min().shift(2),\n",
    "        df['low'].rolling(periods2).min().shift(2),\n",
    "        atr(df, periods=periods3).shift(2),\n",
    "    ]\n",
    "    df2 = pd.concat(l, axis=1)\n",
    "    pl1 = pd.Series(np.full(df2.index.size, np.nan), index=df2.index, name='pl1')\n",
    "    pl2 = pd.Series(np.full(df2.index.size, np.nan), index=df2.index, name='pl2')\n",
    "    ls = entry = stop = np.nan\n",
    "    for row in df2.itertuples():\n",
    "        time, open_, high, low, close, close2, high2, high3, low2, low3, atr_ = row\n",
    "        if np.isnan([close2, high2, high3, low2, low3, atr_]).any():\n",
    "            continue\n",
    "        if (0 < ls and close2 < low3) or (ls < 0 and high3 < close2):\n",
    "            # exit\n",
    "            p = (open_ - entry) * unit * ls - sp\n",
    "            if last(pl2) <= 0.0:\n",
    "                pl1[time] = p\n",
    "            pl2[time] = p\n",
    "            ls = entry = stop = np.nan\n",
    "        if np.isnan(ls) and (high2 < close2 or close2 < low2):\n",
    "            # entry\n",
    "            ls = 1 if high2 < close2 else -1\n",
    "            entry = open_\n",
    "            stop = open_ - (atr_ * 2.0 * ls)\n",
    "        if (0 < ls and low <= stop) or (ls < 0 and stop <= high):\n",
    "            # stop\n",
    "            p = (stop - entry) * unit * ls - sp\n",
    "            if last(pl2) <= 0.0:\n",
    "                pl1[time] = p\n",
    "            pl2[time] = p\n",
    "            ls = entry = stop = np.nan\n",
    "    if not np.isnan(ls):\n",
    "        p = (close - entry) * unit * ls - sp\n",
    "        if last(pl2) <= 0.0:\n",
    "            pl1[time] = p\n",
    "        pl2[time] = p\n",
    "        ls = entry = stop = np.nan\n",
    "    return pd.concat([pl1, pl2], axis=1)\n",
    "\n",
    "%time result = test(df2)\n",
    "result.dropna(how='all').cumsum().fillna(method='ffill').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'トレード数': 77,\n",
       " '勝率（％）': 37.66,\n",
       " '平均利益': 25646.55,\n",
       " '平均損失': 13422.38,\n",
       " '平均利益÷平均損失': 1.91,\n",
       " '1トレード当たりの平均損益': 1291.9,\n",
       " '最大損失額': -142026.84,\n",
       " '総損益': 99475.97,\n",
       " '期待値': 0.1}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mdd(s):\n",
    "    s2 = s.dropna()\n",
    "    s3 = s2.cumsum()\n",
    "    s4 = s3.cummax()\n",
    "    s5 = s4 - s3\n",
    "    r = s5.max()\n",
    "    return r\n",
    "\n",
    "def stat(s):\n",
    "    r = lambda x: round(x, 2)\n",
    "    p = lambda x: r(x * 100)\n",
    "    s2 = s.dropna()\n",
    "    s3 = s2[0 < s2]\n",
    "    s4 = s2[s2 < 0]\n",
    "    profit_probability = s3.count() / s2.count()\n",
    "    risk_reward_ratio = abs(s3.mean() / s4.mean())\n",
    "    expected_value = (profit_probability * risk_reward_ratio) - (1 - profit_probability)\n",
    "    d = {\n",
    "        'トレード数': s2.count(),\n",
    "        '勝率（％）': p(profit_probability),\n",
    "        '平均利益': r(s3.mean()),\n",
    "        '平均損失': r(abs(s4.mean())),\n",
    "        '平均利益÷平均損失': r(risk_reward_ratio),\n",
    "        '1トレード当たりの平均損益': r(s2.mean()),\n",
    "        '最大損失額': r(-mdd(s2)),\n",
    "        '総損益': r(s2.sum()),\n",
    "        '期待値': r(expected_value),\n",
    "    }\n",
    "    return d\n",
    "\n",
    "stat(result['pl1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'トレード数': 113,\n",
       " '勝率（％）': 31.86,\n",
       " '平均利益': 26342.5,\n",
       " '平均損失': 14331.4,\n",
       " '平均利益÷平均損失': 1.84,\n",
       " '1トレード当たりの平均損益': -1373.34,\n",
       " '最大損失額': -276594.32,\n",
       " '総損益': -155187.93,\n",
       " '期待値': -0.1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat(result['pl2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0: symbol\n",
    "# 1: pip\n",
    "# 2: spread\n",
    "data = [\n",
    "    ('AUDJPY', 0.01, 0.6),\n",
    "    ('AUDNZD', 0.0001, 6.0),\n",
    "    ('AUDUSD', 0.0001, 0.8),\n",
    "    ('CADJPY', 0.01, 3.8),\n",
    "    ('CHFJPY', 0.01, 3.8),\n",
    "    ('EURAUD', 0.0001, 2.3),\n",
    "    ('EURGBP', 0.0001, 2.0),\n",
    "    ('EURJPY', 0.01, 0.4),\n",
    "    ('EURUSD', 0.0001, 0.3),\n",
    "    ('GBPAUD', 0.0001, 4.9),\n",
    "    ('GBPJPY', 0.01, 0.9),\n",
    "    ('GBPUSD', 0.0001, 0.8),\n",
    "    ('HKDJPY', 0.01, 2.8),\n",
    "    ('NZDJPY', 0.01, 1.0), # 仮\n",
    "    ('NZDUSD', 0.0001, 2.0),\n",
    "    ('SGDJPY', 0.01, 3.5),\n",
    "    ('USDCAD', 0.0001, 1.0), # 仮\n",
    "    ('USDCHF', 0.0001, 1.0), # 仮\n",
    "    ('USDHKD', 0.0001, 1.0), # 仮\n",
    "    ('USDJPY', 0.01, 0.3),\n",
    "    ('USDSGD', 0.0001, 1.0), # 仮\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = { x[0]: read(f'~/Documents/1/data/D/{x[0]}.csv') for x in data }\n",
    "d2 = { x[0]: drop(d[x[0]]) for x in data }\n",
    "d3 = { x[0]: test(d2[x[0]], pip=x[1], spread=x[2]) for x in data }\n",
    "d4 = { x[0]: [stat(d3[x[0]]['pl1']), stat(d3[x[0]]['pl2'])] for x in data }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(s):\n",
    "    df = d3[s]\n",
    "    l = [\n",
    "        df['pl1'].rename('pl1 (P/L Filter)'),\n",
    "        df['pl2']\n",
    "    ]\n",
    "    df2 = pd.concat(l, axis=1)\n",
    "    df2.dropna(how='all').cumsum().fillna(method='ffill').plot(title=s)\n",
    "    return None\n",
    "\n",
    "plot('EURUSD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('USDJPY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('EURJPY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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>トレード数</th>\n",
       "      <th>勝率（％）</th>\n",
       "      <th>平均利益</th>\n",
       "      <th>平均損失</th>\n",
       "      <th>平均利益÷平均損失</th>\n",
       "      <th>1トレード当たりの平均損益</th>\n",
       "      <th>最大損失額</th>\n",
       "      <th>総損益</th>\n",
       "      <th>期待値</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUDJPY pl1 (P/L Filter)</th>\n",
       "      <td>80</td>\n",
       "      <td>27.50</td>\n",
       "      <td>26292.73</td>\n",
       "      <td>16955.80</td>\n",
       "      <td>1.55</td>\n",
       "      <td>-5062.46</td>\n",
       "      <td>-484703.92</td>\n",
       "      <td>-404996.68</td>\n",
       "      <td>-0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDJPY pl2</th>\n",
       "      <td>110</td>\n",
       "      <td>28.18</td>\n",
       "      <td>35695.81</td>\n",
       "      <td>17206.63</td>\n",
       "      <td>2.07</td>\n",
       "      <td>-2297.76</td>\n",
       "      <td>-482621.11</td>\n",
       "      <td>-252753.98</td>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDNZD pl1 (P/L Filter)</th>\n",
       "      <td>60</td>\n",
       "      <td>38.33</td>\n",
       "      <td>248.67</td>\n",
       "      <td>140.28</td>\n",
       "      <td>1.77</td>\n",
       "      <td>8.81</td>\n",
       "      <td>-1039.29</td>\n",
       "      <td>528.87</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDNZD pl2</th>\n",
       "      <td>92</td>\n",
       "      <td>34.78</td>\n",
       "      <td>271.42</td>\n",
       "      <td>134.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>6.52</td>\n",
       "      <td>-1103.79</td>\n",
       "      <td>599.66</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDUSD pl1 (P/L Filter)</th>\n",
       "      <td>78</td>\n",
       "      <td>30.77</td>\n",
       "      <td>310.54</td>\n",
       "      <td>128.84</td>\n",
       "      <td>2.41</td>\n",
       "      <td>6.35</td>\n",
       "      <td>-1684.93</td>\n",
       "      <td>495.39</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDUSD pl2</th>\n",
       "      <td>116</td>\n",
       "      <td>33.62</td>\n",
       "      <td>306.34</td>\n",
       "      <td>135.15</td>\n",
       "      <td>2.27</td>\n",
       "      <td>13.29</td>\n",
       "      <td>-2451.46</td>\n",
       "      <td>1541.06</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CADJPY pl1 (P/L Filter)</th>\n",
       "      <td>68</td>\n",
       "      <td>38.24</td>\n",
       "      <td>28536.15</td>\n",
       "      <td>19041.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>-850.26</td>\n",
       "      <td>-415775.94</td>\n",
       "      <td>-57817.81</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CADJPY pl2</th>\n",
       "      <td>102</td>\n",
       "      <td>34.31</td>\n",
       "      <td>29434.00</td>\n",
       "      <td>18048.18</td>\n",
       "      <td>1.63</td>\n",
       "      <td>-1755.28</td>\n",
       "      <td>-478378.06</td>\n",
       "      <td>-179038.13</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHFJPY pl1 (P/L Filter)</th>\n",
       "      <td>78</td>\n",
       "      <td>30.77</td>\n",
       "      <td>21564.17</td>\n",
       "      <td>14388.20</td>\n",
       "      <td>1.50</td>\n",
       "      <td>-3325.93</td>\n",
       "      <td>-305868.39</td>\n",
       "      <td>-259422.82</td>\n",
       "      <td>-0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHFJPY pl2</th>\n",
       "      <td>109</td>\n",
       "      <td>29.36</td>\n",
       "      <td>29319.06</td>\n",
       "      <td>14613.03</td>\n",
       "      <td>2.01</td>\n",
       "      <td>-1715.54</td>\n",
       "      <td>-276925.41</td>\n",
       "      <td>-186993.42</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURAUD pl1 (P/L Filter)</th>\n",
       "      <td>64</td>\n",
       "      <td>37.50</td>\n",
       "      <td>435.97</td>\n",
       "      <td>209.39</td>\n",
       "      <td>2.08</td>\n",
       "      <td>32.62</td>\n",
       "      <td>-1355.03</td>\n",
       "      <td>2087.63</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURAUD pl2</th>\n",
       "      <td>100</td>\n",
       "      <td>36.00</td>\n",
       "      <td>412.01</td>\n",
       "      <td>224.42</td>\n",
       "      <td>1.84</td>\n",
       "      <td>4.69</td>\n",
       "      <td>-2315.87</td>\n",
       "      <td>469.20</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURGBP pl1 (P/L Filter)</th>\n",
       "      <td>79</td>\n",
       "      <td>27.85</td>\n",
       "      <td>127.50</td>\n",
       "      <td>96.15</td>\n",
       "      <td>1.33</td>\n",
       "      <td>-33.87</td>\n",
       "      <td>-2840.21</td>\n",
       "      <td>-2675.57</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURGBP pl2</th>\n",
       "      <td>116</td>\n",
       "      <td>31.90</td>\n",
       "      <td>166.34</td>\n",
       "      <td>102.41</td>\n",
       "      <td>1.62</td>\n",
       "      <td>-16.69</td>\n",
       "      <td>-2936.03</td>\n",
       "      <td>-1935.94</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURJPY pl1 (P/L Filter)</th>\n",
       "      <td>63</td>\n",
       "      <td>34.92</td>\n",
       "      <td>44651.36</td>\n",
       "      <td>22001.99</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1273.79</td>\n",
       "      <td>-274874.76</td>\n",
       "      <td>80248.50</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURJPY pl2</th>\n",
       "      <td>97</td>\n",
       "      <td>36.08</td>\n",
       "      <td>50087.14</td>\n",
       "      <td>21324.08</td>\n",
       "      <td>2.35</td>\n",
       "      <td>4442.86</td>\n",
       "      <td>-261197.45</td>\n",
       "      <td>430957.15</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURUSD pl1 (P/L Filter)</th>\n",
       "      <td>72</td>\n",
       "      <td>34.72</td>\n",
       "      <td>383.08</td>\n",
       "      <td>139.67</td>\n",
       "      <td>2.74</td>\n",
       "      <td>41.84</td>\n",
       "      <td>-1675.26</td>\n",
       "      <td>3012.42</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURUSD pl2</th>\n",
       "      <td>107</td>\n",
       "      <td>33.64</td>\n",
       "      <td>385.50</td>\n",
       "      <td>152.19</td>\n",
       "      <td>2.53</td>\n",
       "      <td>28.71</td>\n",
       "      <td>-2012.07</td>\n",
       "      <td>3072.34</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPAUD pl1 (P/L Filter)</th>\n",
       "      <td>63</td>\n",
       "      <td>39.68</td>\n",
       "      <td>619.10</td>\n",
       "      <td>297.75</td>\n",
       "      <td>2.08</td>\n",
       "      <td>66.08</td>\n",
       "      <td>-1584.16</td>\n",
       "      <td>4163.00</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPAUD pl2</th>\n",
       "      <td>95</td>\n",
       "      <td>34.74</td>\n",
       "      <td>669.99</td>\n",
       "      <td>324.69</td>\n",
       "      <td>2.06</td>\n",
       "      <td>20.83</td>\n",
       "      <td>-3476.07</td>\n",
       "      <td>1979.08</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPJPY pl1 (P/L Filter)</th>\n",
       "      <td>73</td>\n",
       "      <td>34.25</td>\n",
       "      <td>46322.80</td>\n",
       "      <td>29068.56</td>\n",
       "      <td>1.59</td>\n",
       "      <td>-3249.60</td>\n",
       "      <td>-502412.52</td>\n",
       "      <td>-237220.89</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPJPY pl2</th>\n",
       "      <td>106</td>\n",
       "      <td>31.13</td>\n",
       "      <td>65398.79</td>\n",
       "      <td>28261.43</td>\n",
       "      <td>2.31</td>\n",
       "      <td>896.94</td>\n",
       "      <td>-713694.71</td>\n",
       "      <td>95075.29</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPUSD pl1 (P/L Filter)</th>\n",
       "      <td>87</td>\n",
       "      <td>29.89</td>\n",
       "      <td>372.81</td>\n",
       "      <td>224.16</td>\n",
       "      <td>1.66</td>\n",
       "      <td>-45.75</td>\n",
       "      <td>-5333.57</td>\n",
       "      <td>-3980.58</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPUSD pl2</th>\n",
       "      <td>121</td>\n",
       "      <td>28.93</td>\n",
       "      <td>406.19</td>\n",
       "      <td>225.52</td>\n",
       "      <td>1.80</td>\n",
       "      <td>-42.80</td>\n",
       "      <td>-7164.52</td>\n",
       "      <td>-5178.23</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKDJPY pl1 (P/L Filter)</th>\n",
       "      <td>68</td>\n",
       "      <td>30.88</td>\n",
       "      <td>4536.67</td>\n",
       "      <td>1989.15</td>\n",
       "      <td>2.28</td>\n",
       "      <td>26.18</td>\n",
       "      <td>-26141.21</td>\n",
       "      <td>1780.12</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKDJPY pl2</th>\n",
       "      <td>92</td>\n",
       "      <td>26.09</td>\n",
       "      <td>4136.25</td>\n",
       "      <td>1999.76</td>\n",
       "      <td>2.07</td>\n",
       "      <td>-399.06</td>\n",
       "      <td>-50667.01</td>\n",
       "      <td>-36713.66</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZDJPY pl1 (P/L Filter)</th>\n",
       "      <td>78</td>\n",
       "      <td>23.08</td>\n",
       "      <td>45558.33</td>\n",
       "      <td>17044.06</td>\n",
       "      <td>2.67</td>\n",
       "      <td>-2597.35</td>\n",
       "      <td>-424161.14</td>\n",
       "      <td>-202593.49</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZDJPY pl2</th>\n",
       "      <td>103</td>\n",
       "      <td>25.24</td>\n",
       "      <td>40894.23</td>\n",
       "      <td>16695.41</td>\n",
       "      <td>2.45</td>\n",
       "      <td>-2158.22</td>\n",
       "      <td>-474219.91</td>\n",
       "      <td>-222296.52</td>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZDUSD pl1 (P/L Filter)</th>\n",
       "      <td>73</td>\n",
       "      <td>39.73</td>\n",
       "      <td>206.66</td>\n",
       "      <td>120.24</td>\n",
       "      <td>1.72</td>\n",
       "      <td>9.62</td>\n",
       "      <td>-1262.21</td>\n",
       "      <td>702.54</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZDUSD pl2</th>\n",
       "      <td>110</td>\n",
       "      <td>34.55</td>\n",
       "      <td>215.41</td>\n",
       "      <td>133.68</td>\n",
       "      <td>1.61</td>\n",
       "      <td>-13.08</td>\n",
       "      <td>-2938.23</td>\n",
       "      <td>-1439.26</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDJPY pl1 (P/L Filter)</th>\n",
       "      <td>69</td>\n",
       "      <td>26.09</td>\n",
       "      <td>20146.67</td>\n",
       "      <td>10830.81</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-2749.73</td>\n",
       "      <td>-210320.56</td>\n",
       "      <td>-189731.15</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDJPY pl2</th>\n",
       "      <td>93</td>\n",
       "      <td>26.88</td>\n",
       "      <td>22667.60</td>\n",
       "      <td>11032.22</td>\n",
       "      <td>2.05</td>\n",
       "      <td>-1973.13</td>\n",
       "      <td>-260468.18</td>\n",
       "      <td>-183500.89</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDCAD pl1 (P/L Filter)</th>\n",
       "      <td>71</td>\n",
       "      <td>36.62</td>\n",
       "      <td>297.92</td>\n",
       "      <td>144.27</td>\n",
       "      <td>2.07</td>\n",
       "      <td>17.66</td>\n",
       "      <td>-2350.47</td>\n",
       "      <td>1253.77</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDCAD pl2</th>\n",
       "      <td>104</td>\n",
       "      <td>31.73</td>\n",
       "      <td>308.74</td>\n",
       "      <td>155.61</td>\n",
       "      <td>1.98</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>-4436.86</td>\n",
       "      <td>-860.04</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDCHF pl1 (P/L Filter)</th>\n",
       "      <td>82</td>\n",
       "      <td>30.49</td>\n",
       "      <td>196.31</td>\n",
       "      <td>139.53</td>\n",
       "      <td>1.41</td>\n",
       "      <td>-37.14</td>\n",
       "      <td>-2942.07</td>\n",
       "      <td>-3045.25</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDCHF pl2</th>\n",
       "      <td>118</td>\n",
       "      <td>30.51</td>\n",
       "      <td>244.03</td>\n",
       "      <td>150.01</td>\n",
       "      <td>1.63</td>\n",
       "      <td>-29.80</td>\n",
       "      <td>-4361.33</td>\n",
       "      <td>-3516.05</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDHKD pl1 (P/L Filter)</th>\n",
       "      <td>61</td>\n",
       "      <td>24.59</td>\n",
       "      <td>123.50</td>\n",
       "      <td>60.60</td>\n",
       "      <td>2.04</td>\n",
       "      <td>-15.33</td>\n",
       "      <td>-1311.54</td>\n",
       "      <td>-935.10</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDHKD pl2</th>\n",
       "      <td>79</td>\n",
       "      <td>22.78</td>\n",
       "      <td>125.02</td>\n",
       "      <td>58.50</td>\n",
       "      <td>2.14</td>\n",
       "      <td>-16.68</td>\n",
       "      <td>-1856.95</td>\n",
       "      <td>-1317.81</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDJPY pl1 (P/L Filter)</th>\n",
       "      <td>77</td>\n",
       "      <td>37.66</td>\n",
       "      <td>25646.55</td>\n",
       "      <td>13422.38</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1291.90</td>\n",
       "      <td>-142026.84</td>\n",
       "      <td>99475.97</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDJPY pl2</th>\n",
       "      <td>113</td>\n",
       "      <td>31.86</td>\n",
       "      <td>26342.50</td>\n",
       "      <td>14331.40</td>\n",
       "      <td>1.84</td>\n",
       "      <td>-1373.34</td>\n",
       "      <td>-276594.32</td>\n",
       "      <td>-155187.93</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDSGD pl1 (P/L Filter)</th>\n",
       "      <td>63</td>\n",
       "      <td>42.86</td>\n",
       "      <td>241.60</td>\n",
       "      <td>96.09</td>\n",
       "      <td>2.51</td>\n",
       "      <td>48.64</td>\n",
       "      <td>-584.39</td>\n",
       "      <td>3064.09</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDSGD pl2</th>\n",
       "      <td>99</td>\n",
       "      <td>37.37</td>\n",
       "      <td>239.89</td>\n",
       "      <td>113.26</td>\n",
       "      <td>2.12</td>\n",
       "      <td>18.73</td>\n",
       "      <td>-887.56</td>\n",
       "      <td>1854.09</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         トレード数  勝率（％）      平均利益      平均損失  平均利益÷平均損失  \\\n",
       "AUDJPY pl1 (P/L Filter)     80  27.50  26292.73  16955.80       1.55   \n",
       "AUDJPY pl2                 110  28.18  35695.81  17206.63       2.07   \n",
       "AUDNZD pl1 (P/L Filter)     60  38.33    248.67    140.28       1.77   \n",
       "AUDNZD pl2                  92  34.78    271.42    134.77       2.01   \n",
       "AUDUSD pl1 (P/L Filter)     78  30.77    310.54    128.84       2.41   \n",
       "AUDUSD pl2                 116  33.62    306.34    135.15       2.27   \n",
       "CADJPY pl1 (P/L Filter)     68  38.24  28536.15  19041.85       1.50   \n",
       "CADJPY pl2                 102  34.31  29434.00  18048.18       1.63   \n",
       "CHFJPY pl1 (P/L Filter)     78  30.77  21564.17  14388.20       1.50   \n",
       "CHFJPY pl2                 109  29.36  29319.06  14613.03       2.01   \n",
       "EURAUD pl1 (P/L Filter)     64  37.50    435.97    209.39       2.08   \n",
       "EURAUD pl2                 100  36.00    412.01    224.42       1.84   \n",
       "EURGBP pl1 (P/L Filter)     79  27.85    127.50     96.15       1.33   \n",
       "EURGBP pl2                 116  31.90    166.34    102.41       1.62   \n",
       "EURJPY pl1 (P/L Filter)     63  34.92  44651.36  22001.99       2.03   \n",
       "EURJPY pl2                  97  36.08  50087.14  21324.08       2.35   \n",
       "EURUSD pl1 (P/L Filter)     72  34.72    383.08    139.67       2.74   \n",
       "EURUSD pl2                 107  33.64    385.50    152.19       2.53   \n",
       "GBPAUD pl1 (P/L Filter)     63  39.68    619.10    297.75       2.08   \n",
       "GBPAUD pl2                  95  34.74    669.99    324.69       2.06   \n",
       "GBPJPY pl1 (P/L Filter)     73  34.25  46322.80  29068.56       1.59   \n",
       "GBPJPY pl2                 106  31.13  65398.79  28261.43       2.31   \n",
       "GBPUSD pl1 (P/L Filter)     87  29.89    372.81    224.16       1.66   \n",
       "GBPUSD pl2                 121  28.93    406.19    225.52       1.80   \n",
       "HKDJPY pl1 (P/L Filter)     68  30.88   4536.67   1989.15       2.28   \n",
       "HKDJPY pl2                  92  26.09   4136.25   1999.76       2.07   \n",
       "NZDJPY pl1 (P/L Filter)     78  23.08  45558.33  17044.06       2.67   \n",
       "NZDJPY pl2                 103  25.24  40894.23  16695.41       2.45   \n",
       "NZDUSD pl1 (P/L Filter)     73  39.73    206.66    120.24       1.72   \n",
       "NZDUSD pl2                 110  34.55    215.41    133.68       1.61   \n",
       "SGDJPY pl1 (P/L Filter)     69  26.09  20146.67  10830.81       1.86   \n",
       "SGDJPY pl2                  93  26.88  22667.60  11032.22       2.05   \n",
       "USDCAD pl1 (P/L Filter)     71  36.62    297.92    144.27       2.07   \n",
       "USDCAD pl2                 104  31.73    308.74    155.61       1.98   \n",
       "USDCHF pl1 (P/L Filter)     82  30.49    196.31    139.53       1.41   \n",
       "USDCHF pl2                 118  30.51    244.03    150.01       1.63   \n",
       "USDHKD pl1 (P/L Filter)     61  24.59    123.50     60.60       2.04   \n",
       "USDHKD pl2                  79  22.78    125.02     58.50       2.14   \n",
       "USDJPY pl1 (P/L Filter)     77  37.66  25646.55  13422.38       1.91   \n",
       "USDJPY pl2                 113  31.86  26342.50  14331.40       1.84   \n",
       "USDSGD pl1 (P/L Filter)     63  42.86    241.60     96.09       2.51   \n",
       "USDSGD pl2                  99  37.37    239.89    113.26       2.12   \n",
       "\n",
       "                         1トレード当たりの平均損益      最大損失額        総損益   期待値  \n",
       "AUDJPY pl1 (P/L Filter)       -5062.46 -484703.92 -404996.68 -0.30  \n",
       "AUDJPY pl2                    -2297.76 -482621.11 -252753.98 -0.13  \n",
       "AUDNZD pl1 (P/L Filter)           8.81   -1039.29     528.87  0.06  \n",
       "AUDNZD pl2                        6.52   -1103.79     599.66  0.05  \n",
       "AUDUSD pl1 (P/L Filter)           6.35   -1684.93     495.39  0.05  \n",
       "AUDUSD pl2                       13.29   -2451.46    1541.06  0.10  \n",
       "CADJPY pl1 (P/L Filter)        -850.26 -415775.94  -57817.81 -0.04  \n",
       "CADJPY pl2                    -1755.28 -478378.06 -179038.13 -0.10  \n",
       "CHFJPY pl1 (P/L Filter)       -3325.93 -305868.39 -259422.82 -0.23  \n",
       "CHFJPY pl2                    -1715.54 -276925.41 -186993.42 -0.12  \n",
       "EURAUD pl1 (P/L Filter)          32.62   -1355.03    2087.63  0.16  \n",
       "EURAUD pl2                        4.69   -2315.87     469.20  0.02  \n",
       "EURGBP pl1 (P/L Filter)         -33.87   -2840.21   -2675.57 -0.35  \n",
       "EURGBP pl2                      -16.69   -2936.03   -1935.94 -0.16  \n",
       "EURJPY pl1 (P/L Filter)        1273.79 -274874.76   80248.50  0.06  \n",
       "EURJPY pl2                     4442.86 -261197.45  430957.15  0.21  \n",
       "EURUSD pl1 (P/L Filter)          41.84   -1675.26    3012.42  0.30  \n",
       "EURUSD pl2                       28.71   -2012.07    3072.34  0.19  \n",
       "GBPAUD pl1 (P/L Filter)          66.08   -1584.16    4163.00  0.22  \n",
       "GBPAUD pl2                       20.83   -3476.07    1979.08  0.06  \n",
       "GBPJPY pl1 (P/L Filter)       -3249.60 -502412.52 -237220.89 -0.11  \n",
       "GBPJPY pl2                      896.94 -713694.71   95075.29  0.03  \n",
       "GBPUSD pl1 (P/L Filter)         -45.75   -5333.57   -3980.58 -0.20  \n",
       "GBPUSD pl2                      -42.80   -7164.52   -5178.23 -0.19  \n",
       "HKDJPY pl1 (P/L Filter)          26.18  -26141.21    1780.12  0.01  \n",
       "HKDJPY pl2                     -399.06  -50667.01  -36713.66 -0.20  \n",
       "NZDJPY pl1 (P/L Filter)       -2597.35 -424161.14 -202593.49 -0.15  \n",
       "NZDJPY pl2                    -2158.22 -474219.91 -222296.52 -0.13  \n",
       "NZDUSD pl1 (P/L Filter)           9.62   -1262.21     702.54  0.08  \n",
       "NZDUSD pl2                      -13.08   -2938.23   -1439.26 -0.10  \n",
       "SGDJPY pl1 (P/L Filter)       -2749.73 -210320.56 -189731.15 -0.25  \n",
       "SGDJPY pl2                    -1973.13 -260468.18 -183500.89 -0.18  \n",
       "USDCAD pl1 (P/L Filter)          17.66   -2350.47    1253.77  0.12  \n",
       "USDCAD pl2                       -8.27   -4436.86    -860.04 -0.05  \n",
       "USDCHF pl1 (P/L Filter)         -37.14   -2942.07   -3045.25 -0.27  \n",
       "USDCHF pl2                      -29.80   -4361.33   -3516.05 -0.20  \n",
       "USDHKD pl1 (P/L Filter)         -15.33   -1311.54    -935.10 -0.25  \n",
       "USDHKD pl2                      -16.68   -1856.95   -1317.81 -0.29  \n",
       "USDJPY pl1 (P/L Filter)        1291.90 -142026.84   99475.97  0.10  \n",
       "USDJPY pl2                    -1373.34 -276594.32 -155187.93 -0.10  \n",
       "USDSGD pl1 (P/L Filter)          48.64    -584.39    3064.09  0.51  \n",
       "USDSGD pl2                       18.73    -887.56    1854.09  0.17  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([\n",
    "    pd.DataFrame([y[0] for x, y in d4.items()], index=[f'{x} pl1 (P/L Filter)' for x in d4.keys()], columns=d4[data[0][0]][0]),\n",
    "    pd.DataFrame([y[1] for x, y in d4.items()], index=[f'{x} pl2' for x in d4.keys()], columns=d4[data[0][0]][1]),\n",
    "]).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot('USDSGD')"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}