ma_sim_explorer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_test_res = pd.read_pickle(\"ma_test_res.pkl\")\n",
    "all_trades = pd.read_pickle(\"all_trades.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66885, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_trades.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(630, 8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma_test_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>pair</th>\n",
       "      <th>num_trades</th>\n",
       "      <th>total_gain</th>\n",
       "      <th>mean_gain</th>\n",
       "      <th>min_gain</th>\n",
       "      <th>max_gain</th>\n",
       "      <th>mashort</th>\n",
       "      <th>malong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>535</td>\n",
       "      <td>412.9</td>\n",
       "      <td>0.771776</td>\n",
       "      <td>-250.1</td>\n",
       "      <td>558.2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>328</td>\n",
       "      <td>-725.5</td>\n",
       "      <td>-2.211890</td>\n",
       "      <td>-232.8</td>\n",
       "      <td>579.9</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>284</td>\n",
       "      <td>-787.8</td>\n",
       "      <td>-2.773944</td>\n",
       "      <td>-183.4</td>\n",
       "      <td>479.7</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>201</td>\n",
       "      <td>-863.3</td>\n",
       "      <td>-4.295025</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>153</td>\n",
       "      <td>-320.3</td>\n",
       "      <td>-2.093464</td>\n",
       "      <td>-190.0</td>\n",
       "      <td>490.6</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pair  num_trades  total_gain  mean_gain  min_gain  max_gain  mashort  \\\n",
       "0  GBP_USD         535       412.9   0.771776    -250.1     558.2        4   \n",
       "1  GBP_USD         328      -725.5  -2.211890    -232.8     579.9        4   \n",
       "2  GBP_USD         284      -787.8  -2.773944    -183.4     479.7        8   \n",
       "3  GBP_USD         201      -863.3  -4.295025    -156.0     476.0        4   \n",
       "4  GBP_USD         153      -320.3  -2.093464    -190.0     490.6        8   \n",
       "\n",
       "   malong  \n",
       "0       8  \n",
       "1      16  \n",
       "2      16  \n",
       "3      32  \n",
       "4      32  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma_test_res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pair', 'num_trades', 'total_gain', 'mean_gain', 'min_gain', 'max_gain',\n",
       "       'mashort', 'malong'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma_test_res.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_test_res = ma_test_res[['pair', 'num_trades', 'total_gain', \n",
    "       'mashort', 'malong']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_test_res[\"CROSS\"] = \"MA_\" + ma_test_res.mashort.map(str) + \"_\" + ma_test_res.malong.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mathew\\AppData\\Local\\Temp\\ipykernel_14072\\1300193441.py:1: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_all_gains = ma_test_res.groupby(by=[\"CROSS\", \"mashort\", \"malong\"], as_index=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_gains.sort_values(by=\"total_gain\", ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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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>CROSS</th>\n",
       "      <th>mashort</th>\n",
       "      <th>malong</th>\n",
       "      <th>num_trades</th>\n",
       "      <th>total_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MA_32_64</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>1439</td>\n",
       "      <td>-1021.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MA_8_96</td>\n",
       "      <td>8</td>\n",
       "      <td>96</td>\n",
       "      <td>1561</td>\n",
       "      <td>-3749.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MA_64_96</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>991</td>\n",
       "      <td>-4799.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MA_16_96</td>\n",
       "      <td>16</td>\n",
       "      <td>96</td>\n",
       "      <td>1205</td>\n",
       "      <td>-5054.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MA_24_96</td>\n",
       "      <td>24</td>\n",
       "      <td>96</td>\n",
       "      <td>1056</td>\n",
       "      <td>-5637.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CROSS  mashort  malong  num_trades  total_gain\n",
       "12  MA_32_64       32      64        1439     -1021.1\n",
       "29   MA_8_96        8      96        1561     -3749.1\n",
       "23  MA_64_96       64      96         991     -4799.1\n",
       "4   MA_16_96       16      96        1205     -5054.6\n",
       "9   MA_24_96       24      96        1056     -5637.4"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_gains.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_8_16 = ma_test_res[ma_test_res.CROSS==\"MA_8_16\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_8_16.sort_values(by=\"total_gain\", ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_p = len(ma_8_16.pair.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma_8_16[ma_8_16.total_gain>0].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19047619047619047"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 / total_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MA_32_64       11   52%\n",
      "MA_8_96         9   43%\n",
      "MA_64_96        7   33%\n",
      "MA_16_96        9   43%\n",
      "MA_24_96       10   48%\n",
      "MA_32_96        9   43%\n",
      "MA_32_256      10   48%\n",
      "MA_4_96         9   43%\n",
      "MA_8_32         5   24%\n",
      "MA_24_64        8   38%\n",
      "MA_64_128       5   24%\n",
      "MA_24_256       9   43%\n",
      "MA_64_256       9   43%\n",
      "MA_16_64        8   38%\n",
      "MA_16_256       8   38%\n",
      "MA_32_128       6   29%\n",
      "MA_8_64         8   38%\n",
      "MA_8_256        7   33%\n",
      "MA_24_128       7   33%\n",
      "MA_4_64         9   43%\n",
      "MA_16_128       6   29%\n",
      "MA_16_32        4   19%\n",
      "MA_8_128        6   29%\n",
      "MA_4_32         5   24%\n",
      "MA_4_128        8   38%\n",
      "MA_8_16         4   19%\n",
      "MA_4_256        5   24%\n",
      "MA_4_8          6   29%\n",
      "MA_24_32        4   19%\n",
      "MA_4_16         4   19%\n"
     ]
    }
   ],
   "source": [
    "for cross in df_all_gains.CROSS.unique():\n",
    "    df_temp = ma_test_res[ma_test_res.CROSS==cross]\n",
    "    total_p = df_temp.shape[0]\n",
    "    n_good = df_temp[df_temp.total_gain>0].shape[0]\n",
    "    print(f\"{cross:12} {n_good:4} {(n_good/total_p)*100:4.0f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "crosses = df_all_gains.CROSS.unique()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MA_32_64', 'MA_8_96', 'MA_64_96'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pair</th>\n",
       "      <th>num_trades</th>\n",
       "      <th>total_gain</th>\n",
       "      <th>mashort</th>\n",
       "      <th>malong</th>\n",
       "      <th>CROSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>535</td>\n",
       "      <td>412.9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>MA_4_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>328</td>\n",
       "      <td>-725.5</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>MA_4_16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pair  num_trades  total_gain  mashort  malong    CROSS\n",
       "0  GBP_USD         535       412.9        4       8   MA_4_8\n",
       "1  GBP_USD         328      -725.5        4      16  MA_4_16"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma_test_res.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_good = ma_test_res[(ma_test_res.CROSS.isin(crosses)) & (ma_test_res.total_gain>0)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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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>pair</th>\n",
       "      <th>num_trades</th>\n",
       "      <th>total_gain</th>\n",
       "      <th>mashort</th>\n",
       "      <th>malong</th>\n",
       "      <th>CROSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>63</td>\n",
       "      <td>1793.5</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>MA_32_64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>64</td>\n",
       "      <td>262.4</td>\n",
       "      <td>8</td>\n",
       "      <td>96</td>\n",
       "      <td>MA_8_96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pair  num_trades  total_gain  mashort  malong     CROSS\n",
       "11  GBP_USD          63      1793.5       32      64  MA_32_64\n",
       "13  GBP_USD          64       262.4        8      96   MA_8_96"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_good.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_pairs = list(df_good.pair.value_counts()[:6].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GBP_USD', 'GBP_CAD', 'GBP_JPY', 'EUR_GBP', 'EUR_CAD', 'NZD_JPY']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "our_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>time</th>\n",
       "      <th>mid_c</th>\n",
       "      <th>DIFF</th>\n",
       "      <th>DIFF_PREV</th>\n",
       "      <th>IS_TRADE</th>\n",
       "      <th>DELTA</th>\n",
       "      <th>GAIN</th>\n",
       "      <th>PAIR</th>\n",
       "      <th>MASHORT</th>\n",
       "      <th>MALONG</th>\n",
       "      <th>DURATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-06-07 10:00:00+00:00</td>\n",
       "      <td>1.24962</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>-0.000026</td>\n",
       "      <td>1</td>\n",
       "      <td>78.8</td>\n",
       "      <td>78.8</td>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2022-06-08 00:00:00+00:00</td>\n",
       "      <td>1.25750</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>-1</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>21.4</td>\n",
       "      <td>GBP_USD</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        time    mid_c      DIFF  DIFF_PREV  IS_TRADE  DELTA  \\\n",
       "21 2022-06-07 10:00:00+00:00  1.24962  0.000761  -0.000026         1   78.8   \n",
       "35 2022-06-08 00:00:00+00:00  1.25750 -0.000060   0.000112        -1  -21.4   \n",
       "\n",
       "    GAIN     PAIR  MASHORT  MALONG  DURATION  \n",
       "21  78.8  GBP_USD        4       8      14.0  \n",
       "35  21.4  GBP_USD        4       8      13.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_trades.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_trades[\"CROSS\"] = \"MA_\" + all_trades.MASHORT.map(str) + \"_\" + all_trades.MALONG.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "trades_cad_jpy = all_trades[(all_trades.CROSS==\"MA_8_16\") & (all_trades.PAIR==\"CAD_JPY\")].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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>time</th>\n",
       "      <th>mid_c</th>\n",
       "      <th>DIFF</th>\n",
       "      <th>DIFF_PREV</th>\n",
       "      <th>IS_TRADE</th>\n",
       "      <th>DELTA</th>\n",
       "      <th>GAIN</th>\n",
       "      <th>PAIR</th>\n",
       "      <th>MASHORT</th>\n",
       "      <th>MALONG</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>CROSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2022-06-09 03:00:00+00:00</td>\n",
       "      <td>106.649</td>\n",
       "      <td>-0.018438</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-149.4</td>\n",
       "      <td>149.4</td>\n",
       "      <td>CAD_JPY</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>90.0</td>\n",
       "      <td>MA_8_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2022-06-12 21:00:00+00:00</td>\n",
       "      <td>105.155</td>\n",
       "      <td>0.009500</td>\n",
       "      <td>-0.022063</td>\n",
       "      <td>1</td>\n",
       "      <td>-57.9</td>\n",
       "      <td>-57.9</td>\n",
       "      <td>CAD_JPY</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>11.0</td>\n",
       "      <td>MA_8_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2022-06-13 08:00:00+00:00</td>\n",
       "      <td>104.576</td>\n",
       "      <td>-0.069500</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>-1</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>CAD_JPY</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>20.0</td>\n",
       "      <td>MA_8_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2022-06-14 04:00:00+00:00</td>\n",
       "      <td>104.490</td>\n",
       "      <td>0.006375</td>\n",
       "      <td>-0.025000</td>\n",
       "      <td>1</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>CAD_JPY</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>MA_8_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2022-06-14 11:00:00+00:00</td>\n",
       "      <td>103.960</td>\n",
       "      <td>-0.006063</td>\n",
       "      <td>0.037313</td>\n",
       "      <td>-1</td>\n",
       "      <td>38.2</td>\n",
       "      <td>-38.2</td>\n",
       "      <td>CAD_JPY</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MA_8_16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time    mid_c      DIFF  DIFF_PREV  IS_TRADE  DELTA  \\\n",
       "61  2022-06-09 03:00:00+00:00  106.649 -0.018438   0.008000        -1 -149.4   \n",
       "103 2022-06-12 21:00:00+00:00  105.155  0.009500  -0.022063         1  -57.9   \n",
       "114 2022-06-13 08:00:00+00:00  104.576 -0.069500   0.005500        -1   -8.6   \n",
       "134 2022-06-14 04:00:00+00:00  104.490  0.006375  -0.025000         1  -53.0   \n",
       "141 2022-06-14 11:00:00+00:00  103.960 -0.006063   0.037313        -1   38.2   \n",
       "\n",
       "      GAIN     PAIR  MASHORT  MALONG  DURATION    CROSS  \n",
       "61   149.4  CAD_JPY        8      16      90.0  MA_8_16  \n",
       "103  -57.9  CAD_JPY        8      16      11.0  MA_8_16  \n",
       "114    8.6  CAD_JPY        8      16      20.0  MA_8_16  \n",
       "134  -53.0  CAD_JPY        8      16       7.0  MA_8_16  \n",
       "141  -38.2  CAD_JPY        8      16       8.0  MA_8_16  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades_cad_jpy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "trades_cad_jpy['CUM_GAIN'] = trades_cad_jpy.GAIN.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_line(df_plot, name):\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=df_plot.time,\n",
    "        y=df_plot.CUM_GAIN,\n",
    "        line=dict(width=2,color=\"#3d825f\"),\n",
    "        line_shape='spline',\n",
    "        name=name,\n",
    "        mode='lines'\n",
    "        ))\n",
    "    fig.update_layout(width=1000,height=400,\n",
    "        margin=dict(l=15,b=15,t=10),\n",
    "        font=dict(size=10,color=\"#e1e1e1\"),\n",
    "        paper_bgcolor=\"#1e1e1e\",\n",
    "        plot_bgcolor=\"#1e1e1e\",\n",
    "        title=name)\n",
    "    fig.update_xaxes(\n",
    "        linewidth=1,\n",
    "        linecolor='#3a4a54',\n",
    "        showgrid=False,\n",
    "        zeroline=False\n",
    "    )\n",
    "    fig.update_yaxes(\n",
    "        linewidth=1,\n",
    "        linecolor='#3a4a54',\n",
    "        showgrid=False,\n",
    "        zeroline=False\n",
    "    )\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "line": {
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          "shape": "spline",
          "width": 2
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         "name": "CAD_JPY",
         "type": "scatter",
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   "source": [
    "c = 'MA_8_16'\n",
    "for p in our_pairs:\n",
    "    temp_df = all_trades[(all_trades.CROSS==c)&(all_trades.PAIR==p)].copy()\n",
    "    temp_df['CUM_GAIN'] = temp_df.GAIN.cumsum()\n",
    "    plot_line(temp_df, p + \"_\" +c)"
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  },
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   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'temp_' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[66], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39mfor\u001b[39;00m c \u001b[39min\u001b[39;00m crosses:\n\u001b[1;32m----> 2\u001b[0m     temp_\n",
      "\u001b[1;31mNameError\u001b[0m: name 'temp_' is not defined"
     ]
    }
   ],
   "source": [
    "for c in crosses:\n",
    "    temp_"
   ]
  },
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