candle_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import utils\n",
    "import plotly.graph_objects as go\n",
    "import instrument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pair = \"EUR_USD\"\n",
    "granularity = \"H1\"\n",
    "ma_list = [16,64]\n",
    "i_pair = instrument.Instrument.get_instrument_by_name(pair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(utils.get_his_data_filename(pair, granularity))\n",
    "non_cols = ['time', 'volume']\n",
    "mod_cols = [x for x in df.columns if x not in non_cols]\n",
    "df[mod_cols] = df[mod_cols].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma = df[['time', 'mid_o', 'mid_h', 'mid_l', 'mid_c']].copy()\n",
    "for ma in ma_list:\n",
    "    df_ma[f'MA_{ma}'] = df_ma.mid_c.rolling(window=ma).mean()\n",
    "df_ma.dropna(inplace=True)\n",
    "df_ma.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>mid_o</th>\n",
       "      <th>mid_h</th>\n",
       "      <th>mid_l</th>\n",
       "      <th>mid_c</th>\n",
       "      <th>MA_16</th>\n",
       "      <th>MA_64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-06T13:00:00.000000000Z</td>\n",
       "      <td>1.11983</td>\n",
       "      <td>1.12032</td>\n",
       "      <td>1.11911</td>\n",
       "      <td>1.11976</td>\n",
       "      <td>1.117484</td>\n",
       "      <td>1.117742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-06T14:00:00.000000000Z</td>\n",
       "      <td>1.11978</td>\n",
       "      <td>1.12002</td>\n",
       "      <td>1.11826</td>\n",
       "      <td>1.11832</td>\n",
       "      <td>1.117631</td>\n",
       "      <td>1.117692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-06T15:00:00.000000000Z</td>\n",
       "      <td>1.11836</td>\n",
       "      <td>1.11902</td>\n",
       "      <td>1.11822</td>\n",
       "      <td>1.11888</td>\n",
       "      <td>1.117808</td>\n",
       "      <td>1.117645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-06T16:00:00.000000000Z</td>\n",
       "      <td>1.11890</td>\n",
       "      <td>1.11898</td>\n",
       "      <td>1.11810</td>\n",
       "      <td>1.11872</td>\n",
       "      <td>1.117956</td>\n",
       "      <td>1.117596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-06T17:00:00.000000000Z</td>\n",
       "      <td>1.11870</td>\n",
       "      <td>1.11961</td>\n",
       "      <td>1.11843</td>\n",
       "      <td>1.11952</td>\n",
       "      <td>1.118158</td>\n",
       "      <td>1.117556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             time    mid_o    mid_h    mid_l    mid_c  \\\n",
       "0  2020-01-06T13:00:00.000000000Z  1.11983  1.12032  1.11911  1.11976   \n",
       "1  2020-01-06T14:00:00.000000000Z  1.11978  1.12002  1.11826  1.11832   \n",
       "2  2020-01-06T15:00:00.000000000Z  1.11836  1.11902  1.11822  1.11888   \n",
       "3  2020-01-06T16:00:00.000000000Z  1.11890  1.11898  1.11810  1.11872   \n",
       "4  2020-01-06T17:00:00.000000000Z  1.11870  1.11961  1.11843  1.11952   \n",
       "\n",
       "      MA_16     MA_64  \n",
       "0  1.117484  1.117742  \n",
       "1  1.117631  1.117692  \n",
       "2  1.117808  1.117645  \n",
       "3  1.117956  1.117596  \n",
       "4  1.118158  1.117556  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ma.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_trade(row):\n",
    "    if row.DIFF >= 0 and row.DIFF_PREV < 0:\n",
    "        return 1\n",
    "    if row.DIFF <= 0 and row.DIFF_PREV > 0:\n",
    "        return -1\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma['DIFF'] = df_ma.MA_16 - df_ma.MA_64\n",
    "df_ma['DIFF_PREV'] = df_ma.DIFF.shift(1)\n",
    "df_ma['IS_TRADE'] = df_ma.apply(is_trade, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trades = df_ma[df_ma.IS_TRADE!=0].copy()\n",
    "df_trades[\"DELTA\"] = ( df_trades.mid_c.diff() / i_pair.pipLocation).shift(-1)\n",
    "df_trades[\"GAIN\"] = df_trades[\"DELTA\"] * df_trades[\"IS_TRADE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.parser import *\n",
    "df_trades[\"time\"] = [parse(x) for x in df_trades.time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trades[\"DURATION\"] = df_trades.time.diff().shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trades[\"DURATION\"] = [x.total_seconds() / 3600 for x in df_trades.DURATION]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>time</th>\n",
       "      <th>DURATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18486</th>\n",
       "      <td>2022-12-22 03:00:00+00:00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18503</th>\n",
       "      <td>2022-12-22 20:00:00+00:00</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18528</th>\n",
       "      <td>2022-12-23 21:00:00+00:00</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18576</th>\n",
       "      <td>2022-12-28 21:00:00+00:00</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18596</th>\n",
       "      <td>2022-12-29 17:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                           time  DURATION\n",
       "18486 2022-12-22 03:00:00+00:00      17.0\n",
       "18503 2022-12-22 20:00:00+00:00      25.0\n",
       "18528 2022-12-23 21:00:00+00:00     120.0\n",
       "18576 2022-12-28 21:00:00+00:00      20.0\n",
       "18596 2022-12-29 17:00:00+00:00       NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trades[['time', 'DURATION']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 373 entries, 2 to 18596\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Non-Null Count  Dtype                  \n",
      "---  ------     --------------  -----                  \n",
      " 0   time       373 non-null    datetime64[ns, tzutc()]\n",
      " 1   mid_o      373 non-null    float64                \n",
      " 2   mid_h      373 non-null    float64                \n",
      " 3   mid_l      373 non-null    float64                \n",
      " 4   mid_c      373 non-null    float64                \n",
      " 5   MA_16      373 non-null    float64                \n",
      " 6   MA_64      373 non-null    float64                \n",
      " 7   DIFF       373 non-null    float64                \n",
      " 8   DIFF_PREV  373 non-null    float64                \n",
      " 9   IS_TRADE   373 non-null    int64                  \n",
      " 10  DELTA      372 non-null    float64                \n",
      " 11  GAIN       372 non-null    float64                \n",
      " 12  DURATION   372 non-null    float64                \n",
      "dtypes: datetime64[ns, tzutc()](1), float64(11), int64(1)\n",
      "memory usage: 40.8 KB\n"
     ]
    }
   ],
   "source": [
    "df_trades.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = pd.read_pickle(\"all_trades.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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>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>MASHORT</th>\n",
       "      <th>MALONG</th>\n",
       "      <th>DURATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>66885.000000</td>\n",
       "      <td>6.688500e+04</td>\n",
       "      <td>6.688500e+04</td>\n",
       "      <td>66885.000000</td>\n",
       "      <td>66885.000000</td>\n",
       "      <td>66885.00000</td>\n",
       "      <td>66885.000000</td>\n",
       "      <td>66885.000000</td>\n",
       "      <td>66885.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.593336</td>\n",
       "      <td>-4.510093e-04</td>\n",
       "      <td>4.534397e-04</td>\n",
       "      <td>-0.001181</td>\n",
       "      <td>-0.353739</td>\n",
       "      <td>-4.05211</td>\n",
       "      <td>11.822741</td>\n",
       "      <td>61.336682</td>\n",
       "      <td>49.985169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.472771</td>\n",
       "      <td>3.387540e-02</td>\n",
       "      <td>3.339264e-02</td>\n",
       "      <td>1.000007</td>\n",
       "      <td>93.981241</td>\n",
       "      <td>93.89451</td>\n",
       "      <td>12.499719</td>\n",
       "      <td>64.796491</td>\n",
       "      <td>83.298226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.552120</td>\n",
       "      <td>-8.646146e-01</td>\n",
       "      <td>-7.054375e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-945.800000</td>\n",
       "      <td>-1139.80000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.926530</td>\n",
       "      <td>-2.662500e-04</td>\n",
       "      <td>-2.587500e-04</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-30.200000</td>\n",
       "      <td>-39.90000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.305740</td>\n",
       "      <td>-1.110223e-16</td>\n",
       "      <td>4.687500e-07</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-14.10000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>84.844000</td>\n",
       "      <td>2.643750e-04</td>\n",
       "      <td>2.629167e-04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>10.40000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>171.560000</td>\n",
       "      <td>6.221250e-01</td>\n",
       "      <td>1.131125e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1327.900000</td>\n",
       "      <td>1327.90000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>1516.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mid_c          DIFF     DIFF_PREV      IS_TRADE         DELTA  \\\n",
       "count  66885.000000  6.688500e+04  6.688500e+04  66885.000000  66885.000000   \n",
       "mean      35.593336 -4.510093e-04  4.534397e-04     -0.001181     -0.353739   \n",
       "std       58.472771  3.387540e-02  3.339264e-02      1.000007     93.981241   \n",
       "min        0.552120 -8.646146e-01 -7.054375e-01     -1.000000   -945.800000   \n",
       "25%        0.926530 -2.662500e-04 -2.587500e-04     -1.000000    -30.200000   \n",
       "50%        1.305740 -1.110223e-16  4.687500e-07     -1.000000      0.300000   \n",
       "75%       84.844000  2.643750e-04  2.629167e-04      1.000000     30.900000   \n",
       "max      171.560000  6.221250e-01  1.131125e+00      1.000000   1327.900000   \n",
       "\n",
       "              GAIN       MASHORT        MALONG      DURATION  \n",
       "count  66885.00000  66885.000000  66885.000000  66885.000000  \n",
       "mean      -4.05211     11.822741     61.336682     49.985169  \n",
       "std       93.89451     12.499719     64.796491     83.298226  \n",
       "min    -1139.80000      4.000000      8.000000      1.000000  \n",
       "25%      -39.90000      4.000000     16.000000      6.000000  \n",
       "50%      -14.10000      8.000000     32.000000     17.000000  \n",
       "75%       10.40000     16.000000     96.000000     64.000000  \n",
       "max     1327.90000     64.000000    256.000000   1516.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1482.7999999999752"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trades[\"GAIN\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = df_ma.iloc[100:250].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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           "bgcolor": "#E5ECF6",
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          "title": {
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          "yaxis": {
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   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Candlestick(\n",
    "    x=df_plot.time, open=df_plot.mid_o, high=df_plot.mid_h, low=df_plot.mid_l, close=df_plot.mid_c,\n",
    "    line=dict(width=1), opacity=1,\n",
    "    increasing_fillcolor='#24A06B',\n",
    "    decreasing_fillcolor=\"#CC2E3C\",\n",
    "    increasing_line_color='#2EC886',\n",
    "    decreasing_line_color='#FF3A4C'\n",
    "))\n",
    "for ma in ma_list:\n",
    "    col = f\"MA_{ma}\"\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=df_plot.time,\n",
    "        y=df_plot[col],\n",
    "        line=dict(width=2),\n",
    "        line_shape='spline',\n",
    "        name=col\n",
    "        ))\n",
    "fig.update_layout(width=1000,height=400,\n",
    "    margin=dict(l=10,b=10,t=10),\n",
    "    font=dict(size=10,color=\"#e1e1e1\"),\n",
    "    paper_bgcolor=\"#1e1e1e\",\n",
    "    plot_bgcolor=\"#1e1e1e\")\n",
    "fig.update_xaxes(\n",
    "    gridcolor=\"#1f292f\",\n",
    "    showgrid=True,fixedrange=True,rangeslider=dict(visible=False)\n",
    ")\n",
    "fig.update_yaxes(\n",
    "    gridcolor=\"#1f292f\",\n",
    "    showgrid=True,\n",
    ")\n",
    "fig.show()"
   ]
  }
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