candle_patterns.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import utils\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_cols = ['ENTRY', 'STOPLOSS', 'TAKEPROFIT']\n",
    "plot_colours = ['#043ef9', '#eb5334', '#34eb37']\n",
    "\n",
    "def plot_candles(df_plot, df_markers=None):\n",
    "    fig = go.Figure()\n",
    "    \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",
    "    if df_markers is not None:\n",
    "        fig.add_trace(go.Candlestick(\n",
    "            x=df_markers.time, open=df_markers.mid_o, high=df_markers.mid_h, low=df_markers.mid_l, close=df_markers.mid_c,\n",
    "            line=dict(width=1), opacity=1,\n",
    "            increasing_fillcolor='#3480eb',\n",
    "            decreasing_fillcolor='#3480eb',\n",
    "            increasing_line_color='#3480eb',\n",
    "            decreasing_line_color='#3480eb'\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",
    "        rangebreaks=[\n",
    "            dict(bounds=[\"sat\", \"mon\"])\n",
    "        ]\n",
    "    )\n",
    "    fig.update_yaxes(\n",
    "        gridcolor=\"#1f292f\",\n",
    "        showgrid=True,\n",
    "    )\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "pair = \"USD_JPY\"\n",
    "granularity = \"H4\"\n",
    "df_raw = pd.read_pickle(utils.get_his_data_filename(pair, granularity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['time', 'mid_o', 'mid_h', 'mid_l', 'mid_c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMALL_BODY = 0.2\n",
    "CLOSE_DISTANCE = 0.15\n",
    "DOJI_BODY = 0.05\n",
    "FULL_BODY = 0.95\n",
    "CENTER_DISTANCE_HIGH = 0.55\n",
    "CENTER_DISTANCE_LOW = 0.45\n",
    "BIG_BODY = 0.6\n",
    "ENGULFING_FACTOR = 1.1\n",
    "\n",
    "def apply_top_end_distance(row):\n",
    "    if row.DIRECTION == 1:\n",
    "        return row.mid_h - row.mid_c\n",
    "    else:\n",
    "        return row.mid_h - row.mid_o\n",
    "    \n",
    "def apply_bottom_end_distance(row):\n",
    "    if row.DIRECTION == 1:\n",
    "        return row.mid_o - row.mid_l\n",
    "    else:\n",
    "        return row.mid_c - row.mid_l\n",
    "    \n",
    "def apply_hammer(row):\n",
    "    if row.BODY_PERC < SMALL_BODY:\n",
    "        if row.DIST_TOP_PERC < CLOSE_DISTANCE or row.DIST_BOTTOM_PERC < CLOSE_DISTANCE:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def apply_spinning_top(row):\n",
    "    if row.BODY_PERC < SMALL_BODY:\n",
    "        if row.DIST_TOP_PERC < CENTER_DISTANCE_HIGH or row.DIST_TOP_PERC < CENTER_DISTANCE_LOW:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def apply_engulfing(row):\n",
    "    if row.PREV_DIRECTION != row.DIRECTION:\n",
    "        if row.PREV_BODY_PERC > BIG_BODY and row.BODY_PERC > BIG_BODY:\n",
    "            if row.BODY_RANGE > row.PREV_BODY_RANGE * ENGULFING_FACTOR:\n",
    "                return True\n",
    "    return False\n",
    "\n",
    "def apply_stats(df):\n",
    "    df['RANGE'] = df.mid_h - df.mid_l\n",
    "    df['BODY_RANGE'] = abs(df.mid_c - df.mid_o)\n",
    "    df['CENTER'] = (df.mid_h - df.mid_l) / 2 + df.mid_l\n",
    "    df['BODY_PERC'] = df.BODY_RANGE / df.RANGE\n",
    "    df['DIRECTION'] = df.mid_c - df.mid_o\n",
    "    df['DIRECTION'] = df['DIRECTION'].apply(lambda x: 1 if x >= 0 else -1)\n",
    "    df['DIST_TOP'] = df.apply(apply_top_end_distance, axis=1)\n",
    "    df['DIST_BOTTOM'] = df.apply(apply_bottom_end_distance, axis=1)\n",
    "    df['DIST_TOP_PERC'] = df.DIST_TOP / df.RANGE\n",
    "    df['DIST_BOTTOM_PERC'] = df.DIST_BOTTOM / df.RANGE\n",
    "    \n",
    "    df['PREV_BODY_RANGE'] = df.BODY_RANGE.shift(1)\n",
    "    df['PREV_BODY_PERC'] = df.BODY_PERC.shift(1)\n",
    "    df['PREV_DIRECTION'] = df.DIRECTION.shift(1)\n",
    "    \n",
    "    df.dropna(inplace=True)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def apply_patterns(df):\n",
    "    df['HAMMER'] = df.apply(apply_hammer, axis=1)\n",
    "    df['SPINNING_TOP'] = df.apply(apply_spinning_top, axis=1)\n",
    "    df['DOJI'] = df['BODY_PERC'].apply(lambda x: True if x < DOJI_BODY else False)\n",
    "    df['MARUBOZU'] = df['BODY_PERC'].apply(lambda x: True if x > FULL_BODY else False)\n",
    "    df['ENGULFING'] = df.apply(apply_engulfing, axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_raw[cols].copy()\n",
    "df = apply_stats(df)\n",
    "df = apply_patterns(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>time</th>\n",
       "      <th>mid_o</th>\n",
       "      <th>mid_h</th>\n",
       "      <th>mid_l</th>\n",
       "      <th>mid_c</th>\n",
       "      <th>RANGE</th>\n",
       "      <th>BODY_RANGE</th>\n",
       "      <th>CENTER</th>\n",
       "      <th>BODY_PERC</th>\n",
       "      <th>DIRECTION</th>\n",
       "      <th>...</th>\n",
       "      <th>DIST_TOP_PERC</th>\n",
       "      <th>DIST_BOTTOM_PERC</th>\n",
       "      <th>PREV_BODY_RANGE</th>\n",
       "      <th>PREV_BODY_PERC</th>\n",
       "      <th>PREV_DIRECTION</th>\n",
       "      <th>HAMMER</th>\n",
       "      <th>SPINNING_TOP</th>\n",
       "      <th>DOJI</th>\n",
       "      <th>MARUBOZU</th>\n",
       "      <th>ENGULFING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02 02:00:00+00:00</td>\n",
       "      <td>108.671</td>\n",
       "      <td>108.758</td>\n",
       "      <td>108.658</td>\n",
       "      <td>108.742</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.071</td>\n",
       "      <td>108.708</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02 06:00:00+00:00</td>\n",
       "      <td>108.744</td>\n",
       "      <td>108.842</td>\n",
       "      <td>108.696</td>\n",
       "      <td>108.839</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.095</td>\n",
       "      <td>108.769</td>\n",
       "      <td>0.650685</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020548</td>\n",
       "      <td>0.328767</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-02 10:00:00+00:00</td>\n",
       "      <td>108.836</td>\n",
       "      <td>108.866</td>\n",
       "      <td>108.658</td>\n",
       "      <td>108.694</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.142</td>\n",
       "      <td>108.762</td>\n",
       "      <td>0.682692</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144231</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.650685</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-02 14:00:00+00:00</td>\n",
       "      <td>108.697</td>\n",
       "      <td>108.748</td>\n",
       "      <td>108.212</td>\n",
       "      <td>108.529</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.168</td>\n",
       "      <td>108.480</td>\n",
       "      <td>0.313433</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095149</td>\n",
       "      <td>0.591418</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.682692</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-02 18:00:00+00:00</td>\n",
       "      <td>108.526</td>\n",
       "      <td>108.572</td>\n",
       "      <td>108.494</td>\n",
       "      <td>108.561</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.035</td>\n",
       "      <td>108.533</td>\n",
       "      <td>0.448718</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141026</td>\n",
       "      <td>0.410256</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.313433</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time    mid_o    mid_h    mid_l    mid_c  RANGE  \\\n",
       "1 2020-01-02 02:00:00+00:00  108.671  108.758  108.658  108.742  0.100   \n",
       "2 2020-01-02 06:00:00+00:00  108.744  108.842  108.696  108.839  0.146   \n",
       "3 2020-01-02 10:00:00+00:00  108.836  108.866  108.658  108.694  0.208   \n",
       "4 2020-01-02 14:00:00+00:00  108.697  108.748  108.212  108.529  0.536   \n",
       "5 2020-01-02 18:00:00+00:00  108.526  108.572  108.494  108.561  0.078   \n",
       "\n",
       "   BODY_RANGE   CENTER  BODY_PERC  DIRECTION  ...  DIST_TOP_PERC  \\\n",
       "1       0.071  108.708   0.710000          1  ...       0.160000   \n",
       "2       0.095  108.769   0.650685          1  ...       0.020548   \n",
       "3       0.142  108.762   0.682692         -1  ...       0.144231   \n",
       "4       0.168  108.480   0.313433         -1  ...       0.095149   \n",
       "5       0.035  108.533   0.448718          1  ...       0.141026   \n",
       "\n",
       "   DIST_BOTTOM_PERC  PREV_BODY_RANGE  PREV_BODY_PERC  PREV_DIRECTION  HAMMER  \\\n",
       "1          0.130000            0.022        0.142857             1.0   False   \n",
       "2          0.328767            0.071        0.710000             1.0   False   \n",
       "3          0.173077            0.095        0.650685             1.0   False   \n",
       "4          0.591418            0.142        0.682692            -1.0   False   \n",
       "5          0.410256            0.168        0.313433            -1.0   False   \n",
       "\n",
       "   SPINNING_TOP   DOJI  MARUBOZU  ENGULFING  \n",
       "1         False  False     False      False  \n",
       "2         False  False     False      False  \n",
       "3         False  False     False       True  \n",
       "4         False  False     False      False  \n",
       "5         False  False     False      False  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = df.iloc[700:800]\n",
    "df_markers = df_plot[(df_plot.ENGULFING==True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
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   ],
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