FTTAI.ipynb

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   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "import requests as rq\n",
    "import numpy as np\n",
    "import pandas_ta as ta\n",
    "import plotly.graph_objects as go\n",
    "from plotly._subplots import make_subplots\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       Time    0\n",
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       " High          0\n",
       " Low           0\n",
       " Close         0\n",
       " Volume        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('EURUSD240.xlsx')\n",
    "df=df[df[' Volume']!=0]\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rsi'] = ta.momentum.rsi(df[' Close'],length=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['       Time'] = pd.to_datetime(df['       Time'])"
   ]
  },
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          "mesh3d": [
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             "outlinewidth": 0,
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            "type": "mesh3d"
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          ],
          "parcoords": [
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             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
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            },
            "type": "parcoords"
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          "pie": [
           {
            "automargin": true,
            "type": "pie"
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          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
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            "type": "scatter"
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            "type": "scattergl"
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            },
            "type": "scattermapbox"
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          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
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            "type": "scatterpolar"
           }
          ],
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            },
            "type": "scatterpolargl"
           }
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          "scatterternary": [
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             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
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             "ticks": ""
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            ],
            "type": "surface"
           }
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          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
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            ],
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            ],
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            ],
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            ],
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            ],
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            ],
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            ],
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            ],
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           ],
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          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
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            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "rangeslider": {
          "visible": false
         }
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0,
          1
         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.575,
          1
         ]
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0,
          0.425
         ]
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfpl = df[0:100]\n",
    "fig = make_subplots(rows=2, cols=1)\n",
    "\n",
    "fig.append_trace(go.Candlestick(x=dfpl.index,\n",
    "                  open=dfpl[' Open'],\n",
    "                  high=dfpl[' High'],\n",
    "                  low=dfpl[' Low'],\n",
    "                  close=dfpl[' Close']),row=1,col=1)\n",
    "fig.append_trace(go.Scatter(x=dfpl.index,\n",
    "                  y=dfpl['Rsi']), row=2, col=1)\n",
    "\n",
    "fig.update_layout(xaxis_rangeslider_visible=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipdiff = 100*1e-4\n",
    "stplRatio = 1\n",
    "\n",
    "def Target(barsupfront, df1):\n",
    "    length = len(df1)\n",
    "    high = list(df1[' High'])\n",
    "    low = list(df1[' Low'])\n",
    "    close = list(df1[' Close']) \n",
    "    open = list(df1[' Open'])\n",
    "    trend = [None] * length\n",
    "    for line in range (0,length-barsupfront-2):\n",
    "        valueOpenLow = 0\n",
    "        valueOpenHigh = 0\n",
    "        for i in range (1,barsupfront+2):\n",
    "            value1 = open[line+1]-low[line+i]\n",
    "            value2 = open[line+1]-high[line+i]\n",
    "            valueOpenLow = max(value1, valueOpenLow)\n",
    "            valueOpenHigh = min(value2, valueOpenHigh)\n",
    "            if ((valueOpenLow >= pipdiff ) and (-valueOpenHigh <= (pipdiff / stplRatio))):\n",
    "                trend[line] = 1\n",
    "                break\n",
    "            elif ((valueOpenLow <= (pipdiff / stplRatio)) and (-valueOpenHigh >= pipdiff)):\n",
    "                trend[line] = 2\n",
    "                break\n",
    "            else:\n",
    "                trend[line] = 0\n",
    "                \n",
    "    return trend\n",
    "    \n",
    "df['Target'] = Target(40,df)\n",
    "df['Target'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Open          High           Low         Close        Volume  \\\n",
      "count  25198.000000  25198.000000  25198.000000  25198.000000  2.519800e+04   \n",
      "mean       1.245722      1.247758      1.243726      1.245719  5.554833e+04   \n",
      "std        0.135562      0.135916      0.135171      0.135563  7.027816e+04   \n",
      "min        0.955040      0.957640      0.953570      0.955050  4.970000e+02   \n",
      "25%        1.126810      1.128420      1.125380      1.126812  1.685025e+04   \n",
      "50%        1.230710      1.232900      1.228790      1.230710  3.470350e+04   \n",
      "75%        1.352657      1.354430      1.350747      1.352618  6.178850e+04   \n",
      "max        1.599200      1.603890      1.596700      1.599040  2.545577e+06   \n",
      "\n",
      "                Rsi        Target  \n",
      "count  25198.000000  25198.000000  \n",
      "mean      49.819971      1.325700  \n",
      "std       12.983333      0.686184  \n",
      "min       10.803056      0.000000  \n",
      "25%       40.373596      1.000000  \n",
      "50%       49.990791      1.000000  \n",
      "75%       59.278428      2.000000  \n",
      "max       88.304762      2.000000  \n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.reset_index(drop=True,inplace=True)\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Rsi</th>\n",
       "      <th>Target</th>\n",
       "      <th>bb_upper</th>\n",
       "      <th>bb_lower</th>\n",
       "      <th>Signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-03 04:00:00</td>\n",
       "      <td>1.32856</td>\n",
       "      <td>1.32856</td>\n",
       "      <td>1.32659</td>\n",
       "      <td>1.32723</td>\n",
       "      <td>185677</td>\n",
       "      <td>71.367025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-01-03 08:00:00</td>\n",
       "      <td>1.32710</td>\n",
       "      <td>1.32736</td>\n",
       "      <td>1.32170</td>\n",
       "      <td>1.32301</td>\n",
       "      <td>186690</td>\n",
       "      <td>50.551053</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-01-03 12:00:00</td>\n",
       "      <td>1.32338</td>\n",
       "      <td>1.32571</td>\n",
       "      <td>1.31596</td>\n",
       "      <td>1.31659</td>\n",
       "      <td>577315</td>\n",
       "      <td>34.205432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-01-03 16:00:00</td>\n",
       "      <td>1.31672</td>\n",
       "      <td>1.31725</td>\n",
       "      <td>1.31461</td>\n",
       "      <td>1.31576</td>\n",
       "      <td>677568</td>\n",
       "      <td>32.731865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-01-03 20:00:00</td>\n",
       "      <td>1.31520</td>\n",
       "      <td>1.31713</td>\n",
       "      <td>1.31520</td>\n",
       "      <td>1.31596</td>\n",
       "      <td>193966</td>\n",
       "      <td>33.475556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25193</th>\n",
       "      <td>2022-12-16 00:00:00</td>\n",
       "      <td>1.06407</td>\n",
       "      <td>1.06624</td>\n",
       "      <td>1.06385</td>\n",
       "      <td>1.06461</td>\n",
       "      <td>57137</td>\n",
       "      <td>60.549380</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.068690</td>\n",
       "      <td>1.060352</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25194</th>\n",
       "      <td>2022-12-16 04:00:00</td>\n",
       "      <td>1.06459</td>\n",
       "      <td>1.06630</td>\n",
       "      <td>1.06252</td>\n",
       "      <td>1.06554</td>\n",
       "      <td>79113</td>\n",
       "      <td>62.466751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.068720</td>\n",
       "      <td>1.060352</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25195</th>\n",
       "      <td>2022-12-16 08:00:00</td>\n",
       "      <td>1.06553</td>\n",
       "      <td>1.06572</td>\n",
       "      <td>1.06093</td>\n",
       "      <td>1.06215</td>\n",
       "      <td>89742</td>\n",
       "      <td>52.458281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.067640</td>\n",
       "      <td>1.060282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25196</th>\n",
       "      <td>2022-12-16 12:00:00</td>\n",
       "      <td>1.06214</td>\n",
       "      <td>1.06487</td>\n",
       "      <td>1.05976</td>\n",
       "      <td>1.06091</td>\n",
       "      <td>144797</td>\n",
       "      <td>49.343986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.066211</td>\n",
       "      <td>1.060317</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25197</th>\n",
       "      <td>2022-12-16 16:00:00</td>\n",
       "      <td>1.06090</td>\n",
       "      <td>1.06190</td>\n",
       "      <td>1.05917</td>\n",
       "      <td>1.05923</td>\n",
       "      <td>69840</td>\n",
       "      <td>45.410528</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.066398</td>\n",
       "      <td>1.059026</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25198 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Time     Open     High      Low    Close   Volume  \\\n",
       "0     2007-01-03 04:00:00  1.32856  1.32856  1.32659  1.32723   185677   \n",
       "1     2007-01-03 08:00:00  1.32710  1.32736  1.32170  1.32301   186690   \n",
       "2     2007-01-03 12:00:00  1.32338  1.32571  1.31596  1.31659   577315   \n",
       "3     2007-01-03 16:00:00  1.31672  1.31725  1.31461  1.31576   677568   \n",
       "4     2007-01-03 20:00:00  1.31520  1.31713  1.31520  1.31596   193966   \n",
       "...                   ...      ...      ...      ...      ...      ...   \n",
       "25193 2022-12-16 00:00:00  1.06407  1.06624  1.06385  1.06461    57137   \n",
       "25194 2022-12-16 04:00:00  1.06459  1.06630  1.06252  1.06554    79113   \n",
       "25195 2022-12-16 08:00:00  1.06553  1.06572  1.06093  1.06215    89742   \n",
       "25196 2022-12-16 12:00:00  1.06214  1.06487  1.05976  1.06091   144797   \n",
       "25197 2022-12-16 16:00:00  1.06090  1.06190  1.05917  1.05923    69840   \n",
       "\n",
       "             Rsi  Target  bb_upper  bb_lower  Signal  \n",
       "0      71.367025     1.0       NaN       NaN       0  \n",
       "1      50.551053     1.0       NaN       NaN       0  \n",
       "2      34.205432     1.0       NaN       NaN       0  \n",
       "3      32.731865     1.0       NaN       NaN       0  \n",
       "4      33.475556     1.0       NaN       NaN       0  \n",
       "...          ...     ...       ...       ...     ...  \n",
       "25193  60.549380     0.0  1.068690  1.060352       0  \n",
       "25194  62.466751     0.0  1.068720  1.060352       0  \n",
       "25195  52.458281     0.0  1.067640  1.060282       0  \n",
       "25196  49.343986     0.0  1.066211  1.060317       0  \n",
       "25197  45.410528     0.0  1.066398  1.059026       0  \n",
       "\n",
       "[25198 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_signals(df):\n",
    "  # Calculate the moving average of the closing price\n",
    "  ma = df[' Close'].rolling(window=10).mean()\n",
    "\n",
    "  # Calculate the standard deviation of the closing price\n",
    "  std = df[' Close'].rolling(window=10).std()\n",
    "\n",
    "  # Create a new column for the upper and lower bounds of the Bollinger Band\n",
    "  df['bb_upper'] = ma + 2*std\n",
    "  df['bb_lower'] = ma - 2*std\n",
    "\n",
    "  # Create a signal column that is 1 when the closing price is above the upper Bollinger Band and -1 when it is below the lower Bollinger Band\n",
    "  df['Signal'] = np.where(df[' Close'] > df['bb_upper'], 1, np.where(df[' Close'] < df['bb_lower'], -1, 0))\n",
    "\n",
    "\n",
    "\n",
    "# Generate the signals\n",
    "generate_signals(df)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Rsi</th>\n",
       "      <th>Target</th>\n",
       "      <th>Signalcat_-1</th>\n",
       "      <th>Signalcat_0</th>\n",
       "      <th>Signalcat_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.367025</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.551053</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.205432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.731865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.475556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25193</th>\n",
       "      <td>60.549380</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25194</th>\n",
       "      <td>62.466751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25195</th>\n",
       "      <td>52.458281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25196</th>\n",
       "      <td>49.343986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25197</th>\n",
       "      <td>45.410528</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25198 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rsi  Target  Signalcat_-1  Signalcat_0  Signalcat_1\n",
       "0      71.367025     1.0             0            1            0\n",
       "1      50.551053     1.0             0            1            0\n",
       "2      34.205432     1.0             0            1            0\n",
       "3      32.731865     1.0             0            1            0\n",
       "4      33.475556     1.0             0            1            0\n",
       "...          ...     ...           ...          ...          ...\n",
       "25193  60.549380     0.0             0            1            0\n",
       "25194  62.466751     0.0             0            1            0\n",
       "25195  52.458281     0.0             0            1            0\n",
       "25196  49.343986     0.0             0            1            0\n",
       "25197  45.410528     0.0             0            1            0\n",
       "\n",
       "[25198 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes = ['Rsi', 'Signal', 'Target']\n",
    "df_model= df[attributes].copy()\n",
    "df_model['Signal'] = pd.Categorical(df_model['Signal'])\n",
    "dfDummies = pd.get_dummies(df_model['Signal'], prefix = 'Signalcat')\n",
    "df_model= df_model.drop(['Signal'], axis=1)\n",
    "df_model = pd.concat([df_model, dfDummies], axis=1)\n",
    "df_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****Train Results****\n",
      "Accuracy: 70.8474%\n",
      "****Test Results****\n",
      "Accuracy: 42.9882%\n"
     ]
    }
   ],
   "source": [
    "attributes = ['Rsi', 'Signalcat_-1','Signalcat_0','Signalcat_1']\n",
    "X = df_model[attributes]\n",
    "y = df_model['Target']\n",
    "\n",
    "train_pct_index = int(0.2 * len(X))\n",
    "X_train, X_test = X[:train_pct_index], X[train_pct_index:]\n",
    "y_train, y_test = y[:train_pct_index], y[train_pct_index:]\n",
    "\n",
    "model = XGBClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "pred_train = model.predict(X_train)\n",
    "pred_test = model.predict(X_test)\n",
    "\n",
    "acc_train = accuracy_score(y_train, pred_train)\n",
    "acc_test = accuracy_score(y_test, pred_test)\n",
    "print('****Train Results****')\n",
    "print(\"Accuracy: {:.4%}\".format(acc_train))\n",
    "print('****Test Results****')\n",
    "print(\"Accuracy: {:.4%}\".format(acc_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  28   61  185]\n",
      " [   2 1141  962]\n",
      " [   3  256 2401]]\n",
      "[[  17  826 2048]\n",
      " [  37 2423 6096]\n",
      " [  46 2440 6226]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.85      0.10      0.18       274\n",
      "         1.0       0.78      0.54      0.64      2105\n",
      "         2.0       0.68      0.90      0.77      2660\n",
      "\n",
      "    accuracy                           0.71      5039\n",
      "   macro avg       0.77      0.52      0.53      5039\n",
      "weighted avg       0.73      0.71      0.69      5039\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.17      0.01      0.01      2891\n",
      "         1.0       0.43      0.28      0.34      8556\n",
      "         2.0       0.43      0.71      0.54      8712\n",
      "\n",
      "    accuracy                           0.43     20159\n",
      "   macro avg       0.34      0.33      0.30     20159\n",
      "weighted avg       0.39      0.43      0.38     20159\n",
      "\n",
      "['Rsi', 'Signalcat_-1', 'Signalcat_0', 'Signalcat_1']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "matrix_train = confusion_matrix(y_train, pred_train)\n",
    "matrix_test = confusion_matrix(y_test, pred_test)\n",
    "\n",
    "print(matrix_train)\n",
    "print(matrix_test)\n",
    "\n",
    "report_train = classification_report(y_train, pred_train)\n",
    "report_test = classification_report(y_test, pred_test)\n",
    "\n",
    "print(report_train)\n",
    "print(report_test)\n",
    "\n",
    "print(model.get_booster().feature_names)"
   ]
  }
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