ts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24236806, 10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "df['date'] = pd.to_datetime(df['timestamp'], unit = 's')\n",
    "df = df.sort_values('date')\n",
    "groups = pd.factorize(df['date'].dt.day.astype(str) + '_' + df['date'].dt.month.astype(str) + '_' + df['date'].dt.year.astype(str))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc = df[df[\"Asset_ID\"]==1].set_index(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "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": {
            "gridcolor": "white",
            "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
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "btc_mini = btc.iloc[-200:]\n",
    "fig = go.Figure(data=[go.Candlestick(x=btc_mini.index, open=btc_mini['Open'], high=btc_mini['High'], low=btc_mini['Low'], close=btc_mini['Close'])])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "pickle_out = open(\"btc_mini.pkl\",\"wb\")\n",
    "pkl.dump(btc_mini,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc.fillna(method=\"ffill\",axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc.drop(columns=['timestamp','Asset_ID','Target','Volume'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "btc[['Count', 'Open', 'High', 'Low', 'Close', 'VWAP']]=scaler.fit_transform(btc[['Count', 'Open', 'High', 'Low', 'Close', 'VWAP']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = btc['Close']\n",
    "X = btc.drop(columns=['Close'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=np.array(X_train)\n",
    "y_train=np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,LSTM,Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1565025, 5, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50,return_sequences=True,activation='relu',input_shape=(X_train.shape[1],1)))\n",
    "model.add(LSTM(50,return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error',optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 5, 50)             10400     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 5, 50)             20200     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "12227/12227 [==============================] - 127s 10ms/step - loss: 1.6308e-04\n",
      "Epoch 2/10\n",
      "12227/12227 [==============================] - 137s 11ms/step - loss: 1.6061e-06\n",
      "Epoch 3/10\n",
      "12227/12227 [==============================] - 153s 12ms/step - loss: 1.2095e-06\n",
      "Epoch 4/10\n",
      "12227/12227 [==============================] - 128s 11ms/step - loss: 1.0005e-06\n",
      "Epoch 5/10\n",
      "12227/12227 [==============================] - 134s 11ms/step - loss: 9.0131e-07\n",
      "Epoch 6/10\n",
      "12227/12227 [==============================] - 149s 12ms/step - loss: 7.4179e-07\n",
      "Epoch 7/10\n",
      "12227/12227 [==============================] - 133s 11ms/step - loss: 7.2587e-07\n",
      "Epoch 8/10\n",
      "12227/12227 [==============================] - 141s 11ms/step - loss: 6.5959e-07\n",
      "Epoch 9/10\n",
      "12227/12227 [==============================] - 132s 11ms/step - loss: 5.7966e-07\n",
      "Epoch 10/10\n",
      "12227/12227 [==============================] - 139s 11ms/step - loss: 5.3343e-07\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2310cccbfc8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,verbose=1,epochs=10,batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses=pd.DataFrame(model.history.history)\n",
    "losses.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"btc.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(391257, 5, 1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reshape(391257,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.50759345],\n",
       "       [0.0740772 ],\n",
       "       [0.01259525],\n",
       "       ...,\n",
       "       [0.96171314],\n",
       "       [0.08548193],\n",
       "       [0.10736696]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=model.predict(X_test)\n",
    "#pred=scaler.inverse_transform(pred)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(391257, 1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0003882518683694214"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse=np.sqrt(np.mean(((pred-y_test)**2)))\n",
    "rmse"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d3ea56757f3b3be2cb7489286f6c6d3faaec445fe5559bac28f43e7042e8a775"
  },
  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit ('myenv': conda)",
   "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": "3.7.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}