commod.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## create and activate virtual environment\n",
    "# !source ./devops/create_env  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from os import pardir, path\n",
    "import sys\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn import ensemble\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/karimkhalil/Coding/development/commodity', '/Users/karimkhalil/Coding/development/commodity/commod_env/lib/python39.zip', '/Users/karimkhalil/Coding/development/commodity/commod_env/lib/python3.9', '/Users/karimkhalil/Coding/development/commodity/commod_env/lib/python3.9/lib-dynload', '', '/Users/karimkhalil/Coding/development/commodity/commod_env/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "mod_path = os.getcwd()\n",
    "if mod_path not in sys.path:\n",
    "    sys.path.append(mod_path)\n",
    "print(sys.path)\n",
    "\n",
    "from package import Model, return_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## mapping\n",
    "\n",
    "mat_codes = {\n",
    "    \"F\": \"01\",\n",
    "    \"G\": \"02\", \n",
    "    \"H\": \"03\", \n",
    "    \"J\": \"04\", \n",
    "    \"K\": \"05\",\n",
    "    \"M\": \"06\",\n",
    "    \"N\": \"07\", \n",
    "    \"Q\": \"08\",\n",
    "    \"U\" : \"09\",\n",
    "    \"V\": \"10\",\n",
    "    \"X\": \"11\",\n",
    "    \"Z\": \"12\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('commodities.csv')\n",
    "df['datetime'] = pd.to_datetime(df['date'], utc=True)\n",
    "df['date_strf']=df.datetime.dt.strftime('%Y%m%d')\n",
    "df['dayofweek'] = df['datetime'].dt.strftime(\"%a\")\n",
    "df['month'] = df['datetime'].dt.month\n",
    "\n",
    "df['datetime_maturity'] = pd.to_datetime(df.maturity.str[-4:] + df.maturity.str[0].map(mat_codes), format='%Y%m', utc=True)\n",
    "\n",
    "df['datetime_maturity'] = df['datetime_maturity']-pd.Timedelta(1, \"d\")\n",
    "df['date_strf_maturity']=df.datetime_maturity.dt.strftime('%Y%m%d')\n",
    "df['time2maturity_d'] = (df.datetime_maturity-df.datetime).dt.days\n",
    "\n",
    "df.loc[df['time2maturity_d'] <0, 'time2maturity_d'] = 0\n",
    "\n",
    "df['time2maturity_m'] = (df.time2maturity_d/30).round()\n",
    "df_settle = df.loc[df['observation'] == 'Settle']\n",
    "df_soy = df_settle.loc[df_settle['instrument'] == 'CBOT.ZS']\n",
    "df_soy.sort_values(['datetime' , 'datetime_maturity'], ascending = [True, False], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## select prices with only 6 months maturity for comparability\n",
    "\n",
    "dates = set(df_soy['date_strf'])\n",
    "\n",
    "concat = []\n",
    "\n",
    "for i in dates:\n",
    "    duration = 6\n",
    "    data = df_soy.loc[(df_soy['date_strf'] == i) & (df_soy['time2maturity_m'] == duration)]\n",
    "    while data.shape[0] ==0:\n",
    "        duration +=1\n",
    "        data = df_soy.loc[(df_soy['date_strf'] == i) & (df_soy['time2maturity_m'] == duration)]\n",
    "    concat.append(data)\n",
    "\n",
    "df_soy_6m = pd.concat(concat)\n",
    "df_soy_6m.set_index('date_strf' , inplace=True, drop=True)\n",
    "df_soy_6m.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Create Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lagged price\n",
    "\n",
    "df_soy_6m['value_t-1'] = df_soy_6m['value'].shift(1)\n",
    "\n",
    "## returns: predicted variable\n",
    "\n",
    "df_soy_6m['pct_t-1'] = df_soy_6m.value.pct_change(1)\n",
    "\n",
    "## calculate returns for the previous 7 days\n",
    "\n",
    "for i in range(7):\n",
    "    df_soy_6m[f'lagged_pct_t-{i+1}'] = df_soy_6m['value_t-1'].pct_change(i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## rolling averages for prices and returns\n",
    "\n",
    "vals = ['value', 'lagged_pct_t-1']\n",
    "\n",
    "for i in [7, 15, 30 , 60]:\n",
    "    for j in vals:\n",
    "        df_soy_6m[f'roll_avg_pct_{i}'] = df_soy_6m[j].rolling(i).mean()\n",
    "\n",
    "for i in vals:\n",
    "    df_soy_6m[f'exp_avg_{i}'] = df_soy_6m[i].expanding(1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_soy_6m[[i for i in df_soy_6m.columns if df_soy_6m[i].dtype != 'datetime64[ns, UTC]']].to_excel(os.path.join(os.getcwd(), 'df_soy_6m_v2.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## drop missing values\n",
    "df_soy_6m_clean = df_soy_6m.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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           "#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
          }
         }
        },
        "title": {
         "text": "Returns Descriptive Statistics"
        },
        "width": 1500,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.45
         ],
         "title": {
          "text": "Date"
         }
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.55,
          1
         ],
         "tickmode": "array"
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0,
          0.45
         ]
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0.55,
          1
         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.625,
          1
         ],
         "title": {
          "text": "USD"
         }
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0,
          0.375
         ]
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0,
          0.375
         ]
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sorted = df_soy_6m_clean.sort_values('date')\n",
    "fig = make_subplots(rows=2, cols=2, subplot_titles=[\"Price\", \"Daily Return Distribution\", 'Price Distribution', 'Return Distribution'])\n",
    "\n",
    "dist = ff.create_distplot([df_soy_6m_clean['pct_t-1'].values.tolist()], [''], bin_size=.01).data[1]\n",
    "hist = ff.create_distplot([df_soy_6m_clean['pct_t-1'].values.tolist()], [''], bin_size=.01).data[0]\n",
    "price = px.line(x=df_soy_6m_clean.value, y=df_soy_6m_clean.index)\n",
    "price = px.line(y=df_sorted.value, x=df_sorted.date, title='Bean Price', labels=dict(x='Date', y='USD')).data[0]\n",
    "# dist.update_layout(width=700, height=700)\n",
    "\n",
    "fig.add_trace(go.Scatter(dist, line=dict(color='red')), row=1, col=2)\n",
    "fig.add_trace(hist, row=1, col=2)\n",
    "fig.add_trace(go.Scatter(price), row=1,col=1)\n",
    "fig.add_trace(go.Box(x=df_sorted.value), row=2,col=1)\n",
    "fig.add_trace(go.Box(x=df_sorted['pct_t-1']), row=2,col=2)\n",
    "\n",
    "fig['layout'].update(height=800, width=1500, title='Returns Descriptive Statistics')\n",
    "\n",
    "fig.update_xaxes(title_text=\"Date\", row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"USD\", row=1, col=1)\n",
    "\n",
    "fig.update_xaxes(tickmode='array', row=1, col=2)\n",
    "\n",
    "# fig.update_xaxes(ticktext =[f'{i:.4f} %' for i in range(-7,7 , 1)], tickvals =[i/100 for i in range(-7,7 , 1)], row=1, col=2)\n",
    "\n",
    "# fig.add_trace(dist, row=1, col=1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object instanciated\n"
     ]
    }
   ],
   "source": [
    "model = Model(df_soy_6m_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = LinearRegression()\n",
    "xgb = ensemble.GradientBoostingRegressor(random_state=137)\n",
    "randforest = ensemble.RandomForestRegressor(random_state=137)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_x = ['lagged_pct_t-1', 'lagged_pct_t-2', 'lagged_pct_t-3',\n",
    "       'lagged_pct_t-4', 'lagged_pct_t-5', 'lagged_pct_t-6', 'lagged_pct_t-7',\n",
    "       'roll_avg_pct_7', 'roll_avg_pct_15', 'roll_avg_pct_30']\n",
    "       \n",
    "cols_y = 'pct_t-1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression error stats:\n",
      "Average MSE train: 6.49543362005166e-05\n",
      "Average MSE test: 0.0011643675794354325\n"
     ]
    }
   ],
   "source": [
    "print('Linear regression error stats:')\n",
    "df_lr, stat_lr = model.skbacktest(model.df, regr, cols_x, cols_y, 'date' ,'value', train_window=16 , test_window=1, test_gap = 0, expanding=False, print_iter=False)\n",
    "# df_lr['value_lr'] = model.backtest(df_lr, 'value', 'predict', 'pct_t-1', 'predict')['value_strat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB regression error stats:\n",
      "Average MSE train: 1.3086016713875701e-10\n",
      "Average MSE test: 0.0004497235256369112\n"
     ]
    }
   ],
   "source": [
    "print('XGB regression error stats:')\n",
    "df_xgb , stat_xgb = model.skbacktest(model.df, xgb, cols_x, cols_y, 'date' ,'value', train_window=16 , test_window=1, test_gap = 0, expanding=False, print_iter=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest regression error stats:\n",
      "Average MSE train: 3.706141730061759e-05\n",
      "Average MSE test: 0.00031504957578602956\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('Random forest regression error stats:')\n",
    "df_rf, stat_rf = model.skbacktest(model.df, randforest, cols_x, cols_y, 'date' ,'value', train_window=16 , test_window=1, test_gap = 0, expanding=False, print_iter=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## price of beans using random forest prediction\n",
    "df_rf = model.backtest(df_rf, 'value', 'predict', 'value_rf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## for validation only\n",
    "# df_rf[['value', 'pct_t-1' ,'predict' , 'MSE', 'strat_return', 'value_strat']].to_excel(os.path.join(os.getcwd(), 'rf_backtest2.xlsx'))\n",
    "# df_rf[['date', 'value', 'value_t-1', 'MSE', 'iteration',  'pct_t-1', 'predict','strat_return', 'value_strat']]\n",
    "# model.backtest(df_rf,'value', 'pct_t-1', 'test')[['value', 'test']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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        "title": {
         "text": "Actual vs Predicted Value"
        },
        "width": 1600,
        "xaxis": {
         "anchor": "y",
         "domain": [
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         "title": {
          "text": "Date"
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        },
        "yaxis": {
         "anchor": "x",
         "domain": [
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         "title": {
          "text": "USD"
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         "title": {
          "text": "USD"
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   "source": [
    "# fig = go.Figure()\n",
    "\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['value'], name='Actual', line=(dict(width=3))))\n",
    "\n",
    "## to hide when showing strategy\n",
    "fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['value_rf'], name='Random forest price prediction'))\n",
    "fig.add_hline(y=1362, annotation_text='Baseline', line_dash=\"dot\", annotation_position=\"bottom right\")\n",
    "\n",
    "######################## To un-hide ########################\n",
    "# fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['value_strat'], name='backtest random forest price'))\n",
    "# # fig.add_vrect(x0=\"2021-5-13\", x1=\"2021-6-10\", fillcolor=\"green\",  opacity=0.25, line_width=0 , annotation_text=\"below base line\", annotation_position=\"top left\")\n",
    "\n",
    "# fig.add_annotation(x=\"2021-6-14\", y=1700, text=\"Plunge prediction\", showarrow=True, arrowhead=1)\n",
    "# fig.add_annotation(x=\"2021-7-2\", y=1700, text=\"\", showarrow=True, arrowhead=1, ax=-50)\n",
    "# fig.add_vrect(x0=\"2021-6-10\", x1=\"2021-6-17\", fillcolor=\"orange\",  opacity=0.25, line_width=0)\n",
    "# fig.add_vrect(x0=\"2021-7-2\", x1=\"2021-7-7\", fillcolor=\"orange\",  opacity=0.25, line_width=0)\n",
    "############################################################\n",
    "\n",
    "fig['layout'].update(height=600, width=1600, title='Actual vs Predicted Value')\n",
    "fig.update_xaxes(title_text=\"Date\")\n",
    "fig.update_yaxes(title_text=\"USD\")\n",
    "\n",
    "# fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['MSE'], name='MSE'), secondary_y=True)\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe ratio (actual): -2.54 %\n",
      "Sharpe ratio (strategy): 3.78 %\n",
      "\n",
      "Average daily return (actual): -0.04 %\n",
      "Average daily return (strategy): 0.06 %\n",
      "\n",
      "Total return (actual): -8.79 %\n",
      "Total return (strategy): 7.27 %\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>pct_t-1</th>\n",
       "      <th>pct_t-1_strat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1325.263072</td>\n",
       "      <td>-0.000386</td>\n",
       "      <td>0.000576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>55.905567</td>\n",
       "      <td>0.015239</td>\n",
       "      <td>0.015232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1212.000000</td>\n",
       "      <td>-0.066481</td>\n",
       "      <td>-0.047742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1279.250000</td>\n",
       "      <td>-0.006965</td>\n",
       "      <td>-0.007315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1318.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1362.000000</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>0.006729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1461.000000</td>\n",
       "      <td>0.063438</td>\n",
       "      <td>0.066481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             value     pct_t-1  pct_t-1_strat\n",
       "count   153.000000  153.000000     152.000000\n",
       "mean   1325.263072   -0.000386       0.000576\n",
       "std      55.905567    0.015239       0.015232\n",
       "min    1212.000000   -0.066481      -0.047742\n",
       "25%    1279.250000   -0.006965      -0.007315\n",
       "50%    1318.750000    0.000000      -0.001650\n",
       "75%    1362.000000    0.007299       0.006729\n",
       "max    1461.000000    0.063438       0.066481"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Actual vs Strategy statistics\n",
    "\n",
    "df_rf['pct_t-1_strat'] = df_rf.value_strat.pct_change(1)\n",
    "\n",
    "cols_desc = ['value' , 'pct_t-1', 'pct_t-1_strat']\n",
    "desc = df_rf[cols_desc].describe()\n",
    "\n",
    "\n",
    "sharpe = desc.loc['mean', 'pct_t-1'] / desc.loc['std', 'pct_t-1']\n",
    "sharpe_strat= desc.loc['mean', 'pct_t-1_strat'] / desc.loc['std', 'pct_t-1_strat']\n",
    "\n",
    "avg_return = desc.loc['mean', 'pct_t-1']\n",
    "avg_return_strat = desc.loc['mean', 'pct_t-1_strat']\n",
    "\n",
    "tot_return_act = return_tot(df_rf, 'date', 'value', 'value')\n",
    "tot_return_predict = return_tot(df_rf, 'date', 'value', 'value_strat')\n",
    "\n",
    "print(f'Sharpe ratio (actual): {100*(sharpe):.2f} %')\n",
    "print(f'Sharpe ratio (strategy): {100*(sharpe_strat):.2f} %')\n",
    "print()\n",
    "\n",
    "print(f'Average daily return (actual): {100*(avg_return):.2f} %')\n",
    "print(f'Average daily return (strategy): {100*(avg_return_strat):.2f} %')\n",
    "\n",
    "print()\n",
    "print(f'Total return (actual): {100*(tot_return_act):.2f} %')\n",
    "print(f'Total return (strategy): {100*(tot_return_predict):.2f} %')\n",
    "\n",
    "desc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = [stat_lr, stat_xgb, stat_rf]\n",
    "mse = [i['MSEtest'] for i in stats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
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    "\n",
    "fig = make_subplots(rows=2, cols=2, subplot_titles=[\"Prices\", \"Returns Distribution\", 'MSE time series', 'MSE by Model'])\n",
    "\n",
    "x_dist_mse = ff.create_distplot([df_rf['MSE'].values.tolist()], [''], bin_size=.01).data[1]['x']\n",
    "y_dist_mse = ff.create_distplot([df_rf['MSE'].values.tolist()], [''], bin_size=.01).data[1]['y']\n",
    "\n",
    "x_dist_act = ff.create_distplot([df_rf['pct_t-1'].values.tolist()], [''], bin_size=.01).data[1]['x']\n",
    "y_dist_act = ff.create_distplot([df_rf['pct_t-1'].values.tolist()], [''], bin_size=.01).data[1]['y']\n",
    "\n",
    "x_dist_predict = ff.create_distplot([df_rf['predict'].values.tolist()], [''], bin_size=.01).data[1]['x']\n",
    "y_dist_predict = ff.create_distplot([df_rf['predict'].values.tolist()], [''], bin_size=.01).data[1]['y']\n",
    "\n",
    "fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['value'], name='actual price'), row=1, col=1)\n",
    "# fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['value_rf'], name='random forest price'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['value_strat'], name='random forest strategy'), row=1, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=x_dist_act, y = y_dist_act, name='actual returns distribution'), row=1, col=2)\n",
    "fig.add_trace(go.Scatter(x=x_dist_predict, y = y_dist_predict, name='random forestreturns distribution'), row=1, col=2)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=df_rf['date'], y = df_rf['MSE'], name='random forest MSE'), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(x=df_lr['date'], y = df_lr['MSE'], name='linear regr MSE'), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(x=df_xgb['date'], y = df_xgb['MSE'], name='XGB regr MSE'), row=2, col=1)\n",
    "fig.add_trace(go.Bar(x=['Linear' , 'XGB', 'Random Forest'], y = mse, name='MSE comparison'), row=2, col=2)\n",
    "\n",
    "fig['layout'].update(height=900, width=1500, title='Model Highlights')\n",
    "fig.show()"
   ]
  }
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