machine_learning_trading_bot.ipynb

{
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    "# Machine Learning Trading Bot\n",
    "\n",
    "In this Challenge, you’ll assume the role of a financial advisor at one of the top five financial advisory firms in the world. Your firm constantly competes with the other major firms to manage and automatically trade assets in a highly dynamic environment. In recent years, your firm has heavily profited by using computer algorithms that can buy and sell faster than human traders.\n",
    "\n",
    "The speed of these transactions gave your firm a competitive advantage early on. But, people still need to specifically program these systems, which limits their ability to adapt to new data. You’re thus planning to improve the existing algorithmic trading systems and maintain the firm’s competitive advantage in the market. To do so, you’ll enhance the existing trading signals with machine learning algorithms that can adapt to new data.\n",
    "\n",
    "## Instructions:\n",
    "\n",
    "Use the starter code file to complete the steps that the instructions outline. The steps for this Challenge are divided into the following sections:\n",
    "\n",
    "* Establish a Baseline Performance\n",
    "\n",
    "* Tune the Baseline Trading Algorithm\n",
    "\n",
    "* Evaluate a New Machine Learning Classifier\n",
    "\n",
    "* Create an Evaluation Report\n",
    "\n",
    "#### Establish a Baseline Performance\n",
    "\n",
    "In this section, you’ll run the provided starter code to establish a baseline performance for the trading algorithm. To do so, complete the following steps.\n",
    "\n",
    "Open the Jupyter notebook. Restart the kernel, run the provided cells that correspond with the first three steps, and then proceed to step four. \n",
    "\n",
    "1. Import the OHLCV dataset into a Pandas DataFrame.\n",
    "\n",
    "2. Generate trading signals using short- and long-window SMA values. \n",
    "\n",
    "3. Split the data into training and testing datasets.\n",
    "\n",
    "4. Use the `SVC` classifier model from SKLearn's support vector machine (SVM) learning method to fit the training data and make predictions based on the testing data. Review the predictions.\n",
    "\n",
    "5. Review the classification report associated with the `SVC` model predictions. \n",
    "\n",
    "6. Create a predictions DataFrame that contains columns for “Predicted” values, “Actual Returns”, and “Strategy Returns”.\n",
    "\n",
    "7. Create a cumulative return plot that shows the actual returns vs. the strategy returns. Save a PNG image of this plot. This will serve as a baseline against which to compare the effects of tuning the trading algorithm.\n",
    "\n",
    "8. Write your conclusions about the performance of the baseline trading algorithm in the `README.md` file that’s associated with your GitHub repository. Support your findings by using the PNG image that you saved in the previous step.\n",
    "\n",
    "#### Tune the Baseline Trading Algorithm\n",
    "\n",
    "In this section, you’ll tune, or adjust, the model’s input features to find the parameters that result in the best trading outcomes. (You’ll choose the best by comparing the cumulative products of the strategy returns.) To do so, complete the following steps:\n",
    "\n",
    "1. Tune the training algorithm by adjusting the size of the training dataset. To do so, slice your data into different periods. Rerun the notebook with the updated parameters, and record the results in your `README.md` file. Answer the following question: What impact resulted from increasing or decreasing the training window?\n",
    "\n",
    "> **Hint** To adjust the size of the training dataset, you can use a different `DateOffset` value&mdash;for example, six months. Be aware that changing the size of the training dataset also affects the size of the testing dataset.\n",
    "\n",
    "2. Tune the trading algorithm by adjusting the SMA input features. Adjust one or both of the windows for the algorithm. Rerun the notebook with the updated parameters, and record the results in your `README.md` file. Answer the following question: What impact resulted from increasing or decreasing either or both of the SMA windows?\n",
    "\n",
    "3. Choose the set of parameters that best improved the trading algorithm returns. Save a PNG image of the cumulative product of the actual returns vs. the strategy returns, and document your conclusion in your `README.md` file.\n",
    "\n",
    "#### Evaluate a New Machine Learning Classifier\n",
    "\n",
    "In this section, you’ll use the original parameters that the starter code provided. But, you’ll apply them to the performance of a second machine learning model. To do so, complete the following steps:\n",
    "\n",
    "1. Import a new classifier, such as `AdaBoost`, `DecisionTreeClassifier`, or `LogisticRegression`. (For the full list of classifiers, refer to the [Supervised learning page](https://scikit-learn.org/stable/supervised_learning.html) in the scikit-learn documentation.)\n",
    "\n",
    "2. Using the original training data as the baseline model, fit another model with the new classifier.\n",
    "\n",
    "3. Backtest the new model to evaluate its performance. Save a PNG image of the cumulative product of the actual returns vs. the strategy returns for this updated trading algorithm, and write your conclusions in your `README.md` file. Answer the following questions: Did this new model perform better or worse than the provided baseline model? Did this new model perform better or worse than your tuned trading algorithm?\n",
    "\n",
    "#### Create an Evaluation Report\n",
    "\n",
    "In the previous sections, you updated your `README.md` file with your conclusions. To accomplish this section, you need to add a summary evaluation report at the end of the `README.md` file. For this report, express your final conclusions and analysis. Support your findings by using the PNG images that you created.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import hvplot.pandas\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from pandas.tseries.offsets import DateOffset\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Establish a Baseline Performance\n",
    "\n",
    "In this section, you’ll run the provided starter code to establish a baseline performance for the trading algorithm. To do so, complete the following steps.\n",
    "\n",
    "Open the Jupyter notebook. Restart the kernel, run the provided cells that correspond with the first three steps, and then proceed to step four. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: mport the OHLCV dataset into a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
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       "      <th>open</th>\n",
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       "      <th>date</th>\n",
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       "      <th>2018-10-19 09:30:00</th>\n",
       "      <td>16.90</td>\n",
       "      <td>17.18</td>\n",
       "      <td>16.90</td>\n",
       "      <td>17.095</td>\n",
       "      <td>11522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-19 09:45:00</th>\n",
       "      <td>17.11</td>\n",
       "      <td>17.44</td>\n",
       "      <td>17.11</td>\n",
       "      <td>17.400</td>\n",
       "      <td>70593</td>\n",
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       "      <th>2018-10-19 10:00:00</th>\n",
       "      <td>17.40</td>\n",
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       "      <td>17.25</td>\n",
       "      <td>17.280</td>\n",
       "      <td>38885</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-19 10:15:00</th>\n",
       "      <td>17.27</td>\n",
       "      <td>17.27</td>\n",
       "      <td>17.18</td>\n",
       "      <td>17.200</td>\n",
       "      <td>37046</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-19 10:30:00</th>\n",
       "      <td>17.21</td>\n",
       "      <td>17.37</td>\n",
       "      <td>17.19</td>\n",
       "      <td>17.200</td>\n",
       "      <td>46874</td>\n",
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       "                      open   high    low   close  volume\n",
       "date                                                    \n",
       "2018-10-19 09:30:00  16.90  17.18  16.90  17.095   11522\n",
       "2018-10-19 09:45:00  17.11  17.44  17.11  17.400   70593\n",
       "2018-10-19 10:00:00  17.40  17.40  17.25  17.280   38885\n",
       "2018-10-19 10:15:00  17.27  17.27  17.18  17.200   37046\n",
       "2018-10-19 10:30:00  17.21  17.37  17.19  17.200   46874"
      ]
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     "execution_count": 103,
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    }
   ],
   "source": [
    "# Import the OHLCV dataset into a Pandas Dataframe\n",
    "ohlcv_df = pd.read_csv(\n",
    "    Path(\"./Resources/ohlcv.csv\"), \n",
    "    index_col='date', \n",
    "    infer_datetime_format=True, \n",
    "    parse_dates=True\n",
    ")\n",
    "\n",
    "# Review the DataFrame\n",
    "ohlcv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
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       "    <tr>\n",
       "      <th>2018-10-19 09:45:00</th>\n",
       "      <td>17.40</td>\n",
       "      <td>0.017841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-19 10:00:00</th>\n",
       "      <td>17.28</td>\n",
       "      <td>-0.006897</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-19 10:15:00</th>\n",
       "      <td>17.20</td>\n",
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       "      <th>2018-10-19 10:30:00</th>\n",
       "      <td>17.20</td>\n",
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       "      <th>2018-10-19 10:45:00</th>\n",
       "      <td>17.12</td>\n",
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       "                     close  Actual Returns\n",
       "date                                      \n",
       "2018-10-19 09:45:00  17.40        0.017841\n",
       "2018-10-19 10:00:00  17.28       -0.006897\n",
       "2018-10-19 10:15:00  17.20       -0.004630\n",
       "2018-10-19 10:30:00  17.20        0.000000\n",
       "2018-10-19 10:45:00  17.12       -0.004651"
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th>2020-09-04 14:45:00</th>\n",
       "      <td>6.250</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-04 15:15:00</th>\n",
       "      <td>6.275</td>\n",
       "      <td>0.004000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:30:00</th>\n",
       "      <td>6.255</td>\n",
       "      <td>-0.003187</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-04 15:45:00</th>\n",
       "      <td>6.250</td>\n",
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      "text/plain": [
       "                     close  Actual Returns\n",
       "date                                      \n",
       "2020-09-04 14:45:00  6.250        0.003210\n",
       "2020-09-04 15:00:00  6.250        0.000000\n",
       "2020-09-04 15:15:00  6.275        0.004000\n",
       "2020-09-04 15:30:00  6.255       -0.003187\n",
       "2020-09-04 15:45:00  6.250       -0.000799"
      ]
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    }
   ],
   "source": [
    "# Filter the date index and close columns\n",
    "signals_df = ohlcv_df.loc[:, [\"close\"]]\n",
    "\n",
    "# Use the pct_change function to generate  returns from close prices\n",
    "signals_df[\"Actual Returns\"] = signals_df[\"close\"].pct_change()\n",
    "\n",
    "# Drop all NaN values from the DataFrame\n",
    "signals_df = signals_df.dropna()\n",
    "\n",
    "# Review the DataFrame\n",
    "display(signals_df.head())\n",
    "display(signals_df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Generate trading signals using short- and long-window SMA values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
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       "      <th>close</th>\n",
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       "      <th>SMA_Fast</th>\n",
       "      <th>SMA_Slow</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2018-10-24 15:00:00</th>\n",
       "      <td>15.615</td>\n",
       "      <td>-0.002874</td>\n",
       "      <td>15.65250</td>\n",
       "      <td>16.3403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:15:00</th>\n",
       "      <td>15.530</td>\n",
       "      <td>-0.005443</td>\n",
       "      <td>15.61875</td>\n",
       "      <td>16.3216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:30:00</th>\n",
       "      <td>15.410</td>\n",
       "      <td>-0.007727</td>\n",
       "      <td>15.55375</td>\n",
       "      <td>16.3029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:45:00</th>\n",
       "      <td>15.350</td>\n",
       "      <td>-0.003894</td>\n",
       "      <td>15.47625</td>\n",
       "      <td>16.2844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-25 09:30:00</th>\n",
       "      <td>15.320</td>\n",
       "      <td>-0.001954</td>\n",
       "      <td>15.40250</td>\n",
       "      <td>16.2656</td>\n",
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      ],
      "text/plain": [
       "                      close  Actual Returns  SMA_Fast  SMA_Slow\n",
       "date                                                           \n",
       "2018-10-24 15:00:00  15.615       -0.002874  15.65250   16.3403\n",
       "2018-10-24 15:15:00  15.530       -0.005443  15.61875   16.3216\n",
       "2018-10-24 15:30:00  15.410       -0.007727  15.55375   16.3029\n",
       "2018-10-24 15:45:00  15.350       -0.003894  15.47625   16.2844\n",
       "2018-10-25 09:30:00  15.320       -0.001954  15.40250   16.2656"
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       "      <th>2020-09-04 14:45:00</th>\n",
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       "      <th>2020-09-04 15:00:00</th>\n",
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       "      <th>2020-09-04 15:30:00</th>\n",
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       "      <th>2020-09-04 15:45:00</th>\n",
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       "                     close  Actual Returns  SMA_Fast  SMA_Slow\n",
       "date                                                          \n",
       "2020-09-04 14:45:00  6.250        0.003210   6.22875   6.27030\n",
       "2020-09-04 15:00:00  6.250        0.000000   6.23875   6.26985\n",
       "2020-09-04 15:15:00  6.275        0.004000   6.25125   6.26910\n",
       "2020-09-04 15:30:00  6.255       -0.003187   6.25750   6.26855\n",
       "2020-09-04 15:45:00  6.250       -0.000799   6.25750   6.26785"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the short window and long window\n",
    "short_window = 4\n",
    "long_window = 100\n",
    "\n",
    "# Generate the fast and slow simple moving averages (4 and 100 days, respectively)\n",
    "signals_df['SMA_Fast'] = signals_df['close'].rolling(window=short_window).mean()\n",
    "signals_df['SMA_Slow'] = signals_df['close'].rolling(window=long_window).mean()\n",
    "\n",
    "signals_df = signals_df.dropna()\n",
    "\n",
    "# Review the DataFrame\n",
    "display(signals_df.head())\n",
    "display(signals_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>SMA_Fast</th>\n",
       "      <th>SMA_Slow</th>\n",
       "      <th>Signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:00:00</th>\n",
       "      <td>15.615</td>\n",
       "      <td>-0.002874</td>\n",
       "      <td>15.65250</td>\n",
       "      <td>16.3403</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:15:00</th>\n",
       "      <td>15.530</td>\n",
       "      <td>-0.005443</td>\n",
       "      <td>15.61875</td>\n",
       "      <td>16.3216</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:30:00</th>\n",
       "      <td>15.410</td>\n",
       "      <td>-0.007727</td>\n",
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       "      <td>16.3029</td>\n",
       "      <td>-1.0</td>\n",
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       "      <th>2018-10-24 15:45:00</th>\n",
       "      <td>15.350</td>\n",
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       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-25 09:30:00</th>\n",
       "      <td>15.320</td>\n",
       "      <td>-0.001954</td>\n",
       "      <td>15.40250</td>\n",
       "      <td>16.2656</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      close  Actual Returns  SMA_Fast  SMA_Slow  Signal\n",
       "date                                                                   \n",
       "2018-10-24 15:00:00  15.615       -0.002874  15.65250   16.3403    -1.0\n",
       "2018-10-24 15:15:00  15.530       -0.005443  15.61875   16.3216    -1.0\n",
       "2018-10-24 15:30:00  15.410       -0.007727  15.55375   16.3029    -1.0\n",
       "2018-10-24 15:45:00  15.350       -0.003894  15.47625   16.2844    -1.0\n",
       "2018-10-25 09:30:00  15.320       -0.001954  15.40250   16.2656    -1.0"
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       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>SMA_Fast</th>\n",
       "      <th>SMA_Slow</th>\n",
       "      <th>Signal</th>\n",
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       "    <tr>\n",
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       "      <th>2020-09-04 14:45:00</th>\n",
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       "      <th>2020-09-04 15:30:00</th>\n",
       "      <td>6.255</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-04 15:45:00</th>\n",
       "      <td>6.250</td>\n",
       "      <td>-0.000799</td>\n",
       "      <td>6.25750</td>\n",
       "      <td>6.26785</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     close  Actual Returns  SMA_Fast  SMA_Slow  Signal\n",
       "date                                                                  \n",
       "2020-09-04 14:45:00  6.250        0.003210   6.22875   6.27030     1.0\n",
       "2020-09-04 15:00:00  6.250        0.000000   6.23875   6.26985     1.0\n",
       "2020-09-04 15:15:00  6.275        0.004000   6.25125   6.26910     1.0\n",
       "2020-09-04 15:30:00  6.255       -0.003187   6.25750   6.26855    -1.0\n",
       "2020-09-04 15:45:00  6.250       -0.000799   6.25750   6.26785    -1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the new Signal column\n",
    "signals_df['Signal'] = 0.0\n",
    "\n",
    "# When Actual Returns are greater than or equal to 0, generate signal to buy stock long\n",
    "signals_df.loc[(signals_df['Actual Returns'] >= 0), 'Signal'] = 1\n",
    "\n",
    "# When Actual Returns are less than 0, generate signal to sell stock short\n",
    "signals_df.loc[(signals_df['Actual Returns'] < 0), 'Signal'] = -1\n",
    "\n",
    "# Review the DataFrame\n",
    "display(signals_df.head())\n",
    "display(signals_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    6334\n",
       "-1.0    5635\n",
       "Name: Signal, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signals_df['Signal'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Actual Returns</th>\n",
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       "  <tbody>\n",
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       "      <th>2018-10-24 15:00:00</th>\n",
       "      <td>15.615</td>\n",
       "      <td>-0.002874</td>\n",
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       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2018-10-24 15:15:00</th>\n",
       "      <td>15.530</td>\n",
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       "      <th>2018-10-24 15:30:00</th>\n",
       "      <td>15.410</td>\n",
       "      <td>-0.007727</td>\n",
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       "      <td>16.3029</td>\n",
       "      <td>-1.0</td>\n",
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       "      <th>2018-10-24 15:45:00</th>\n",
       "      <td>15.350</td>\n",
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       "      <td>0.003894</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-25 09:30:00</th>\n",
       "      <td>15.320</td>\n",
       "      <td>-0.001954</td>\n",
       "      <td>15.40250</td>\n",
       "      <td>16.2656</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.001954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      close  Actual Returns  SMA_Fast  SMA_Slow  Signal  \\\n",
       "date                                                                      \n",
       "2018-10-24 15:00:00  15.615       -0.002874  15.65250   16.3403    -1.0   \n",
       "2018-10-24 15:15:00  15.530       -0.005443  15.61875   16.3216    -1.0   \n",
       "2018-10-24 15:30:00  15.410       -0.007727  15.55375   16.3029    -1.0   \n",
       "2018-10-24 15:45:00  15.350       -0.003894  15.47625   16.2844    -1.0   \n",
       "2018-10-25 09:30:00  15.320       -0.001954  15.40250   16.2656    -1.0   \n",
       "\n",
       "                     Strategy Returns  \n",
       "date                                   \n",
       "2018-10-24 15:00:00               NaN  \n",
       "2018-10-24 15:15:00          0.005443  \n",
       "2018-10-24 15:30:00          0.007727  \n",
       "2018-10-24 15:45:00          0.003894  \n",
       "2018-10-25 09:30:00          0.001954  "
      ]
     },
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       "      <th>2020-09-04 14:45:00</th>\n",
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       "      <th>2020-09-04 15:00:00</th>\n",
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       "      <td>6.275</td>\n",
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       "      <td>6.26910</td>\n",
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       "      <th>2020-09-04 15:45:00</th>\n",
       "      <td>6.250</td>\n",
       "      <td>-0.000799</td>\n",
       "      <td>6.25750</td>\n",
       "      <td>6.26785</td>\n",
       "      <td>-1.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                     close  Actual Returns  SMA_Fast  SMA_Slow  Signal  \\\n",
       "date                                                                     \n",
       "2020-09-04 14:45:00  6.250        0.003210   6.22875   6.27030     1.0   \n",
       "2020-09-04 15:00:00  6.250        0.000000   6.23875   6.26985     1.0   \n",
       "2020-09-04 15:15:00  6.275        0.004000   6.25125   6.26910     1.0   \n",
       "2020-09-04 15:30:00  6.255       -0.003187   6.25750   6.26855    -1.0   \n",
       "2020-09-04 15:45:00  6.250       -0.000799   6.25750   6.26785    -1.0   \n",
       "\n",
       "                     Strategy Returns  \n",
       "date                                   \n",
       "2020-09-04 14:45:00          0.003210  \n",
       "2020-09-04 15:00:00          0.000000  \n",
       "2020-09-04 15:15:00          0.004000  \n",
       "2020-09-04 15:30:00         -0.003187  \n",
       "2020-09-04 15:45:00          0.000799  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the strategy returns and add them to the signals_df DataFrame\n",
    "signals_df['Strategy Returns'] = signals_df[\"Actual Returns\"] * signals_df['Signal'].shift()\n",
    "\n",
    "# Review the DataFrame\n",
    "display(signals_df.head())\n",
    "display(signals_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Strategy Returns to examine performance\n",
    "(1 + signals_df['Strategy Returns']).cumprod().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig(\"Strategy Return plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Split the data into training and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>SMA_Fast</th>\n",
       "      <th>SMA_Slow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:15:00</th>\n",
       "      <td>15.65250</td>\n",
       "      <td>16.3403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:30:00</th>\n",
       "      <td>15.61875</td>\n",
       "      <td>16.3216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:45:00</th>\n",
       "      <td>15.55375</td>\n",
       "      <td>16.3029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-25 09:30:00</th>\n",
       "      <td>15.47625</td>\n",
       "      <td>16.2844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-25 09:45:00</th>\n",
       "      <td>15.40250</td>\n",
       "      <td>16.2656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     SMA_Fast  SMA_Slow\n",
       "date                                   \n",
       "2018-10-24 15:15:00  15.65250   16.3403\n",
       "2018-10-24 15:30:00  15.61875   16.3216\n",
       "2018-10-24 15:45:00  15.55375   16.3029\n",
       "2018-10-25 09:30:00  15.47625   16.2844\n",
       "2018-10-25 09:45:00  15.40250   16.2656"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign a copy of the sma_fast and sma_slow columns to a features DataFrame called X\n",
    "X = signals_df[['SMA_Fast', 'SMA_Slow']].shift().dropna()\n",
    "\n",
    "# Review the DataFrame\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    6334\n",
       "-1.0    5635\n",
       "Name: Signal, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the target set selecting the Signal column and assiging it to y\n",
    "y = signals_df['Signal']\n",
    "\n",
    "# Review the value counts\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-24 15:15:00\n"
     ]
    }
   ],
   "source": [
    "# Select the start of the training period\n",
    "training_begin = X.index.min()\n",
    "\n",
    "# Display the training begin date\n",
    "print(training_begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-10-24 15:15:00\n"
     ]
    }
   ],
   "source": [
    "# Select the ending period for the training data with an offset of 3 months\n",
    "training_end = X.index.min() + DateOffset(months=12)\n",
    "\n",
    "# Display the training end date\n",
    "print(training_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMA_Fast</th>\n",
       "      <th>SMA_Slow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2019-10-24 14:15:00</th>\n",
       "      <td>12.5075</td>\n",
       "      <td>12.73130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-24 14:30:00</th>\n",
       "      <td>12.4950</td>\n",
       "      <td>12.72785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-24 14:45:00</th>\n",
       "      <td>12.4925</td>\n",
       "      <td>12.72435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-24 15:00:00</th>\n",
       "      <td>12.4925</td>\n",
       "      <td>12.72195</td>\n",
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       "      <td>12.4900</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     SMA_Fast  SMA_Slow\n",
       "date                                   \n",
       "2019-10-24 14:15:00   12.5075  12.73130\n",
       "2019-10-24 14:30:00   12.4950  12.72785\n",
       "2019-10-24 14:45:00   12.4925  12.72435\n",
       "2019-10-24 15:00:00   12.4925  12.72195\n",
       "2019-10-24 15:15:00   12.4900  12.71835"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the X_train and y_train DataFrames\n",
    "X_train = X.loc[training_begin:training_end]\n",
    "y_train = y.loc[training_begin:training_end]\n",
    "\n",
    "# Review the X_train DataFrame\n",
    "X_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMA_Fast</th>\n",
       "      <th>SMA_Slow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:15:00</th>\n",
       "      <td>15.65250</td>\n",
       "      <td>16.3403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:30:00</th>\n",
       "      <td>15.61875</td>\n",
       "      <td>16.3216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 15:45:00</th>\n",
       "      <td>15.55375</td>\n",
       "      <td>16.3029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-25 09:30:00</th>\n",
       "      <td>15.47625</td>\n",
       "      <td>16.2844</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-25 09:45:00</th>\n",
       "      <td>15.40250</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     SMA_Fast  SMA_Slow\n",
       "date                                   \n",
       "2018-10-24 15:15:00  15.65250   16.3403\n",
       "2018-10-24 15:30:00  15.61875   16.3216\n",
       "2018-10-24 15:45:00  15.55375   16.3029\n",
       "2018-10-25 09:30:00  15.47625   16.2844\n",
       "2018-10-25 09:45:00  15.40250   16.2656"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the X_test and y_test DataFrames\n",
    "X_test = X.loc[training_end+DateOffset(hours=1):]\n",
    "y_test = y.loc[training_end+DateOffset(hours=1):]\n",
    "\n",
    "# Review the X_test DataFrame\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the features DataFrames\n",
    "\n",
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Apply the scaler model to fit the X-train data\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Transform the X_train and X_test DataFrames using the X_scaler\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Use the `SVC` classifier model from SKLearn's support vector machine (SVM) learning method to fit the training data and make predictions based on the testing data. Review the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From SVM, instantiate SVC classifier model instance\n",
    "svm_model = svm.SVC()\n",
    " \n",
    "# Fit the model to the data using the training data\n",
    "svm_model = svm_model.fit(X_train_scaled, y_train)\n",
    " \n",
    "# Use the testing data to make the model predictions\n",
    "svm_pred = svm_model.predict(X_train_scaled)\n",
    "\n",
    "# Review the model's predicted values\n",
    "svm_pred[:10]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Review the classification report associated with the `SVC` model predictions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.54      0.02      0.04      2972\n",
      "         1.0       0.53      0.99      0.69      3365\n",
      "\n",
      "    accuracy                           0.53      6337\n",
      "   macro avg       0.54      0.50      0.36      6337\n",
      "weighted avg       0.54      0.53      0.38      6337\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use a classification report to evaluate the model using the predictions and testing data\n",
    "svm_testing_report = classification_report(y_train, svm_pred)\n",
    "\n",
    "# Print the classification report\n",
    "print(svm_testing_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When I ran the Dataoffset longer, ie 6 months and then 12 months, the results were that accuracy improved over 6 months, and also recall for the buy signal, went from 90 to 97.  Compare this to the 12 months offset and accuracy was also similar at 53%, however the recall continued to rise \n"
     ]
    }
   ],
   "source": [
    "print (\"When I ran the Dataoffset longer, ie 6 months and then 12 months, the results were that accuracy improved over 6 months, and also recall for the buy signal, went from 90 to 97.  Compare this to the 12 months offset and accuracy was also similar at 53%, however the recall continued to rise \")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Create a predictions DataFrame that contains columns for “Predicted” values, “Actual Returns”, and “Strategy Returns”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>Predicted</th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>Strategy Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-25 09:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 09:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 10:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.002408</td>\n",
       "      <td>-0.002408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 10:15:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.000805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 10:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006431</td>\n",
       "      <td>0.006431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Predicted  Actual Returns  Strategy Returns\n",
       "date                                                            \n",
       "2019-10-25 09:30:00        1.0        0.000000          0.000000\n",
       "2019-10-25 09:45:00        1.0        0.000000          0.000000\n",
       "2019-10-25 10:00:00        1.0       -0.002408         -0.002408\n",
       "2019-10-25 10:15:00        1.0        0.000805          0.000805\n",
       "2019-10-25 10:30:00        1.0        0.006431          0.006431"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>Strategy Returns</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.003210</td>\n",
       "    </tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>-0.003187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000799</td>\n",
       "      <td>-0.000799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                     Predicted  Actual Returns  Strategy Returns\n",
       "date                                                            \n",
       "2020-09-04 14:45:00        1.0        0.003210          0.003210\n",
       "2020-09-04 15:00:00        1.0        0.000000          0.000000\n",
       "2020-09-04 15:15:00        1.0        0.004000          0.004000\n",
       "2020-09-04 15:30:00        1.0       -0.003187         -0.003187\n",
       "2020-09-04 15:45:00        1.0       -0.000799         -0.000799"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new empty predictions DataFrame.\n",
    "testing_signal_predictions = svm_model.predict(X_test_scaled)\n",
    "# Create a predictions DataFrame\n",
    "predictions_df = pd.DataFrame(index=X_test.index)\n",
    "\n",
    "# Add the SVM model predictions to the DataFrame\n",
    "predictions_df['Predicted'] = testing_signal_predictions\n",
    "\n",
    "# Add the actual returns to the DataFrame\n",
    "predictions_df['Actual Returns'] = signals_df['Actual Returns']\n",
    "\n",
    "# Add the strategy returns to the DataFrame\n",
    "predictions_df['Strategy Returns'] = predictions_df['Actual Returns'] * predictions_df['Predicted']\n",
    "\n",
    "# Review the DataFrame\n",
    "display(predictions_df.head())\n",
    "display(predictions_df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: Create a cumulative return plot that shows the actual returns vs. the strategy returns. Save a PNG image of this plot. This will serve as a baseline against which to compare the effects of tuning the trading algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>Strategy Returns</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>1.000805</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1.003210</td>\n",
       "      <td>1.003210</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-04 15:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>2020-09-04 15:15:00</th>\n",
       "      <td>1.004000</td>\n",
       "      <td>1.004000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:30:00</th>\n",
       "      <td>0.996813</td>\n",
       "      <td>0.996813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:45:00</th>\n",
       "      <td>0.999201</td>\n",
       "      <td>0.999201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5629 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Actual Returns  Strategy Returns\n",
       "date                                                 \n",
       "2019-10-25 09:30:00        1.000000          1.000000\n",
       "2019-10-25 09:45:00        1.000000          1.000000\n",
       "2019-10-25 10:00:00        0.997592          0.997592\n",
       "2019-10-25 10:15:00        1.000805          1.000805\n",
       "2019-10-25 10:30:00        1.006431          1.006431\n",
       "...                             ...               ...\n",
       "2020-09-04 14:45:00        1.003210          1.003210\n",
       "2020-09-04 15:00:00        1.000000          1.000000\n",
       "2020-09-04 15:15:00        1.004000          1.004000\n",
       "2020-09-04 15:30:00        0.996813          0.996813\n",
       "2020-09-04 15:45:00        0.999201          0.999201\n",
       "\n",
       "[5629 rows x 2 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the actual returns versus the strategy returns\n",
    "(1 + predictions_df[[\"Actual Returns\", \"Strategy Returns\"]]).cumprod().plot()\n",
    "(1 + predictions_df[[\"Actual Returns\", \"Strategy Returns\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig(\"CumulativeReturnplot12mthoffset.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Tune the Baseline Trading Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Use an Alternative ML Model and Evaluate Strategy Returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, you’ll tune, or adjust, the model’s input features to find the parameters that result in the best trading outcomes. You’ll choose the best by comparing the cumulative products of the strategy returns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Tune the training algorithm by adjusting the size of the training dataset. \n",
    "\n",
    "To do so, slice your data into different periods. Rerun the notebook with the updated parameters, and record the results in your `README.md` file. \n",
    "\n",
    "Answer the following question: What impact resulted from increasing or decreasing the training window?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Tune the trading algorithm by adjusting the SMA input features. \n",
    "\n",
    "Adjust one or both of the windows for the algorithm. Rerun the notebook with the updated parameters, and record the results in your `README.md` file. \n",
    "\n",
    "Answer the following question: What impact resulted from increasing or decreasing either or both of the SMA windows?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Choose the set of parameters that best improved the trading algorithm returns. \n",
    "\n",
    "Save a PNG image of the cumulative product of the actual returns vs. the strategy returns, and document your conclusion in your `README.md` file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Evaluate a New Machine Learning Classifier\n",
    "\n",
    "In this section, you’ll use the original parameters that the starter code provided. But, you’ll apply them to the performance of a second machine learning model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1:  Import a new classifier, such as `AdaBoost`, `DecisionTreeClassifier`, or `LogisticRegression`. (For the full list of classifiers, refer to the [Supervised learning page](https://scikit-learn.org/stable/supervised_learning.html) in the scikit-learn documentation.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import a new classifier from SKLearn\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Initiate the model instance\n",
    "RF_model = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
    "    min_samples_split=2, random_state=0)\n",
    "\n",
    "RF_model \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Using the original training data as the baseline model, fit another model with the new classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1., -1.,  1.,  1., -1.,  1., -1.])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model using the training data\n",
    "RF_model = RF_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Use the testing dataset to generate the predictions for the new model\n",
    "RF_model_predictions = RF_model.predict(X_train_scaled)\n",
    "\n",
    "# Review the model's predicted values\n",
    "RF_model_predictions[:10]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Backtest the new model to evaluate its performance. \n",
    "\n",
    "Save a PNG image of the cumulative product of the actual returns vs. the strategy returns for this updated trading algorithm, and write your conclusions in your `README.md` file. \n",
    "\n",
    "Answer the following questions: \n",
    "Did this new model perform better or worse than the provided baseline model? \n",
    "Did this new model perform better or worse than your tuned trading algorithm?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00      2661\n",
      "         1.0       0.53      1.00      0.69      2968\n",
      "\n",
      "    accuracy                           0.53      5629\n",
      "   macro avg       0.26      0.50      0.35      5629\n",
      "weighted avg       0.28      0.53      0.36      5629\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jimp\\anaconda3\\envs\\pyvizenv\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\jimp\\anaconda3\\envs\\pyvizenv\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\jimp\\anaconda3\\envs\\pyvizenv\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Use a classification report to evaluate the model using the predictions and testing data\n",
    "RF_training_report = classification_report(y_test, testing_signal_predictions)\n",
    "\n",
    "# Print the classification report\n",
    "print(RF_training_report)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>Strategy Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0.002408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 10:15:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.000805</td>\n",
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      ],
      "text/plain": [
       "                     Predicted  Actual Returns  Strategy Returns\n",
       "date                                                            \n",
       "2019-10-25 09:30:00       -1.0        0.000000         -0.000000\n",
       "2019-10-25 09:45:00       -1.0        0.000000         -0.000000\n",
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       "2019-10-25 10:15:00        1.0        0.000805          0.000805\n",
       "2019-10-25 10:30:00        1.0        0.006431          0.006431"
      ]
     },
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       "      <td>-0.003210</td>\n",
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      "text/plain": [
       "                     Predicted  Actual Returns  Strategy Returns\n",
       "date                                                            \n",
       "2020-09-04 14:45:00       -1.0        0.003210         -0.003210\n",
       "2020-09-04 15:00:00       -1.0        0.000000         -0.000000\n",
       "2020-09-04 15:15:00       -1.0        0.004000         -0.004000\n",
       "2020-09-04 15:30:00       -1.0       -0.003187          0.003187\n",
       "2020-09-04 15:45:00       -1.0       -0.000799          0.000799"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new empty predictions DataFrame.\n",
    "testing_signal_predictions = RF_model.predict(X_test_scaled)\n",
    "# Create a predictions DataFrame\n",
    "predictions_df = pd.DataFrame(index=X_test.index)\n",
    "\n",
    "# Add the SVM model predictions to the DataFrame\n",
    "predictions_df['Predicted'] = testing_signal_predictions\n",
    "\n",
    "# Add the actual returns to the DataFrame\n",
    "predictions_df['Actual Returns'] = signals_df['Actual Returns']\n",
    "\n",
    "# Add the strategy returns to the DataFrame\n",
    "predictions_df['Strategy Returns'] = predictions_df['Actual Returns'] * predictions_df['Predicted']\n",
    "\n",
    "# Review the DataFrame\n",
    "display(predictions_df.head())\n",
    "display(predictions_df.tail())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the actual returns versus the strategy returns\n",
    "(1 + predictions_df[[\"Actual Returns\", \"Strategy Returns\"]]).cumprod().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig(\"RF Prediction - 12mth Offset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will look at AdaBoostClassifier as a comparison supervised learning moduel \n",
    "# Import a new classifier from SKLearn\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(n_estimators=100)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_model = AdaBoostClassifier(n_estimators=100)\n",
    "\n",
    "ada_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1., -1.,  1.,  1., -1.,  1., -1.])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_model = ada_model.fit(X_train_scaled, y_train)\n",
    "ada_model_predictions = ada_model.predict(X_train_scaled)\n",
    "# Review the model's predicted values\n",
    "ada_model_predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.47      0.88      0.61      2661\n",
      "         1.0       0.50      0.11      0.18      2968\n",
      "\n",
      "    accuracy                           0.47      5629\n",
      "   macro avg       0.48      0.49      0.39      5629\n",
      "weighted avg       0.49      0.47      0.38      5629\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use a classification report to evaluate the model using the predictions and testing data\n",
    "ada_training_report = classification_report(y_test, testing_signal_predictions)\n",
    "\n",
    "# Print the classification report\n",
    "print(ada_training_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>Predicted</th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>Strategy Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-25 09:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 09:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 10:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.002408</td>\n",
       "      <td>-0.002408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 10:15:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.000805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-25 10:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006431</td>\n",
       "      <td>0.006431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Predicted  Actual Returns  Strategy Returns\n",
       "date                                                            \n",
       "2019-10-25 09:30:00        1.0        0.000000          0.000000\n",
       "2019-10-25 09:45:00        1.0        0.000000          0.000000\n",
       "2019-10-25 10:00:00        1.0       -0.002408         -0.002408\n",
       "2019-10-25 10:15:00        1.0        0.000805          0.000805\n",
       "2019-10-25 10:30:00        1.0        0.006431          0.006431"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Predicted</th>\n",
       "      <th>Actual Returns</th>\n",
       "      <th>Strategy Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-04 14:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.003210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:15:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.004000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.003187</td>\n",
       "      <td>-0.003187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-04 15:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000799</td>\n",
       "      <td>-0.000799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Predicted  Actual Returns  Strategy Returns\n",
       "date                                                            \n",
       "2020-09-04 14:45:00        1.0        0.003210          0.003210\n",
       "2020-09-04 15:00:00        1.0        0.000000          0.000000\n",
       "2020-09-04 15:15:00        1.0        0.004000          0.004000\n",
       "2020-09-04 15:30:00        1.0       -0.003187         -0.003187\n",
       "2020-09-04 15:45:00        1.0       -0.000799         -0.000799"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new empty predictions DataFrame.\n",
    "testing_signal_predictions = ada_model.predict(X_test_scaled)\n",
    "# Create a predictions DataFrame\n",
    "predictions_df = pd.DataFrame(index=X_test.index)\n",
    "\n",
    "# Add the ADA boost model predictions to the DataFrame\n",
    "predictions_df['Predicted'] = testing_signal_predictions\n",
    "\n",
    "# Add the actual returns to the DataFrame\n",
    "predictions_df['Actual Returns'] = signals_df['Actual Returns']\n",
    "\n",
    "# Add the strategy returns to the DataFrame\n",
    "predictions_df['Strategy Returns'] = predictions_df['Actual Returns'] * predictions_df['Predicted']\n",
    "\n",
    "# Review the DataFrame\n",
    "display(predictions_df.head())\n",
    "display(predictions_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the actual returns versus the strategy returns\n",
    "(1 + predictions_df[[\"Actual Returns\", \"Strategy Returns\"]]).cumprod().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig(\"ADA Boost Prediction 12mth Offset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer - the Random Forest model is more accurate, and has done better than the Ada Boost model, and the strategy returns are in general above the actual returns, which is what we are searching for, even though it started out the opposite, and it is important to note that the Random Forest seems to predict with 100% accuracy a buy signal, as evidenced by the recall result\n"
     ]
    }
   ],
   "source": [
    "print(\"Answer - the Random Forest model is more accurate, and has done better than the Ada Boost model, and the strategy returns are in general above the actual returns, which is what we are searching for, even though it started out the opposite, and it is important to note that the Random Forest seems to predict with 100% accuracy a buy signal, as evidenced by the recall result\")\n"
   ]
  }
 ],
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