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GrovesD2/trend_predictor.ipynb

Created October 29, 2023 17:01
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A simple model for predicting today's trend in the SPY
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trend_predictor.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "c334e834",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import yfinance as yf\n",
"\n",
"from plotly import graph_objects as go\n",
"from scipy.signal import savgol_filter"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2afa60b7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[*********************100%***********************] 1 of 1 completed\n"
]
}
],
"source": [
"df = yf.download('SPY').reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "693b424d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\danny\\AppData\\Local\\Temp\\ipykernel_43184\\4120236351.py:6: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
"\n",
"C:\\Users\\danny\\anaconda3\\lib\\site-packages\\plotly\\graph_objs\\_deprecations.py:378: DeprecationWarning:\n",
"\n",
"plotly.graph_objs.Line is deprecated.\n",
"Please replace it with one of the following more specific types\n",
" - plotly.graph_objs.scatter.Line\n",
" - plotly.graph_objs.layout.shape.Line\n",
" - etc.\n",
"\n",
"\n"
]
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"line": {
"color": "blue"
},
"name": "Prices",
"type": "scatter",
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"source": [
"df['smooth_prices'] = savgol_filter(df['Adj Close'].values, window_length=51, polyorder=7)\n",
"df['smooth_gradient'] = df['smooth_prices'].diff()\n",
"df['uptrend'] = np.where(df['smooth_gradient'] > 0, 1, 0)\n",
"\n",
"df_plot = df[df['Date'] > '2021-01-01']\n",
"df_plot['colour'] = np.where(df_plot['uptrend'] == 1, 'green', 'red')\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Line(x=df_plot['Date'], y=df_plot['Adj Close'], line={'color': 'blue'}, name='Prices'))\n",
"fig.add_trace(go.Scatter(x=df_plot['Date'], y=df_plot['smooth_prices'], mode='markers', marker_color=df_plot['colour'], name = 'trend'))\n",
"\n",
"fig.show()"
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},
{
"cell_type": "code",
"execution_count": 30,
"id": "820c1036",
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{
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" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
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" <th>100</th>\n",
" <td>1993-06-23</td>\n",
" <td>44.62500</td>\n",
" <td>44.62500</td>\n",
" <td>44.21875</td>\n",
" <td>44.21875</td>\n",
" <td>25.398325</td>\n",
" <td>227600</td>\n",
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" <td>0.008553</td>\n",
" <td>-0.002081</td>\n",
" <td>-0.003607</td>\n",
" <td>-0.034789</td>\n",
" <td>0.021661</td>\n",
" <td>-0.010397</td>\n",
" <td>-0.002232</td>\n",
" <td>-0.035446</td>\n",
" <td>0.032847</td>\n",
" <td>0.018320</td>\n",
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" <tr>\n",
" <th>101</th>\n",
" <td>1993-06-24</td>\n",
" <td>44.34375</td>\n",
" <td>44.81250</td>\n",
" <td>44.34375</td>\n",
" <td>44.81250</td>\n",
" <td>25.739384</td>\n",
" <td>243700</td>\n",
" <td>25.676672</td>\n",
" <td>0.009770</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.022096</td>\n",
" <td>0.012738</td>\n",
" <td>0.009686</td>\n",
" <td>-0.021828</td>\n",
" <td>0.035379</td>\n",
" <td>0.004286</td>\n",
" <td>0.010920</td>\n",
" <td>-0.022495</td>\n",
" <td>0.046715</td>\n",
" <td>0.024707</td>\n",
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" <tr>\n",
" <th>102</th>\n",
" <td>1993-06-25</td>\n",
" <td>44.78125</td>\n",
" <td>44.90625</td>\n",
" <td>44.75000</td>\n",
" <td>44.78125</td>\n",
" <td>25.721422</td>\n",
" <td>44800</td>\n",
" <td>25.698155</td>\n",
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" <td>0.021383</td>\n",
" <td>0.024242</td>\n",
" <td>0.008908</td>\n",
" <td>-0.022510</td>\n",
" <td>0.034657</td>\n",
" <td>0.003585</td>\n",
" <td>0.009996</td>\n",
" <td>-0.023177</td>\n",
" <td>0.045985</td>\n",
" <td>0.021827</td>\n",
" </tr>\n",
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" <th>103</th>\n",
" <td>1993-06-28</td>\n",
" <td>45.00000</td>\n",
" <td>45.28125</td>\n",
" <td>44.93750</td>\n",
" <td>45.28125</td>\n",
" <td>26.008606</td>\n",
" <td>439900</td>\n",
" <td>25.725857</td>\n",
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" <td>0.032787</td>\n",
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" <td>0.046209</td>\n",
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" <th>104</th>\n",
" <td>1993-06-29</td>\n",
" <td>45.21875</td>\n",
" <td>45.21875</td>\n",
" <td>45.00000</td>\n",
" <td>45.06250</td>\n",
" <td>25.882965</td>\n",
" <td>207500</td>\n",
" <td>25.741172</td>\n",
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" <td>0.027798</td>\n",
" <td>0.027757</td>\n",
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" <td>-0.016371</td>\n",
" <td>0.041155</td>\n",
" <td>0.014119</td>\n",
" <td>0.015977</td>\n",
" <td>-0.017042</td>\n",
" <td>0.052555</td>\n",
" <td>0.013249</td>\n",
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" Date Open High Low Close Adj Close Volume \n",
"100 1993-06-23 44.62500 44.62500 44.21875 44.21875 25.398325 227600 \\\n",
"101 1993-06-24 44.34375 44.81250 44.34375 44.81250 25.739384 243700 \n",
"102 1993-06-25 44.78125 44.90625 44.75000 44.78125 25.721422 44800 \n",
"103 1993-06-28 45.00000 45.28125 44.93750 45.28125 26.008606 439900 \n",
"104 1993-06-29 45.21875 45.21875 45.00000 45.06250 25.882965 207500 \n",
"\n",
" smooth_prices smooth_gradient uptrend ... feat_dist_min_30 \n",
"100 25.666902 0.013305 1 ... 0.008553 \\\n",
"101 25.676672 0.009770 1 ... 0.022096 \n",
"102 25.698155 0.021483 1 ... 0.021383 \n",
"103 25.725857 0.027702 1 ... 0.032787 \n",
"104 25.741172 0.015316 1 ... 0.027798 \n",
"\n",
" feat_dist_price_30 feat_dist_ma_50 feat_dist_max_50 feat_dist_min_50 \n",
"100 -0.002081 -0.003607 -0.034789 0.021661 \\\n",
"101 0.012738 0.009686 -0.021828 0.035379 \n",
"102 0.024242 0.008908 -0.022510 0.034657 \n",
"103 0.036413 0.019870 -0.011596 0.046209 \n",
"104 0.027757 0.014656 -0.016371 0.041155 \n",
"\n",
" feat_dist_price_50 feat_dist_ma_100 feat_dist_max_100 \n",
"100 -0.010397 -0.002232 -0.035446 \\\n",
"101 0.004286 0.010920 -0.022495 \n",
"102 0.003585 0.009996 -0.023177 \n",
"103 0.014791 0.021044 -0.012270 \n",
"104 0.014119 0.015977 -0.017042 \n",
"\n",
" feat_dist_min_100 feat_dist_price_100 \n",
"100 0.032847 0.018320 \n",
"101 0.046715 0.024707 \n",
"102 0.045985 0.021827 \n",
"103 0.057664 0.022427 \n",
"104 0.052555 0.013249 \n",
"\n",
"[5 rows x 46 columns]"
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"source": [
"def get_features(df):\n",
" \n",
" for m in [2, 3, 5, 10, 15, 20, 30, 50, 100]:\n",
" \n",
" # Distance from the moving averages\n",
" df[f'feat_dist_ma_{m}'] = df['Adj Close']/df['Adj Close'].rolling(m).mean() - 1\n",
" \n",
" # Distance from the rolling maximum/minimum\n",
" df[f'feat_dist_max_{m}'] = df['Close']/df['High'].rolling(m).max() - 1\n",
" df[f'feat_dist_min_{m}'] = df['Close']/df['Low'].rolling(m).min() - 1\n",
" \n",
" # Price distance\n",
" df[f'feat_dist_price_{m}'] = df['Adj Close']/df['Adj Close'].shift(m) - 1\n",
" \n",
" return df\n",
" \n",
"df_fe = get_features(df.copy())\n",
"df_fe = df_fe.dropna()\n",
"\n",
"df_fe.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "012e8bc3",
"metadata": {},
"outputs": [],
"source": [
"feat_cols = [col for col in df_fe.columns if 'feat' in col]\n",
"\n",
"train_until = '2020-01-01'\n",
"\n",
"x_train = df_fe[df_fe['Date'] < train_until][feat_cols]\n",
"y_train = df_fe[df_fe['Date'] < train_until]['uptrend']\n",
"\n",
"x_test = df_fe[df_fe['Date'] >= train_until][feat_cols]\n",
"y_test = df_fe[df_fe['Date'] >= train_until]['uptrend']"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "871a221d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accuracy Score: 0.7736240913811008\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import confusion_matrix, accuracy_score\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"rf_classifier = RandomForestClassifier(n_estimators=250, random_state=42)\n",
"rf_classifier.fit(x_train, y_train)\n",
"\n",
"y_pred = rf_classifier.predict(x_test)\n",
"\n",
"conf_matrix = confusion_matrix(y_test, y_pred)\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
"plt.xlabel('Predicted')\n",
"plt.ylabel('Actual')\n",
"plt.title('Confusion Matrix')\n",
"plt.show()\n",
"\n",
"# Print the accuracy score\n",
"print(\"\\nAccuracy Score:\", accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "1e30be52",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\danny\\AppData\\Local\\Temp\\ipykernel_43184\\963459811.py:2: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
"\n"
]
},
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},
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],
"source": [
"df_test = df_fe[df_fe['Date'] >= train_until]\n",
"df_test['predicted_trend'] = rf_classifier.predict(x_test)\n",
"\n",
"uptrends = df_test[df_test['predicted_trend'] == 1]\n",
"downtrends = df_test[df_test['predicted_trend'] == 0]\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=uptrends['Date'], y=uptrends['Adj Close'], mode='markers', marker_color='green', name='Uptrend'))\n",
"fig.add_trace(go.Scatter(x=downtrends['Date'], y=downtrends['Adj Close'], mode='markers', marker_color='red', name='Downtrend'))\n",
"\n",
"fig.update_layout(title='SPY Predicted Trends')\n",
"\n",
"fig.show()"
]
}
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