diff --git a/DAX40/DAX_Quant_Model.ipynb b/DAX40/DAX_Quant_Model.ipynb index c39f86a..5031129 100644 --- a/DAX40/DAX_Quant_Model.ipynb +++ b/DAX40/DAX_Quant_Model.ipynb @@ -22,12 +22,14 @@ "source": [ "## Data retrieval ($)\n", "\n", - "We want historical data for our model. This isn't free." + "We want historical data for our model. \n", + "\n", + "This isn't free." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 237, "id": "ec60e3a9-efcc-4619-9fbd-0d09fedcf4f7", "metadata": {}, "outputs": [], @@ -52,6 +54,14 @@ "read_api_keys_from_env()" ] }, + { + "cell_type": "markdown", + "id": "270e5063-6928-4138-adf9-f25913be521c", + "metadata": {}, + "source": [ + "## Data download via Quant APIs" + ] + }, { "cell_type": "code", "execution_count": 31, @@ -439,6 +449,16 @@ "gdax_df.head(1)" ] }, + { + "cell_type": "markdown", + "id": "daf6d537-f095-4a27-a2b7-b629f1e17c76", + "metadata": {}, + "source": [ + "# We use a Kalman Filter for estimates\n", + "\n", + "This is one feature we will use." + ] + }, { "cell_type": "code", "execution_count": 5, @@ -576,7 +596,7 @@ }, { "cell_type": "markdown", - "id": "2fd93317-ea7b-4edf-a2d4-b7ebdbe5021e", + "id": "5267ca35-aced-4b12-bb9b-bfba6cee5a62", "metadata": {}, "source": [ "# Get foundational features ($)\n", @@ -586,29 +606,33 @@ "* Export stats\n", "* Inflation\n", "\n", - "https://www.econdb.com/series/REXPDE/germany-real-exports-of-goods-and-services/?from=2019-08-25&to=2024-08-25 " + "https://www.econdb.com/series/REXPDE/germany-real-exports-of-goods-and-services/?from=2019-08-25&to=2024-08-25 \n", + "\n", + "We know DE is exporting a lot, and that DAX companies are export focused.\n", + "(For very adv. analysis, we could incorporate the cash flow etc. - all possible)" ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "53f74d83-3ebf-4cac-b93e-547cb4d9d4e4", + "cell_type": "markdown", + "id": "31cca246-87d6-4952-bf3a-cc481e7bad4b", "metadata": {}, - "outputs": [], "source": [ - "from openbb import obb" + "## RPXDE = Germany - Real exports of goods and services (Level USD)\n", + "\n", + "These statistics aren't available since '91. \n", + "We could go all out and merge certain other stats. But it doesn't seem to be necessary." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "de0c2354-e0aa-4034-a4bd-46e25963fd2a", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", - "# define API_TOKEN\n", + "API_TOKEN = os.getenv('ECONDB_API_KEY')\n", "\n", "export_stats_usd_de_df = pd.read_csv(\n", " 'https://www.econdb.com/api/series/REXPDE~TUSD/?token=%s&format=csv' % API_TOKEN,\n", @@ -617,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 219, "id": "0269e523-0a5c-4e09-8805-f6a53016e90a", "metadata": {}, "outputs": [ @@ -664,7 +688,7 @@ "2024-01-01 441754.0" ] }, - "execution_count": 17, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -675,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 217, "id": "78ff5b8d-1ab9-4688-86a0-b75e09103cd7", "metadata": {}, "outputs": [ @@ -712,22 +736,6 @@ " 1991-01-01\n", " NaN\n", " \n", - " \n", - " 1991-04-01\n", - " NaN\n", - " \n", - " \n", - " 1991-07-01\n", - " NaN\n", - " \n", - " \n", - " 1991-10-01\n", - " NaN\n", - " \n", - " \n", - " 1992-01-01\n", - " NaN\n", - " \n", " \n", "\n", "" @@ -735,20 +743,24 @@ "text/plain": [ " REXPDE~TUSD\n", "Date \n", - "1991-01-01 NaN\n", - "1991-04-01 NaN\n", - "1991-07-01 NaN\n", - "1991-10-01 NaN\n", - "1992-01-01 NaN" + "1991-01-01 NaN" ] }, - "execution_count": 25, + "execution_count": 217, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "export_stats_usd_df.head(5)" + "export_stats_usd_df.head(1)" + ] + }, + { + "cell_type": "markdown", + "id": "41020bb4-5100-49a7-ba3f-bdc7c0fadb01", + "metadata": {}, + "source": [ + "## CPI = Germany - Consumer Price Index (Level USD would be better)" ] }, { @@ -973,9 +985,17 @@ "print(combined_df.info())" ] }, + { + "cell_type": "markdown", + "id": "5cb9f86d-5605-4388-9048-67c55ba2cf5c", + "metadata": {}, + "source": [ + "# Calculation of a volatility-based indicator (Lorentzian) and the Kalman estimation" + ] + }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 195, "id": "bd1f6548-d845-4a26-b0be-52951a332ffe", "metadata": {}, "outputs": [ @@ -1099,9 +1119,17 @@ "# Now we fit Prophet" ] }, + { + "cell_type": "markdown", + "id": "7b712cba-df13-4ef7-9a20-4e83fb0ff80a", + "metadata": {}, + "source": [ + "## Prophet model for the next day" + ] + }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 205, "id": "73e18e29-154b-43ed-947b-c0ac49e54f8f", "metadata": {}, "outputs": [ @@ -1116,10 +1144,85 @@ "name": "stderr", "output_type": "stream", "text": [ - "07:41:25 - cmdstanpy - INFO - Chain [1] start processing\n", - "07:41:32 - cmdstanpy - INFO - Chain [1] done processing\n" + "18:44:45 - cmdstanpy - INFO - Chain [1] start processing\n", + "18:44:52 - cmdstanpy - INFO - Chain [1] done processing\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model training completed and saved.\n" + ] + } + ], + "source": [ + "from prophet import Prophet\n", + "import pandas as pd\n", + "import numpy as np\n", + "from datetime import datetime, timedelta\n", + "\n", + "# Assuming your dataframe is named combined_df\n", + "df = combined_df.copy()\n", + "\n", + "# Convert 'index' to datetime if it's not already\n", + "df['index'] = pd.to_datetime(df['index'])\n", + "\n", + "# Filter for the last 10 years of data\n", + "ten_years_ago = datetime.now() - timedelta(days=365*10)\n", + "df = df[df['index'] >= ten_years_ago]\n", + "\n", + "# Remove rows with NaN values\n", + "df = df.dropna()\n", + "\n", + "# Rename columns to match Prophet's requirements\n", + "df = df.rename(columns={'index': 'ds', 'close': 'y'})\n", + "\n", + "# Define potential regressors (all columns except 'ds' and 'y')\n", + "regressors = [col for col in df.columns if col not in ['ds', 'y']]\n", + "\n", + "def train_prophet_model(df, regressors, include_monthly_seasonality=True):\n", + " model = Prophet(\n", + " yearly_seasonality=True,\n", + " daily_seasonality=True,\n", + " weekly_seasonality=True,\n", + " changepoint_prior_scale=0.05,\n", + " seasonality_prior_scale=10.0,\n", + " seasonality_mode='additive'\n", + " )\n", + " \n", + " if include_monthly_seasonality:\n", + " model.add_seasonality(name='monthly', period=30.5, fourier_order=5)\n", + " \n", + " for regressor in regressors:\n", + " if regressor in df.columns:\n", + " model.add_regressor(regressor)\n", + " \n", + " model.fit(df)\n", + " return model\n", + "\n", + "# Filter regressors to only include those present in the dataframe\n", + "regressors = [reg for reg in regressors if reg in df.columns]\n", + "print(f\"Using regressors: {regressors}\")\n", + "\n", + "# Train the model\n", + "model = train_prophet_model(df, regressors)\n", + "\n", + "# Save model and data for future use\n", + "df.to_pickle('dax_data.pkl')\n", + "import pickle\n", + "with open('prophet_model.pkl', 'wb') as f:\n", + " pickle.dump(model, f)\n", + "\n", + "print(\"Model training completed and saved.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "93f6d266-070c-4fbb-a638-43c3a28c6096", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", @@ -19340,3641 +19443,3641 @@ "2024-08-23T00:00:00" ], "y": [ - 9463.02130911165, - 9462.874764886805, - 9462.87488203546, - 9471.669697093797, - 9499.65065015972, - 9619.401286668266, - 9716.965394075889, - 9739.617925914858, - 9740.043158443701, - 9740.158000363206, - 9751.103605092412, - 9703.762573204025, - 9693.25471175727, - 9684.238963404803, - 9643.614669592145, - 9644.227913059913, - 9643.86713261404, - 9652.149725732712, - 9625.616018416782, - 9654.763136746393, - 9791.449311055732, - 9791.890773160372, - 9792.142631462044, - 9791.984172305707, - 9742.630701613853, - 9588.520829046041, - 9654.965501626119, - 9503.297818409723, - 9483.728343991374, - 9483.62870223477, - 9483.415102499468, - 9416.139237057962, - 9467.39871325026, - 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import pickle\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error\n", + "import numpy as np\n", + "import plotly.graph_objects as go\n", + "\n", + "# Load the trained model and data\n", + "df = pd.read_pickle('dax_data_month.pkl')\n", + "with open('prophet_model_month.pkl', 'rb') as f:\n", + " model = pickle.load(f)\n", + "\n", + "# Ensure all regressors are present in the future dataframe\n", + "regressors = ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE']\n", + "future = df[['ds'] + regressors].copy()\n", + "\n", + "# Make predictions on the existing data (no future dates, just evaluation)\n", "forecast = model.predict(future)\n", "\n", - "# Evaluate the model\n", + "# Extract the true values and the predicted values\n", "y_true = df['y'].values\n", "y_pred = forecast['yhat'].values\n", + "\n", + "# Calculate evaluation metrics\n", "mse = mean_squared_error(y_true, y_pred)\n", "rmse = np.sqrt(mse)\n", "mae = mean_absolute_error(y_true, y_pred)\n", "\n", - "print(f\"\\nModel Results:\")\n", - "print(f\"Root Mean Squared Error: {rmse}\")\n", - "print(f\"Mean Absolute Error: {mae}\")\n", + "# Print the results\n", + "print(f\"\\nModel Evaluation Results:\")\n", + "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n", + "print(f\"Mean Absolute Error (MAE): {mae}\")\n", "\n", - "# Plot the results with Plotly\n", + "# Plot the evaluation results with Plotly\n", "fig = go.Figure()\n", + "\n", + "# Plot the actual historical data\n", "fig.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='Actual'))\n", + "\n", + "# Plot the forecasted line\n", "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))\n", + "\n", + "# Plot the confidence interval\n", "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n", "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n", - "fig.update_layout(title='Prophet Forecast for DAX (Last 10 Years)', xaxis_title='Date', yaxis_title='Value', hovermode='x unified')\n", - "fig.update_xaxes(range=[df['ds'].min(), current_date])\n", - "fig.show()\n", "\n", - "# Plot component-wise effects\n", - "component_names = ['trend', 'yearly', 'monthly'] + regressors\n", - "fig = make_subplots(rows=len(component_names), cols=1, subplot_titles=component_names)\n", - "for i, component in enumerate(component_names, 1):\n", - " fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast[component], mode='lines', name=component), row=i, col=1)\n", - "fig.update_layout(height=300*len(component_names), title_text=\"Prophet Components for DAX\", hovermode='x unified')\n", - "fig.update_xaxes(range=[df['ds'].min(), current_date])\n", - "fig.show()\n", + "# Update the layout\n", + "fig.update_layout(title='Prophet Forecast vs Actual',\n", + " xaxis_title='Date',\n", + " yaxis_title='Value',\n", + " hovermode='x unified')\n", "\n", - "# Analyze regressor importance\n", - "if regressors:\n", - " beta_values = model.params['beta'].flatten()\n", - " regressor_importance = pd.DataFrame({\n", - " 'regressor': regressors,\n", - " 'importance': np.abs(beta_values[:len(regressors)])\n", - " })\n", - " regressor_importance = regressor_importance.sort_values('importance', ascending=False)\n", - " print(\"\\nRegressor Importance:\")\n", - " print(regressor_importance)\n", + "# Show the plot\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "id": "c1cfde03-c238-456f-b12a-404e6e8470ad", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import pickle\n", + "from datetime import datetime\n", + "import plotly.graph_objects as go\n", "\n", - " # Plot regressor importance\n", - " fig = go.Figure([go.Bar(x=regressor_importance['regressor'], y=regressor_importance['importance'])])\n", - " fig.update_layout(title='Regressor Importance for DAX', xaxis_title='Regressor', yaxis_title='Absolute Importance')\n", - " fig.show()\n", - "else:\n", - " print(\"\\nNo regressors were included in the model.\")\n", - "\n", - "# Make future predictions (next 30 days)\n", - "future_dates = pd.date_range(start=current_date + timedelta(days=1), periods=30)\n", + "# Load the trained model and data\n", + "df = pd.read_pickle('dax_data_month.pkl')\n", + "with open('prophet_model_month.pkl', 'rb') as f:\n", + " model = pickle.load(f)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "id": "55dcaecb-9f35-468d-81e7-6f929ab6a2d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Prediction for the next first day of the month:\n", + " ds yhat yhat_lower yhat_upper\n", + "0 2024-09-01 18082.128156 18074.147232 18090.528226\n" + ] + } + ], + "source": [ + "# Make a prediction for the next first day of the month\n", + "future_dates = pd.date_range(start=datetime.now().date(), periods=1, freq='MS') # MS = Month Start\n", "future_df = pd.DataFrame({'ds': future_dates})\n", "\n", - "# Estimate future regressor values (using last 30 days average)\n", - "for regressor in regressors:\n", - " future_df[regressor] = df[regressor].tail(30).mean()\n", + "# Include all regressors, set them to a default value or use last known values\n", + "for regressor in ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE']:\n", + " if regressor in df.columns:\n", + " future_df[regressor] = df[regressor].iloc[-1] # Use last known value as a simple approach\n", "\n", - "future_forecast = model.predict(future_df)\n", + "# Make the prediction\n", + "forecast = model.predict(future_df)\n", "\n", - "# Plot future predictions\n", + "# Display the prediction\n", + "print(\"\\nPrediction for the next first day of the month:\")\n", + "print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "id": "b4e247da-1d82-4349-8218-32a75f3f2fab", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "name": "Historical", + "type": "scatter", + "x": [ + "2014-09-01T00:00:00", + "2014-10-01T00:00:00", + "2014-11-01T00:00:00", + "2014-12-01T00:00:00", + "2015-01-01T00:00:00", + "2015-02-01T00:00:00", + "2015-03-01T00:00:00", + "2015-04-01T00:00:00", + "2015-05-01T00:00:00", + "2015-06-01T00:00:00", + "2015-07-01T00:00:00", + "2015-08-01T00:00:00", + "2015-09-01T00:00:00", + "2015-10-01T00:00:00", + "2015-11-01T00:00:00", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the historical data and forecast\n", "fig = go.Figure()\n", - "fig.add_trace(go.Scatter(x=df['ds'].tail(365), y=df['y'].tail(365), mode='lines', name='Historical'))\n", - "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat'], mode='lines', name='Forecast'))\n", - "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n", - "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n", - "fig.update_layout(title='30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Value', hovermode='x unified')\n", - "fig.update_xaxes(range=[current_date - timedelta(days=365), future_dates[-1]])\n", - "fig.show()\n", "\n", - "print(\"\\nFuture Predictions (next 30 days):\")\n", - "print(future_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])" + "# Plot the actual historical data\n", + "fig.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='Historical'))\n", + "\n", + "# Plot the forecasted line\n", + "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines+markers', name='Forecast'))\n", + "\n", + "# Add a violet dot for the next month's prediction\n", + "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='markers', name='Next Month Prediction', marker=dict(color='violet', size=12, symbol='circle')))\n", + "\n", + "# Plot the confidence interval\n", + "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n", + "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n", + "\n", + "# Update the layout\n", + "fig.update_layout(title='Prediction for Next First Day of the Month with Violet Dot',\n", + " xaxis_title='Date',\n", + " yaxis_title='Value',\n", + " hovermode='x unified')\n", + "\n", + "# Show the plot\n", + "fig.show()\n" ] }, { @@ -152067,12 +163436,20 @@ "id": "c7fda70a-555e-4503-8aff-2142f32ca046", "metadata": {}, "source": [ - "# Time-series AutoML with H2O" + "# Time-series AutoML with H2O (alternative to Prophet)" + ] + }, + { + "cell_type": "markdown", + "id": "ee5e0c74-2fec-4662-b5be-cc757a14e587", + "metadata": {}, + "source": [ + "## Day range " ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 224, "id": "401eaf48-91bb-42c3-a9a0-c85e89f3a690", "metadata": {}, "outputs": [ @@ -152089,15 +163466,15 @@ "\n", " \n", - "
\n", + "
\n", " \n", " \n", " \n", " \n", - "\n", + "\n", "\n", "\n", "\n", @@ -152139,11 +163516,11 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -152163,14 +163540,14 @@ ], "text/plain": [ "-------------------------- -----------------------------\n", - "H2O_cluster_uptime: 56 mins 11 secs\n", + "H2O_cluster_uptime: 6 mins 46 secs\n", "H2O_cluster_timezone: Etc/UTC\n", "H2O_data_parsing_timezone: UTC\n", "H2O_cluster_version: 3.46.0.4\n", "H2O_cluster_version_age: 1 month and 15 days\n", - "H2O_cluster_name: H2O_from_python_marius_tlzho8\n", + "H2O_cluster_name: H2O_from_python_marius_2e9dqw\n", "H2O_cluster_total_nodes: 1\n", - "H2O_cluster_free_memory: 1.784 Gb\n", + "H2O_cluster_free_memory: 2.910 Gb\n", "H2O_cluster_total_cores: 6\n", "H2O_cluster_allowed_cores: 6\n", "H2O_cluster_status: locked, healthy\n", @@ -152242,20 +163619,20 @@ "H2OFrame column types:\n", "{'open': 'real', 'high': 'real', 'low': 'real', 'close': 'real', 'volume': 'int', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'int', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'int', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'real', 'close_return_1': 'real', 'close_return_7': 'real', 'close_return_30': 'real', 'REXPDE~TUSD_lag_1': 'int', 'REXPDE~TUSD_lag_7': 'int', 'REXPDE~TUSD_lag_30': 'int', 'REXPDE~TUSD_return_1': 'real', 'REXPDE~TUSD_return_7': 'real', 'REXPDE~TUSD_return_30': 'real', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'real', 'LorentzianIndicator_return_1': 'real', 'LorentzianIndicator_return_7': 'real', 'LorentzianIndicator_return_30': 'real', 'volume_lag_1': 'int', 'volume_lag_7': 'int', 'volume_lag_30': 'int', 'volume_return_1': 'real', 'volume_return_7': 'real', 'volume_return_30': 'real', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'real', 'CPIDE_return_1': 'real', 'CPIDE_return_7': 'real', 'CPIDE_return_30': 'real', 'date': 'time'}\n", "AutoML progress: |\n", - "15:20:05.489: Stopping tolerance set by the user is < 70% of the recommended default of 0.01989286700048564, so models may take a long time to converge or may not converge at all.\n", + "19:25:03.665: Stopping tolerance set by the user is < 70% of the recommended default of 0.01989286700048564, so models may take a long time to converge or may not converge at all.\n", "\n", "███████████████████████████████████████████████████████████████| (done) 100%\n", "\n", "Best model: Model Details\n", "=============\n", "H2OGradientBoostingEstimator : Gradient Boosting Machine\n", - "Model Key: GBM_grid_1_AutoML_4_20240825_152005_model_10\n", + "Model Key: GBM_grid_1_AutoML_1_20240825_192503_model_10\n", "\n", "\n", "Model Summary: \n", " number_of_trees number_of_internal_trees model_size_in_bytes min_depth max_depth mean_depth min_leaves max_leaves mean_leaves\n", "-- ----------------- -------------------------- --------------------- ----------- ----------- ------------ ------------ ------------ -------------\n", - " 265 265 180303 6 6 6 17 64 49.4604\n", + " 265 265 180294 6 6 6 17 64 49.4604\n", "\n", "ModelMetricsRegression: gbm\n", "** Reported on train data. **\n", @@ -152278,27 +163655,27 @@ "Scoring History: \n", " timestamp duration number_of_trees training_rmse training_mae training_deviance validation_rmse validation_mae validation_deviance\n", "--- ------------------- ---------- ----------------- ------------------- ------------------- ------------------- ------------------ ------------------ ---------------------\n", - " 2024-08-25 15:28:22 26.170 sec 0.0 2267.8070877442683 1831.7651450271476 5142948.98722314 2167.37146448559 1727.5369870745326 4697499.06506641\n", - " 2024-08-25 15:28:22 26.237 sec 5.0 1339.4290444956703 1081.8047211212654 1794070.1652385842 1280.086231604434 1020.2502613844603 1638620.7603432408\n", - " 2024-08-25 15:28:23 26.288 sec 10.0 791.1037600180482 638.9889297153245 625845.1591146935 756.3318833032002 602.8763082947705 572037.9177009657\n", - " 2024-08-25 15:28:23 26.384 sec 15.0 467.2540165384569 377.4190457898447 218326.31597132055 446.80941625421246 356.0965450093761 199638.6544534301\n", - " 2024-08-25 15:28:23 26.468 sec 20.0 275.9894821285658 222.902427996389 76170.19424559394 263.99987481691744 210.28850343236857 69695.93390334808\n", - " 2024-08-25 15:28:23 26.534 sec 25.0 163.02393409515125 131.66268286876732 26576.803087860215 156.19649878065823 124.21944497011569 24397.346231336167\n", - " 2024-08-25 15:28:23 26.595 sec 30.0 96.30989622211466 77.77900896876237 9275.596110314496 92.4303735515898 73.35618719650957 8543.37395488643\n", - " 2024-08-25 15:28:23 26.666 sec 35.0 56.914897549685065 45.96736766360803 3239.3055630911467 54.950025285150154 43.38128926287381 3019.505278838641\n", - " 2024-08-25 15:28:23 26.747 sec 40.0 33.660746942232926 27.186723619286703 1133.045884709043 32.96765657283606 25.71040371955034 1086.8663799044607\n", - " 2024-08-25 15:28:23 26.848 sec 45.0 19.948569567649148 16.112147401810446 397.9454277953377 20.23255715365955 15.543300604837833 409.35636897610016\n", + " 2024-08-25 19:33:32 22.322 sec 0.0 2267.8070877442683 1831.7651450271476 5142948.98722314 2167.37146448559 1727.5369870745326 4697499.06506641\n", + " 2024-08-25 19:33:32 22.384 sec 5.0 1339.4290444956703 1081.8047211212654 1794070.1652385842 1280.086231604434 1020.2502613844603 1638620.7603432408\n", + " 2024-08-25 19:33:32 22.438 sec 10.0 791.1037600180482 638.9889297153245 625845.1591146935 756.3318833032002 602.8763082947705 572037.9177009657\n", + " 2024-08-25 19:33:32 22.490 sec 15.0 467.2540165384569 377.4190457898447 218326.31597132055 446.80941625421246 356.0965450093761 199638.6544534301\n", + " 2024-08-25 19:33:32 22.540 sec 20.0 275.9894821285658 222.902427996389 76170.19424559394 263.99987481691744 210.28850343236857 69695.93390334808\n", + " 2024-08-25 19:33:32 22.587 sec 25.0 163.02393409515125 131.66268286876732 26576.803087860215 156.19649878065823 124.21944497011569 24397.346231336167\n", + " 2024-08-25 19:33:32 22.636 sec 30.0 96.30989622211466 77.77900896876237 9275.596110314496 92.4303735515898 73.35618719650957 8543.37395488643\n", + " 2024-08-25 19:33:32 22.682 sec 35.0 56.914897549685065 45.96736766360803 3239.3055630911467 54.950025285150154 43.38128926287381 3019.505278838641\n", + " 2024-08-25 19:33:32 22.724 sec 40.0 33.660746942232926 27.186723619286703 1133.045884709043 32.96765657283606 25.71040371955034 1086.8663799044607\n", + " 2024-08-25 19:33:32 22.769 sec 45.0 19.948569567649148 16.112147401810446 397.9454277953377 20.23255715365955 15.543300604837833 409.35636897610016\n", "--- --- --- --- --- --- --- --- --- ---\n", - " 2024-08-25 15:28:25 29.217 sec 220.0 0.5774730392218062 0.43098327129501385 0.3334751110280697 6.293659693034171 2.8169700225909162 39.61015233172298\n", - " 2024-08-25 15:28:26 29.281 sec 225.0 0.5605945566221089 0.41884599450929955 0.3142662569143388 6.296492897239999 2.813847540411579 39.64582280499376\n", - " 2024-08-25 15:28:26 29.346 sec 230.0 0.5470047646209211 0.4076891447368421 0.2992142125179893 6.298017085944119 2.810778540880968 39.66501921484405\n", - " 2024-08-25 15:28:26 29.420 sec 235.0 0.5334259621145044 0.3975111761723387 0.2845432570577847 6.2948378182961955 2.805359711464768 39.62498315865201\n", - " 2024-08-25 15:28:26 29.485 sec 240.0 0.5230718751480744 0.38953675306687774 0.2736041865709227 6.2946263141075365 2.8042309477880623 39.62232043425503\n", - " 2024-08-25 15:28:26 29.534 sec 245.0 0.5137845356151425 0.3816628073060942 0.26397454903726764 6.29695562568025 2.8024185682891063 39.65165015178615\n", - " 2024-08-25 15:28:26 29.601 sec 250.0 0.5030422046621342 0.3730978865749901 0.2530514596713405 6.296888610241514 2.8008790858133437 39.650806169789305\n", - " 2024-08-25 15:28:26 29.659 sec 255.0 0.4944384601924157 0.36583414745745946 0.24446939091744704 6.297635582539977 2.7996777626978813 39.66021393047364\n", - " 2024-08-25 15:28:26 29.714 sec 260.0 0.4808029471585699 0.3563606796596755 0.2311714739963666 6.2955211429398314 2.796881209253175 39.63358646120244\n", - " 2024-08-25 15:28:26 29.784 sec 265.0 0.46050773361855435 0.34181078724772457 0.21206737272249743 6.293705534193468 2.792280000891665 39.610729351137486\n", + " 2024-08-25 19:33:33 24.176 sec 220.0 0.5774730392218062 0.43098327129501385 0.3334751110280697 6.293659693034171 2.8169700225909162 39.61015233172298\n", + " 2024-08-25 19:33:33 24.215 sec 225.0 0.5605945566221089 0.41884599450929955 0.3142662569143388 6.296492897239999 2.813847540411579 39.64582280499376\n", + " 2024-08-25 19:33:33 24.261 sec 230.0 0.5470047646209211 0.4076891447368421 0.2992142125179893 6.298017085944119 2.810778540880968 39.66501921484405\n", + " 2024-08-25 19:33:34 24.302 sec 235.0 0.5334259621145044 0.3975111761723387 0.2845432570577847 6.2948378182961955 2.805359711464768 39.62498315865201\n", + " 2024-08-25 19:33:34 24.342 sec 240.0 0.5230718751480744 0.38953675306687774 0.2736041865709227 6.2946263141075365 2.8042309477880623 39.62232043425503\n", + " 2024-08-25 19:33:34 24.379 sec 245.0 0.5137845356151425 0.3816628073060942 0.26397454903726764 6.29695562568025 2.8024185682891063 39.65165015178615\n", + " 2024-08-25 19:33:34 24.421 sec 250.0 0.5030422046621342 0.3730978865749901 0.2530514596713405 6.296888610241514 2.8008790858133437 39.650806169789305\n", + " 2024-08-25 19:33:34 24.460 sec 255.0 0.4944384601924157 0.36583414745745946 0.24446939091744704 6.297635582539977 2.7996777626978813 39.66021393047364\n", + " 2024-08-25 19:33:34 24.496 sec 260.0 0.4808029471585699 0.3563606796596755 0.2311714739963666 6.2955211429398314 2.796881209253175 39.63358646120244\n", + " 2024-08-25 19:33:34 24.536 sec 265.0 0.46050773361855435 0.34181078724772457 0.21206737272249743 6.293705534193468 2.792280000891665 39.610729351137486\n", "[54 rows x 10 columns]\n", "\n", "\n", @@ -152329,7 +163706,8 @@ "[43 rows x 4 columns]\n", "\n", "\n", - "Model saved to: /home/marius/finance_code/GBM_grid_1_AutoML_4_20240825_152005_model_10\n" + "Model saved to: /home/marius/finance_code/GBM_grid_1_AutoML_1_20240825_192503_model_10\n", + "DataFrame and model path saved.\n" ] } ], @@ -152339,6 +163717,7 @@ "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime, timedelta\n", + "import pickle\n", "\n", "# Initialize H2O\n", "h2o.init()\n", @@ -152425,13 +163804,18 @@ "model_path = h2o.save_model(model=best_model, path=\"./\", force=True)\n", "print(f\"\\nModel saved to: {model_path}\")\n", "\n", - "# Don't shutdown H2O here if you plan to use it for predictions immediately after" + "# Save the DataFrame to a pickle file for later use\n", + "df.to_pickle('dax_data.pkl')\n", + "with open('model_path.txt', 'w') as f:\n", + " f.write(model_path)\n", + "\n", + "print(\"DataFrame and model path saved.\")\n" ] }, { "cell_type": "code", - "execution_count": 120, - "id": "fb91d2e9-c615-4c5b-b4d2-12bf12f04101", + "execution_count": 263, + "id": "d3d6cc09-23ad-4e34-a962-4218b183030b", "metadata": {}, "outputs": [ { @@ -152447,15 +163831,15 @@ "\n", " \n", - "
\n", + "
\n", "
H2O_cluster_uptime:56 mins 11 secs
6 mins 46 secs
H2O_cluster_timezone:Etc/UTC
H2O_data_parsing_timezone:
H2O_cluster_version_age:1 month and 15 days
H2O_cluster_name:H2O_from_python_marius_tlzho8
H2O_from_python_marius_2e9dqw
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:1.784 Gb
2.910 Gb
H2O_cluster_total_cores:6
H2O_cluster_allowed_cores:
\n", " \n", " \n", " \n", - "\n", + "\n", "\n", "\n", "\n", @@ -152497,11 +163881,11 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -152521,14 +163905,14 @@ ], "text/plain": [ "-------------------------- -----------------------------\n", - "H2O_cluster_uptime: 1 hour 13 mins\n", + "H2O_cluster_uptime: 42 mins 19 secs\n", "H2O_cluster_timezone: Etc/UTC\n", "H2O_data_parsing_timezone: UTC\n", "H2O_cluster_version: 3.46.0.4\n", "H2O_cluster_version_age: 1 month and 15 days\n", - "H2O_cluster_name: H2O_from_python_marius_tlzho8\n", + "H2O_cluster_name: H2O_from_python_marius_2e9dqw\n", "H2O_cluster_total_nodes: 1\n", - "H2O_cluster_free_memory: 1.427 Gb\n", + "H2O_cluster_free_memory: 2.498 Gb\n", "H2O_cluster_total_cores: 6\n", "H2O_cluster_allowed_cores: 6\n", "H2O_cluster_status: locked, healthy\n", @@ -152546,233 +163930,266 @@ "name": "stdout", "output_type": "stream", "text": [ - "Future DataFrame column types:\n", - "open float64\n", - "high float64\n", - "low float64\n", - "close float64\n", - "volume float64\n", - "vwap float64\n", - "adj_close float64\n", - "unadjusted_volume float64\n", - "change float64\n", - "change_percent float64\n", - "KalmanEstimate float64\n", - "LorentzianIndicator float64\n", - "REXPDE~TUSD float64\n", - "CPIDE float64\n", - "close_lag_1 float64\n", - "close_lag_7 float64\n", - "close_lag_30 float64\n", - "close_return_1 float64\n", - "close_return_7 float64\n", - "close_return_30 float64\n", - "REXPDE~TUSD_lag_1 float64\n", - "REXPDE~TUSD_lag_7 float64\n", - "REXPDE~TUSD_lag_30 float64\n", - "REXPDE~TUSD_return_1 float64\n", - "REXPDE~TUSD_return_7 float64\n", - "REXPDE~TUSD_return_30 float64\n", - "LorentzianIndicator_lag_1 float64\n", - "LorentzianIndicator_lag_7 float64\n", - "LorentzianIndicator_lag_30 float64\n", - "LorentzianIndicator_return_1 float64\n", - "LorentzianIndicator_return_7 float64\n", - "LorentzianIndicator_return_30 float64\n", - "volume_lag_1 float64\n", - "volume_lag_7 float64\n", - "volume_lag_30 float64\n", - "volume_return_1 float64\n", - "volume_return_7 float64\n", - "volume_return_30 float64\n", - "CPIDE_lag_1 float64\n", - "CPIDE_lag_7 float64\n", - "CPIDE_lag_30 float64\n", - "CPIDE_return_1 float64\n", - "CPIDE_return_7 float64\n", - "CPIDE_return_30 float64\n", - "date datetime64[ns]\n", - "dtype: object\n", "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", + "AutoML progress: |\n", + "20:00:35.757: Stopping tolerance set by the user is < 70% of the recommended default of 0.01989286700048564, so models may take a long time to converge or may not converge at all.\n", "\n", - "Future H2OFrame column types:\n", - "{'open': 'real', 'high': 'real', 'low': 'real', 'close': 'real', 'volume': 'real', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'real', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'real', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'enum', 'close_return_1': 'int', 'close_return_7': 'int', 'close_return_30': 'enum', 'REXPDE~TUSD_lag_1': 'real', 'REXPDE~TUSD_lag_7': 'real', 'REXPDE~TUSD_lag_30': 'enum', 'REXPDE~TUSD_return_1': 'int', 'REXPDE~TUSD_return_7': 'int', 'REXPDE~TUSD_return_30': 'enum', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'enum', 'LorentzianIndicator_return_1': 'int', 'LorentzianIndicator_return_7': 'int', 'LorentzianIndicator_return_30': 'enum', 'volume_lag_1': 'real', 'volume_lag_7': 'real', 'volume_lag_30': 'enum', 'volume_return_1': 'int', 'volume_return_7': 'int', 'volume_return_30': 'enum', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'enum', 'CPIDE_return_1': 'int', 'CPIDE_return_7': 'int', 'CPIDE_return_30': 'enum', 'date': 'time'}\n", - "gbm prediction progress: | (failed)\n" - ] - }, - { - "ename": "OSError", - "evalue": "Job with key $03017f00000132d4ffffffff$_8a43f9d4d026c494916fc4baa9e4fc2 failed with an exception: java.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\nstacktrace: \njava.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\n\tat hex.Model.adaptTestForTrain(Model.java:1833)\n\tat hex.Model.adaptTestForTrain(Model.java:1643)\n\tat hex.Model.adaptTestForTrain(Model.java:1639)\n\tat hex.Model.adaptFrameForScore(Model.java:1981)\n\tat hex.Model.score(Model.java:1999)\n\tat water.api.ModelMetricsHandler$1.compute2(ModelMetricsHandler.java:555)\n\tat water.H2O$H2OCountedCompleter.compute(H2O.java:1704)\n\tat jsr166y.CountedCompleter.exec(CountedCompleter.java:468)\n\tat jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)\n\tat jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:976)\n\tat jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1479)\n\tat jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)\n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[120], line 81\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28mprint\u001b[39m(future_h2o\u001b[38;5;241m.\u001b[39mtypes)\n\u001b[1;32m 80\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[0;32m---> 81\u001b[0m future_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mbest_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfuture_h2o\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 82\u001b[0m future_pred_df \u001b[38;5;241m=\u001b[39m h2o\u001b[38;5;241m.\u001b[39mas_list(future_predictions, use_pandas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# Plot future predictions\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/model/model_base.py:334\u001b[0m, in \u001b[0;36mModelBase.predict\u001b[0;34m(self, test_data, custom_metric, custom_metric_func)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(test_data, h2o\u001b[38;5;241m.\u001b[39mH2OFrame): \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_data must be an instance of H2OFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 332\u001b[0m j \u001b[38;5;241m=\u001b[39m H2OJob(h2o\u001b[38;5;241m.\u001b[39mapi(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOST /4/Predictions/models/\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m/frames/\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_id, test_data\u001b[38;5;241m.\u001b[39mframe_id), data \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcustom_metric_func\u001b[39m\u001b[38;5;124m'\u001b[39m: custom_metric_func}),\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_json[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malgo\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m prediction\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 334\u001b[0m \u001b[43mj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpoll\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m h2o\u001b[38;5;241m.\u001b[39mget_frame(j\u001b[38;5;241m.\u001b[39mdest_key)\n", - "File \u001b[0;32m~/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/job.py:88\u001b[0m, in \u001b[0;36mH2OJob.poll\u001b[0;34m(self, poll_updates)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFAILED\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob, \u001b[38;5;28mdict\u001b[39m)) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstacktrace\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob)):\n\u001b[0;32m---> 88\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJob with key \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m failed with an exception: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mstacktrace: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob_key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstacktrace\u001b[39m\u001b[38;5;124m\"\u001b[39m]))\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJob with key \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m failed with an exception: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob_key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception))\n", - "\u001b[0;31mOSError\u001b[0m: Job with key $03017f00000132d4ffffffff$_8a43f9d4d026c494916fc4baa9e4fc2 failed with an exception: java.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\nstacktrace: \njava.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\n\tat hex.Model.adaptTestForTrain(Model.java:1833)\n\tat hex.Model.adaptTestForTrain(Model.java:1643)\n\tat hex.Model.adaptTestForTrain(Model.java:1639)\n\tat hex.Model.adaptFrameForScore(Model.java:1981)\n\tat hex.Model.score(Model.java:1999)\n\tat water.api.ModelMetricsHandler$1.compute2(ModelMetricsHandler.java:555)\n\tat water.H2O$H2OCountedCompleter.compute(H2O.java:1704)\n\tat jsr166y.CountedCompleter.exec(CountedCompleter.java:468)\n\tat jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)\n\tat jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:976)\n\tat jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1479)\n\tat jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)\n" + "███████████████████████████████████████████████████████████████| (done) 100%\n", + "\n", + "Model saved to: /home/marius/finance_code/GBM_grid_1_AutoML_2_20240825_200035_model_10\n", + "DataFrame and model path saved.\n" ] } ], "source": [ "import h2o\n", + "from h2o.automl import H2OAutoML\n", "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime, timedelta\n", - "from pykalman import KalmanFilter\n", - "import plotly.graph_objects as go\n", + "import pickle\n", "\n", - "# Initialize H2O (if not already initialized)\n", + "# Initialize H2O\n", "h2o.init()\n", "\n", - "# Load the saved model\n", - "best_model = h2o.load_model(\"./GBM_grid_1_AutoML_4_20240825_152005_model_10\") # Use your actual model path\n", + "# Assuming your dataframe is named combined_df\n", + "df = combined_df.copy()\n", "\n", - "# Assuming df is your original dataframe used for training\n", - "# If it's not in memory, you'll need to load and preprocess it again\n", + "# Convert 'index' to datetime if it's not already\n", + "df['index'] = pd.to_datetime(df['index'])\n", + "df.set_index('index', inplace=True)\n", "\n", - "def forecast_feature_kalman(series, forecast_horizon):\n", - " kf = KalmanFilter(transition_matrices=np.array([[1]]),\n", - " observation_matrices=np.array([[1]]),\n", - " initial_state_mean=np.array([series.iloc[-1]]),\n", - " initial_state_covariance=np.array([[1]]),\n", - " observation_covariance=np.array([[1]]),\n", - " transition_covariance=np.array([[0.01]]))\n", - " \n", - " state_means, _ = kf.filter(series.values.reshape(-1, 1))\n", - " \n", - " last_state = state_means[-1]\n", - " forecast = np.zeros(forecast_horizon)\n", - " for i in range(forecast_horizon):\n", - " last_state = np.dot(kf.transition_matrices, last_state)\n", - " forecast[i] = last_state.flatten()[0]\n", - " \n", - " return pd.Series(forecast, index=pd.date_range(start=series.index[-1] + pd.Timedelta(days=1), periods=forecast_horizon))\n", + "# Filter for the last 10 years of data\n", + "ten_years_ago = datetime.now() - timedelta(days=365*10)\n", + "df = df[df.index >= ten_years_ago]\n", "\n", - "def generate_future_features(df, future_dates, features):\n", - " future_df = pd.DataFrame(index=future_dates)\n", - " \n", - " # Forecast main features\n", - " for feature in df.columns:\n", - " if feature != 'date':\n", - " if feature == 'CPIDE':\n", - " future_df[feature] = df[feature].iloc[-1]\n", - " else:\n", - " future_df[feature] = forecast_feature_kalman(df[feature], len(future_dates))\n", - " \n", - " # Calculate derived features\n", - " for feature in features:\n", - " if '_lag_' in feature:\n", - " base_feature, lag = feature.split('_lag_')\n", - " future_df[feature] = future_df[base_feature].shift(int(lag))\n", - " elif '_return_' in feature:\n", - " base_feature, period = feature.split('_return_')\n", - " future_df[feature] = future_df[base_feature].pct_change(int(period))\n", - " \n", - " future_df['date'] = future_dates\n", - " \n", - " # Ensure all dtypes match the original dataframe\n", - " for col in future_df.columns:\n", - " if col in df.columns:\n", - " future_df[col] = future_df[col].astype(df[col].dtype)\n", - " \n", - " return future_df\n", + "# Function to calculate returns for CPIDE\n", + "def calculate_cpide_returns(series, periods):\n", + " returns = series.pct_change(periods)\n", + " mask = returns != 0\n", + " idx = np.arange(len(returns))\n", + " idx_valid = idx[mask]\n", + " returns = pd.Series(np.interp(idx, idx_valid, returns[mask]), index=returns.index)\n", + " return returns\n", "\n", - "# Generate future dates\n", - "last_date = df.index.max()\n", - "future_dates = pd.date_range(start=last_date + timedelta(days=1), periods=30)\n", + "# Feature Engineering\n", + "for col in ['close', 'REXPDE~TUSD', 'LorentzianIndicator', 'volume']:\n", + " df[f'{col}_lag_1'] = df[col].shift(1)\n", + " df[f'{col}_lag_7'] = df[col].shift(7)\n", + " df[f'{col}_lag_30'] = df[col].shift(30)\n", + " df[f'{col}_return_1'] = df[col].pct_change(1)\n", + " df[f'{col}_return_7'] = df[col].pct_change(7)\n", + " df[f'{col}_return_30'] = df[col].pct_change(30)\n", "\n", - "# Generate future features\n", - "future_df = generate_future_features(df, future_dates, df.columns)\n", + "# Special handling for CPIDE\n", + "df['CPIDE_lag_1'] = df['CPIDE'].shift(1)\n", + "df['CPIDE_lag_7'] = df['CPIDE'].shift(7)\n", + "df['CPIDE_lag_30'] = df['CPIDE'].shift(30)\n", + "df['CPIDE_return_1'] = calculate_cpide_returns(df['CPIDE'], 1)\n", + "df['CPIDE_return_7'] = calculate_cpide_returns(df['CPIDE'], 7)\n", + "df['CPIDE_return_30'] = calculate_cpide_returns(df['CPIDE'], 30)\n", "\n", - "print(\"Future DataFrame column types:\")\n", - "print(future_df.dtypes)\n", + "# Remove rows with NaN values\n", + "df = df.dropna()\n", "\n", - "# Convert future DataFrame to H2OFrame\n", - "future_h2o = h2o.H2OFrame(future_df)\n", + "# Set date as a separate column for H2O\n", + "df['date'] = df.index\n", "\n", - "print(\"\\nFuture H2OFrame column types:\")\n", - "print(future_h2o.types)\n", + "# Ensure data types\n", + "df['volume_return_7'] = df['volume_return_7'].astype('float')\n", "\n", - "# Make predictions\n", - "future_predictions = best_model.predict(future_h2o)\n", - "future_pred_df = h2o.as_list(future_predictions, use_pandas=True)\n", + "# Select all features except 'close' and 'date'\n", + "features = [col for col in df.columns if col not in ['close', 'date']]\n", + "target = 'close'\n", "\n", - "# Plot future predictions\n", - "fig = go.Figure()\n", - "fig.add_trace(go.Scatter(x=df.index[-365:], y=df['close'].tail(365), mode='lines', name='Historical'))\n", - "fig.add_trace(go.Scatter(x=future_dates, y=future_pred_df['predict'], mode='lines', name='Forecast'))\n", - "fig.update_layout(title='H2O AutoML: 30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Close Price')\n", - "fig.show()\n", + "# Convert to H2OFrame\n", + "h2o_df = h2o.H2OFrame(df)\n", "\n", - "print(\"\\nFuture Predictions (next 30 days):\")\n", - "print(pd.concat([future_df['date'], future_pred_df], axis=1))\n", + "# Split the data into train, validation, and test sets\n", + "train, valid, test = h2o_df.split_frame(ratios=[0.7, 0.15], seed=1234)\n", "\n", - "# Shutdown H2O cluster\n", - "h2o.shutdown()" + "# Run AutoML\n", + "aml = H2OAutoML(max_runtime_secs=3600, # 1 hour\n", + " seed=1234,\n", + " sort_metric='RMSE',\n", + " stopping_rounds=20,\n", + " stopping_tolerance=0.01,\n", + " stopping_metric='RMSE',\n", + " max_models=100,\n", + " exclude_algos=['DeepLearning'],\n", + " nfolds=0)\n", + "\n", + "aml.train(x=features, y=target, training_frame=train, validation_frame=valid)\n", + "\n", + "# Get the best model\n", + "best_model = aml.leader\n", + "\n", + "# Save the model\n", + "model_path = h2o.save_model(model=best_model, path=\"./\", force=True)\n", + "print(f\"\\nModel saved to: {model_path}\")\n", + "\n", + "# Save the DataFrame to a pickle file for later use\n", + "df.to_pickle('dax_data.pkl')\n", + "with open('model_path.txt', 'w') as f:\n", + " f.write(model_path)\n", + "\n", + "print(\"DataFrame and model path saved.\")\n" ] }, { "cell_type": "code", - "execution_count": 77, - "id": "d3d6cc09-23ad-4e34-a962-4218b183030b", + "execution_count": 264, + "id": "cfad0910-1eb6-4acf-ab79-a18144858114", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "
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H2O_cluster_uptime:1 hour 13 mins
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H2O_cluster_timezone:Etc/UTC
H2O_data_parsing_timezone:
H2O_cluster_version_age:1 month and 15 days
H2O_cluster_name:H2O_from_python_marius_tlzho8
H2O_from_python_marius_2e9dqw
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:1.427 Gb
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H2O_cluster_total_cores:6
H2O_cluster_allowed_cores:
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H2O_cluster_uptime:54 mins 08 secs
H2O_cluster_timezone:Etc/UTC
H2O_data_parsing_timezone:UTC
H2O_cluster_version:3.46.0.4
H2O_cluster_version_age:1 month and 16 days
H2O_cluster_name:H2O_from_python_marius_2e9dqw
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:2.150 Gb
H2O_cluster_total_cores:6
H2O_cluster_allowed_cores:6
H2O_cluster_status:locked, healthy
H2O_connection_url:http://localhost:54321
H2O_connection_proxy:{\"http\": null, \"https\": null}
H2O_internal_security:False
Python_version:3.11.9 final
\n", + "
\n" + ], + "text/plain": [ + "-------------------------- -----------------------------\n", + "H2O_cluster_uptime: 54 mins 08 secs\n", + "H2O_cluster_timezone: Etc/UTC\n", + "H2O_data_parsing_timezone: UTC\n", + "H2O_cluster_version: 3.46.0.4\n", + "H2O_cluster_version_age: 1 month and 16 days\n", + "H2O_cluster_name: H2O_from_python_marius_2e9dqw\n", + "H2O_cluster_total_nodes: 1\n", + "H2O_cluster_free_memory: 2.150 Gb\n", + "H2O_cluster_total_cores: 6\n", + "H2O_cluster_allowed_cores: 6\n", + "H2O_cluster_status: locked, healthy\n", + "H2O_connection_url: http://localhost:54321\n", + "H2O_connection_proxy: {\"http\": null, \"https\": null}\n", + "H2O_internal_security: False\n", + "Python_version: 3.11.9 final\n", + "-------------------------- -----------------------------" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n" + ] + } + ], + "source": [ + "import h2o\n", + "import pandas as pd\n", + "import pickle\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error\n", + "import numpy as np\n", + "\n", + "# Initialize H2O\n", + "h2o.init()\n", + "\n", + "# Load the trained model and data\n", + "df = pd.read_pickle('dax_data.pkl')\n", + "model_path = open('model_path.txt', 'r').read().strip()\n", + "best_model = h2o.load_model(model_path)\n", + "\n", + "# Convert the DataFrame to an H2OFrame\n", + "h2o_df = h2o.H2OFrame(df)\n", + "\n", + "# Split the data back into train, validation, and test sets\n", + "train, valid, test = h2o_df.split_frame(ratios=[0.7, 0.15], seed=1234)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "id": "7aba7967-c3d7-4b1b-97dc-88301eaebf2e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Future DataFrame Data Types:\n", - "date datetime64[ns]\n", - "open float64\n", - "high float64\n", - "low float64\n", - "volume float64\n", - "vwap float64\n", - "adj_close float64\n", - "unadjusted_volume float64\n", - "change float64\n", - "change_percent float64\n", - "KalmanEstimate float64\n", - "LorentzianIndicator float64\n", - "REXPDE~TUSD float64\n", - "CPIDE float64\n", - "close_lag_1 float64\n", - "close_lag_7 float64\n", - "close_lag_30 float64\n", - "close_return_1 float64\n", - "close_return_7 float64\n", - "close_return_30 float64\n", - "REXPDE~TUSD_lag_1 float64\n", - "REXPDE~TUSD_lag_7 float64\n", - "REXPDE~TUSD_lag_30 float64\n", - "REXPDE~TUSD_return_1 float64\n", - "REXPDE~TUSD_return_7 float64\n", - "REXPDE~TUSD_return_30 float64\n", - "LorentzianIndicator_lag_1 float64\n", - "LorentzianIndicator_lag_7 float64\n", - "LorentzianIndicator_lag_30 float64\n", - "LorentzianIndicator_return_1 float64\n", - "LorentzianIndicator_return_7 float64\n", - "LorentzianIndicator_return_30 float64\n", - "CPIDE_lag_1 float64\n", - "CPIDE_lag_7 float64\n", - "CPIDE_lag_30 float64\n", - "CPIDE_return_1 float64\n", - "CPIDE_return_7 float64\n", - "CPIDE_return_30 float64\n", - "volume_lag_1 float64\n", - "volume_lag_7 float64\n", - "volume_lag_30 float64\n", - "volume_return_1 float64\n", - "volume_return_7 float64\n", - "volume_return_30 float64\n", - "dtype: object\n", - "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", - "\n", - "H2OFrame Column Types After Conversion:\n", - "{'date': 'time', 'open': 'real', 'high': 'real', 'low': 'real', 'volume': 'real', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'real', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'real', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'real', 'close_return_1': 'real', 'close_return_7': 'real', 'close_return_30': 'real', 'REXPDE~TUSD_lag_1': 'real', 'REXPDE~TUSD_lag_7': 'real', 'REXPDE~TUSD_lag_30': 'real', 'REXPDE~TUSD_return_1': 'real', 'REXPDE~TUSD_return_7': 'real', 'REXPDE~TUSD_return_30': 'real', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'real', 'LorentzianIndicator_return_1': 'real', 'LorentzianIndicator_return_7': 'real', 'LorentzianIndicator_return_30': 'real', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'real', 'CPIDE_return_1': 'real', 'CPIDE_return_7': 'real', 'CPIDE_return_30': 'real', 'volume_lag_1': 'real', 'volume_lag_7': 'real', 'volume_lag_30': 'real', 'volume_return_1': 'real', 'volume_return_7': 'real', 'volume_return_30': 'real'}\n", "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n" ] }, @@ -152787,3907 +164204,11338 @@ "\n" ] }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "mode": "lines", - "name": "Historical", - "type": "scatter", - "x": [ - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Future Predictions (next 30 days):\n", - " date predict\n", - "0 2024-08-24 18226.278084\n", - "1 2024-08-25 18226.278084\n", - "2 2024-08-26 18226.278084\n", - "3 2024-08-27 18226.278084\n", - "4 2024-08-28 18226.278084\n", - "5 2024-08-29 18224.811445\n", - "6 2024-08-30 18223.730802\n", - "7 2024-08-31 18223.500556\n", - "8 2024-09-01 18223.500556\n", - "9 2024-09-02 18223.500556\n", - "10 2024-09-03 18223.500556\n", - "11 2024-09-04 18223.500556\n", - "12 2024-09-05 18223.500556\n", - "13 2024-09-06 18223.345046\n", - "14 2024-09-07 18223.345046\n", - "15 2024-09-08 18223.345046\n", - "16 2024-09-09 18223.345046\n", - "17 2024-09-10 18223.345046\n", - "18 2024-09-11 18223.345046\n", - "19 2024-09-12 18223.345046\n", - "20 2024-09-13 18223.345046\n", - "21 2024-09-14 18223.345046\n", - "22 2024-09-15 18223.345046\n", - "23 2024-09-16 18223.345046\n", - "24 2024-09-17 18223.345046\n", - "25 2024-09-18 18223.345046\n", - "26 2024-09-19 18223.345046\n", - "27 2024-09-20 18223.345046\n", - "28 2024-09-21 18223.345046\n", - "29 2024-09-22 18223.345046\n" - ] - } - ], - "source": [ - "import h2o\n", - "import pandas as pd\n", - "import numpy as np\n", - "from datetime import datetime, timedelta\n", - "import plotly.graph_objects as go\n", - "\n", - "# Assuming df and best_model are already defined\n", - "\n", - "# Future predictions\n", - "last_date = df.index.max()\n", - "future_dates = pd.date_range(start=last_date + timedelta(days=1), periods=30)\n", - "future_df = pd.DataFrame(index=future_dates)\n", - "\n", - "# Create future feature values\n", - "for feature in features:\n", - " if feature in df.columns:\n", - " future_df[feature] = np.zeros(30, dtype=np.float64)\n", - " \n", - " if 'lag' in feature:\n", - " lag = int(feature.split('_')[-1])\n", - " base_feature = feature.split('_')[0]\n", - " future_df[feature] = [float(df[base_feature].iloc[-lag:].values[0])] * 30\n", - " elif 'return' in feature:\n", - " period = int(feature.split('_')[-1])\n", - " base_feature = feature.split('_')[0]\n", - " future_df[feature] = [float(df[feature].iloc[-period:].mean())] * 30\n", - " elif feature in ['REXPDE~TUSD', 'CPIDE']:\n", - " future_df[feature] = [float(df[feature].iloc[-1])] * 30\n", - " elif feature in ['LorentzianIndicator', 'KalmanEstimate']:\n", - " last_30_days = df[feature].tail(30)\n", - " trend = np.polyfit(range(30), last_30_days, 1)[0]\n", - " future_df[feature] = df[feature].iloc[-1] + trend * np.arange(1, 31)\n", - " else:\n", - " # For other features, use the average of the last 30 days\n", - " future_df[feature] = [float(df[feature].tail(30).mean())] * 30\n", - "\n", - "# Ensure all dtypes are float64\n", - "for col in future_df.columns:\n", - " future_df[col] = future_df[col].astype(np.float64)\n", - "\n", - "# Reset index and rename to 'date' for consistency\n", - "future_df.reset_index(inplace=True)\n", - "future_df.rename(columns={'index': 'date'}, inplace=True)\n", - "\n", - "# Double-check data types\n", - "print(\"\\nFuture DataFrame Data Types:\")\n", - "print(future_df.dtypes)\n", - "\n", - "# Convert future DataFrame to H2OFrame\n", - "future_h2o = h2o.H2OFrame(future_df)\n", - "\n", - "# Force all columns except 'date' to be numeric\n", - "for col in future_h2o.columns:\n", - " if col != \"date\":\n", - " future_h2o[col] = future_h2o[col].asnumeric()\n", - "\n", - "# Check column types after conversion\n", - "print(\"\\nH2OFrame Column Types After Conversion:\")\n", - "print(future_h2o.types)\n", - "\n", - "# Make predictions\n", - "future_predictions = best_model.predict(future_h2o)\n", - "future_pred_df = h2o.as_list(future_predictions, use_pandas=True)\n", - "\n", - "# Plot future predictions\n", - "fig = go.Figure()\n", - "fig.add_trace(go.Scatter(x=df.index[-365:], y=df['close'].tail(365), mode='lines', name='Historical'))\n", - "fig.add_trace(go.Scatter(x=future_dates, y=future_pred_df['predict'], mode='lines', name='Forecast'))\n", - "fig.update_layout(title='H2O AutoML: 30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Close Price')\n", - "fig.show()\n", - "\n", - "print(\"\\nFuture Predictions (next 30 days):\")\n", - "print(pd.concat([future_df['date'], future_pred_df], axis=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "fb8d4613-0840-46bd-aab4-547902fa7c83", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - "
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H2O_cluster_uptime:11 mins 07 secs
H2O_cluster_timezone:Etc/UTC
H2O_data_parsing_timezone:UTC
H2O_cluster_version:3.46.0.4
H2O_cluster_version_age:1 month and 15 days
H2O_cluster_name:H2O_from_python_marius_st2i25
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:2.910 Gb
H2O_cluster_total_cores:6
H2O_cluster_allowed_cores:6
H2O_cluster_status:locked, healthy
H2O_connection_url:http://localhost:54321
H2O_connection_proxy:{\"http\": null, \"https\": null}
H2O_internal_security:False
Python_version:3.11.9 final
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|██████████████████████████████████████████████████████████| (done) 100%\n", - "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", - "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n", - "Export File progress: |██████████████████████████████████████████████████████████| (done) 100%\n", - "Future DataFrame first few rows:\n", - " open high low close \\\n", - "2024-08-24 18180.224652 18274.179719 18092.658184 18205.351114 \n", - "2024-08-25 18177.261576 18270.905789 18089.133413 18182.966541 \n", - "2024-08-26 18177.239431 18270.881321 18089.107070 18190.571284 \n", - "2024-08-27 18177.239281 18270.881156 18089.106892 18186.453103 \n", - "2024-08-28 18177.239280 18270.881155 18089.106891 18190.222285 \n", - "\n", - " volume vwap adj_close unadjusted_volume \\\n", - "2024-08-24 4.771495e+07 18185.695815 18195.404213 4.771495e+07 \n", - "2024-08-25 4.776941e+07 18182.351951 18191.788029 4.776941e+07 \n", - "2024-08-26 4.776982e+07 18182.326960 18191.761002 4.776982e+07 \n", - "2024-08-27 4.776982e+07 18182.326791 18191.760820 4.776982e+07 \n", - "2024-08-28 4.776982e+07 18182.326790 18191.760818 4.776982e+07 \n", - "\n", - " change change_percent ... volume_return_1 volume_return_7 \\\n", - "2024-08-24 15.179561 0.000838 ... 0.0 0.000000 \n", - "2024-08-25 14.526453 0.000803 ... 0.0 -0.252057 \n", - "2024-08-26 14.521572 0.000803 ... 0.0 0.091381 \n", - "2024-08-27 14.521539 0.000803 ... 0.0 0.113204 \n", - "2024-08-28 14.521538 0.000803 ... 0.0 0.324434 \n", - "\n", - " volume_return_30 CPIDE_lag_1 CPIDE_lag_7 CPIDE_lag_30 \\\n", - "2024-08-24 -0.192544 119.8 119.8 119.8 \n", - "2024-08-25 -0.042443 119.8 119.8 119.8 \n", - "2024-08-26 0.116914 119.8 119.8 119.8 \n", - "2024-08-27 0.056974 119.8 119.8 119.8 \n", - "2024-08-28 -0.025336 119.8 119.8 119.8 \n", - "\n", - " CPIDE_return_1 CPIDE_return_7 CPIDE_return_30 date \n", - "2024-08-24 0.0 0.0 0.0 2024-08-24 \n", - "2024-08-25 0.0 0.0 0.0 2024-08-25 \n", - "2024-08-26 0.0 0.0 0.0 2024-08-26 \n", - "2024-08-27 0.0 0.0 0.0 2024-08-27 \n", - "2024-08-28 0.0 0.0 0.0 2024-08-28 \n", - "\n", - "[5 rows x 45 columns]\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "mode": "lines", - "name": "Historical", - "type": "scatter", - "x": [ - "2023-08-10T00:00:00", - "2023-08-11T00:00:00", - "2023-08-12T00:00:00", - "2023-08-13T00:00:00", - "2023-08-14T00:00:00", - "2023-08-15T00:00:00", - "2023-08-16T00:00:00", - "2023-08-17T00:00:00", - "2023-08-18T00:00:00", - "2023-08-19T00:00:00", - "2023-08-20T00:00:00", - "2023-08-21T00:00:00", - "2023-08-22T00:00:00", - "2023-08-23T00:00:00", - "2023-08-24T00:00:00", - "2023-08-25T00:00:00", - "2023-08-26T00:00:00", - "2023-08-27T00:00:00", - "2023-08-28T00:00:00", - "2023-08-29T00:00:00", - "2023-08-30T00:00:00", - "2023-08-31T00:00:00", - "2023-09-01T00:00:00", - 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"title": { - "text": "H2O AutoML: 30-Day Future Forecast for DAX" - }, - "xaxis": { - "autorange": true, - "range": [ - "2023-08-10", - "2024-09-22" - ], - "title": { - "text": "Date" - }, - "type": "date" - }, - "yaxis": { - "autorange": true, - "range": [ - 14455.079647777777, - 19101.689892222224 - ], - "title": { - "text": "Close Price" - }, - "type": "linear" - } - } - }, - "image/png": 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For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n", - "Deprecated, use ``h2o.cluster().shutdown()``.\n", "\n" ] } ], + "source": [ + "# Generate predictions on the validation set\n", + "predictions = best_model.predict(valid)\n", + "\n", + "# Convert H2OFrame to Pandas DataFrame\n", + "y_true = valid['close'].as_data_frame().values.flatten() # Actual values\n", + "y_pred = predictions.as_data_frame().values.flatten() # Predicted values\n", + "\n", + "# Calculate evaluation metrics\n", + "mse = mean_squared_error(y_true, y_pred)\n", + "rmse = np.sqrt(mse)\n", + "mae = mean_absolute_error(y_true, y_pred)\n", + "\n", + "# Print the evaluation metrics\n", + "print(f\"\\nModel Evaluation Results on Validation Set:\")\n", + "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n", + "print(f\"Mean Absolute Error (MAE): {mae}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "# Create a plot to visualize the predictions vs actual values\n", + "fig = go.Figure()\n", + "\n", + "# Plot the actual values\n", + "fig.add_trace(go.Scatter(x=np.arange(len(y_true)), y=y_true, mode='lines', name='Actual'))\n", + "\n", + "# Plot the predicted values\n", + "fig.add_trace(go.Scatter(x=np.arange(len(y_pred)), y=y_pred, mode='lines', name='Predicted'))\n", + "\n", + "# Update layout\n", + "fig.update_layout(title='Actual vs Predicted Close Values on Validation Set',\n", + " xaxis_title='Index',\n", + " yaxis_title='Close Price',\n", + " hovermode='x unified')\n", + "\n", + "# Show the plot\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "id": "22d0f533-c280-4f9f-a8c5-eb1e0dd463f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure 'volume_return_7' is correctly set as a float\n", + "next_day_df['volume_return_7'] = next_day_df['volume_return_7'].astype('float')\n", + "\n", + "# Ensure all other columns match their training data types\n", + "for col in next_day_df.columns:\n", + " if training_types[col] == 'enum': # If the column was treated as categorical during training\n", + " next_day_df[col] = next_day_df[col].astype('category')\n", + " elif training_types[col] == 'real': # If the column was treated as real (float) during training\n", + " next_day_df[col] = next_day_df[col].astype('float')\n", + " elif training_types[col] == 'int': # If the column was treated as integer during training\n", + " next_day_df[col] = next_day_df[col].astype('int')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "id": "96d1be6c-516d-4d65-8747-460bc3a429c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n" + ] + } + ], + "source": [ + "# Convert the DataFrame to an H2OFrame\n", + "next_day_h2o = h2o.H2OFrame(next_day_df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "id": "c1e6c26e-2a7a-4991-89c7-0c8431681be2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", + "\n", + "H2OFrame column types:\n", + "{'open': 'real', 'high': 'real', 'low': 'real', 'close': 'real', 'volume': 'int', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'int', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'int', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'real', 'close_return_1': 'real', 'close_return_7': 'real', 'close_return_30': 'real', 'REXPDE~TUSD_lag_1': 'int', 'REXPDE~TUSD_lag_7': 'int', 'REXPDE~TUSD_lag_30': 'int', 'REXPDE~TUSD_return_1': 'real', 'REXPDE~TUSD_return_7': 'real', 'REXPDE~TUSD_return_30': 'real', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'real', 'LorentzianIndicator_return_1': 'real', 'LorentzianIndicator_return_7': 'real', 'LorentzianIndicator_return_30': 'real', 'volume_lag_1': 'int', 'volume_lag_7': 'int', 'volume_lag_30': 'int', 'volume_return_1': 'real', 'volume_return_7': 'real', 'volume_return_30': 'real', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'real', 'CPIDE_return_1': 'real', 'CPIDE_return_7': 'real', 'CPIDE_return_30': 'real', 'date': 'time'}\n" + ] + } + ], + "source": [ + "import h2o\n", + "import pandas as pd\n", + "\n", + "# Load the DataFrame and model\n", + "df = pd.read_pickle('dax_data.pkl')\n", + "\n", + "# Convert the DataFrame to an H2OFrame\n", + "h2o_df = h2o.H2OFrame(df)\n", + "\n", + "# Print data types to confirm\n", + "print(\"\\nH2OFrame column types:\")\n", + "print(h2o_df.types)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "id": "369db021-ab2b-49ec-a612-5c745b9b0a94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n" + ] + } + ], "source": [ "import h2o\n", "import pandas as pd\n", - "import numpy as np\n", "from datetime import datetime, timedelta\n", - "from pykalman import KalmanFilter\n", + "\n", + "# Prepare the data for the next day prediction\n", + "last_row = df.iloc[-1].copy()\n", + "\n", + "# Increment the date to predict for the next day\n", + "next_day = last_row['date'] + timedelta(days=1)\n", + "last_row['date'] = next_day\n", + "\n", + "# Convert to DataFrame\n", + "next_day_df = pd.DataFrame([last_row])\n", + "\n", + "# Ensure 'volume_return_7' and all similar columns are set as float\n", + "next_day_df['volume_return_7'] = next_day_df['volume_return_7'].astype('float')\n", + "\n", + "# Convert the DataFrame to an H2OFrame with specified types\n", + "types_dict = {\n", + " 'open': 'real',\n", + " 'high': 'real',\n", + " 'low': 'real',\n", + " 'close': 'real',\n", + " 'volume': 'int',\n", + " 'vwap': 'real',\n", + " 'adj_close': 'real',\n", + " 'unadjusted_volume': 'int',\n", + " 'change': 'real',\n", + " 'change_percent': 'real',\n", + " 'KalmanEstimate': 'real',\n", + " 'LorentzianIndicator': 'real',\n", + " 'REXPDE~TUSD': 'int',\n", + " 'CPIDE': 'real',\n", + " 'close_lag_1': 'real',\n", + " 'close_lag_7': 'real',\n", + " 'close_lag_30': 'real',\n", + " 'close_return_1': 'real',\n", + " 'close_return_7': 'real',\n", + " 'close_return_30': 'real',\n", + " 'REXPDE~TUSD_lag_1': 'int',\n", + " 'REXPDE~TUSD_lag_7': 'int',\n", + " 'REXPDE~TUSD_lag_30': 'int',\n", + " 'REXPDE~TUSD_return_1': 'real',\n", + " 'REXPDE~TUSD_return_7': 'real',\n", + " 'REXPDE~TUSD_return_30': 'real',\n", + " 'LorentzianIndicator_lag_1': 'real',\n", + " 'LorentzianIndicator_lag_7': 'real',\n", + " 'LorentzianIndicator_lag_30': 'real',\n", + " 'LorentzianIndicator_return_1': 'real',\n", + " 'LorentzianIndicator_return_7': 'real',\n", + " 'LorentzianIndicator_return_30': 'real',\n", + " 'volume_lag_1': 'int',\n", + " 'volume_lag_7': 'int',\n", + " 'volume_lag_30': 'int',\n", + " 'volume_return_1': 'real',\n", + " 'volume_return_7': 'real',\n", + " 'volume_return_30': 'real',\n", + " 'CPIDE_lag_1': 'real',\n", + " 'CPIDE_lag_7': 'real',\n", + " 'CPIDE_lag_30': 'real',\n", + " 'CPIDE_return_1': 'real',\n", + " 'CPIDE_return_7': 'real',\n", + " 'CPIDE_return_30': 'real',\n", + " 'date': 'time'\n", + "}\n", + "\n", + "# Convert the DataFrame to an H2OFrame with explicitly specified column types\n", + "next_day_h2o = h2o.H2OFrame(next_day_df, column_types=types_dict)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "id": "bc4bdca0-b3fb-424f-8c71-f95d6288e22f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n", + "Prediction for 2024-08-24: 18633.202683363976\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/marius/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/frame.py:1981: H2ODependencyWarning:\n", + "\n", + "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Predict the 'close' value for the next day\n", + "try:\n", + " prediction = best_model.predict(next_day_h2o)\n", + " predicted_close = prediction.as_data_frame().iloc[0, 0]\n", + " print(f\"Prediction for {next_day.date()}: {predicted_close}\")\n", + "except Exception as e:\n", + " print(f\"An error occurred during prediction: {str(e)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "b5e585bf-d251-41d5-83c1-4d86235c409d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "name": "Historical", + "type": "scatter", + "x": [ + "2014-10-01T00:00:00", + "2014-10-02T00:00:00", + "2014-10-03T00:00:00", + "2014-10-04T00:00:00", + 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "import plotly.graph_objects as go\n", "\n", - "# Initialize H2O (if not already initialized)\n", - "h2o.init()\n", - "\n", - "# Load the saved model\n", - "best_model = h2o.load_model(\"./GBM_grid_1_AutoML_4_20240825_152005_model_10\") # Use your actual model path\n", - "\n", - "def forecast_feature_kalman(series, forecast_horizon=1):\n", - " kf = KalmanFilter(transition_matrices=np.array([[1]]),\n", - " observation_matrices=np.array([[1]]),\n", - " initial_state_mean=np.array([series.iloc[-1]]),\n", - " initial_state_covariance=np.array([[1]]),\n", - " observation_covariance=np.array([[1]]),\n", - " transition_covariance=np.array([[0.01]]))\n", - " \n", - " state_means, _ = kf.filter(series.values.reshape(-1, 1))\n", - " \n", - " last_state = state_means[-1]\n", - " forecast = np.zeros(forecast_horizon)\n", - " for i in range(forecast_horizon):\n", - " last_state = np.dot(kf.transition_matrices, last_state)\n", - " forecast[i] = last_state.flatten()[0]\n", - " \n", - " return forecast[0]\n", - "\n", - "def generate_next_day_features(df, last_date, features):\n", - " next_day = pd.DataFrame(index=[last_date + timedelta(days=1)])\n", - " \n", - " for feature in features:\n", - " if feature == 'date':\n", - " next_day[feature] = next_day.index[0]\n", - " elif '_lag_' in feature:\n", - " base_feature, lag = feature.split('_lag_')\n", - " lag = int(lag)\n", - " next_day[feature] = df[base_feature].iloc[-lag] if len(df) > lag else df[base_feature].iloc[-1]\n", - " elif '_return_' in feature:\n", - " base_feature, period = feature.split('_return_')\n", - " period = int(period)\n", - " if len(df) >= period:\n", - " current_value = df[base_feature].iloc[-1]\n", - " past_value = df[base_feature].iloc[-period]\n", - " # Use np.divide with a where condition to avoid division by zero\n", - " return_value = np.divide(current_value - past_value, past_value, \n", - " out=np.zeros_like(current_value, dtype=float), \n", - " where=past_value!=0)\n", - " next_day[feature] = return_value\n", - " else:\n", - " next_day[feature] = 0 # Use 0 as default return if not enough data\n", - " elif feature != 'close':\n", - " next_day[feature] = forecast_feature_kalman(df[feature])\n", - " \n", - " return next_day\n", - "\n", - "# Assuming df is your original dataframe with historical data\n", - "# Make sure df is sorted by date and the index is a DatetimeIndex\n", - "\n", - "# Initialize future DataFrame with last 30 days of historical data\n", - "future_df = df.tail(30).copy()\n", - "future_dates = pd.date_range(start=df.index[-1] + timedelta(days=1), periods=30)\n", - "\n", - "for future_date in future_dates:\n", - " next_day = generate_next_day_features(future_df, future_df.index[-1], df.columns)\n", - " \n", - " # Convert to H2OFrame and make prediction\n", - " h2o_next_day = h2o.H2OFrame(next_day)\n", - " for col in h2o_next_day.columns:\n", - " if col != 'date':\n", - " h2o_next_day[col] = h2o_next_day[col].asnumeric()\n", - " \n", - " prediction = best_model.predict(h2o_next_day)\n", - " # Use multi-threaded conversion\n", - " pred_df = prediction.as_data_frame(use_pandas=True, use_multi_thread=True)\n", - " next_day['close'] = pred_df['predict'].values[0]\n", - " \n", - " future_df = pd.concat([future_df, next_day])\n", - "\n", - "# Keep only the future predictions\n", - "future_predictions = future_df.iloc[-30:]\n", - "\n", - "print(\"Future DataFrame first few rows:\")\n", - "print(future_predictions.head())\n", - "\n", - "# Plot future predictions\n", + "# Historical data\n", "fig = go.Figure()\n", - "fig.add_trace(go.Scatter(x=df.index[-365:], y=df['close'].tail(365), mode='lines', name='Historical'))\n", - "fig.add_trace(go.Scatter(x=future_predictions.index, y=future_predictions['close'], mode='lines', name='Forecast'))\n", - "fig.update_layout(title='H2O AutoML: 30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Close Price')\n", - "fig.show()\n", + "fig.add_trace(go.Scatter(x=df['date'], y=df['close'], mode='lines', name='Historical'))\n", "\n", - "print(\"\\nFuture Predictions (next 30 days):\")\n", - "print(future_predictions[['close']])\n", + "# Plot the prediction with a violet dot\n", + "fig.add_trace(go.Scatter(x=[next_day], y=[predicted_close], mode='markers', name='Next Day Prediction', marker=dict(color='violet', size=12, symbol='circle')))\n", "\n", - "# Shutdown H2O cluster\n", - "h2o.shutdown()" + "# Update layout for better style\n", + "fig.update_layout(\n", + " title='Next Day Prediction with Violet Dot',\n", + " xaxis_title='Date',\n", + " yaxis_title='Close Price',\n", + " hovermode='x unified',\n", + " plot_bgcolor='rgba(0,0,0,0)',\n", + " xaxis=dict(showgrid=True, gridcolor='LightGray'),\n", + " yaxis=dict(showgrid=True, gridcolor='LightGray')\n", + ")\n", + "\n", + "# Show the plot\n", + "fig.show()\n" ] }, { @@ -156695,7 +175543,9 @@ "id": "a8750240-2e84-4d6e-8f9a-a4ed82f7ea56", "metadata": {}, "source": [ - "# Kalman estimate" + "# Kalman estimate (high, low, close)\n", + "\n", + "Useful for stop-loss and take-profit estimation." ] }, {