{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":96164,"databundleVersionId":12993472,"sourceType":"competition"}],"dockerImageVersionId":31040,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"### 🔹 Importing Libraries\n\nI start by importing core libraries for data handling (`pandas`, `numpy`), visualization (`matplotlib`), modeling (`Ridge` from `sklearn`), and evaluation (`pearsonr`). These choices ensure a lightweight, fast modeling environment while still allowing for effective predictive analysis.\n","metadata":{"execution":{"iopub.status.busy":"2025-07-24T09:21:04.411189Z","iopub.execute_input":"2025-07-24T09:21:04.411510Z","iopub.status.idle":"2025-07-24T09:21:04.417026Z","shell.execute_reply.started":"2025-07-24T09:21:04.411475Z","shell.execute_reply":"2025-07-24T09:21:04.415744Z"}}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import Ridge\nfrom sklearn.preprocessing import StandardScaler\nfrom scipy.stats import pearsonr\nimport matplotlib.pyplot as plt","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:10:00.015880Z","iopub.execute_input":"2025-07-24T10:10:00.017548Z","iopub.status.idle":"2025-07-24T10:10:00.026933Z","shell.execute_reply.started":"2025-07-24T10:10:00.017486Z","shell.execute_reply":"2025-07-24T10:10:00.025495Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Data Loading\n\nThe provided training and test datasets are in `.parquet` format, which is efficient for large-scale numerical data. Then, load the `sample_submission.csv` to ensure our output format aligns with DRW's expectations. Understanding the structure early on helps guide the modeling workflow.\n","metadata":{}},{"cell_type":"code","source":"# === Load Data ===\ntrain = pd.read_parquet('/kaggle/input/drw-crypto-market-prediction/train.parquet')\ntest = pd.read_parquet('/kaggle/input/drw-crypto-market-prediction/test.parquet')\nsubmission = pd.read_csv('/kaggle/input/drw-crypto-market-prediction/sample_submission.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:10:01.927511Z","iopub.execute_input":"2025-07-24T10:10:01.927846Z","iopub.status.idle":"2025-07-24T10:10:59.801663Z","shell.execute_reply.started":"2025-07-24T10:10:01.927825Z","shell.execute_reply":"2025-07-24T10:10:59.800476Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Feature Engineering\n\nTwo simple but powerful derived features were introduced:\n\n- `order_imbalance` captures liquidity pressure between buyers and sellers.\n- `trade_aggressiveness` shows the direction and strength of recent trades.\n\nThese features emulate real trading signals and help translate market microstructure into model-readable data.\n","metadata":{}},{"cell_type":"code","source":"# === Feature Engineering ===\n# Derived features\nfor df in [train, test]:\n    df['order_imbalance'] = (df['bid_qty'] - df['ask_qty']) / (df['bid_qty'] + df['ask_qty'] + 1e-6)\n    df['trade_aggressiveness'] = (df['buy_qty'] - df['sell_qty']) / (df['volume'] + 1e-6)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:39.903899Z","iopub.execute_input":"2025-07-24T10:11:39.904353Z","iopub.status.idle":"2025-07-24T10:11:40.236841Z","shell.execute_reply.started":"2025-07-24T10:11:39.904321Z","shell.execute_reply":"2025-07-24T10:11:40.235648Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Selecting Features\n\nThe dataset contains anonymized `X_` features likely representing internal quantitative signals. While their meaning is opaque, including them leverages potential hidden structure. We also combine them with engineered and public features to create a robust feature matrix.\n","metadata":{}},{"cell_type":"code","source":"# Proprietary features\nX_cols = [col for col in train.columns if col.startswith(\"X_\")]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:44.397832Z","iopub.execute_input":"2025-07-24T10:11:44.398483Z","iopub.status.idle":"2025-07-24T10:11:44.403197Z","shell.execute_reply.started":"2025-07-24T10:11:44.398451Z","shell.execute_reply":"2025-07-24T10:11:44.402222Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Final feature set\nfeature_cols = ['order_imbalance', 'trade_aggressiveness', 'bid_qty', 'ask_qty',\n                'buy_qty', 'sell_qty', 'volume'] + X_cols","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:45.091545Z","iopub.execute_input":"2025-07-24T10:11:45.091845Z","iopub.status.idle":"2025-07-24T10:11:45.096692Z","shell.execute_reply.started":"2025-07-24T10:11:45.091824Z","shell.execute_reply":"2025-07-24T10:11:45.095704Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Target\ntarget = train['label']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:45.823721Z","iopub.execute_input":"2025-07-24T10:11:45.824059Z","iopub.status.idle":"2025-07-24T10:11:45.829807Z","shell.execute_reply.started":"2025-07-24T10:11:45.824035Z","shell.execute_reply":"2025-07-24T10:11:45.828551Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Time-Based Validation\n\nIn financial markets, using a **time-aware split** is critical to avoid data leakage. We simulate a real-time scenario by training on the first 80% of the data and validating on the last 20%, ensuring that future data is never leaked into the past.\n","metadata":{}},{"cell_type":"code","source":"# === Time-Based Split ===\nsplit_idx = int(len(train) * 0.8)\nX_train = train[feature_cols].iloc[:split_idx]\ny_train = train['label'].iloc[:split_idx]\nX_val = train[feature_cols].iloc[split_idx:]\ny_val = train['label'].iloc[split_idx:]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:49.567726Z","iopub.execute_input":"2025-07-24T10:11:49.568092Z","iopub.status.idle":"2025-07-24T10:11:49.878551Z","shell.execute_reply.started":"2025-07-24T10:11:49.568062Z","shell.execute_reply":"2025-07-24T10:11:49.877594Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Feature Scaling\n\nRidge regression is sensitive to the scale of input features. Standardizing the data ensures all features contribute equally, preventing domination by those with larger magnitudes. This is especially important when mixing features with different distributions (e.g. volume vs. synthetic signals).\n","metadata":{}},{"cell_type":"code","source":"# === Scale Features ===\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_val_scaled = scaler.transform(X_val)\nX_test_scaled = scaler.transform(test[feature_cols])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:51.954502Z","iopub.execute_input":"2025-07-24T10:11:51.954840Z","iopub.status.idle":"2025-07-24T10:11:52.131644Z","shell.execute_reply.started":"2025-07-24T10:11:51.954817Z","shell.execute_reply":"2025-07-24T10:11:52.130926Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Training Ridge Model\n\nRidge regression is chosen for its simplicity and ability to handle multicollinearity via L2 regularization. It offers a strong baseline in noisy environments like crypto markets. Despite its linear nature, Ridge can perform surprisingly well in high-dimensional problems.\n","metadata":{}},{"cell_type":"code","source":"# === Train Ridge Regression ===\nridge = Ridge(alpha=1.0)\nridge.fit(X_train_scaled, y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:53.322635Z","iopub.execute_input":"2025-07-24T10:11:53.323070Z","iopub.status.idle":"2025-07-24T10:11:53.372354Z","shell.execute_reply.started":"2025-07-24T10:11:53.323041Z","shell.execute_reply":"2025-07-24T10:11:53.371201Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Evaluation with Pearson Correlation\n\nPearson correlation is the official competition metric. It evaluates the linear relationship between our predictions and true market movements. This reflects how well the model captures **direction**, rather than raw price values — a core requirement for actionable trading signals.\n","metadata":{}},{"cell_type":"code","source":"# === Validate Using Pearson Correlation ===\nval_preds = ridge.predict(X_val_scaled)\npearson = pearsonr(y_val, val_preds)[0]\nprint(f\"Validation Pearson Correlation (Ridge): {pearson:.5f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:56.984035Z","iopub.execute_input":"2025-07-24T10:11:56.984380Z","iopub.status.idle":"2025-07-24T10:11:57.000428Z","shell.execute_reply.started":"2025-07-24T10:11:56.984332Z","shell.execute_reply":"2025-07-24T10:11:56.999502Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Generating Test Predictions\n\nOnce validated, the model is used to make predictions on the test set. These predictions represent a directional signal — how the model expects crypto prices to move in the near term. This signal would hypothetically be consumed by a trading algorithm.\n","metadata":{}},{"cell_type":"code","source":"# === Predict on Test Set ===\ntest_preds = ridge.predict(X_test_scaled)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:11:58.996298Z","iopub.execute_input":"2025-07-24T10:11:58.996602Z","iopub.status.idle":"2025-07-24T10:11:59.007166Z","shell.execute_reply.started":"2025-07-24T10:11:58.996580Z","shell.execute_reply":"2025-07-24T10:11:59.006203Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Submission File Creation\n\nThe predictions are inserted into the sample submission template and saved in CSV format. This step finalizes the workflow, ensuring our results are formatted according to the competition rules and ready for upload and scoring.\n","metadata":{}},{"cell_type":"code","source":"# === Create Submission ===\n# Load the sample submission to get correct format\nsubmission = pd.read_csv('/kaggle/input/drw-crypto-market-prediction/sample_submission.csv')\n\n# Replace the correct column with your predictions\n# Replace 'label' below if the column name in sample_submission is different\nsubmission['label'] = test_preds  # <- make sure test_preds is your final prediction array\n\n# Save the submission file in proper format\nsubmission.to_csv(\"submission.csv\", index=False)\n\n# Confirm the structure\nprint(\"Submission file saved. Preview:\")\ndisplay(submission.head())\nprint(\"Columns:\", submission.columns.tolist())\nprint(\"Shape:\", submission.shape)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:21:42.665081Z","iopub.execute_input":"2025-07-24T10:21:42.665754Z","iopub.status.idle":"2025-07-24T10:21:45.264722Z","shell.execute_reply.started":"2025-07-24T10:21:42.665726Z","shell.execute_reply":"2025-07-24T10:21:45.263812Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 🔹 Prediction Distribution\n\nVisualizing the distribution of test predictions helps confirm model behavior. A normal-looking spread suggests the model is learning patterns rather than collapsing to a constant value — a common failure mode in noisy datasets like crypto.\n","metadata":{}},{"cell_type":"code","source":"# === Optional: Plot Prediction Distribution ===\nplt.figure(figsize=(8,4))\nplt.hist(test_preds, bins=100, alpha=0.7)\nplt.title(\"Test Predictions Distribution\")\nplt.xlabel(\"Predicted Label\")\nplt.ylabel(\"Frequency\")\nplt.grid(True)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-24T10:12:34.194490Z","iopub.execute_input":"2025-07-24T10:12:34.194820Z","iopub.status.idle":"2025-07-24T10:12:34.549041Z","shell.execute_reply.started":"2025-07-24T10:12:34.194796Z","shell.execute_reply":"2025-07-24T10:12:34.547765Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 📘 Summary \n\nThis end-to-end pipeline highlights how even a simple, well-structured linear model can extract meaningful directional signals from noisy, high-dimensional financial data. The combination of engineered insights, robust validation, and alignment with trading-oriented evaluation metrics like Pearson correlation helps build a strong foundation for future development.\n\nIn a production context, this model could be extended with ensemble methods, deeper architectures (e.g., neural nets), and more sophisticated feature selection strategies — especially if interpretability or latency is important in real-time trading applications.\n","metadata":{}}]}