Analysis Time // Assuming you have a DataFrame with a datetime index // In JavaScript, you might use a library like 'pandas-js' for DataFrame functionalities
// 1. Data Acquisition and Cleaning // Replace the following with your data and date formatting const data = /* Your DataFrame with datetime index and relevant columns */; data.dropna(); // Handle missing values
// 2. Hexagonal Binning and Interpolation const x = data["Close"]; const y = data.index; // Assuming datetime index const grid_x = [...x]; // Copy x to grid_x const grid_y = y.map((date) => date.getTime()); // Convert datetime to timestamp const grid_z = /* Use griddata method or any other interpolation method */;
// 3. Data Visualization
// Your plotting code remains the same with 'Volume' replaced by timestamp
plt.figure(figsize=(10, 6));
sns.hexbin(x, y, C=data["Close"], gridsize=20, cmap="viridis");
plt.contour(grid_x, grid_y, grid_z, levels=10, colors="black", linewidths=0.5);
plt.xlabel("Closing Price");
plt.ylabel("Timestamp");
plt.title(Hexagonal Interpolation for ${ticker});
plt.show();
// 4. Feature Engineering (example) // Assuming you have a DataFrame named 'data' data["MA50"] = data["Close"].rolling(window=50).mean(); data["Volatility"] = data["Close"].rolling(window=20).std();
// 5. Model Building and Prediction (example using linear regression) // Assuming you have a library similar to scikit-learn in JavaScript const { LinearRegression } = require('your-machine-learning-library');
const features = ["MA50", "Volatility"]; const X = data[features]; const y = data["Close"];
const model = new LinearRegression(); const { X_train, X_test, y_train, y_test } = /* Split your data */; model.fit(X_train, y_train);
const predictions = model.predict(X_test);
// Evaluate model performance (example using RMSE) // Assuming you have a library similar to scikit-learn in JavaScript const { mean_squared_error } = require('your-machine-learning-library');
const rmse = Math.sqrt(mean_squared_error(y_test, predictions)); console.log("RMSE:", rmse);