Time-series foundations
Read temporal order, trends, seasonality, volatility, and noise.
Official MLIR book · Forecasting
Machine Learning for Portfolio Management
A practical guide to forecasting Indonesian and US stocks, gold, and silver through data workflows, model evaluation, and Python.
$4.99 one-time payment · lifetime access
Four asset groupsIndonesian and US stocks, gold, silver
Python workflowFrom data to evaluation
Lab accessRegression, optimization, and signals
Lifetime accessStored in your account
Forecasting that starts with data, not guesses.
The learning path connects financial time series, signal transformation, models, evaluation, and responsible interpretation.
Data preparation
Handle date ranges, features, missing values, and leakage-aware splits.
Regression and forecasting
Connect baselines, regression, interpolation, extrapolation, and prediction.
Model optimization
Understand loss, gradient descent, learning rates, and parameter updates.
Signal processing
Explore frequency components, filters, trends, seasonality, and residuals.
Realistic evaluation
Test models chronologically and interpret errors without promising returns.
Supporting forecasting labs
- 01Linear regression and fitted lines.
- 02Interpolation and extrapolation.
- 03Gradient descent and learning rates.
- 04Signal decomposition and filtering.
- 05FFT and frequency spectra.
Related guides
Common questions
Is this a stock recommendation book?
No. It focuses on the technical workflow of data, forecasting, Python, and model evaluation.
Is it suitable for beginners?
Basic Python familiarity is helpful; the machine learning concepts are introduced step by step.
How is lab access activated?
After payment, the book and supporting labs appear on your account page.
Experiments you can explain.
- 01Understand the data and its limitations.
- 02Build a baseline before complex models.
- 03Run chronological experiments.
- 04Compare errors and interpret results.
Build a cleaner forecasting workflow.
Get the book and supporting labs with lifetime access.