Machine-learning results depend heavily on how market data is prepared. A powerful model cannot repair leakage, inconsistent timestamps, or a poorly defined target.
Define the Prediction Target
Specify whether the model predicts a future return, volatility, direction, or another measurable outcome, including the horizon and exact decision time.
Use Chronological Splits
Keep older data for training, a later period for validation, and the newest period for testing. Random splitting can leak information across market regimes.
Build Causal Features
Lagged returns, rolling volatility, ranges, volume ratios, and time-of-day variables are common inputs. Fit scaling and preprocessing on training data only.
Validate Robustly
Check gaps, holidays, halts, corporate actions, class balance, transaction costs, and performance across multiple walk-forward windows.
Review our OHLCV data guide and browse historical datasets.
For research and education only; not investment advice.