Build a financial forecasting model
Follow the steps from collecting data to testing your model, putting it to work, and checking its performance.
Start with the data
A repeatable process lets you test a market forecast and learn from its mistakes.
Collect your data
Gather historical prices and trading volume, along with economic indicators and alternative data. Sources such as Bloomberg, Refinitiv, and the St. Louis Fed (FRED) provide APIs for collecting data.
Prepare the data
Handle missing values and investigate outliers before changing them. Correct data errors without automatically removing genuine extreme events. Fit scaling and other learned preprocessing only on training data.
Choose useful variables
Find variables that help predict your target, such as RSI and MACD or lagged economic indicators. Use only information available at the forecast date, including the original release dates and revisions of economic data.
Step 4: Choose and train a model
Use the techniques overview to choose a model that fits your data. Linear regression models can describe linear relationships. For more complex, nonlinear behavior, consider random forests or LSTM (long short-term memory) neural networks.
Start with a simple baseline model. Add complexity only when it meaningfully improves performance on data the model has not seen during training.
Step 5: Backtest and validate
Test the model on later data it has not seen during training. Backtesting simulates forecasts using historical information available at each point in time. Keep future observations out of feature selection and preprocessing.
To evaluate the forecast itself, compare errors against a simple baseline using measures such as mean absolute error (MAE) or root mean squared error (RMSE). Trading results also depend on costs and execution.
- Walk-forward validation trains on one window of data and tests on the next period, then shifts the window forward.
- Sharpe ratio analysis measures risk-adjusted returns to assess trading results based on a forecast.
- Drawdown measures a fall from a previous peak. Historical drawdowns describe past losses, not the worst loss that could occur in the future.
Tools for forecasting
| Category | Examples | What they help with |
|---|---|---|
| Programming | Python, R, Julia | Data science and statistical modeling. |
| Libraries | pandas, scikit-learn, TensorFlow | Preparing data and building machine learning models. |
| Data visualization | Tableau, Power BI, Plotly | Explaining forecast results to the people who use them. |
| Cloud | AWS, Azure, Google Cloud | Running large simulations and processing data in real time. |
Keep checking performance
Compare your financial forecasts with actual results on an ongoing basis. As markets change, model drift can reduce accuracy.
If errors keep growing, review your variables or retrain the model. Keep checking data quality as well as the forecast itself.
Read about economic indicators and other inputs to a forecast on our markets page.
Choose your next step
Review the basics on our home page, or compare forecasting methods before choosing one for your model.