How to Become a AI Trading Signal Generator

Build a basic signal that buys when price touches the lower Bollinger Band and sells at the upper band. Use Python and Pandas to implement, backtest with transaction costs, and visualize performance.

Create a pipeline that scrapes financial news headlines, applies a pre-trained sentiment model (like FinBERT), and generates a daily aggregate sentiment score for major stocks. Backtest a strategy that goes long on positive sentiment and short on negative.

Identify a cointegrated pair of stocks (e.g., within the same sector). Develop a signal based on the z-score of their spread, with dynamic thresholds for entry and exit. Implement proper in-sample/out-of-sample testing.

Build a complete pipeline: ingest daily market data via API into S3, perform feature engineering with SageMaker Processing, train a gradient boosting model (XGBoost) using SageMaker Training, and deploy the model as an API endpoint via SageMaker Endpoint. Set up a simple monitoring dashboard.

Combine three distinct signals (e.g., momentum, value factor, and sentiment) using a meta-learner (like a simple neural network) that takes the sub-signals as inputs and outputs a final composite signal. Use robust walk-forward optimization to determine weights.