ai-signal-aggregator

To consolidate multiple trading strategy signals, you can aggregate and weight outputs from trend-following, mean-reversion, and momentum strategies into a single composite signal using weighted voting and ensemble meta-models.

Ensemble signal fusion combines directional views from multiple trading strategies across various assets and timeframes into a unified signal, reducing conflicting guidance and improving overall decision quality.

You can calculate confidence metrics for buy or sell recommendations by applying a calibrated random forest ensemble meta-model to generate probabilistic assessments and risk guidance for position sizing.

Yes, signal aggregation can be applied across various assets and timeframes for portfolio-wide decision making, allowing you to align diversified strategies and compare performance using a master signal.

Use weighted voting for straightforward strategy combination, while a random forest ensemble meta-model with calibration is suited for deeper signal fusion, providing probabilistic confidence metrics for risk management.

Conflicting trading signals arise because different strategies like trend-following and mean-reversion generate opposing directional views; aggregating these signals into a composite signal resolves this conflict.