Artificial Intelligence and Multi-Indicator Confluence Frameworks for Cryptocurrency Trading Signal Optimisation: Evidence from Major Digital Assets
“Comprehensive backtesting reveals that the AI-enhanced confluence framework substantially outperforms passive buy-and-hold strategies, with a Sharpe ratio of 1.81 versus 0.81, a maximum drawdown reduced from 78% to 37%, and a win rate of 69%.”
AssetCryptospot crypto
StrategyMomentum
TechniqueTree ensemble
GeographyGlobal
Also stated: Daily, 2016–2026, Sharpe ratio, Excess return, Drawdown, Long-only, Price and volume, benchmark, out-of-sample test
Missing from abstract: costs, multiple testing, universe
Institutional-grade audit of this strategy.
£5 — Request the audit- Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
- Reproduction code integratable into Bloomberg, IBKR, etc.
- Bias-free datasets for backtesting included
- Delivered in 2 working days
The paper's neighbourhoodMomentum and trend following by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| Anomaly detection in European cryptocurrency exchange-traded products by Kończal and Połoczański | 2026 | ||||
| Cross-Sectional Return Prediction in Cryptocurrency Markets Using Variational Latent Factor Models by Boyer | 2026 | ||||
| On-Chain Factors and Cryptocurrency Asset Pricing: Evidence from Ethereum-Based Tokens by Byun et al. | 2026 | ||||
| A Forecast Model For Crypto Currencies by Chen and Chen | 2026 | ||||
| Systematic Trend-Following with Adaptive Portfolio Construction: Enhancing Risk-Adjusted Alpha in Cryptocurrency Markets by Bui and Nguyen | 2026 |