AI Powered Cryptotrademate Backtester

Abstract - The swift expansion of cryptocurrency markets necessitates the assessment of clever and flexible trading strategies. In order to optimize cryptocurrency trading strategies, CryptoTradeMate is an AI-powered backtesting framework that combines machine learning, deep learning, and reinforcement learning with historical and on-chain data. The system reduces overfitting and improves robustness by supporting walk-forward optimization, purged cross-validation, and real-time data ingestion. Results from experiments conducted on Bitcoin, Ethereum, and other assets between 2018 and 2024 show that AI-augmented strategies outperform conventional rule-based approaches in terms of stability and risk-adjusted returns, particularly in times of market volatility. Key Words: risk management, machine learning, reinforcement learning, backtesting, cryptocurrency, on-chain data, and optimization.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-10-11
DOI
https://doi.org/10.55041/ijsrem52948
Primary Topic
Cryptographic Implementations and Security
Type
article
Field-Weighted Citation Impact
0.00
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article

AI Powered Cryptotrademate Backtester

D Nandhini, Sri Harshini M
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Cryptographic Implementations and Security
article

AI Powered Cryptotrademate Backtester

D Nandhini, Sri Harshini M
article en

Abstract

Abstract - The swift expansion of cryptocurrency markets necessitates the assessment of clever and flexible trading strategies. In order to optimize cryptocurrency trading strategies, CryptoTradeMate is an AI-powered backtesting framework that combines machine learning, deep learning, and reinforcement learning with historical and on-chain data. The system reduces overfitting and improves robustness by supporting walk-forward optimization, purged cross-validation, and real-time data ingestion. Results from experiments conducted on Bitcoin, Ethereum, and other assets between 2018 and 2024 show that AI-augmented strategies outperform conventional rule-based approaches in terms of stability and risk-adjusted returns, particularly in times of market volatility. Key Words: risk management, machine learning, reinforcement learning, backtesting, cryptocurrency, on-chain data, and optimization.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(10)
Openalex Percentile: Top 85%
Cryptographic Implementations and Security
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