This strategy focuses on backtesting and algorithmic trading by applying the hill climbing method to find liquid levels at support and resistance levels. The strategy entails evaluating price movements and setting the threshold value for trading at these key levels. By closely studying the market, the technique seeks to find optimal entry and exit points based on recognized support and resistance levels. This method allows for a systematic approach to trading decisions using the concepts of liquidity and market dynamics. Implementing this technique entails testing historical data to determine its effectiveness and tweaking the threshold values for higher accuracy. By introducing the hill-climbing method into the backtesting process, traders may make informed decisions and perhaps increase their algorithmic trading performance.
Veritabanları (dc.source.platform) | Scopus |
Department (dc.contributor.department) | Bilgisayar Mühendisliği |
Yazar (dc.contributor.author) | Yelghi, Aref |
Tür (dc.type) | Kitap Bölümü |
Eser Adı (dc.title) | An Approach for Backtesting and Algorithmic Trading with Liquidity and Hill Climbing Algorithm |
Konu Başlıkları (dc.subject) | Algorithmic trading |
Konu Başlıkları (dc.subject) | Backtesting |
Konu Başlıkları (dc.subject) | Hill climbing |
Konu Başlıkları (dc.subject) | Liquidity |
Yayın Yılı (dc.date.issued) | 2024 |
Yayıncı (dc.publisher) | Springer Science and Business Media Deutschland GmbH |
Kitap Adı (dc.identifier.kitap) | Studies in Computational Intelligence |
ISSN (dc.identifier.issn) | 1860949X |
Açık Erişim Tarihi (dc.date.available) | 2040-01-01 |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/20.500.14081/2158 |
Özet (dc.description.abstract) | This strategy focuses on backtesting and algorithmic trading by applying the hill climbing method to find liquid levels at support and resistance levels. The strategy entails evaluating price movements and setting the threshold value for trading at these key levels. By closely studying the market, the technique seeks to find optimal entry and exit points based on recognized support and resistance levels. This method allows for a systematic approach to trading decisions using the concepts of liquidity and market dynamics. Implementing this technique entails testing historical data to determine its effectiveness and tweaking the threshold values for higher accuracy. By introducing the hill-climbing method into the backtesting process, traders may make informed decisions and perhaps increase their algorithmic trading performance. |
Dil (dc.language.iso) | En |
DOI (dc.identifier.doi) | 10.1007/978-3-031-57708-6_5 |
Araştırma Alanı (dc.relation.arastirmaalani) | 10.1007/978-3-031-57708-6_5 |