An Approach for Backtesting and Algorithmic Trading with Liquidity and Hill Climbing Algorithm

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.

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Son Erişim Tarihi
05 Eylül 2024 17:42
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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
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