Cormac Kinsella

Cormac Kinsella

Mathematics Master Student and Software Engineer

Posts by Tags

algorithmic trading

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

backtesting

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

bcms upload

Public Documentation on BCMS Upload Services

5 minute read

Published:

I provide public documentation of my work regarding BCMS Upload Services in this article where you can see how I structured the application and what decisions led me to those structures be implemented in the application. I show my own way of creating an application and apply my principles which are shown in my work in trin-app and trin-react.

biased

breakout trading

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

cluster analysis

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

coding challenge

coin

cryptocurrency

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

data science

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

ensemble learning

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

fear and greed index

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

financial modeling

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

heuristic

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

k-means clustering

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

kelly criterion

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

machine learning

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

market accumulation

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

market sentiment analysis

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

pattern recognition

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

portfolio optimization

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

python

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

quantitative finance

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

risk management

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

scikit-learn

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

selection model

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

sharpe ratio

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

simulate

simulation

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

state-based model

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

stochastic modeling

Harnessing Deterministic Signals through Stochastic Sampling: A State-Based Approach to Algorithmic Trading

12 minute read

Published:

Abstract: This article details the architecture and methodology of an automated, quantitative trading system designed for cryptocurrency markets. The system’s core is a machine learning strategy that leverages unsupervised clustering to identify distinct market patterns and applies cluster-specific models to generate daily trade signals. Trade execution is managed through a sophisticated two-phase lifecycle, incorporating a momentum-based entry confirmation and dynamic in-trade risk management. The software is designed with a dual-mode operational framework, allowing for both live trading and robust, parallelized backtesting on historical data. This document outlines the system’s modular components, the intricacies of the trading and capital management strategies, and the implementation of its operational modes.

stochastic process

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.

technical details

Public Documentation on BCMS Upload Services

5 minute read

Published:

I provide public documentation of my work regarding BCMS Upload Services in this article where you can see how I structured the application and what decisions led me to those structures be implemented in the application. I show my own way of creating an application and apply my principles which are shown in my work in trin-app and trin-react.

trading strategy

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

unbiased

unsupervised learning

Identifying Market Accumulation Patterns with K-Means Clustering

4 minute read

Published:

Abstract: This paper details the “ClusterBuster” algorithm, a methodology for identifying potentially profitable market conditions using unsupervised machine learning. The approach leverages K-Means clustering to group historical financial data, with features primarily derived from rolling modes of the “Fear and Greed Index” over various lookback periods. Each resulting cluster is evaluated using custom performance metrics: the “breakthrough ratio” to quantify upside potential and the “loss ratio” to assess downside risk. The core of the algorithm is an optimization loop that systematically tests different numbers of clusters to identify a model that maximizes the proportion of high-performing, or “breakthrough,” clusters. The final output is an optimized clustering model that effectively isolates historical data patterns correlated with significant positive returns, providing a systematic approach to identifying trading opportunities.

xgboost

Improving the Odds: A Heuristic Method for Selecting Crypto Breakouts

13 minute read

Published:

Abstract: This document details a proprietary algorithm designed to predict significant upward price movements, or “breakouts,” in crypto-assets. The methodology is built on a multi-stage process that transforms raw market data into a powerful selection heuristic. The algorithm leverages historical price action, market sentiment, and a novel, stability-focused machine learning approach to identify and leverage a more favorable, informed subset of the crypto-asset universe for potential trading opportunities.