TensorFlow

Fundamental theory and practice in Data Science (DS). 🧮

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

Trading Bot – Algorithmic Crypto Trading with AI Integration

This project is a powerful algorithmic trading bot for cryptocurrency markets. It combines traditional technical analysis with modern machine learning to generate accurate and intelligent trading decisions.

Key Features:

Candlestick Pattern Detection: Identifies classic reversal patterns such as Hammer, Doji, Engulfing, Shooting Star, and complex formations like triangle patterns.

Technical Indicators: Includes standard indicators (RSI, MACD, Moving Averages, Bollinger Bands) and advanced tools like Ichimoku Clouds, SuperTrend, Fibonacci Retracements, and more.

Machine Learning Integration: Uses LSTM-based models for time-series forecasting and momentum strategies, combined with indicator signals through weighted evaluation.

Dynamic Signal Weighting: Customizable signal weighting for patterns, indicators, and ML predictions with automatic adjustments to market volatility.

Trade Execution Engine: Supports long/short positions with stop-loss, take-profit, and trailing stop features. Automatically includes fees and tax deductions in profit calculations.

Backtesting & Debugging: Simulates strategies on historical data with detailed equity/value curve visualization and comprehensive debug logs.

Robust Error Handling: Detects and logs data inconsistencies, index errors, and processing issues to ensure stability.

Modular architecture with key components such as TraderBot, SignalHandler, PatternManager, IndicatorManager, MLModelHandler, SequenceManager, DataAPI, and CryptoCurrency. Additional support provided by PatternCalculator, IndicatorCalculator, and DataProcessing.

Version: V1.3.0.0 | GUI: V1.0.0 Author: Marian Seeger – info@seegersoftwaredevelopment.de

web-interface

Old functions to play with neural networks. Developed in December 2024

ML model training

This project focuses on building a Lyric Classification Model using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Networks). The models' purpose is to identify which artist a given lyric belongs to. Users can input lyrics and the models will predict the associated artist, aligning with the course's focus on the practical implementation of Natural Language Processing (NLP) tasks.

A TensorFlow/Keras neural network for regression on noisy sine wave data, predicting continuous values with real-time visualization of predictions and loss using Matplotlib.

Android implementation of an offline computer vision algorithm for Malaria parasite detection and classification in thick blood smears (see research project).

A Computer Vision algorithm for Malaria parasite detection and classification in digital images of thick blood smears.

A project that utilizes machine learning to predict shrimp growth to optimize the amount of feed used.

This repository is a testament to my journey of learning Tensor Flow from scratch (basics to intermediate level) using Google’s official Tensor-Flow documentation.

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

Tutorials from tensorflow, for learning purposes

Repository of real applications of neural networks coded in Python with TensorFlow/Keras and PyTorch.

Cyber determetion toolskit

This project provides a deep learning approach to learn machining features from CAD models using a hierarchical graph convolutional neural network.

Linear regression refresher