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Flask API Development

Flash USDT: Advanced Peer-to-Peer Temporal Tether Transaction Solution Overview

Imagine having the power to execute seamless, instant USDT (Tether) transactions with unparalleled speed and security, all while leveraging a temporary coin designed for ultimate flexibility. Flash USDT is a sophisticated software platform engineered for instantaneous peer-to-peer (P2P) transactions using a temporal version of USDT, valid for 60 to 350 days. This temporary coin is fully transferable, tradable, and swappable across supported platforms. Tailored for traders, businesses, and cryptocurrency enthusiasts, Flash USDT offers rapid transaction processing, competitive fees, and seamless integration with major platforms, including leading exchanges such as Binance, Trust Wallet, and MetaMask, as well as casino and betting platforms. It ensures secure, efficient, and versatile cryptocurrency operations for a wide range of use cases.

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

A Web API project for University of Oulu Programmable web project course

Saleor Commerce Customer-centric e-commerce on a modern stack

This is a Collaborative-Based Product Recommendation Engine which recommends most correlated products to the Customer based on the Ratings patterns of other customers who bought that same product also.

This model is developed on the CarDekho.com Dataset for predicting Prices of cars. I have used RandomForestRegressor() algorithms for creating the model, plus I have used RandomSearchCV() function to do the Hypertuning.

This is a Content-Based Movie_Recommendation_Engine based on the IMDB Movies Dataset