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  • 5000 USD

    Bot for cryptocurrency arbitration Dex/Cex

    Python
    Arbitrage bot CEX/DEX

    Stack: Python (requests, Flask), API Binance/BingX/MEXC, integration with Solana

    Developed a highly automated bot for analyzing and executing arbitrage opportunities between centralized and decentralized exchanges. Key features:

    Monitoring and analyzing token prices on multiple exchanges through their official APIs.

    Automatic calculation of profitable opportunities for buying and selling assets.

    Integration with the Solana network for direct interaction with blockchain transactions.

    REST API on Flask for managing the bot and tracking the status of operations.

    Mechanisms for secure key storage and managing API request limits.

    The project demonstrates skills in: financial automation, working with APIs, blockchain integrations, and building reliable services for processing large volumes of data in real-time.


  • 6 USD

    Bot manager

    Python
    Bot where you can add and manage tasks


  • 226 USD

    Control of residues and prices

    Python
    Local web application, features, Revaluation reporting, collection of information on balances for positions where the price has changed, printing price tags, auto-saving drafts, login for the office to control submitted reports and generate summary reports !! test login 159357


  • 1500 USD

    Telegram Botnet

    Python
    Multifunctional modular utility for Telegram, created in 2020 for mass management of user bots. Functionality in the screenshot.
    The project was sold in 2022 and abandoned.


  • 2 USD

    Current-Voltage Characteristic Graph

    Python
    Graph of current strength dependence on voltage for a copper wire electrode


  • 338 USD

    Telegram bot for extended link management and analytics

    Python
    Telegram bot for creating and tracking unique links. Allows analyzing the effectiveness of advertising campaigns through a convenient admin panel, tracking clicks and user interactions.


  • 226 USD

    Signal Bot Pokect Option

    Python
    Project: Trading Telegram Bot for Pocket Option
    This is a Telegram bot created to simplify trading on the Pocket Option platform. It automates the process of receiving trading signals, allowing users to make quick decisions. The bot has a secure authorization system, an administrative panel for management, and a reliable architecture for stable operation.
    Main technologies:
    Python
    Aiogram (Telegram Bot API)
    Selenium (Web automation)
    Asyncio (Asynchronous programming)
    WebSockets (Real-time communication)


  • 1353 USD

    Candlestick Dashboard on Django, Dash, and Flask

    Python
    Project Description

    This project is a web application built on Django, Dash, and Flask that allows users to display interactive charts for financial data analysis. The main goal of the project is to provide a convenient tool for working with exchange data, including candlestick analysis, asset correlation, and data collection from the OKX exchange.

    Key Features

    1. Dynamic Candlestick Chart

    Users can select a currency pair and timeframe.

    Data is loaded from the database and displayed as a candlestick chart.

    The chart automatically updates when parameters change.

    2. Asset Correlation Chart

    The correlation coefficient between currency pairs is calculated.

    A table of pairs with a coefficient above a specified level is displayed.

    Users can select pairs with checkboxes and build their chart.

    Automatic assignment of different colors for visual distinction of pairs on the chart.

    3. User Registration and Authentication (Flask)

    User system implemented via Flask.

    Access to chart pages is restricted to authorized users only.

    JWT tokens are used for session security.

    4. OKX Exchange API Integration

    a) Real-time Candlestick Data Parser

    Continuously collects data for 75 currency pairs with a market cap over $1 billion.

    Automatically updates the database with new candlestick data.

    b) Historical Data Parser

    Allows users to download historical data for a specified period.

    Supports the addition of other exchanges in the future.

    Technologies and Tools

    Django – Backend framework and database management.

    Dash/Plotly – Interactive charts.

    Flask – User authentication (registration, login, access control).

    Pandas – Data processing.

    SQLite/PostgreSQL – Data storage.

    OKX API – Source for market data.


  • 45 USD

    Ceneo.pl parser

    Python
    This application is for collecting product data from the Ceneo.pl website.

    Capabilities:

    - Automatic data collection: Collects information about products (name, ID, price, availability) from all pages of the category.
    - Pagination handling: Automatically navigates to the next pages of the category.
    - Data extraction from JSON-LD: Finds and extracts product data from the JSON-LD markup on the page.
    - Saving to Excel: Saves the collected data to an Excel file with separate sheets for each category.
    - User-friendly interface: Allows easy selection of a file with links to categories and a file for saving results.
    - Logging: Displays the progress of the parsing in the application window.

    Easy to use:

    - Select a file with links to Ceneo.pl categories.
    - Select an Excel file to save the data.
    - Click "Start parsing."

    Technologies:

    - PyQt5: for creating the graphical interface.
    - Playwright: for browser automation and interaction with web pages.
    - Pandas: for processing and saving data to Excel.
    - JSON: for working with JSON-LD data.


  • 4000 USD

    AI-Powered PDF Processing for UPS & FDX Contracts

    Python
    I led the development of an AI-powered PDF processing application as a Proof of Concept (POC) for a client, focusing on the intricate task of extracting and processing data from UPS and FDX contracts. This project combined cutting-edge OCR technology, advanced PDF mining techniques, and the latest in LLM prompt engineering to deliver a comprehensive solution.

    Key Contributions:

    - Advanced OCR & PDF Mining: Leveraged a combination of OCR tools and specialized PDF miners such as Camelot and PuMuPDF to accurately extract data from complex, structured documents.
    - LLM Prompt Engineering: Implemented and fine-tuned prompt engineering techniques to enhance the accuracy and relevance of extracted information.
    - Streamlit Integration: Developed a cheap and intuitive user interface using Streamlit, enabling seamless interaction with the AI models and easy access to review the extracted data.
    - Client Collaboration: Engaged closely with the client to gather detailed requirements, ensuring the solution aligned with their specific business logic and operational needs.
    - Comprehensive Reporting: Delivered a 40-page data science report detailing the methodologies, findings, cost analysis, and recommendations, providing the client with deep insights into the processing of their contracts.


  • 79 USD

    Telegram bot - Crane TRX

    Python
    Functionality:
    - Local SQLite3 database
    - TRX collection and crediting to balance every hour
    - Bot's internal wallet with deposit and withdrawal
    - Referral system with reward

    #Python3 #telebot #trx #python-telegram-bot


  • 400 USD

    ScraiGen Admin Panel Implementation | AI Improvements

    Python
    As part of the project, a ready-made AI script was implemented in a Django (Python) project. A custom admin panel was added.
    Accelerated the speed of generating responses from AI using multithreading