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Juan Diego G. - Fullstack Developer, Data Science, Data Analysis

Working at Softaims has been an experience that continues to shape my perspective on what it means to build quality software. I’ve learned that technology alone doesn’t solve problems—understanding people, processes, and context is what truly drives innovation. Every project begins with a question: what value are we creating, and how can we make it lasting? This mindset has helped me develop systems that are both adaptable and reliable, designed to evolve as business needs change. I take a thoughtful approach to problem-solving. Instead of rushing toward quick fixes, I prioritize clarity, sustainability, and collaboration. Every decision in development carries long-term implications, and I strive to make those decisions with care and intention. This philosophy allows me to contribute to projects that are not only functional, but also aligned with the values and goals of the people who use them. Softaims has also given me the opportunity to work with diverse teams and clients, exposing me to different perspectives and problem domains. I’ve come to appreciate the balance between technical excellence and human-centered design. What drives me most is seeing our solutions empower businesses and individuals to operate more efficiently, make better decisions, and achieve meaningful outcomes. Every challenge here is a chance to learn something new—about technology, teamwork, or the way people interact with digital systems. As I continue to grow with Softaims, my focus remains on delivering solutions that are innovative, responsible, and enduring.

Main technologies

  • Fullstack Developer

    1 year

  • Python

    1 Year

  • SQL

    1 Year

  • Microsoft Power BI

    1 Year

Additional skills

  • Python
  • SQL
  • Microsoft Power BI
  • Stata
  • Linux
  • C
  • Java
  • AWS Glue
  • AWS Cloud9
  • AWS Lambda
  • Machine Learning Model
  • Machine Learning Algorithm
  • Machine Learning Framework
  • TensorFlow
  • Python Scikit-Learn
  • Data Engineering
  • Data Science
  • Data Analysis

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Experience Highlights

Automated Trading Bot with AI and Telegram Integration

Developed a comprehensive trading bot using Python, integrating market data collection, data cleaning, model training, and live trading. The bot fetches data from multiple markets, including forex, stocks, and cryptocurrencies, cleans it, and uses a machine learning model for trading decisions. It integrates reinforcement learning to improve strategies and provides live updates and trading signals via a Telegram bot. Additionally, a Flask web app is included to facilitate user interaction and monitoring.

Risk-Reward Simulator for Trading Strategies

Built a simulation tool to analyze the performance of trading strategies based on risk-reward ratios and win rates. The tool models the outcome of multiple trading scenarios given parameters like account size, risk per trade, total trades, win rate, and risk-to-reward ratio. The results of each simulation are saved in CSV format and visualized through plots to help traders assess the viability and robustness of their trading approach over a series of trades.

Machine Learning-Driven Trading Strategy with Alpaca API

Developed an algorithmic trading strategy using Python and the Alpaca API for real-time stock trading and backtesting. The strategy integrates with the Alpaca brokerage for executing trades based on a machine learning model's analysis of the latest news headlines and market prices. The trading logic includes dynamic position sizing based on cash at risk and uses bracket orders to manage profit-taking and stop-loss, maximizing returns while mitigating risks. The strategy was successfully backtested over a defined period to assess its profitability.

Forex Trading Assistant with Fibonacci Retracement Analysis

Developed a Python-based assistant tool for forex trading analysis. The program reads forex data, identifies key price points using Fibonacci retracement levels, and analyzes the overall price trend for the USD/CAD pair. The assistant provides graphical representations of price trends and advice for potential trading strategies, enhancing the user's ability to interpret market data effectively.

Neural Network Stock Price Predictor

Developed a neural network model to predict stock price movements for key indices such as Apple and S&P 500. Implemented data preprocessing, model training, and evaluation techniques to achieve high directional accuracy in price change prediction. Visualized predictions and error trends over time to assess model performance, leading to better insights for stock trading strategies.

Education

  • Pontificia Universidad Católica del Perú (PUCP)

    Bachelor's degree in Economics

    2017-01-01-2023-01-01

Languages

  • English
  • Spanish

Personal Accounts