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Schedule Interview NowJuan Diego G. - , Data Science, Data Analysis
My name is Juan Diego G. and I have over 1 year years of experience in the tech industry. I specialize in the following technologies: Python, SQL, Microsoft Power BI, Stata, Linux, etc.. I hold a degree in Bachelor's degree. Some of the notable projects I’ve worked on include: Automated Trading Bot with AI and Telegram Integration, Risk-Reward Simulator for Trading Strategies, Machine Learning-Driven Trading Strategy with Alpaca API, Forex Trading Assistant with Fibonacci Retracement Analysis, Neural Network Stock Price Predictor, etc.. I am based in Lima, Peru. I've successfully completed 18 projects while developing at Softaims.
I specialize in architecting and developing scalable, distributed systems that handle high demands and complex information flows. My focus is on building fault-tolerant infrastructure using modern cloud practices and modular patterns. I excel at diagnosing and resolving intricate concurrency and scaling issues across large platforms.
Collaboration is central to my success; I enjoy working with fellow technical experts and product managers to define clear technical roadmaps. This structured approach allows the team at Softaims to consistently deliver high-availability solutions that can easily adapt to exponential growth.
I maintain a proactive approach to security and performance, treating them as integral components of the design process, not as afterthoughts. My ultimate goal is to build the foundational technology that powers client success and innovation.
Main technologies
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
Direct hire
Potentially possible
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
Github