Migrate to Server-Based Database for Trading and Investment

Applying OOP by building a Bank System 🚀 As part of my Python learning journey, I built a simple Bank Management System using Object-Oriented Programming 🐍 💻 Project: Bank System 🔹 Features: • Create a bank account • Deposit and withdraw money • Check account balance • Basic validation for transactions 🔹 Concepts I used: • Classes and Objects • init method • Instance methods • Class variables • @classmethod and @staticmethod • Input validation This project helped me understand how real-world systems can be modeled using classes and objects. 💡 Biggest learning: Breaking problems into smaller parts and organizing them using OOP makes code much cleaner and scalable. Excited to keep building more real-world projects 🚀 #Python #OOP #MiniProject #BankSystem #CodingJourney #FullStackDeveloper #LearningInPublic

I built a fully automated trading system using Python. Not a simulation. A real system handling real money. It processes live market data, executes trades, and manages multiple positions in real time. At first, I thought automation would eliminate risk. But I was wrong. It doesn’t remove risk. It moves risk into system design. Now I focus more on: - execution reliability - risk control - handling unexpected market conditions Still improving the system every day.

I built a fully automated stock trading system using Python. At first, it looked perfect. Backtesting results were strong. Execution was smooth. No human emotion involved. But then the market did something unexpected. A sudden drop triggered conditions I hadn’t fully prepared for. That’s when I realized: Automation doesn’t remove risk. It just shifts where the risk lives. Now I focus less on “making profits” and more on “controlling damage.” Because in trading, survival comes first. Still improving the system every day.

🚀 Excited to Share My Latest Project: SIP Calculator 💰 I have created a SIP Calculator using Python that helps users estimate their investment growth over time. 📌 Project Features: Calculates total investment value Shows estimated returns Uses compound interest logic Simple and user-friendly input system 🧠 What I Learned: Python functions and logic building Financial concepts like SIP & compound interest Real-world problem solving using programming 💡 This project made me understand how small monthly investments can grow into a large amount over time. 🔗 GitHub: [https://lnkd.in/dgKR3Dnj] #Python #SIPCalculator #Programming #StudentDeveloper #Finance #Coding

Accelerate your algorithmic trading strategies with C++ (without having to learn C++). *aat* can do that. Here's how: aat is an asynchronous, event-driven framework for writing algorithmic trading strategies in Python with optional acceleration in C++. The engine is composed of 4 parts: 1. trading engine 2. risk management engine 3. execution engine 4. backtest engine https://lnkd.in/gc2cYndb

What is Escape Analysis in Go Today I learned about an important concept in the Go programming language called Escape Analysis. Escape Analysis is a process used by the Go compiler to decide where a variable should be stored, either in the stack or in the heap. If a variable is used only inside a function, it stays in the stack and is removed when the function execution is completed. But if a variable is needed outside the function (for example, returning its address), it cannot safely stay in the stack. In that case, the compiler moves it to the heap. This is called “escape to heap”. If a value is returned normally (by value), Go simply copies the value, so no escape happens. In simple terms, Go automatically manages memory by deciding whether a variable should stay in the stack or move to the heap using Escape Analysis.

Just deployed my first full-stack Python project: an interactive dashboard for ETF portfolio analysis. The idea builds on my Master's thesis at LUISS on personal finance and cognitive biases in Italian retail investing — where the case for low-cost, passive strategies emerged clearly from the data. So I built a tool that makes that case visible. ETF Portfolio Dashboard lets anyone: → Analyze risk-return metrics (CAGR, Sharpe, max drawdown) → Compare a portfolio to any benchmark using CAPM (alpha, beta, correlation) → Simulate a DCA (Piano d'Accumulo) vs lump-sum strategy → Detect FX bias when mixing different quotation currencies Stack: Python, Streamlit, pandas, NumPy, Plotly, yfinance. 🌐 Try it: https://lnkd.in/dfWTDfKF 💻 Code: https://lnkd.in/dXTxvftK #QuantitativeFinance #Python #ETF #DataAnalytics

Pine Script has hundreds of thousands of developers on TradingView. Most of them can't actually run their code anywhere else. That's the gap I've been closing for the past year with PyneCore — a Pine-compatible Python runtime (Apache 2.0) where ta.rsi(), strategy.entry() and the rest of the Pine namespace work the same way they do on TV, validated against TradingView reference values. Today I shipped two integration examples with FreqTrade, the open-source crypto trading bot (~42k stars on GitHub): → Pine indicators inside a Python strategy → A complete Pine strategy with FreqTrade as the execution layer The practical meaning for Pine developers: you don't have to choose between "the language I like writing in" and "automated execution on a real exchange." You keep the Pine code, your bot runs on whatever FreqTrade supports. This is one piece of a larger plan — a hosted Pine→Python compiler, a managed bot platform, and an embeddable WASM-based execution layer that runs Pine directly in the browser. But the FreqTrade bridge works today, no paid tier required. Repo: https://lnkd.in/d8PD8KTV #algotrading #pinescript #fintech #python #opensource

One step toward what PyneSys is really building: making Pine Script work everywhere — not just on TradingView. Today's piece is a public example showing how PyneCore plugs into FreqTrade. The hosted compiler, managed bot platform, and browser-native execution come next.

Comprehensive guide: Building Python trading bots with the Interactive Brokers API in 2025. This article walks through system architecture, live trading implementation, and best practices for scaling strategies across futures and options. A must-read for developers and quant traders. Read more: https://wix.to/3GbNDvy #AlgoTrading #Python #InteractiveBrokers #QuantFinance #TradingEngineering