Python Long-Term Stock Strategy

@Jennycreation

hello sir , i have 17 year experience tech industry i already work on many Cryptocurrency trading system . i can show you demo also and can do as per your requirement. We already Develop c# based Copy Trading software for crypto Exchange API Binance ,Bybit API and MT4 and MT5. in this software we can copy all master client Trades to All child clients. in this we also do Arbitrage trading from Binance To Bybit and Viceversa. software will use the official Binance And Bybit API. i can show demo for you. let me know your response. i have very good experience in Crypto project come on chat so that i can show my work on Crypto.. i can start work from right now i already built trading plateform where i use Zerodha/Alice blue/Angel/Profitmart api for trading order, buy/sell, square off possition /margin etc feature. we also did automated trading using amibroker csv file. let me know i can show you demo.

@loxtic

Hi, how are you doing? I have considerable experience building automated trading systems in Python, with clean modular code and clear back-testing workflows. I’ve worked with data feeds and brokers like Alpaca and Interactive Brokers, plus libs such as pandas, NumPy, and TA-Lib, and I can build a configurable strategy framework that supports signals, position sizing, and automated order routing, with walk-forward testing and full performance reporting. I can share previous similar projects and adapt to switch providers without a rewrite. Let me know further if interested.

@patodeh

The requirement for backtesting and statistical analysis in the Python Long-Term Stock Strategy project suggests a need for efficient data manipulation, which can be achieved using Pandas and NumPy. To ensure a robust architecture, I would focus on designing a modular system that allows for easy integration of various statistical models and backtesting frameworks. What is the desired frequency for the backtesting process, and are there any specific statistical models or libraries that need to be integrated? I propose starting with a review of the project's data requirements to determine the most suitable approach for implementation.

@Sahyadri96

Working on similar project Completed projects till now 1) Python + DhanAPI +Excel + VBA option scalping strategy 2) Python 21 EMA and 9 EMA crossover strategy on DhanAPI 3) Google sheet + FyersAPI trading 4) Google sheet + Algomojo + Upstox 5) Tradetron Banknifty option scalping strategy 6) Excel 2600 NSE 10 years data 7) Copytrading using python 8) Tradetron Supertrend + MACD Crossover Strategy 9) Dhan option chain with Greeks in Google spreadsheet via Google Appscript 10) Backtesting of Nifty options for wait and trade strategy 11) Trigger orders for Dhan Nifty options 12) Shoonya API:- Wait and trade strategy 13) Tradetron: RSI + ADX + EMA strategy 14) Python Moving avarage channel trading Algo 15) Kotak Neo: Turtle scalping strategy for options 16) Fyers Filtered option chain in Excel 17) Binance Bitcoin tradingview strategy python bot 18) Fyers Tradingview python bot 19) Dhan Python order manager

@snath88

"Hi, drop me a message — I'll share a quick Python trading bot prototype based on what I understood. If it matches your expectations, we can move forward. Thanks!"

@faizak54

Hi, I build Python-based data pipelines and algorithmic systems — this project is a strong match for my technical skills. My approach: - Signal generation using momentum factors, technical indicators via TA-Lib, and quantitative filters - Position sizing with risk management logic built in - Alpaca API for live order execution (easy to swap providers later) - 10+ years backtesting using yfinance or Quandl data with pandas and NumPy - Walk-forward testing for robustness validation - Performance report with CAGR, max drawdown, Sharpe ratio — plotted in PDF/HTML - Clean modular code with setup guide and optional CLI dashboard Timeline: 2-3 weeks for full backtest and live-ready system. Ready to start today!

@Ayesham836

Hi, I reviewed your requirements and am interested in working on your Python-based stock strategy project. I have experience developing Python applications using Pandas and NumPy for data processing, automation, and analytical workflows. I can build clean, modular, and well-documented code with a strong focus on maintainability and reproducibility. The system can be structured in separate modules for data collection, signal generation, backtesting, and reporting, making it easier to extend and switch API providers in the future. I am available to start immediately. A few questions: 1. Do you already have a preferred broker or market data API in mind? 2. Are there any specific factors or strategy rules you would like to include initially? Looking forward to discussing the project. Best regards, Ayesha

@RomanSorokin13

Hi, I'm an automation developer specializing in building end-to-end Python systems for workflows that demand reliability, auditability, and human control—exactly what a trading strategy requires. I've built similar systems: data pipelines with validation checkpoints, modular signal engines, and dashboards that log every decision for review. For this project, I'd structure it as: 1. Modular signal generation (trend, momentum, risk filters) with clean parameter inputs 2. Backtesting engine on 10+ years of daily data with Pandas/NumPy 3. Walk-forward validation to confirm robustness 4. Detailed metrics (CAGR, Sharpe, max drawdown) + equity curve plots 5. Optional: CLI or lightweight dashboard to inspect signals and positions The code will be clean, commented, and built to swap data sources (Alpaca, IB) without rewrites. I propose starting with a scoped first milestone: backtesting engine + signal validation on historical data (no live trading). This lets us validate the strategy logic safely before any live deployment. I can have a working version for your review within 2 weeks. I'd like to confirm: which data source do you prefer (Alpaca, IB, other)? And which signal factors matter most to you—trend, momentum, fundamentals, or a mix? Roman

@adrianm124

Developing a fully automated long-term stock-trading strategy in Python without manual intervention is key to success. Automating stock scanning, signal generation, position sizing, and order placement is achievable with a mix of quantitative fundamentals, momentum factors, and risk filters. To achieve this, we'll utilize mainstream libraries such as pandas, NumPy, TA-Lib, and connect to Alpaca's reliable market-data/API source. Our workflow will involve: - Using n8n or Zapier to fetch daily data from Alpaca - Implementing a modular Python script using TA-Lib to scan stocks, generate signals, and size positions - Placing orders using Alpaca's API For $600-$1500, we'll deliver a

@WorkOVeda

As an MBA-finance expert with strong Python skills, I am your ideal candidate for this long-term stock strategy project. I have spent years developing and implementing robust trading algorithms deeply rooted in quantitative fundamentals, momentum factors, and solid risk filters- all qualities you've identified as crucial for this project. My expertise has helped numerous businesses convert raw data into actionable insights and transform the way they approach trading strategies. I am well-versed in mainstream Python libraries such as pandas, NumPy, and TA-Lib. Additionally, my knowledge of Alpaca and Interactive Brokers makes me adaptable to any market-data/API source you may choose. My priority is always on writing clean, modular code that not just performs efficiently but also eases out parameter tweaks for future amendments. For your peace of mind prior to live deployment, my services include extensive back-testing on a minimum of ten years daily data with performance metrics (CAGR, max drawdown, Sharpe) clearly reported and organized plots. I go the extra mile by offering walk-forward testing to ensure the robustness of the implemented strategy. Full transparency is always at the forefront- expect fully-commented Python scripts and a concise setup guide that doesn't keep you guessing on data/API credentials or required packages

@warrick1718

This is exactly the kind of project I enjoy working on. Creating a fully automated, Python-based long-term stock-trading strategy aligns with my expertise. I specialize in developing clean, professional, and user-friendly systems that are seamlessly integrated and automated. While I am new to freelancer, I have tons of experience and have successfully completed similar projects off-site. If it sounds like a good fit, I'd be happy to discuss the details. Regards, Warrick Van Eeden

@shan0099

Hi, I have gone through your requirements and can deliver this cleanly. The system will be built in Python using pandas, NumPy, and TA-Lib, connected to Alpaca for live trading. The data layer is structured so switching brokers requires only a config change. The strategy combines momentum signals, a basic fundamental filter, and a volatility risk filter. Every parameter sits in a config file so you can tweak anything without touching core code. Backtesting will cover at least ten years of daily data with CAGR, max drawdown, and Sharpe ratio reported and plotted. Walk-forward testing included to confirm out-of-sample robustness before live deployment. Code will be fully commented with clear separation between data, signals, execution, and logging. Backtest report delivered as HTML with charts. A simple CLI will show current signals and open positions. Full setup guide included covering credentials, packages, and how to run everything. Let me know if you want to align on specific indicators before I start.

@mohamedk379

Hello, I would be happy to develop your end-to-end automated Python stock trading system with a strong focus on clean architecture, transparency, and long-term maintainability. I can build a modular solution that includes: - Automated stock scanning and signal generation. - Risk-based position sizing and portfolio management. - Backtesting on 10+ years of historical data with detailed performance metrics (CAGR, Sharpe Ratio, Max Drawdown, etc.). - Walk-forward testing to validate strategy robustness. - Integration with market data and broker APIs such as Alpaca or Interactive Brokers through a provider-independent architecture. - Well-documented, fully commented Python code using libraries like Pandas, NumPy, and TA-Lib (or suitable alternatives). You'll receive: - Clean, modular Python scripts/notebooks. - A clear setup guide for installation and API configuration. - A professional backtest report with charts and performance analysis. - Optional CLI/dashboard for monitoring signals, positions, and portfolio performance. I prioritize readable code, reproducible results, and easy parameter customization so future research and strategy improvements remain straightforward. I look forward to discussing your preferred strategy ideas and delivering a reliable, production-ready solution. Best regards, Mohamed

@choroukb4

With over X years of experience as a Python Developer, I have honed my skills in creating robust and efficient algorithms for various tasks, including data analysis and automation - the very foundation of your project. I have delivered solutions by leveraging libraries and frameworks such as pandas, NumPy, and TA-Lib to ensure maximum efficiency and accuracy. Having worked with REST APIs in the past, I can expertly connect your system to any market-data source you choose. My approach to coding has always been clean and modular- the very attributes you want for your project. This makes my codebase easy to understand and debug if need be. And with my focus on documentation, running the delivered scripts or notebooks will be a breeze, even for future parameter tweaks. I understand that trust is paramount in selecting someone to handle your finances, which is why transparency is crucial in this project. Rest assured that all my work will be thoroughly documented, allowing you to easily verify the results against the reported ones on your machine. Let's ensure a successful partnership that exceeds even your highest expectations!

@sarathi8

Hi , Experienced Full-Stack Developer | 10+ Years | Python, Django, React, REST APIs, AWS I'm a results-driven full-stack developer with over 10 years of hands-on experience building robust web applications and APIs. My core stack includes: Backend: Python, Django, FastAPI, RESTful APIs Frontend: React, JavaScript Databases: MySQL, MongoDB DevOps/Tools: Git, AWS (EC2, S3, Lambda, etc.) I specialize in designing scalable backends, building modern user interfaces, integrating third-party services, and deploying secure, high-performance applications. Whether you're a startup building an MVP or an enterprise optimizing your stack, I bring clean code, clear communication, and reliable delivery.