Stock Day-Trading Algo Strategy

@mahads31

Drawing on my extensive experience in software development and system architecture, I am confident I can create a powerful and customized solution for your stock day-trading algorithmic strategy using Moving Averages. Developing systems that are accessible, scalable, and adaptable has always been my priority. Given this, I assure you that the code I provide will be well-commented and understandable, making it easy for you to adjust parameters without requiring frequent modifications at the core level. With diligent back-testing and thorough documentation as part of my process, you can expect reliable results and a detailed README to aid the installation process. Moreover, my ability to build long-term technology partnerships aligns perfectly with your future-oriented project demands. Along with the project deliverables outlined, I am also excited to propose exploring additional enhancements such as an RSI or MACD filter down the line. Let's create an algorithmic trading system that not only meets your current Stock market trading needs but also anticipates future requirements - ensuring your system can adapt to changing market conditions with ease.

@ashinnd84

Hello sir/mem, we are a team of AI ML automation Full Stack Web and Mobile developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@ravichedwal

As an experienced Full Stack Developer with a solid background in Python and Software Architecture, I believe I'm perfectly positioned to take on your Stock Day-Trading Algo Strategy project. Over the past 8+ years, I've successfully developed numerous web and mobile solutions with a strong emphasis on reliability, efficiency, and security -- all traits that are essential for a trading algorithm. My expertise extends to core Python programming as well as leveraging languages/platforms like TradingView Pine Script and QuantConnect that you mentioned as potentially suitable. Whether it's developing clean, well-documented code or integrating APIs for seamless data exchange between your chosen platform and a broker, rest assured that I'll deliver a robust solution that suits your exact needs.

@prabhathk5

Hi Sir Hope you doing well! I’ve reviewed your requirement and would like to discuss it further. I’m Prabhath — an MQL4/MQL5, Pine Script, Python, and C++ developer with strong experience in building trading systems, advanced studies, and institutional-grade algorithmic solutions. I develop Expert Advisors, indicators, dashboards, data tools, and custom trading utilities for MT4/MT5 and TradingView. As an active trader, I work with concepts like ICT, SMT, market structure, liquidity models, order blocks, FVGs, VWAP, and volume-based logic. I build EAs and indicators that follow precise rules and match the user’s trading methodology perfectly. My expertise includes: Institutional-grade EA and indicator development ICT/SMT-based trading systems Pine Script indicators and automated strategies Python tools for data analysis, signals, and external integrations C++ modules for high-performance components Backtesting, forward testing, and full strategy optimization Strategy development, refinement, and consulting Once we discuss your project and you select me as the developer, I prefer a short call to confirm all details before starting. This ensures perfect clarity and avoids misunderstandings. I fix and optimize existing trading code by removing errors, correcting logic issues, and making your EA/indicator/bot stable and production-ready. Thank you.

@Algokage

Hello, I hope you're doing well. I am an experienced algorithmic trading developer with over 6 years of expertise in designing and implementing automated strategies for various financial markets. My background includes creating robust systems that leverage technical indicators like Moving Averages, RSI, and MACD, ensuring precise intraday execution and risk management. For this project, Python with a broker API integration would be the most suitable choice, given its flexibility for live execution and parameter adjustments. The approach will focus on developing a clean, modular codebase that allows seamless tweaking of Moving Average lengths without altering the core logic. Back-testing will be conducted over a recent, meaningful period to ensure reliability, and all deliverables will include well-commented code, a detailed README, and comprehensive back-test results. Optional enhancements, such as integrating RSI or MACD filters, can be outlined for future consideration. I deliver clean, well-documented code and ensure reproducibility for all strategies. If you’d like to discuss this further or review relevant samples, feel free to send a message. Thanks, Adegoke M.

@nexvative

Hi, I can develop a fully coded, reproducible, and back-tested intraday trading strategy using Moving Averages as the core signal engine, with clean parameter controls so you can adjust MA lengths without modifying the source code. I can deliver: • Complete, well-structured and commented strategy source code • Configurable MA periods, entry/exit rules, position sizing, stop-loss, take-profit, and risk limits • Reliable historical data handling and reproducible back-testing • Detailed performance metrics including returns, drawdown, win rate, Sharpe ratio, and trade statistics • Equity curve and back-test report over a meaningful recent period • Broker/data-feed integration for paper or live execution where supported • Clear README covering installation, configuration, broker connection, and parameter tuning • Modular architecture allowing future RSI, MACD, volume, or other filters to be added easily I can recommend the best platform based on your execution requirements, Python with a broker API for maximum flexibility, or TradingView/QuantConnect for faster strategy deployment and testing.

@alluankit01

Hi, I can build your fully coded moving-average-based day-trading strategy with configurable parameters, backtesting, risk controls, source code, README, and reproducible test results. The best solution is to first confirm the market, timeframe, MA types, entry/exit logic, stop-loss, target, position sizing, and preferred platform. I can build it in Python for broker/API flexibility or TradingView Pine Script if you want easier chart-based testing and parameter control. I’m comfortable with Python, Pine Script, backtesting, moving-average systems, intraday strategy logic, broker/data-feed integration, risk management, parameter optimization, equity-curve reporting, and clean technical documentation. Deliverables will include: * Complete source code * Moving Average entry/exit logic * Configurable MA lengths * Stop-loss and target rules * Position sizing controls * Backtesting module/results * Equity curve and key metrics * README setup guide * Parameter adjustment instructions * Clean comments in code * Optional RSI/MACD filter notes I’ll focus on a disciplined, reproducible, and easy-to-adjust strategy so you can test it on your own data before sign-off. Trading profit is not guaranteed, but the system will be built with clear rules and proper risk controls. Best regards Ankit

@Hrolgar

Start with the data contract and backtest harness, because your own validation data needs to produce the same trades, equity curve, and metrics without guessing what the strategy did. I'd code this as a parameterized Python pipeline, likely pandas with a vectorbt-style backtest layer, where MA lengths, session times, symbol list, fees/slippage, sizing, stop rules if needed, and data path/feed adapter are all config values. The moving-average logic itself stays simple and auditable, with generated trades, equity curve, drawdown, win rate, Sharpe/Sortino where relevant, and exported CSV/plots so you can compare runs against your own intraday data. The 30000 INR is based on the brief as written. It may need tightening once I know the exact data format, broker target, and whether this is backtest-only or also needs live-paper execution wiring. M1: Strategy spec, config schema, data loader, and first reproducible backtest run, 9900 INR, 2 days. M2: Metrics, equity curve outputs, trade ledger, parameterized MA tests, and cleanup around fees/slippage/session filters, 10100 INR, 2 days. M3: README covering install, data feed, broker setup notes, reproducible result package, and final validation pass, 10000 INR, 1 day. I'll keep the output boring and checkable: one command to run the backtest, one config file to change MA lengths, and result files that make it clear exactly what happened.

@Sahyadri96

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 I can deliver any project in Trading. Readymade setups for Python available

@snath88

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

@thiagofoz87

I can deliver a complete MA-based intraday trading strategy with configurable parameters, reproducible backtesting, and clear execution flow for live or paper trading. My recommendation is to implement this in Python using Backtrader or vectorbt for research/backtesting and a broker-compatible execution layer for live trading. This provides flexibility for parameter optimization, reliable historical testing, and future expansion without rewriting the strategy core. The strategy would include: - Configurable fast/slow moving averages - Intraday entry/exit logic - Risk controls such as stop-loss, take-profit, and position sizing - Externalized parameters so MA lengths and thresholds can be adjusted without code changes - Backtest reporting with equity curve, drawdown, win rate, and Sharpe-style metrics The delivery will include fully documented source code, setup instructions, broker/data-feed integration guidance, and reproducible backtest execution steps. I can also structure the project so optional filters like RSI, MACD, volatility filters, or session/time constraints can be added later with minimal changes. Estimated delivery for the first production-ready version is 4 days, including validation and documentation.

@Gurpreetsing343

Drawing from over a decade of experience in IT solutions and software development, GSINFOTECHH OPC Pvt. Ltd. is the perfect choice to deliver your stock day-trading algo strategy. Our expertise covers not just C++ Programming and Python – the languages well-suited to building this system – but also other important technologies like Django and React JS which can be used to ensure efficiency and scalability in your project. What sets us apart is our commitment to complete client satisfaction through transparent workflow, secure technology, optimism, and on-time project delivery. We are already excited about creating a fully coded algorithmic strategy that leverages moving averages as its core signal engine, while providing you the ability to fine-tune key MA lengths without cumbering with code modifications each time. In terms of deliverables, we will provide you with a well-commented and comprehensive source code that meets your parameter control requirements. Additionally, you can expect a README guide for easy installation and adjustment of the program. Moreover, we understand the significance of backtesting in day-trading strategies so we ensure that our delivered project speaks stably well on relevant equity curve metrics. Make no mistake: Buckingham & Sons is primed to turn your idea into a robust solution for market-ready use. Let's take your project to new heights!

@harshbuilds7

I can develop a clean, modular, and fully documented algorithmic trading strategy based on Moving Averages with adjustable parameters, robust risk management, and reliable backtesting. The solution will include well-commented source code, a detailed README, reproducible backtest results, and a scalable architecture that makes it easy to add indicators like RSI or MACD in the future. I prioritise clean code, clear communication, and on-time delivery, and I'm ready to start immediately.

@ibtemirlan

I can deliver a bounded first milestone: a reproducible Python backtest of a configurable moving-average intraday strategy. The milestone includes: 1. Implement fast/slow MA entry and exit rules with editable parameters. 2. Add position sizing, stop-loss, commission and slippage settings. 3. Run the strategy on one agreed symbol and recent dataset supplied by you or available through an agreed source. 4. Export an equity curve and core metrics: return, drawdown, win rate, trade count and Sharpe ratio. 5. Provide commented source code and a concise installation/reproduction README. Delivery: up to 5 hours, fixed price 10,000 RUB equivalent. This stage excludes live broker execution, production hosting and any profitability guarantee; those can be scoped separately after you validate the backtest. Which broker/data source and bar interval do you intend to use? Should entries be long-only or both long and short?

@upretik92

Hello, I am interested in developing your Moving Average-based algorithmic day trading strategy. I have experience with Python and automated trading concepts and can build a clean, modular, and well-documented solution that is easy to customize. The deliverables will include: * Complete, well-commented source code * Configurable Moving Average parameters without modifying the code * Intraday entry/exit logic with risk management (stop loss, take profit, and position sizing) * Backtesting framework with performance metrics and equity curve * Clear README with installation, setup, broker/data feed connection, and parameter configuration instructions The code will be written with maintainability in mind, making it easy to add future enhancements such as RSI, MACD, ATR, or VWAP filters. I focus on writing clean, reliable, and reproducible code, and I will ensure the strategy is easy to test on your own historical data before deployment. I look forward to discussing your exact trading rules, preferred broker, and target market to deliver a solution that meets your requirements. Thank you for your consideration.

@bhargav922002

Hello, I'm bharghav, with 10 years of experience in Software Architecture, C++, and Python programming. My expertise in crafting robust systems aligns perfectly with developing precise algorithmic solutions. I understand you need a fully coded, back-tested day-trading strategy using Moving Averages for fast intraday entries and exits. I will deliver this using Python, leveraging its robust libraries for backtesting and broker API integration, ensuring easy parameter adjustment and reliable live execution. The solution will include well-commented source code, a clear README, and comprehensive back-test results. Please initiate a chat so we can discuss the specifics further. Best regards,

@farhans31

Hello, I can develop a robust algorithmic day trading strategy centered on Moving Averages with clean, modular code and reproducible backtesting. The strategy will be built so that key parameters (fast/slow MA lengths, stop-loss, take-profit, position sizing, etc.) can be adjusted through configuration without modifying the source code. My deliverables include: * Fully documented and well-structured source code * Backtesting over historical market data with an equity curve and performance metrics (win rate, drawdown, CAGR/returns, Sharpe ratio where applicable) * A clear README explaining installation, setup, broker/data feed integration, and parameter customization * Code designed for future enhancements such as RSI, MACD, ATR, volume filters, or additional risk management rules I recommend Python for flexibility, extensive backtesting libraries, and straightforward integration with broker APIs for live execution. If you prefer TradingView Pine Script or another platform, I can adapt the implementation accordingly. The solution will emphasize reproducibility, clean architecture, and transparent logic so you can independently validate the results on your own historical data before deployment. I look forward to discussing your preferred broker, market, and execution platform to ensure the strategy fits your trading workflow.

@AndileBotha

Hello, I see you need a fully coded day trading strategy built around Moving Averages for fast intraday stock entries and exits with tight risk and adjustable MA lengths. I already deliver these kinds of reproducible MA signal engines for traders who want clean automation and easy parameter control. I write the complete strategy in Python with Backtrader for backtesting and Interactive Brokers API for live execution. Parameters sit in a simple config file so MA lengths change without touching the code. I include full comments throughout the source, a concise README covering install data feed connection and parameter tweaks, plus equity curve and key metrics from a recent multi month back test that stays fully reproducible on your own data. Happy to hear more about the specific stock universe or risk rules you want prioritised. Kind regards Andile

@Sumanravivirat59

Hi, I can build you a backtested MA crossover strategy with parameter control. I suggest Python with broker API + backtrader/zipline for backtesting. Deliverables: 1. Well commented source code 2. README for setup + broker connection 3. Equity curve + Sharpe, Max Drawdown metrics My bid: ₹X,XXX for complete project. Timeline: X days. All payment via Freelancer milestones only. Questions: Which broker will you use for live trading?