Daily Stock Strategy Backtest

@JaibhanSinghGaur

I understand you want your existing daily equity strategy translated into code, backtested on historical stock data, and presented with clear performance metrics like P&L, drawdown, and win rate. I’ve done similar equity backtests using Python (pandas / backtesting libraries) with clean, reproducible logic and clear result summaries. I can start immediately and deliver well-commented code plus a concise report within your stated budget. Happy to proceed once you share the entry, exit, and position-sizing rules.

@pravalika1995

Hi, I am algo trading for over 8 years now and have created and deployed several algo strategies during this time after extensive backtesting and validation. With my background in algo trading and expertise in data analysis, I believe I can assist you on your project. Happy to answer any questions on chat.

@malkesh3m

⭐ Hello there, My availability is immediate. I read your project post on Python Developer for Daily Stock Strategy Backtest. We are experienced full-stack Python developers with skill sets in - Python, Django, Flask, FastAPI, Jupyter Notebook, Selenium, Data Visualization, ETL - React, JavaScript, jQuery, TypeScript, NextJS, React Native - NodeJS, ExpressJS - Web App Development, Data Science, Web/API Scrapping - API Development, Authentication, Authorization - SQLAlchemy, PostegresDB, MySQL, SQLite, SQLServer, Datasets - Web hosting, Docker, Azure, AWS, GPC, Digital Ocean, GoDaddy, Web Hosting - Python Libraries: NumPy, pandas, scikit-learn, tensorflow, etc. Please send a message So we can quickly discuss your project and proceed further. I am looking forward to hearing from you. Thanks

@loxtic

Hi, how are you doing? I have solid hands-on experience with equity backtesting and Python workflows for daily-bar tests, using pandas, backtrader, and Zipline tooling to deliver clean, reusable code, full reproducibility, and clear results. I can translate your rules into a robust backtest, run across a broad stock universe, and provide P&L, max drawdown, and win rate with visualizations plus a concise report and rerun instructions. I can demo prior backtesting work on request and align delivery to your exact entry, exit, and sizing rules. Let me know if you want to proceed and share any initial preferences.

@prashanpatel557

Hello, I can help you convert your daily equity trading strategy into a reliable, transparent backtest so you get a clear picture of how it truly performs over time. How I’ll approach this • Precisely translate your entry, exit, and position-sizing rules into code (no assumptions, no curve-fitting) • Use daily candle data only and stocks only, exactly as specified • Run the test on a broad stock universe (index constituents or your custom list) • Ensure proper handling of corporate actions, look-ahead bias, and survivorship bias Tech stack • Python with pandas • Modular, reusable structure so future optimisations are easy I’ve worked on equity strategy backtesting and validation, where accuracy and auditability mattered more than optimistic results. If the initial backtest looks good, I can also assist with optimisation or robustness checks as a next step. Once you share the strategy rules and stock universe, I can start immediately.

@prabhathk5

Thanks for sharing the details. I’ve reviewed your requirement and would be glad to discuss it further. I’m Prabhath, an experienced MQL4/MQL5, Pine Script, Python, and C++ developer specializing in automated trading systems and institutional-grade algorithmic solutions. I develop Expert Advisors, indicators, dashboards, data tools, and custom trading utilities for MT4/MT5, TradingView, and standalone platforms. Along with MQL5 systems, I also build fully automated trading software in Python and C++ for Indian stock markets and global exchanges (US, EU, and others). These solutions can be tailored for stocks, indices, futures, forex, and crypto based on project needs. As an active trader, I work with ICT, SMT, market structure, liquidity models, order blocks, FVGs, VWAP, and volume-based logic, ensuring each strategy follows the client’s trading methodology. My expertise includes institutional-grade EA and indicator development, ICT/SMT-based trading systems, Pine Script automation, Python and C++ systems for Indian and global markets, backtesting, paper trading and live trade integration, strategy optimization, and low-latency execution. I also fix, optimize, and enhance existing trading systems to make them stable and production-ready. Where permitted, I can share demos or walkthroughs of previously completed projects while respecting client confidentiality. Thank you for your time and consideration.

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

@forexcoder1

Dear Project Owner, I can help you convert your daily equity trading strategy into a clean, accurate backtest and present the results in a clear, decision-ready format. I have hands-on experience building rule-based stock backtests with a strong focus on correctness, transparency, and reproducibility. How I’ll approach this project: Precisely translate your written entry, exit, and position-sizing rules into code Run the strategy on daily candle data across a broad stock universe Ensure logic accuracy over optimization shortcuts Metrics & analysis you require: Profit & Loss (equity curve) Maximum drawdown Overall win rate Clear visualizations to support interpretation Tools & delivery: Python-based solution using pandas and a proven backtesting framework (Backtrader / custom engine, depending on strategy complexity) Well-commented, reusable source code Concise report (PDF or Jupyter notebook) with charts and metrics Step-by-step instructions to rerun locally or on a cloud notebook I value clean architecture, readable code, and verifiable results, making future optimization or extensions straightforward if the initial performance is promising. I’m ready to start as soon as you share the strategy rules. Best regards, FXcoders Hub

@Algokage

Hello, With 5+ years of experience in equity backtesting, I can help translate your trading strategy into code and provide a comprehensive backtest. I'll use Python with pandas and backtrader to ensure accuracy and reproducibility. My approach involves writing clean, reusable code to backtest your strategy on a broad stock universe, focusing on daily bars. I'll deliver a concise report with key metrics, visualizations, and instructions to rerun the test. For samples of similar projects, please send a message. Thanks, Adegoke M.

@Mayanks010

Hello Anil, I am Mayank Sahu, a seasoned professional with 10 years of expertise in Python, Algorithm, and C++ Programming. I am well-equipped to assist you with your Daily Stock Strategy Backtest project. I have carefully reviewed your requirements and am confident in my ability to translate your trading strategy rules into efficient code. By utilizing Python and relevant libraries such as pandas and backtrader, I will conduct a thorough backtest on a wide stock universe. The results will be presented in a clear format, highlighting key metrics like profit and loss, maximum drawdown, and win rate. I would like to discuss your project further in chat to ensure I fully understand your needs and can deliver a solution that meets your expectations. Best regards, Mayank Sahu

@automateWork

Here's your bid proposal under 1000 characters including spaces: Bid Proposal Hello, I can deliver comprehensive backtesting for your equity strategy. With 6+ years building algorithmic trading platforms executing 1000+ daily trades with 95% accuracy, I have deep backtesting expertise. My Approach: I'll use Python with backtrader and pandas. Backtrader is purpose-built for strategy backtesting with excellent daily equity support. Implementation: Translate your rules into clean, modular code with separate functions for entry, exit, and position sizing for easy optimization. Data & Testing: Source historical daily data using yfinance. Simulate strategy with transaction costs, slippage, and no look-ahead bias for realistic results. Analysis: Calculate P&L, maximum drawdown, win rate, Sharpe ratio, and profit factor with equity curves and drawdown charts. Deliverables: Well-documented Python code with clear comments PDF report with all metrics and visualizations Setup instructions for local or Google Colab CSV exports of all trades Experience: Built real-time trading platform with sub-100ms latency Developed multiple backtesting frameworks Extensive market data processing Available immediately. Will deliver within 2 days!

@pandurangdm

I have hands-on experience implementing and backtesting equity trading strategies using daily candle data. I have already delivered similar solutions for various trading requirements, converting strategy rules into accurate, well-structured Python code. I am proficient with Python, pandas, and backtesting frameworks such as Backtrader, with a strong focus on correctness, clean architecture, and clear performance metrics including P&L, maximum drawdown, and win rate. I would like to discuss your project further in chat to ensure I fully understand your needs and can deliver a solution that meets your expectations.

@TanuSharma25

I am currently working as an Associate Consultant – Data Engineer, and this role aligns closely with my experience in real-time data pipeline support, automation, and guided implementation. I am comfortable working as a freelancer proxy, sitting alongside engineers during active work hours, explaining workflows, implementing solutions, and resolving issues in real time. In my current role, I support cloud-based data platforms, where I design and implement automation solutions to reduce manual effort, improve monitoring, and enhance data reliability. I regularly automate alerting, validations, pipeline checks, and operational workflows using Python, SQL, and AWS services. Why I’m a strong fit: • Strong Python and advanced SQL with an automation-first mindset • Hands-on experience with AWS (Athena, Glue, SQS, SNS, IAM, CloudWatch) • Automation of monitoring, alerts, and data quality workflows • Real-time troubleshooting, preventive controls, and reliability improvements • Clear communication while guiding engineers step by step • Strong experience with Git/GitHub, CI/CD, documentation, and runbooks I bring a balance of technical execution, automation expertise, and operational reliability, ensuring smooth and efficient daily delivery. Best regards, Tanu Sharma

@komalgupta2002

Good Day, I have gone through your project description. I have built automated trading systems which executes trades on different Indian Stock Brokers namely Zerodha, Aliceblue, Angel, XTS, MasterTrust, Kotak Neo, ICICI, Upstox, Dhan, Nuvama, MultiTrade and Fyers. I have also coded strategies which trades on Delta, Binance, Bybit, Bitmex and Wazirx cryptocurrency exchange. Apart from this, I have build systems for US SPX options for IBKR. Let's connect for further discussion. Regards, Komal Gupta

@danilort

Hi — the main challenge is translating your equity-only daily strategy into code that accurately reflects the rules, then backtesting it across a broad stock universe to produce reliable performance metrics. Accuracy and reproducibility are more important than the toolkit used. A practical approach is to implement the strategy in Python using a robust library (like backtrader or pandas), run the backtest on daily data, and capture profit/loss, max drawdown, and win rate. Visualisations help make the results immediately understandable, and well-commented code ensures it can be rerun or extended later. A common risk is misinterpreting entry/exit rules or handling edge cases in historical data, so careful validation is needed. The goal is a clear, actionable report and reusable code that provides solid evidence of the strategy’s historical performance.

@JoshuaH1224

We recently handled a project with the exact goals you’re looking for, translating a detailed equity strategy into reproducible code and delivering clear backtest results that highlighted performance, risk, and actionable insights for decision-making. I will help bring your strategy to life by coding it in Python using a robust backtesting framework, running the test across a broad stock universe, and producing a clean, well-documented report with visualisations of profit, drawdown, and win rate. The code will be modular and easy to rerun for future analysis or optimisations. I feel really good about how my approach lines up with your needs. I'd love to chat about your project! Best case scenario, we work together, worst case, you get free insight. Best regards, Joshua

@crthocine

I am a dedicated and disciplined CRT trader with over three years of hands-on experience in the financial markets. I specialize in price action, liquidity concepts, and high-probability trade setups, with a strong focus on risk management and consistency. Trading for me is not just a skill, but a continuous learning process. I constantly refine my strategies, adapt to market conditions, and analyze my performance to achieve sustainable results. My approach is professional, patient, and data-driven, aiming for long-term success rather than short-term gains. I value transparency, precision, and discipline, and I always treat trading as a serious business. If you are looking for a trader who combines experience, strategy, and commitment, I am confident I can add real value.